Methylphenidate compositions for treating attention-deficit hyperactivity disorder

Abstract

To provide methylphenidate compositions for treating attention-deficit hyperactivity disorder.SOLUTION: A solid oral pharmaceutical composition is described. The solid oral pharmaceutical composition includes methylphenidate or a pharmaceutical salt thereof, where an in vivo absorption model 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 model. A correlation of a plurality of fractions of an in vitro dissolution of the solid oral pharmaceutical composition with a same plurality of fractions of an in vivo absorption of the solid oral pharmaceutical composition is non-linear. A method of treating a condition in a subject having a disorder or condition responsive to the administration of methylphenidate is also described. The method includes orally administering to the subject an effective amount of the solid oral pharmaceutical composition.SELECTED DRAWING: None

Description

[Technical field]

[0001] Related Applications This application claims priority to U.S. Provisional Patent Application No. 62 / 796,918 (filed January 25, 2019) and U.S. Provisional Patent Application No. 62 / 962,355 (filed January 17, 2020), the disclosures of which are hereby incorporated by reference in their entireties.

[0002] 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 technology]

[0003] background Methylphenidate is a central nervous system stimulant that is used to treat ADD and ADHD in children and adults.Despite its usefulness, methylphenidate is accompanied by rebound as the drug is metabolized and its effect decreases.Rebound can cause significant changes in attitude, excessive moodiness, irritability, anger, nervousness, sadness, screaming, fatigue, and even increase in the severity of ADD or ADHD symptoms during rebound period. Summary of the Invention [Means for solving the problem]

[0004] overview According to a first aspect, a solid oral pharmaceutical composition is provided. The solid oral pharmaceutical composition comprises methylphenidate or a pharmaceutical salt thereof, and an in vivo absorption model of the solid oral pharmaceutical composition is: Single Weibull function:

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[0005] In any of the disclosed implementations, the solid oral pharmaceutical composition may further include the following details, which may be combined with each other in any combination, except where expressly mutually exclusive:

[0006] (i) The nonlinear correlation between fractions of in vitro dissolution and fractions of in vivo absorption of a solid oral pharmaceutical composition can be best fitted to a fifth order polynomial function.

[0007] (ii) The nonlinear correlation between the fractions of in vitro dissolution and the fractions of in vivo absorption of a solid oral pharmaceutical composition may be best fitted to 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.

[0008] (iii) The fractions of in vitro dissolution and fractions of in vivo absorption of the solid oral pharmaceutical composition may include values ​​from 0 to 1.

[0009] (iv) The solid oral pharmaceutical composition can be a multi-layer solid oral pharmaceutical composition comprising methylphenidate or a pharmaceutical salt thereof, an extended release layer, and a delayed release layer.

[0010] (v) The solid oral pharmaceutical composition can include a core comprising methylphenidate or a pharmaceutical 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 can surround the core.

[0011] (vi) A sustained release layer may surround the core, and a delayed release layer may surround the sustained release layer.

[0012] (vii) The sustained release layer and the delayed release layer may incompletely surround the surface of the core.

[0013] (viii) The delayed release layer may incompletely surround the surface of the sustained release layer and / or the surface of the core.

[0014] (ix) The sustained release layer and delayed release layer may surround at least 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99% or 99.9% of the surface of the core.

[0015] (x) The delayed release layer may surround 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.

[0016] (xi) The multi-layered solid oral pharmaceutical composition can comprise a multi-layered core, the multi-layered core having a surface, and the multi-layered core comprises a first layer comprising methylphenidate or a pharmaceutical salt thereof and a second layer comprising a swellable layer comprising a superdisintegrant or an osmotic agent.

[0017] (xii) The multi-layered solid oral pharmaceutical composition can comprise a multi-layered core, the multi-layered core having a surface, and the multi-layered core comprises a first layer comprising methylphenidate or a pharmaceutical salt thereof and a second layer comprising a sustained release layer.

[0018] (xiii) The multi-layered core may further comprise a third layer, which comprises a sustained release layer.

[0019] (xiv) The multi-layered core may further comprise a third layer, which comprises a swellable layer comprising a superdisintegrant or an osmotic agent.

[0020] (xv) A delayed release layer may surround the multi-layered core.

[0021] (xvi) The delayed release layer may incompletely surround the surface of the multi-layered core.

[0022] (xvii) The delayed release layer may surround at least 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99% or 99.9% of the surface of the multi-layered core.

[0023] According to a second aspect, a method for treating a condition in a subject having a disorder or condition responsive to administration of methylphenidate is described.The method comprises orally administering to the subject an effective amount of a solid oral pharmaceutical composition comprising methylphenidate or a pharmaceutical salt thereof, and an in vivo absorption model of the solid oral pharmaceutical composition is: Single Weibull function:

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[0024] In any of the disclosed implementations, the method may further include the following details, which may be combined with each other in any combination, except where expressly mutually exclusive:

[0025] (i) A period may start at 8:00am, 9:00am, 10:00am, 11:00am, 12:00pm, 1:00pm, 2:00pm, 3:00pm, 4:00pm, 5:00pm, 6:00pm or 7:00pm.

[0026] (ii) The period may begin 8, 9, 10, 11, 12, 13, 14, 15, or 16 hours after administration of the composition.

[0027] (iii) Improvement may be measured by a validated rating scale, score, or combination score.

[0028] (iv) The validated rating scale, score, or combination score can be the Swanson, Kotkin, Agler, M-Flynn and Pelham (SKAMP) score, or the SKAMP-CS combination score.

[0029] (v) The variability in potency is measured using the following variability index (FI):

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[0030] (vi) The Fluctuation Index (FI) may have an absolute value less than 1.0.

[0031] (vii) A 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 8:00pm.

[0032] (viii) The period may end 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, or 16 hours after the period began.

[0033] (ix) The period may end when the plasma concentration of methylphenidate in the subject is less than 5 ng / mL.

[0034] (x) The period may end 3, 4, 5, 6, 7, 8, 9, 10, 11, or 12 hours after the methylphenidate Tmax in the subject.

[0035] (xi) The period may end when the subject falls asleep after Tmax.

[0036] (xii) Over the period of time, the value of the SKAMP score may not change by more than, or by more than about, 6, 7, 8, 9, or 10.

[0037] (xiii) the rate of change in methylphenidate plasma concentration over time during the period may be less than or equal to +2.5 ng.hr / mL following doses of up to 100 mg methylphenidate.

[0038] (xiv) The period can be between Tmax and 6 hours after Tmax during which the rate of change in methylphenidate plasma concentrations is greater than or equal to -1.2 ng.hr / mL following doses of methylphenidate up to 100 mg.

[0039] (xv) The period may include a period during which the methylphenidate plasma concentration is between Cmax and at least 40% of Cmax and the rate of change of the methylphenidate plasma concentration is less than or equal to +1.5 ng.hr / mL and greater than or equal to -1.5 ng.hr / mL.

[0040] (xvi) Methylphenidate or a pharmaceutical salt thereof can be absorbed in the colon.

[0041] (xvii) At least 90% of methylphenidate or a pharmaceutical salt thereof can be absorbed in the colon.

[0042] (xviii) The subject may have Attention Deficit Hyperactivity Disorder (ADHD) or Attention Deficit Hyperactivity Disorder (ADD), and Autism Spectrum Disorder (ASD).

[0043] (xix) The improvement may be dose-dependent.

[0044] (xx) The dose-dependent improvement may include a dose-dependent increase in duration.

[0045] (xxi) The increase in duration may include an increase in duration after Tmax.

[0046] (xxii) The solid oral pharmaceutical composition can be a multi-layer solid oral pharmaceutical composition comprising methylphenidate or a pharmaceutical salt thereof, an extended release layer, and a delayed release layer.

[0047] (xxiii) The solid oral pharmaceutical composition may comprise a core comprising methylphenidate or a pharmaceutical 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 surround the core.

[0048] (xxiv) A sustained release layer may surround the core, and a delayed release layer may surround the sustained release layer.

[0049] (xxv) The sustained release layer and the delayed release layer may incompletely surround the surface of the core.

[0050] (xxvi) The delayed release layer may incompletely surround the surface of the sustained release layer and / or the surface of the core.

[0051] (xxvii) The sustained release layer and delayed release layer may surround at least 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99% or 99.9% of the surface of the core.

[0052] (xxviii) The delayed release layer may surround 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.

[0053] (xxix) The multi-layered solid oral pharmaceutical composition can comprise a multi-layered core, the multi-layered core having a surface, and the multi-layered core comprises a first layer comprising methylphenidate or a pharmaceutical salt thereof and a second layer comprising a swellable layer comprising a superdisintegrant or an osmotic agent.

[0054] (xxx) The multi-layered solid oral pharmaceutical composition can include a multi-layered core, the multi-layered core having a surface, and the multi-layered core includes a first layer comprising methylphenidate or a pharmaceutical salt thereof and a second layer comprising a sustained release layer.

[0055] (xxxi) The multi-layered core may further comprise a third layer, which comprises a sustained release layer.

[0056] (xxxii) The multi-layered core may further comprise a third layer, which comprises a swellable layer comprising a superdisintegrant or an osmotic agent.

[0057] (xxxiii) A delayed release layer may surround the multi-layered core.

[0058] (xxxiv) The delayed-release layer may incompletely surround the surface of the multi-layered core.

[0059] (xxxv) The delayed release layer may surround at least 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99% or 99.9% of the surface of the multi-layered core. [Brief description of the drawings]

[0060] For a more complete understanding of the present disclosure and associated features and advantages, reference is made to the following description taken in conjunction with the accompanying drawings, which are not to scale.

[0061] [Figure 1] FIG. 1 is a schematic of the pharmacokinetic (PK) model for delayed / extended release methylphenidate formulation HLD200.

[0062] [Diagram 2] FIG. 2 is an exemplary scatter plot on a linear scale of the PK profiles of individual subjects by dose of HLD200.

[0063] [Diagram 3] FIG. 3 is an exemplary scatter plot on a log-linear scale of the PK profiles of individual subjects by dose of HLD200.

[0064] [Figure 4] FIG. 4 is an exemplary scatter plot of the mean (±standard deviation (SD)) PK profile by dose of HLD200 on a linear scale in the entire study adult subject population.

[0065] [Figure 5A] FIG. 5A is an exemplary scatter plot of the mean (±SD) PK profile for the 20 mg dose of HLD200 on a linear scale by subject gender.

[0066] [Figure 5B] FIG. 5B is an exemplary scatter plot of the mean (±SD) PK profile for the 100 mg dose of HLD200 on a linear scale by subject gender.

[0067] [Figure 6] FIG. 6 is an exemplary scatter plot of the mean (±SD) PK profile by dose of HLD200 on a log-linear scale in the entire study subject population.

[0068] [Figure 7A] FIG. 7A is an exemplary scatter plot of mean (±SD) PK profiles for the 20 mg dose of HLD200 on a log-linear scale by subject gender.

[0069] [Figure 7B] FIG. 7B is an exemplary scatter plot of the mean (±SD) PK profile for the 100 mg dose of HLD200 on a log-linear scale by subject gender.

[0070] [Figure 8] FIG. 8 is an exemplary goodness of fit plot for the underlying subject population PK model for the 20 mg dose of HLD200.

[0071] [Figure 9] FIG. 9 is an exemplary goodness of fit plot for the underlying subject population PK model for the 100 mg dose of HLD200.

[0072] [Figure 10]FIG. 10 is a set of exemplary exploratory covariate analysis plots of the relationship between body weight and kel, V, TD, and SS in subjects administered HLD200.

[0073] [Figure 11] FIG. 11 is a set of exemplary exploratory covariate analysis plots of the relationship between gender (0=F, 1=M) and kel, V, TD, and SS in subjects administered HLD200.

[0074] [Figure 12] FIG. 12 is a set of exemplary goodness-of-fit plots for the final study subject population PK model at a dose of 20 mg HLD200.

[0075] [Figure 13] FIG. 13 is a set of exemplary goodness-of-fit plots for the final study population PK model at a dose of 100 mg HLD200.

[0076] [Figure 14] FIG. 14 is a set of exemplary graphs reporting model-predicted and observed PK concentrations of HLD200 versus time for individual subjects.

[0077] [Figure 15] FIG. 15 is a set of exemplary graphs reporting model-predicted and observed PK concentrations of HLD200 versus time for individual subjects.

[0078] [Figure 16] FIG. 16 is a set of exemplary graphs reporting model-predicted and observed PK concentrations of HLD200 versus time for individual subjects.

[0079] [Figure 17] FIG. 17 is a set of exemplary graphs reporting model-predicted and observed PK concentrations of HLD200 versus time for individual subjects.

[0080] [Figure 18] FIG. 18 is a set of exemplary graphs reporting model-predicted and observed PK concentrations of HLD200 versus time for individual subjects.

[0081] [Figure 19] 19 is a set of graphs reporting an exemplary Visual Predictive Check. The solid line represents the median predicted MPH concentration, the circles represent the observed MPH concentration, the thick dashed line represents the median observed concentration, and the area between the thin dashed lines represents the 90% prediction interval for the simulated data. If the majority of the observed data falls within the model's confidence interval (the area between the thin dashed lines), then the Visual Predictive Check is successful.

[0082] [Figure 20] FIG. 20 is a set of graphs reporting exemplary effects of body weight and gender on the predicted PK profile of HLD200 at doses of 20 mg or 100 mg.

[0083] [Figure 21] FIG. 21 is a graph reporting exemplary simulated methylphenidate (MPH) exposure following repeated administration of HLD200.

[0084] [Figure 22] 22 is a set of graphs reporting an exemplary comparison of HLD200 exposure to Concerta® (ALZA Corporation) exposure in a study subject population, where the time of HLD drug intake was in the evening, 8 hours (top panel) or 10 hours (bottom panel) before the start of school at 8:00 am.

[0085] [Diagram 23]23 is a set of graphs reporting an exemplary comparison of HLD200 exposure to Metadate® (UCB, Inc) exposure in a study subject population. Time of HLD drug intake was in the evening, 8 hours (top panel) or 10 hours (bottom panel) before the start of school at 8:00 am.

[0086] [Figure 24] 24 is a set of graphs reporting an exemplary comparison of HLD200 exposure to Ritalin LA® (Novartis AG) exposure in a study subject population, where the time of HLD drug intake was in the evening, 8 hours (upper panel) or 10 hours (lower panel) before the start of school at 8:00 am.

[0087] [Diagram 25] 25 is a set of graphs reporting an exemplary comparison of HLD200 exposure to Quillivant XR® (NextWave Pharmaceuticals, Inc.) exposure in a study subject population. Time of HLD drug intake was in the evening, 8 hours (top panel) or 10 hours (bottom panel) before the start of school at 8:00 am.

[0088] [Figure 26] FIG. 26 is a set of plots reporting exemplary patient individual Swanson, Kotkin, Agler, M-Flynn and Pelham Scale (SKAMP) scores by treatment.

[0089] [Figure 27] FIG. 27 is a graph reporting exemplary mean (±SD) SKAMP score profiles by treatment in the entire study patient population.

[0090] [Figure 28] FIG. 28 is a graph reporting exemplary mean (±SD) SKAMP score profiles by treatment and gender in the entire study patient population.

[0091] [Figure 29] FIG. 29 is a graph reporting exemplary mean (±SD) estimated MPH concentrations in the entire study patient population.

[0092] [Diagram 30] FIG. 30 is a graph reporting exemplary mean (±SD) estimated MPH concentrations by gender in the entire study patient population.

[0093] [Diagram 31] FIG. 31 is a set of graphs reporting exemplary goodness-of-fit plots for the comparable placebo response model.

[0094] [Diagram 32] 32 is a graph reporting an exemplary visual predictive performance assessment for the placebo response model. The thick dashed line represents the median predicted SKAMP score, the circles represent the observed SKAMP score, the thick solid line represents the median observed SKAMP score, and the area between the thin dashed lines represents the 90% prediction interval for the simulated data.

[0095] [Diagram 33] FIG. 33 is a set of graphs reporting exemplary goodness-of-fit plots for the foundation population PK / PD model.

[0096] [Diagram 34] FIG. 34 is a graph reporting an exemplary exploratory analysis for covariates, namely, the relationship between weight, age, and sex for EMAX and EC50.

[0097] [Diagram 35] FIG. 35 is a graph reporting exemplary goodness-of-fit plots for the final population PK / PD model.

[0098] [Diagram 36]Figure 36 is a graph reporting an exemplary PK / pharmacodynamic (PD) model-visual predictive performance assessment. The thick dashed line represents the median predicted concentration, the circles represent the observed scores, the thick solid line represents the median score, and the area between the thin dashed lines represents the 90% prediction interval for the simulated data.

[0099] [Figure 37] FIG. 37 is a graph reporting an exemplary relationship between HLD200 drug exposure and clinical response in study patients defined by change from placebo response, along with 90% prediction intervals (shaded areas).

[0100] [Figure 38] FIG. 38 is a schematic illustrating an exemplary definition of Clinical Benefit (CB).

[0101] [Figure 39] FIG. 39 is a set of graphs reporting an exemplary effect of time of HLD200 intake on clinical benefit in the overall study patient population.

[0102] [Diagram 40] FIG. 40 is a graph reporting exemplary simulated time courses of HLD200 plasma concentrations (thick solid line) and SKAMP scores (thick dashed line) following a 60 mg HLD200 dose and SKAMP scores (thin solid line) following a placebo dose.

[0103] [Diagram 41] FIG. 41 is a graph reporting exemplary simulated time courses of HLD200 plasma concentrations (thick solid line) and SKAMP scores (thick dashed line) following an 80 mg HLD200 dose and SKAMP scores (thin solid line) following a placebo dose.

[0104] [Diagram 42]FIG. 42 is a graph reporting exemplary simulated time courses of HLD200 plasma concentrations (thick solid line) and SKAMP scores (thick dashed line) following a 100 mg HLD200 dose and SKAMP scores (thin solid line) following a placebo dose.

[0105] [Diagram 43] FIG. 43 is a graph reporting an exemplary simulation of the time course of SKAMP scores following HLD200 doses of 60 mg, 80 mg, and 100 mg, as well as following a placebo dose.

[0106] [Diagram 44] Figure 44 is a set of graphs reporting exemplary data from the COMACS study, namely, mean SKAMP total score by MCD dose level versus time. Sonuga-Barke EJ, Swanson JM, Coghill D, DeCory HH, Hatch SJ. Efficacy of two once-daily methylphenidate formulations compared across dose levels at different times of the day: preliminary indications from a secondary analysis of the COMACS study data. From BMC Psychiatry (2004 Sep 30; 4: 28), which is incorporated herein by reference.

[0107] [Diagram 45]Figure 45 is a graph reporting exemplary data from the d-MPH ER study, i.e., mean SKAMP total raw scores from pre-dose (hour 0) to hour 12. From Raul R. Silva et al., Efficacy and Duration of Effect of Extended-Release Dexmethylphenidate Versus Placebo in School children With Attention-Deficit / Hyperactivity Disorder. Journal Of Child And Adolescent Psychopharmacology, Vol. 16, No. 3, 2006, which is incorporated herein by reference.

[0108] [Figure 46] Figure 46 is a graph reporting exemplary data from the NWP06 (Quillivant XR® (NextWave Pharmaceuticals, Inc.)) study, i.e., mean SKAMP combined scores from pre-dose (time 0) to time 12. From Sharon B. Wigal et al., NWP06, an Extended-Release Oral Suspension of Methylphenidate, Improved Attention-Deficit / Hyperactivity Disorder Symptoms Compared with Placebo in a Laboratory Classroom Study. Journal of Child And Adolescent Psychopharmacology, Vol. 23, No. 1, 2013, which is incorporated herein by reference.

[0109] [Figure 47]FIG. 47 is a graph reporting an exemplary comparison of change in SKAMP scores for patients receiving HLD200 compared to taking a placebo, along with data for Concerta® (ALZA Corporation) (at doses of 18 mg, 36 mg, and 54 mg), data for Quillivant XR® (NextWave Pharmaceuticals, Inc.), and data for d-MPH.

[0110] [Figure 48] FIG. 48 is a graph reporting an exemplary comparison of change in SKAMP scores for patients receiving HLD200 compared to placebo, along with data for Metadate CD® (UCB, Inc.) (at doses of 20 mg, 40 mg, and 60 mg), data for Quillivant XR® (NextWave Pharmaceuticals, Inc.), and data for d-MPH.

[0111] [Figure 49] FIG. 49 is a set of graphs reporting exemplary in vitro dissolution model predicted and observed fractional release for fast, slow and final formulations of delayed / extended release MPH.

[0112] [Figure 50] FIG. 50 is a set of graphs reporting exemplary observed mean MPH concentrations (dots) and predicted mean MPH concentrations (lines) following administration of 20 mg of Ritalin® (Novartis) IR.

[0113] [Figure 51] FIG. 51 is a graph reporting an exemplary fraction of in vitro released methylphenidate relative to in vivo released methylphenidate of HLD200 for combined fast and slow 54 formulations.

[0114] [Figure 52]FIG. 52 is a set of graphs reporting an exemplary internal validation of the IVIVC model, i.e., observed methylphenidate concentrations (dots) and IVIVC predicted methylphenidate concentrations (lines) for a fast formulation (Study HLD200-101, 54 mg) and a slow formulation (Study HLD200-101, 54 mg).

[0115] [Figure 53] FIG. 53 is a graph reporting an exemplary external validation of the IVIVC model, namely, the observed methylphenidate concentrations (dots) and IVIVC predicted methylphenidate concentrations (line) for the Slow 100 formulation and the final formulation.

[0116] [Figure 54A] FIG. 54A is an exemplary schematic representation of a bead pharmaceutical composition in which a drug-containing core is surrounded by sustained release and delayed release layers.

[0117] [Figure 54B] FIG. 54B is an exemplary schematic representation of a composition as in FIG. 54A in which an additional swellable layer is disposed between the sustained release layer and the drug-containing core.

[0118] [Figure 55A] FIG. 55A is an exemplary schematic representation of a mini-tablet pharmaceutical composition in which a drug-containing core is surrounded by an extended release layer and a delayed release layer.

[0119] [Figure 55B] FIG. 55B is an exemplary schematic representation of a composition as in FIG. 55A in which an additional swellable layer is disposed between the sustained release layer and the drug-containing core.

[0120] [Figure 56] FIG. 56 is an exemplary schematic representation of a bead pharmaceutical composition comprising a core surrounded by four layers: an inert inner core, a swelling polymer, a drug layer, a sustained release layer and an enteric layer.

[0121] [Figure 57] Figure 57 is a graph reporting exemplary data on the fraction of methylphenidate dissolved in vitro (FDISS) versus the fraction of methylphenidate absorbed in vivo (Fabs) from Concerta® (ALZA Corporation). From R. Gomeni, F. Bressolle, TJ Spencer, SV Faraone. Meta-analytic approach to evaluate alternative models for characterizing the PK profiles of extended release formulations of MPH. ASCPT 2016 Annual Meeting (March 8-12, 2016, Hilton Bayfront, San Diego, Calif.), which is incorporated herein by reference.

