Methylphenidate compositions for the treatment of attention deficit hyperactivity disorder

文档序号：491492 发布日期：2022-01-04 浏览：11次中文

阅读说明：本技术 用于治疗注意力缺陷多动障碍的哌甲酯组合物 (Methylphenidate compositions for the treatment of attention deficit hyperactivity disorder ) 是由 J·戈布鲁 D·利克利斯 B·J·因克尔登 R·戈梅尼于 2020-01-24 设计创作，主要内容包括：描述了一种固体口服药物组合物。所述固体口服药物组合物包括哌甲酯或其药用盐,其中所述固体口服药物组合物的体内吸收模型具有选自以下的函数：单威布尔函数、双威布尔函数和sigmoid eMax函数。所述固体口服药物组合物的体外溶出的多个分数与固体口服药物组合物的体内吸收的同一多个分数的相关性是非线性的。还描述了一种治疗患有对施用哌甲酯有反应的障碍或病症的受试者的病症的方法。所述方法包括向所述受试者口服施用有效量的所述固体口服药物组合物。(A solid oral pharmaceutical composition is described. The solid oral pharmaceutical composition comprises methylphenidate or a pharmaceutically acceptable salt thereof, wherein the model of in vivo absorption of the solid oral pharmaceutical composition has a function selected from the group consisting of: a single weibull function, a double weibull function, and a sigmoid eMax function. The correlation of the multiple fractions of in vitro dissolution of the solid oral pharmaceutical composition to the same multiple fractions of in vivo absorption of the solid oral pharmaceutical composition is non-linear. Also described is a method of treating a disorder in a subject having a disorder or condition responsive to administration of methylphenidate. The method comprises orally administering to the subject an effective amount of the solid oral pharmaceutical composition.)

1. A solid, oral pharmaceutical composition comprising:

(ii) methylphenidate or a pharmaceutically acceptable salt thereof,

wherein the in vivo absorption model of the solid oral pharmaceutical composition has a function selected from the group consisting of:

(i) single weibull function:

wherein td is the time required to absorb 63.2% of the released methylphenidate or pharmaceutically acceptable salt thereof, and ss is the sigmoidal factor;

(ii) double weibull function:

where ff is the fraction of the dose released during procedure 1, td is the time required to absorb 63.2% of the released dose during procedure 1, td1 is the time required to absorb 63.2% of the released dose during procedure 2, ss is the sigmoid factor of the first procedure, ss1 is the sigmoid factor of the second procedure; and

(iii) sigmoid eMax function:

wherein EC is the time to release 50% of the methylphenidate or pharmaceutically acceptable salt thereof, and ga is a parameter characterizing the shape of the absorption curve of the methylphenidate or pharmaceutically acceptable salt thereof,

and

the correlation of the multiple fractions of in vitro dissolution of the solid oral pharmaceutical composition to the same multiple fractions of in vivo absorption of the solid oral pharmaceutical composition is non-linear.

2. The solid, oral pharmaceutical composition of claim 1, wherein the non-linear correlation of the plurality of fractions of in vitro dissolution and the plurality of fractions of in vivo absorption of the solid, oral pharmaceutical composition best fits a quintic polynomial function.

3. The solid, oral pharmaceutical composition of claim 1, wherein the non-linear correlation of the plurality of fractions of in vitro dissolution of the solid, oral pharmaceutical composition and the plurality of fractions of in vivo absorption best fits a quadratic polynomial function, a cubic polynomial function, a quartic polynomial function, a quintic polynomial function, or a sextic polynomial function.

4. The solid, oral pharmaceutical composition of claim 1, wherein the plurality of fractions of in vitro dissolution and the plurality of fractions of in vivo absorption of the solid, oral pharmaceutical composition comprise a plurality of values from 0 to 1.

5. The solid oral pharmaceutical composition of claim 1, wherein the solid oral pharmaceutical composition is a multi-layered solid oral pharmaceutical composition comprising

Methylphenidate or a pharmaceutically acceptable salt thereof;

a sustained release layer; and

a delayed release layer.

6. The solid, oral pharmaceutical composition of claim 5, comprising:

a core comprising methylphenidate or a pharmaceutically acceptable salt thereof;

wherein

The core, the sustained release layer and the delayed release layer each have a surface; and

the slow release layer and the delayed release layer wrap the core.

7. The solid, oral pharmaceutical composition of claim 6, wherein:

the sustained release layer surrounds a core, and the delayed release layer surrounds the sustained release layer.

8. The solid, oral pharmaceutical composition of claim 6, wherein:

the sustained release layer and the delayed release layer do not completely wrap the surface of the core.

9. The solid, oral pharmaceutical composition of claim 6, wherein:

the delayed release layer does not completely wrap the surface of the delayed release layer and/or the surface of the core.

10. The solid, oral pharmaceutical composition of claim 8, wherein:

the sustained release layer and the delayed release layer wrap at least 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99% or 99.9% of the core surface.

11. The solid, oral pharmaceutical composition of claim 9, wherein:

the delayed release layer wraps at least 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99% or 99.9% of the surface of the core and/or the surface of the sustained release layer.

12. The solid, oral pharmaceutical composition of claim 5, wherein:

the multilayer solid oral pharmaceutical composition comprises a multilayer core, wherein:

the multilayer core having a surface, an

The multilayer core includes a first layer comprising methylphenidate or a pharmaceutically acceptable salt thereof and a second layer comprising a swellable layer that includes a superdisintegrant or osmagent.

13. The solid, oral pharmaceutical composition of claim 5, wherein:

the multilayer solid oral pharmaceutical composition comprises a multilayer core, wherein

The multilayer core having a surface, an

The multilayered core includes a first layer comprising methylphenidate or a pharmaceutically acceptable salt thereof and a second layer comprising a sustained release layer.

14. The solid, oral pharmaceutical composition of claim 12, wherein the multilayered core further comprises a third layer comprising a sustained release layer.

15. The solid, oral pharmaceutical composition of claim 13, wherein the multilayer core further comprises a third layer comprising a swellable layer comprising a superdisintegrant or an osmagent.

16. A solid oral pharmaceutical composition according to any one of claims 12-15, wherein the delayed release layer surrounds the multilayer core.

17. A solid, oral pharmaceutical composition according to claim 16, wherein the delayed release layer does not completely wrap the surface of the multi-layered core.

18. A solid, oral pharmaceutical composition according to claim 17, wherein the delayed release layer wraps at least 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99% or 99.9% of the surface of the multilayer core.

19. A method of treating a disorder or condition in a subject having a disorder or condition responsive to administration of methylphenidate comprising:

orally administering to the subject an effective amount of a solid oral pharmaceutical composition comprising: (ii) methylphenidate or a pharmaceutically acceptable salt thereof,

wherein the in vivo absorption model of the solid oral pharmaceutical composition has a function selected from the group consisting of:

(i) single weibull function:

wherein td is the time required to absorb 63.2% of the released methylphenidate or pharmaceutically acceptable salt thereof, and ss is the sigmoidal factor;

(ii) double weibull function:

(iii) sigmoid eMax function:

and

thereby producing an improvement in the behavior or ability associated with the disorder or condition over a period of time,

wherein the administration reduces the time period of efficacy change, or the likelihood or severity of rebound, or both.

20. The method of claim 19, wherein the time period begins at 8:00am, 9:00am, 10:00am, 11:00am, 12:00pm, 1:00pm, 2:00pm, 3:00pm, 4:00pm, 5:00pm, 6:00pm, or 7:00 pm.

21. The method of claim 19, wherein the period of time begins 8, 9, 10, 11, 12, 13, 14, 15, or 16 hours after administration of the composition.

22. The method of claim 19, wherein the improvement is measured by a validated rating scale, score, or combined score.

23. The method of claim 22, wherein the validated rating scale, score, or combined score is a Swanson, Kotkin, Agler, M-Flynn and pelham (SKAMP) score, or a SKAMP-CS combined score.

24. The method of claim 22, wherein the change in efficacy is measured by the volatility index (FI):

wherein CHP is the change in SKAMP score over the period of time relative to placebo.

25. The method of claim 24, wherein the Fluctuation Index (FI) has an absolute value of less than 1.0.

26. The method of claim 19, wherein the period of time ends at 9:00am, 10:00am, 11:00am, 12:00pm, 1:00pm, 2:00pm, 3:00pm, 4:00pm, 5:00pm, 6:00pm, 7:00pm, or 8:00 pm.

27. The method of claim 19, wherein the period of time ends 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, or 16 hours after the period of time begins.

28. The method of claim 19 wherein said period of time ends when the plasma concentration of methylphenidate in said subject is less than 5 ng/mL.

29. The method of claim 19, wherein said period of time ends 3, 4, 5, 6, 7, 8, 9, 10, 11, or 12 hours after Tmax of methylphenidate in said subject.

30. The method of claim 19, wherein the period of time ends when the subject falls asleep after Tmax.

31. The method of claim 23, wherein during the period of time, the change in the SKAMP score does not exceed or exceed about 6, 7, 8, 9, or 10.

32. The method of claim 19 wherein the rate of change of methylphenidate plasma concentration over time during said period of time is no greater than +2.5ng.hr/mL following administration of up to 100mg of methylphenidate.

33. The method of claim 19 wherein said period of time is between Tmax and 6 hours after Tmax and the rate of change in the plasma concentration of methylphenidate over time is not less than-1.2 ng.hr/mL following administration of up to 100mg of methylphenidate.

34. The method of claim 19 wherein said period of time comprises a period wherein the methylphenidate plasma concentration is between Cmax and at least 40% Cmax and the rate of change of the methylphenidate plasma concentration is no greater than +1.5ng.hr/mL and no less than-1.5 ng.hr/mL.

35. The method of claim 19 wherein the methylphenidate or pharmaceutically acceptable salt thereof is absorbed in the colon.

36. The method of claim 35 wherein at least 90% of the methylphenidate or pharmaceutically acceptable salt thereof is absorbed in the colon.

37. The method of claim 19, wherein the subject has Attention Deficit Hyperactivity Disorder (ADHD) or Attention Deficit Disorder (ADD) and Autism Spectrum Disorder (ASD).

38. The method of claim 19, wherein the improvement is a dose-dependent improvement.

39. The method of claim 38, wherein said dose-dependent improvement comprises a dose-dependent increase over said period of time.

40. The method of claim 39, wherein the increase in the period of time comprises an increase in the period of time after Tmax.

41. The method of claim 19, wherein the solid oral pharmaceutical composition is a multi-layered solid oral pharmaceutical composition comprising:

methylphenidate or a pharmaceutically acceptable salt thereof;

a sustained release layer; and

a delayed release layer.

42. The method of claim 41, wherein the solid oral pharmaceutical composition comprises:

a core comprising methylphenidate or a pharmaceutically acceptable salt thereof;

wherein:

the core, the sustained release layer and the delayed release layer each have a surface; and

the slow release layer and the delayed release layer wrap the core.

43. The method according to claim 42, wherein said sustained release layer surrounds said core and said delayed release layer surrounds said sustained release layer.

44. The method of claim 42, wherein:

the sustained release layer and the delayed release layer do not completely wrap the core.

45. The method of claim 42, wherein:

the delayed release layer does not completely wrap the surface of the delayed release layer and/or the surface of the core.

46. The method of claim 44, wherein:

the sustained release layer and the delayed release layer wrap at least 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99% or 99.9% of the core surface.

47. The method of claim 45, wherein:

the delayed release layer wraps at least 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99% or 99.9% of the surface of the core and/or the surface of the sustained release layer.

48. The method of claim 19, wherein:

the multilayer solid oral pharmaceutical composition comprises a multilayer core, wherein:

the multilayer core having a surface, an

49. The method of claim 19, wherein:

the multilayer solid oral pharmaceutical composition comprises a multilayer core, wherein

The multilayer core having a surface, an

The multilayered core includes a first layer comprising methylphenidate or a pharmaceutically acceptable salt thereof and a second layer comprising a sustained release layer.

50. The method of claim 48, wherein the multilayer core further comprises a third layer comprising a sustained release layer.

51. The method of claim 49, wherein the multilayer core further comprises a third layer comprising a swellable layer comprising a superdisintegrant or an osmagent.

52. The method of any one of claims 48-51, wherein the delayed release layer wraps the multilayer core.

53. The method of claim 52, wherein the delayed release layer does not completely wrap around the surface of the multi-layer core.

54. The method according to claim 53, wherein said delayed release layer wraps at least 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99% or 99.9% of the surface of said multilayer core.

Technical Field

The present disclosure relates to pharmaceutical compositions and methods for treating Attention Deficit Disorder (ADD) or Attention Deficit Hyperactivity Disorder (ADHD) and related disorders.

Background

Methylphenidate is a central nervous system stimulant used to treat ADD and ADHD in children and adults. Although useful, methylphenidate is associated with a rebound as the drug is metabolized and its action is reduced. Rebound can cause significant changes in behavior (demeanor), excessive emotionalization (moodiness), irritability, nervousness, sadness, crying, fatigue, and even during the rebound period the severity of ADD or ADHD symptoms increases.

Brief description of the invention

According to a first aspect, a solid oral pharmaceutical composition is described. The solid oral pharmaceutical composition comprises methylphenidate or a pharmaceutically acceptable salt thereof, wherein the model of in vivo absorption of the solid oral pharmaceutical composition has a function selected from the group consisting of: single Weibull function (Weibull function):

wherein td is the time required to absorb 63.2% of the released methylphenidate or pharmaceutically acceptable salt thereof, ss is the sigmoidicfactor (sigmoidicy factor); double weibull function:

In any of the disclosed embodiments, the solid oral pharmaceutical composition may further include the following details, which may be combined with each other in any combination, unless specifically mutually exclusive:

(i) the non-linear dependence of the multiple fractions of in vitro dissolution of the solid oral pharmaceutical composition on the multiple fractions of in vivo absorption may best fit a quintic polynomial function.

(ii) The non-linear correlation of the multiple fractions of in vitro dissolution with the multiple fractions of in vivo absorption of the solid oral pharmaceutical composition may best fit a quadratic polynomial function, a cubic polynomial function, a quartic polynomial function, a quintic polynomial function, or a sextic polynomial function.

(iii) The plurality of fractions of in vitro dissolution and the plurality of fractions of in vivo absorption of the solid oral pharmaceutical composition may comprise a plurality of values from 0 to 1.

(iv) The solid oral pharmaceutical composition may be a multi-layered solid oral pharmaceutical composition comprising methylphenidate or a pharmaceutically acceptable salt thereof, a sustained release layer; and a delayed release layer.

(v) The solid oral pharmaceutical composition may comprise a core comprising methylphenidate or a pharmaceutically acceptable salt thereof, wherein the core, the sustained release layer and the delayed release layer each have a surface; and the sustained release layer and the delayed release layer may surround the core.

(vi) The sustained release layer may surround the core, and the delayed release layer may surround the sustained release layer.

(vii) The sustained release layer and the delayed release layer may not completely wrap the surface of the core.

(viii) The delayed release layer may not completely encapsulate the surface of the sustained release layer and/or the surface of the core.

(ix) The sustained release layer and the delayed release layer may wrap at least 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99% or 99.9% of the surface of the core.

(x) The delayed release layer may wrap at least 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99% or 99.9% of the surface of the core and/or the surface of the delayed release layer.

(xi) A multilayer solid oral pharmaceutical composition can include a multilayer core, wherein the multilayer core has a surface and the multilayer core includes a first layer comprising methylphenidate or a pharmaceutically acceptable salt thereof and a second layer comprising a swellable layer comprising a superdisintegrant or osmagent.

(xii) The multilayer solid oral pharmaceutical composition can include a multilayer core, wherein the multilayer core has a surface and the multilayer core includes a first layer comprising methylphenidate or a pharmaceutically acceptable salt thereof and a second layer comprising a sustained release layer.

(xiii) The multi-layer core may also include a third layer comprising a sustained release layer.

(xiv) The multi-layer core may further include a third layer comprising a swellable layer comprising a superdisintegrant or an osmotic agent.

(xv) The delayed release layer may surround a multi-layer core.

(xvi) The delayed release layer may not completely wrap around the surface of the multi-layer core.

(xvii) The delayed release layer may wrap at least 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99% or 99.9% of the surface of the multilayer core.

According to a second aspect, a method of treating a disorder or condition in a subject suffering from a disorder or condition responsive to administration of methylphenidate is described. The method comprises orally administering to a subject an effective amount of a solid oral pharmaceutical composition comprising methylphenidate or a pharmaceutically acceptable salt thereof, wherein the in vivo absorption model of the solid oral pharmaceutical composition has a function selected from the group consisting of: single weibull function:

wherein td is the time required to absorb 63.2% of the released methylphenidate or pharmaceutically acceptable salt thereof, and ss is the sigmoidal factor; double weibull function:

wherein EC is the time to release 50% of methylphenidate or a pharmaceutically acceptable salt thereof, and ga is a parameter characterizing the shape of the absorption curve of methylphenidate or a pharmaceutically acceptable salt thereof. The correlation of the plurality of fractions of the solid oral pharmaceutical composition that are dissolved in vitro to the same plurality of fractions of the solid oral pharmaceutical composition that are absorbed in vivo is non-linear. The method produces an improvement in the behavior or ability associated with the disorder or condition over a period of time, wherein the administration reduces the likelihood or severity or both of a change in efficacy or rebound over the period of time.

In any of the disclosed embodiments, the method may further include the following details, which may be combined with each other in any combination, unless expressly mutually exclusive:

(i) the time period may begin at 8:00am, 9:00am, 10:00am, 11:00am, 12:00pm, 1:00pm, 2:00pm, 3:00pm, 4:00pm, 5:00pm, 6:00pm, or 7:00 pm.

(ii) The period of time may begin 8, 9, 10, 11, 12, 13, 14, 15, or 16 hours after administration of the composition.

(iii) The improvement may be measured by a verified rating scale, score, or combined score.

(iv) The validated rating scale, score, or combined score may be a Swanson, Kotkin, Agler, M-Flynn, and Pelham (SKAMP) score, or a SKAMP-CS combined score.

(v) The change in efficacy can be measured by the Fluctuation Index (FI):

wherein CHP is the change in SKAMP score over the time period relative to placebo.

(vi) The undulation index (FI) may have an absolute value of less than 1.0.

(vii) The time period may end at 9:00am, 10:00am, 11:00am, 12:00pm, 1:00pm, 2:00pm, 3:00pm, 4:00pm, 5:00pm, 6:00pm, 7:00pm, or 8:00 pm.

(viii) The time period may end 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, or 16 hours after the beginning of a time period.

(ix) This time period may end when the plasma concentration of methylphenidate in the subject is below 5 ng/mL.

(x) The period of time may end 3, 4, 5, 6, 7, 8, 9, 10, 11, or 12 hours after Tmax of methylphenidate in the subject.

(xi) The period of time may end when the subject falls asleep after Tmax.

(xii) During this time period, the change in the value of the SKAMP score may not exceed or exceed about 6, 7, 8, 9, or 10.

(xiii) During this period, the rate of change of the methylphenidate plasma concentration over time may be no greater than +2.5ng.hr/mL after administration of up to 100mg of methylphenidate.

(xiv) The time period may be between Tmax and 6 hours after Tmax, with a rate of change in plasma concentration of methylphenidate of no less than-1.2 ng.hr/mL after administration of up to 100mg of methylphenidate.

(xv) The time period may include a period in which the methylphenidate plasma concentration is between Cmax and at least 40% Cmax, and the rate of change of the methylphenidate plasma concentration is no greater than +1.5ng.hr/mL and no less than-1.5 ng.hr/mL.

(xvi) Methylphenidate or a pharmaceutically acceptable salt thereof may be absorbed in the colon.

(xvii) At least 90% of the methylphenidate or pharmaceutically acceptable salt thereof may be absorbed in the colon.

(xviii) The subject may have Attention Deficit Hyperactivity Disorder (ADHD) or Attention Deficit Disorder (ADD) and Autism Spectrum Disorder (ASD).

(xix) The improvement may be dose-dependent.

(xx) The dose-dependent improvement may comprise a dose-dependent increase over said period of time.

(xxi) The increase in the period of time may include an increase in a period of time after Tmax.

(xxii) The solid oral pharmaceutical composition may be a multi-layered solid oral pharmaceutical composition comprising methylphenidate or a pharmaceutically acceptable salt thereof, a sustained release layer; and a delayed release layer.

(xxiii) The solid oral pharmaceutical composition may comprise a core comprising methylphenidate or a pharmaceutically acceptable salt thereof, wherein the core, the sustained-release layer and the delayed-release layer each have a surface; and the sustained and delayed release layers may surround the core.

(xxiv) The sustained release layer may surround the core, and the delayed release layer may surround the sustained release layer.

(xxv) The sustained release layer and the delayed release layer may not completely wrap the surface of the core.

(xxvi) The delayed release layer may not completely encapsulate the surface of the sustained release layer and/or the surface of the core.

(xxvii) The sustained release layer and the delayed release layer may wrap at least 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99% or 99.9% of the surface of the core.

(xxviii) The delayed release layer may wrap at least 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99% or 99.9% of the surface of the core and/or the surface of the delayed release layer.

(xxix) A multilayer solid oral pharmaceutical composition can include a multilayer core, wherein the multilayer core has a surface and the multilayer core includes a first layer comprising methylphenidate or a pharmaceutically acceptable salt thereof and a second layer comprising a swellable layer comprising a superdisintegrant or osmagent.

(xxx) A multilayer solid oral pharmaceutical composition can include a multilayer core, wherein the multilayer core has a surface and the multilayer core includes a first layer comprising methylphenidate or a pharmaceutically acceptable salt thereof and a second layer comprising a sustained release layer.

(xxxi) The multilayer core may further include a third layer comprising a sustained release layer.

(xxxii) The multi-layer core may further include a third layer comprising a swellable layer comprising a superdisintegrant or an osmotic agent.

(xxxiii) The delayed release layer may surround a multi-layer core.

(xxxiv) The delayed release layer may not completely wrap around the surface of the multi-layer core.

(xxxv) The delayed release layer may wrap at least 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99% or 99.9% of the surface of the multilayer core.

Brief Description of Drawings

For a more complete understanding of the present disclosure and the related features and advantages, reference is now made to the following description, taken in conjunction with the accompanying drawings, which are not to scale, and wherein:

FIG. 1 is a schematic representation of a Pharmacokinetic (PK) model of a delayed release/extended release methylphenidate formulation HLD 200;

fig. 2 is an exemplary scatter plot of individual subject PK characteristics administered HLD200, shown in linear scale;

fig. 3 is an exemplary scatter plot of individual subject PK profiles administered HLD200 doses, shown on a log-linear scale;

fig. 4 is an exemplary scatter plot of the mean (± Standard Deviation (SD)) PK profile of administered HLD200 across the entire population of experimental adult subjects, shown on a linear scale;

FIG. 5A is an exemplary scatter plot of the mean (± SD) PK characteristics of HLD200 at a 20mg dose by subject gender, shown on a linear scale;

FIG. 5B is an exemplary scatter plot of the mean (± SD) PK characteristics of HLD200 at a 100mg dose by subject gender, shown on a linear scale;

FIG. 6 is an exemplary scatter plot of mean (± SD) PK characteristics in terms of HLD200 dose, shown on a log-linear scale, across the entire population of subjects tested;

FIG. 7A is an exemplary scatter plot of the mean (± SD) PK characteristics of HLD200 at a 20mg dose by subject gender, shown on a log-linear scale;

FIG. 7B is an exemplary scatter plot of the mean (± SD) PK characteristics of HLD200 at a 100mg dose by subject gender, shown on a log-linear scale;

FIG. 8 is an exemplary goodness-of-fit plot for a basal subject population PK model at a 20mg dose of HLD 200;

FIG. 9 is an exemplary goodness-of-fit plot for a basal subject population PK model at a 100mg dose of HLD 200;

FIG. 10 is an exemplary exploratory covariate analysis plot of the relationship between body weight and kel, V, TD and SS in a group of subjects administered HLD 200;

fig. 11 is an exemplary exploratory covariate analysis plot of the relationship between gender (0 ═ female (F), 1 ═ male (M)) and kel, V, TD and SS in a group of subjects administered HLD 200;

FIG. 12 is an exemplary goodness-of-fit plot for a group of subject population PK models tested last at a 20mg dose of HLD 200;

FIG. 13 is an exemplary goodness-of-fit plot for a panel of PK models for a population of subjects last tested at a dose of 100mg of HLD 200;

figure 14 is an exemplary set of graphs reporting predicted and observed concentrations of HLD200 PK versus time for individual subject models;

figure 15 is an exemplary set of graphs reporting predicted and observed concentrations of HLD200 PK versus time for individual subject models;

figure 16 is an exemplary set of graphs reporting predicted and observed concentrations of HLD200 PK versus time for individual subject models;

figure 17 is an exemplary set of graphs reporting predicted and observed concentrations of HLD200 PK versus time for individual subject models;

figure 18 is an exemplary set of graphs reporting predicted and observed concentrations of HLD200 PK versus time for individual subject models;

FIG. 19 is a set of diagrams reporting an exemplary Visual Predictive Check. The solid lines represent the median of the predicted MPH concentrations, the circles represent the observed MPH concentrations, the thick dashed lines represent the median of the observed concentrations, and the regions between the thin dashed lines represent the 90% prediction intervals of the simulated data. Visual Predictive Check was successful if most of the observed data fell within the confidence interval (region between the light dashed lines) of the model;

figure 20 is a set of graphs reporting exemplary effects of weight and gender on expected PK profiles for HLD200 at 20mg or 100mg doses;

fig. 21 is a graph reporting exemplary simulated Methylphenidate (MPH) exposure following repeated administration of HLD 200;

FIG. 22 is a set of reports reporting HLD200 exposure in a test subject populationExemplary comparative figures of exposure (ALZA corporation). The timing of HLD drug intake was at night, 8 hours (top panel) or 10 hours (bottom panel) before 8:00am school;

FIG. 23 is a set of reports reporting HLD200 exposure in a test subject populationExemplary comparative graphs of (UCB, Inc) exposure. The timing of HLD drug intake was at night, 8 hours (top panel) or 10 hours (bottom panel) before 8:00am school;

FIG. 24 is a panel reporting HLD200 exposure and Ritalin in a population of test subjectsExemplary comparative graphs of (Novartis AG) exposures. The timing of HLD drug intake was at night, 8 hours (top panel) or 10 hours (bottom panel) before 8:00am school; (ii) a

FIG. 25 is a panel reporting HLD200 exposure and Quillivant in a population of test subjects(NextWave Pharmaceuticals, Inc.) exemplary comparative plots of exposures. The time of HLD drug intake was at night, 8 hours (top panel) or 10 hours (bottom panel) before school at 8:00 am;

FIG. 26 is a graph reporting the Swanson, Kotkin, Agler, M-Flynn, and Pelham Scale (SKAMP) scores of an exemplary set of individual patients according to treatment;

figure 27 is a graph reporting exemplary mean (± SD) SKAMP scores according to treatment in a patient population for all trials;

FIG. 28 is a graph reporting exemplary mean (+ -SD) SKAMP score curves by treatment and gender in a patient population for all trials;

FIG. 29 is a graph reporting exemplary mean (± SD) estimated MPH concentrations in a patient population across all trials;

FIG. 30 is a graph reporting an exemplary mean (. + -. SD) estimated MPH concentration by gender in a patient population for all trials;

FIG. 31 is a set of graphs reporting an exemplary goodness-of-fit graph for a comparative placebo response model;

FIG. 32 is a set of graphs reporting an exemplary Visual Predictive Check for a placebo response model. The solid line represents the median of the predicted SKAMP scores, the circles represent the median of the observed SKAMP scores, the thick dashed lines represent the median of the observed SKAMP scores, and the regions between the thin dashed lines represent the 90% predicted intervals of the simulated data;

FIG. 33 is a graph of a set of exemplary goodness-of-fit maps reported for a baseline population PK/PD model;

FIG. 34 is a diagram reporting an exemplary exploratory analysis of covariates: the relationship between body weight, age and gender for EMAX and EC 50;

FIG. 35 is a graph reporting an exemplary goodness of fit map for a final population PK/PD model

Fig. 36 is a graph reporting an exemplary PK/Pharmacodynamic (PD) model-visual predictive test. Thick dashed lines represent median predicted concentrations, circles represent observed scores, solid thick lines represent median scores, and the regions between the light dashed lines represent 90% predicted intervals of the simulated data;

figure 37 is a graph reporting an exemplary relationship between HLD200 drug exposure and clinical response in a test patient, the relationship being defined by a change in the 90% prediction interval (shaded area) relative to placebo response;

FIG. 38 is a schematic diagram illustrating an exemplary definition of Clinical Benefit (CB);

FIG. 39 is a set of graphs reporting exemplary effects of HLD200 intake time on clinical benefit across the test patient population;

FIG. 40 is a graph reporting an exemplary simulated time course of HLD200 plasma concentrations (thick solid line) and SKAMP scores following a 60mg HLD200 dose (thick dashed line) and a placebo dose (thin solid line);

FIG. 41 is a graph reporting an exemplary simulated time course of HLD200 plasma concentrations (thick solid line) and SKAMP scores following an 80mg HLD200 dose (thick dashed line) and a placebo dose (thin solid line);

FIG. 42 is a graph reporting an exemplary simulated time course of HLD200 plasma concentrations (thick solid line) and SKAMP scores following a 100mg HLD200 dose (thick dashed line) and a placebo dose (thin solid line);

FIG. 43 is a graph reporting an exemplary simulation of SKAMP scores following 60mg, 80mg, and 100mg HLD200 doses over time and placebo doses;

FIG. 44 is a set of graphs reporting exemplary data from a COMACS study: mean SKAMP total score versus time as per MCD dose level. From Sonuga-Barke EJ, Swanson JM, Coghill D, Decory HH, Hatch SJ. effectiveness of two-way across multiple access levels at differential times of the day, prediction indices from a second analysis of the study data, BMC 2004Sep 30; 4:28, which is incorporated herein by reference.

FIG. 45 is a graph reporting exemplary data from a d-MPH ER study: mean SKAMP-composite raw score from pre-dose (0 hours) to 12 hours. From Raul R.Silva et al.Effect And Duration Of Effect Of Extended-Release Dexmethylphenate Versus plant in School chip With Attention-Definit/Hyperactivity Disporter. journal Of gold And additive Psychopharmacology. volume 16, Number 3,2006, which is incorporated herein by reference.

FIG. 46 is a report from NWP06 (Quillivant)(NextWave Pharmaceuticals, Inc.)) graph of exemplary data for the study: mean integrated SKAMP scores from pre-dose (0 hours) to 12 hours. From Sharon B.Wigal et al, NWP06, an Extended-Release Oral subsystem Of Methylphenidate, Improved Attention-Definite/Hyperactivity recorder combinations with plant in a Laboratory Classoom study. journal Of Child And added following Pharmacology. volume 23, Number 1, 2013, which are incorporated herein by reference.

FIG. 47 is a graph of the change in placebo amount reported compared to SKAMP scores for patients receiving HLD200 versus that reported forData (ALZA Corp.) on dosage levels of 18mg, 36mg and 54mg, and information on QuilliantAnd graphs of exemplary comparisons with data on d-MPH (NextWave Pharmaceuticals, Inc.);

FIG. 48 is a graph reporting changes compared to placebo reporting SKAMP scores for patients receiving HLD200 versus MetadateData (dosage of 20mg, 40mg and 60mg) for (UCB, Inc), and for QuilliantAnd graphs of exemplary comparisons with data on d-MPH (NextWave Pharmaceuticals, Inc.);

FIG. 49 is a set of graphs reporting in vitro release fractions predicted and observed by an exemplary dissolution model for fast, slow and final formulations of delayed release/extended release MPH;

FIG. 50 is a set of reports on 20mg administered(Novartis) plots of exemplary observed (point) and predicted (line) mean MPH concentrations after IR;

FIG. 51 is a table reporting exemplary scores for in vitro release of methylphenidate versus in vivo release of methylphenidate for combined rapid and slow 54 formulations of HLD 200;

fig. 52 is a diagram of an exemplary internal validation of a set of reporting ivivivc models: observed (spot) and ivivivc predicted (line) methylphenidate concentrations for both fast (study HLD200-101, 54mg) and slow (study HLD200-101, 54mg) formulations;

fig. 53 is a diagram reporting an exemplary external validation of an ivivivc model: observed (dots) and ivivivc predicted (lines) methylphenidate concentrations for slow 100 and final formulations;

fig. 54A is an exemplary schematic representation of a pharmaceutical composition representative of beads having a drug-containing core surrounded by a sustained release layer and a delayed release layer.

FIG. 54B is an exemplary schematic representation of a composition as in FIG. 54A having an additional swellable layer disposed between the extended release layer and the drug-containing core;

FIG. 55A is an exemplary schematic representation of a mini-tablet pharmaceutical composition having a drug-containing core surrounded by a sustained release layer and a delayed release layer.

FIG. 55B is an exemplary schematic representation of a composition as in FIG. 55A having an additional swellable layer disposed between the extended release layer and the drug-containing core;

fig. 56 is an exemplary schematic representation of a pharmaceutical composition of beadlets comprising a core surrounded by four layers: an inert core, a swelling polymer, a drug layer, a slow release layer and an enteric layer;

FIG. 57 is a report on slave(ALZA Corporation) a plot of the fraction of methylphenidate dissolved in vitro (FDISS) versus the fraction of methylphenidate dissolved in vivo. From R.Gomeni, F.Bressole, T.J.Spencer, S.V.Faraone, Meta-analytical approach to evaluation analytical modules for the characterization of the PK profiles of extended release formulations of MPH.ASCPT 2016 analytical testing, March 8-12,2016, Hilton Bayont, San Diego, Calif., which is incorporated herein by reference.

FIG. 58 is a set of graphs reporting exemplary observed (dot) and predicted (line) mean methylphenidate concentrations following administration of a given drug. From R.Gomeni, F.Bressole, T.J. Spencer, S.V.Faraone.Meta-analytical approach to evaluation analytical modules for the characterization of the PK profiles of extended release formulations of MPH.ASCPT 2016 analytical Meeting, March 8-12,2016, Hilton Bayfront, San Diego, Calif., which is incorporated herein by reference.

FIG. 59 is a report(ALZA Corporation) a graph of exemplary data of cumulative fractions of absorptions;

fig. 60 is a graph reporting exemplary "initial" and "optimized" absorption score curves as determined by r.gomeni et al (ascopt 2016 artificial Meeting, March 8-12,2016, Hilton Bayfront, San Diego, ca., incorporated herein by reference);

fig. 61 is a graph of an exemplary fractional absorption curve of a reporting HLD 200.

FIG. 62 is a report of HLD20054 mg (study 200-A graph of exemplary data for the mean rate of change of 54mg of methylphenidate plasma concentration over time (ng/mL/hour);

FIG. 63 is a graph reporting an exemplary fraction in vitro versus in vivo release of amphetamine from an exemplary amphetamine formulation HLD100-102 having a composition similar to the HLD200 methylphenidate intermediate formulation;

fig. 64 is a graph reporting exemplary cumulative colon arrival time% for replacement beads radiolabeled with 111 indium of no more than 1 MBq. The figure shows the results from two independent experiments, denoted as "F1" and "F2";

fig. 65 is a graph reporting an exemplary visual predictive review of PK data following an IR formulation. The thick solid line represents the median prediction with 95% prediction interval (shaded area). Points represent observed MPH concentrations;

fig. 66 is a graph reporting exemplary average dissolution data for slow, medium and fast dissolution rate formulations (points) using a model to predict dissolution profiles (as indicated by the solid and dashed lines);

fig. 67 is a schematic diagram showing an exemplary Convolution-based model for fitting in vivo PKs for fast, medium and slow HLD200 formulations;

fig. 68 is a graph reporting exemplary mean PK data for 3 formulations using model prediction curves (upper panel) and in vivo release rates (lower panel);

fig. 69 is a graph reporting concentration (points) observed for exemplary average PKs versus predicted values by a convolution model (solid and dashed lines as shown) for fast, medium, and slow HLD200 formulations;

figure 70 is a graph reporting an exemplary regression analysis of in vivo absorption rates versus in vitro dissolution rates for slow, medium, and fast release HLD200 formulations;

fig. 71 is a graph reporting an exemplary regression analysis of concentrations predicted by the rolling model versus observed concentrations (points) for slow, medium, and fast release HLD200 formulations;

FIG. 72 is a graph of an exemplary scattergram reporting TD parameters characterizing in vitro dissolution and in vivo BI (left panel) and Kel (right panel) versus model prediction (dashed line);

fig. 73 is a schematic diagram showing an exemplary modified ivivivc model that includes a dependence between dissolution properties and estimated in vivo relative bioavailability;

fig. 74 is a graph reporting concentration (points) observed for exemplary mean PK versus predicted values by a modified convolution model (solid and dashed lines as shown) for slow, medium and fast release HLD200 formulations;

fig. 75 is a graph reporting an exemplary regression analysis of concentrations predicted by a modified convolution model versus observed concentrations (points) for slow, medium, and fast release HLD200 formulations;

fig. 76 is a graph reporting exemplary average dissolution data (points) for formulations used in external verification (top graph) and formulations with slow, medium, and fast dissolution rate formulations used in model development (bottom graph) using model predicted dissolution profiles (solid and dashed lines as shown);

FIG. 77 is a graph reporting an exemplary comparison of the mean PK concentration time course of formulations with median dissolution rates in study HLD200-111 with the PK concentration time course in study HLD200-109 (fasted group);

FIG. 78 is a diagram of an exemplary visual predictive test reporting PK data for study HLDs 200-109. The thick solid line represents the median prediction with 95% prediction interval (shaded area). Dots represent observed MPH concentrations;

FIG. 79 is a graph reporting an exemplary comparison of a typical PK time course (solid curve) with a convolution-based estimate of expected PK characteristics (dashed curve) in a study HLD 200-109;

FIG. 80 is a diagram reporting an exemplary external verification: regression analysis of predicted concentrations versus observed concentrations (points) by the modified convolution model;

figure 81 is a graph reporting an exemplary in vitro dissolution profile of an intermediate formulation (also referred to herein as the final formulation) of HLD200 and an exemplary in vitro dissolution profile of an amphetamine formulation (also referred to herein as HLD 100-102);

fig. 82 is a graph reporting an exemplary regression analysis of in vivo absorption rate versus in vitro dissolution rate for a rapid release HLD200 formulation;

fig. 83 is a graph reporting an exemplary regression analysis of in vivo absorption rate versus in vitro dissolution rate for a mediator release HLD200 formulation; and

fig. 84 is a graph reporting an exemplary regression analysis of in vivo absorption rate versus in vitro dissolution rate for a slow release HLD200 formulation.

Fig. 85 is an exemplary diagram depicting a modified ivivivc model that includes a dependence between dissolution properties and estimated in vivo relative bioavailability;

fig. 86A is a graph reporting exemplary values for plasma MPH concentration versus time (hours), which allows for visual predictive review of the IR MPH model (step 1).

Fig. 86B is a graph reporting exemplary values of in vitro release dose fraction (%) versus time (hours) for model predicted in vitro DR/ER-MPH dissolution profiles (step 2).

FIG. 86C is a graph reporting exemplary values of DR/ER-MPH PK curves predicted for the median model (step 3).

Fig. 86D is a graph reporting exemplary values for mean plasma MPH concentrations predicted by the convolution model (step 4).

Fig. 87 is a graph reporting an exemplary regression analysis of in vivo absorption rate versus in vitro dissolution rate for slow, medium, and fast release HLD200 formulations.

Figure 88 is a graph reporting an exemplary comparison of dissolution data observed and predicted using the single weibull model (solid line) and the double weibull model (dashed line).

Fig. 89 is a graph reporting an exemplary comparison of dissolution data observed using sigmoid Emax (solid line) and the double weibull model (dashed line) and dissolution data predicted by the model.

Fig. 90 is an exemplary diagram depicting a convolution model for evaluating ivivivc relationships.

Fig. 91 is a graph reporting exemplary values for an immediate release formulation: absorption fraction versus dissolution fraction (upper panel) and Levy plot (lower panel).

Fig. 92 is a graph reporting exemplary values for a medium release formulation: absorption fraction versus dissolution fraction (upper panel) and Levy plot (lower panel).

Fig. 93 is a graph reporting exemplary values for a slow release formulation: absorption fraction versus dissolution fraction (upper panel) and Levy plot (lower panel).

Detailed Description

In the following description, details are set forth, by way of example, in order to facilitate discussion of the disclosed subject matter. However, it will be apparent to those of ordinary skill in the art that the disclosed embodiments are exemplary and not exhaustive of all possible embodiments.

The present disclosure relates to compositions and methods that provide minimal changes in efficacy of methylphenidate in a patient over a period of time for reducing the likelihood or severity of rebound or both.

The term "subject" as used herein refers to a single human, including but not limited to a child, adult or adolescent in various embodiments. In some embodiments, a subject may be a "patient," meaning a subject that may be under the care of a physician and/or that may have been diagnosed as having ADD or ADHD, and may also have other conditions (e.g., autism or autism spectrum disorder).

Methylphenidate is useful for the treatment of various disorders, particularly ADD and ADHD. Methylphenidate is generally effective over a period of time. Typically, the time period includes the daytime period when the child is at school. For example, the period of time may typically last from 4 to 12 hours. The actual efficacy of many methylphenidate drugs during this period can show significant variation. For example, the efficacy of improving performance may vary according to a validated rating scale, such as the SKAMP score. In some embodiments, the compositions and methods disclosed herein can reduce the change in efficacy, which can be measured, for example, by a reduction in the change in SKAMP score. In some embodiments, the compositions and methods disclosed herein can reduce adverse events reported in the afternoon or evening, e.g., one or more of increased aggressiveness, influenzability (affectability), emotional instability and irritability, and the like.

At or near the end of the effective period, many patients exhibit rebound during which other symptoms occur or ADD and ADHD symptoms reappear, and may be worse than they would normally have not been treated. Bounce can interfere with home operations and bedtime. The compositions and methods disclosed herein may have a longer effective period with a more gradual decrease in methylphenidate plasma levels at later times during the day. The longer potency and absence of a sharp drop in methylphenidate plasma levels may help to reduce or avoid rebound.

Some patients may particularly experience or be sensitive to adverse effects of changes in efficacy, rebound, or both. These change-sensitive, rebound-sensitive, and both change-and rebound-sensitive patients are particularly likely to benefit from the compositions and methods disclosed herein.

Patients that are change-sensitive, rebound-sensitive and both change-and rebound-sensitive generally include patients with co-morbidities, particularly with another behavioral or mental health disorder. In particular, patients with ADD or ADHD and Autism Spectrum Disorder (ASD) can be change-sensitive, rebound-sensitive, and both change-and rebound-sensitive patients.

The present disclosure provides compositions and methods for treating Attention Deficit Disorder (ADD), Attention Deficit Hyperactivity Disorder (ADHD), or other CNS stimulant-responsive conditions or disorders by providing dosage forms that deliver therapeutic amounts of active agents in a delayed and controlled release pattern so as to provide therapeutic amounts of the agents in the active portion of the day. For pediatric patients, including adolescents, and for adults, treatment is desired in the early morning (claiming) and throughout the morning and afternoon where work or homework is required.

The compositions of the present disclosure include solid oral pharmaceutical compositions having a core comprising methylphenidate or a pharmaceutically acceptable salt thereof and at least one pharmaceutically acceptable excipient, a sustained release layer surrounding the core, and a delayed release layer surrounding the sustained release layer.

The disclosed formulations can provide therapeutic amounts of a drug over an extended period of the day in a single administration. The dosage form provides delayed release so that the dosage form can be conveniently administered before the patient sleeps. A small percentage of the drug may be released within the first 6 hours after administration so that the patient has received the minimum therapeutic dose at normal wake up time. Thus, patients do not need to be awake to take pills, then need to eat breakfast and prepare for their day before experiencing a therapeutic effect.

Individuals diagnosed with ADHD are treated with prescription stimulant drugs (e.g., methylphenidate and amphetamine and prodrugs). According to the national institute of health, all stimulants work by increasing dopamine levels in the brain. Dopamine is a brain chemical (or neurotransmitter) associated with happiness, movement and attention. The therapeutic effect of the stimulant is achieved by a slow and steady increase in dopamine, similar to that naturally produced by the brain. The dose prescribed by the physician is initially low and gradually increased until the therapeutic effect is achieved.

Treatment of ADHD with stimulants, often in combination with psychotherapy, helps to ameliorate the symptoms of ADHD, as well as the patient's self-esteem, cognition, social and familial relationships. Most commonly prescribed drugs include amphetamine and methylphenidate. These drugs have a sedative and "attentive" effect from paradox in individuals with ADHD. Researchers speculate that because methylphenidate enhances dopamine release, it may improve attention and concentration in individuals with poor dopamine signaling.

Methylphenidate can be formulated as a racemic mixture of dextrorotatory and levorotatory conformations or as the pure dextrorotatory isomer. Methylphenidate has two chiral centers in the molecule and can therefore also be further refined to enrich the dsu isomer. The use of pharmaceutically acceptable salts of methylphenidate, such as methylphenidate hydrochloride, is also contemplated by the present disclosure.

It is to be understood that the active pharmaceutical ingredient of the present disclosure may be present as a prodrug that is activated in the body of the user. One form of prodrug has an amino acid conjugated to an active ingredient. When the amino acid is enzymatically cleaved, the active drug is released. Prodrugs comprising lysyl, isoleucyl, or aspartyl conjugates are contemplated for use in the practice of the present disclosure.

The formulations of the present disclosure are designed to provide a new release and plasma profile that includes a first lag phase followed by a sigmoidal release phase. By providing this property, the dosage form provides a timed, extended therapeutic effect when taken once a day. Based on the release profile in which the dosage form passes through the stomach prior to release, the formulations disclosed herein provide at least the following additional advantages: low variability of gastric emptying, low risk of sudden dose dumping, low incidence of gastric discomfort, and low intra-and inter-individual variability.

In some embodiments, the solid oral pharmaceutical composition of the present disclosure may be multilayered. In some embodiments, the solid oral pharmaceutical compositions of the present disclosure may comprise methylphenidate or a pharmaceutically acceptable salt thereof, a sustained release layer, and a delayed release layer. In some embodiments, the solid oral pharmaceutical composition of the present disclosure may include a core comprising methylphenidate or a pharmaceutically acceptable salt thereof, and a sustained release layer and a delayed release layer may surround the core. In some embodiments, a sustained release layer may surround the core, and a delayed release layer may surround the sustained release layer. In some embodiments, the solid oral pharmaceutical compositions of the present disclosure have a core comprising methylphenidate or a pharmaceutically acceptable salt thereof and at least one pharmaceutically acceptable excipient, a sustained release layer surrounding the core, and a delayed release layer surrounding the release layer.

In some embodiments, the sustained release layer and the delayed release layer may not completely surround the core. For example, in some embodiments, the sustained release layer and the delayed release layer may individually or together wrap at least 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99%, or 99.9% of the core surface. In some embodiments, the delayed release layer may not completely surround the sustained release layer and/or the core. For example, in some embodiments, the delayed release layer may coat at least 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99%, or 99.9% of the surface of the core and/or the sustained release layer.

In some embodiments, the multilayer solid oral pharmaceutical compositions of the present disclosure may include a multilayer core. In some embodiments, the multilayered core may comprise methylphenidate or a pharmaceutically acceptable salt thereof and at least one pharmaceutically acceptable excipient.

A first example of a dosage form is a single population of beads that can be administered in a capsule or liquid or gel suspension containing the beads. An example of a bead structure 10 is shown schematically in fig. 54A and 54B. In figure 54A, the inner circle represents the drug-containing core, which includes the active ingredient or prodrug, a suitable excipient, and optionally a super disintegrant or osmotic agent. The core may include, for example, an active agent, a disintegrant, an osmotic agent, or a pore former and a binder. An exemplary core comprises about 20-25% active agent, about 45-60% microcrystalline cellulose, about 10-30% potassium chloride, and about 3-5% binder, such as polyvinylpyrrolidone or hydroxypropyl cellulose. The drug-containing core may be prepared by a variety of methods known in the art, including wet granulation, extrusion, and spheronization. In some embodiments, two layers wrap the core. In some embodiments, the first layer is a sustained release layer and the outer layer is an optionally pH-dependent delayed release layer. In certain embodiments, the core as shown in fig. 54A may be an inert non-Pareil bead. The inner core may be beads of sugar and starch, or it may consist of microcrystalline cellulose. Any spherical bead suitable for forming a core bead and which is pharmaceutically acceptable may be used. In such embodiments, the drug and excipient of the core may be layered onto the core bead, resulting in a three-layered formulation.

In some embodiments, the outermost layer 14 is a delayed release or enteric coating. In certain embodiments, the layer comprises a water soluble polymer, a water insoluble polymer, a plasticizer, and a lubricant. The delay time of drug release can be controlled by the ratio of water soluble and insoluble polymers, plasticizer concentration, amount of lubricant and coating weight gain, which can be as high as 35-45%. In some embodiments, the delayed release layer may be a pH-dependent polymer that dissolves at a pH above 5.5.

In some embodiments, the extended release layer 16 is designed to provide a slower initial release rate that increases over a period of up to 8-10 hours after exposure of the layer to an aqueous environment. In some embodiments, the delayed release layer and/or the sustained release layer form a semipermeable membrane. Increased drug distribution can be achieved by membranes that become more permeable over time. Examples of the sustained-release layer include water-soluble polymers, water-insoluble polymers, plasticizers, and lubricants. The drug release rate can be controlled or maintained by varying the ratio of water soluble and water insoluble polymers and by varying the coating thickness to a weight gain of 15-45%.

In some embodiments, the solid oral pharmaceutical compositions of the present disclosure may include a swellable layer comprising a superdisintegrant or an osmotic agent. In some embodiments, a solid oral pharmaceutical composition of the present disclosure may include a delayed release layer surrounding a swellable layer, a sustained release layer, and a core comprising methylphenidate or a pharmaceutically acceptable salt thereof. In some embodiments, the extended release delayed release layer may not completely encapsulate the swellable layer, the extended release layer, and/or the core comprising methylphenidate or a pharmaceutically acceptable salt thereof. For example, in some embodiments, the delayed release layer may encapsulate at least 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99% or 99.9% of the surface of the swellable layer, the sustained release layer, and/or the core comprising methylphenidate or a pharmaceutically acceptable salt thereof. In some embodiments, the solid oral pharmaceutical compositions of the present disclosure may include a multi-layer core comprising a swellable layer, a sustained release layer, and a core comprising methylphenidate or a pharmaceutically acceptable salt thereof.

In some embodiments, the solid oral pharmaceutical compositions of the present disclosure comprise a multilayered core. In some embodiments, the multilayer core comprises a first layer comprising methylphenidate or a pharmaceutically acceptable salt thereof and a second layer comprising a superdisintegrant or osmagent.

In some embodiments, the solid oral pharmaceutical compositions of the present disclosure comprise a delayed release layer surrounding a multi-layer core. In some embodiments, the delayed release layer may not completely surround the multilayer core. For example, in some embodiments, the delayed release layer may wrap at least 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99%, or 99.9% of the surface of the multilayer core.

For example, in some embodiments, such as shown in FIG. 54B, a swellable layer 18 comprising a superdisintegrant or an osmotic agent is disposed between the core and the sustained release layer.

In certain embodiments, the compositions and methods of the present disclosure comprise a 4-layer 30 formulation, as shown in fig. 56. The formulation may comprise an inner core 15 of non-pareil beads and 4 concentric layers from the inside to the outside, depicted as a swollen polymer layer 18, a drug layer 12, a sustained release layer 16 and a pH dependent delayed release layer 14 (which may be a pH dependent layer). It should be understood that the layer may have a surface. Fig. 56 shows, for example, surface 121 of drug layer 12, surface 161 of sustained release layer 16, and surface 141 of delayed release layer 14.

In certain embodiments, the 4-layer composition may be prepared in a step-wise manner. In a first step, a hydrophilic polymer coating suspended in ethanol was applied with a binder to a 30-50% weight gain on the nonpareil beads. In certain embodiments, PolyOx Coogulant SFP (PEO) sold by Dow Chemical Company is a hydrophilic polymer and hydroxypropyl cellulose (HPC LF) is added as a binder. Then, using hydroxypropyl cellulose such asEF sealed the PolyOx layer to 10% weight gain. Then, the Active Pharmaceutical Ingredient (API) is mixed with a binderSuspended together in ethanol and coated onto the layered beads, and a slow release and delayed release coating applied as described herein.