[0122] [Figure 58] Figure 58 is a set of graphs reporting exemplary observed mean methylphenidate concentrations (points) and predicted mean methylphenidate concentrations (lines) after the indicated drugs are administered. From R. Gomeni, F. Bressolle, TJ Spencer, SV Faraone. Meta-analytic approach to evaluate alternative models for characterizing the PK profiles of extended release formulations of MPH. ASCPT 2016 Annual Meeting, March 8-12, 2016, Hilton Bayfront, San Diego, Calif., which is incorporated herein by reference.

[0123] [Figure 59] FIG. 59 is a graph reporting exemplary data of cumulative fraction absorbed for Concerta® (ALZA Corporation).

[0124] [Figure 60] FIG. 60 is a graph reporting exemplary "initial" and "optimized" fractional absorption profiles as determined by R. Gomeni et al. (ASCPT 2016 Annual Meeting, March 8-12, 2016, Hilton Bayfront, San Diego, Calif.), which is incorporated herein by reference.

[0125] [Figure 61] FIG. 61 is a graph reporting an exemplary fractional absorption profile of HLD200.

[0126] [Figure 62] FIG. 62 is a graph reporting exemplary data regarding the mean percent change in methylphenidate plasma concentrations (ng / mL / hour) over time for HLD200 54 mg (Study 200-101), HLD200 100 mg (Study 200-109), and CONCERTA® 54 mg.

[0127] [Figure 63] FIG. 63 is a graph reporting an exemplary fraction of in vitro released amphetamine relative to in vivo released amphetamine from an exemplary amphetamine formulation, HLD100-102, having a similar composition as the HLD200 methylphenidate medium formulation.

[0128] [Figure 64] Figure 64 is a graph reporting exemplary cumulative % colon arrival times for surrogate beads radiolabeled with up to 1 MBq of Indium-111. The plot shows results from two separate experiments, designated "F1" and "F2."

[0129] [Figure 65]Figure 65 is a graph reporting an exemplary visual predictive performance assessment of PK data after IR compounding. The thick solid line represents the median prediction with 95% prediction intervals (shaded areas). The points represent the measured MPH concentrations.

[0130] [Figure 66] FIG. 66 is a graph reporting exemplary average dissolution data (dots) for slow, medium and fast dissolution rate formulations along with model predicted dissolution curves (solid and dashed lines as indicated).

[0131] [Figure 67] FIG. 67 is a schematic diagram showing an exemplary convolution model used to fit the in vivo PK of fast, medium and slow HLD200 formulations.

[0132] [Figure 68] FIG. 68 is a graph reporting exemplary mean PK data for the three formulations along with model predicted curves (top panel) and in vivo release rates (bottom panel).

[0133] [Figure 69] FIG. 69 is a graph reporting exemplary mean observed PK concentrations (dots) along with predicted values ​​by the convolution model (solid and dashed lines as indicated) for fast, medium and slow HLD200 formulations.

[0134] [Figure 70] FIG. 70 is a graph reporting an exemplary regression analysis of in vivo absorption rate versus in vitro dissolution rate for slow, intermediate and fast releasing HLD200 formulations.

[0135] [Figure 71]FIG. 71 is a graph reporting an exemplary regression analysis of predicted concentrations by the convolution model against observed concentrations (points) for slow, medium and fast releasing HLD200 formulations.

[0136] [Figure 72] FIG. 72 is a graph reporting exemplary scatter plots of TD parameters characterizing in vitro dissolution and in vivo BI (left panel) and Kel (right panel) with model-predicted relationships (dashed lines).

[0137] [Figure 73] FIG. 73 is a schematic diagram showing an exemplary refined IVIVC model including the dependence between dissolution properties and estimated relative in vivo bioavailability.

[0138] [Figure 74] FIG. 74 is a graph reporting exemplary mean PK observed concentrations (dots) along with predicted values ​​by the refined convolution model (solid and dashed lines as indicated) for slow, intermediate and fast release HLD200 formulations.

[0139] [Figure 75] FIG. 75 is a graph reporting an exemplary regression analysis of predicted concentrations by the refined convolution model against observed concentrations (points) for slow, medium and fast releasing HLD200 formulations.

[0140] [Figure 76] FIG. 76 is a graph reporting exemplary average dissolution data (dots) along with model-predicted dissolution curves (solid and dashed lines as indicated) for formulations used in external validation (top panel) and slow, intermediate and fast dissolution rate formulations used in model development.

[0141] [Figure 77] FIG. 77 is a graph reporting an exemplary comparison of the mean PK concentration time courses for median dissolution rate formulations in study HLD200-111 with the PK concentration time courses in study HLD200-109 (fasted group).

[0142] [Figure 78] Figure 78 is a graph reporting an exemplary visual predictive performance assessment of the PK data from Study HLD200-109. The thick solid line represents the median prediction with 95% prediction intervals (shaded areas). The points represent the measured MPH concentrations.

[0143] [Figure 79] FIG. 79 is a graph reporting an exemplary comparison of typical PK time courses (solid curves) in study HLD200-109 with estimates based on convolution of expected PK profiles (dashed curves).

[0144] [Figure 80] FIG. 80 is a graph reporting an exemplary external validation, i.e., regression analysis of predicted concentrations by the refined convolution model against observed concentrations (points).

[0145] [Figure 81] FIG. 81 is a graph reporting an exemplary in vitro dissolution plot of the HLD200 mid formulation (also referred to herein as the final formulation) along with an exemplary in vitro dissolution plot of the amphetamine formulation (also referred to herein as the HLD100-102).

[0146] [Figure 82] FIG. 82 is a graph reporting an exemplary regression analysis of in vivo absorption rate versus in vitro dissolution rate for rapid release HLD200 formulations.

[0147] [Figure 83]FIG. 83 is a graph reporting an exemplary regression analysis of in vivo absorption rate versus in vitro dissolution rate for intermediate release HLD200 formulations.

[0148] [Figure 84] FIG. 84 is a graph reporting an exemplary regression analysis of in vivo absorption rate versus in vitro dissolution rate for slow release HLD200 formulations.

[0149] [Figure 85] FIG. 85 is an exemplary schematic diagram describing a refined IVIVC model including the dependence between dissolution properties and estimated relative in vivo bioavailability.

[0150] [Figure 86A] FIG. 86A is a graph reporting exemplary values ​​for plasma MPH concentration versus time (hours) that enable visual predictive performance assessment (Step 1) for the IR MPH model.

[0151] [Figure 86B] FIG. 86B is a graph reporting exemplary values ​​for the fraction of in vitro released dose (%) versus time (hr) for the model-predicted in vitro DR / ER-MPH dissolution profile (Step 2).

[0152] [Figure 86C] Figure 86C is a graph reporting exemplary values ​​for the median model predicted PK curve (Step 3) of DR / ER-MPH.

[0153] [Fig. 86D] FIG. 86D is a graph reporting exemplary values ​​for mean plasma MPH concentrations predicted by the convolution model (Step 4).

[0154] [Figure 87]FIG. 87 is a graph reporting an exemplary regression analysis of in vivo absorption rate versus in vitro dissolution rate for slow, intermediate, and fast release formulations.

[0155] [Figure 88] FIG. 88 is a graph reporting an exemplary comparison of observed dissolution data with model-predicted dissolution data using a single Weibull model (solid line) and a double Weibull model (dotted line).

[0156] [Figure 89] FIG. 89 is a graph reporting an exemplary comparison of observed and model-predicted dissolution data using a sigmoidal Emax (solid line) and dual Weibull model (dotted line).

[0157] [Figure 90] FIG. 90 is an exemplary schematic diagram describing the convolution model used for evaluation of IVIVC relationships.

[0158] [Figure 91] FIG. 91 is a graph reporting exemplary values ​​for fast release formulations: fraction absorbed versus fraction dissolved (top panel) and Levy plot (bottom panel).

[0159] [Figure 92] FIG. 92 is a graph reporting exemplary values ​​for an intermediate release formulation: fraction absorbed versus fraction dissolved (top panel) and Levy plot (bottom panel).

[0160] [Figure 93] FIG. 93 is a graph reporting exemplary values ​​for slow release formulations: fraction absorbed versus fraction dissolved (top panel) and Levy plot (bottom panel). DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0161] Detailed Description In the following description, details are given as examples to facilitate discussion of the disclosed subject matter, however, it should be apparent to those skilled in the art that the disclosed implementations are exemplary and do not encompass all possible implementations.

[0162] The present disclosure relates to compositions and methods that provide minimal fluctuations in methylphenidate efficacy in a patient over a period of time to reduce the likelihood or severity, or both, of rebound.

[0163] The term "subject" as used herein refers to an individual human being, including, but not limited to, a child, an adult, or an adolescent in various implementations. In some implementations, the subject may be a "patient," which means a subject who may be under the care of a physician and / or who may be diagnosed with ADD or ADHD, and possibly other disorders (such as, for example, autism or autism spectrum disorder).

[0164] Methylphenidate is used to treat various disorders, particularly ADD and ADHD. Methylphenidate is generally effective over a period of time. Often, the period includes the daytime hours when children are at school. For example, this period may typically last between 4 hours and 12 hours. The actual efficacy of many methylphenidate drugs during this period may exhibit significant variation. For example, efficacy to improve performance according to a validated assessment scale, such as SKAMP score, may vary. In some implementations, the compositions and methods disclosed herein can reduce the variation in efficacy that may be measured, for example, by reduced variation in SKAMP score. In some implementations, the compositions and methods disclosed herein can reduce adverse events reported in the late afternoon or at night, such as one or more of increased aggression, emotionality, moodiness and irritability, among others.

[0165] At or near the end of the effective period, many patients will experience rebound, during which other symptoms will appear, or ADD and ADHD symptoms will return, typically worse than without treatment.Rebound can interfere with tasks and bedtime.The compositions and methods disclosed herein can have a longer period of efficacy with a slower decline in methylphenidate plasma levels later in the day.The longer efficacy and lack of a sharp decline in methylphenidate plasma levels can help reduce or avoid rebound.

[0166] Some patients are particularly likely to experience or be susceptible to the negative effects of fluctuations in efficacy, rebound, or both. These fluctuation-sensitive, rebound-sensitive, and fluctuation-sensitive and rebound-sensitive patients are particularly likely to benefit from the compositions and methods disclosed herein.

[0167] Patients with fluctuation sensitivity, rebound sensitivity, and fluctuation sensitivity and rebound sensitivity often include patients with comorbid conditions, particularly those with another behavioral or mental health disorder. In particular, patients with both ADD or ADHD and an autism spectrum disorder (ASD) may be patients with fluctuation sensitivity, rebound sensitivity, and fluctuation sensitivity and rebound sensitivity.

[0168] The present disclosure provides therapeutic compositions and methods for treating Attention Deficit Disorder (ADD), Attention Deficit Hyperactivity Disorder (ADHD), or other conditions or disorders responsive to CNS stimulants, by providing dosage forms that deliver a therapeutic amount of an active drug in a delayed and controlled release pattern to maintain a therapeutic amount of the drug throughout the active portion of the day. For pediatric patients, including adolescents, and also for adults, a therapeutic dose is desirable upon waking and throughout the morning, as well as throughout the afternoon when work or homework needs to be performed.

[0169] The compositions of the present disclosure include solid oral pharmaceutical compositions having a core comprising methylphenidate or a pharmaceutical salt thereof and at least one pharma- ceutically acceptable excipient, an extended release layer surrounding the core, and a delayed release layer surrounding the extended release layer.

[0170] The disclosed formulations can provide therapeutic amounts of drug over an extended period of the day with a single administration. The dosage form provides delayed release so that the dosage form can be conveniently administered prior to the patient's sleep. A small portion of the drug can be released over the first 6 hours after administration so that the patient already receives a minimum therapeutic dose upon normal awakening. Thus, the patient does not need to be woken up, take medication, and then is not required to have breakfast and get ready for the day before experiencing the therapeutic effect.

[0171] Stimulant medication drugs (e.g., methylphenidate and amphetamines and prodrugs) are often prescribed to treat individuals diagnosed with ADHD. According to the National Institutes of Health, all stimulants work by increasing dopamine levels in the brain. Dopamine is a brain chemical (or neurotransmitter) associated with pleasure, movement and attention. The therapeutic effect of stimulants is achieved by a slow and steady increase in dopamine, similar to the natural production by the brain. The dose prescribed by the doctor starts low and gradually increases until a therapeutic effect is reached.

[0172] Treatment of ADHD with stimulants, often in conjunction with psychotherapy, helps improve the symptoms of ADHD as well as the patient's self-esteem, cognition, and social and family interactions. The most commonly prescribed medications include amphetamines and methylphenidate. These medications have a paradoxical calming and "focusing" effect on individuals with ADHD. Researchers speculate that methylphenidate amplifies the release of dopamine, and therefore can improve attention and focus in individuals with weak dopamine signals.

[0173] Methylphenidate can be formulated in the racemic mixture of dextrorotatory conformation and levorotatory conformation, or can be formulated as pure dextrorotatory isomer.Methylphenidate has two chiral centers in the molecule, and therefore can be further purified to enrich d-threo isomer.The use of methylphenidate's pharma-ceutically acceptable salt, such as methylphenidate hydrochloride, is also contemplated by the present disclosure.

[0174] It is understood that the active pharmaceutical ingredient of the present disclosure can exist as a prodrug that is activated in the user's body.In one form of prodrug, an amino acid is conjugated to the active ingredient.When the amino acid is enzymatically cleaved, the active drug is released.It is believed that prodrugs including lysyl conjugate, isoleucyl conjugate or aspartyl conjugate are useful in the implementation of the present disclosure.

[0175] The formulation of the present disclosure is designed to provide a novel release profile and plasma profile, which includes a first lag phase followed by a sigmoid release phase.By providing this profile, the dosage form provides a time-limited, long-term therapeutic effect when taken once a day.Based on the release characteristics of the dosage form passing through the stomach before release, the formulation disclosed herein provides at least the following additional advantages: low variability in gastric emptying, low risk of sudden dose dumping, low incidence of gastric discomfort, and low intra- and inter-individual variability.

[0176] In some embodiments, the solid oral pharmaceutical composition of the present disclosure may be multi-layered.In some embodiments, the solid oral pharmaceutical composition of the present disclosure may comprise methylphenidate or its pharmaceutical salt, a sustained release layer and a delayed release layer.In some embodiments, the solid oral pharmaceutical composition of the present disclosure may comprise a core comprising methylphenidate or its pharmaceutical salt, and a sustained release layer and a delayed release layer may surround the core.In some embodiments, the sustained release layer may surround the core, and the delayed release layer may surround the sustained release layer.In some embodiments, the solid oral pharmaceutical composition of the present disclosure has a core comprising methylphenidate or its pharmaceutical salt and at least one pharma- ceutically acceptable excipient, a sustained release layer surrounding the core, and a delayed release layer surrounding the sustained release layer.

[0177] In some embodiments, the sustained release layer and the delayed release layer may incompletely surround the core. For example, in some embodiments, the sustained release layer and the delayed release layer may individually or together surround at least 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99% or 99.9% of the surface of the core. In some embodiments, the delayed release layer may incompletely surround the sustained release layer and / or the core. For example, in some embodiments, the delayed release layer may surround 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.

[0178] In some embodiments, the multi-layered solid oral pharmaceutical composition of the present disclosure may comprise a multi-layered core.In some embodiments, the multi-layered core may comprise methylphenidate or its pharmaceutical salt and at least one pharma-ceutical acceptable excipient.

[0179] A first example of a dosage form is a single population of beads that can be administered in a capsule, or a liquid or gel suspension containing such beads. An example of a bead structure 10 is shown in schematic form in Figures 54A and 54B. In Figure 54A, the inner circle represents a drug-containing core, which contains an active ingredient or prodrug, suitable excipients, and optionally a superdisintegrant or osmotic agent. The core can contain, for example, an active agent, a disintegrant, an osmotic agent, or a pore-forming agent, and a binder. An exemplary core contains, for example, about 20-25% active agent, about 45-60% microcrystalline cellulose, about 10-30% potassium chloride, and about 3-5% binder (such as polyvinylpyrrolidone or hydroxypropylcellulose). The drug-containing core can be made by various methods known in the art, including wet granulation, extrusion, and spheronization. In some embodiments, two layers surround the core. In some embodiments, the first layer is a sustained release layer and the outer layer is a delayed release layer, which is optionally pH-dependent. In certain embodiments, the core, as shown in Figure 54A, can be an inert non-pareil bead. The inner core can be a sugar and starch bead or can be composed of microcrystalline cellulose. Any suitable and pharma- ceutical acceptable spherical bead can be used to form the core bead. In such an embodiment, the core drug and excipients can be layered on the core bead, resulting in a three-layered formulation.

[0180] In some embodiments, the outermost layer 14 is a delayed release or enteric coating. In certain embodiments, this layer includes a water-soluble polymer, a water-insoluble polymer, a plasticizer, and a lubricant. The delay time of drug release may be controlled by the ratio of water-soluble and water-insoluble polymers, the plasticizer concentration, the amount of lubricant, and the coating weight gain, which can be up to 35-45%. In some embodiments, the delayed release layer may be a pH-dependent polymer that dissolves at a pH above 5.5.

[0181] In some embodiments, the sustained release layer 16 is designed to provide a slower initial release rate that increases over a period of up to 8-10 hours after the layer is exposed to an aqueous environment. In some embodiments, the delayed release layer and / or sustained release layer forms a semipermeable membrane. An increasing drug profile can be achieved by a membrane that becomes more permeable over time. One example of a sustained release layer includes a water-soluble polymer, a water-insoluble polymer, a plasticizer, and a lubricant. 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 up to a weight gain of 15-45%.

[0182] In some embodiments, the solid oral pharmaceutical composition of the present disclosure may comprise a swellable layer that comprises a superdisintegrant or an osmotic agent.In some embodiments, the solid oral pharmaceutical composition of the present disclosure may comprise a delayed release layer, a sustained release layer, and a core that comprises methylphenidate or a pharmaceutical salt thereof, surrounding the swellable layer.In some embodiments, the sustained delayed release layer may incompletely surround the swellable layer, the sustained release layer, and / or the core that comprises methylphenidate or a pharmaceutical salt thereof.For example, in some embodiments, the delayed release layer may surround 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 that comprises methylphenidate or a pharmaceutical salt thereof.In some embodiments, the solid oral pharmaceutical composition of the present disclosure may comprise a multi-layered core that comprises a swellable layer, a sustained release layer, and / or a core that comprises methylphenidate or a pharmaceutical salt thereof.

[0183] In some embodiments, the solid oral pharmaceutical composition of the present disclosure comprises a multi-layered core.In some embodiments, the multi-layered core comprises a first layer that comprises methylphenidate or its pharmaceutical salt, and a second layer that comprises a superdisintegrant or an osmotic agent.

[0184] In some embodiments, the solid oral pharmaceutical composition of the present disclosure comprises a delayed release layer surrounding the multi-layered core. In some embodiments, the delayed release layer may incompletely surround the multi-layered core. For example, in some embodiments, the delayed release layer may surround at least 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99%, or 99.9% of the surface of the multi-layered core.

[0185] For example, in some embodiments, such as the embodiment shown in FIG. 54B, a swellable layer 18 comprising a superdisintegrant or osmotic agent is disposed between the core and the sustained release layer.

[0186] In certain embodiments, the compositions and methods of the disclosure include a four layer 30 formulation as shown in Figure 56. The formulation can include an inner core 15 of nonpareil beads and four concentric layers from inside to outside described as a swellable polymer layer 18, a drug layer 12, a sustained release layer 16, and a pH-dependent delayed release layer 14, which can be a pH-dependent layer. It will be understood that the layers can have surfaces. Figure 56, for example, shows a surface 121 of the drug layer 12, a surface 161 of the sustained release layer 16, and a surface 141 of the delayed release layer 14.

[0187] In certain embodiments, this four-layer composition can be made in a stepwise fashion. In the first step, a hydrophilic polymer suspended in ethanol with a binder is coated onto the nonpareil beads to a weight gain of 30-50%. In certain embodiments, PolyOx Coagulant SFP (PEO) marketed by Dow Chemical Company is the hydrophilic polymer, and hydroxypropyl cellulose (HPC LF) is added as a binder. The PolyOx layer is then sealed with hydroxypropyl cellulose (e.g., Klucel® EF, etc.) to a weight gain of 10%. An active pharmaceutical ingredient (API) is then suspended in ethanol with a binder and coated onto the layered beads, and sustained release and delayed release coatings are applied as described herein.

[0188] Figures 55A and 55B depict an embodiment in which the core is a mini-tablet 20 rather than a bead. The core and layers in Figures 55A and 55B are functionally the same as the similarly numbered layers in the beads in Figures 54A and 54B, except for the absence of the optional inert core.

[0189] A variety of water-soluble polymers can be used in the disclosed formulations. Such polymers include polyethylene oxide (PEO), ethylene oxide-propylene oxide copolymers, polyethylene-polypropylene glycols (e.g., poloxamers), carbomers, polycarbophil, chitosan, polyvinylpyrrolidone (PVP), polyvinyl alcohol (PVA), hydroxyalkylcelluloses such as hydroxypropylcellulose (HPC), hydroxyethylcellulose, hydroxymethylcellulose, and hydroxypropylmethylcellulose, sodium carboxymethylcellulose, methylcellulose, hydroxyethylmethylcellulose, hydroxypropyl ... These include, but are not limited to, propyl methylcellulose, polyacrylates such as carbomer, polyacrylamides, polymethacrylamides, polyphosphazines, polyoxazolidines, polyhydroxyalkyl carboxylic acids, alginic acid and its derivatives such as carrageenan alginates, ammonium alginate and sodium alginate, starch and starch derivatives, polysaccharides, carboxypolymethylene, polyethylene glycols, natural gums such as guar gum, gum arabic, tragacanth, karaya gum and xanthan gum, povidone, gelatin or the like.

[0190] In certain embodiments, at least the delayed release layer is made of one or more polymers, such as acrylic polymers, acrylic copolymers, methacrylic polymers or methacrylic copolymers (these include Eudragit® L100, Eudragit® L100-55, Eudragit® L30 D-55, Eudragit® 5100, Eudragit® 4135F, Eudragit® RS, copolymers of acrylic and methacrylic acid, methyl methacrylate, methyl methacrylate copolymers, ethoxyethyl methacrylate, cyanoethyl methacrylate, aminoalkyl methacrylate copolymers, polyacrylic acid, polymethacrylic acid, methacrylic acid alkylamine copolymers, polymethyl methacrylate, polymethacrylic anhydride, polymethacrylate, polyacrylamide, polymethacrylic anhydride and glycidyl methacrylate). cellulose acetate copolymers, alkylcelluloses such as, but not limited to, ethylcellulose, methylcellulose, calcium carboxymethylcellulose, certain substituted cellulose polymers such as, for example, hydroxypropyl methylcellulose phthalate, and hydroxypropyl methylcellulose acetate succinate, cellulose acetate butyrate, cellulose acetate phthalate, and cellulose acetate trimellarate, polyvinyl acetate phthalate, polyesters, waxes, shellac, zein, or the like.