Fig. 55A and 55B show an embodiment in which the core is a platelet 20 rather than a bead. The core and layer in fig. 55A and 55B are functionally the same as the identically numbered layers on the beads in fig. 54A and 54B, except that there is no optional inert core.

Various water-soluble polymers may be used in the formulations disclosed herein. Such polymers include, but are not limited to, polyethylene oxide (PEO), ethylene oxide-propylene oxide copolymers, polyethylene glycol-polypropylene glycols (e.g., poloxamers), carbomers, polycarbophil, chitosan, polyvinylpyrrolidone (PVP), polyvinyl alcohol (PVA), hydroxyalkyl celluloses such as hydroxypropyl cellulose (HPC), hydroxyethyl cellulose, hydroxymethyl and hydroxypropyl methylcellulose, sodium carboxymethyl cellulose, methyl cellulose, hydroxyethyl methyl cellulose, hydroxypropyl methyl cellulose, polyacrylates such as carbomers, polyacrylamides, polymethacrylamides, polyphosphazines, polyoxazolidines, polyhydroxyalkylcarboxylic acids, alginic acid and derivatives thereof such as carrageenan alginates, ammonium alginate and sodium alginate, starch and starch derivatives, sodium alginate, calcium alginate, and starch derivatives, Polysaccharides, carboxyvinyl polymers, polyethylene glycols, natural gums such as guar gum, gum arabic, tragacanth gum, karaya gum and xanthan gum, povidone, gelatin, and the like.

In certain embodiments, at least the delayed release layer comprises one or more polymers, such as an acrylic polymer, an acrylic copolymer, a methacrylic polymer, or a methacrylic copolymer, including but not limited toL100、L100-55、 L 30D-55、5100、4135F、RS, acrylic and methacrylic acid copolymers, methyl methacrylate copolymers, ethoxyethyl methacrylate, cyanopropyl methacrylate, aminoalkylmethacrylate copolymers, polyacrylic acid, polymethacrylic acid, alkylamine methacrylate copolymers, polymethyl methacrylate, polymethacrylic anhydride, polymethacrylate, polyacrylamide, polymethacrylic anhydride and glycidyl methacrylate copolymers, alkylcelluloses such as ethylcellulose, methylcellulose, carboxymethylcellulose calcium, certain substituted cellulose polymers such as hydroxypropyl methylcellulose phthalate, and hydroxypropyl methylcellulose acetate succinate, cellulose acetate butyrate, cellulose acetate phthalate, and cellulose acetate trimaleate, and mixtures thereof, Polyvinyl acetate phthalate, polyesters, waxes, shellac, zein, and the like.

Eudragits are well known polymers and copolymers for controlled release applications. For enteric coatingsThe grades are anionic polymers based on methacrylic acid and methacrylic acid esters. They contain-COOH as a functional group. They dissolve in the range of pH5.5 to pH7.FS30D is an aqueous dispersion based on an anionic copolymer of methyl acrylate, methyl methacrylate and methacrylic acid. It is insoluble in acidic media, but dissolves by forming a salt at pH above 7.0. Eudragit L100-55 and L30-55 dissolved at pH above 5.5. Eudragit L100 and S100 dissolved at pH above 6.0.

For many oral dosage forms, sustained release is usedThe preparation can control the release of active ingredient regularly. Drug delivery can be controlled throughout the gastrointestinal tract to increase therapeutic efficacy and patient compliance.Different polymer combinations of RL (easy-to-penetrate) and RS (difficult-to-penetrate) grades enable tailoring of the (custom-tailed) release profile and can be selected within a wide range to achieve desired drug delivery properties.The NE polymer is a neutral ester dispersion that does not require plasticizers and is particularly useful in the granulation process for making matrix tablets and sustained release coatings.

Exemplary osmotic agents (osmagents) or osmotic agents (osmotic agents) include organic and inorganic compounds such as salts, acids, bases, chelating agents, sodium chloride, lithium chloride, magnesium sulfate, lithium sulfate, potassium chloride, sodium sulfite, calcium bicarbonate, sodium sulfate, calcium lactate, d-mannitol, urea, tartaric acid, raffinose, sucrose, alpha-d-lactose monohydrate, glucose, combinations thereof, and other analogs or equivalents thereof generally known in the art.

The term "disintegrant" as used herein is meant to refer to a compound used in solid dosage forms to promote the breakdown of the solid (layer) into smaller particles that are more readily dispersed or dissolved. Exemplary disintegrants include, for example and without limitation, starches such as corn starch, potato starch, pregelatinized starch and modified starches thereof, sweeteners, clays, bentonite, microcrystalline cellulose (e.g., Avicel^TM) Calcium carboxymethylcellulose, croscarmellose sodium, alginic acid, sodium alginate, potassium cellulose polacrilin (e.g. Amberlite)^TM) Alginates, sodium starch glycolate, gums, agar, guar gumLocust bean, karaya, pectin, tragacanth, crospovidone, and other materials known to those of ordinary skill in the art. The super disintegrant is a fast acting disintegrant. Exemplary super disintegrants include crospovidone and low substituted HPC.

In a preferred embodiment, a plasticizer is also included in the oral dosage form. Plasticizers suitable for use in the present invention include, but are not limited to, low molecular weight polymers, oligomers, copolymers, oils, small organic molecules, low molecular weight polyols having aliphatic hydroxyl groups, ester plasticizers, glycol ethers, poly (propylene glycol), multi-block polymers, mono-block polymers, low molecular weight poly (ethylene glycol), citrate plasticizers, triacetin, propylene glycol, and glycerin. Such plasticizers may also include ethylene glycol, 1, 2-butanediol, 2, 3-butanediol, styrene glycol, diethylene glycol, triethylene glycol, tetraethylene glycol and other poly (ethylene glycol) compounds, monopropylene glycol monoisopropyl ether, propylene glycol monoethyl ether, ethylene glycol monoethyl ether, diethylene glycol monoethyl ether, sorbitol lactate, ethyl lactate, butyl lactate, ethyl glycolate, dibutyl sebacate, tributyl acetylcitrate, triethyl citrate, triethyl acetylcitrate, tributyl citrate and allyl glycolate.

One aspect of the compositions and methods of the present disclosure is that the formulation or dosage form may also include one or more ingredients that prevent or prevent abuse of the active ingredient by crushing and inhaling the formulation in powder form. Thus, a nasal irritant may be included as a separate layer, or incorporated into the outer layer, the sustained release layer or the core of the dosage form. Exemplary stimulants include, but are not limited to, sodium lauryl sulfate, which is also known as sodium lauryl sulfate, or capsaicinoids including capsaicin and synthetic capsaicinoids. In certain embodiments, the dosage form comprises 1 to 10% sodium lauryl sulfate.

The compositions of the present disclosure may also include one or more functional excipients such as lubricants, thermal lubricants (thermal lubricants), antioxidants, buffers, alkalizing agents, binders, diluents, sweeteners, chelating agents, colorants, flavorants, surfactants, solubilizing agents, wetting agents, stabilizers, hydrophilic polymers, hydrophobic polymers, waxes, lipophilic materials, absorption enhancers, preservatives, absorbents, crosslinking agents, bioadhesive polymers, retarding agents, porogens, and fragrances.

Lubricants or thermal lubricants useful in the present invention include, but are not limited to, fatty acid esters, glyceryl monooleate, glyceryl monostearate, wax, carnauba wax, beeswax, vitamin E succinate, and combinations thereof.

The term "antioxidant" as used herein is meant to refer to an agent that inhibits oxidation and thus serves to prevent deterioration of the formulation by oxidation due to the presence of free oxygen radicals or free metals in the composition. Such compounds include, for example and without limitation, ascorbic acid, ascorbyl palmitate, Butylated Hydroxyanisole (BHA), Butylated Hydroxytoluene (BHT), hypophosphorous acid (hypophosphorous acid), monothioglycerol, sodium ascorbate, sodium formaldehyde sulfoxylate, and sodium metabisulfite, and those known to one of ordinary skill in the art. Other suitable antioxidants include, for example, vitamin C, sodium bisulfite, vitamin E and its derivatives, propyl gallate or sulfite derivatives.

Binders suitable for use in the present invention include beeswax, carnauba wax, cetyl palmitate, glyceryl behenate, glyceryl monostearate, glyceryl palmitostearate, glyceryl stearate, hydrogenated castor oil, microcrystalline wax, paraffin wax, stearic acid, stearyl alcohol, stearate 6000WL1644, gelucire 50/13, poloxamer 188, and polyethylene glycol (PEG)2000, 3000, 6000, 8000, 10000, or 20000.

Buffers are used to resist changes in pH upon dilution or addition of acids or bases. Such compounds include, for example and without limitation, potassium metaphosphate, potassium phosphate, disodium acetate, and sodium citrate, both anhydrous and dihydrate, salts of inorganic or organic acids, salts of inorganic or organic bases, and other buffers known to those of ordinary skill in the art. ,

the term "alkalizing agent" as used herein is meant to refer to compounds that are commonly used to provide an alkaline medium for product stability. Such compounds include, for example and without limitation, ammonia solution, ammonium carbonate, diethanolamine, monoethanolamine, potassium hydroxide, sodium borate, sodium carbonate, sodium bicarbonate, sodium hydroxide, triethanolamine and trolamine, and other alkalizing agents known to those of ordinary skill in the art.

Exemplary adhesives include: polyethylene oxide; polypropylene oxide; polyvinylpyrrolidone; polyvinylpyrrolidone-co-vinyl acetate; acrylate and methacrylate copolymers; polyethylene; polycaprolactone; polyethylene-co-polypropylene; alkylcelluloses and cellulose derivatives, such as low-substituted HPC (L-HPC), methylcellulose; hydroxyalkyl celluloses such as hydroxymethyl cellulose, hydroxyethyl cellulose, hydroxypropyl cellulose and hydroxybutyl cellulose; hydroxyalkyl alkylcelluloses such as hydroxyethyl methylcellulose and hydroxypropyl methylcellulose; starch, pectin; PLA and PLGA, polyesters (shellac), waxes such as carnauba wax, beeswax; polysaccharides such as cellulose, gum tragacanth, gum acacia, guar gum and xanthan gum.

Exemplary chelating agents include EDTA and salts thereof, alpha hydroxy acids such as citric acid, polycarboxylic acids, polyamines, derivatives thereof, and other chelating agents known to those of ordinary skill in the art.

The term "colorant" as used herein is meant to refer to compounds commonly used to impart color to solid (e.g., tablet) pharmaceutical formulations. Such compounds include, for example and without limitation, FD & C Red No.3, FD & C Red No.20, FD & CYellow No.6, FD & C Blue No.2, D & C Green No.5, D & C Orange No.5, D & C Red No.8, caramel and iron oxide, danese Red, other f.d. & c. dyes and natural colorants such as grape skin extract, beet Red powder, beta carotene, annatto (annato), carmine (carmine), turmeric, chili powder, and others known to those of ordinary skill in the art. The amount of colorant used will vary as desired.

The term "fragrance" as used herein is meant to refer to compounds commonly used to impart a pleasant and common taste to pharmaceutical preparations. Exemplary flavoring agents or spices include synthetic flavoring oils and spices, and/or natural oils, extracts derived from plants, leaves, flowers, fruits, and the like, and combinations thereof. These may also include cinnamon oil, oil of wintergreen, peppermint oil, clove oil, bay oil, anise oil, eucalyptus oil, thyme oil, cedar leaf oil, oil of nutmeg, oil of sage, oil of bitter almonds, and cassia oil. Other useful flavorings include vanilla, lemon oil, including lemon, orange, grape, lime (lime) and grapefruit, and fruit essences, including apple, pear, peach, strawberry, raspberry, cherry, plum, pineapple, apricot, and so forth. Flavoring agents that have been found to be particularly useful include commercially available orange, grape, cherry, and bubble gum flavors and mixtures thereof. The amount of flavoring agent may depend on a variety of factors, including the desired organoleptic effect. The flavoring agent will be present in any amount desired by one of ordinary skill in the art. Particular flavourings are grape and cherry flavourings and citrus flavourings, such as oranges.

Suitable surfactants include polysorbate 80, sorbitan monooleate, polyoxymers, sodium lauryl sulfate, or other surfactants known in the art. Soaps and syndets may be used as surfactants. Suitable soaps include fatty acid alkali metal, ammonium and triethanolamine salts. Suitable detergents include cationic detergents such as dimethyl dialkyl ammonium halides, alkyl pyridinium halides, and alkylamine acetates; anionic detergents such as alkyl, aryl and olefin sulfonates, alkyl, olefin, ether and monoglyceride sulfates, and sulfosuccinates; nonionic detergents, for example, fatty amine oxides, fatty acid alkanolamides, and poly (oxyethylene) -block-poly (oxypropylene) copolymers; and amphoteric detergents such as alkyl beta-aminopropionates and 2-alkylimidazoline quaternary ammonium salts; and mixtures thereof.

Wetting agents are agents that lower the surface tension of liquids. Wetting agents include alcohols, glycerol, proteins, peptide water-miscible solvents such as glycols, hydrophilic polymers polysorbate 80, sorbitan monooleate, sodium lauryl sulfate, fatty acid alkali metals, ammonium, and triethanolamine salts, dimethyl dialkyl ammonium halides, alkyl pyridinium halides, and alkylamine acetates; anionic detergents such as alkyl, aryl and olefin sulfonates, alkyl, olefin, ether and monoglyceride sulfates and sulfosuccinates; nonionic detergents such as fatty amine oxides, fatty acid alkanolamides, and poly (oxyethylene) -block-poly (oxypropylene) copolymers; and amphoteric detergents such as alkyl beta-aminopropionates, and 2-alkylimidazoline quaternary ammonium salts; and mixtures thereof.

Solubilizers include cyclodextrins, povidone, combinations thereof, and other solubilizers known to one of ordinary skill in the art.

Exemplary waxes include carnauba wax, beeswax, microcrystalline wax, and other waxes known to those of ordinary skill in the art.

Exemplary absorption enhancers include dimethyl sulfoxide, vitamin E PGS, sodium cholate, and other absorption enhancers known to those of ordinary skill in the art.

Preservatives include compounds commonly used to prevent microbial growth. Suitable preservatives include, for example and without limitation, benzalkonium chloride, benzethonium chloride, benzyl alcohol, cetylpyridinium chloride, chlorobutanol, phenol, phenyl ethyl alcohol, phenylmercuric nitrate and thimerosal, and other preservatives known to those of ordinary skill in the art.

Examples of absorbents include sodium starch glycolate (Explotab @)^TM,Primojel^TM) And croscarmellose sodium (Ac-Di-Sol)^TM) Crosslinked PVP (Polyplasdone)^TMXL 10), magnesium aluminium silicate (veegum), clays, alginates, PVP, alginic acid, calcium carboxymethylcellulose, microcrystalline cellulose (e.g. Avicel)^TM) Polacrillin (polacrillin) potassium (e.g., Amberlite)^TM) Sodium alginate, corn starch, potato starch, pregelatinized starch, modified starch, cellulose agents, montmorillonite (e.g., bentonite) clay, gums, agar, locust bean gum, karaya gum, pectin, tragacanth gum, and other disintegrants known to those of ordinary skill in the art.

A crosslinker is defined as any compound that will form crosslinks between polymer moieties. The crosslinking agent may include, for example and without limitation, organic acids, alpha-hydroxy acids, and beta-hydroxy acids. Suitable crosslinking agents include tartaric acid, citric acid, fumaric acid, succinic acid, and other crosslinking agents known to those of ordinary skill in the art.

Bioadhesive polymers include polyethylene oxide KLUCEL (hydroxypropyl cellulose), CARBOPOL, polycarbophil, GANTREZ, poloxamers, and combinations thereof, and other bioadhesive polymers known to those of ordinary skill in the art.

Retarders are insoluble or sparingly soluble polymers having a glass transition temperature (Tg) above 45 ℃ or above 50 ℃ before being plasticized by other agents in the formulation including other polymers and other excipients necessary for processing. Such excipients include waxes, acrylic acid, cellulosics, lipids, proteins, glycols, and the like.

Exemplary porogens include water soluble polymers such as polyethylene glycol, propylene glycol, poloxamers, and povidone; binders such as lactose, calcium sulfate, calcium phosphate, and the like; salts such as sodium chloride, magnesium chloride, and the like; combinations thereof, and other analogs or equivalents thereof generally known in the art.

The term "sweetener" as used herein is meant to refer to compounds commonly used to impart sweetness to formulations. Such compounds include, for example and without limitation, aspartame, dextrose, glycerin, mannitol, saccharin sodium, sorbitol, sucrose, fructose, and other such materials known to those of ordinary skill in the art.

It is understood that compounds used in the field of pharmaceutical formulation are generally used for various functions or purposes. Thus, if a compound specified herein is referred to only once, or is used to define more than one term herein, its purpose or function should not be deemed to be limited to only that specified.

In some embodiments, the invention relates to pharmaceutical formulations for once daily administration of CNS stimulants for the treatment of conditions responsive to these drugs, such as ADD, ADHD, bipolar depression, narcolepsy, sleep disorders and fatigue. The dose is formulated to be taken before bedtime and to begin to be released after a lag of several hours, so that the patient has absorbed a sufficient amount of the drug to have a therapeutic effect when awake and ready to leave work or school. Another aspect of the formulation is that the drug is released in ascending doses throughout the day to overcome any acute toleragenic effects and maintain therapeutic levels of the drug.

One embodiment of the compositions and methods of the present disclosure is a dosage form comprising a capsule encapsulating a single bead or population of tablets comprising a core and 2 or more coatings around the core. The inner core is a bead or a mini-tablet containing the API and one or more excipients. The core is wrapped in a sustained release layer and an outer delayed release layer.

In certain embodiments, the sustained release layer comprises a combination of a water soluble polymer and a water insoluble polymer. The extended release coating may contain a combination of polyethylene oxide and, for example, ethyl cellulose, or a combination of hydroxypropyl methylcellulose and ethyl cellulose. The ethylcellulose product useful in the disclosed dosage forms is Ethocel^TMSold under the trademark Dow Chemical Company. The dissolution rate of the sustained release layer can be controlled by adjusting the ratio of water-soluble polymer to water-insoluble polymer in the coating or layer. The weight ratio of water insoluble polymer to water soluble polymer may be adjusted, for example, but not limited to, 90:10 to 10:90, 80:20 to 20:80, 75:25 to 25:75, 70:30 to 30:70, 67.5:33.5 to 33.5:67.5, 60:40 to 40:60, 56:44 to 44:56, or to 50: 50.

The extended release coating may also contain a plasticizer, such as triethyl citrate (TEC), in an amount of 3% to 50% by weight of the total polymer. Other additives for the coating may include titanium dioxide, talc, colloidal silica or citric acid.

Some examples of sustained release layers are shown in example 29. Various formulations include those in which the ratio of water-insoluble polymer to water-soluble polymer is varied, and those in which the ratio is reversed. In certain embodiments, the active ingredient or API may be included in a sustained release layer. Any of the disclosed APIs may be incorporated into the sustained release layer.

While the disclosed compositions of capsules containing a single bead or population of tablets having a sustained release layer and an outer delayed release layer are shown herein to be effective delivery systems with new release characteristics and surprisingly low variability of absorption when administered to humans, it will be appreciated that alternative compositions may be used in accordance with the present disclosure.

In certain embodiments, the drug-containing core bead or mini-tablet is coated with a delayed release layer comprising one or more water-insoluble polymers, one or more water-soluble polymers and silicone oils to achieve a desired delay or lag time as in the present disclosure prior to release. The lag time and release are controlled by the ratio of the two polymers and the thickness of the layer. In such embodiments, the delayed release layer may include, but is not limited to, cellulose acetate phthalate, cellulose acetate trimaleate, hydroxypropyl methylcellulose phthalate, polyvinyl acetate phthalate, acrylic polymers, polyvinyl acetate diethylaminoacetate, hydroxypropyl methylcellulose acetate succinate, cellulose acetate trimellitate, shellac, methacrylic acid copolymer, Eudragit L30D, Eudragit L100, Eudragit FS30D, Eudragit S100, or any combination thereof. The delayed release layer may also include a plasticizer, or in certain embodiments, the delayed release layer may include a methacrylic acid copolymer type B, mono-and diglycerides, dibutyl sebacate, and polysorbate 80. The delayed release layer may also comprise a cellulose ether derivative, an acrylic resin, a copolymer of acrylic acid and a methacrylate ester having a quaternary ammonium group, a copolymer of acrylic acid and a methacrylate ester, or any combination thereof. The layer may further comprise a powder component such as talc as a carrier for the silicone oil.

In certain embodiments, the CNS stimulating agent can be contained in a delayed and/or controlled release capsule. In such embodiments, the water-insoluble capsule contains one or more compartments in which the drug or active agent is contained. Additionally, one or more absorbents, super absorbents or osmolytes are included in the drug-containing compartment. The capsule further comprises one or more holes (at least one in fluid communication with each compartment) filled with a water-soluble polymer and a delayed release layer surrounding the entire capsule.

In such embodiments, the length of the initial delay may be controlled by the composition and thickness of the outer delay release layer. This layer may be a pH dependent layer or a pH independent layer as disclosed herein. When the capsule is administered to a human, the delayed release layer begins to lose integrity as the capsule passes through the gastrointestinal tract. When the water-soluble plug is exposed and dissolves, the aqueous fluid enters the drug-containing compartment and is absorbed by the absorbent or osmotic agent, driving the active agent from the capsule through the pores. The release profile can be controlled by the concentration and absorption characteristics of the absorbent or osmotic agent to achieve the desired profile.

Other examples of compositions described herein are provided in examples 30 to 42.

In some embodiments described herein, "HLD 200" refers to exemplary compositions of the present disclosure, e.g., as described in the examples. FDA approved HLD200 formulation is also known as JORNAY(Ironshore Pharmaceuticals&Development,Inc.)。

As described herein, with other methylphenidate drugs, e.g.(ALZA Co., Ltd.), Ritalin(Novartis AG)、Metadate(UCB Corp.) and Quilliant(NextWave Pharmaceuticals Inc.) the compositions of the present disclosure exhibit significantly different pharmacokinetics, including in vitro dissolution profiles, in vivo absorption profiles, and in vitro-in vivo correlations (IVIVIVC). In contrast to the compositions of the present disclosure, these other methylphenidate drugs are formulatedTo have an Immediate Release (IR) component and an Extended Release (ER) component. Models that can be used to describe the pharmacokinetics of these other drugs exhibit a linear and time-invariant relationship between dissolution and absorption, with multiphasic (IR and ER) release profiles of absorption in vivo modeled using the double weibull function r2 (t):

wherein: ff is the fraction of the dose released in the 1 st pass (IR), td is the time required to absorb 63.2% of the released dose in the 1 st pass (IR), td1 is the time required to absorb 63.2% of the released dose in the 2 nd pass (ER), ss is the sigmoid factor of the first pass (IR), ss1 is the sigmoid factor of the second pass (ER) (R.Gomeni, F. Bresolle, T.J.Spencer, S.V.Faraone.Meta-analytical approach to evaluation of the PK profiles for processing the MP profiles of MPH.ASCPT 2016Annual, March 8-12,2016, Hilton front, Bayer, Saneo, CA, incorporated herein by reference). The in vivo absorption model is believed to follow a weibull distribution, and therefore the absorption curve is believed to be sigmoidal.

The pharmacokinetics of a drug can be described in terms of the in vitro dissolution-in vivo absorption correlation (IVIVC). For example, IVIVIVC is generally defined by the U.S. Food and Drug Administration (FDA) as a predictive mathematical model that describes the relationship between in vitro properties of a dosage form and in vivo inverse vice versa. In general, the in vitro property may be the rate or extent of drug dissolution or release, while the in vivo response may be the plasma drug concentration or the amount of drug absorbed. The United States Pharmacopeia (USP) also defines IVIVC as the establishment of a relationship between biological properties produced by a dosage form or parameters derived from biological properties and physicochemical properties of the same dosage form. Typically, the parameter derived from the biological property may be, for example, AUC or Cmax, while the physicochemical property may be the in vitro dissolution profile.

For example, for drugs such as(ALZA Corporation)、Ritalin(Novartis AG)、Metadate(UCB, Inc) and Quillivant(NextWave Pharmaceuticals, Inc.), the relationship between the fraction of methylphenidate dissolved in vitro and the fraction of methylphenidate absorbed in vivo is linear or nearly linear. For example, FIG. 57 is a graph of the fraction of methylphenidate reporting in vitro dissolution (FDISS) versus the fraction of methylphenidate from(ALZA Corporation) of the fraction of methylphenidate absorbed in vivo (Fabs). As above, FIG. 57 shows the fraction of methylphenidate dissolved in vitro (FDISS) and resulting fromThe in vitro-in vivo correlation (IVIVC) between the fractions (Fabs) of methylphenidate absorbed in vivo (ALZA Corporation) is linear or nearly linear.

As will be appreciated by those skilled in the art, the linear relationship may be described by a linear polynomial function, or referred to as a first order polynomial. The degree of a polynomial is generally understood to mean the highest degree of a monomial (individual term or variable, e.g. x) with non-zero coefficients. Thus, a polynomial function of one degree can generally be understood to mean a mathematical function or equation in which a variable in the function, such as x, has a non-negative integer exponent with a maximum value of 1. Similarly, a quintic polynomial function may generally be understood to represent a mathematical function or equation in which the highest degree of its monomials with non-zero coefficients has a sum of non-negative integer exponentials with a maximum value of 5, for example. For example, when referring to the "degree" of a function, each uncertainty is added, e.g., x³+x²5 times.

In some cases, the term "approximately linear" may mean that a linear polynomial function best fits a series of data points. For example, a series of data points may be said to be approximately linear when a first order polynomial function better fits the series of data points than, for example, a second order polynomial function, a third order polynomial function, a fourth order polynomial function, a fifth order polynomial function, or a sixth order polynomial function.

A curve fitting process can be used to construct a polynomial function that has the best fit to a series of data points. Examples of curve fitting include polynomial regression, polynomial interpolation, and the like. Statistical packages and numerical software such as R, such as GNU Scientific Library, MLAB, Maple, MATLAB, Mathemica, GNU Octave, and SciPy, include commands for curve fitting in each case.

FIG. 58 is a set of graphs reporting exemplary observed (dot) and predicted (line) mean methylphenidate concentrations following administration of a given drug. As above, as shown in FIG. 58, the double Weibull function model can accurately predict(ALZA Corporation)、Ritalin(Novartis AG)、Metadate(UCB, Inc) and Quillivant(NextWave Pharmaceuticals,Inc.),and also(Rhodes Pharmaceuticals l.p.) to demonstrate a linear or near linear relationship of weibull absorption in multiple phases for each of these drugs (as described above).

In some embodiments, the pharmacokinetics of the methylphenidate compositions described herein can be modeled using the double weibull function r2 (t):

In some embodiments, the pharmacokinetics of the methylphenidate compositions described herein may be described by a time-varying in vivo absorption model comprising a single [ r1(t) ] weibull function, as follows:

wherein: td is the time required to absorb 63.2% of the released dose and ss is the s-shaped factor, such as described in examples 1 to 22.

In some embodiments, the pharmacokinetics of the methylphenidate compositions described herein may be described by an in vivo absorption model that includes a sigmoid eMax function, as follows: :

where EC is the time to release 50% of the dose and ga is a parameter characterizing the shape of the absorption curve (see examples 50-53).

The compositions described herein are formulated such that, when administered to a human subject, methylphenidate is released and absorbed according to a significantly different mechanism than other methylphenidate drugs, such as(ALZA Corporation)、Ritalin(Novartis AG)、 Metadate(UCB, Inc) and Quillivant(NextWave Pharmaceuticals, Inc.). In various embodiments, the compositions described herein do not exhibit linear or near linear ivivivivc. See, for example, examples 22 to 25 and examples 50 to 53. For example, as described in example 24, in some embodiments, a quintic polynomial function may best fit the ivivivc of a composition of the present disclosure.

Thus, in some embodiments, the present disclosure relates to a solid oral pharmaceutical composition comprising methylphenidate or a pharmaceutically acceptable salt thereof, wherein the in vivo absorption model of the solid oral pharmaceutical composition has a single weibull function:

or the double Weibull function r2(t)

Or sigmoid eMax function:

and wherein the correlation of the plurality of fractions of in vitro dissolution of the solid oral pharmaceutical composition to the plurality of fractions of in vivo absorption of the solid oral pharmaceutical composition is non-linear.

In some embodiments, the non-linear correlation of the plurality of fractions of in vitro dissolution and the plurality of fractions of in vivo absorption of the solid oral pharmaceutical composition may best fit a quadratic polynomial function, a cubic polynomial function, a quartic polynomial function, a quintic polynomial function, or a sextic polynomial function.

As will be understood by one of ordinary skill in the art upon reading this disclosure, ivivivc maps can be generated according to various methods using data sets from individual subjects or populations of subjects. For example, in one approach, the PK profile of an individual subject can be predicted and then the average of the individual subject data is calculated to generate an ivivivc map of the subject population. Alternatively, for example, in another method, an ivivivc map of a population of subjects is generated by using an average observed PK profile from the population of subjects.

In some embodiments, the plurality of fractions of in vitro dissolution and the plurality of fractions of in vivo absorption of the solid oral pharmaceutical composition may comprise a plurality of values from 0 to 1.

The methods of the present disclosure include methods of treating a disorder or condition in a subject having a disorder or condition responsive to administration of methylphenidate. The method comprises orally administering to a subject an effective amount of a solid oral pharmaceutical composition described herein.

In some embodiments, administration of a composition described herein to a population of subjects provides a significant improvement in ADHD-related behavior or ability over a period of time, e.g., at least 12 consecutive hours, as measured by a validated rating scale, score, or combined score. A validated rating scale, as used herein, is a scale, score, or combined score validated or disclosed in a peer review journal, or deemed valid by one of skill in the art, or deemed valid by a drug regulatory agency, such as the U.S. food and drug administration or the european drug administration, for example, to provide a valid measure of treatment efficacy.

A validated rating scale, score, or combined score as used herein includes methods approved by a governmental drug management agency, methods disclosed and/or relied upon in or in peer-reviewed journals, or in clinical trials reported in peer-reviewed journals (peer-reviewed journels), or methods approved by an association of a physician or clinician. Examples of such methods or measurements include, but are not limited to, ADHD-RS-IV scale based on psychiatric disorder diagnostic and statistical manuals, fourth edition criteria, preschool functional Questionnaire (BEFore School functional Questionaire) (BSFQ), Clinical Global Impression (Clinical Global Impression) (CGI), Clinical Global Impression of Improvement (CGI-S), Consortium Global Index Parent (Consortium Global Index Parent) (CGI-P), psychiatric disease diagnostic and statistical manuals-fourth edition text revision (DSM-IV-TR), Parent of modified late and early behaviors (event Rating of event and Mobile viewing review) (RMMB-PRE), Product Performance of measurement (RMP), Swanson, Kotkin, M-Flynn and Pelham (SKAMP) rating Scale, SKAGlin-CS composite score, AM or PM Adult ADHD drug rebound Scale (AMRS), or AM or PM Adult ADHD drug Effect Smoothness Scale (additive ADHD medical of Effect Scale) (AMSES).

In some embodiments, the present invention relates to a method wherein the composition provides a significant improvement in the SKAMP score during about 12 consecutive hours when the composition is administered to a population, e.g., a population of juvenile or pediatric subjects, and wherein the composition provides a significant improvement in the SKAMP score during about 11 hours to about 23 hours after administration, or wherein the composition provides a significant improvement in the SKAMP score during 10-12 hours of about 7AM to about 9PM the following day when the composition is administered at about 8-10 PM in the afternoon.

The methods of the present disclosure can reduce the change in efficacy of an improvement in ADHD-related behavior or ability over a period of time, or reduce the likelihood or severity of rebound, or both, as measured by a validated rating scale. In some embodiments, administration of a composition described herein to a population of subjects provides a clinical response, e.g., an improvement in the SKAMP score over a period of time, wherein the improvement provides a minimal change in efficacy, a reduced likelihood or severity of rebound, or both over the period of time.

In some embodiments, described herein is a model that can accurately predict changes in SKAMP scores when using methylphenidate PK characteristics as input variables. See, for example, examples 10 to 22. In particular, for example, examples 15 to 18 examples 15-18 describe the sensitivity of SKAMP response of the compositions of the present disclosure with respect to time of day of administration and sigmoid factor (β). In some embodiments, the compositions and methods of the present disclosure provide minimal changes, reduced rebound potential or severity or both in efficacy as a function of the in vivo release rate of the methylphenidate composition described herein and/or as a function of time of administration prior to the morning classroom. In some exemplary embodiments (see, e.g., examples 15-18), efficacy may be defined as the area of change in SKAMP score (AUEC) from a placebo-treated population of subjects over a period of time, e.g., from 8:00am to 8:00 pm. In other embodiments, efficacy may be defined as the area of change in SKAMP score for a population of untreated subjects over a period of time, e.g., 8:00am to 8:00 pm. An exemplary illustration of efficacy assessment, also referred to herein in certain embodiments as "clinical benefit" or "CB", is shown in fig. 38, where the light dashed line represents the SKAMP score for placebo, the thick dashed line curve represents the SKAMP score for HLD200, and the shaded area represents AUEC.

In some embodiments, the disclosure relates to compositions and methods that provide minimal changes in efficacy, reduced likelihood or severity of rebound, or both, in improving ADHD-related behavior or ability, such as SKAMP scores, over a period of time.

In some embodiments, the time period can begin at or near 8:00am, 9:00am, 10:00am, 11:00am, 12:00pm, 1:00pm, 2:00pm, 3:00pm, 4:00pm, 5:00pm, 6:00pm, or 7:00 pm.

In some embodiments, the time period may begin at or near 8, 9, 10, 11, 12, 13, 14, 15, or 16 hours after administration of the composition. For example, in some embodiments, the composition can be administered at or near the time period, or at 12:00am on the day prior to the time period, at 4:00pm, 5:00pm, 6:00pm, 7:00pm, 8:00pm, 9:00pm, 10:00pm, 11:00 pm. For example, as described in examples 16 and 17, the composition can be administered at 8:00am 12 hours before the start of the morning classroom.

In some embodiments, the time-varying in vivo absorption model for a solid oral pharmaceutical composition having a core comprising methylphenidate or a pharmaceutically acceptable salt thereof, as described herein, has a single weibull function:

wherein td absorbs 63.2% of the released methylphenidate or pharmaceutically acceptable salt thereof and ss is the sigmoid factor, and the sigmoid factor (ss) may have a value of or about 4.5 to 8.5, such as at or about 4.5, 5.5, 6.5, 7.5 or 8.5. For example, in some embodiments, as described in example 17, the sigmoid factor (ss) may have a value of from or about 4.5 to 8.5, such as at or about 4.5, 5.5, 6.5, 7.5, or 8.5, and the time period may begin about 10-12 hours or about 10-12 hours after administration of the composition (e.g., see table 21).

In some embodiments, the change in efficacy may be described by a volatility index (FI). For example, examples 20 to 22 describe exemplary compositions of the present disclosure (HLD200) with(ALZA Corporation)、Metadate(UCB,Inc)、NWP06 (Quillivant(NextWave Pharmaceuticals, Inc.) and d-MPH ER (Focalin)(Novartis AG)). As described in examples 20-22, in some embodiments, efficacy can be calculated as the mean simulated value of the change in SKAMP score (CHP) compared to placebo calculated from 8:00am to 8:00 pm. The response and variability of response of different drugs can be evaluated using the mean of the change in SKAMP scores compared to placebo and using the volatility index (FI). The fluctuation index can be defined as:

in some embodiments, the compositions and methods described herein show an improvement in ADHD-related behavior or ability, e.g., a decrease in SKAMP score, a decrease in change in efficacy during the day, and start at an earlier time of day, e.g., with other drugs such as e.g., as(ALZA Corporation)、Metadate(UCB,Inc)、NWP06 (Quillivant(NextWave Pharmaceuticals, Inc.) and d-MPH ER (Focalin)(Novartis AG) for comparison. As described in examples 20-22, the earlier onset of improvement in SKAMP score correlates with less change in efficacy in improving SKAMP score during the day. The description is given in example 22In one exemplary embodiment, administration of an exemplary composition of the present disclosure HLD200 provides a fluctuation index of-0.87. The fluctuation index of HLD200 was over 50% lower than that of the other drugs evaluated.

Thus, in some embodiments, the disclosure relates to compositions and methods that provide improved minimal changes in ADHD-related behavior or ability, such as SKAMP scores, over a period of time or for reducing the likelihood or severity of rebound, or both. In some embodiments, this change can be measured by the Fluctuation Index (FI):

wherein CHP is a change in SKAMP score over the time period as compared to placebo. In some embodiments, FI may have an absolute value of less than 1.0.

In some embodiments, the time period may begin after a measurable increase in plasma MPH concentration. For example, in various embodiments, a measurable increase in plasma MPH concentration may begin at or about 4, 5, 6, 7, 8, 9, 10, 11, or 12 hours after administration of a composition described herein.

In some embodiments, the time period may end at the time Cmax occurs (Tmax).

In some embodiments, the time period may begin at the time Cmax occurs (Tmax).

In some embodiments, the time period may end at or about 9:00am, 10:00am, 11:00am, 12:00pm, 1:00pm, 2:00pm, 3:00pm, 4:00pm, 5:00pm, 6:00pm, 7:00pm, or 8:00 pm.

In some embodiments, the time period may last up to or up to 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, or 16 hours after the beginning of the time period.

In some embodiments, this period of time may continue until or until the plasma concentration of about MPH is less than 5ng/mL or less than about 5 ng/mL. In some embodiments, this period of time may continue until or about until the plasma concentration of MPH is less than 3 to 5ng/mL or less than about 3 to 5 ng/mL.

In some embodiments, this period of time may continue until or until about 3, 4, 5, 6, 7, 8, 9, 10, 11, or 12 hours after the Tmax of methylphenidate in the subject.

In some embodiments, the period of time may last until the subject falls asleep after Tmax.

In some embodiments, the change in the value of the SKAMP score may not exceed or exceed about 6, 7, 8, 9, or 10 during the entire time period.

In some embodiments of the compositions and methods described herein, the rate of change of MPH plasma concentration over time may be no greater than +2.5ng.hr/mL throughout the time period following administration of a dose of up to 100mg MPH. For example, as described in example 46, FIG. 62 showsRate of change of the exemplary HLD200 formulation compared to the 54mg dose. For example, HLD20054 mg has a maximum rate of change of about +1.0 ng/mL/hour for an increase in methylphenidate plasma concentration and a maximum rate of change of about-0.5 ng/mL/hour for a decrease in methylphenidate plasma concentration. FIG. 62 also shows that HLD 200100 mg has a maximum rate of change of about +2.5 ng/mL/hour for increased plasma concentrations of methylphenidate and a maximum rate of change of about-1.2 ng/mL/hour for decreased plasma concentrations of methylphenidate. In contrast to this, in the case of,54mg had a maximum rate of change of the methylphenidate plasma concentration increase of about +3.6 ng/mL/hour and a maximum rate of change of the methylphenidate plasma concentration decrease of about-1.0 ng/mL/hour. Will be provided withData normalized to 100mg1.85, expected for the dose multiplied by 100/54100mg, the maximum rate of change that will give an increase in methylphenidate plasma concentration is about +3.6 × 1.85 ═ 6.7 ng/mL/hour, and the maximum rate of change in methylphenidate plasma concentration is about-1.0 × 1.85 ═ 1.85 ng/mL/hour.

In some embodiments of the compositions and methods described herein, the rate of change of MPH plasma concentration over time may be no greater than +2.5ng after dosing of a dose of 20mg to 100mg MPH over the period of time. hr/mL. In some embodiments, the rate of change in plasma concentration of MPH between Tmax and 6 hours after Tmax may not have a value less than-1.2 ng/hr/mL following administration of a dose of 20mg to 100mg MPH.

In some embodiments, the slope of the MPH plasma concentration curve may be calculated in consideration of body weight, dosage, etc., and thus may be determined in consideration of the mg/kg dose of the subject.

Thus, in various embodiments, when the rate of change of MPH plasma concentration over time between time points Tmax and 6 hours after Tmax has a value of no less than-1.2 ng/hr/mL after dosing of a dose of up to 100mg MPH, the SKAMP score may not have a fluctuation index greater than 1 and the subject may experience less change and/or less rebound as compared to other MPH drugs described herein.

In some embodiments, the time period may comprise a time period in which the MPH plasma concentration is between Cmax and at least 40% Cmax. During this time period, the index of fluctuation of the SKAMP score over the time period may be no greater than 1, and/or the rate of change of the MPH plasma concentration over time may have a value of no greater than +1.5ng.hr/mL or no less than-1.5 ng.hr/mL. In other words, in some embodiments, the rate of change of the plasma concentration of MPH over time may not have an absolute value greater than 1.5, for example, during a period in which the plasma concentration of MPH is between Cmax and at least 40% Cmax. Thus, in such embodiments, the subject may experience minimal changes in efficacy or a reduced likelihood and/or severity of efficacy rebound during the period.

In some embodiments, the present disclosure relates to compositions and methods that provide minimal change in the efficacy of methylphenidate during a time window for reducing the likelihood or severity or both of rebound, wherein methylphenidate or a pharmaceutically acceptable salt thereof is absorbed in the colon. In some embodiments, at least 90% is absorbed in the colon.

Typical gastrointestinal transit times are: the stomach is emptied for 90 minutes, the transit time of the small intestine is 4-6 hours, and the colon arrival time is about 8 hours.

For example, as described in example 45, using the PK/PD model described herein, it was estimated that an exemplary maximum therapeutic effect over a 24 hour period was related to the absorption of methylphenidate starting after about 8 hours, related to the time of arrival in the colon. In some embodiments, the compositions and methods described herein provide that absorption of methylphenidate begins after about 8 hours, correlated with the time of arrival in the colon. In contrast, for example, exemplary simulation results show that for other methylphenidate drugs, for example(ALZA Corporation), 50% of the methylphenidate is absorbed within 4-6 hours, associated with absorption in the small intestine, and where all of the methylphenidate is absorbed for about 10-12 hours, which is much faster than the exemplary optimal rate described in example 45.

Fig. 64 is a graph reporting exemplary cumulative colon arrival time% for replacement beads radiolabeled with no more than 1MBq111 indium. The replacement beads have the same size shape and density as the beads of the methylphenidate formulation, but lack the methylphenidate active ingredient and are not coated. The figure shows the results from two independent experiments, denoted as "F1" and "F2". Figure 64 shows that all the radiolabeled beads have reached the colon 10 hours after administration.

Examples 48 and 49 of the present disclosure describe the differences in ivivivc of exemplary fast, medium and slow formulations of HLD 200. For example, fig. 69 shows a medium formulation (also referred to herein as a final formulation, as opposed to Jornay)) From about 8 hours after applicationThere was an increasing plasma concentration of methylphenidate, while the rapid formulation had an earlier increasing plasma concentration of methylphenidate from about 6 hours after administration, corresponding to more upper intestinal absorption than the medium formulation. In addition, the slow formulation showed an increasing plasma concentration of methylphenidate from about 10 hours after administration, corresponding to a further release into the colon than the medium formulation. Fig. 70 and fig. 82-84 show that the patterns of data points (dots) for the slow, medium and fast formulations have different curvatures. The data points (points) of the sustained release formulation are best fit to a fifth order polynomial. In contrast, the plot of the point of IVIVIVC for the fast formulation is more linear, associated with the most upper intestinal absorption. Ivivivc plots for medium preparations were best fit to a cubic polynomial.

In some embodiments, the present disclosure relates to methods of treating subjects susceptible to adverse effects of changes in efficacy, rebound, or both. In some embodiments, the disclosure relates to compositions and methods for treating subjects suffering from a co-disease, in particular another behavioral or mental health disorder, e.g., patients suffering from both ADD or ADHD and Autism Spectrum Disorder (ASD).

For example, in children with both ADHD and ASD, a dose effect of methylphenidate on rebound hyperactivity and aggressive behavior has been observed (Soo-Jeong Kim et al, 2017) Autosm Dev disorder 47:2307-2313, incorporated herein by reference).

Thus, in some embodiments, the present disclosure relates to compositions and methods of treating subjects having ADD or ADHD and Autism Spectrum Disorder (ASD). The method comprises orally administering to the subject an effective amount of a solid oral pharmaceutical composition described herein, wherein the administration provides minimal change in efficacy of methylphenidate during a time window for reducing the likelihood or severity of rebound, or both.

Examples

The methods and compositions disclosed herein are further illustrated in the following examples, which are provided by way of illustration, and not by way of limitation.

Examples 1 to 22 relate to PK/PD modeling and modeling for methylphenidate formulations described herein. The following methods were used:

PK data.HLD200(20mg and 100mg) was administered to 20 adult volunteers at 21:00h using two sequential, two-cycle crossover design studies (student HLD200-104) on the Latin square (Latin square). The following morning, subjects received a medium fat breakfast. Gender and weight of each individual were collected and studied as potential covariates in the PK model.

And (4) developing a PK model.Population PK analysis was performed in the following order of steps: 1. exploratory data analysis, 2 basic structure model development, 3 covariate analysis, 4 model refinement and 5 model evaluation, wherein the following steps are carried out:

1. exploratory data analysis: exploratory data analysis is performed to understand the information content of the analyzed data set relative to the expected model, to find extrema and/or potential outliers, to check for correlations between covariates, and to evaluate possible trends in the data. Linear and log-linear scatter plots of concentration versus time were generated using individual PK values and mean values at different sampling times.

scatterplots

2. Basic structure model development: preliminary evaluation of PK results indicated that HLD200 concentration-time curves show a configuration/clearance profile consistent with the single compartment PK model. Thus, a single compartment based PK model was evaluated.

Preliminary evaluation of PK results indicated that HLD200 concentration-time curves show a configuration/elimination profile consistent with the single compartment PK model. Thus, a single compartment based PK model was evaluated.

Where kel is the elimination rate constant, f (t) is the time-varying in vivo release rate, r (t) is an input function, and Cp is the MPH concentration.

Convolution-based modeling methods are applied using prescribed input functions (r.gomeni, f.bressole, t.j.spencer, s.v. farone.meta-analytical approach to evaluation adaptive models for characterizing the PK profiles of extended release formulations of mph.ascpt 2016 artificial Meeting, March 8-12,2016, hillon Bayfront, San Diego, ca., which is incorporated herein by reference). FIG. 1 is a schematic of the HLD200 PK model.

Two time-varying in vivo absorption models were evaluated: mono [ r1(t) ] and bis [ r2(t) ] weibull functions.

Wherein: ff is the fraction of the dose released during the 1 st pass, td is the time required to absorb 63.2% of the released dose during the 1 st pass, td1 is the time required to absorb 63.2% of the released dose during the 2 nd pass, ss is the sigmoid factor of the first pass and ss1 is the sigmoid factor of the second pass.

The estimated model parameters are: average structural model parameters, magnitude of inter-individual variability (IIV), magnitude of intra-temporal variability (IOV), and magnitude of residual variability.

The following variance component models were evaluated:

inter-individual variability: inter-individual variability (IIV) models describe unexplained random variability (fixed effects) in individual values of structural model parameters. The IIV of the model parameters is assumed to be lognormally distributed. Thus, the relationship between the model parameters (P) and their variances is represented as:

P_j＝P_TVe^ηp

wherein P is_jIs the parameter value of the jth individual, P_TVIs a representative value of P of the population, η P represents the difference between Pj and PTV, with the average being zero and the squareDifference is ω p²Normal distribution of (2). If the goodness-of-fit map reveals potential deviations in the error model, an alternative error model may be considered.

Internally Variability (Inter-Ocvasion variabilty): since all patients had PK samples at both occasions, an exponential error model was used to assess the inter-occasion variability of the population PK parameters.

Residual variability: residual variability, including but not limited to intra-individual variability, experimental error, process noise, and/or misfixing of the model, is modeled using additive, proportional, and combined error structures as described below:

additional error: yij ═ ytij + epsilon 1ij

Proportional error: yij ═ ytij (1+ epsilon 1ij)

Combining additive and proportional errors: yij ═ ytij (1+ epsilon 1ij) + epsilon 2ij

Where yij is the jth observation in the ith individual, ytij is the corresponding model prediction, and ε 1ij (or ε 2ij) is the normal distribution random error with a mean of zero and a variance of σ 2. If the goodness-of-fit map reveals potential deviations in the residual variability model, other residual error models are also revealed.

The parameter estimation procedure was performed using a non-linear mixed effects modeling method as implemented in the NONMEM software (version 7.3). The following parameters were estimated:

fixing effect:

kel first order elimination Rate constant

V distribution volume of the Central Chamber (V ═ V/F)

Parameters of the Weibull time varying input function ss, td

Random effect

IIV on kel, V, ss, td

V, ss and td (assuming this parameter does not change from one occasion to another, the IOV on kel is not considered)

Residual error:

add (Add _ error) and proportion (Prop _ error) model components

Comparison of surrogate models: comparison of surrogate models was performed using log-likelihood ratio tests. A surrogate model is considered to be a significantly better data descriptor when the reduction in the Objective Function Value (OFV) associated with the model is ≧ 3.84, and χ 2 for 1 degree of freedom (df) is < 0.05.

Model diagnosis: and generating a goodness-of-fit map for evaluating the result of model fitting. These figures include the following figures: a) observed data versus concentration for individual and model predictions, b) absolute weighted residuals versus individual predictions, and c) conditional weighted residuals versus time.

3. Covariate analysis: the mathematical form of the relationship between covariates on pharmacokinetic parameters of a population that must be examined and evaluated is identified using graphical and statistical methods and considering basic scientific principles. The covariate effect was explored for all model parameters. The covariates expected to be identified were: body weight and sex.

Covariate model building is a step-by-step process consisting of forward and backward selection procedures. Likelihood ratio tests are used to evaluate the significance of incorporating or removing a fixed effect into a population model based on a priori set alpha levels. For the forward selection, a significance level of 0.05 for FOCE-I was used.

Step-by-step forward addition procedure: initially, each covariate was included individually in the base model to identify significant covariates, where significance was a reduction of the Objective Function Value (OFV) > 3.84, and χ 2 <0.05 for 1 degree of freedom (df) using FOCE-I. Next, significant covariates were added to the base model, one covariate at a time to one parameter. First, the most significant covariates are included in the model. This new model serves as a new starting model for the next iteration. The significance check and additional steps are repeated until all the significant covariates are included and the final model is defined.

And (3) backward elimination process: after the complete model is defined, the significance of each covariate is checked separately by removing one at a time from the complete model. The significance of the covariates examined was determined using the following evaluation criteria:

if after removal, FOCE-I, OFV was used to increase by more than 6.63 points (χ 2 < 0.01 for 1 df), covariates were retained in the model.

With the exception of OFV, the same criteria as above were used to evaluate the significance of covariates in the program. The least significant covariates were excluded from the model. The least significant covariates were excluded from the model. The elimination step is repeated until all non-significant covariates are excluded and the final model is defined.

4. And (3) improving the model: simulations were performed to illustrate the effect of the retained covariates and their combinations on the MPH exposure.

PD data.The clinical data used for this analysis was SKAMP scores collected in phase 3 (HLD200-107 study), multicenter, open label treatment optimization, double-blind, randomized, placebo-controlled, forced exit, parallel group study to evaluate the safety and efficacy of overnight administration of HLD200, a novel methylphenidate delayed and extended release formulation (DELEXIS), in children 6-12 years of age with Attention Deficit Hyperactivity Disorder (ADHD) in a laboratory classroom setting.

Development of PK/PD model.In this study, no MPH samples were collected. Thus, individual exposure for PK/PD analysis was assessed based on the results of the population PK model and the individual demographic (weight and sex) covariate values.

The model included a parametric description of the trajectory of the placebo and HLD200 processed SKAMP scores.