[0191] The various Eudragits are widely known polymers and copolymers that are useful for controlled release applications. EUDRAGIT® grades for enteric coating are based on anionic polymers of methacrylic acid and methacrylic acid esters. They contain -COOH as functional group. They are soluble in the pH range of 5.5 to 7. EUDRAGIT® FS 30D is an aqueous dispersion of an anionic copolymer based on methyl acrylate, methyl methacrylate and methacrylic acid. It is insoluble in acidic media, but dissolves by salt formation above pH 7.0. Eudragit L100-55 and L30-55 dissolve at a pH above 5.5. Eudragit L100 and S100 dissolve at a pH above 6.0.

[0192] Sustained release EUDRAGIT® formulations are used for many oral dosage forms to allow time-controlled release of active ingredients. Drug delivery can be controlled throughout the gastrointestinal tract for increased therapeutic efficacy and patient compliance. Various polymer combinations of EUDRAGIT® RL (easy permeability) and EUDRAGIT® RS (difficult permeability) grades allow for tailored release profiles and a wide range of alternatives to achieve the desired drug delivery outcome. EUDRAGIT® NE polymers are neutral ester dispersions that do not require any plasticizers and are particularly suitable for granulation processes in the manufacture of matrix tablets and sustained release coatings.

[0193] Exemplary osmotic agents or osmotic agents include organic and inorganic compounds, such as salts, acids, bases, chelating agents, sodium chloride, lithium chloride, magnesium chloride, magnesium sulfate, lithium sulfate, potassium chloride, sodium sulfite, calcium bicarbonate, sodium sulfate, calcium sulfate, calcium lactate, d-mannitol, urea, tartaric acid, raffinose, sucrose, alpha-d-lactose monohydrate, glucose, combinations thereof, and other similar or equivalent substances commonly known in the art.

[0194] As used herein, the term "disintegrant" is intended to mean a compound used in solid dosage forms to promote the breakup of solid mass (layer) into smaller particles that are easier to disperse or dissolve.Exemplary disintegrants include, by way of example and without limitation, starch, such as corn starch, potato starch, pregelatinized starch and modified starch, sweeteners, clay, bentonite, microcrystalline cellulose (e.g., Avicel™), carboxymethylcellulose calcium, croscarmellose sodium, alginic acid, sodium alginate, cellulose polyacrylin potassium (e.g., Amberlite™), alginates, sodium starch glycolate, gum, agar, guar, locust bean, karaya, pectin, tragacanth, crospovidone, and other materials known to those skilled in the art.Superdisintegrants are fast acting disintegrants.Exemplary superdisintegrants include crospovidone and low-substituted HPC.

[0195] 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 with aliphatic hydroxyls, ester-type plasticizers, glycol ethers, poly(propylene glycol), multiblock polymers, single block polymers, low molecular weight poly(ethylene glycol), citrate-type plasticizers, triacetin, propylene glycol, and glycerin. Such plasticizers can also include ethylene glycol, 1,2-butylene glycol, 2,3-butylene glycol, 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, acetyl tributyl citrate, triethyl citrate, acetyl triethyl citrate, tributyl citrate and allyl glycolate.

[0196] It is an aspect of the compositions and methods of the present disclosure that the formulation or dosage form can also incorporate one or more components that discourage or prevent abuse of the active ingredient by grinding the formulation and inhaling the powdered form. As such, the nasal irritant can be included as a separate layer or can be incorporated into the outer layer, sustained release layer or core of the dosage form. Exemplary irritants include, but are not limited to, sodium lauryl sulfate (also called sodium dodecyl sulfate), or capsaicinoids, including capsaicin and synthetic capsaicin. In certain embodiments, the dosage form contains 1%-10% sodium lauryl sulfate.

[0197] The compositions of the present disclosure can also include one or more functional excipients, such as lubricants, thermal lubricants, antioxidants, buffering agents, alkalizing agents, binders, diluents, sweeteners, chelating agents, colorants, flavoring agents, surfactants, solubilizers, wetting agents, stabilizers, hydrophilic polymers, hydrophobic polymers, waxes, lipophiles, absorption enhancers, preservatives, absorbents, crosslinking agents, bioadhesive polymers, retarders, pore formers, and fragrances.

[0198] Lubricants or thermal lubricants useful in the present invention include, but are not limited to, fatty acid esters, glyceryl monooleate, glyceryl monostearate, wax, camauba wax, beeswax, vitamin E succinate, and combinations thereof.

[0199] As used herein, the term "antioxidant" is intended to mean an agent that inhibits oxidation and is therefore used to prevent the deterioration of preparations due to oxidation caused by the presence of oxygen free radicals or free metals in the composition.Such compounds include, by way of example and without limitation, ascorbic acid, ascorbyl palmitate, butylated hydroxyanisole (BHA), butylated hydroxytoluene (BHT), hypophosphorous acid, monothioglycerol, sodium ascorbate, sodium formaldehyde sulfoxylate and sodium metabisulfite, and others known to those skilled in the art.Other suitable antioxidants include, for example, vitamin C, sodium bisulfite, vitamin E and its derivatives, propyl gallate or sulfite derivatives.

[0200] Binders suitable for use in the present invention include beeswax, camauba wax, cetyl palmitate, glycerol behenate, glyceryl monostearate, glyceryl palmitostearate, glyceryl stearate, hydrogenated castor oil, microcrystalline wax, paraffin wax, stearic acid, stearic alcohol, stearate 6000 WL1644, gelucire 50 / 13, poloxamer 188, and polyethylene glycol (PEG) 2000, 3000, 6000, 8000, 10000 or 20000.

[0201] Buffering agents are used to prevent changes in pH upon dilution or addition of acid or alkali. Such compounds include, by way of example and without limitation, potassium metaphosphate, potassium phosphate, monobasic sodium acetate, and sodium citrate anhydrous and dihydrate, salts of inorganic or organic acids, salts of inorganic or organic bases, and others known to those skilled in the art.

[0202] As used herein, the term "alkalinizing agent" is intended to mean a compound used to provide an alkaline medium for product stability. Such compounds include, by way of example and without limitation, ammonia solution, ammonium carbonate, diethanolamine, monoethanolamine, potassium hydroxide, sodium borate, sodium carbonate, sodium bicarbonate, sodium hydroxide, triethanolamine and trolamine, and others known to those skilled in the art.

[0203] Exemplary binders include polyethylene oxide; polypropylene oxide; polyvinylpyrrolidone; polyvinylpyrrolidone-co-vinyl acetate; acrylate and methacrylate copolymers; polyethylene; polycaprolactone; polyethylene-co-polypropylene; alkyl celluloses and cellulose derivatives such as low-substituted HPC (L-HPC), methyl cellulose, and the like; hydroxyalkyl celluloses such as hydroxymethyl cellulose, hydroxyethyl cellulose, hydroxypropyl cellulose, and hydroxybutyl cellulose, and the like; hydroxyalkyl alkyl celluloses such as hydroxyethyl methyl cellulose and hydroxypropyl methyl cellulose, and the like; starch, pectin; PLA and PLGA, polyesters (shellac), waxes such as camauba wax, beeswax, and the like; polysaccharides such as cellulose, tragacanth, gum arabic, guar gum, and xanthan gum, and the like.

[0204] Exemplary chelating agents include EDTA and its salts, alpha hydroxy acids, such as citric acid, polycarboxylic acids, polyamines, their derivatives, and others known to those of skill in the art.

[0205] As used herein, the term "colorant" is intended to mean a compound used to impart color to solid (e.g., tablet) pharmaceutical preparations. Such compounds include, by way of example and without limitation, FD&C Red No. 3, FD&C Red No. 20, FD&C Yellow No. 6, FD&C Blue No. 2, D&C Green No. 5, D&C Orange No. 5, D&C Red No. 8, caramel, as well as ferric oxide, red, other FD&C dyes and natural colors such as grape skin extract, beet red powder, beta carotene, annatto, carmine, turmeric, paprika, and others known to those skilled in the art. The amount of coloring agent used will vary as desired.

[0206] As used herein, the term "flavoring agent" is intended to mean a compound used to impart a pleasant taste and often an odor to pharmaceutical preparations. Exemplary flavoring agents or flavoring agents include synthetic flavor oils and flavoring aromatics and / or natural oils, extracts from plants, leaves, flowers, fruits, and the like, and combinations thereof. They may also include cinnamon oil, wintergreen oil, peppermint oil, clove oil, bay oil, anise oil, eucalyptus, thyme oil, cedar oil, nutmeg oil, sage oil, bitter almond oil, and cassia oil. Other useful flavoring agents include citrus oils, including vanilla, lemon, orange, grape, lime, and grapefruit, and fruit essences, including apple, pear, peach, strawberry, raspberry, cherry, plum, pineapple, and apricot, and the like. Flavoring agents that have been found to be particularly useful include commercially available orange flavor, grape flavor, cherry flavor, and bubble gum flavor, and mixtures thereof. The amount of flavoring material may depend on several factors, including the organoleptic effect desired. The flavoring may be present in any amount as desired by one of ordinary skill in the art. Specific flavorings include grape and cherry flavors, and citrus flavors such as orange.

[0207] Suitable surfactants include polysorbate 80, sorbitan monooleate, polyoxymers, sodium lauryl sulfate, or others known in the art. Soaps and synthetic detergents may be used as surfactants. Suitable soaps include alkali metal, ammonium and triethanolamine salts of fatty acids. Suitable detergents include cationic detergents such as dimethyldialkylammonium halides, alkylpyridinium halides and alkylamine acetates; anionic detergents such as alkylsulfonates, arylsulfonates and olefinsulfonates, alkylsulfates, olefinsulfates, ethersulfates 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 β-aminopropionates and 2-alkylimidazoline quaternary ammonium salts, and mixtures thereof.

[0208] Wetting agents are agents that reduce the surface tension of liquid.Wetting agents may include alcohol, glycerin, protein, peptide water-miscible solvents, such as glycols, hydrophilic polymer polysorbate 80, sorbitan monooleate, sodium lauryl sulfate, alkali metal salts, ammonium salts and triethanolamine salts of fatty acids, dimethyldialkylammonium halides, alkylpyridinium halides and alkylamine acetates; anionic detergents, such as alkylsulfonates, arylsulfonates and olefinsulfonates, alkylsulfates, olefinsulfates, ethersulfates and monoglycerides 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 β-aminopropionates and 2-alkylimidazoline quaternary ammonium salts, and mixtures thereof.

[0209] Solubilizing agents include cyclodextrin, povidone, combinations thereof, and others known to those of skill in the art.

[0210] Exemplary waxes include carnauba wax, beeswax, microcrystalline wax, and others known to those skilled in the art.

[0211] Exemplary absorption enhancers include dimethylsulfoxide, Vitamin E PGS, sodium cholate, and others known to those of skill in the art.

[0212] Preservatives include compounds used to prevent the growth of microorganisms. Suitable preservatives include, by way of example and without limitation, benzalkonium chloride, benzethonium chloride, benzyl alcohol, cetylpyridinium chloride, chlorobutanol, phenol, phenylethyl alcohol, phenylmercuric nitrate, and thimerosal, as well as others known to those skilled in the art.

[0213] Examples of absorbents include sodium starch glycolate (Explotab™, Primojel™) and croscarmellose sodium (Ac-Di-Sol™), cross-linked PVP (Polyplasdone™ XL10), veegum, clay, alginates, PVP, alginic acid, calcium carboxymethylcellulose, microcrystalline cellulose (e.g., Avicel™), potassium polacrillin (e.g., Amberlite™), sodium alginate, corn starch, potato starch, pregelatinized starch, modified starch, cellulosics, montmorrilonite clay (e.g., bentonite), gums, agar, locust bean gum, karaya gum, pectin, tragacanth, and other disintegrants known to those skilled in the art.

[0214] A crosslinker is defined as any compound that will form crosslinks between portions of a polymer. Crosslinkers can include, by way of example and without limitation, organic acids, alpha-hydroxy acids, and beta-hydroxy acids. Suitable crosslinkers include tartaric acid, citric acid, fumaric acid, succinic acid, and others known to those skilled in the art.

[0215] Bioadhesive polymers include polyethylene oxide, KLUCEL (hydroxypropyl cellulose), CARBOPOL, polycarbophil, GANZTREZ, poloxamer, and combinations thereof, as well as others known to those of skill in the art.

[0216] Retarders are agents that are insoluble or sparingly soluble polymers with a glass transition temperature (Tg) above 45° C. or above 50° C. before being plasticized by other agents in the formulation with other polymers and other excipients required for processing. Excipients include waxes, acrylics, cellulosics, lipids, proteins, glycols and the like.

[0217] Exemplary pore-forming agents include water soluble polymers such as polyethylene glycol, propylene glycol, polaxamer, 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 similar or equivalents commonly known in the art.

[0218] As used herein, the term "sweetening agent" is intended to mean a compound used to impart sweetness to a preparation. Such compounds include, by way of example and without limitation, aspartame, dextrose, glycerin, mannitol, sodium saccharin, sorbitol, sucrose, fructose, and others such as are known to those skilled in the art.

[0219] It should be understood that compounds used in the technical field of pharmaceutical formulations generally perform various functions or purposes.Therefore, if a compound listed herein is mentioned only once in this specification, or is used to define more than one term in this specification, its purpose or function should not be interpreted as being limited to its or their listed purpose or function.

[0220] In some implementations, the present disclosure relates to a pharmaceutical preparation for administering a CNS stimulant once a day to treat a condition, the condition being responsive to such a drug, for example, ADD, ADHD, bipolar depression, narcolepsy, sleep disorder and fatigue. The dosage is formulated to be taken before going to bed and begins to release after a delay of several hours, so that the patient has absorbed a sufficient amount of drug to have a therapeutic effect while waking up and preparing to go to work or school. It is a further aspect of the formulation that the drug is released in increasing doses throughout the day to overcome any acute tolerance effects and maintain therapeutic levels of the drug.

[0221] One embodiment of the disclosed compositions and methods is a dosage form that includes a capsule that encloses a single population of beads or mini-tablets that includes a core and two or more coatings that surround the core. The inner core is a bead or mini-tablet that contains an API and one or more excipients. The core is surrounded by a sustained release layer and an outer delayed release layer.

[0222] In certain embodiments, the sustained release layer comprises a combination of a water-soluble polymer and a water-insoluble polymer. The sustained release coating can comprise a combination of polyethylene oxide and ethylcellulose, for example, or a combination of hydroxypropylmethylcellulose and ethylcellulose. An ethylcellulose product that can be used in the disclosed dosage form is Ethocel™, which is commercially available under the trademark of The 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 to water-soluble polymer can be adjusted, for example, and without limitation, from 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 even 50:50.

[0223] The sustained release coating may also contain a plasticizer, such as triethyl citrate (TEC), at a level of 3% to 50% of the total weight of the polymer. Other additives to the coating may include titanium dioxide, talc, colloidal silicone dioxide, or citric acid.

[0224] Some examples of sustained release layer are shown in Example 29. Various formulations include formulations in which the ratio of water-insoluble polymer to water-soluble polymer is changed, and the ratio is reversed. In certain embodiments, active ingredient or API can be included in sustained release layer. Any of the disclosed API can be added to sustained release layer.

[0225] Although the disclosed composition of a capsule containing a single population of beads or minitablets having an extended release layer and an outer delayed release layer is shown herein to be an effective delivery system with novel release characteristics and surprisingly low variability in absorption when administered to humans, it is understood that alternative compositions may be used in light of the present disclosure.

[0226] In certain embodiments, the drug-containing core bead or core minitablet is coated with a delayed release layer that comprises one or more water-insoluble polymers, one or more water-soluble polymers, and silicone oil to achieve the desired delay or lag time before release as in the present disclosure.The delay time and release are controlled by the ratio of these two types of polymers and the thickness of the layer.In such embodiments, the delayed release layer can comprise, but is not limited to, cellulose acetate phthalate, cellulose acetate trimellitate, hydroxylpropylmethylcellulose phthalate, polyvinyl acetate phthalate, acrylic polymer, polyvinyl acetal diethylamino acetate, hydroxypropylmethylcellulose 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 can also include a plasticizer, or in certain embodiments, the delayed release layer can include methacrylic acid copolymer type B, mono- and diglycerides, dibutyl sebacate, and polysorbate 80. The delayed release layer can also include cellulose ether derivatives, acrylic resins, copolymers of acrylic and methacrylic acid esters with quaternary ammonium groups, copolymers of acrylic and methacrylic acid esters, or any combination thereof. This layer can further include a powder component such as talc as a carrier for the silicone oil.

[0227] In certain embodiments, CNS stimulants can be contained in delayed release and / or controlled release capsules.In such embodiments, the water-insoluble capsule contains one or more compartments in which the drug or active agent is held.In addition, one or more absorbents, superabsorbents or osmagents are contained in the drug-containing compartment.The capsule also contains one or more openings (provided that at least one is in fluid communication with each compartment) that are blocked by water-soluble polymers, and a delayed release layer that surrounds the entire capsule.

[0228] In such an embodiment, the length of the initial delay can be controlled by the composition and thickness of the outer delayed release layer. This layer can be a pH-dependent or 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 GI tract. When the water-soluble plug is exposed and dissolved, aqueous fluid enters the drug-containing compartment and is absorbed by the absorbent or ozmagent, thus allowing the active agent to exit the capsule through the opening. The release profile can be controlled by the concentration and absorption characteristics of the absorbent or ozmagent to obtain a desired profile.

[0229] Further illustrative examples of the compositions described herein are provided in Examples 30-42.

[0230] In some implementations described herein, "HLD200" refers to an exemplary composition of the present disclosure, such as those described in the Examples. An FDA approved formulation of HLD200 is also known as JORNAY PM® (Ironshore Pharmaceuticals & Development, Inc.).

[0231] As described herein, the compositions of the present disclosure exhibit significantly different pharmacokinetics, including in vitro dissolution profile, in vivo absorbance profile, and in vitro-in vivo correlation (IVIVC), compared with other methylphenidate drugs, such as Concerta (ALZA Corporation), Ritalin LA (Novartis AG), Metadate CD (UCB, Inc) and Quillivant XR (NextWave Pharmaceuticals, Inc.).In contrast to the compositions of the present disclosure, these other methylphenidate drugs are formulated to have an immediate release (IR) component and an extended release (ER) component.The model that can be used to describe the pharmacokinetics of these other drugs exhibits a linear and time-invariant relationship between dissolution and absorption, where the multiphasic (IR and ER) release profile of in vivo absorption is modeled using the following double Weibull function r2(t):

number

[0232] The pharmacokinetics of a drug can be described in terms of in vitro dissolution-in vivo absorption correlation (IVIVC). For example, IVIVC is generally defined by the US Food and Drug Administration (FDA) as a predictive mathematical model that describes the relationship between the in vitro properties and the in vivo response of a dosage form. Generally, the in vitro properties can be the rate or extent of drug dissolution or drug release, while the in vivo response can 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 a biological property, or a parameter derived from a biological property resulting from a dosage form, and a physicochemical property of the same dosage form. Typically, the parameter derived from the biological property can be, for example, AUC or Cmax, while the physicochemical property can be an in vitro dissolution profile.

[0233] For example, for drugs such as Concerta (ALZA Corporation), Ritalin LA (Novartis AG), Metadate CD (UCB, Inc) and Quillivant XR (NextWave Pharmaceuticals, Inc.), the relationship between the fraction of in vitro dissolved methylphenidate and the fraction of in vivo absorbed methylphenidate is linear or approximately linear.For example, Figure 57 is a graph reporting an exemplary correlation of the fraction of in vitro dissolved methylphenidate (FDISS) to the fraction of in vivo absorbed methylphenidate (Fabs) from Concerta (ALZA Corporation).Ibid. Figure 57 shows that the in vitro-in vivo correlation (IVIVC) between the fraction of in vitro dissolved methylphenidate (FDISS) to the fraction of in vivo absorbed methylphenidate (Fabs) from Concerta (ALZA Corporation) is linear or approximately linear.

[0234] As will be understood by those skilled in the art, linear relationships can be described by linear polynomial functions, otherwise known as first order polynomials. The degree of a polynomial is generally understood to mean the highest degree of its monomials (individual terms or variables, e.g., x) that have non-zero coefficients. Thus, a first order polynomial function may be generally understood to mean a mathematical function or formula in which a variable in the function (e.g., x, etc.) has a non-negative integer exponent with a maximum value of 1. Similarly, for example, a fifth order polynomial function may be generally understood to mean a mathematical function or formula in which the highest degree of its monomials with non-zero coefficients has a sum of non-negative integer exponents with a maximum value of 5. For example, when referring to the "degree" of a function, the degrees of the respective indeterminates are summed: for example, x 3 +x 2 =5th order.

[0235] The term "approximately linear" can sometimes mean that a linear polynomial function is best fitted to the set of data points. For example, a set of data points can be said to be approximately linear when a first order polynomial function best fits the set of data points better 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, etc.

[0236] The process of curve fitting can be used to construct a polynomial function that has the best fit to a set of data points. Examples of curve fitting include polynomial regression and polynomial interpolation, among others. Statistical packages (such as R) and numerical software (such as GNU Scientific Library, MLAB, Maple, MATLAB, Mathematica, GNU Octave, and SciPy) include commands to perform curve fitting in a variety of scenarios.

[0237] Figure 58 is a set of graphs reporting exemplary observed mean methylphenidate concentrations (points) and predicted mean methylphenidate concentrations (lines) after administration of the indicated drugs. Ibid. As shown in Figure 58, the double Weibull model can accurately predict the in vivo plasma levels of Concerta (ALZA Corporation), Ritalin LA (Novartis AG), Metadate CD (UCB, Inc), and Quillivant XR (NextWave Pharmaceuticals, Inc.), as well as Aptensio (Rhodes Pharmaceuticals LP), confirming a linear or near-linear relationship with the Weibull absorption in multiple phases for each of these drugs (Ibid.).

[0238] In some embodiments, the pharmacokinetics of the methylphenidate compositions described herein may be modeled using the following double Weibull function r2(t):

number

[0239] In some embodiments, the pharmacokinetics of the methylphenidate compositions described herein can be described by a time-varying in vivo absorption model that includes a single [r1(t)] Weibull function, as follows:

number

[0240] In some embodiments, the pharmacokinetics of the methylphenidate compositions described herein can be described by an in vivo absorption model including a sigmoidal eMax function, as follows:

number

[0241] 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 relative to other methylphenidate drugs, such as Concerta® (ALZA Corporation), Ritalin LA® (Novartis AG), Metadate CD® (UCB, Inc), and Quillivant XR® (NextWave Pharmaceuticals, Inc.). In various implementations, the compositions described herein do not exhibit linear or near-linear IVIVC. See, for example, Examples 22-25 and Examples 50-53. For example, as described in Example 24, in some implementations, a fifth-order polynomial function can best fit the IVIVC for the compositions of the present disclosure.