The trace of SKAMP scores after placebo administration was described using the indirect response model [ r (t) ]. The effect of HLD200 was described by drug-related changes compared to placebo using the Emax model:

r (t) is a placebo response defined as: 4

p(t)＝AA·e^-t·P1(equation 7)

Wherein k is_inDenotes the zero-order rate constant, k, which produces the response (R)_outIs the first order rate constant of the loss response, AA is the amplitude of the placebo effect, P₁Is the rate of change of placebo effect, Emax is the maximum achievable HLD 200-related effect, EC₅₀Is the concentration of MPH associated with 50% of the maximal response, and g is the shape of the exposure-response relationship. When the system is assumed to be stationary, the response variable (R) starts at a predetermined baseline value (Bas), changes over time and returns to Bas. Thus:

and (6) performance of the model.Model performance/validation and stability were evaluated using visual predictive checks. Visual predictive inspection (VPC) was used to evaluate the appropriateness of the final model, including the effect of statistically significant covariates. This assumes that the parameter uncertainty is negligible with respect to inter-individual and residual variances. The basic premise is that the models and parameters derived from the observed data set should produce simulated data similar to the original observed data. Based on the final model, five hundred replicates of the original dataset were simulated, and a 90% prediction interval was calculated based on the simulated dataset. Observed concentrations versus time data were plotted on a prediction interval plot to visually assess the agreement between simulated and observed data. The quantile (5 th, median, 95 th) of the simulated data was plotted against the observed data quantile.

SoftwarePreparation of all data, summary statistics (mean, median, standard deviation and other measures as appropriate), reporting and graphical display were performed using SAS (version 9.3) and R (version 3.2.5). Any SAS and R scripts used for data preparation and final analysis are obtained. Population PK and PK/PD analyses were performed using NONMEM software, version 7.3(ICON Development Solutions). R-based package Xpose (version 4.3) was used as a model construction aid for population analysis using NONMEM. Statistical evaluation of results from clinical trial simulations using the SAS statistical package (version 9.3)。

A total of 20 subjects were included in the population PK analysis dataset, for a total of 960 PK measurements.

The following abbreviations are used herein: ADHD (attention deficit hyperactivity disorder), AUEC (area under the effect curve), CB (clinical benefit), CI (confidence interval), df (degree of freedom), ER (extended release), F (bioavailability), FI (fluctuation index), FOCE-I (first order conditional approach with interaction), IIV (intra-subject variability), MAX (maximum), MIN (minimum), MPH (methylphenidate), OFV (objective function value), PK (pharmacokinetics), RSE (relative square error), SD (standard deviation), SE (standard error), VPC (visual predictive test).

Examples 1-9 relate to PK analysis results

Example 1.PK population: demographic data.

Table 1 shows the gender distribution of the subjects included in the study. Table 2 shows the descriptive statistics of body weight, and table 3 shows the descriptive statistics of body weight by gender.

Table 1-gender distribution of subjects:

sex	Frequency of	Percentage of	Cumulative frequency	Cumulative percentage of
					Female with a view to preventing the formation of wrinkles	14	70.00	14	70.00
Male sex	6	30.00	20	100.00

Table 2-descriptive statistics of body weight:

TABLE 3 descriptive statistics of body weight by gender

Example 2 PK population: descriptive analysis.

Fig. 2 and 3 show exemplary scatter plots of individual PK characteristics by dose on a linear and log-linear scale, respectively.

Fig. 4 and 5A and 5B show exemplary scatter plots of mean (± SD) PK profiles by dose and gender on a linear scale throughout the population.

Fig. 6 and 7A and 7B show exemplary scatter plots of mean (± SD) PK profiles by dose and sex on a log linear scale throughout the population.

Example 3 PK model development: comparison between one and two weibull input functions.

The comparison between one and two weibull input functions is presented in table 4 in the time order in which they were evaluated. The first column of the table lists the model for each trial. The second column lists the reference run compared to the test run (run). The third and fourth columns list the change in OFV for each test run and OFV from the reference run (test-reference), respectively. The fifth column briefly describes the hypothesis or goal tested by the model. The test results in the last column describe the conclusion, which is not statistically significant or statistically significant, which can be derived based on chi-squared statistics compared to a reference run.

TABLE 4 comparison between one and two Weibull input functions: :

OFV-maximum likelihood ratio target function value

(ii) statistically significant

The comparison results show that the model of the single weibull input function is a suitable model. Thus, the model is considered a reference base model.

Example 4 PK model development: evaluation of variability between timings.

Given the randomized administration of two treatments (20mg or 100mg) per subject in separate treatment periods (occasions), inter-occasion variability (IOV) in PK response was evaluated in addition to intra-individual variability (IIV). A comparison of the performance of the base model with and without the opportunity-to-opportunity variability assessment is shown in table 5.

Table 5-evaluation of the time-to-time variability:

OFV-maximum likelihood ratio target function value

(ii) statistically significant

The comparison shows that the presence of the IOV parameter significantly (p < 0.0001) improves the performance of the model. Thus, model "run 3" is considered a new reference base model.

Example 5 PK model development: and estimating the parameters of the basic model.

The estimated fixed and random effect parameters are shown in tables 6 and 7, respectively. In these tables, SE represents the standard error of the parameter estimates, RSE represents the root mean square error, and CV% represents the coefficient of variation of the IIV and IOV variability.

Table 6-basal population PK model: fixed effect parameter estimation:

table 7-basal population PK model: estimation of random Effect parameters:

goodness of fit (GOF) diagnostic plots for the basal population PK models are shown by dose in fig. 8 and 9. Identical lines, reference lines and trend lines overlap when appropriate.

In summary, there were no significant deviations in these diagnostic plots, suggesting that the basal population PK model is sufficient to describe HLD200 PK.

Example 6 PK model development: and (5) analyzing covariates.

The mathematical form of the relationship between the parameters and covariates is evaluated using graphical and statistical methods and considering basic scientific principles to identify covariates to be included in the PK models of the underlying population. The covariates expected to be identified were: sex and body weight.

An estimate of Empirical Bayesian of individual parameters was obtained from the basic model in the NONMEM analysis (run 3). A graphical study of the potential impact of covariates on PK parameter variability was performed by analyzing scatter plots of individual PK parameters versus selected covariates (fig. 10 and 11).

Analysis of the distribution of individual parameter estimates relative to the selected covariates indicated the potential dependence of V, TD and SS on body weight and TD on gender.

The characteristics of the different models that were studied to explain the effects of the covariates with reference to the base model (in the absence of covariates) are summarized in table 8.

Table 8-list of all intermediate population PK models evaluated:

covariate analysis showed that the best working model is a model of the effect of body weight on distribution volume and gender on TD: the volume increase with body weight and the dose release Time (TD) of 63.8% was longer (-20%) in females relative to males.

The details of the model illustrating the effect of covariates are as follows:

run 3 (reference base model):

TD＝θ₁

kel＝θ₂

V＝θ₃

SS＝θ₄

run 4 (for body weight of V, using the heterologous growth quantification model):

run 5 (body weight for V and body weight for TD):

run 6 (body weight for V and sex for TD):

wherein if the gender is M, then TD is theta₁Otherwise TD ═ theta₅

Run 7 (body weight for V and for SS):

run (body weight for V, body weight for TD and sex for TD):

wherein if the gender is M, then TD is theta₁Otherwise TD ═ theta₅

Run (sex for TD): wherein if the gender is M, then TD is theta₁Otherwise TD ═ theta₅

The estimated fixed and random effect parameters of the final model are given in tables 9 and 10, respectively. In these tables, SE represents the standard error of the evaluation parameters, RSE represents the root mean square error, and CV% represents the coefficient of variation of the IIV and IOV variability.

Table 9-final population PK model: fixed effect parameter estimation:

table 10-final population PK model: estimation of random Effect parameters:

goodness of fit (GOF) diagnostic plots for the basal population PK models are shown by dose in fig. 12 and 13. Identical lines, reference lines and trend lines overlap when appropriate.

In summary, there were no significant deviations in these diagnostic plots, suggesting that the basal population PK model is sufficient to describe HLD200 PK.

The individual model predicted and observed PK concentrations versus time are shown in figures 14 to 18.

The VPC of HLD200 at 20mg and 100mg doses is shown in figure 19.

VPC showed that the model performed well at different doses: the PK median and the scatter of data around the median are well described by 5%, 50% (median) and 95%, indicating that this population model correctly describes the observed data.

Example 7 PK model development: effect of covariates on HLD200 Exposure after Single HLD200 dosing

Simulations were performed to evaluate the effect of retained covariates (weight and gender) and their combinations on MPH exposure.

To illustrate the expected impact of covariates on MPH exposure, covariate values were classified as follows: sex: f and M, body weight: 55kg, 65kg and 75kg, dose: 20mg and 100 mg.

The median MPH concentration for each category was calculated and presented in fig. 20.

The results of this evaluation indicate that peak values increase with decreasing body weight and peak arrival times increase in female subjects relative to male subjects.

Example 8 PK model development: effect of covariates on HLD200 Exposure after repeated HLD200 dosing

Simulations were performed to evaluate MPH exposure at a pre-stage following repeated administration of HLD200 as a function of the selected covariates.

Consider the following simulation scenario: sex: f and M, body weight: female 63kg, and male 77 (these values correspond to the mean body weight of the study population), dose: 20mg and 100mg, once daily during 6 days.

The median MPH concentration for each class was calculated and graphically presented in fig. 21. Estimated Ctrough and Cmax values after repeated administration of HLD200 are reported in table 11.

Table 11-estimated Ctrough and Cmax values after repeated administration of HLD 200:

example 9 PK model development: exposure of HLD200 with other ERs Comparison of exposures of MPH formulations

MPH exposure at doses of 20mg and 100mg of HLD200 has been performed relative to administration(ALZA Corporation)(18mg,36mg,54mg)、 Ritalin(Novartis AG)(40mg)、Metadate(UCB, Inc) (20mg, 40mg, and 60mg) and Quillivant(NextWave Pharmaceuticals, Inc.) (60 mg.) comparison of the exposures observed thereafter.

The HLD200 was administered for comparison, assuming 8 or 10 hours prior to school at 8:00am on the next day, prior to sleeping at night. It is contemplated that another ER MPH formulation is administered 8:00am in the morning. Simulated MPH concentrations following administration of HLD200 have been generated using previously estimated population PK parameter values.

The comparison results are presented in fig. 22 to 25.

Examples 10 to 15 relate to the PK/PD assay results. In study HLD200-107, a total of 117 subjects were included: 53 were treated with placebo and 64 with HLD 200. A sequential modeling approach is applied. In a first step, using a previously developed population PK model, the individual MPH concentrations were derived using individual demographic data and individual HLD200 dosing history, then modeled on placebo data, and finally SKAMP scores were analyzed by fixing placebo and previously estimated PK exposure values

Example 10 PK/PD assay results: PK/PD populations: descriptive analysis

Table 12 shows the gender and treatment profiles of the subjects included in the study. Table 13 shows descriptive statistics of age, weight and height with gender and treatment.

Table 12-distribution of subjects included in the study by gender and treatment: :

table 13-descriptive statistics of demographics:

fig. 26 shows a scatter plot of the individual SKAMP score curves after processing.

Fig. 27 and 28 show scatter plots that characterize mean (± SD) SKAMP scores by treatment and gender across the population.

This descriptive analysis appears to indicate that a stronger response (change compared to placebo) is observed in women than in men.

Example 11 PK/Pd analysis results estimated PK Exposure

Individual MPH concentrations for each subject treated with HLD200 were derived using a previously developed population PK model, in conjunction with individual demographics (weight and gender) and individual HLD200 dosing history. The MPH concentration was estimated to match the time of measurement of the SKAMP score. The mean (± SD) MPH concentration values are shown in fig. 29, and the mean MPH concentrations are shown in fig. 30 by nature.

Example 12 PK/PD assay results: placebo model development

The analytical data set included data collected for 53 subjects, for a total of 470 SKAMP measurements.

The estimated fixed effect and random effect parameters are shown in tables 14 and 15, respectively. In these tables, SE represents the standard error of the parameter estimates, RSE represents the root mean square error, and CV% represents the coefficient of variation of the IIV variability.

Table 14-placebo response model: fixed effect parameter estimation:

table 15-placebo response model: estimation of random Effect parameters:

a goodness of fit (GOF) diagnostic plot for the placebo response model is shown in figure 31. Identical lines, reference lines and trend lines overlap when appropriate.

In summary, there were no significant deviations in these diagnostic plots, indicating that the placebo-response model was sufficient to describe SKAMP scores in placebo-treated subjects.

The VPC of the placebo-responsive model is shown in figure 32. VPC showed that the model performed well. The median SKAMP score and the scatter of the data around the median are well described by 5%, 50% (median) and 95%, indicating that the population model correctly describes the observed data.

Example 13 PK/PD assay results: PK/Pd model development

The analytical data set included data collected in 64 subjects for a total of 557 SKAMP score measurements.

And (5) developing a basic model. The base model was developed by using the following information: (1) individual MPH concentrations estimated by a previously developed population PK model, and (2) placebo responses defined by fixed and random effects parameters estimated in a previously developed placebo response model.

The parameters estimated in this analysis (run 1) were: EMAX, EC50 and g.

The estimated fixed effect and random effect parameters are shown in tables 16 and 17, respectively. In these tables, SE represents the standard error of the parameter estimates, RSE represents the root mean square error, and CV% represents the coefficient of variation of the IIV variability.

Table 16-basal population PK/PD model: fixed effect parameter estimation: :

table 17-basal population PK/PD model: estimation of random Effect parameters:

in this analysis, it was not possible to evaluate the random effect of the 'g' parameter. A goodness of fit (GOF) diagnostic plot for the basal population PK/PD model is shown in figure 33. Identical lines, reference lines and trend lines overlap when appropriate.

In summary, there were no significant deviations in these diagnostic plots, indicating that the basal population PK/Pd model was sufficient to describe the SKAMP score associated with HLD200 treatment.Example 14 PK/PD assay results: development of PK/Pd model: covariate analysis

The mathematical form of the relationship between the parameters and covariates is evaluated using graphical and statistical methods and considering basic scientific principles to identify covariates to be included in the PK/PD model of the basal population. The covariates expected to be identified were: sex and body weight.

An estimate of Empirical Bayesian of individual parameters was obtained from the basic model in the NONMEM analysis (run 1). A graphical study of the potential impact of covariates on PK/PD parameter variability was performed by analyzing scatter plots of individual PK/PD parameters versus selected covariates (figure 34).

Analysis of the distribution of individual parameter estimates relative to the selected covariates indicated the potential dependence of EC50 on weight, age, and gender.

The characteristics of the different models that were studied to explain the effects of the covariates with reference to the base model (in the absence of covariates) are summarized in table 18.

Table 18-list of all intermediate population PK/PD models evaluated:

covariate analysis showed that the best operating model was the model with gender effect on the EC50 parameter (run 4): men appear to have twice as much EC50 as women.

The details of the model illustrating the effect of covariates are as follows:

run 1 (reference base model):

EMAX＝θ₁

EC50＝θ₂

operation 2:

where 30.86 is the median weight in the analysis data set.

Run 3:

where 10 is the median age in the analysis dataset.

And 4, running:

EC50＝θ₂if the gender is great

EC50＝θ₃If gender is male

And (5) running:

EC＝θ₂if the gender is great

EC＝θ₃If gender is male

Where 10 is the median age in the analysis dataset.

The estimated fixed effect and random effect parameters are shown in tables 19 and 20, respectively. In these tables, SE represents the standard error of the parameter estimates, RSE represents the root mean square error, and CV% represents the coefficient of variation of the IIV variability.

Table 19-final population PK/PD model: fixed effect parameter estimation:

table 20-final population PK/PD model: parameter of random effectEstimation value:

a goodness of fit (GOF) diagnostic plot for the basal population PK/PD model is shown in figure 35. Identical lines, reference lines and trend lines overlap when appropriate.

In summary, there were no significant deviations in these diagnostic plots, indicating that the basal population PK/PD model was sufficient to describe the SKAMP score of subjects treated with HLD 200.

The VPC of the SKAMP score associated with HLD200 processing is shown in fig. 36.

VPC showed that the model performed well at different doses: the median of the score values in PK and the scatter of data around the score values in medium PK are well described by 5%, 50% (median) and 95%, indicating that this population model correctly describes the observed data.

Example 15 PK/PD assay results: development of PK/PD model: exposing response relationships

The PK/PD model identifies the relationship between MPH exposure and clinical response, as assessed by the ability of the drug to reverse placebo response.

In the absence of drug (Cp ═ 0), the clinical response was considered consistent with that observed following administration of placebo.

In the presence of the drug, the SKAMP clinical score increased relative to placebo response by a factor proportional to drug exposure.

The PK/PD model is defined by the following equation:

wherein r (t) is placebo response and% change compared to placebo is defined as:

fig. 37 shows the relationship between% change compared to placebo and MPH concentration. This analysis shows that a drug concentration of-15 ng/ml is required to induce a-40% change compared to placebo.

Examples 16 to 19 relate to the sensitivity of clinical response with respect to time of in vivo release and HLD200 uptake. The purpose of this analysis was to evaluate the expected Clinical Benefit (CB) as a function of the in vivo release rate of HLD200, and as a function of the time of drug intake of HLD200 in the morning, in the evening, in the morning and in the evening.

The effect of different in vivo release rates and different HLD200 intake times on the expected clinical benefit was assessed by using experimental simulations.

Example 16 clinical response sensitivity with respect to in vivo release and HLD200 uptake time: determination of clinical benefit Yi (Chinese character)

CB is defined as the area of change in estimated SKAMP score (AUEC) from 8:00am to 20:00pm. compared to placebo. The evaluation of CB is presented graphically in fig. 38.

Example 17 clinical response sensitivity with respect to in vivo release and HLD200 uptake time: rate of in vivo release Effects on CB

The absorption rate is characterized by two parameters: td and ss in the PK model developed for HLD 200. Therefore, CB is estimated assuming that the td parameter varies from 8 hours to 16 hours and the ss parameter varies from 4.5 to 8.5. Typical values for these parameters evaluated in the population PK analysis are: td is 12 hours and ss is 7.5. The results of the simulation are shown in table 21.

Table 21-effect of in vivo release rate on CB. Shaded cells identify in vivo release of current formulations of HLD200 Value and associated CB value: :

the results of the simulations indicate that only a slight improvement in clinical benefit is expected with the change in vivo release rate used.

Example 18. clinical response sensitivity with respect to in vivo release and HLD200 uptake time: HLD200 drug uptake Effect of incoming time on CB

CB was evaluated assuming that the dose intake time varied from 4 to 14 hours before the start of the classroom time at 8:00am morning. The simulation results are shown in table 22 and fig. 39.

Table 22-clinical benefit as a function of HLD200 drug intake time:

simulation results show that clinical benefit is strongly dependent on the time of drug intake of HLD200 before the start of the class was caused. The optimal time for drug intake was estimated 10-12 hours before the start of the classroom in the morning.

Example 19 evaluation of the morphology of the clinical response of various doses of HLD200

The purpose of this analysis was to evaluate the morphology of the clinical response determined by the trace of the combined score of SKAMP at 60mg, 80mg and 100mg doses using the PK/PD model previously developed.

The following assumptions for the simulation were retained: HLD200 is administered in the evening, 10 hours before the start of the classroom. HLD200 concentration was simulated in 34kg of subjects. the td parameter value (found to be different in males and females) was fixed to 12.05 hours as the average value for males and females. As shown by the PK/PD model previously developed, the maximum variation compared to placebo is expected to be-40% for HLD200 doses up to 100 mg. The simulated response was evaluated from 8:00am to 30 hours post dose administration.

Simulated traces of SKAMP scores for HLD200 doses of 60mg, 80mg, and 100mg are shown in FIGS. 40, 41, and 42.

FIG. 43 shows a comparative plot of SKAMP scores after three doses of HLD 200.

Thus, this example shows a dose-dependent improvement in clinical benefit. In particular, this example shows a dose-dependent increase over a period of time of significant clinical benefit. Notably, the time period after Tmax indicated a dose-dependent increase showing clinical benefit.

Example 20 HLD200 and (ALZA Corporation)、Metadate (UCB, Inc)、NWP06(Quillivant (NextWave pharmaceuticals, Inc.) and d-MPHER (Focalin) (NovartisAG)) comparison of clinical manifestations

Examples 20 to 22 relate to HLD200 and(ALZA Corporation)、 Metadate(UCB,Inc)、NWP06(Quillivant(NextWave Pharmaceuticals, Inc.) and d-MPH ER (Focalin)(Novartis AG)) in a clinical setting. The purpose of this analysis was to compare the clinical performance of different MPH products, including different from HLD200,(ALZA Corporation)、Metadate(UCB,Inc)、 Quillivant(NextWave Pharmaceuticals, Inc.) and FocalinThe PK time course of (Novartis AG) is described by a convolution-based model, using a double weibull function to characterize MPH delivery in vivo (equation 4) (r. gomeni, f. bressole, t.j. Spencer, s.v. factor. meta-analytical approach to evaluation alternative models for the characterization of the PK profiles of extended release formulations of MPH. ascot 2016 artificial measuring, March 8-12,2016, Hilton baffront, San Diego, ca., which is incorporated herein by reference). The estimated PK parameters are shown in table 23.

TABLE 23- (ALZA Corporation)、Metadate (UCB,Inc)、 Quillivant (NextWave Pharmaceuticals, Inc.) and Focalin (Novartis AG) PK parameter of

Clinical performance of the different products was compared to the performance of HLD200 using mean simulated values of SKAMP scores (CHP) calculated from 8:00am to 8:00pm as compared to placebo. The response and variability of response of different products was evaluated using the mean of the variation of the SKAMP scores compared to placebo and using the volatility index (FI).

The fluctuation index is defined as:

example 21 HLD200 and (ALZA Corporation)、Metadate (UCB, Inc)、NWP06(Quillivant (NextWave pharmaceuticals, Inc.) and d-MPH ER (Focalin) (Novartis AG)) reference clinical trial

A COMACS study.This is Metadate(UCB,Inc),、(ALZA Corporation) and placebo, wherein each treatment is administered for 1 week (Sonega-Barke EJ, Swanson JM, Coghill D, Decory HH, Hatch SJ. efficacy of two-way polymerization at differential time of the day: clinical indications from a syndrome analysis of the study data. BMC Psychiatry. 200Sep 30; 4:28, which are incorporated herein by reference). Children were assigned to high doses of MPH (Metadate) according to pre-test dose(UCB,Inc) 60mg；(ALZA Corporation)54mg), medium dose (Metadate)(UCB,Inc)40mg；(ALZA Corporation)36mg) or low dose (Metadate)(UCB,Inc)20mg；(ALZA Corporation)18mg) and was enrolled in a laboratory school on day 7 for evaluation over 7 courses of treatment during the day. The SKAMP score values are shown in table 24 and figure 44.

TABLE 24-COMACS study-mean (+ -SD) SKAMP per observation period for each treatment at each dose level Total score:

wherein a lower SKAMP score indicates greater efficacy.(ALZA Corporation)，MCD＝Metadate(UCB, Inc), PLA-placebo, low-low dose (CON 18 mg; MCD 20mg), medium-medium dose (CON 36 mg; MCD 40mg), Hi-high dose (CON 54 mg; MCD 60mg).

d-MPH-ER(Focalin (Novartis AG)).. This is a double-blind, Placebo-controlled, crossover study involving 54 children 6-12 years old, stabilized With MPH20-40 mg/day (Raul R.Silva et al. efficacy And Duration Of Effect Of Extended-Release Dexphenylate Versus plant in Schoolhildren With Attention-Deficit/Hyperactivity disorder recorder. Journal Of Child And additive Psychopharmacology. Vol.16, No.3, 2006, which is incorporated herein by reference). The patients participated in the practice day and then received treatment with d-MPH-ER 20 mg/day or placebo for 5 days. After 1 day of clearance, they returned to the classroom and received 1 dose of their assigned treatment. Evaluations occurred before and 1,2, 4, 6, 8, 9, 10, 11, and 12 hours after dosing. Then, using the same protocol, the child was subjected to a cross over (cross over) replacement treatment. The main efficacy variable was the SKAMP-composite score. FIG. 45 is a graph reporting exemplary data from a d-MPH ER study: mean SKAMP-composite raw score from pre-dose (0 hours) to 12 hours.

NWP06(Quillivant (NextWave Pharmaceuticals, Inc.)).This is a double-blind, Placebo-controlled, cross-designed, Laboratory Classroom study designed to evaluate the efficacy And safety Of NWP06 in ADHD Child patients between 6 And 12 years Of age (Sharon b. wide et al, NWP06, an Extended-Release Oral delivery Of methyl phenate, advanced attachment-determination/hyper availability disorders complex with a plant in a Laboratory class study, Journal Of Child And advanced additive psychopharmacology, vol 23, No. 1, 2013, which is incorporated herein by reference). A total of 45 subjects 6-12 years of age were enrolled in the dose optimization study. Following open-marker dose optimization, subjects received 2 weeks of double-blind treatment (1 week of NWP06 and 1 week of placebo). Efficacy measurements were based on SKAMP rating scale-combination measurements evaluated in each laboratory class at pre-dose and 0.75, 2, 4, 8, 10 and 12 hours post-dose. FIG. 46 is a report from NWP06 (Quillivant)(NextWave Pharmaceuticals, Inc.)) graph of exemplary data for the study: average SKAMP composite scores from pre-dose (0 hours) to 12 hours.

The average composite SKAMP scores for the placebo and treated groups with d-MPH-ER and NWP06 were digitized using Plot Digitator software (University of South Alabama, version 2.0) according to the graphs reported in the reference publications.

Example 22 HLD200 and (ALZA Corporation)、Metadate (UCB, Inc)、NWP06(Quillivant (NextWave pharmaceuticals, Inc.) and d-MPH ER (Focalin) (Novartis AG)) clinical performance: clinical manifestations

Variation of SKAMP score for HLD200 (observed in the 107 study) compared to placebo(ALZA Corporation) (dosages of 18mg, 36mg, and 54mg), and information on QuillivantThe data of (NextWave Pharmaceuticals, Inc.) and the results of comparing with the data for d-MPH are shown in FIG. 47.

Changes in SKAMP scores for HLD200 (observed in the 107 study) compared to placebo versus for Metadate(UCB, Inc) (dosages of 20mg, 40mg and 60mg), and information on QuillivantThe data of (NextWave Pharmaceuticals, Inc.), and the results of comparing with the data for d-MPH are shown in FIG. 48.

Table 25 shows the estimated mean of the variation of SKAMP scores and volatility indices compared to placebo for the different treatment evaluations.