[0242] Thus, in some implementations, the present disclosure provides a solid oral pharmaceutical composition comprising methylphenidate or a pharmaceutical salt thereof, wherein the in vivo absorption model of the solid oral pharmaceutical composition is a single Weibull function:

number

[0243] or the double Weibull function r2(t):

number

number

[0244] In some implementations, the nonlinear correlation between fractions of in vitro dissolution and fractions of in vivo absorption of a solid oral pharmaceutical composition may be best fitted to a fifth order polynomial function.

[0245] In some implementations, the nonlinear correlation between fractions of in vitro dissolution and fractions of in vivo absorption of a solid oral pharmaceutical composition may be best fitted to 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.

[0246] As will be understood by those skilled in the art upon reading this disclosure, IVIVC plot can be made according to various methods using data sets obtained from individual subjects or populations of subjects.For example, in one method, the PK curve of individual subjects can be predicted, and then the average value of the data of individual subjects can be calculated to make the IVIVC plot for the population of subjects.In the alternative, for example, in another method, the IVIVC plot for the population of subjects can be made by using the average actual PK curve from the population of subjects.

[0247] In some implementations, the fractions of in vitro dissolution and fractions of in vivo absorption of the solid oral pharmaceutical composition may include values ​​between 0 and 1.

[0248] The various methods of the present disclosure include a method for treating a condition in a subject having a disorder or condition that is responsive to administration of a methylphenidate-based drug.The method involves orally administering to the subject an effective amount of the solid oral pharmaceutical composition described herein.

[0249] In some implementations, administering the compositions described herein to a group of subjects results in significant improvement in the behavior or ability associated with ADHD over a period of time, for example, for at least 12 consecutive hours, as measured by validated assessment scale, score or combination score.As used herein, a validated assessment scale is a scale, score or combination score that is verified or published in a peer-reviewed journal, or a scale, score or combination score that is recognized as valid by those skilled in the art, or a scale, score or combination score that is deemed by a drug regulatory agency, for example, the US Food and Drug Administration or the European Medicines Agency, to provide a valid measure of therapeutic efficacy.

[0250] As used herein, a validated rating scale, score, or combination score includes a method approved by a government drug regulatory agency, or a method published and / or relied upon in a peer-reviewed journal or in a clinical trial reported in a peer-reviewed journal, or a method endorsed by a medical or clinician association. Examples of such methods or measures include the ADHD-RS-IV scale based on the Diagnostic and Statistical Manual of Mental Disorders (4th ed. criteria), the Preschool Functioning Questionnaire (BSFQ), Clinical Global Impression (CGI), Clinical Global Impression of Improvement (CGI-I), Clinical Global Impression of Severity (CGI-S), Conors Global Index Parent (CGI-P), Diagnostic and Statistical Manual of Mental Disorders-4th Edition Text Revision (DSM-IV-TR), Parents Rating of Evening and Morning Behavior Revised (PREMB-R), Permanent Product Measure of Performance (PERMP) score, Swanson, Kotkin, Agler, M-Flynn and Pelham (SKAMP) rating scale, SKAMP-CS combined score, Adult ADHD Medication Rebound These include, but are not limited to, the Adult ADHD Medication Smoothness of Effect Scale (AMRS) for AM or PM, or the Adult ADHD Medication Smoothness of Effect Scale (AMSES) for AM or PM.

[0251] In some implementations, the disclosure relates to methods in which when the composition is administered to a population, such as a population of adolescent or pediatric subjects, the composition provides a significant improvement in SKAMP score over a continuous period of about 12 hours, and the composition provides a significant improvement in SKAMP score over a period of about 11 hours to about 23 hours after dosing, or when the composition is administered between about 8-10 PM, the composition provides a significant improvement in SKAMP score over a 10-12 hour period from about 7 AM to about 9 PM the following day.

[0252] The disclosed methods may reduce variability in efficacy, or may reduce the likelihood or severity, or both, of rebound improvement in behavior or ability associated with ADHD, as measured by a validated rating scale. In some implementations, administering the compositions described herein to a population of subjects results in a clinical response, such as an improvement in SKAMP score, over a period of time, where the improvement results in minimal variability in efficacy, reduced likelihood or severity, or both, of rebound improvement over that period of time.

[0253] In some implementations, a model is described herein that can accurately predict the change in SKAMP score when methylphenidate PK profile is used as an input variable.See, for example, Examples 10-22.In particular, for example, Examples 15-18, the sensitivity of SKAMP response is described with respect to the administration time of the composition of the present disclosure and the sigmoid coefficient (ss).In some implementations, the compositions and methods of the present disclosure provide minimal variation in efficacy, reduced likelihood or severity of rebound, or both, as a function of the in vivo release rate of the composition of methylphenidate described herein and / or as a function of administration time on the night before class in the morning.In some exemplary implementations (see, for example, Examples 15-18), efficacy can be defined as the area of ​​change (AUEC) from the population of placebo-treated subjects in SKAMP score during a period, for example, the period from 8:00am to 8:00pm. In other implementations, efficacy can be defined as the area of ​​change in SKAMP score from the population of untreated subjects over a period of time, for example, from 8:00am to 8:00pm. An exemplary graphical representation of efficacy assessment (also referred to herein in certain implementations as "clinical benefit" or "CB") is shown in Figure 38, in which the light dashed curve represents the SKAMP score for placebo, the thick dashed curve represents the SKAMP score for HLD200, and the shaded area represents AUEC.

[0254] In some implementations, the disclosure relates to compositions and methods that provide minimal variability in efficacy, reduced likelihood or severity of rebound, or both, over time in improving behaviors or abilities associated with ADHD, such as SKAMP scores.

[0255] In some implementations, the time period may begin at or about 8:00am, 9:00am, 10:00am, 11:00am, 12:00pm, 1:00pm, 2:00pm, 3:00pm, 4:00pm, 5:00pm, 6:00pm, or 7:00pm.

[0256] In some implementations, the period may begin 8, 9, 10, 11, 12, 13, 14, 15 or 16 hours after administration of the composition.For example, in some implementations, the composition may be administered at or about 4:00pm, 5:00pm, 6:00pm, 7:00pm, 8:00pm, 9:00pm, 10:00pm or 11:00pm on the day before the period, or at or about 12:00am on the day of the period.For example, as described in Examples 16 and 17, the composition may be administered 12 hours before morning classes start at 8:00am.

[0257] In some implementations, the time-varying in vivo absorption model of the solid oral pharmaceutical compositions described herein having a core comprising methylphenidate or a pharmaceutical salt thereof has the following single Weibull function:

number

[0258] In some implementations, the variability of efficacy can be described by the variability index (FI). For example, Examples 20-22 describe the comparison of efficacy of an exemplary composition of the present disclosure (HLD200) with Concerta® (ALZA Corporation), Metadate CD® (UCB, Inc), NWP06 (Quillivant XR® (NextWave Pharmaceuticals, Inc.)) and d-MPH ER (Focalin XR® (Novartis AG)). As described in Examples 20-22, in some implementations, efficacy can be calculated as the mean simulated value of the calculated SKAMP score change from placebo (CHP) from 8:00am to 8:00pm. The response of these different drugs and the variability in response can be evaluated using the mean value of the change from placebo in the SKAMP score and using the variability index (FI). The variability index can be defined as follows:

number

[0259] In some implementations, the compositions and methods described herein provide an improved efficacy and safety profile compared to other drugs, such as, for example, Concerta® (ALZA Corporation), Metadate CD® (UCB, Inc.), NWP06 (Quillivant XR® (NextWave Pharmaceuticals, Inc.)) and d-MPH ER (Focalin XR® (Novartis Compared with HLD200 (AG), HLD200 exhibits improvement in behaviors or abilities associated with ADHD, such as a decrease in SKAMP score, with less variability in efficacy during the day and with an earlier onset in the day. As described in Examples 20-22, the earlier onset in improving SKAMP score is accompanied by less variability in efficacy in improving SKAMP score during the day. In the exemplary implementation described in Example 22, administration of an exemplary composition of the present disclosure, i.e., HLD200, resulted in a variability index of -0.87. The variability index of HLD200 was more than 50% lower than the variability index of the other drugs evaluated.

[0260] Thus, in some implementations, the present disclosure relates to compositions and methods that provide minimal variability over time in improvement in behaviors or abilities associated with ADHD, such as SKAMP scores, or to reduce the likelihood or severity of rebound, or both. In some implementations, variability may be measured by the Fluctuation Index (FI), as follows:

number

[0261] In some implementations, the period may begin when there is a measurable increase in plasma MPH concentration. For example, in various implementations, 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.

[0262] In some implementations, the period may end when Cmax occurs (Tmax).

[0263] In some implementations, the period may begin when Cmax occurs (Tmax).

[0264] In some implementations, the 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 8:00pm.

[0265] In some implementations, the period may last up to or about 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, or 16 hours after the start of the period.

[0266] In some implementations, the period may last until the plasma concentration of MPH is less than or about 5 ng / mL, or may last long enough to cause the plasma concentration of MPH to be less than or about 5 ng / mL. In some implementations, the period may last until the plasma concentration of MPH is less than or about 3-5 ng / mL, or may last long enough to cause the plasma concentration of MPH to be less than or about 3-5 ng / mL.

[0267] In some implementations, the period may last until or about 3, 4, 5, 6, 7, 8, 9, 10, 11, or 12 hours after the methylphenidate Tmax in the subject.

[0268] In some implementations, the period may last until the subject falls asleep after Tmax.

[0269] In some implementations, the value of the SKAMP score may not change by more than or about 6, 7, 8, 9, or 10 over the entire period of time.

[0270] In some implementations of the compositions and methods described herein, the rate of change of MPH plasma concentration over time may not exceed +2.5ng.hr / mL after a maximum dose of 100mg MPH throughout the entire period.For example, as described in Example 46, Figure 62 shows the rate of change of an exemplary HLD200 formulation compared with a dose of 54mg CONCERTA®.For example, 54mg of HLD200 has a maximum rate of change in increasing methylphenidate plasma concentration of about +1.0ng / mL / hour, and a maximum rate of change in decreasing methylphenidate plasma concentration of about -0.5ng / mL / hour.Figure 62 also shows that 100mg of HLD200 has a maximum rate of change in increasing methylphenidate plasma concentration of about +2.5ng / mL / hour, and a maximum rate of change in decreasing methylphenidate plasma concentration of about -1.2ng / mL / hour. In contrast, 54mg of CONCERTA® has a maximum rate of change in increasing methylphenidate plasma concentration of about +3.6ng / mL / hour, and a maximum rate of change in decreasing methylphenidate plasma concentration of about -1.0ng / mL / hour.If the data of CONCERTA® is normalized to the dose of 100mg of CONCERTA® by multiplying 100 / 54=1.85, it would be expected that for 100mg of CONCERTA®, the maximum rate of change in increasing methylphenidate plasma concentration would be about +3.6×1.85=+6.7ng / mL / hour, and the maximum rate of change in decreasing methylphenidate plasma concentration would be about -1.0×1.85=-1.85ng / mL / hour.

[0271] In some implementations of the compositions and methods described herein, the rate of change of MPH plasma concentration over time may not exceed +2.5 ng.hr / mL after a dose of 20 mg to 100 mg MPH over the entire period of time. In some implementations, the rate of change of MPH plasma concentration may not have a value less than -1.2 ng / hr / mL between Tmax and 6 hours after Tmax after a dose of 20 mg to 100 mg MPH.

[0272] In some implementations, the slope of the MPH plasma concentration curve can be calculated to take into account body weight, dosage, etc., and therefore can be determined taking into account the dose in mg / kg in the subject.

[0273] Thus, in various implementations, when the rate of change in MPH plasma concentration over time between T and 6 hours after T has a value of -1.2 ng / hr / mL or greater after a dose of up to 100 mg of MPH, the SKAMP score may not exceed a fluctuation index of 1, and the subject may experience less fluctuation and / or less rebound compared to other MPH drugs described herein.

[0274] In some implementations, a given period may include a period during which the MPH plasma concentration is between Cmax and at least 40% of Cmax. During the period, the SKAMP score may not have a fluctuation index greater than 1 during the period, and / or the rate of change in the MPH plasma concentration over time may not have a value greater than +1.5 ng.hr / mL or less than -1.5 ng.hr / mL. In other words, in some implementations, the rate of change in the MPH plasma concentration over time may not exceed an absolute value of 1.5, such as during the period during which the MPH plasma concentration is between Cmax and at least 40% of Cmax. Thus, in such implementations, a subject may experience minimal fluctuation in efficacy, or reduced likelihood and / or severity of rebound in efficacy during the period.

[0275] In some implementations, the present disclosure relates to compositions and methods that provide minimal fluctuations in methylphenidate efficacy over a time frame to reduce the likelihood or severity or both of rebound, wherein methylphenidate or its pharmaceutical salt is absorbed in the colon, hi some implementations, at least 90% is absorbed in the colon.

[0276] Typical gastrointestinal transit times are as follows: gastric emptying is 90 minutes, small intestinal transit time is 4-6 hours, and colonic transit time is approximately 8 hours.

[0277] For example, as described in Example 45, using the PK / PD model described herein, it is estimated that the exemplary maximum therapeutic efficacy over a 24-hour period is accompanied by the absorption of methylphenidate beginning approximately 8 hours after arrival in the colon.In some implementations, the compositions and methods described herein result in the absorption of methylphenidate beginning approximately 8 hours after arrival in the colon.In contrast, for example, exemplary modeling results show that for other methylphenidate drugs, such as Concerta (ALZA Corporation), 50% of methylphenidate is absorbed in 4-6 hours after absorption in the small intestine, and in this case, all methylphenidate is absorbed in approximately 10-12 hours, which is much faster than the exemplary optimal rate described in Example 45.

[0278] Figure 64 shows the results for 1MBq or less. 111 Figure 64 is a graph reporting the exemplary cumulative % colon arrival time for the surrogate bead radiolabeled with indium.The surrogate bead has the same size, shape and density as the bead of methylphenidate formulation, but lacks methylphenidate active ingredient and is not coated.The plot shows the results from two separate experiments, which are shown as "F1" and "F2".Figure 64 shows that all radiolabeled beads reach the colon by 10 hours after administration.

[0279] Examples 48 and 49 of the present disclosure describe the difference in IVIVC of the exemplary fast, medium and slow formulations of HLD200.For example, Figure 69 shows that the medium formulation (also referred to herein as the final formulation corresponding to Jornay PM®) has an increasing methylphenidate plasma concentration from about 8 hours after administration, whereas the fast formulation has a faster increase in methylphenidate plasma concentration from about 6 hours after administration, which corresponds to a higher upper intestinal absorption than the medium formulation.In addition, the slow formulation shows an increasing methylphenidate plasma concentration from about 10 hours after administration, which corresponds to a release further into the colon than the medium formulation.Figure 70 and Figures 82-84 show that the patterns of data points (points) for the slow, medium and fast formulations have different curvatures.The data points (points) of the slow release formulation are best fitted to a 5th order polynomial. In contrast, the plot of points for IVIVC of the rapid formulation is more linear, associated with most upper intestinal absorption. The IVIVC plot for the intermediate formulation is best fitted to a third order polynomial.

[0280] In some implementations, the disclosure relates to methods of treating subjects susceptible to the negative adverse effects of fluctuations in efficacy, rebound, or both. In some implementations, the disclosure relates to compositions and methods for treating patients with co-morbidities, particularly patients with another behavioral or mental health disorder, such as patients with both ADD or ADHD and an autism spectrum disorder (ASD).

[0281] For example, methylphenidate dose effects on relapsed hyperactivity and aggression have been observed in children with both ADHD and ASD (Soo-Jeong Kim et al., 2017, Autism Dev Disord 47:2307-2313, which is incorporated herein by reference).

[0282] Thus, in some implementations, the present disclosure relates to compositions and methods for treating subjects with ADD or ADHD and autism spectrum disorder (ASD).The method comprises orally administering to the subject an effective amount of the solid oral pharmaceutical composition described herein, said administration provides minimal fluctuation in methylphenidate efficacy during a time window to reduce the likelihood or severity or both of rebound. EXAMPLES

[0283] The methods and compositions disclosed herein are further illustrated in the following examples, which are provided by way of illustration and are not intended to be limiting.

[0284] Examples 1-22 relate to PK / PD modeling and simulations for the formulations of methylphenidate described herein. The following methods were used:

[0285] PK Data. HLD200 (20 mg and 100 mg) was administered at 21:00 h to 20 adult volunteers using a Latin Square, two-sequence, two-period crossover design study (Study HLD200-104). The following morning, subjects received a moderate-fat breakfast. Gender and body weight were collected for each individual and investigated as potential covariates in the PK model.

[0286] PK model development. Population PK analysis was performed in the following sequential steps: 1. Exploratory data analysis, 2. Basic structural model development, 3. Covariate analysis, 4. Model refinement, 5. Model validation:

[0287] 1. Exploratory Data Analysis: Exploratory data analysis was performed to understand the information content of the analysis data set with respect to the expected model, to look for extreme values ​​and / or potential outliers, to examine 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.

[0288] 2. Basic structural model development: Initial evaluation of the PK results indicated that the HLD200 concentration-time profile exhibited a disposition / elimination shape consistent with a one-compartment PK model. Therefore, a PK model based on a one-compartment was evaluated.

[0289] Initial evaluation of the PK results indicated that the HLD200 concentration-time profile exhibited a disposition / elimination shape consistent with a one-compartment PK model. Therefore, a one-compartment based PK model was evaluated.

number

[0290] A convolution-based modeling approach was applied using a predefined input function (R. Gomeni, F. Bressolle, T. J. Spencer, SV Faraone. Meta-analytic approach to evaluate alternative models for characterizing the PK profiles of extended release formulations of MPH. ASCPT 2016 Annual Meeting, March 8-12, 2016, Hilton Bayfront, San Diego, Calif., which is incorporated herein by reference. Figure 1 is a schematic of the PK model of HLD200.

[0291] Two time-varying in vivo absorption models were evaluated: single Weibull function [r1(t)] and double Weibull function [r2(t)].

number

[0292] Model parameters estimated were the mean structural model parameters, the magnitude of inter-individual variation (IIV), the magnitude of intra-occasion variation (IOV), and the magnitude of residual variation.

[0293] The following variance component models were evaluated:

[0294] Inter-individual variability: The inter-individual variability (IIV) model describes the unexplained random variation in the individual values ​​of the structural model parameters (fixed effects). The IIV of the model parameters was assumed to be log-normally distributed. Thus, the relationship between the model parameters (P) and their variance was expressed as follows:

number

number

number

[0295] Intertemporal variability: Because all patients had PK samples at two time points, intertemporal variability was assessed for population PK parameters using an exponential error model.

[0296] Residual Variability: Residual variability, consisting of (but not limited to) intraindividual variability, experimental error, process noise and / or model misspecification, was modeled using additive, proportional and total error structures as described below: Additional error: yij=ytij+ε1ij Proportional error: yij=ytij(1+ε1ij) Additive and proportional total error: yij=ytij(1+ε1ij)+ε2ij where yij is the jth observed value in the ith individual, ytij is its corresponding model predicted value, and ε1ij (or ε2ij) is a normally distributed random error with mean 0 and variance σ2. If the goodness-of-fit plot reveals potential bias in the residual variation model, we consider a different residual error model.

[0297] The parameter estimation procedure was performed using a nonlinear mixed-effects modeling approach as implemented in the NONMEM software (version 7.3). The following parameters were estimated:

[0298] Fixation effects: kel: primary discharge rate constant V: volume of distribution of the central compartment (V=V / F) ss, td: parameters of the Weibull time-varying input function

[0299] Random Effects IIV on kel, V, ss, td IOV with respect to V, ss and td (IOV was not considered with respect to kel, assuming that these parameters do not change over time)

[0300] Residual error: Additive (Add_error) and proportional (Prop_error) model components

[0301] Comparison of alternative models: Comparison of alternative models was performed using the log-likelihood ratio test. An alternative model was considered to be a significantly better descriptor of the data when the reduction in objective function value (OFV) associated with this model was ≥ 3.84 (χ2 < 0.05 for 1 degree of freedom (df)).

[0302] Model diagnostics: Goodness-of-fit plots were generated to assess the results of the model fitting. These plots included a) observed data versus individual and model-predicted concentrations, b) absolute weighted residuals versus individual predictions, and c) conditional weighted residuals versus time.

[0303] 3. Covariate Analysis: Graphical and statistical methods, in addition to considering the underlying scientific rationale, were used to identify which covariates had to be examined on the population pharmacokinetic parameters and to evaluate the mathematical form of their relationships. Covariate effects were examined for all model parameters. Pre-specified covariates were weight and sex.

[0304] Covariate model building was a stepwise process consisting of forward and backward selection procedures. A likelihood ratio test was used to assess the significance of including or removing fixed effects in the population model based on an a priori set alpha level. For forward selection, a significance level of 0.05 for FOCE-I was used.

[0305] Stepwise forward addition procedure: First, each covariate was included individually in the base model to identify significant covariates whose significance was a decrease in the objective function value (OFV) of >3.84 (χ2 <0.05 for 1 degree of freedom (df)) using FOCE-I. The significant covariates were then added to the base model, one parameter at a time. The most significant covariate was included in the model first. This new model served as the new starting model for the next iteration. The significance tests and addition steps are repeated until all significant covariates are included and a final model is defined.

[0306] Backward elimination procedure: After the full model was defined, the significance of each covariate was tested individually by removing it one by one from the full model. The following evaluation criteria were used to judge the significance of the tested covariates:

[0307] Covariates were retained in the model if, when removed, OFV increased by more than 6.63 points (χ2 < 0.01 for 1 df) using FOCE-I.

[0308] In addition to OFV, the significance of covariates in the procedure was assessed using the same criteria described above. The least significant covariates were removed from the model. Elimination steps were repeated until all non-significant covariates were removed and a final model was defined.

[0309] 4. Model refinement: Simulations were performed to illustrate the effect of the retained covariates and their combinations on MPH exposure.

[0310] PD Data. The clinical data used in this analysis were SKAMP scores collected in a Phase 3 (HLD200-107 study), multicenter, open-label treatment-optimized, double-blind, randomized, placebo-controlled, forced withdrawal, parallel-group study to evaluate the safety and efficacy of HLD200, a novel delayed and extended release formulation of methylphenidate hydrochloride (DELEXIS), taken at night, in children aged 6 to 12 years with attention-deficit hyperactivity disorder (ADHD) in a laboratory classroom setting.

[0311] PK / PD Model Development. No MPH samples were collected in this study. Therefore, individual exposures used in the PK / PD analysis were estimated based on the population PK model results and individual demographic (weight and sex) covariate values.

[0312] The model included a parametric description of the trajectories of SKAMP scores for placebo and HLD200 treatments.