Table 25-estimated mean of change in SKAMP scores and volatility indices compared to placebo in different treatment evaluations:

HLD200 shows the lowest FI, indicating less wave response during the day, as shown by the shaded cell of table 25.

In summary, examples 1 to 22 describe the following:

a population PK model was developed to characterize the concentration of MPH following HLD200 administration. One suitable model is a single compartment model with a time varying absorption rate, which is well described by the in vivo release function of the single weibull.

Covariate analysis identified the effect of body weight on volume of distribution and the effect of gender on the time to release MPH from HLD200 formulations: relative to men, the volume increases with body weight in women and the time to release 63.8% of the dose (td) is longer (-20%).

In general, the population PK model may be considered sufficient to characterize the MPH PK after administration of HLD200, given the goodness-of-fit plot, good appropriateness of individual model predictions versus observations, and visual predictive review analysis.

The clinical study (HLD200-107) did not plan for the collection of PK measurements, and, given the parallel study design, placebo measurements without SKAMP scores were available for subjects treated with HLD 200.

The development of a PK/PD model requires the evaluation of individual PK exposure and the evaluation of individual placebo response. Therefore, PK/PD analysis was performed according to the sequential modeling method. In the first step, individual MPH concentrations were extrapolated using a previously developed population PK model, combining individual demographic data and HLD200 dosing history, and placebo responses were then evaluated using data collected in the placebo group of this study. Finally, SKAMP scores for subjects treated with HLD200 were modeled as a function of HLD200 exposure and placebo response models.

The placebo response model suitably describes the shape of the SKAMP score trajectory in view of the goodness of fit plot and visual predictive review analysis.

The population PK/PD model provides a reasonable estimate of the effect of HLD 200. Covariate analysis showed that the best operating model was a model of gender effect on EC50 parameters: male EC50 appears to be twice as high as females, indicating that sensitivity of response may be higher in the female population.

Finally, an exposure-response relationship may be established. This analysis shows that drug concentrations of 15ng/ml are required to induce a 40% improvement in clinical response.

The PK/PD model was developed using data collected in the HLD200-107 study. The objective of this study was to select individual doses of HLD200 suitable for maximizing clinical response, rather than establishing dose (or exposure response) relationships. The investigator was allowed to escalate the dose in increments of 20mg to 40mg (up or down) until the "best" daily dose was reached or a maximum daily dose of 100 mg/day and/or a maximum dose of no more than 3.7mg/kg was reached, whichever was first reached. The optimal therapeutic dose was defined as the dose that produced the greatest symptom control in the morning and throughout the day, while maintaining safety and good tolerability, with minimal improvement from at least one third (33%) of the total score from baseline (visit 2) to the randomly grouped total score (visit 8) for each of the 3 scales.

Comparison of the expected Clinical Benefit (CB) of treatment with HLD200 as a function of in vivo release rate shows that the formulations of the invention provide the best response and with any altered in vivo release rate, the expected clinical benefit is only marginally improved.

Analysis of clinical benefit as a function of time of drug intake in the morning before and evening of the classroom indicated that clinical benefit was strongly dependent on the time of drug intake of HLD 200. The optimal time for drug intake was estimated to be 12 hours before the start of the classroom in the morning.

Simulations were performed using PK/PD model results to evaluate the morphology of clinical responses (traces of SKAMP composite scores) at 60mg, 80mg and 100mg doses. The simulation results indicate that an increase in the dose of HLD200 administration provides an extended duration of clinical response from morning to evening.

Final analysis was performed using composite SKAMP clinical scores to compare clinical performance of different MPH products, including HLD200, c,(ALZA Corporation)、Metadate(UCB,Inc)、Quillivant(NextWave Pharmaceuticals, Inc.) and d-MPH ER.

Comparison shows that HLD200 provides a medium-high dose(ALZA Corporation) and Metadate(UCB, Inc.) considerable clinical benefit. However, the clinical manifestations of HLD200 show a more stable clinical response throughout the day, with clinical benefit beginning at an earlier time of day being different from other compounds. An earlier onset of response is also associated with more consistent control of the SKAMP response throughout the day. The characteristics were evaluated using the fluctuation index (variability of response over the day): the fluctuation index of HLD200 was over 50% lower than that of the other products evaluated.

Example 23 in vitro-in vivo correlation (IVIVC) analysis

Examples 23 to 28 relate to in vitro-in vivo correlations (IVIVIVC) for methylphenidate formulations described herein. Analysis was performed to develop and validate the in vitro-in vivo correlation (ivivivc) of HLD 200. Three formulations of HLD200, fast, slow and final, were included in current IVIVC assays.

The 2 formulations used in clinical study HLD200-101 were identified as B-HLD200 (batch N450137), which has a "slow" dissolution profile, and C-HLD200 (batch N451299), which has a "fast" dissolution profile for clinical study HLD 200-101. The difference in dissolution profiles was achieved by adjusting the amount of DR coating applied using the same DR coating formulation (30% WG versus 15% WG, respectively). Two batches were coated to an ER coating of 20% WG. For the clinical study of HLD200-104, the final formulation (batch 3125683) was used and identified as HLD 200. The batch was coated to 21% WG ER coating and 30% WG DR coating. See table 26 for description of batches.

Table 26: description of the Using batches

Wherein represents 100mg prepared from 54mg capsules.

The following methods were used:

and (4) data.In vitro dissolution and in vivo pharmacokinetic data are available considering formulations for IVIVC modeling and validation.

And (4) dissolving in vitro.Details of the formulations are provided in table 26. All formulations were subjected to the same dissolution experiment. The dissolution test was carried out in 0.1N HCl for 2 hours (T ═ 0-2 hours), then in ph6.0 phosphate buffer for 4 hours (T ═ 2-6 hours), and finally in ph7.2 phosphate buffer for the remaining time.

For details of the dissolution method, see tables 27, 28 and 29.

TABLE 27 dissolution apparatus and conditions:

wherein with respect to "sampling time point" up to 6 hours of sampling can be done manually or with an automatic sampler. Samples will be collected after 6 hours with an auto sampler.

Table 28: HPLC conditions for dissolution:

table 29: media, mobile phase and diluent for dissolution:

for both the fast and slow formulations, the amount of methylphenidate released from in vitro dissolution at the 6 hour time point was reported to be 0.0%. At the 4 and 7 hour time points, the release of methylphenidate was reported to be 1.0%. Dissolution results for drug release levels below 5% are variable, as the confirmation range of dissolution method measured during dissolution method validation is 5% to 130% drug release. Values below 5% will be below the quantitation limit of the method, which will lead to variability of results at this low level. Therefore, for this model, a value of 1.0% at 6 hours is assumed.

Study of HLD200-101The study and procedure are described in the study of the HLD200-101 clinical protocol. A single dose 3-way crossover Latin formulation with three sequences included comparing 2 methylphenidate HCl MR capsule formulations 54mg MPH00400 (trial 1, referred to herein as Slow 54, treatment B HLD200) and 54mg MPH00500 (trial 2, referred to herein as Rapid, treatment C-HLD200) with the reference formulation(Novartis)20mg Immediate Release (IR) tablet (treatment A). The subjects were healthy volunteers (6 males and 6 females) and were organized into 3 groups, each group including 4 subjects. MR formulations were administered to 12 subjects at-9: 00pm,and IR comparator (at 8:00 am)(Novartis)), both under fasting conditions. The PK data for the IR formulation was used to derive the unit pulse function for methylphenidate, and the PK data for the slow (B-HLD200) and fast (C-HLD200) formulations were used for ivivivc analysis.

Study of HLD200-104The study and procedure are described in the study HLD200-104 clinical protocol. In part 1 of the study, the dose ratio of the lowest dose to the highest dose of pharmacokinetic parameters of 20mg and 100mg of HLD200 final formulations administered in the evening under fasting conditions was studied in 20 normal volunteers. The breakfast provided in the morning of the second of the two treatments was a medium fat composition (medium caloric value). Ivivivc analysis was performed using PK data from a 100mg dose (which represents the final formulation).

Study of HLD200-103The study and procedure are described in the study of the HLD200-103 clinical protocol. In the evening (about 9:00pm), eighteen subjects were orally administered a single dose of 100mg of B-HLD200 and 240ml of ambient temperature water. For the purposes of this study, formulation B-HLD-200(54mg) was modified to produce a dose equivalent to a strength of 100mg in a single capsule. The three treatments compared were randomized to different treatment series of high-fat meals (a), sprinkled on food (applesauce) (B) and fasted (C) in 6 clusters with 7 days of clearance between the three treatment periods. Ivivivc analysis was performed using PK data from a 100mg dose fasted group (C), which represents a slow formulation (referred to herein as slow 100).

Dissolution modelDevelop dissolution models using slow, fast and in vitro dissolution data of the final formulation. For this purpose, a model of the Emax type with a sigmoid factor (γ) was used.

Unit impulse responseUse of(Novartis) average PK data for IR formulations the unit pulse function was derived. A single compartment model with absorption hysteresis is sufficient to describe each namePK of the subject.

Deconvolution and convolutionIndividual PK data from both the fast and slow formulations of study HLD200-101 were deconvoluted using a unit pulse function for each subject to evaluate cumulative in vivo release rates. The average cumulative in vivo release rate for the fast and slow formulations was then calculated. These average in vivo release rates are then correlated to in vitro release rates using a polynomial function (ivivivc model). Internal validation was performed by convolving the in vitro release data with the cumulative in vivo release rate predicted by the IVIVIC model. The predicted and observed PK characteristics were compared using non-chamber parameters (AUC (zero to infinity) and Cmax) according to recommendations of IVIVIVC guidelines (Guidance for Industry: Extended Release order Forms: Development, Evaluation, and Application of In Vitro/In Vivo correlation, U.S. department of Health and Human Services Food and Drug Administration Center for Drug Evaluation and Research (CDER) September 1997BP 2, which is incorporated herein by reference).

For external validation, ivivivc models were used to predict PK in the final formulation in vivo. The predicted PK was compared to the observed PK (AUC, Cmax) from study HLD200-103 and study HLD 200-104.

Software and evaluationAll evaluations were performed using6.4(Certara, Cary, NC) and Microsoft(Redmont,WA)。

Example 24 in vitro-in vivo correlation (IVIVC) analysis: dissolution model

The mean in vitro release fraction data for the 3 formulations are shown in table 30 and table 31.

Table 30: mean in vitro release fraction of fast and slow formulations: :

table 31: mean in vitro release fraction of final formulation:

data were collected for more time points for the final formulation. The Emax type model adequately describes the observed dissolution data, as shown in FIG. 49.

The maximum possible dissolution fraction was fixed at 1 (100%). The time to reach half maximum dissolution was estimated to be 14.48 hours, 12.57 hours and 14.49 hours for the slow, fast and final formulations, respectively. The γ for the slow, fast and final formulations were 8.05, 6.87 and 7.98, respectively, as shown in table 32.

Table 32: dissolution model parameters (slow, fast and final formulation):

parameter(s)	Slow down	Fast speed	Finally, the product is processed
				Dmax	100	100	100
DT50	14.48	12.57	14.49
				gamma	8.05	6.87	7.98

Wherein Dmax is the maximum dissolution fraction; DT50 time to half maximum dissolution; gamma is a sigmoid factor.

Example 25 in vitro-in vivo correlation (IVIVC) analysis: unit impulse response

Use of(Novartis) IR data derive unit pulse function parameters. The methylphenidate concentration rises rapidly to a maximum concentration within about 1.5 hours and then falls rapidly. For this purpose, a single compartment model with time-delayed absorption was used. For each of the 12 subjects, the model parameters were evaluated and the predictions are shown in figure 50. FIG. 50 is a report of 20mg administeredGraph of exemplary observed (dots) and predicted (lines) mean methylphenidate concentrations after (Novartis) IR. The model parameters are absorption rate constant 1/hr, volume L, elimination rate constant 1/hr, absorption lag time hr, percent proportional error and additive error ug/L. The model describes the observed data very well. The inverse of the volume of distribution provides a measure of the value, and the rate of elimination constant determines the rate of concentration decline. Because these properties are inherent to methylphenidate, they can be used to estimate the rate of release in vivo by deconvolution.

Example 26 in vitro-in vivo correlation (IVIVC) analysis: IVIVC model

Cumulative in vivo drug release scores were assessed using unit pulse functions for both the fast and slow 54 formulations and observed concentrations of methylphenidate alone. The in vivo drug release of each subject was deduced using the pulse function per subject. The cumulative drug release score in an individual is used to calculate an average cumulative drug release score. Table 33 describes the ratio of the mean cumulative in vitro and in vivo release fractions of the drug.

TABLE 33 fraction released in vitro and in vivo using the fast and slow 54 formulations in study HLD 200-101. Body The internal release score was calculated as the average of the individual release profiles: :

the in vitro and in vivo release rates were then correlated to derive an ivivivc model, as shown in fig. 51, which shows IVIVC of a combined fast and slow 54 formulation. The 5 th order polynomial function successfully describes the relationship between in vitro and in vivo release. As indicated in fig. 51, the ivivivc relationship is not linear. Low-order polynomial functions have also been tried, but a polynomial of degree 5 provides the best fit and prediction quality. The ivivivc model parameters are given in table 34.

Table 34: IVIVIVC model parameters will use the fraction of in vitro dissolution (Fdis) derived for the fast and slow 54 formulations and fraction of in vivo absorption (Fabs) correlates: :

where the 5 th order polynomial best describes the following relationship: fabs ═ f (fdis)⁵+ (Fdiss)⁴+(Fdiss)³+(Fdiss)²+(Fdiss).

Example 27 in vitro-in vivo correlation (IVIVC) analysis: internal authentication

In vitro dissolution data and ivivivc models were used to predict the PK profile in vivo for both the fast and slow 54 formulations. Cumulative input predictions for the fast and slow 54 formulations are shown in tables 35 and 36, respectively.

Table 35: cumulative absorption fraction prediction for rapid preparation (54mg) based on in vitro dissolution data and ivivivc model: :

where fdis the fraction eluted in vitro and Fabs the fraction absorbed in vivo.

Table 36: cumulative absorption fractions of slow formulations (54mg and 100mg) based on in vitro dissolution data and IVIVC model Predicting the value: :

where fdis the fraction eluted in vitro and Fabs the fraction absorbed in vivo.

The in vivo predictive value and observed mean methylphenidate concentrations for the fast and slow 54 formulations are shown in figure 52 and table 37.

Table 37: rapid (intra) prediction using IVIVC modelPartial verification), final (internal verification) and slow (internal and external verification) Department validation) in vivo PK profile of the formulation: :

the ivivivivc model predicted values were nearly consistent with the observed concentrations. As shown in table 38, the Absolute Prediction Error (APE) for AUC and Cmax were 4.13% and 14.64%, respectively, for the rapid formulation. For the slow 54 formulation, the Absolute Prediction Error (APE) for AUC and Cmax were 5.20% and 6.21%, respectively. For both formulations, the mean APEs for AUC and Cmax were 4.67% and 10.43%, respectively.

Table 38: internal and external verification results:

example 28 in vitro-in vivo correlation (IVIVC) analysis: internal authentication

In vitro dissolution data and IVIVIVC models were used to study HLD200-103 (slow 100 formulation) and HLD200-104 (final formulation) to predict PK behavior in vivo for slow formulations. The cumulative input prediction for the 100mg slow formulation is shown in table 36. The cumulative input prediction for the 100mg final formulation is shown in table 39.

Table 39: based on in vitro dissolutionData and ivivivc model, cumulative absorption fraction predicted for the final formulation (100 mg):

the in vivo predicted values and observed methylphenidate concentrations for the final formulations are shown in figure 53 and table 37. The ivivivivc model predicted values were nearly consistent with the observed concentrations. As shown in Table 38, the Absolute Prediction Error (APE) for AUC and Cmax was 9.20% and 0.81%, respectively, for 100mg of the slow 100 formulation (study HLD 200-103). AUC and Cmax of APE were 13.30% and 0.79% respectively for the 100mg final formulation (study HLD 200-104).

In summary, examples 23 to 28 describe the following:

the dissolution model yields a good correlation with the fractions that dissolved over time. IVIVC models were developed using the fast and slow 54 formulations from study HLD 200-101. Fraction of in vitro dissolution enables prediction of fraction of in vivo absorption, R²Is 0.99. The F2 for both formulations was 45.15%. The mean ivivivivc prediction error for AUC and Cmax of the final formulation and the slow 54 formulation was 4.67% and 10.43%, close to the 10% FDA recommendation. The prediction error for each formulation was within the allowable 15%. The mean prediction error for both formulations was about 10% for AUC and close to 10% for Cmax.

External validation was also performed using the formulations tested in study HLD200-103 and study HLD 200-104. Ivivivc model predictions closely agreed with the observed concentrations. For the 100mg slow 100 formulation (study HLD200-103), the Absolute Prediction Error (APE) for AUC and Cmax was 9.20% and 0.81%, respectively. AUC and Cmax of APE were 13.30% and 0.79% respectively for the 100mg final formulation (study HLD 200-104). The AUC prediction for the final formulation is on the boundary line, while Cmax passes the 10% threshold. It should be noted that subjects with HLD200-104 received light breakfast during the study, which may result in slightly higher variability. In the study of HLD200-104, the fed and fasted estimated AUC of HLD200 was 1.09 and Cmax was 1.004. When adjusted for the slight effect of breakfast, the AUC of study HLD200-104 was 155.29ug hr/L. Upon this modulation, the APE of the AUC of the final formulation (study HLD200-104) became 5.5%. Slight effects of food (breakfast) may be a potential cause of the difference. In general, a successful ivivivc model was developed and validated as recommended by the FDA in its industry guidelines: extended release oral dosage forms: development, evaluation and application of in vitro/in vivo correlations.

EXAMPLE 29 example of extended Release layer

Some examples of sustained release layers are shown in table 40. Citric acid is added to the formulation to keep the microenvironment pH in the membrane low to inhibit dissolution of HPMCAS-LF, which dissolves at pH5.5 or more, creating a lag in the onset of dissolution profile.

Table 40. exemplary sustained release layer and core: :

due to rounding, the total in the diagram may not be 100.

Exemplary cores were synthesized as shown in table 41. In this example, the osmotic agent is added to the core.

Table 41. exemplary core:

a sustained release layer having the structural formula shown in the right column (F) of table 40 was synthesized on the API-containing beads. When the sustained release layer provided a 25% weight gain, the formulation was named 2009-. Additional layers were synthesized as shown in columns a and B of table 40. In column B, the formulation in column A was changed to remove the colloidal silica and increase the plasticizer to 50% w/w of the polymer content. All other ratios as shown in column a are maintained.

The formulation of column a was also changed to produce the sustained release layer of column C of table 40. In this formulation, the colloidal silica was removed and citric acid was added. Ethocel: the HPMCAS ratio decreased from 75:25 to 56: 44. The formulation is expected to provide a lower pH in the microenvironment to increase lag time. The samples that delaminated to produce a 25% weight gain and another sample with a 45% weight gain were subjected to dissolution testing.

Another embodiment of the sustained release layer is prepared wherein the drug or API is included in the sustained release layer. This layer is depicted in the formulation in column D of table 40, with a ratio between Ethocel and HPMCAS of 75: 25. The micronized drug is added to the formulation in the form of a suspension. Dissolution tests were performed on samples with 25% weight gain.

Core tablets as described in table 41 were coated with a sustained release layer formulated as in column a of table 40. The formulation showed an initial slow drug release (3% over the first 3 hours).

Another embodiment of the extended release coating is designed with a ratio of polyethylene oxide (PEO) to ethylcellulose of 37.5: 62.5. Talc was also added to one sample at 10% to improve the coating process. The presence of talc does not affect drug release. The release profiles of these formulations processed to 25% weight gain and 40% weight gain were also determined. The formulation showed a1 hour lag with essentially complete release of the drug within 9 hours.

Example 30 example of a sustained release coating.

Examples of sustained release coatings applied to core pellets were prepared with the following ingredients (see table 42).

Core batch-1100.0 g

The weight of the coating is increased by-30 percent

Solids content-12.0%

Table 42:

example 31 example of pH-dependent coating.

An exemplary S100 pH-dependent coating formulated for 30% weight gain was formulated with the following components (see table 43).

The weight of the coating is increased by-30 percent

Solid content-10.0%

Batch-715 g

Core pellet amount-550 g

Table 43:

example 32 example of pH-dependent coating.

An exemplary S100 pH-dependent coating formulated for 50% weight gain contained the following components (see table 44).

The coating weight is increased by-50.0 percent

Solid content-10.0%

Batch-715 g

Core pellet amount-550 g

Table 44:

example 33 example of a sustained release coating.

One example of a sustained release coating with an alternative ratio of water soluble (Klucel) to water insoluble polymer (Ethocel) was prepared with the following components to obtain a faster release profile (see table 45).

Core batch-1100.0 g

The weight of the coating is increased by-30 percent

Solids content-12.0%

Table 45:

example 34 example of a sustained release coating.

Another example of a sustained release coating according to the present disclosure is prepared with the following components (see table 46).

Core batch-1100.0 g

The weight of the coating is increased by-30 percent

Solids content-12.0%

Table 46:

example 35 example of a sustained release coating.

One example of a sustained release coating as described herein for sustained release formulations (1 and 2) is a 25% SR + 20% or 30% pH coating (see table 47).

For table 47, SR coating sustained release (1 and 2):

coating weight gain 25.0

12.0% of solids

Table 47:

example 36 example of Slow enteric coating.

One example of an enteric coating as described herein for the sustained release formulations (1 and 2) is a 25% SR + 20% or 30% pH coating (see tables 48 and 49).

For table 48, S100 pH-dependent coated sustained release (1):

the weight of the coating is increased by 20 percent

10 percent of solid

Batch (g)1500

Core pellet amount (g)1250

Table 48:

components	mg/g	Ratio of solvents	％w/w	g/batch
					Methacrylic acid copolymer type-B	133.7		13.37	202
Mono-and diglycerides, NF	13.4		1.34	20
					Dibutyl sebacate, NF	13.4		1.34	20
Polysorbate 80, NF	5.3		0.53	8
					Ethanol (denatured)		94.4		2138
DI water		5.6		113

Theoretical amount (g) of coated pellet	1500

For table 49, S100 pH-dependent coated sustained release (2):

the weight of the coating is increased by 30 percent

10 percent of solid

Batch (g)1625

Core pellet amount (g)1250

Table 49:

example 37 example of a moderate slow release coating.

One example of a medium slow release coating as described herein for medium release formulations (1 and 2) is a 20% SR + 20% or 30% pH coating (see tables 50, 51 and 52).

For table 50, SR coating medium release (1 and 2):

the weight of the coating is increased by 20 percent

The solid percentage is 12%

Table 50:

for table 51, S100 pH-dependent coating medium release (1):

coating weight gain of 20.0

10.0% of solids

Batch (g)1440

The amount of core pellet (g) is 1000

Table 51:

for table 52, S100 pH-dependent coating medium release (2):

the weight of the coating is increased by 30 percent

10 percent of solid

Batch (g)1560

Core pellet amount (g)1200

Table 52:

example 38. example of immediate release coating.

An example of an immediate release coating for an immediate release formulation is a 20% SR + 20% pH coating (see tables 53 and 54).

For table 53, SR coating fast release:

coating weight gain of 20.0

12% by solids

Table 53:

for table 54, S100 pH-dependent coated immediate release:

the weight of the coating is increased by 20 percent

10 percent of solid

Batch (g)1440

Core pellet amount (g)1200

Table 54:

example 39 examples of methylphenidate compositions.

This example describes an exemplary composition of 54mg methylphenidate capsules (sustained release formulation, 25% SR weight gain + 30% pH dependent weight gain) (see table 55).

Table 55:

example 40 example of a methylphenidate composition.

This example describes an exemplary composition of 54mg methylphenidate capsules (sustained release formulation, 20% SR weight gain + 20% pH dependent weight gain) (see table 56).

Table 56:

example 41 example of a methylphenidate composition.

This example describes an exemplary composition of 54mg methylphenidate capsules (sustained release formulation, 20% SR weight gain + 30% EC pH dependent weight gain) (see table 57).

Table 57:

this example describes an exemplary composition of 54mg methylphenidate capsules (immediate release formulation, 20% SR weight gain + 15% EC pH dependent weight gain) (see table 58).

Table 58:

example 42. Process for preparing coated methylphenidate capsules.

In one example of a manufacturing process, methylphenidate hydrochloride and microcrystalline cellulose (Avicel PH-101) are mixed in a Hobart mixer. Purified water was added to the dry mixture and the wet granulation was extruded (MG-55 multiparticulate granulator). The extrudate was then spheronized into pellets (Caleva Model # SPH 250). The wet granulation was dried (Fluid Air Model #0050) and sieved (30 mesh < acceptable < 20 mesh).

The slow release coating was added as follows: a dispersion of ethyl cellulose NF (Ethocel Standard 10Premium), Klucel EF, dibutyl sebacate, NF, magnesium stearate, NF, ethanol and purified water USP was prepared in an overhead stirrer. The dispersion was applied to uncoated methylphenidate micropellets in a fluidized bed and the coated micropellets were sieved as previously described. It should be understood that in the context of this embodiment, the term "dispersion" may refer to various two-phase systems in which at least some of the solids are dispersed in a liquid phase. Thus, the term "dispersion" as used herein may include, but is in no way limited in whole or in part to the concept of a colloid, emulsion, and/or suspension.

The following enteric coatings were prepared: a dispersion of methacrylic acid copolymer type B (Eudragit S100), mono-and diglycerides, NF (initiator 900K), dibutyl sebacate, NF, polysorbate 80, NF, ethanol and purified water USP was mixed in a top mixer to obtain a dispersion. The dispersion was applied to slow release coated methylphenidate micropellets in a fluidized bed. And (4) encapsulating the enteric-coated pellets into capsules to obtain methylphenidate capsules.

Example 43. IVIVC of (ALZA corporation)

FIG. 57 is a report on(ALZA Corporation) a plot of the fraction of methylphenidate dissolved in vitro (FDISS) versus the fraction of methylphenidate absorbed in vivo (Fabs). From R.Gomenii, F.Bressole, T.J.Spencer, S.V.Faraone, Meta-analytical approach to evaluation analytical modules for the characterization of the PK profiles of extended release formulations of MPH.ASCPT 2016 analytical testing, March 8-12,2016, Hilton Bayont, San Diego, Calif., which is incorporated herein by reference).