[0313] An indirect response model [R(t)] was used to describe the trajectory of SKAMP scores after placebo administration. The effect of HLD200 was described by the drug-related change from placebo using the following Emax model:

number

[0314] R(t) is the placebo response defined by:

number

number

[0315] Model performance. Model performance / validation and stability were assessed using visual predictive performance assessment. Visual predictive performance assessment (VPC) was utilized to assess the validity of the final model including the effects of statistically significant covariates. It assumes that parameter uncertainty is negligible compared to inter-individual and residual variances. The basic premise is that the model and parameters derived from the actual data set must result in simulated data similar to the original actual data. 500 replicates of the original data set were simulated based on the final model and 90% prediction intervals were calculated based on the simulated data set. The actual concentration versus time data were plotted with prediction intervals to visually assess the agreement between the simulated and actual data. The amount of simulated data (5th, median, 95th) was compared graphically to the amount of actual data.

[0316] Software. All data preparation, summary statistics (means, medians, standard deviations and other measures as appropriate), reporting and presentation of graphical displays were performed using SAS (version 9.3) and R (version 3.2.5). All SAS and R scripts used for data preparation and final analysis were accomplished. Population PK and PK / PD analyses were performed using NONMEM software, version 7.3 (ICON Development Solutions). The R-based package Xpose (version 4.3) was used as a model building aid for population analyses using NONMEM. The SAS statistics package (version 9.3) was used to perform statistical evaluation of the results of the clinical trial simulations.

[0317] A total of 20 subjects were included in the population PK analysis dataset with a total of 960 PK measurements.

[0318] The following abbreviations are used in the examples: ADHD (attention deficit hyperactivity disorder), AUEC (area under the effect curve), CB (clinical benefit), CI (confidence interval), df (degrees of freedom), ER (extended release), F (bioavailability), FI (variability index), FOCE-I (first-order conditional method with interaction), IIV (intrasubject 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 performance assessment).

[0319] Examples 1 to 9 relate to the results of PK analysis. Example 1. PK population: demographic data.

[0320] Table 1 shows the distribution of subjects included in the study by gender, Table 2 shows the descriptive statistics for weight and Table 3 shows the descriptive statistics for weight by gender. [Table 1] [Table 2] [Table 3]

[0321] Example 2. PK Population: Descriptive Analytics. Figures 2 and 3 show exemplary scatter plots of individual PK profiles by dose on a linear scale and a log-linear scale, respectively.

[0322] FIG. 4 and FIG. 5A and FIG. 5B show exemplary scatter plots of the mean (±SD) PK profile by dose on a linear scale in the entire population, and exemplary scatter plots of the mean (±SD) PK profile by gender.

[0323] FIG. 6 and FIG. 7A and FIG. 7B show exemplary scatter plots of the mean (±SD) PK profiles by dose on a log-linear scale in the entire population, and exemplary scatter plots of the mean (±SD) PK profiles by gender.

[0324] Example 3. PK model development: Comparison of uninomial and binomial Weibull input functions. A comparison of the uninomial and binomial Weibull input functions is presented in Table 4 in the chronological order in which they were evaluated. The first column of the table lists each model that was tested. The second column lists the reference run to which the test runs were compared. The third and fourth columns list the OFV for each test run and the change in OFV from the reference run (test minus reference), respectively. The fifth column briefly describes the hypothesis or objective that is being tested by the model. The test results in the final column describe the conclusions that can be drawn from the comparison with the reference run based on chi-square statistics (which were either not statistically significant or were statistically significant). [Table 4]

[0325] The comparison results showed that the single Weibull input function model was the appropriate model, and therefore this model was considered as the reference base model.

[0326] Example 4. PK model development: assessment of time-to-time variability. Considering that the two treatments (20 mg or 100 mg) were randomly administered to each subject in separate treatment periods, inter-temporal variability (IOV) was evaluated in addition to intraindividual variability (IIV) in the PK responses. Comparison of the performance of the basic model with and without evaluation of inter-temporal variability is shown in Table 5. [Table 5]

[0327] The comparison results showed that the presence of the IOV parameter significantly (p<0.0001) improved the model performance. Therefore, model “Run 3” was considered as the new reference base model.

[0328] Example 5. PK model development: Basic model parameter estimates. 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 estimate, RSE represents the root mean square error, and CV% represents the coefficient of variation of IIV and IOV variations. [Table 6] [Table 7]

[0329] Goodness of fit (GOF) diagnostic plots for the underlying population PK model by dose are shown in Figures 8 and 9. Discrimination lines, zero lines and trend lines are overlaid where appropriate.

[0330] Overall, no obvious bias was observed in these diagnostic plots, suggesting that the underlying population PK model was adequate in describing the PK of HLD200.

[0331] Example 6. PK model development: covariate analysis. Graphical and statistical methods, in addition to considering the underlying scientific rationale, were used to identify covariates to be included in the underlying population PK model and to evaluate the mathematical form of the relationships between parameters and covariates. Prespecified covariates were sex and body weight.

[0332] Empirical Bayes estimates of individual parameters were obtained from the base model (Run 3) in the NONMEM analysis. Graphical exploration of the potential influence of covariates on the variability of PK parameters was performed by analyzing scatter plots of individual PK parameters against selected covariates (Figures 10 and 11).

[0333] Analysis of the distribution of individual parameter estimates for selected covariates showed a potential dependence of V, TD and SS on body weight, and of TD on sex.

[0334] The characteristics of the reference base model (without covariates) and the various models explored to explain the covariate effects are summarized in Table 8. [Table 8]

[0335] Covariate analysis showed that the best performing model was one with the effect of body weight on volume of distribution and the effect of sex on TD: volume of distribution increased with body weight and the time to release 63.8% of the dose (TD) was longer in women versus men (approximately 20%).

[0336] Details of the model accounting for covariate effects are shown below:

[0337] Run 3 (reference base model):

number

[0338] Run 4 (body weight relative to V using allometric scaling model):

number

[0339] Run 5 (Weight for V and Weight for TD):

number

[0340] Run 6 (Weight for V and Gender for TD):

number

[0341] Run 7 (Weight on V and Weight on SS):

number

[0342] Run 8 (Weight for V, Weight for TD, and Gender for TD):

number

number

[0343] Run 9 (weight related to TD): If gender=M, then TD=θ 1 , otherwise TD=θ 5

[0344] The estimated fixed and random effect parameters of the final model are shown in Tables 9 and 10, respectively. In these tables, SE represents the standard error of the parameter estimate, RSE represents the root mean square error, and CV% represents the coefficient of variation of IIV variation and IOV variation. [Table 9] [Table 10]

[0345] Goodness of fit (GOF) diagnostic plots for the underlying population PK model are shown by dose in Figure 12 and Figure 13. Discrimination lines, zero lines and trend lines are overlaid where appropriate.

[0346] Overall, no obvious bias was observed in these diagnostic plots, suggesting that the underlying population PK model was adequate in describing the PK of HLD200.

[0347] Individual model predicted and observed PK concentrations versus time are shown in Figures 14-18.

[0348] The VPC for HLD200 at doses of 20 mg and 100 mg is shown in FIG.

[0349] VPC showed that the model performed well at these different doses: the median PK value, as well as the variance of the data around the median value, were satisfactorily described by the 5th, 50th (median) and 95th percentiles indicating that the population model adequately described the observed data.

[0350] Example 7. PK model development: Effect of covariates on HLD200 exposure after a single HLD200 dose. Simulations were performed to assess the effect of the retained covariates (weight and sex) and their combinations on MPH exposure.

[0351] To illustrate the expected effects of covariates on MPH exposure, covariate values ​​were categorized as follows: gender: F and M, weight: 55 kg, 65 kg and 75 kg, dose: 20 mg and 100 mg.

[0352] Median MPH concentrations were calculated for each category and are displayed graphically in FIG.

[0353] The results of this evaluation showed that peak values ​​increased with weight loss, while peak times increased in female versus male subjects.

[0354] Example 8. PK Model Development: Effect of Covariates on HLD200 Exposure Following Repeated HLD200 Dosing Simulations were performed to assess predicted MPH exposure following repeated administration of HLD200 as a function of selected covariates.

[0355] The following simulation scenarios were considered: gender: F and M, weight: 63 kg for F and 77 kg for M (these values ​​correspond to the average weight in the study population), dose: 20 mg and 100 mg administered once per day during a period of 6 days.

[0356] Median MPH concentrations were calculated for each category and are displayed graphically in Figure 21. Estimated Ctrough and Cmax values ​​after multiple doses of HLD200 are reported in Table 11. [Table 11]

[0357] Example 9. PK Model Development: Comparison of Exposure of HLD200 with Other ER MPH Formulations A comparison of MPH exposure of HLD200 at doses of 20 mg and 100 mg was performed with respect to exposure observed following administration of Concerta® (ALZA Corporation) (18 mg, 36 mg, 54 mg), Ritalin LA® (Novartis AG) (40 mg), Metadate CD® (UCB, Inc) (20 mg, 40 mg, and 60 mg), and Quillivant XR® (NextWave Pharmaceuticals, Inc.) (60 mg).

[0358] Comparisons are made assuming that HLD200 was administered in the evening before bedtime, 8 hours or 10 hours before the start of school at 8:00 a.m. the following day. The other ER MPH formulations were expected to be administered in the morning, at 8:00 a.m. Simulated MPH concentrations following administration of HLD200 were obtained using the previously estimated population PK parameter values.

[0359] The results of the comparison are shown in FIGS.

[0360] Examples 10-15 relate to the PK / PD analysis results. In study HLD200-107, a total of 117 subjects were included: 53 were treated with placebo and 64 were treated with HLD200. A sequential modeling approach was applied. In the first step, individual MPH concentrations were derived using a previously developed population PK model together with individual demographic data and individual HLD200 dosing history, then placebo data was modeled, and finally, SKAMP scores were analyzed by fixing placebo and previously estimated PK exposure values.

[0361] Example 10. PK / PD Analysis Results: PK / PD Population: Descriptive Analysis Table 12 shows the distribution of subjects included in the study by sex and treatment. Table 13 shows descriptive statistics for age, weight and height by sex and treatment. [Table 12] [Table 13]

[0362] FIG. 26 shows a scatter plot of individual SKAMP score profiles by treatment.

[0363] Figures 27 and 28 show scatter plots of the mean (±SD) SKAMP score profiles by treatment and by gender in the entire population.

[0364] This descriptive analysis appears to indicate that a more robust response (change from placebo) was observed in women versus men.

[0365] Example 11. PK / PD Analysis Results: Estimated PK Exposure Individual MPH concentrations were derived for each subject treated with HLD200 using a previously developed population PK model together with individual demographic data and individual HLD200 dosing history. MPH concentrations were estimated to coincide with the time of measurement of SKAMP scores.

[0366] Mean (±SD) MPH concentration values ​​are shown in FIG. 29, and mean MPH concentrations by gender are shown in FIG.

[0367] Example 12. PK / PD Analysis Results: Placebo Model Development The analysis dataset included data collected on 53 subjects with a total of 470 SKAMP measurements.

[0368] The estimated fixed and random effect parameters are shown in Tables 14 and 15, respectively. In these tables, SE represents the standard error of the parameter estimate, RSE represents the root mean square error, and CV% represents the coefficient of variation of the IIV variation. [Table 14] [Table 15]

[0369] Goodness-of-fit (GOF) diagnostic plots for the placebo response model are shown in Figure 31. Discrimination lines, zero lines and trend lines are overlaid where appropriate.

[0370] Overall, no obvious bias was observed in these diagnostic plots, suggesting that the placebo response model was adequate in describing the SKAMP scores in placebo-treated subjects.

[0371] The VPC for the placebo response model is shown in Figure 32. The VPC showed that the model performed well. The median SKAMP score, as well as the distribution of the data around the median value, were well described by the 5th, 50th (median) and 95th percentiles indicating that the population model adequately described the observed data.

[0372] Example 13. PK / PD Analysis Results: PK / PD Model Development The analysis dataset includes data collected in 64 subjects for a total of 557 SKAMP measurements.

[0373] Baseline Model Development. A base model was developed using the following information: (1) individual MPH concentrations estimated by a previously developed population PK model, and (2) placebo response defined by the fixed and random effects parameters estimated in a previously developed placebo response model.

[0374] Parameters estimated in this analysis (Run 1) were EMAX, EC50 and g.

[0375] The estimated fixed and random effect parameters are shown in Table 16 and Table 17, respectively. In these tables, SE represents the standard error of the parameter estimate, RSE represents the root mean square error, and CV% represents the coefficient of variation of the IIV variation. [Table 16] [Table 17]

[0376] In this analysis, it was not possible to estimate random effects for the "g" parameter. Goodness-of-fit (GOF) diagnostic plots for the underlying population PK / PD model are shown in Figure 33. Discrimination lines, zero lines and trend lines are overlaid where appropriate.

[0377] Overall, no obvious bias was observed in these diagnostic plots, suggesting that the underlying population PK / PD model was adequate in describing the SKAMP scores associated with HLD200 treatment.

[0378] Example 14. PK / PD Analysis Results: PK / PD Model Development: Covariate Analysis Graphical and statistical methods, in addition to considering the underlying scientific rationale, were used to identify covariates to be included in the underlying population PK / PD model and to evaluate the mathematical form of the relationships between parameters and covariates. Prespecified covariates were sex and body weight.

[0379] Empirical Bayes estimates of individual parameters were obtained from the base model (Run 1) in the NONMEM analysis. Graphical exploration of the potential influence of covariates on the variability of PK / PD parameters was performed by analyzing scatter plots of individual PK / PD parameters against selected covariates (Figure 34).

[0380] Analysis of the distribution of individual parameter estimates for selected covariates indicated a potential dependence of the EC50 on age and sex.

[0381] The characteristics of the reference base model (without covariates) and the various models explored to explain the covariate effects are summarized in Table 18. [Table 18]

[0382] Covariate analysis showed that the best performing model was the one with an effect of gender on the EC50 parameter (Run 4): the EC50 appears to be 2-fold higher in males than in females.

[0383] Details of the model accounting for covariate effects are shown below:

[0384] Run 1 (reference base model):

number

[0385] Run 2:

number

[0386] Run 3:

number

[0387] Run 4:

number

[0388] Run 5:

number

[0389] The estimated fixed and random effect parameters are shown in Tables 19 and 20, respectively. In these tables, SE represents the standard error of the parameter estimate, RSE represents the root mean square error, and CV% represents the coefficient of variation of the IIV variation. [Table 19] [Table 20]

[0390] Goodness of fit (GOF) diagnostic plots for the underlying population PK / PD model are shown in Figure 35. Discrimination lines, zero lines and trend lines are overlaid where appropriate.

[0391] Overall, no obvious bias was observed in these diagnostic plots, suggesting that the underlying population PK / PD model was adequate in describing the SKAMP scores in subjects treated with HLD200.

[0392] The VPC for SKAMP scores associated with HLD200 treatment is shown in FIG.

[0393] VPC showed that the model performed well at these different doses: the median SKAMP score value, as well as the variance of the data around the median value, were satisfactorily described by the 5th, 50th (median) and 95th percentiles indicating that the population model adequately described the observed data.

[0394] Example 15. PK / PD Analysis Results: PK / PD Model Development: Exposure-Response Relationships The PK / PD model identified the relationship between MPH exposure and clinical response, as assessed by the ability of the drug to reverse the placebo response.

[0395] In the absence of drug (Cp=0), the clinical response was assumed to be consistent with that observed following placebo administration.

[0396] In the presence of drug, the SKAMP clinical score improves relative to the placebo response by a factor proportional to drug exposure.

[0397] The PK / PD model was defined by the following equation:

number

number

[0398] Figure 37 shows the relationship between % change from placebo and MPH concentration. This analysis showed that a drug concentration of about 15 ng / ml was required to induce about a 40% change from placebo.

[0399] Examples 16-19 relate to the sensitivity of clinical response to in vivo release and time of HLD200 ingestion. The objective of this analysis was to evaluate the predicted clinical benefit (CB) as a function of the in vivo release rate of HLD200 and as a function of the time of HLD200 drug ingestion the night before a morning class.

[0400] The effects of different in vivo release rates and different HLD200 uptake times on the predicted clinical benefit were evaluated by using trial simulations.

[0401] Example 16. Sensitivity of clinical response to in vivo release and time of HLD200 uptake: Defining clinical benefit CB was defined as the area under change from placebo (AUEC) in estimated SKAMP score from 8:00am to 20:00pm. Assessment of CB is shown graphically in Figure 38.

[0402] Example 17. Sensitivity of clinical response to in vivo release and time of HLD200 intake: Effect of in vivo release rate on CB The absorption rate was characterized by two parameters, namely td and ss in the PK model developed for HLD200. Therefore, the CB was estimated assuming that the td parameter varied from 8 to 16 hours and the ss parameter varied from 4.5 to 8.5. Typical values ​​of these parameters estimated in the population PK analysis were td=12hr and ss=7.5. The results of the simulations are shown in Table 21. [Table 21]

[0403] The simulation results indicated that only a slight improvement in clinical benefit was expected due to the altered in vivo release rates used.

[0404] Example 18. Sensitivity of in vivo release and clinical response to HLD200 intake time: Effect of HLD200 drug intake time on CB CB was estimated assuming that the time of dose intake varied from 4 hours to 14 hours before the start of morning classroom activities at 8:00 am. The results of the simulation are shown in Table 22 and Figure 39. [Table 22]

[0405] Simulation results showed that the clinical benefit was strongly dependent on the time of HLD200 drug intake before the start of morning classes. The optimal time of drug intake was estimated to be 10 to 12 hours before the start of morning classes.

[0406] Example 19. Evaluation of the shape of the clinical response of HLD200 at different doses The objective of this analysis was to evaluate the shape of clinical response, as defined by the trajectory of the SKAMP composite score at doses of 60 mg, 80 mg, and 100 mg, using a previously developed PK / PD model.

[0407] The following assumptions for the simulation were retained: HLD200 was administered in the evening, i.e., 10 hours before the start of classroom activities. HLD200 concentrations were simulated in a 34 kg subject. The td parameter value, which was found to be different for men and women, was fixed at mean value for men and women = 12.05 hr. The maximum change from placebo expected for HLD200 doses up to 100 mg was approximately 40% as indicated by the previously developed PK / PD model. The simulated responses were evaluated from 8:00 am up to 30 hours after dosing.

[0408] The simulated trajectories of SKAMP scores are shown in Figures 40, 41 and 42 for HLD200 doses of 60 mg, 80 mg and 100 mg, respectively.

[0409] FIG. 43 shows the comparative profile of SKAMP scores following three doses of HLD200.

[0410] Thus, this example shows a dose-dependent improvement in clinical benefit. In particular, this example shows a dose-dependent increase in the duration of clinical benefit. Notably, the period after Tmax shows a dose-dependent increase in the duration of clinical benefit.

[0411] Example 20. Comparison of clinical outcomes of HLD200 with Concerta® (ALZA Corporation), Metadate CD® (UCB, Inc), NWP06 (Quillivant XR® (NextWave Pharmaceuticals, Inc.)) and d-MPH ER (Focalin XR® (Novartis AG)) Examples 20-22 relate to a comparison of the clinical performance of HLD200 with Concerta® (ALZA Corporation), Metadate CD® (UCB, Inc), NWP06 (Quillivant XR® (NextWave Pharmaceuticals, Inc.)) and d-MPH ER (Focalin XR® (Novartis AG)). The purpose of the analysis was to compare the clinical performance of different MPH products, including HLD200, Concerta® (ALZA Corporation), Metadate CD® (UCB, Inc), Quillivant XR® (NextWave Pharmaceuticals, Inc.) and Focalin XR® (Novartis AG), using the composite SKAMP clinical score. Unlike HLD200, the PK time courses of Concerta® (ALZA Corporation), Metadate CD® (UCB, Inc), Quillivant XR® (NextWave Pharmaceuticals, Inc.) and Focalin XR® (Novartis AG) were described by a convolution type model using a double Weibull function (Equation 4) to characterize in vivo MPH delivery (R. Gomeni, F. Bressolle, TJ Spencer, SV Faraone. Meta-analytic approach to evaluate alternative models for characterizing the PK profiles of extended release formulations of MPH. ASCPT 2016 Annual Meeting, March 8-12, 2016, Hilton Bayfront, San Diego, CA., which is incorporated herein by reference). The estimated PK parameters are shown in Table 23. [Table 23]

[0412] The clinical outcomes of the different products were compared against the HLD200 outcomes using the mean simulated values ​​of the change from placebo in the SKAMP score (CHP) calculated from 8:00am to 8:00pm. The response of the different products and the variability in the response were assessed using the mean values ​​of the change from placebo in the SKAMP score and using the Fluctuation Index (FI).

[0413] The variability index was defined as follows:

number

[0414] Example 21. Comparison of Clinical Performance of HLD200 with Concerta® (ALZA Corporation), Metadate CD® (UCB, Inc), NWP06 (Quillivant XR® (NextWave Pharmaceuticals, Inc.)) and d-MPH ER (Focalin XR® (Novartis AG)): Reference Clinical Trial COMACS study. This was a multicenter, double-blind, crossover study of Metadate CD® (UCB, Inc), Concerta® (ALZA Corporation), and placebo, with each treatment administered for 1 week (Sonuga-Barke EJ, Swanson JM, Coghill JJ, D, DeCory HH, Hatch SJ. Efficacy of two once-daily methylphenidate formulations compared across dose levels at different times of the day: preliminary indications from a secondary analysis of the COMACS study data. BMC Psychiatry (2004 Sep 30;4:28), which is incorporated herein by reference. Children were assigned to either high (Metadate CD® (UCB, Inc), 60 mg; Concerta® (ALZA Corporation), 54 mg), medium (Metadate CD® (UCB, Inc), 40 mg; Concerta® (ALZA Corporation), 36 mg) or low (Metadate CD® (UCB, Inc), 20 mg; Concerta® (ALZA Corporation), 18 mg) doses of MPH based on pretrial dosing, and children attended the institute's school on day 7 for evaluation at seven periods throughout the day. The SKAMP score values ​​are shown in Table 24 and FIG. [Table 24] In the table, lower SKAMP scores indicate greater efficacy. CON=Concerta® (ALZA Corporation), MCD=Metadate CD® (UCB, Inc), PLA = placebo, Low = low dose (CON, 18 mg; MCD, 20 mg), Med = medium dose (CON, 36 mg; MCD, 40 mg), Hi = high dose (CON, 54 mg; MCD, 60 mg).

[0415] d-MPH-ER (Focalin XR® (Novartis AG)) Study. This was a double-blind, placebo-controlled crossover study involving 54 children aged 6-12 years stabilized on 20-40 mg / day MPH (Raul R. Silva et al., Efficacy and Duration of Effect of Extended-Release Dexmethylphenidate Versus Placebo in Schoolchildren With Attention-Deficit / Hyperactivity Disorder. Journal Of Child And Adolescent Psychopharmacology, Vol. 16, No. 3, 2006, which is incorporated herein by reference). Patients attended a clinic day followed by 5 days of treatment with d-MPH-ER (20 mg / day) or placebo. After a 1-day washout, patients returned to the classroom and received one of their assigned treatments. Assessments were performed pre-dose and 1, 2, 4, 6, 8, 9, 10, 11, and 12 hours post-dose. Children were then transferred to alternative treatment using the same protocol. The primary efficacy variable was the SKAMP combined score. Figure 45 is a graph reporting exemplary data from the d-MPH ER study, namely, the mean SKAMP total raw score from pre-dose (hour 0) to hour 12.

[0416] NWP06 (Quillivant XR® (NextWave Pharmaceuticals, Inc.)) Study. This was a double-blind, placebo-controlled, crossover design, laboratory classroom study designed to evaluate the efficacy and safety of NWP06 in pediatric patients aged 6 to 12 years with ADHD (Sharon B. Wigal et al., NWP06, an Extended-Release Oral Suspension of Methylphenidate, Improved Attention-Deficit / Hyperactivity Disorder Symptoms Compared with Placebo in a Laboratory Classroom Study. Journal of Child And Adolescent Psychopharmacology, Vol. 23, No. 1, 2013, which is incorporated herein by reference). A total of 45 subjects aged 6-12 years were enrolled in this dose optimization study. After open-label dose optimization, subjects received 2 weeks of double-blind treatment (1 week of NWP06 and 1 week of placebo). Efficacy measures were based on a combination of the SKAMP rating scale sums measured and assessed pre-dose and at 0.75, 2, 4, 8, 10 and 12 hours post-dose on each laboratory day. Figure 46 is a graph reporting exemplary data from the NWP06 (Quillivant XR® (NextWave Pharmaceuticals, Inc.)) study, i.e., mean SKAMP combination scores from pre-dose (hour 0) to hour 12.

[0417] The mean composite SKAMP scores for the placebo group and the groups treated with d-MPH-ER and NWP06 were digitized from figures reported in the referenced publications using Plot Digitizer software (University of South Alabama, version 2.0).

[0418] Example 22. Comparison of Clinical Outcomes of HLD200 with Concerta® (ALZA Corporation), Metadate CD® (UCB, Inc), NWP06 (Quillivant XR® (NextWave Pharmaceuticals, Inc.)) and d-MPH ER (Focalin XR® (Novartis AG)): Clinical Outcomes A comparison of HLD200 SKAMP score change from placebo (change observed in 107 studies) with data for Concerta® (ALZA Corporation) (at doses of 18 mg, 36 mg, and 54 mg), Quillivant XR® (NextWave Pharmaceuticals, Inc.), and d-MPH is shown in Figure 47.

[0419] A comparison of HLD200 SKAMP score change from placebo (change observed in 107 studies) with data for Metadate CD® (UCB, Inc) (at doses of 20 mg, 40 mg, and 60 mg), Quillivant XR® (NextWave Pharmaceuticals, Inc.), and d-MPH is shown in Figure 48.

[0420] Table 25 shows the estimated mean changes from placebo in SKAMP scores, and the variability index, for the different treatments evaluated. [Table 25]

[0421] The HLD200, as shown in the shaded cells of Table 25, indicates that the lowest FI indicates less variable response over the course of a day.

[0422] In conclusion, Examples 1-22 describe the following:

[0423] A population PK model was developed to characterize MPH concentrations following administration of HLD 200. The appropriate model was a one-compartment model with a time-varying absorption rate that was well described by a single Weibull in vivo release function.

[0424] Covariate analysis identified an effect of body weight on the volume of distribution and an effect of sex on the time to release MPH from the HLD200 formulation: the volume increased with body weight and the time (td) for release of 63.8% of the dose was longer in women versus men (approximately 20%).

[0425] Overall, the population PK model can be considered appropriate to characterize MPH PK following HLD200 dosing, given the goodness-of-fit plots, good validation of individual model predictions with respect to observed values, and visual predictive performance assessment.

[0426] The clinical study (HLD200-107) was not planned to collect PK measurements, and given the parallel study design, placebo measurements of SKAMP scores could not be obtained for subjects treated with HLD200.

[0427] The development of the PK / PD model required estimates of individual PK exposure and estimates of individual placebo response. Therefore, the PK / PD analysis was performed according to a sequential modeling approach. In the first step, individual MPH concentrations were derived using the previously developed population PK model together with demographic data and individual HLD200 dosing history, and then placebo response was estimated using the data collected in the placebo group of the study. Finally, the SKAMP scores of subjects treated with HLD200 were modeled as a function of the HLD200 exposure and placebo response models.

[0428] The placebo response model adequately described the shape of the SKAMP score trajectories, taking into account goodness-of-fit plots and visual predictive performance assessment.

[0429] The population PK / PD model provided reasonable estimates of the HLD200 effect. Covariate analysis showed that the best performing model was the one with a gender effect on the EC50 parameter: the EC50 appeared to be two-fold greater in men than in women, indicating that greater sensitivity in response is possible in the female population.

[0430] Finally, it was possible to establish an exposure-response relationship. This analysis showed that a drug concentration of about 15 ng / ml was required to induce an improvement in clinical response of about 40%.

[0431] A PK / PD model was developed using data collected in the HLD200-107 study. The purpose of this study was to select the appropriate individual dose of HLD200 to maximize clinical response, not to establish a dose (or exposure-response) relationship. Investigators could titrate (increase or decrease) the dose in 20-40 mg increments until an "optimal" daily dose was achieved or a maximum daily dose of 100 mg / day and / or a maximum dose not exceeding 3.7 mg / kg was reached, whichever was achieved first. An optimal treatment dose was defined as that which provided maximum symptom suppression in the morning and throughout the day while still being safe and well tolerated, with a minimal improvement from at least one-third (33%) of the total score at baseline (Visit 2) to the total score at randomization (Visit 8) for each of the three scales.

[0432] Comparison of the predicted clinical benefit (CB) of treatment with HLD200 as a function of in vivo release rate showed that the current formulation produced the optimal response and that only slight improvements in clinical benefit were expected with modified in vivo release rates at any given in vivo release rate.

[0433] Analysis of the change in clinical benefit as a function of time of drug intake the night before morning classes showed that clinical benefit was strongly dependent on time of HLD200 drug intake. The optimal time of drug intake was estimated to be 12 hours before morning classes began.

[0434] Simulations were performed using the PK / PD model results to evaluate the shape of the clinical response (trajectory of the SKAMP composite score) at doses of 60 mg, 80 mg, and 100 mg. Simulation results showed that increasing the HLD200 dose resulted in an extended duration of clinical response from morning to evening.

[0435] A final analysis was performed to compare the clinical outcomes of different MPH products, including HLD200, Concerta® (ALZA Corporation), Metadate CD® (UCB, Inc), Quillivant XR® (NextWave Pharmaceuticals, Inc.) and d-MPH ER, using the composite SKAMP clinical score.

[0436] The comparison is as follows: HLD200 is a 200 mg / kg / day, 100 mg / kg / day, 150 mg / kg / day, and 100 mg / kg / day. HLD200 was shown to provide comparable clinical benefit to HLD200 (UCB, Inc.) and Metadate CD® (UCB, Inc.). However, clinical performance of HLD200 showed a more stable clinical response during the day with clinical benefit beginning earlier in the day, unlike the other compounds. The earlier onset of response was also accompanied by a more stable control of SKAMP response during the day. This feature was evaluated using the variability index (variability in response over the course of a day): the variability index of HLD200 was more than 50% lower than the variability index of the other products evaluated.

[0437] Example 23. In vitro-in vivo correlation (IVIVC) analysis Examples 23-28 relate to the in vitro-in vivo correlation (IVIVC) for the formulations of methylphenidate described herein.Analysis was carried out to develop and validate the in vitro-in vivo correlation (IVIVC) for HLD200.Three formulations of HLD200 (fast, slow and late) were included in this IVIVC analysis.

[0438] The two formulations used in clinical study HLD200-101 were identified as B-HLD200 (batch N450137) with a "slow" dissolution profile for clinical study HLD200-101, and C-HLD200 (batch N451299) with a "fast" dissolution profile. The difference in dissolution profile was achieved by adjusting the amount of DR coating applied using the same DR coating formulation (30% WG vs. 15% WG, respectively). Both batches were coated with an ER coating of 20% WG. For clinical study HLD200-104, the final formulation (batch 3125683) was used and identified as HLD200. This batch was coated with an ER coating of 21% WG and a DR coating of 30% WG. See Table 26 for batch description. [Table 26] In the table, * represents 100 mg prepared from a 54 mg capsule.

[0439] The following method was used:

[0440] Data. In vitro dissolution data and in vivo pharmacokinetic data were available for the formulations considered for IVIVC modeling and validation.

[0441] In vitro dissolution. The formulation details are shown 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 pH 6.0 phosphate buffer for 4 hours (T=2-6 hours) and finally in pH 7.2 phosphate buffer for the remaining time.

[0442] See Tables 27, 28 and 29 for details on dissolution methods. [Table 27] In the table, for "sampling time points," sampling up to 6 hours may be done manually or by autosampler. Samples after 6 hours will be collected by the autosampler. [Table 28] [Table 29]

[0443] The amount of methylphenidate released in vitro by dissolution at 6 hours for both the fast and slow formulations was reported as 0.0%. The amount of methylphenidate released at 4 and 7 hours was reported as 1.0%. Dissolution results for drug release levels below 5% will be inconsistent since the validation range of the dissolution method is 5% to 130% drug release as required during the dissolution method validation period. Values ​​below 5% may be less than the quantitation limit of the method which may result in variability in results at this low level. Therefore, a value of 1.0% was assumed at 6 hours for the purposes of this model.

[0444] Study HLD200-101. The study and procedures are described in Study HLD200-101 clinical protocol. A three-way crossover Latin square design of single dose with three sequences involved comparing two methylphenidate HCl MR capsule formulations, namely, 54mg MPH00400 (Study 1, herein referred to as slow 54, treatment B HLD200) and 54mg MPH00500 (Study 2, herein referred to as fast, treatment C-HLD200), with a reference formulation, namely, 20mg immediate release (IR) tablets (treatment A) of Ritalin® (Novartis). The subjects were healthy volunteers (6 males and 6 females) and were organized into three cohorts, each containing 4 subjects. The MR formulation was administered to 12 subjects at approximately 9:00 pm, whereas the IR comparator (Ritalin® (Novartis)) was administered in the morning at approximately 8:00 am, both under fasting conditions. The PK data for the IR formulation was used to derive the unit impulse function for methylphenidate, and the PK data for the slow formulation (B-HLD200) and the fast formulation (C-HLD200) were used for the IVIVC analysis.

[0445] Study HLD200-104. The study and procedures are described in the Study HLD200-104 clinical protocol. Dose proportionality of pharmacokinetic parameters of the lowest dose of the final formulation of HLD200 to the highest dose (20 mg and 100 mg, respectively, administered at night under fasting conditions) was investigated in 20 normal volunteers in the first part of the study. The breakfast provided the next morning for both treatments was of medium fat composition (medium calorific value). PK data from the 100 mg dose representing the final formulation was used for IVIVC analysis.

[0446] Study HLD200-103. The study and procedures are described in the Study HLD200-103 clinical protocol. Eighteen subjects received a single dose of 100 mg B-HLD200 administered po late at night (approximately 9:00 pm) with 240 ml of ambient temperature water. For the purposes of this study, formulation B-HLD200 (54 mg) was modified to prepare a dose equivalent to the 100 mg strength in one capsule. The three treatments compared were given on a high fat meal (A), sprinkled on food (applesauce) (B), and fasted (C) in six cohorts with a 7-day washout between the three treatment periods, with random crossover to the different treatment sequences. PK data from the fasted group (C) with a 100 mg dose representing the slow formulation (referred to herein as slow 100) were used for IVIVC analysis.

[0447] Dissolution Model. A dissolution model was developed using the in vitro dissolution data for the slow, fast and final formulations. An Emax type model with a sigmoidal coefficient (gamma) was used for this purpose.

[0448] Unit impulse response. Mean PK data for the Ritalin® (Novartis) IR formulation was utilized to derive a unit impulse function. A one-compartment model with an absorption delay was appropriate to describe the PK for each subject.

[0449] Deconvolution and convolution. Individual PK data for the fast and slow formulations from study HLD200-101 were deconvoluted using a unit impulse function for each subject to estimate the cumulative in vivo release rate. The average cumulative in vivo release rates for the fast and slow formulations were then calculated. These average in vivo release rates were then correlated with the in vitro release rates using a polynomial function (IVIVC model). Internal validation was performed by convolving the in vitro release data with the IVIVC predicted cumulative in vivo release rates. The predicted and observed PK profiles were compared using non-compartmental parameters (AUC(0-infinity) and Cmax) as recommended by the IVIVC guidance (Guidance for Industry: Extended Release Oral Dosage Forms: Development, Evaluation, and Application of In Vitro / In Vivo Correlations, USDepartment of Health and Human Services Food and Drug Administration Center for Drug Evaluation and Research (CDER), September 1997 BP 2, which is incorporated herein by reference).

[0450] The IVIVC model was then used to predict the in vivo PK of the final formulation for external validation. The predicted PK was compared to the observed PK (AUC, Cmax) from studies HLD200-103 and HLD200-104.

[0451] Software and Estimations. All estimations were performed using Phoenix® 6.4 (Certara, Cary, NC) and Microsoft Excel® (Redmont, WA).

[0452] Example 24. In Vitro-In Vivo Correlation (IVIVC) Analysis: Dissolution Model The mean fractional in vitro release data for the three formulations are presented in Tables 30 and 31. [Table 30] [Table 31]

[0453] Data for more time points were collected for the final formulation. An Emax-type model adequately described the observed dissolution data, as shown in FIG.

[0454] The maximum possible dissolution fraction was fixed at 1 (100%). The times to half maximum dissolution were estimated to be 14.48 hr, 12.57 hr and 14.49 hr for the slow, fast and final formulations, respectively. The gammas for the slow, fast and final formulations are 8.05, 6.87 and 7.98, respectively, as shown in Table 32. [Table 32] In the table, Dmax = maximum fraction dissolved; DT50 = time to half maximum dissolution; gamma is the sigmoidicity coefficient.

[0455] Example 25. In Vitro-In Vivo Correlation (IVIVC) Analysis: Unit Impulse Response Ritalin® (Novartis) IR data was used to derive unit impulse function parameters. Methylphenidate concentration increased rapidly to a maximum concentration at about 1.5 hours, then rapidly decreased. A one-compartment model with time-delayed absorption was used for this purpose. Model parameters were estimated for each of the 12 subjects, and predicted values ​​are shown in Figure 50. Figure 50 is a graph reporting exemplary observed mean methylphenidate concentration (points) and predicted mean methylphenidate concentration (line) after administration of 20 mg of Ritalin® (Novartis) IR. The model parameters were absorption rate constant (1 / hr), volume (L), elimination rate constant (1 / hr), absorption lag time (hr), proportional error (%), and additive error (ug / L). This model describes the observed data very well. The inverse of the distribution volume provides a measure of magnitude, and the elimination rate constant determines the rate at which the concentration decreases. These properties are unique to methylphenidate and can therefore be used to estimate the in vivo release rate by deconvolution.

[0456] Example 26. In vitro-in vivo correlation (IVIVC) analysis: IVIVC model The unit impulse function and measured individual methylphenidate concentration for fast formulation and slow 54 formulation are used to estimate cumulative in vivo drug release fraction.The unit impulse function for each subject is used to derive the in vivo drug release in that subject.The individual in vivo cumulative drug release fraction is used to calculate the average cumulative drug release fraction.Table 33 shows the average cumulative fraction drug release rate in vitro and in vivo. [Table 33]

[0457] The in vitro and in vivo release rates were then correlated to derive the IVIVC model as shown in FIG. 51, which shows the IVIVC for both the fast and slow 54 formulations combined. A fifth order polynomial function successfully described the relationship between in vitro and in vivo release. As suggested by FIG. 51, the IVIVC relationship is not linear. Lower order polynomial functions were also attempted, but the fifth order polynomial provided the best fit and prediction quality. The IVIVC model parameters are shown in Table 34. [Table 34] In this case, the following relationship was best described by a fifth order polynomial: Fabs = (Fdiss) 5 +(Fdiss) 4 +(Fdiss) 3 +(Fdiss) 2 +(Fdiss).

[0458] Example 27. In vitro-in vivo correlation (IVIVC) analysis: internal validation The in vitro dissolution data and the IVIVC model were used to predict the in vivo PK profiles of the fast and slow 54 formulations. The cumulative input predicted values ​​for the fast and slow 54 formulations are shown in Tables 35 and 36, respectively. [Table 35] In the table, Fdis = in vitro dissolved fraction and Fabs = in vitro absorbed fraction. [Table 36] In the table, Fdis = in vitro dissolved fraction and Fabs = in vitro absorbed fraction.

[0459] The predicted and mean observed in vivo methylphenidate concentrations for the fast and slow 54 formulations are shown in FIG. 52 and Table 37. [Table 37-1] [Table 37-2]

[0460] The IVIVC model predictions are in excellent agreement with the observed concentrations. As shown in Table 38, the absolute prediction errors (APE) for the fast formulation are 4.13% and 14.64% for AUC and Cmax, respectively. The absolute prediction errors (APE) for the slow 54 formulation are 5.20% and 6.21% for AUC and Cmax, respectively. The average APE across the two formulations is 4.67% and 10.43% for AUC and Cmax, respectively. [Table 38]

[0461] Example 28. In vitro-in vivo correlation (IVIVC) analysis: external validation The in vitro dissolution data and the IVIVC model were used to predict the in vivo PK profiles of the slow formulations from Study HLD200-103 (slow 100 formulation) and Study HLD200-104 (final formulation). The cumulative input predictions for the 100 mg slow formulation are shown in Table 36. The cumulative input predictions for the 100 mg final formulation are shown in Table 39. [Table 39]

[0462] The in vivo predicted and measured methylphenidate concentrations for the final formulation are shown in Figure 53 and Table 37. The IVIVC model predictions are in very good agreement with the measured concentrations. As shown in Table 38, the absolute prediction error (APE) for the 100mg slow 100 formulation (study HLD200-103) is 9.20% and 0.81% for AUC and Cmax, respectively. The APE for the 100mg final formulation (study HLD200-104) is 13.30% and 0.79% for AUC and Cmax, respectively.

[0463] In conclusion, Examples 23-28 describe the following:

[0464] The dissolution model provided good correlation to fractional dissolution over time. An IVIVC model was developed using the fast and slow 54 formulations from study HLD200-101. The in vitro dissolution fraction correlated with the in vivo absorption fraction with an R of 0.99. 2 f2 for these two formulations is 45.15%. The mean IVIVC prediction errors for AUC and Cmax for the Final and Slow54 formulations are 4.67% and 10.43%, close to the 10% suggested by FDA. The prediction errors for each of these formulations are less than the allowed 15% range. The mean prediction errors for both formulations are about 10% for AUC and close to 10% for Cmax.

[0465] External validation was also performed using the formulations tested in Study HLD200-103 and Study HLD200-104. The IVIVC model predictions are in very good agreement with the observed concentrations. The absolute prediction error (APE) for the 100mg slow 100 formulation (Study HLD200-103) is 9.20% and 0.81% for AUC and Cmax, respectively. The APE for the 100mg final formulation (Study HLD200-104) is 13.30% and 0.79% for AUC and Cmax, respectively. The AUC predictions for the final formulation are borderline, while Cmax exceeds the 10% threshold. It should be noted that in Study HLD200-104, subjects received a light breakfast, which may be considered to be a contributor to slightly larger variability. The fed to fasted ratio of HLD200 in study HLD200-104 was estimated to be 1.09 for AUC and 1.004 for Cmax. The AUC for study HLD200-104 when adjusted for the slight effect of breakfast is 155.29ug.hr / L. Making this adjustment results in an APE of 5.5% for the AUC of the final formulation (study HLD200-104). The slight effect of food (breakfast) may be considered a potential source of discrepancy. Overall, a successful IVIVC model was developed and validated as recommended by the FDA in its Guidance for Industry:Extended Release Oral Dosage Forms:Development, Evaluation, and Application of In Vitro / In Vivo Correlations.

[0466] Example 29. Sustained release layer example Some examples of sustained release layers are shown in Table 40. Citric acid was added to the formulation to keep the microenvironmental pH low in the coating to inhibit dissolution of HPMCAS-LF, which dissolves at pH 5.5 and above, thus causing a delay in the onset of the dissolution curve. [Table 40]

[0467] Exemplary cores were synthesized as shown in Table 41. In this example, an osmotic agent is added to the core. [Table 41]

[0468] A sustained release layer having the formulation shown in the right column (F) of Table 40 was synthesized on the surface of the API-containing beads. The formulation was designated 2009-043-10A when the sustained release layer provided a 25% weight gain and 2009-043-10 when the sustained release layer provided a 35% gain. Additional layers were synthesized as shown in columns A and B of Table 40. In column B, the formulation of column A was modified such that the colloidal silicon dioxide was removed and the plasticizer was increased to 50% (w / w) of the polymer level. All other ratios as shown in column A were maintained.

[0469] The formulation of row A was also modified to produce the sustained release layer of row C of Table 40. The formulation had the colloidal silicon dioxide removed and citric acid added. The ratio of Ethocel:HPMCAS was reduced from 75:25 to 56:44. This formulation was expected to result in a lower pH in the microenvironment, increasing the lag time. A sample layered to provide a 25% weight gain and another sample with a 45% weight gain were subjected to dissolution testing.

[0470] Another embodiment of the sustained release layer was prepared where the drug or API was included in the sustained release layer. This layer is described in column D of Table 40. In this formulation, the ratio of Ethocel to HPMCAS was 75:25. The micronized drug was added to the formulation as a suspension. Samples with 25% weight gain were subjected to dissolution testing.

[0471] Core tablets as described in Table 41 were coated with a sustained release layer formulated as in column A of Table 40. This formulation exhibited an initial slow drug release (3% in the first 3 hours).

[0472] Another embodiment of the sustained release coating was designed with a polyethylene oxide (PEO) to ethylcellulose ratio of 37.5:62.5. Talc was also added to one sample at 10% to improve the coating process. The presence of talc did not affect drug release. The release profiles for these formulations processed with 25% and 40% weight gains were also determined. The formulations exhibited a lag of 1 hour, with the drug being substantially completely released within 9 hours.

[0473] Example 30. Sustained release coating example. An example of a sustained release coating to be applied to the core pellets is prepared with the following ingredients (see Table 42).

[0474] Core batch size-1100.0g

[0475] Coating weight increase -30%

[0476] Solid content - 12.0% [Table 42]

[0477] Example 31. Example of a pH dependent coating. An exemplary S100 pH dependent coating formulated for a 30% weight gain is formulated with the following ingredients (see Table 43):

[0478] Coating weight increase -30%

[0479] Solid content -10.0%

[0480] Batch size- 715g

[0481] Core pellet quantity - 550g [Table 43]

[0482] Example 32. Example of a pH dependent coating. An exemplary S100 pH dependent coating formulated for a 50% weight gain contains the following ingredients (see Table 44):

[0483] Coating weight increase -50.0%

[0484] Solid content -10.0%

[0485] Batch size- 715g

[0486] Core pellet quantity - 550g [Table 44]

[0487] Example 33. Sustained release coating example. An example of a sustained release coating with an alternative ratio of water soluble polymer (Klucel) to water insoluble polymer (Ethocel) is prepared with the following ingredients to obtain a faster release profile (see Table 45).