Example 44 validation of the double Weibull IVIVC model in an exemplary MPH formulation

Fig. 58 is a graph reporting exemplary (dots) and predicted (lines) average methylphenidate concentrations observed after administration of a given drug. From R.Gomeni, F.Bressole, T.J.Spencer, S.V. Faraone.Meta-analytical approach to evaluation analytical modules for the characterization of the PK profiles of extended release formulations of MPH.ASCPT 2016 analytical testing, March 8-12,2016, Hilton Bayont, San Diego, Calif., which is incorporated herein by reference.

EXAMPLE 45 measurement of exemplary optimal MPH Release characteristics of products that release MPH upon absorption in the Dual Weibull body Said products being, for example (ALZA Corporation)、Ritalin (Novartis AG)、 Metadate (UCB,Inc)

And Quillivant (NextWave Pharmaceuticals,Inc.)

Using the PK/PD model described herein, the profile of methylphenidate absorption required to deliver maximal clinical benefit over a 24 hour period following in vivo absorption in double Weibull is determined for MPH-releasing products such as, for example, products such as(ALZA Corporation)、 Ritalin(Novartis AG)、Metadate(UCB,Inc)And Quillivant(NextWave Pharmaceuticals, Inc.) (R.Gomeni, F.Bressole, T.J. Spencer, S.V.Faraone.Meta-analytical application to evaluation analytical modules for processing the PK profiles of extended release formulations of MPH.ASCPT analytical testing, March 8-12,2016, Hilton Baysint, San Diego, Calif., which is incorporated herein by reference).

Table 59 shows the comparison of the calculated "optimized" methylphenidate absorption profile for products such as(ALZA Corp.) measured values (indicated in the line "initial marked").

Table 59:

where td is the time to deliver 63.2% of the immediate release portion and td1 is the time to release 63.2% of the controlled release portion. The terms ss and ss1 refer to sigmoid factors as previously described herein. Table 59 shows that for these drugs, the optimal delivery has a much lower sigmoidal factor (much shallower) release than observed, and 63.2% of the drug takes 10.8 hours to reach after the start of delivery. FIG. 59 is a reportA plot of exemplary data for cumulative absorption scores (ALZA corporation) showing that 50% of the drug was absorbed in 4-6 hours, and all the drug was absorbed in about 10-12 hours (indicated by the vertical and horizontal lines intersecting the curve), which is much faster than optimal.

Fig. 60 is a graph reporting exemplary "initial" and "optimized" partial absorption curves as determined by r.gomeni et al (ascopt 2016 artificial Meeting, March 8-12,2016, Hilton Bayfront, San Diego, ca., incorporated herein by reference). Figure 60 shows that the optimal 50% release is reached about 7-8 hours after the onset of drug absorption, with the maximum cumulative absorption occurring at about 20 hours. Comparison of this curve with the fractional absorption curve for HLD200 (figure 61) indicates that HLD200 exhibits 50% absorption (corrected for 75-80% relative bioavailability) about 7 hours after the onset of drug absorption and maximum cumulative absorption occurs about 20-24 hours after the onset of absorption.

Typical gastrointestinal transit times are: the stomach is emptied for 90 minutes, the transit time of the small intestine is 4-6 hours, and the colon arrival time is about 8 hours. Thus, comprising(ALZA Corporation)、 Ritalin(Novartis AG)、Metadate(UCB, Inc) and QuillivantThe products of (NextWave Pharmaceuticals, Inc.) appear to deliver a large portion, if not all, of the product in the small intestine, and they all exhibit the same absorption mechanism, with approximately linear ivivivivc. In contrast, compositions of the present disclosure, such as HLD200, deliver the product after about 8 hours, and thus in the colon, and also exhibit nonlinear and time-varying IVIVC. Without being limited by theory, if absorption in the colon is associated with non-linear IVIVC, and the optimal curve takes 50% of the absorption to occur after 10 hours, then this is relevant for other products such as(ALZA Corporation)、Ritalin(Novartis AG)、 Metadate(UCB, Inc) and Quillivant(NextWave Pharmaceuticals, Inc.) in contrast, the compositions and methods of the present disclosure allow absorption in the colon. In particular, the delayed release formulation of the compositions described herein and the methods of the present disclosure including administration in the evening of the previous day allow the compositions described herein to be delivered to the colon and begin absorption of methylphenidate before waking in the morning, thereby providing an optimal methylphenidate release and absorption profile.

FIG. 64 is a graph of reporting using no more than 1MBq¹¹¹Graph of exemplary cumulative colon arrival time% for indium radiolabeled surrogate beads. The figure shows the results from two independent experiments, designated "F1" and "F2". Radiolabeled beads are administered to the subject and the time to reach the colon is assessed using scintigraphy. Fig. 64 shows that all the radiolabeled beads in the exemplary formulation were in the colon at 10 hours.

Example 46 mean Rate of Change of Methylphenidate plasma concentration with time

FIG. 62 is a report of HLD20054 mg (study 200-Graph of exemplary data for the mean rate of change of 54mg of methylphenidate plasma concentration over time (ng/mL/hour).

FIG. 62 showsRate of change of the comparative exemplary HLD200 formulation. Specifically, HLD20054 mg anda comparison of 54mg shows that HLD20054 mg has a maximum rate of change of increase in methylphenidate plasma concentration of about +1.0 ng/mL/hr and a maximum rate of change of decrease in methylphenidate plasma concentration of about-0.5 ng/mL/hr, while54mg had a maximum rate of change of methylphenidate plasma concentration increase of about +3.6 ng/mL/hour and a maximum rate of change of methylphenidate plasma concentration decrease of about-1.0 ng/mL/hour.

FIG. 62 also shows that HLD 200100 mg has a maximum rate of change of methylphenidate plasma concentration increase of about +2.5 ng/mL/hour and a maximum rate of change of methylphenidate plasma concentration decrease of about-1.2 ng/mL/hour.

Will be provided withData normalized to 100mg1.85, expected for the dose multiplied by 100/54100mg, the maximum rate of change that will give an increase in plasma methylphenidate concentration is about +3.6 × 1.85 ═ 6.7 ng/mL/hour, and the maximum rate of change in plasma methylphenidate concentration is about-1.0 × 1.85 ═ -1.85 ng/mL/hour.

Further, the graph of FIG. 62 shows the peak time at 16 hours fromThe plasma concentration rate of methylphenidate released for the second time was increased, with HLD200 instead providing a smoother rate change over the entire curve, with fewer peaks.

Example 47 comparative data: ivivivc model of amphetamine preparation.

Exemplary delayed release, extended release formulations of amphetamine, referred to herein as HLD100-102, were prepared and had excipient coatings and bead physical properties similar to the medium methylphenidate formulation of HLD 200. An exemplary amphetamine formulation has the same film coating components in the same relative proportions as the medium HLD200 methylphenidate formulation. The amount of coating (weight gain) applied was varied such that amphetamine formulation had a 25% weight gain for ER film coating and a 20% weight gain for DR film coating. In contrast, the exemplary methylphenidate HLD200 moderate formulation had 20% ER weight gain and 30% DR weight gain.

Figure 81 is a graph reporting an exemplary in vitro dissolution profile of a medium formulation of HLD200 and an exemplary in vitro dissolution profile of an amphetamine formulation of HLD 100-102. FIG. 81 shows that the in vitro dissolution profiles of the exemplary HLD100-102 amphetamine formulation and the HLD200 intermediate formulation are very similar.

In vitro and in vivo release rates of exemplary amphetamine formulations were obtained following a deconvolution-based ivivivc procedure similar to that described in examples 23-28 above.

The in vitro and in vivo release rates of amphetamine HLD100-102 formulations were correlated with a derived IVIVC model. Figure 63 is a graph reporting exemplary scores for amphetamine released in vitro versus in vivo from HLD100-102 formulations.

As shown in fig. 63, the ivivivc relationship for amphetamine formulations was not linear. However, in contrast to the exemplary IVIVIVC of the methylphenidate formulation described in example 26, in which the 5 th order polynomial function successfully describes the relationship between in vitro and in vivo release, the 2 nd order polynomial function of the exemplary amphetamine formulation successfully describes the relationship between in vitro and in vivo release.

Thus, comparison of an ivivivc model suitable for an exemplary amphetamine formulation with an ivivivc model suitable for an exemplary methylphenidate formulation indicates that altering the active ingredient can alter the mechanism of release and absorption, since the physical properties of the coatings and beads of the exemplary methylphenidate formulation and the exemplary amphetamine formulation are very similar.

Because the in vitro dissolution profiles of the exemplary amphetamine HLD100-102 formulation and the exemplary methylphenidate HLD200 intermediate formulation are very similar, the ivivivc model demonstrates the difference in vivo absorption properties between amphetamine and methylphenidate formulations, as shown in figure 81.

Example 48 additional IVIVIVC modeling of HLD200 formulations

This example describes an additional ivivivc analysis of the fast, slow and final formulation of HLD 200.

Ivivivc was evaluated using a convolution-based modeling method.

The following method was used.

Convolution-based models.The HLD200 plasma concentration (Cp) resulting from any dose is described by convolution as:

C_p(t)＝f(t)*I(t)

where F (t) is the in vivo input function, I (t) is the unit impulse response (defined by the volume of distribution (V) and the first order elimination rate (kel) estimated using IR formulation data), which is the convolution operator, r (t) is the fraction of released dose over time defined by the Weibull model, A is the amount of drug, and F is the fraction of absorbed dose.

And (4) IVIVC modeling.The analysis presented in this example focuses on the assessment of the fraction of drug absorbed in vivo (r)_{In vivo}(t))(r_vivo(t)) and the fraction of drug dissolved (r)_{In vitro}(t))(r_vitror(t)) develop a level a ivivc (level a ivivc). In ivivivc assessment, in vitro dissolution and in vivo entry curves can be directly superimposable, or they can be made superimposable by using a "scaling factor". A time scaling function is included in the model to account for potential time differences in vitro and in vivo processes (e.g., when dissolution is faster than in vivo input rate). A general time scaling model was applied in the assessment of ivivivc:

r_{in vivo}(t)＝a₁+a₂·r_{In vitro}(tt)

At r_{In vivo}(r_vivo) And r_{In vitro}(r_vitro) Without time scaling in between: a is₁＝0、 a₂＝1、b₁＝0、b₂1 and b₃1. Otherwise, the time scaling may be defined by estimating appropriate values for the parameters a1, a2, b1, b2, and b 3. Equation 3 includes a linear component (intercept of a1 and slope of a 2) and a nonlinear component describing the time shift (b1), time scaling (b2) and time scaling factor (shaping factor) (b 3).

And (5) verifying the model.The final step in ivivivc analysis is to validate the model by providing quantitative evidence of the predictive performance of the model. Model validation (internal validation) was done using data from the formulation used to build the model. Validation was performed using the ivivivc model: prediction of relevant exposure parameters (Cmax and AUC) using a model for each formulation_inf) And compared to the observed values. The prediction error (% PE) for each PK parameter was calculated using the following equation:

where n is the number of formulations. For each PK parameter, the criteria used to assess the level of predictability were: average% PE < 10%, with no individual values > 15%. If the criteria are not met, then the external predictability needs to be evaluated.

IVIVC was performed.Ivivivc analysis was performed using a 6-step method:

1. fitting PK time course of IR formulation (step 1);

2. fitting the mean in vitro dissolution data of the slow, medium and fast formulations individually using the release function defined by the weibull model (step 2);

3. fitting the in vivo PK of 3 formulations by fixing the configuration (V) and elimination (kel) parameters estimated from the IR formulation analysis (step 3);

4. ivivivc is evaluated by applying the convolution model jointly to the in vivo data of the slow, medium and fast preparations (step 4) and by: (a) fixing the in vivo drug release parameters of each formulation to the values estimated in step (2), (b) estimating a time scaling factor common to all formulations, (c) estimating the relative bioavailability of each formulation.

5. Predicted Cmax, AUC (estimated in step 3) by comparison_infAnd the observed values to evaluate in vivo predictability (step 5).

6. The external predictability of the final ivivivc model was evaluated by predicting the in vivo performance of formulations not used in ivivivc model development.

The following data were used in the analysis.

Immediate releaseAt a dose of 20mgPK data for IR formulations derive unit pulse function. These data are in study number: produced in HLD 200-111: phase I, single-center, single-dose, open label, randomized, crossover, comparative bioavailability studies of HLD200 methylphenidate hcl delayed-release and extended-release capsules in healthy adult volunteers with a commercial formulation of methylphenidate hcl. In this study, a total of 12 subjects were randomly assigned to 2 treatment series groups in a crossover fashion (hld200,100mg and20mg), 6 subjects per group.

In vitro dissolutionBatches of formulation used in dissolution testing were: 749A-1811-B003-FAST, 749A-1606-B008-MID, 749A-1747-B006-SLOW. The dissolution test was carried out in 0.1N HCl for 2 hours (T ═ 0-2 hours), then in ph6.0 phosphate buffer for 4 hours (T ═ 2-6 hours), and finally in ph7.2 phosphate buffer for the remaining time. For details of the dissolution method, see table 60, table 61 and table 62. Dissolution results with drug release levels below 5% are variable because the demonstrated range of dissolution methods is 5% to 130% drug release as determined during dissolution method validation. Values below 5% will be below the quantitation limit of the method, which will lead to variability of results at this low level.

Table 60. dissolution apparatus and conditions:

where "1" indicates that sampling up to 6 hours can be performed manually or with an autosampler. Samples were collected after 6 hours with an autosampler.

TABLE 61 HPLC conditions for dissolution:

Table 62. media, mobile phase and diluent for dissolution::

in vivo PKPK data were generated in the HLD200-111 study. This is a phase 1, single-center, single-dose, open label, random, crossover, comparative bioavailability study of three delayed release/extended release capsule (HLD200) formulations of methylphenidate hcl in healthy adults. A total of 18 subjects were randomly assigned to six treatment series groups in a crossover fashion, with 3 subjects per group (table 63). The different release rates of the active compound distinguished three HLD 200100 mg formulations, defined as: treatment A (HLD200-F, quick Release Curve), treatment B (HLD200-M, Jornay, commercially available)(Ironshore Pharmaceuticals&Development, Inc.) formulation and reference treatment) andphysical C (HLD-200-S, slow release profile). All subjects were dosed under fasting conditions.

TABLE 63 study of HLD 200-111-crossover design: :

external authenticationBatches of the formulation used in the dissolution test were: 749A-1510-B009. The dissolution test was carried out according to the same method as described above.

The PK data for study HLD200-109 at 100mg in the fasted group was used to evaluate the external validation. The study is a phase I, single-center clinical trial examining the pharmacokinetic effects of 100mg of HLD200, methylphenidate hcl modified release capsules in healthy adult volunteers of fasting, fed and springled State under a randomized three-way crossover design. A total of 18 subjects were randomized in equal proportions into one of six treatment series with a single dose of 100mg of HLD200 (table 64), where treatment a is fed, treatment B is dusted (on applesauce), and treatment C is fasted. Six subjects who were withdrawn or replaced after administration of at least one dose of study product (IP) were enrolled to achieve three evaluable subjects per treatment series, thus a total of 24 subjects were enrolled in the study.

TABLE 64 study of HLD 200-109-crossover design:

the following results were observed.

Step 1-fitting PK time course for IR formulation.Individual PK data using IR formulations characterize the configuration and elimination of MPH. Absorption, configuration and elimination of MPH following administration of IR formulationsIt is best characterized by a single compartment model with first order absorption and lag time. The model parameters are: ka (first order absorption rate constant), kel (elimination rate constant), lag (lag time), V (volume of distribution), and a combination of additive and proportional residual errors. Population PK analysis of IR data was performed in NONMEM using the ADVAN14 subroutine and the FOCE-I method. The estimated population parameter values are presented in table 65, where SE represents the standard error and RSE represents the relative standard error of the estimated parameters.

Table 65. estimated PK parameter values for IR formulations:

the model description IR data was evaluated for appropriateness using a visual predictive inspection method. The basic premise is that the model and parameters derived from the observed data set should produce simulated data similar to the original observed data. Based on the final model, five hundred replicates of the original dataset were simulated, and a 90% prediction interval was calculated based on the simulated dataset. Observed drug concentration values were plotted against time on a prediction interval plot to visually assess the agreement between the simulated data and the observed data. A visual predictive review of the population PK model for the IR formulation is presented in figure 65.

VPC showed that a model was developed to characterize the temporal course of MPH plasma concentration after a well-performed IR formulation: the distribution of the observed data was well predicted based on the typical distribution (median curve determined by the thick solid line) and the inter-individual variability (90% prediction interval determined by the shaded region), indicating that the population model correctly described the observed data.

Step 2-fitting the mean dissolution dataIn vitro dissolution data for each extended release formulation was described by the double weibull model:

where ff is the fraction of the dose dissolved in procedure 1, td and td1 are the times of the 63.2% dose dissolved in procedure 1 and procedure 2, and ss1 are the sigmoidal factors for procedure 1 and procedure 2.

A weibull model (r (t)) was fitted to the mean in vitro dissolution data for slow, medium and fast dissolving formulations. Each formulation was evaluated for the following parameters: ff (fraction of the available dose released in the 1 st pass), td and td1 (time to release 63.2% of the dose in the 1 st and 2 nd passes), ss and ss1 (sigmoidal factor for the 1 st and 2 nd pass dissolution method). Analysis of dissolution data was performed in NONMEM using the FOCE-I method.

The mean dissolution data for each formulation with the model prediction curve is presented in figure 66. The estimated parameter values are presented in table 66. In the table, RSE denotes the relative standard error of the estimated parameters.

Table 66 estimated dissolution data parameters for slow, medium and fast dissolution rate formulations (RSE ═ relative standard error Poor):

step 3-fitting 3 Using convolution-based model by fixing the configuration and cancellation parameter values estimated from IR preparations In vivo PK of the formulation.The purpose of this analysis was to provide an assessment of the drug release rate in vivo and to assess the inter-individual variability of the elimination rate constants for the 3 formulations. To this end, the convolution model shown in fig. 67 was used to combine data fitted to 3 agents using a non-linear mixing effect approach. In vivo drug release was evaluated by fixing the mean value of volume (V) and the mean value of elimination rate (kel) as estimated values in IR data analysis.

The in vivo PK release time course for the different formulations is assumed to be characterized by two drug release phases: the first phase is associated with an initial release of a portion of the dose and the second phase provides an extended release of the dose. The r (t) function of MPH is modeled using a double Weibull function. In this model, ff represents the fraction of the available dose released during procedure 1, td and td1 are the times at which 63.2% of the dose was released during procedure 1 and procedure 2, and ss1 are sigmoidal factors for procedure 1 and procedure 2.

The estimated parameter values are presented in table 67. In the table, SE denotes a standard error, and RSE denotes a relative standard error of the estimated parameter.

Table 67 estimation of in vivo PK parameters for slow, medium and fast dissolution rate formulations:

the average PK observations for each formulation with model predicted curves (upper panel) and in vivo release rates (lower panel) are presented in figure 68.

Step 4-IVIVC was evaluated using a convolution modeling method.The convolution model used to evaluate ivivivc relationships is presented in fig. 67. Data for 3 formulations were fit together. The following parameters were fixed as estimated in the previous analysis: mean Kel and V estimated from IR data analysis (step 1), variability among Kel individuals estimated for analysis performed in step 3, and dissolution data estimated in step 2 (ff, td, ss, td1, and ss1 for 3 formulations). The convolution model was performed in NONMEM using the ADVAN13 subroutine and the FOCE-I method.

The data for the three formulations (slow, medium and fast) were jointly fitted to evaluate the following parameters: time scaling parameters (a1, a2, b1, b2, and b3), the fraction of the administered dose available for systemic circulation (F _ slow, F _ medium (Med), and F _ Fast (Fast)), and additional residual error (err). The f (t) function is estimated by using a finite difference method. F _ parameters represent dose table values relative to a reference IR formulation. It is reasonable to use this parameter to take into account any differences in bioavailability that may exist between the reference formulation and the slow, medium and fast release formulations.

The time scaling parameters (a1, a2, b1, b2, and b3) were evaluated as fixed effect parameters (no random effect) since these parameters were expected to take the same values for all formulations. No random effects are associated with any parameters of the convolution model.

The estimated parameter values are presented in table 68. The mean observed and model predicted in vivo concentrations of MPH for the slow, medium and fast release rate formulations are presented in fig. 69.

Table 68 estimated parameter values in the convolution analysis for ivivivc evaluation. With SE as standard error and RSE as parameter Relative standard error:

the results of the analysis showed relative bioavailabilities of the slow, medium and fast release formulations to the IR formulation of 0.88%, 0.48% and 0.24%, respectively.

The analysis was performed to evaluate the correlation between in vitro and in vivo release resulting from ivivivc analysis (including time scaling correction).

The regression analysis is shown in fig. 70, and the results of the regression analysis are shown in table 69. Analysis indicated that a statistically significant correlation was found between in vitro dissolution and in vivo absorption data (p < 0.001).

TABLE 69 results of regression analysis of in vivo versus in vitro release::

step 5. internal verification

a) Correlation analysis between observed PK and PK predicted from IVIVC analysis.

In vitro dissolution data and convolution models were used to predict the PK profile in vivo for the three formulations. The convolution model predicts close agreement with the observed concentration. FIG. 71 shows a graph of predicted concentration versus observed concentration using regression lines.

The results of the regression analysis are presented in table 70. The analysis showed that the intercept was statistically not different from zero and the slope was statistically not different from 1, since the 95% confidence limits included intercept of 0 and slope of 1.

Table 70. results of regression analysis of predicted versus observed concentrations:

b) internal predictability assessment.

For both observed and model predicted data, the area under the plasma-concentration time curve between zero and the last measurable concentration (AUC) value was estimated using the log-linear trapezoidal rule. Prediction error (% PE) was estimated using AUC and Cmax values for the observed and model predicted concentrations. The estimated% PE values are presented in table 71. The maximum% PE of AUC of the fast formulation was estimated (═ 11.05%). For AUC and Cmax, the mean% PE of the three formulations was 6.0% and 4.31%, respectively.

TABLE 71 estimated Cmax and AUC values on observed PK data and estimated values on convolution model predicted PK data A comparison was made and the prediction error (% PE): :

step 6-external verification.

a) Modified IVIVC model.

The ivivivivc model, configuration (V/F ═ 2.11), and cancellation (Kel ═ 0.256) parameter values defined previously were fixed to the values estimated in step 3. In addition, the inter-formulation difference of V/F was fixed to 0, and the inter-formulation difference of kel was fixed to 0.438 of step 3). The model is adapted to support internal verification. However, this model is not suitable for predicting PK in vivo for formulations different from the formulation used in model development. This is because no rules have been established to estimate in vivo relative Bioavailability (BI) and kel values from in vitro dissolution data. To overcome this limitation, the initial ivivivc model was modified by including sub-models describing the relationship between in vitro properties, in vivo relative bioavailability and in vivo Kel. As shown in figure 72, the polynomial relationship between the in vitro estimated TD parameter (time required to dissolve 63.2% of the dose during release 1) and the in vivo estimated relative Bioavailability (BI) and Kel for each formulation (F slow, F moderate and F fast) was determined empirically. These models were adapted to estimate by interpolation the BI and Kel values of the new formulations used in the external validation when their TD parameters were taken to be values within the range of TD values of the formulations used in the development of the ivivivc model: 818 to 20.8 (hours).

A modified convolution model including the dependence between dissolution properties and estimated in vivo relative BI and kel is presented in fig. 73.

The estimated parameter values are presented in table 72. The mean observed and model predicted in vivo concentrations of MPH for the slow, medium and fast release rate formulations are presented in fig. 74.

Table 72 estimated parameter values in the modified convolution analysis for ivivivc evaluation. SE is the standard error and RSE is Relative standard error of parameters::

and (5) performing model verification on the modified IVIVC model.

Correlation analysis between observed PK and PK predicted from IVIVC analysis.

In vitro dissolution data and convolution models were used to predict the PK profile in vivo for the three formulations. The convolution model predicts close agreement with the observed concentration. FIG. 75 is a graph showing the predicted concentration versus the observed concentration by regression line.

The results of the regression analysis are presented in table 73. The analysis showed that the intercept was statistically not different from zero and the slope was statistically not different from 1, since the 95% confidence limits included intercept of 0 and slope of 1.

Table 73. regression analysis results of predicted versus observed concentrations for the corrected ivivivivc model:

predictability assessment

For both observed and model predicted data, the area under the plasma-concentration time curve between zero and the last measurable concentration (AUC) value was estimated using the log-linear trapezoidal rule. Prediction error (% PE) was estimated using AUC and Cmax values for the observed and model predicted concentrations. The estimated% PE values are presented in table 74. The maximum% PE of AUC for the slow formulation was estimated (═ 11.08%). For AUC and Cmax, the mean% PE of the three formulations was 5.93% and 4.34%, respectively.

TABLE 74 estimated Cmax and AUC values on observed PK data and modified convolution model predicted PK data The values of the meters are compared and the prediction error (% PE) is evaluated: :

dissolution data

The in vitro dissolution data formulation was described by the same weibull model previously applied.

The average dissolution data with the model prediction curve is presented in figure 76. The estimated parameter values are presented in table 75. In the table, RSE denotes the relative standard error of the estimated parameters.

Table 75. estimated dissolution data parameters (RSE ═ relative standard error) for the formulations used in the external verification:

in vivo PK for use in external validation

External validation was evaluated using PK data for study HLD200-109 collected in 18 subjects at 100mg in the fasted group.

FIG. 77 shows the mean concentration time course of the formulation with median dissolution rate in study HLD200-111 compared to the time course of PK concentration in study HLD200-109 (fasted group).

While the dissolution data for the formulation with median dissolution rate in study HLD200-111 and the dissolution data for the formulation used in study HLD200-109 (fasted group) showed nearly overlapping curves (fig. 76), the mean in vivo Cmax in the HLD200-109 study was-30% lower than that observed in the HLD200-111 study.

The PK data were characterized using the same model as above using a population PK modeling method: a single compartment model with time varying absorption described by a double weibull function and a first order elimination rate constant.

The estimated parameter values are presented in table 76. In the table, SE denotes a standard error, and RSE denotes a relative standard error of the estimated parameter.