[0488] Core batch size-1100.0g

[0489] Coating weight increase -30%

[0490] Solid content - 12.0% [Table 45]

[0491] Example 34. Sustained release coating example. Another example of a sustained release coating according to the present disclosure is prepared from the following ingredients (see Table 46):

[0492] Core batch size-1100.0g

[0493] Coating weight increase -30%

[0494] Solid content - 12.0% [Table 46]

[0495] Example 35. An example of a slow sustained release coating. An example of a slow sustained release coating as described herein for use in a 25% SR+20% pH coating or 30% pH coating in slow release formulations (1 and 2) (see Table 47)

[0496] For Table 47, SR Coated Slow Release (1 and 2):

[0497] Coating weight gain: 25.0

[0498] Solids%: 12.0 [Table 47]

[0499] Example 36. Example of slow enteric coating. An example of a slow release enteric coating as described herein for use in a 25% SR+20% pH coating or a 30% pH coating in slow release formulations (1 and 2) (see Tables 48 and 49)

[0500] For Table 48, S100 pH-dependent coated slow release (1):

[0501] Coating weight increase: 20%

[0502] Solids%: 10%

[0503] Batch size (g) 1500

[0504] Core pellet quantity (g) 1250

[0505] [Table 48]

[0506] For Table 49, S100 pH-dependent coated slow release (2):

[0507] Coating weight increase: 30%

[0508] Solids%: 10%

[0509] Batch size(g) 1625

[0510] Core pellet quantity (g) 1250

[0511] [Table 49]

[0512] Example 37. An example of a moderate sustained release coating. An example of an intermediate sustained release coating as described herein for use in a 20% SR+20% pH coating or a 30% pH coating in intermediate release formulations (1 and 2) (see Tables 50, 51 and 52)

[0513] For Table 50, SR Coat Medium Release (1 and 2):

[0514] Coating weight increase: 20%

[0515] Solids%: 12%

[0516] [Table 50]

[0517] For Table 51, S100 pH-dependent coated medium release (1):

[0518] Coating weight gain: 20.0

[0519] Solids%: 10.0

[0520] Batch size(g) 1440

[0521] Core pellet quantity (g) 1000

[0522] [Table 51]

[0523] For Table 52, S100 pH-dependent coated medium release (2):

[0524] Coating weight increase: 30%

[0525] Solids%: 10%

[0526] Batch size(g) 1560

[0527] Core pellet quantity (g) 1200

[0528] [Table 52]

[0529] Example 38. Rapid release coating example. Rapid release coating examples for 20% SR + 20% pH coating in rapid release formulation (see Tables 53 and 54).

[0530] For Table 53, SR Coat Fast Release:

[0531] Coating weight gain: 20.0

[0532] Solids%: 12 [Table 53]

[0533] For Table 54, S100 pH dependent coat fast release:

[0534] Coating weight increase: 20%

[0535] Solids%: 10%

[0536] Batch size(g) 1440

[0537] Core pellet quantity (g) 1200 [Table 54]

[0538] Example 39. Methylphenidate composition examples. This example describes an exemplary composition of methylphenidate: 54 mg capsule (slow release formulation, 25% SR weight gain + 30% pH dependent weight gain) (see Table 55). [Table 55]

[0539] Example 40. Methylphenidate composition examples. This example describes an exemplary composition of methylphenidate: 54 mg capsule (slow release formulation, 20% SR weight gain + 20% pH dependent weight gain) (see Table 56). [Table 56]

[0540] Example 41. Methylphenidate composition examples. This example describes an exemplary composition of methylphenidate: 54 mg capsule (slow release formulation, 20% SR weight gain + 30% EC pH dependent weight gain) (see Table 57). [Table 57]

[0541] This example also describes an exemplary composition of methylphenidate: 54 mg capsule (immediate release formulation, 20% SR weight gain + 15% EC pH dependent weight gain) (see Table 58). [Table 58]

[0542] Example 42. Method of fabricating coated methylphenidate capsules. In one example of manufacturing process, methylphenidate HCl and microcrystalline cellulose (Avicel PH-101) are blended in a Hobart mixer. Purified water is added to the dry mixture, and the wet granulation is extruded (MG-55 multi-granulator). The extrudate is then spheronized into pellets (Caleva model #SPH250). The wet pellets are dried (Fluid Air model #0050) and sieved (30 mesh<acceptable<20 mesh).

[0543] The sustained release coating is added as follows: Prepare a dispersion of ethylcellulose NF (Ethocel Standard 10 Premium), Klucel EF, dibutyl sebacate (NF), magnesium stearate (NF), ethanol and purified water (USP) in an overhead mixer. Apply the dispersion to uncoated methylphenidate pellets in a fluidized bed, and sieve the coated pellets as described above. In the context of this embodiment, it is understood that the term "dispersion" can refer to various two-phase systems in which at least some solid is dispersed in a liquid phase. Thus, the term "dispersion" as used herein can encompass, but is in no way limited to, the concepts of colloid, emulsion and / or suspension, whether in whole or in part.

[0544] Enteric coating is prepared as follows: the dispersion of methacrylic acid copolymer type B (Eudragit S100), mono- and di-glycerides (NF) (Inwitor 900K), dibutyl sebacate (NF), polysorbate 80 (NF), ethanol and purified water (USP) is mixed in an overhead mixer to obtain a dispersion.The dispersion is applied to the sustained release coated methylphenidate pellets in a fluidized bed.The enteric coated pellets are encapsulated to obtain methylphenidate capsules.

[0545] Example 43. IVIVC of Concerta® (ALZA Corporation) Figure 57 is a graph reporting exemplary data on the fraction of methylphenidate dissolved in vitro (FDISS) versus the fraction of methylphenidate absorbed in vivo (Fabs) from Concerta® (ALZA Corporation). From R. Gomeni, F. Bressolle, TJ Spencer, SV Faraone. Meta-analytic approach to evaluate alternative models for characterizing the PK profiles of extended release formulations of MPH. ASCPT 2016 Annual Meeting (March 8-12, 2016, Hilton Bayfront, San Diego, Calif.), which is incorporated herein by reference.

[0546] Example 44. Validation of the Dual Weibull IVIVC Model in Exemplary MPH Formulations Figure 58 is a graph reporting exemplary observed mean methylphenidate concentrations (points) and predicted mean methylphenidate concentrations (lines) after the indicated drugs are administered.R. Gomeni, F. Bressolle, TJ Spencer, SV Faraone.Meta-analytic approach to evaluate alternative models for characterizing the PK profiles of extended release formulations of MPH.From ASCPT 2016 Annual Meeting, March 8-12, 2016, Hilton Bayfront, San Diego, Calif., which is incorporated herein by reference.

[0547] Example 45. Products that release MPH according to a dual Weibull in vivo absorption pattern, e.g., Concerta® (ALZA Corporation), Ritalin LA® (Novartis AG), Metadate CD® (UCB, Inc.) and Quillivant XR® (NextWave Determination of an Exemplary Optimal MPH Release Profile for Medicinal Pharmaceuticals, Inc. Using the PK / PD model described herein, the methylphenidate absorption profile required to provide maximum clinical benefit over a 24 hour period was determined for formulations that release MPH according to a dual Weibull in vivo absorption curve, e.g., Concerta® (ALZA Corporation), Ritalin LA® (Novartis AG), Metadate CD® (UCB, Inc), and Quillivant XR® (NextWave Pharmaceuticals, Inc.) (R. Gomeni, F. Bressolle, T.J. Spencer, SV Faraone. Meta-analytic approach to evaluate alternative models for characterizing the PK profiles of extended release formulations of MPH. ASCPT 2016 Annual Meeting, March 8-12, 2016, Hilton Bayfront, San Diego, CA., which is incorporated herein by reference).

[0548] Table 59 shows the values ​​determined for a product, such as Concerta® (ALZA Corporation) (shown by the line labeled "Initial") compared to the calculated "optimized" methylphenidate absorption profile. [Table 59] In the table, 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 the sigmoid coefficient as previously described herein. Table 59 shows that the optimal delivery has a much lower sigmoid coefficient (much shallower) release than observed for these drugs, requiring 10.8 hours after the start of delivery to reach 63.2% of the drug. Figure 59 is a graph reporting exemplary data of the cumulative fraction absorbed for Concerta® (ALZA Corporation), showing that 50% of the drug is absorbed in 4-6 hours, and all of the drug is absorbed by approximately 10-12 hours (indicated by the vertical and horizontal lines intersecting the curve), although much faster than optimal.

[0549] FIG. 60 is a graph reporting exemplary "initial" and "optimized" fractional absorption profiles as determined by R. Gomeni et al. (ASCPT 2016 Annual Meeting, March 8-12, 2016, Hilton Bayfront, San Diego, Calif.), which is incorporated herein by reference). FIG. 60 shows that the optimized 50% release is achieved approximately 7-8 hours after drug absorption begins, with maximum cumulative absorption occurring at approximately 20 hours. Comparing this profile to the fractional absorption profile of HLD200 (FIG. 61) shows that 50% absorption (corrected for a relative bioavailability of 75-80%) occurs approximately 7 hours after drug absorption begins for HLD200, with maximum cumulative absorption occurring approximately 20-24 hours after absorption begins.

[0550] Typical gastrointestinal transit times are as follows: gastric emptying is 90 minutes, small intestinal transit time is 4-6 hours, and colonic arrival time is approximately 8 hours. Thus, products including Concerta® (ALZA Corporation), Ritalin LA® (Novartis AG), Metadate CD® (UCB, Inc.) and Quillivant XR® (NextWave Pharmaceuticals, Inc.) appear to deliver most, if not all, of their product to the small intestine, and all of them exhibit the same absorption mechanism with a nearly linear IVIVC. In contrast, compositions of the present disclosure, such as HLD200, deliver their product approximately 8 hours later and therefore to the colon, and also exhibit nonlinear and time-varying IVIVC. Without being limited by theory, if absorption in the colon is associated with a non-linear IVIVC and the optimal curve requires that 50% absorption occurs after 10 hours, the compositions and methods of the present disclosure may be more effective than other products, such as Concerta® (ALZA In contrast to methylphenidate, which is a methylphenidate that ...

[0551] Figure 64 shows the results for 1MBq or less. 11164 is a graph reporting exemplary cumulative % colon arrival time for surrogate beads radiolabeled with indium. The plot shows results from two separate experiments, shown as "F1" and "F2". Subjects were administered radiolabeled beads and arrival time in the colon was assessed using scintigraphy. Figure 64 shows that all radiolabeled beads in the exemplary formulation are present in the colon at 10 hours.

[0552] Example 46. Mean percent change in methylphenidate plasma concentrations over time FIG. 62 is a graph reporting exemplary data regarding the mean percent change in methylphenidate plasma concentrations (ng / mL / hour) over time for HLD200 54 mg (Study 200-101), HLD200 100 mg (Study 200-109), and CONCERTA® 54 mg.

[0553] Figure 62 shows the rate of change of exemplary HLD200 formulation compared with CONCERTA®.In particular, comparing 54mg of HLD200 with 54mg of CONCERTA®, it shows that 54mg of HLD200 has a maximum rate of change in increasing methylphenidate plasma concentration of about +1.0ng / mL / hour and a maximum rate of change in decreasing methylphenidate plasma concentration of about -0.5ng / mL / hour, whereas 54mg of CONCERTA® has a maximum rate of change in increasing methylphenidate plasma concentration of about +3.6ng / mL / hour and a maximum rate of change in decreasing methylphenidate plasma concentration of about -1.0ng / mL / hour.

[0554] FIG. 62 also shows that 100 mg of HLD200 had a maximum rate of change in increasing methylphenidate plasma concentrations of about +2.5 ng / mL / hr and a maximum rate of change in decreasing methylphenidate plasma concentrations of about -1.2 ng / mL / hr.

[0555] If the CONCERTA® data were normalized to a CONCERTA® dose of 100 mg by multiplying by 100 / 54 = 1.85, then for 100 mg of CONCERTA®, the maximum rate of change in increasing methylphenidate plasma concentrations would be expected to be approximately +3.6 x 1.85 = +6.7 ng / mL / hr, and the maximum rate of change in decreasing methylphenidate plasma concentrations would be expected to be approximately -1.0 x 1.85 = -1.85 ng / mL / hr.

[0556] In addition, the graph in Figure 62 shows the methylphenidate plasma concentration rate increase from the second release of CONCERTA® at 16 hours, where, in contrast, HLD200 provides a smoother change in rate across the profile with fewer peaks.

[0557] Example 47. Comparative Data: IVIVC Model for Amphetamine Formulations. An exemplary delayed release extended release formulation of amphetamine was prepared, which is referred to herein as HLD100-102, and has similar excipient coating and bead physical properties as the HLD200 medium methylphenidate formulation.This exemplary amphetamine formulation has the same film-coated components in the same relative proportions as the medium HLD200 methylphenidate formulation.The amount of coating applied (weight gain) is different, so that the amphetamine formulation has a weight gain of 25% for the ER film-coated and 20% for the DR film-coated.In contrast, the exemplary methylphenidate HLD200 medium formulation has a 20% ER weight gain and a 30% DR weight gain.

[0558] Figure 81 is a graph reporting an exemplary in vitro dissolution plot of an HLD200 mid-range formulation along with an exemplary in vitro dissolution plot of an HLD100-102 amphetamine formulation. Figure 81 shows that the in vitro dissolution profiles of the exemplary HLD100-102 amphetamine formulation and the HLD200 mid-range formulation are very similar.

[0559] The in vitro and in vivo release rates of exemplary amphetamine formulations were obtained following a deconvoluted IVIVC procedure similar to that described above in Examples 23-28.

[0560] The in vitro and in vivo release rates of amphetamine HLD100-102 formulations were correlated to derive an IVIVC model. Figure 63 is a graph reporting exemplary fractions of amphetamine released in vitro vs. in vivo from HLD100-102 formulations.

[0561] As suggested by Figure 63, the IVIVC relationship for this amphetamine formulation is not linear. However, in contrast to the exemplary IVIVC for the methylphenidate formulation described in Example 26, in which a fifth order polynomial function successfully described the relationship between in vitro release and in vivo release, for the exemplary amphetamine formulation, a second order polynomial function successfully described the relationship between in vitro release and in vivo release.

[0562] Thus, when the IVIVC model fitted to the exemplary amphetamine formulation is compared to the IVIVC model fitted to the exemplary methylphenidate formulation, the physical properties of the coatings and beads of the exemplary methylphenidate formulation and the exemplary amphetamine formulation are very similar, indicating that changing the active ingredient can alter the mechanism of release and absorption.

[0563] Since the in vitro dissolution profiles of the exemplary amphetamine HLD100-102 formulation and the exemplary methylphenidate HLD200 mid formulation are very similar as shown in FIG. 81, the IVIVC models indicate that the in vivo absorption characteristics differ between the amphetamine and methylphenidate formulations.

[0564] Example 48. Further IVIVC modeling of HLD200 formulations In this example, further IVIVC analysis of fast, slow and final formulations of HLD200 is described.

[0565] IVIVC was assessed using a convolution-based modeling approach.

[0566] The following method was used:

[0567] Convolution-based model. The plasma concentration (Cp) of HLD200 resulting from any dose was described by convolution as the following equation:

number

[0568] IVIVC Modeling. The analysis presented in this example estimates the fraction of drug absorbed in vivo (r vivo (t)) and the fraction of drug that dissolves (r vitro The present study aims to develop a Level A IVIVC by evaluating the two-point correlation between the in vitro dissolution curve and the in vivo input curve (t). In an IVIVC evaluation, the in vitro dissolution curve and the in vivo input curve may be directly superimposed, or may be made to be superimposed by using a "scaling factor". A time-scaling function was included in the model to account for potential time differences in the in vitro and in vivo processes (e.g., when dissolution is faster than the in vivo input rate). A general time-scaling model was applied in the evaluation of the IVIVC:

number

[0569] Time scaling is r vivo andr vitro If not between:a 1 =0, a 2 =1, b 1 =0, b 2 = 1, and b 3 = 1. If not, the time scaling can be defined by estimating appropriate values ​​for the parameters a1, a2, b1, b2 and b3. Equation 3 is expressed as the linear component (a 1 Intercept of and a 2 slope of the time-shifting coefficient (b 1 ), time scaling factor (b 2 ) and a nonlinear component describing time-shaping (b3).

[0570] Model validation. The final step in the IVIVC analysis is to validate the model by providing quantified evidence of the model's predictive performance. Model validation was achieved using data from the formulations used to build the model (internal validation). Validation was performed using the IVIVC model: relevant exposure parameters (Cmax and AUCinf) were predicted using the model for each formulation and compared to the actual values. Prediction error (%PE) was calculated for each PK parameter using the following formula:

number

[0571] Conducting IVIVC. IVIVC analysis was performed using a six-step approach:

[0572] 1. Match the PK time course of the IR formulation (Step 1);

[0573] 2. Fit the average in vitro dissolution data of the slow, medium and fast formulations individually using a release function defined by the Weibull model (step 2);

[0574] 3. Fit the in vivo PK of the three formulations by fixing the disposition parameters (V) and elimination parameters (kel) estimated from the analysis of the IR formulations (step 3);

[0575] 4. Evaluate the IVIVC by jointly applying the convolution model to the in vivo data of the slow, intermediate and fast release formulations and by (a) fixing the in vivo drug release parameters for each formulation to the values ​​estimated in step (2), (b) estimating a time scaling coefficient common to all formulations, and (c) estimating the relative bioavailability of each formulation (step 4).

[0576] 5. Internal predictability is assessed by comparing the predicted (estimated in step 3) Cmax, AUCinf with the observed values ​​(step 5).

[0577] 6. The external predictability of the final IVIVC model will be assessed by predicting the in vivo performance of formulations not used in the development of the IVIVC model.

[0578] The following data were used in the analysis:

[0579] Immediate release. PK data for the Ritalin® IR formulation at a dose of 20 mg was utilized to derive unit impulse functions. These data were obtained in Study No. HLD200-111 (a phase 1, single-center, single-dose, open-label, randomized, crossover, comparative bioavailability study of HLD200, delayed-release and extended-release capsules of methylphenidate HCl versus the commercial formulation of immediate-release methylphenidate HCl in healthy adult volunteers). In this study, a total of 12 subjects were randomly assigned to two treatment sequence cohorts (HLD200, 100 mg, and Ritalin®, 20 mg) of 6 subjects each in a crossover fashion.

[0580] In Vitro Dissolution. The formulation lots used in the dissolution test were 749A-1811-B003-FAST, 749A-1606-B008-MID, 749A-1747-B006-SLOW. Dissolution tests were performed in 0.1N HCl for 2 hours (T=0-2 hours), then in pH 6.0 phosphate buffer for 4 hours (T=2-6 hours), and finally in pH 7.2 phosphate buffer for the remaining time. See Table 60, Table 61, and Table 62 for details of the dissolution method. Dissolution results for drug release levels below 5% will be inconsistent since the demonstration range of the dissolution method is 5%-130% drug release as required during the dissolution method validation period. Values ​​below 5% may be less than the quantification limit of the method which may result in variability in the results at this low level.

[0581] [Table 60] In the table, a "1" indicates that sampling up to 6 hours may be done manually or by autosampler. Samples after 6 hours were collected by autosampler.

[0582] [Table 61]

[0583] [Table 62]

[0584] In vivo PK. PK data was obtained in the HLD200-111 study. This was a phase 1, single-center, single-dose, open-label, randomized, crossover, comparative bioavailability study of three formulations of delayed / extended release capsules of methylphenidate hydrochloride (HLD200) in healthy adults. A total of 18 subjects were randomly assigned to six treatment sequence cohorts of three subjects each in a crossover fashion (Table 63). The three HLD200 100mg formulations were differentiated by different release rates of the active compound, which were defined as treatment A (HLD200-F, the fastest release profile), treatment B (HLD200-M, the commercial Jornay PM® (Ironshore Pharmaceuticals & Development, Inc.) formulation and reference treatment), and treatment C (HLD-200-S, the slowest release profile). All subjects were dosed under fasting conditions.

[0585] [Table 63]

[0586] External Validation: The formulation lot used in the dissolution test was 749A-1510-B009. The dissolution test was performed following the same methodology set forth above.

[0587] The PK data of study HLD200-109 at 100mg in fasting group was used to evaluate external validation. This study was a phase I single-center clinical study to investigate the pharmacokinetic effects of 100mg HLD200, methylphenidate HCl modified release capsules in healthy adult volunteers in fasted, fed and sprinkled states under a randomized three-way crossover design. A total of 18 subjects were randomized in equal proportions to one of the following six treatment sequences of a single dose of 100mg HLD200 (Table 64): Treatment A=fed, Treatment B=sprinkled (with applesauce), and Treatment C=fasted. Six subjects who discontinued or were discontinued after taking at least one dose of investigational product (IP) were replaced to achieve three evaluable subjects per treatment sequence, thus enrolling a total of 24 subjects in the study.

[0588] [Table 64]

[0589] The following results were observed:

[0590] Step 1-Fit the PK time course of the IR formulation. Individual PK data for the IR formulation were utilized to characterize the disposition and elimination of MPH. Absorption, disposition, and elimination of MPH after taking the IR formulation were best characterized by a one-compartment model with first-order absorption with lag time. Model parameters were 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 the IR data was performed in NONMEM using the ADVAN14 subroutine and FOCE-I method. The estimated population parameter values ​​are shown in Table 65. In this table, SE represents standard error and RSE represents the relative standard error of the estimated parameters.

[0591] [Table 65]

[0592] A visual predictive performance assessment method was utilized to evaluate the suitability of the model to describe the IR data. The basic premise is that the model and parameters derived from the actual data set must result in simulated data similar to the original actual data. 500 replicates of the original data set were simulated based on the final model, and 90% prediction intervals were calculated based on the simulated data set. The actual drug concentration values ​​versus time were plotted in the prediction interval to visually assess the agreement between the simulated and actual data. The visual predictive performance assessment of the population PK modeling for the IR formulation is shown in Figure 65.

[0593] The VPC showed that the model developed to characterize the MPH plasma concentration time course after IR formulation performed well: the distribution of the observed data was well predicted in terms of typical profile (median curve identified by the thick solid line) and inter-individual variability (90% prediction interval identified by the shaded area), indicating that the population model adequately described the observed data.

[0594] Step 2 - Fit the average dissolution data. The in vitro dissolution data of each extended release formulation was described by the following double Weibull model:

number

[0595] A Weibull model (r(t)) was fitted to the average in vitro dissolution data of the slow, medium and fast dissolution formulations. The following parameters were estimated for each formulation: ff (fraction of the available dose released in the first process), td and td1 (time to release 63.2% of the dose in the first and second processes), ss and ss1 (sigmoidality coefficients for the dissolution process in the first and second processes). Analysis of the dissolution data was performed in NONMEM using the FOCE-I method.

[0596] The average dissolution data for each formulation along with the model prediction curves are shown in Figure 66. The estimated parameter values ​​are shown in Table 66. In this table, RSE represents the relative standard error of the estimated parameters.