TABLE 76 study of population PK parameter estimates for HLD200-109 data: :

based on the final model, five hundred replicates of the original dataset were simulated, and a 90% prediction interval was calculated based on the simulated dataset. Observed drug concentration values were plotted against time on a prediction interval chart to visually assess the agreement between the simulated data and the observed data. A visual predictive review of population PK modeling is presented in figure 78.

VPC showed that a model was developed to characterize the temporal course of MPH plasma concentrations in well-performed studies of HLD 200-109: the distribution of the observation data was well predicted based on the typical distribution (median curve determined by the thick solid line) and the inter-individual variability (90% prediction interval determined by the gray shaded region), indicating that the population model correctly described the observation data.

External predictability assessment

The expected typical characteristics of PK in vivo in study HLD200-109 were predicted using a modified ivivivc model (table 76) in combination with dissolution release parameters for the formulations used in the external validation (table 75). In this model, in vivo PK data were estimated by calculating the model results while all parameter values were fixed to the values shown in table 77.

TABLE 77 values of parameters used in the assessment of in vivo PK for the evaluation of external predictability:

FIG. 79 shows a comparison of typical PK time courses (solid curves) with pre-phase PK curves (dashed curves) in study HLDs 200-109.

In vitro dissolution data and convolution models were used to predict the in vivo PK profile of the formulations used in the external validation. Fig. 80 presents a graph of predicted concentration versus observed concentration using a regression line.

The results of the regression analysis are presented in table 78. The analysis showed that the intercept was statistically not different from zero and the slope was statistically not different from 1, since the 95% confidence limits included intercept of 0 and slope of 1.

Table 78. external verification: regression analysis of predicted versus observed concentrations:

external validation was assessed by comparing the area under the plasma concentration time curve (AUC) and Cmax values of observed and model predicted data.

The estimated% PE values are presented in table 79.

Table 79. external verification: comparing the estimated Cmax and AUC values with model predicted Cmax and AUC values and evaluating Measurement error (% PE):

the predicted AUC values are characterized by% PE < 10%, while the% PE for Cmax is-24%. This value is consistent with the comparison of the mean PK concentration time course for the formulation with median dissolution rate in study HLD200-111 and the PK concentration time course in study HLD200-109 (fasted group), as shown in figure 15. However, this value exceeds the maximum threshold of 10% determined by the FDA to meet the criteria for external predictability.

In summary, the ivivivc analysis described in example 48 was performed using a convolution-based modeling method.

Convolution-based methods for evaluating IVIVC have been shown to have many benefits over conventional evaluation of IVIVC using convolution and deconvolution methods (Buchwald P (2003) Direct directed-equalization-based in vision-in vivo correlation (IVIVC) method. J Pharm Pharmacol 55: 495-504; Gaynor C, Dunne A, Costello C, Davis J.A position adaptation to in vision-in vivo correlation modeling for combining with non-linear dynamics. J Pharmacokinetic Pharman.2011un; 38(3):317-32).

For example, using a convolution-based approach, the overall ivivivc evaluation can be achieved using a set of differential equations that integrate directly without applying a convolution or deconvolution process.

In this framework, various functional dependencies (e.g., time scaling) can be easily introduced to describe or combine dissolution and absorption properties. This may provide improved performance and increased modeling flexibility, since no specific software tools are required other than the standard software used for modeling (e.g. nommem).

Furthermore, traditional methods based on deconvolution and convolution methods require systematic linear assumptions of interest and are therefore not suitable for use with compounds exhibiting non-linear kinetics. Under this limited variation, convolution-based approaches can easily accommodate the potential non-linearity in the pharmacokinetic process by integrating the description of the potential non-linearity in the differential equations used to define the ivivivc model (Gaynor et al,2011).

Ivivivc models were developed using in vitro dissolution and in vivo PK of three sustained release formulations (fast, medium and slow). Data for IR formulations were used to characterize the distribution of MPH.

In vitro dissolution data enables prediction of the PK time course in vivo for each formulation evaluated with high accuracy.

Comparison of the corrected ivivivc model predicted values with the observed concentrations showed a strong correlation between the two measurements: the regression line is characterized by a zero intercept and a single slope.

The mean prediction errors for AUC and Cmax were 5.93% and 4.34%, respectively, for slow, medium and fast release formulations, below the FDA recommended 10%. Furthermore, the prediction error for each formulation was below the allowable 15%. These results support a class a IVIVC correlation.

The modified ivivivc model was used to evaluate external predictability to predict in vivo performance of formulations not used to develop IVIVC models.

The predicted AUC values are characterized by% PE < 10%, while the% PE for Cmax is-24%. This value is consistent with the comparison of the mean PK concentration time course for the formulation with median dissolution rate in study HLD200-111 and the PK concentration time course in study HLD200-109 (fasted group), as shown in figure 15. However, this value exceeds the maximum threshold of 10% determined by the FDA to meet the criteria for external predictability.

In summary, a successful ivivivc model was developed and validated according to the internal predictive criteria recommended by the FDA in its Guidance for industrial Extended Release Oral Dosage Forms (guide to the Industry Oral Dosage Forms): development, evaluation and application of in vitro/in vivo correlations.

Example 49 polynomial curve fitting of slow, medium and fast methylphenidate formulations.

Various pharmacokinetic data for the immediate release methylphenidate formulation were used to characterize the absorption, distribution and elimination of methylphenidate.

The weibull model described above was fitted to the average in vitro dissolution data for slow, medium and fast dissolving methylphenidate formulations.

For the three formulations, the in vivo drug release rate was estimated along with an estimate of the inter-vivo variability in the elimination rate constant. A non-linear mixing effect method was used to fit a volume model to the data for these three formulations.

In vivo drug release was evaluated by fixing the mean value of volume (V) and the mean value of elimination rate (kel) as estimates in the analysis of immediate release formulation data.

As described in example 48, assays were performed to evaluate the correlation between in vitro release and in vivo release obtained from IVIVC assays (including time scaling corrections).

Graphs reporting regression analyses of the fast, medium and slow release MPH formulations are presented in fig. 82, 83 and 84, and the results of the respective regression analyses are presented in table 80, table 81 and table 82. Analysis showed that a statistically significant correlation (p <0.001) was found between the in vitro dissolution and in vivo absorption data.

TABLE 80 IVIVIVC regression analysis of methylphenidate HLD200 fast formulations.

Table 80 shows that using the linear model, the rapid formulations show statistically significant correlation between in vitro and in vivo absorption (p < 0.0001).

TABLE 81 IVIVIVC regression analysis of the intermediate formulation of methylphenidate HLD 200.

Table 80 shows that using the 3 rd order polynomial model, the intermediate formulations showed statistically significant correlation between in vitro and in vivo absorption (p < 0.0001).

TABLE 82 IVIVIVC regression analysis of methylphenidate HLD200 slow formulations.

Table 82 shows that using the 5 th order polynomial model, the slow formulations showed statistically significant correlation between in vitro and in vivo absorption (p < 0.0001).

The methylphenidate HLD200 fast formulation has the same composition and release mechanism as the medium and slow formulation of methylphenidate HLD200, differing only in% weight gain in the ER layer. However, the fast formulation of methylphenidate HLD200 releases methylphenidate earlier and therefore closer to the upper GI tract, compared to the medium and slow formulation of methylphenidate HLD200 that releases methylphenidate later and therefore further in the colon. Thus, a fast formulation of methylphenidate is shown and describedIVIVC similar to other products. The medium and slow formulation of methylphenidate HLD200 showed non-linear (high degree polynomial) ivivivc, which can be assigned to sites of release and absorption. Furthermore, the release began the further into the colon the correlation deviated from linearity, and the slow methylphenidate HLD200 formulation showed suitability for IVIVC of higher order polynomials than the medium methylphenidate HLD200 formulation.

Example 50: HLD200, in vitro/in vivo correlation of evening-administered delayed and extended release methylphenidate (IVIVIVC) model

This embodiment includes information related to sigmoidal Emax modeling that can be used as an alternative to weibull function modeling, but results in the same clinical indications. Thus, unless expressly excluded or impossible to implement, the sigmoidal Emax modeling discussed in this example and the data presented in relation to that modeling and any conclusions in relation to that data can be combined with or substituted for all other aspects of the present disclosure, either with or from the weibull function modeling or the information and conclusions resulting therefrom.

HLD200 is a delayed-release and extended-release formulation for the first night administration of methylphenidate (DR/ER-MPH) that is specifically designed to delay the initial release of MPH and provide a clinically meaningful onset of therapeutic effect when awakened and sustained to night.

In some embodiments, the Pharmacokinetics (PK) of DR/ER-MPH is characterized by an initial MPH release delay of 8-10 hours, followed by an extended controlled release period with an absorption peak of 14 hours post-administration and a drug exposure of > 50% after reaching peak plasma concentrations (Liu T, et al, J Child Adolesc Psychopharmacol.2019; 29(3):181-191, incorporated herein by reference). In two critical phase 3 trials in children (6-12 years) with attention deficit/hyperactivity disorder (ADHD), DR/ER-MPH demonstrated significant improvement over placebo in ADHD symptom control and functional impairment during morning, all day, and afternoon/evening (Childress AC, et al, J Child Adolesc Psychopharmacol, 2019Aug 29.doi:10.1089/cap.2019.0070.Epub ahead of print; and Pliszka SR, et al, J Child Adolesc Psychopharmacol, 2017; 27(6): 474-. The U.S. Food and Drug Administration (FDA) encourages the Development of In Vitro/In Vivo correlations (IVIVC), i.e., the time course of predicting In Vivo plasma concentrations from In Vitro dissolution profiles for extended Release Drug products (U.S. Department of Health and Human Services, Food and Drug Administration Center for Drug Evaluation and Research (CDER). guide for industry. extended Release organic systems: Development, Evaluation, and Application of In/In Vivo correlation. separation 1997, incorporated herein by reference). IVIVC can be used to establish dissolution profiles and can allow for some formulation and production variations without in vivo bioequivalence studies. The class a correlation (the most informative type in the IVIVC model) describes the relationship between the overall in vitro dissolution time course and the time course of the plasma drug concentration (point-to-point relationship).

The purpose of this study was to establish and validate class A IVIVC for DR/ER-MPH.

The following methods were used in these examples:

data sourceIn vitro MPH dissolution rates in three DR/ER-MPH formulations (slow, medium and fast) were determined in a phase 3 environment (in the case of simulated oral administration) using United States Pharmacopeia (USP) apparatus type 1. In phase 1, single-center, single-dose, open-label, randomized, 3-way crossover, comparative bioavailability studies of three DR/ER-MPH formulations, in vivo plasma MPH concentrations (Ironshore Pharmaceuticals) were measured in 18 healthy adults&Development, Inc. data on File, HLD200-111, incorporated herein by reference). Approximately three hours after a standard low-fat diet, 100mg of DR/ER-MPH was administered at 9:00 pm. Blood samples were collected up to 48 hours after dosing. A 96 hour washout period was performed between each administration. PK data from immediate release MPH (ir MPH) was used to characterize the distribution and elimination of MPH. Plasma levels were measured in a phase 1, single-center, single-dose, open-label, randomized, crossover study of 12 healthy adults, which has been previously described (Liu T, et al. J Child Adolesc psychopharmacol. 2019; 29(3):181-Incorporated herein by reference).

IVIVC model development

Study and evaluation of grade A IVIVIVC, MPH score in vitro dissolution [ r_{In vitro}(t)]([r_vitro (t)]) (Eq.9) and fraction of MPH absorbed in vivo [ r_{In vivo}(t)]([r_vivo(t)]) (Eq.14) point-to-point correlation. By a convolution-based modeling method, the following steps are used: study and evaluation of IVIVC models (Gomeni R, et al. Pharmacommunications Syst Pharmacol.2019; 8(2):97-106, which are incorporated herein by reference):

step 1. fitting PK time course for IR MPH formulation: to characterize the absorption, processing and elimination of IR MPH, PK is best described by a single compartment model with first order absorption and lag time. The model parameters are ka (first order absorption rate constant), kel (elimination rate constant), lag (lag time), and V (volume of distribution, and a combination of additive and proportional residual errors).

Step 2, respectively fitting the average in vitro dissolution data: in vitro dissolution data for slow, medium and fast formulations of DR/ER-MPH are described by sigmoidal Emax model:

where EC is the time to release 50% of the dose and ga is a parameter characterizing the shape of the absorption curve.

Step 3. fitting in vivo PK data: to estimate the inter-individual variability of in vivo release rate and elimination rate constants, a nonlinear mixed effect approach was used, using a convolution model to fit the data for the three agents together. In vivo PK data were fitted by fixing the mean of volume (V) and the mean of elimination rate (kel) to the values estimated in the IR MPH data analysis. The PK time course was assumed to be characterized by a sigmoidal Emax model. The DR/ER-MPH plasma concentration (Cp) resulting from any dose is described by convolution as (see fig. 85):

BI_i＝-0.0019*EC_i**2+0.0019*EC_i+ 1.1351 (equation 11)

Wherein:

and

where BI is the relative bioavailability in vivo, i is the index for the ith agent, f (t) is the in vivo input function, V is the volume of distribution, kel is the first order elimination rate estimated using IR MPH data, x is the convolution operator, r (t) is the fraction of released dose that varies with time, A is the drug content, EC is the time to release 50% of the dose, and ga is a parameter characterizing the shape of the absorption curve.

Fig. 85 is an exemplary schematic depicting a modified ivivivc model that includes a dependence between dissolution properties and estimated in vivo relative bioavailability.

Step 4. using the convolution modeling method to evaluate IVIVIVC by:

a. the in vivo drug release parameters for each DR/ER-MPH formulation were fixed at the values estimated in the previous analysis: (i) average kel and V estimated from analysis of IR MPH data (step 1); (ii) in vitro dissolution data (EC and ga) were estimated for the three formulations (step 2); and (iii) inter-individual variability of Kel and V, estimated from analysis of in vivo PK data (step 3).

b. The time scaling factor (fixation effect) common to all formulations was estimated: (i) time scaling functions were included in the model to account for potential time differences in vitro and in vivo procedures (e.g., when dissolution times were faster than in vivo infusion rates (Gomeni R, et al. Pharmacommunications Syst Pharmacol.2019; 8(2):97-106, incorporated herein by reference):

r_{in vivo}(t)＝a₁+a₂·r_{In vitro}(tt) (equation 14)

The equation includes linear components (intercept of a1 and slope of a 2) and nonlinear components describing time shift (b1), time scaling (b2) and time-shaping (b3) factors. At r_vitroAnd r_vivoWithout time scaling in between: a1 ═ 0, a2 ═ 1, b1 ═ 0, b2 ═ 1, and b3 ═ 1; in addition, the time scaling is defined by estimating appropriate parameter values.

c. The fraction of the administered dose available for systemic circulation (fway, fway and fway) and the additional residual error were estimated: the F parameter represents the dose scale value relative to the reference IR MPH formulation; this allows for any differences in bioavailability that may exist between the IR MPH and DR/ER-MPH formulations.

Step 5, internal verification: in vitro dissolution data and convolution models were used to predict the PK profile in vivo for the three formulations. The model for each formulation was used to predict the peak plasma concentration (Cmax) and the area between zero and infinity (AUC) on the plasma concentration-concentration time curve and compared to the mean observed value. For both observed and model predicted data, AUC values were estimated using a log-linear trapezoidal rule, extrapolated to infinity using the last measurable concentration and elimination rate constant. The prediction error (% PE) for each PK parameter was calculated using the following equation, where n is the number of formulations (table 84):

the criteria for assessing the level of predictability of each PK parameter was an average% PE < 10%, with individual values not > 15% (U.S. department of Health and Human Services, Food and Drug Administration Center for Drug Evaluation and Research (CDER). guideline for industry. extended Release Oral procedures: Development, Evaluation, and Application of In Vitro/In Vivo correlation. separator 1997, which is incorporated herein by reference).

Step 6, external verification: external validation of the IVIVIVC model was performed using PK data from a phase 1, single-center, open-label study of 18 healthy adult volunteers receiving 100mg DR/ER-MPH, which has been previously described (Liu T, et al.J. Child Adolesc Psychopharmacol.2019; 29(3): 181-. The in vitro dissolution data of the formulations used in the study are described by the same Sigmoid Emax model applied before. Ivivivc was used in combination with the dissolution release parameters from study eq.16 to predict the expected typical profile of PK in vivo, which was used to calculate% PE from the observed and predicted Cmax and AUC values.

And (3) software.All analyses were in(Regents of the University of California Corporation, California) using the first order conditional interaction assessment (FOCE-I) method. Population PK analysis of IR MPH data was performed using the ADVAN14 subroutine. The convolution model was implemented using the ADVAN6 subroutine.

The following results were obtained in this example.

FIG. 86A is a graph reporting exemplary plasma MPH concentration values versus time (hours), which allows for a visual predictive review of the immediate release methylphenidate (IR MPH) model (step 1). The thick solid line depicts the median curve; the shaded area depicts the 90% prediction interval; the dots depict individual participant data.

Step 1 provides values for MPH elimination (kel, elimination ratio) and distribution (V, distribution volume).

FIG. 86B is a graph reporting exemplary values of in vitro release dose fraction (%) versus time (hours) for model predicted in vitro delayed release and extended release methylphenidate (DR/ER-MPH) dissolution profiles (step 2). The dots represent the average dissolution values; the solid line depicts the distribution of model predictions.

Step 2 provides in vitro dissolution values including EC (time to release 50% of the dose) and ga (parameters characterizing the shape of the curve).

FIG. 86C is a graph reporting exemplary values of the median model predicted DR/ER-MPH PK curve (step 3).

Step 3 provides K_elAnd the value of inter-individual variability of V.

Fig. 86D is a graph reporting exemplary values of mean plasma MPH concentrations predicted by the convolution model (step 4). The circles indicate the mean observed PK concentrations and the solid lines indicate the PK profile predicted by the model.

IVIVC model assessment and validation

In the IVIVC model, all three DR/ER-MPH formulations demonstrated monophasic plasma-time concentration profiles with rapid formulations, showing shorter delay before initial MPH release, higher Cmax, faster peak absorption time (Tmax) and higher relative bioavailability compared to the intermediate formulations (fig. 86D).

The relative bioavailability of the fast, medium and slow DR/ER-MPH formulations relative to the IR MPH formulation was 100%, 80% and 32%, respectively.

Regression analysis confirmed a statistically significant correlation (P <0.001) between observed plasma MPH concentrations versus convolution predicted concentrations (predicted from in vitro dissolution data and convolution models), including time scaling corrections (fig. 87). In fig. 87, thick solid line, regression analysis; point, individual participant data; shaded area, 95% confidence interval; dashed line, 95% prediction interval.

The parameter estimation values related to fig. 87 are shown in table 83.

Table 83:

in table 83, "DF" is the degree of freedom, "MPH" is methylphenidate, and "Pr" is the probability.

The established ivivivivc model enables prediction of the in vivo PK of each formulation evaluated from in vitro dissolution data with high accuracy; the mean% PE for the slow, medium and fast formulations was 6.07% for Cmax and 1.64% for AUC (table 84).

TABLE 84. internal testEvidence: prediction error estimation

In Table 84, "a" represents the data source of the observed values, LD200-111 clinical study report (Ironshore Pharmaceuticals & Development, Inc. data on File: HLD200-111), "AUC" represents the area under the plasma concentration-time curve between zero and infinity; "Cmax" refers to the peak plasma concentration; "PE" refers to prediction error.

The average% PE for AUC and Cmax were less than 10% each, and the% PE for each formulation was less than 15% (see Table 84), meeting the FDA criteria for successful IVIVIVC (U.S. Department of Health and Human Services, Food and Drug Administration Center for Drug Evaluation and Research (CDER).

The estimated% PE of the external validation data was 0.79% for Cmax and 9.85% for AUC, which established the external predictability of IVIVC ((U.S. department of Health and Human Services, Food and Drug Administration Center for Drug Evaluation and Research (CDER). guide for industry. Extended Release organic document Forms: Development, Evaluation, and Application of Vitro/In Vivo correlation. separator 1997, which is incorporated herein by reference) (see Table 85).

Table 85. external verification: estimation of prediction error:

in Table 85, "a" refers to the source of the observed values (Liu T, et al. J Child Adolesc psychopharmacol.2019; 29(3): 181-; "Cmax" refers to the peak plasma concentration; "PE" refers to prediction error.

In this example, a convolution-based class A IVIVC model of DR/ER-MPH was developed and validated. In vitro dissolution data enables prediction of PK time course in vivo with high accuracy. The mean prediction error for AUC and Cmax for slow, medium and fast formulations of DR/ER-MPH was less than 10% and less than 15% for each formulation, meeting FDA criteria for a class a IVIVC correlation of success levels. IVIVC also meets the FDA standards for external predictability.

Example 51 IVIVIVC was evaluated using the convolution modeling method.

This example further describes the evaluation of ivivivc using a convolution modeling method (step 4 described in example 50).

The convolution model used to evaluate ivivivc relationships is presented in fig. 90. The data for the three formulations were independently fitted using the nonlinear mixing effect method using the convolution model presented in figure 90. In vivo drug release was evaluated by fixing the mean value of volume (V) and the mean value of elimination rate (kel) as estimated values in IR data analysis. The PK time course of in vivo release of the different formulations was assumed to be characterized by the sigmoidal Emax model.

The mean data for the three formulations were fit together. The following parameters were fixed as estimates in the previous analysis: mean kel and V (estimated from IR data analysis in step 1), and variance of kel and V (inter-individual variability) (estimated in step 3), dissolution data estimated in step 2 (EC and GA for 3 formulations). In NONMEM, the convolution model was implemented using the ADVAN6 subroutine and the FOCE-I method.

The mean data for the three formulations (slow, medium and fast) were combined with fitting to estimate the following parameters: time scaling parameters (a1, a2, b1, b2, and b3), fraction of the administered dose available for systemic circulation (F _ slow, F _ medium, and F _ fast), and additional residual error (err). The f (t) function is estimated by using a finite difference method. The F parameters (parameters) represent dose-scale values relative to the reference IR formulation. The rationale for using this parameter is to allow for any differences in bioavailability that may exist between the reference formulation and the sustained, medium and fast release formulations.

The time scaling parameters (a1, a2, b1, b2, and b3) were estimated as fixed effect parameters (no random effect) since these parameters were expected to take the same values for all formulations. There is no random effect on any of the parameters of the convolution model.

The estimated parameter values are presented inWatch 86In (1).

Table 86, values of parameters estimated in the convolution analysis for ivivivc evaluation. With SE as standard error and RSE as parameter Relative standard error:

fixed by analysis of IR data

Fixed by reevaluation of IIV

# evaluation as fixed parameters

The mean observed and model predicted in vivo concentrations of MPH for the slow, medium and fast release rate formulations are presented in fig. 86D.

The results of the analysis showed that the relative bioavailability of the fast, medium and slow release formulations relative to the IR formulation was 100%, 79.8% and 32.2%, respectively.

Assays were performed to evaluate the correlation between in vitro release and in vivo release from ivivivc assays (including time scaling corrections).

The regression analysis is presented in fig. 87, and the results of the regression analysis are presented in table 83. Analysis showed that a statistically significant correlation (p <0.001) was found between in vitro dissolution and in vivo absorption data.

Example 52. consider a comparison of models used to describe in vitro dissolution data for each formulation.

Three different models were considered to describe the in vitro dissolution data for each formulation. Table 87 shows the functional equations associated with these three models.

Table 87:

comparison of surrogate models was performed using the log likelihood ratio test of nested models or Akaike information standard (AIC) of non-nested models. For nested models, the reduction in the value of an Objective Function (OFV) associated with the model is used>3.84, 1 degree of freedom (df)% of²<At 0.05, the surrogate model is considered to be a significantly better descriptor of the data. For non-nested models, models with lower AIC values are considered to be preferred models. The AIC criteria were calculated as: AIC ═ 2LL + 2n_p. Wherein n is_pIs the total number of parameters in the model. Of the two models, the most informative will be the model with the lowest AIC value.

Comparing the performance of the single and double weibull models using log-likelihood ratio testing when the two models are nested models: (Watch 88). When the two models are not nested models, the AIC standard is used to compare the performance of the double Weibull model and the sigmoid Emax model (S) ((S))Watch 89)。

Table 88. comparing the performance of the single and double weibull models using log-likelihood ratio tests:

comparison of Powerburg function to double Weibull function

TABLE 89 comparison of sigmoid Emax and double Weibull model Performance using AIC criteria:

comparison of model performance shows that the double weibull model performs better than the single weibull model, but the sigmoid Emax model performs better than the double weibull model. Therefore, the sigmoid Emax model remains as the preferred model. Figure 88 shows the observed dissolution data and the model predicted dissolution data from the single and double weibull model analyses. Figure 89 shows the observed dissolution data and the dissolution data predicted by the model obtained from sigmoid Emax model and double weibull model analysis.

Example 53 Using scaling parameters as submodels to fit a Single model to Slow, Medium and fast Release And (4) preparing the preparation.

This example involves using scaling parameters as submodels to fit a single model to three formulations (sustained, medium and rapid release formulations) that exhibit different PK profiles, such as different bioavailability and/or absorption sites.

Graphs of the absorption portion versus the dissolution portion for the rapid, moderate and slow release formulations and the Levy graph (T in vivo versus T in vitro) (Tvivo vs Tvitro) are presented in fig. 91, fig. 92 and fig. 93, respectively.

For example, the Levy plots shown in fig. 91, fig. 92, and fig. 93 provide further evidence of the non-linearity of colonic absorption. The fast release formulation (which is not absorbed in the colon) has evenly distributed points above and below the line of the Levy plot, while for the medium and slow formulations absorbed at the more distal end, most of the points fall below the line.

＊＊＊＊＊＊＊＊＊＊

The above-disclosed subject matter is to be considered illustrative, and not restrictive, and the appended claims are intended to cover all such modifications, enhancements, and other embodiments, which fall within the true spirit and scope of the present disclosure. Thus, to the maximum extent allowed by law, the scope of the present disclosure is to be determined by the broadest permissible interpretation of the following claims and their equivalents, and shall not be restricted or limited by the foregoing detailed description.

As used in this specification and the appended claims, the singular forms "a", "an", and "the" include plural referents unless the content clearly dictates otherwise. The term "plurality" includes two or more indications unless the context clearly dictates otherwise. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure belongs.

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