[0597] [Table 66]

[0598] Step 3 - Fit the in vivo PK of the three formulations using a convolution-based model by fixing the configuration and elimination parameter values ​​estimated from the IR formulation. The objective of this analysis was to provide estimates of the in vivo drug release rates along with estimates of the inter-individual variability of the elimination rate constants for the three formulations. To this end, the convolution model shown in Figure 67 was used to fit the data of the three formulations together using a non-linear mixed-effects approach. The in vivo drug release was estimated by fixing the mean values ​​of the volume (V) and the mean values ​​of the elimination rate (kel) to the values ​​estimated in the analysis of the IR data.

[0599] The PK time course of in vivo release of different formulations was assumed to be characterized by two phases of drug release, a first phase for the initial release of a portion of the dose, and a second phase to provide a prolonged release of the dose. The r(t) function for MPH was modeled using a double Weibull function. In this model, ff is the fraction of the available dose released in the first process, td and td1 are the times to release 63.2% of the dose in the first and second processes, and ss and ss1 are the sigmoidality coefficients for the first and second processes.

[0600] The estimated parameter values ​​are shown in Table 67. In this table, SE stands for standard error and RSE stands for relative standard error of the estimated parameters.

[0601] [Table 67]

[0602] The mean observed PK values ​​for each formulation are shown in Figure 68 along with the model predicted curves (top panel) and in vivo release rates (bottom panel).

[0603] Step 4 - Evaluate IVIVC using a convolution modeling approach. The convolution model used for evaluation of the IVIVC relationship is shown in Figure 67. The data of the three formulations were fitted together. The following parameters were fixed to values ​​estimated in previous analyses: mean kel, and V estimated from analysis of IR data (step 1), inter-individual variability in kel estimated for analysis performed in step 3, dissolution data estimated in step 2 (ff, td, ss, td1 and ss1 for the three formulations). The convolution model was implemented in NONMEM using the ADVAN13 subroutine and the FOCE-I method.

[0604] The data for the three formulations (slow, medium and fast) were fitted together 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_Med and F_Fast) and additive residual error (err). The f(t) function was estimated using a finite difference approach. The F_parameter represents the dose scaling value with respect to the reference IR formulation. The rationale for using this parameter is to allow for any differences that may exist in bioavailability between the reference formulation and the slow, medium and fast release formulations.

[0605] The time scaling parameters (a1, a2, b1, b2, and b3) were estimated as fixed effect parameters (without random effects) because these parameters were expected to take the same values ​​for all formulations. No random effects were associated with any of the parameters in the convolution model.

[0606] The estimated parameter values ​​are shown in Table 68. The mean observed and model predicted MPH in vivo concentrations for the slow, medium and fast release rate formulations are shown in Figure 69.

[0607] [Table 68]

[0608] The results of the analysis showed that the relative bioavailability of the slow, intermediate and fast release formulations to the IR formulation was 0.88%, 0.48% and 0.24%, respectively.

[0609] Analyses were performed to assess the correlation between the in vitro release and the in vivo release obtained from the IVIVC analysis, including time scaling correction.

[0610] The regression analysis is shown in Figure 70 and the results of the regression analysis are shown in Table 69. The analysis showed that a statistically significant correlation was found between the in vitro dissolution data and the in vivo absorption data (p<0.001).

[0611] [Table 69]

[0612] Step 5. Internal validation.

[0613] a) Correlation analysis between observed PK and predicted PK from IVIVC analysis.

[0614] The in vitro dissolution data and the convolution model were used to predict the in vivo PK profiles of the three formulations. The convolution model predictions were in excellent agreement with the observed concentrations. Figure 71 shows a plot of predicted versus observed concentrations with the regression line.

[0615] The results of the regression analysis are shown in Table 70. The analysis indicated that the intercept was not statistically different from 0 and the slope was not statistically different from 1, as the 95% confidence limits included 0 for the intercept and 1 for the slope.

[0616] [Table 70]

[0617] b) Internal predictability assessment.

[0618] Area under the plasma concentration-time curve (AUC) values ​​between 0 and the last measurable concentration were estimated using the log-linear trapezoidal rule for the observed and model-predicted data. Prediction errors (%PE) were estimated using the AUC values ​​and the Cmax values ​​for the observed and model-predicted concentrations. The estimated %PE values ​​are shown in Table 71. The maximum %PE (=11.05%) was estimated for the AUC of the rapid formulation. The average %PE across the three formulations was 6.0% and 4.31% for AUC and Cmax, respectively.

[0619] [Table 71]

[0620] Step 6 - External validation.

[0621] a) Refined IVIVC model.

[0622] The IVIVC model defined above, the disposition parameter value (V / F=2.11) and elimination parameter value (kel=0.256) were fixed to the values ​​estimated in step 3. In addition, the interformulation variability for V / F was fixed to 0, and the interformulation variability for kel was fixed to 0.438 (step 3). This model was adequate to support internal validation. However, this model was not adequate to predict the in vivo PK of formulations different from those used in the model development. This is because no rules were established for estimating in vivo relative bioavailability (BI) and kel values ​​from in vitro dissolution data. To overcome this limitation, the initial IVIVC model was refined by including submodels describing the relationships between in vitro properties, in vivo relative bioavailability, and in vivo kel. As shown in Figure 72, polynomial relationships were empirically identified between the in vitro estimated TD parameters (time required to dissolve 63.2% of the dose in the first release process) and the in vivo estimated values ​​of relative bioavailability (BI) and Kel (F_Slow, F_Med, and F_Fast) of each formulation. When the TD parameters of the new formulations took values ​​in the range of TD values ​​of the formulations used in the IVIVC model development, i.e., in the range of 818-20.8 (hr), these models were suitable for estimating by interpolation the values ​​of BI and Kel of the new formulations used in the external validation.

[0623] The refined convolution model including the dependence between dissolution properties and estimated in vivo relative BI and kel is shown in FIG.

[0624] The estimated parameter values ​​are shown in Table 72. The mean observed and model predicted MPH in vivo concentrations for the slow, medium and fast release rate formulations are shown in Figure 74.

[0625] [Table 72]

[0626] Model validation of the refined IVIVC model.

[0627] Correlation analysis between observed PK and predicted PK from IVIVC analysis.

[0628] The in vitro dissolution data and the convolution model were used to predict the in vivo PK profiles of the three formulations. The convolution model predictions were in excellent agreement with the observed concentrations. Figure 75 shows a plot of predicted versus observed concentrations with the regression line.

[0629] The results of the regression analysis are shown in Table 73. The analysis showed that the intercept was not statistically different from 0 and the slope was not statistically different from 1, as the 95% confidence limits included 0 for the intercept and 1 for the slope.

[0630] [Table 73]

[0631] Predictability Assessment

[0632] Area under the plasma concentration-time curve (AUC) values ​​between 0 and the last measurable concentration were estimated using the log-linear trapezoidal rule for the observed and model-predicted data. Prediction errors (%PE) were estimated using the AUC values ​​and the Cmax values ​​for the observed and model-predicted concentrations. The estimated %PE values ​​are shown in Table 74. The maximum %PE (=11.08%) was estimated for the AUC of the slow formulation. The average %PE across the three formulations was 5.93% and 4.34% for AUC and Cmax, respectively.

[0633] [Table 74]

[0634] Dissolution Data

[0635] In vitro dissolution data formulations were described by the same Weibull model applied previously.

[0636] The mean dissolution data along with the model prediction curves are shown in Figure 76. The estimated parameter values ​​are shown in Table 75. In this table, RSE stands for the relative standard error of the estimated parameters.

[0637] [Table 75]

[0638] In vivo PK used in external validation

[0639] PK data from study HLD200-109, collected in 18 subjects at 100 mg in the fasting group, were used to assess the external validation.

[0640] FIG. 77 shows an exemplary comparison of the mean concentration time courses for the median dissolution rate formulations in study HLD200-111 and the PK concentration time courses in study HLD200-109 (fasted group).

[0641] The dissolution data for the formulation with the median dissolution rate in study HLD200-111 and the dissolution data for the formulation used in study HLD200-109 (fasted group) showed nearly superimposable profiles (Figure 76), however the mean in vivo Cmax in the HLD200-109 study was approximately 30% lower than that observed in the HLD200-111 study.

[0642] A population PK modeling approach was used to characterize the PK data using the same model described above, i.e., a one-compartment model with time-varying absorption described by a double Weibull function and a first-order elimination rate constant.

[0643] The estimated parameter values ​​are shown in Table 76. In this table, SE stands for standard error and RSE stands for relative standard error of the estimated parameters.

[0644] [Table 76]

[0645] Five hundred replicates of the original data set were simulated based on the final model, and 90% prediction intervals were calculated based on the simulated data set. The observed drug concentration values ​​versus time were plotted in the prediction intervals to visually assess the agreement between the simulated and observed data. A visual prediction performance assessment of the population PK modeling is shown in Figure 78.

[0646] The VPC showed that the model developed to characterize the MPH plasma concentration time course in study HLD200-109 performed well: the distribution of the observed data was well predicted in terms of typical profile (median curve identified by the thick solid line) and inter-individual variability (90% prediction interval identified by the grey shaded area), indicating that the population model adequately described the observed data.

[0647] External predictability assessment

[0648] The expected typical profile of in vivo PK in Study HLD200-109 was predicted using the refined IVIVC model (Table 76) together with the dissolution release parameters of the formulation used in the external validation (Table 75). In this model, the in vivo PK data was estimated by calculating the model results when all parameter values ​​were fixed to the values ​​shown in Table 77.

[0649] [Table 77]

[0650] FIG. 79 shows a comparison of typical PK time courses (solid curves) from study HLD200-109 with estimates based on convolution of the predicted PK profiles (dashed curves).

[0651] The in vitro dissolution data and the convolution model were used to predict the in vivo PK profile of the formulation used in the external validation. Figure 80 shows a plot of predicted versus observed concentrations with the regression line.

[0652] The results of the regression analysis are shown in Table 78. The analysis indicated that the intercept was not statistically different from 0 and the slope was not statistically different from 1, as the 95% confidence limits included 0 for the intercept and 1 for the slope.

[0653] [Table 78]

[0654] External validation was assessed by comparing the area under the plasma concentration-time curve (AUC) and Cmax values ​​of observed and model-predicted data.

[0655] The estimated %PE values ​​are shown in Table 79.

[0656] [Table 79]

[0657] The predicted AUC values ​​were characterized by a %PE of less than 10%, while the %PE for Cmax was about 24%. This value was consistent with a comparison of the mean PK concentration time courses for the median dissolution rate formulation in study HLD200-111 with the PK concentration time courses in study HLD200-109 (fasted group) as shown in Figure 15. However, this value exceeded the maximum threshold of 10% established by the FDA to meet the criteria for external predictability.

[0658] In summary, the IVIVC analysis described in Example 48 was performed using a convolution-based modeling approach.

[0659] Convolution-based methods for assessing IVIVC have been shown to offer several benefits with respect to traditional assessment of IVIVC using convolution and deconvolution methodologies (Buchwald P(2003)Direct differential-equation-based in vitro-in vivo correlation(IVIVC)method. J Pharm Pharmacol 55:495-504; Gaynor C, Dunne A, Costello C, Davis JA population approach to in vitro-in vivo correlation modelling for compounds with nonlinear kinetics. J Pharmacokinet Pharmacodyn.2011 Jun;38(3):317-32).

[0660] For example, using convolution-based methods, a global IVIVC assessment can be performed using a set of differential equations that are directly integrated without the need to apply convolution or deconvolution procedures.

[0661] In this framework, various functional dependencies (e.g., time scaling) can be easily introduced to describe or couple the dissolution and absorption profiles, which can lead to improved performance and increased modeling flexibility, since no specific software tools are required apart from standard software for modeling (e.g., NONMEM, etc.).

[0662] Furthermore, traditional methods based on deconvolution and convolution techniques require the assumption of linearity of the system being studied and are therefore not suitable for use with compounds that exhibit nonlinear dynamics. Unlike this limitation, convolution-based techniques can easily accommodate potential nonlinearities in the pharmacokinetic process by integrating a description of the potential nonlinearities in the differential equations used to define the IVIVC model (Gaynor et al., 2011).

[0663] An IVIVC model was developed using in vitro dissolution and in vivo PK of three extended release formulations (fast, intermediate and slow). The disposition of MPH was characterized using data from the IR formulation.

[0664] The in vitro dissolution data were able to predict with high accuracy the in vivo PK time course for each formulation evaluated.

[0665] Comparison of the refined IVIVC model predictions with the measured concentrations indicated the presence of a strong correlation between the two measurements: the regression line was characterized by a zero intercept and a unit slope.

[0666] The mean prediction errors for AUC and Cmax for the slow, intermediate, and fast release formulations were 5.93% and 4.34%, respectively, for AUC and Cmax, which are below the FDA suggested 10%. Furthermore, the prediction errors for each of these formulations were all below the permitted 15%. These results support a Level A IVIVC correlation.

[0667] External predictability was assessed using the refined IVIVC model to predict the in vivo performance of formulations that were not used in developing the IVIVC model.

[0668] The predicted AUC values ​​were characterized by a %PE of less than 10%, while the %PE for Cmax was about 24%. This value was consistent with a comparison of the mean PK concentration time courses for the median dissolution rate formulation in study HLD200-111 with the PK concentration time courses in study HLD200-109 (fasted group) as shown in Figure 15. However, this value exceeded the maximum threshold of 10% established by the FDA to meet the criteria for external predictability.

[0669] Overall, a successful IVIVC model was developed and validated according to internal predictability criteria as recommended by the FDA in its Guidance to the Industry Extended Release Oral Dosage Forms: Development, Evaluation, and Application of In Vitro / In Vivo Correlations.

[0670] Example 49. Polynomial curve fitting for slow, medium and fast methylphenidate formulations. Individual pharmacokinetic data for the immediate release methylphenidate formulations were utilized to characterize the absorption, disposition, and excretion of methylphenidate.

[0671] The Weibull model described above was fitted to the mean in vitro dissolution data for the slow, moderate and fast dissolution methylphenidate formulations.

[0672] In vivo drug release rates were estimated along with estimates of interindividual variability of elimination rate constants for these three formulations. A convolution model was fitted to the data for these three formulations using a nonlinear mixed effects approach.

[0673] In vivo drug release was estimated by fixing the mean volume (V) and mean excretion rate (kel) to those estimated from the analysis of the immediate release formulation data.

[0674] Analyses were performed to assess the correlation between the in vitro release and the in vivo release obtained from the IVIVC analysis, including time scaling correction, as described in Example 48.

[0675] Graphs reporting the regression analyses for the fast, moderate and slow release MPH formulations are shown in Figure 82, Figure 83 and Figure 84, and the results of the respective regression analyses are shown in Tables 80, 81 and 82. The analysis showed that a statistically significant correlation was found between the in vitro dissolution and in vivo absorption data (p<0.001). [Table 80]

[0676] Table 80 shows that the rapid formulations show a statistically significant correlation (p<0.0001) between in vitro and in vivo absorption using a linear model. [Table 81]

[0677] Table 80 shows that the mid-range formulations show a statistically significant correlation (p<0.0001) between in vitro and in vivo absorption using a third order polynomial model. [Table 82]

[0678] Table 82 shows that the slow formulations show a statistically significant correlation (p<0.0001) between in vitro and in vivo absorption using a fifth order polynomial model.

[0679] Methylphenidate HLD200 fast formulation has the same composition and release mechanism as methylphenidate HLD200 medium formulation and slow formulation, but only differs in the % weight increase of ER layer.However, methylphenidate HLD200 fast formulation releases methylphenidate faster, and therefore more proximal in upper GI tract, compared with methylphenidate HLD200 medium formulation and slow formulation, which releases methylphenidate slower, and therefore more distal, i.e., in colon.Therefore, methylphenidate fast formulation shows similar IVIVC to Concerta (registered trademark) and other products.Methylphenidate HLD200 medium formulation and slow formulation show nonlinear (high order polynomial) IVIVC, which may be due to the site of release and absorption. Furthermore, the further into the colon release begins, the more the correlation deviates from linearity, such that the methylphenidate HLD200 slow formulation exhibits an IVIVC that fits a higher order polynomial than the methylphenidate HLD200 medium formulation.

[0680] Example 50: In Vitro / In Vivo Correlation (IVIVC) Model for HLD200, a Nighttime Delayed- and Extended-Release Methylphenidate This example includes information related to sigmoidal Emax modeling, which may be used as an alternative to Weibull modeling, but which results in the same clinical indications. Thus, the sigmoidal Emax modeling discussed in this example, and the data presented with respect to this modeling, as well as any conclusions regarding that data, may be combined with all other aspects of this disclosure, either in combination with or as an alternative to Weibull modeling or information and conclusions arising therefrom, unless expressly excluded or non-functional.

[0681] HLD200 is a first evening dose delayed and extended release formulation of methylphenidate (DR / ER-MPH) specifically designed to delay the initial release of MPH and provide a clinically meaningful treatment effect beginning upon awakening and continuing into the night.

[0682] In some embodiments, the pharmacokinetic (PK) profile of DR / ER-MPH is characterized by an 8-10 hour delay in initial MPH release, followed by a period of prolonged controlled release with absorption peaking at approximately 14 hours post-dose, and 50% or greater drug exposure occurring after peak plasma concentrations are reached (Liu T et al., J Child Adolesc Psychopharmacol. 2019;29(3):181-191, which is incorporated herein by reference). In two pivotal Phase 3 studies in children (ages 6-12) with Attention Deficit / Hyperactivity Disorder (ADHD), DR / ER-MPH demonstrated significant improvements versus placebo in ADHD symptom suppression and functional impairment during the early morning period, throughout the day, and into the late evening / night (Childress AC et al., J Child Adolesc Psychopharmacol. 2019 Aug 29. doi:10.1089 / cap.2019.0070. Epub ahead of print; and Pliszka SR et al., J Child Adolesc Psychopharmacol. 2017;27(6):474-482, both of which are incorporated herein by reference.) The United States Food and Drug Administration (FDA) has encouraged the detailed description of in vitro / in vivo correlations (IVIVCs) for extended release drug products, i.e., the prediction of in vivo plasma concentrations over time from in vitro dissolution profiles (USDepartment of Health and Human Services, Food and Drug Administration Center for Drug Evaluation and Research (CDER). Guidance for Industry. Extended Release Oral Dosage Forms: Development, Evaluation, and Application of In Vitro / In Vivo Correlations (September 1997), which is incorporated herein by reference). IVIVC can be useful in establishing dissolution specifications and allows some formulation and manufacturing changes to be permitted without in vivo bioequivalence studies. Level A correlation, the most informative type of IVIVC model, describes the relationship between the entire in vitro dissolution time course and the time course of plasma drug concentration (a two-point relationship).

[0683] The aim of this study was to establish and validate a level A IVIVC for DR / ER-MPH.

[0684] The following methods were used in this example.

[0685] Data Sources. In vitro MPH dissolution rates were measured in three formulations of DR / ER-MPH (slow, intermediate and fast) in three environments (conditions mimicking oral administration) using United States Pharmacopeia (USP) apparatus type 1. In vivo plasma MPH concentrations were measured in a Phase 1, single-center, single-dose, open-label, randomized, three-way crossover, comparative bioavailability study of the three DR / ER-MPH formulations in 18 healthy adults (Ironshore Pharmaceuticals & Development, Inc. internal data: HLD200-111, which is incorporated herein by reference). 100 mg of DR / ER-MPH was administered at 9:00 pm, approximately 3 hours after a standard low-fat meal. Blood samples were collected up to 48 hours after dosing. A 96-hour washout period was allowed between each dose. PK data from immediate release MPH (IR MPH) was used to characterize MPH disposition and elimination. Plasma levels were measured in a previously described phase 1, single-center, single-dose, open-label, randomized, crossover study in 12 healthy adults (Liu T et al., J Child Adolesc Psychopharmacol. 2019;29(3):181-191, which is incorporated herein by reference).

[0686] IVIVC model development.

[0687] Level A IVIVC, i.e., the fraction of in vitro dissolved MPH [r vitro (t)] (Eq. 9) and the fraction of MPH absorbed in vivo [r vivo (t)] (Eq. 14) was developed and evaluated. The IVIVC model was developed and evaluated by a convolution-based modeling approach (Gomeni R et al., Pharmacometrics Syst Pharmacol. 2019;8(2):97-106, which is incorporated herein by reference) using the following steps:

[0688] Step 1. Fit the PK time course of the IR MPH formulation: To characterize the absorption, disposition, and elimination of IR MPH, the PK was best described by a one-compartment model with first-order absorption with a lag time. The model parameters were 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).

[0689] Step 2. Individually fit the mean in vitro dissolution data: The in vitro dissolution data for the slow, medium and fast formulations of DR / ER-MPH were described by the following sigmoidal Emax model:

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[0690] where EC is the time to release 50% of the dose and g a is a parameter that characterizes the shape of the absorption curve.

[0691] Step 3. Fit the in vivo PK data: To estimate the in vivo release rates and inter-individual variability of the elimination rate constants, a convolution model was used to fit the data of the three formulations together using a non-linear mixed effects approach. The in vivo PK data were fitted by fixing the mean values ​​of the volume (V) and the mean elimination rate (kel) to the values ​​estimated in the analysis of the IR MPH data. It was assumed that the PK time course was characterized by a sigmoidal Emax model. The DR / ER-MPH plasma concentration (Cp) resulting from any dose was described by convolution as the following equation (see Figure 85):

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[0692] FIG. 85 is an exemplary schematic diagram describing a refined IVIVC model including the dependence between dissolution properties and estimated relative in vivo bioavailability.

[0693] Step 4. Evaluate the IVIVC using a convolution modeling approach by:

[0694] a. Fixing the in vivo drug release parameters for each DR / ER-MPH formulation to the following values ​​estimated in previous analyses: (i) mean kel and V estimated from analysis of IR MPH data (Step 1); (ii) estimated in vitro dissolution data (EC and ga) for the three formulations (Step 2); (iii) inter-individual variability of kel and V estimated from analysis of in vivo PK data (Step 3).

[0695] b. Estimating time scaling coefficients (fixed effects) common to all formulations: (i) A time scaling function was included in the model to account for potential time differences in in vitro and in vivo processes (e.g., when dissolution times are faster than in vivo input rates) (Gomeni R et al., Pharmacometrics Syst Pharmacol. 2019;8(2):97-106, which is incorporated herein by reference):

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[0696] The above formula is the linear component (a 1 Intercept of and a 2 ) and the time shifting coefficient (b 1 ), time scaling factor (b 2 ) and time shaping (b 3 ) and a nonlinear component describing the time scaling r vitro andr vivo If not between:a 1 =0, a 2 =1, b 1 =0, b 2 = 1, and b 3 = 1; otherwise, the time scaling was defined by estimating appropriate values ​​of the parameters.

[0697] c. Estimating the fraction of the administered dose available to the systemic circulation (Fslow, Fmed, and Ffast) and the additive residual error: these parameters represent dose scaling values ​​with respect to the reference IR MPH formulation; this was done to account for any differences that may exist in bioavailability between the IR MPH formulation and the DR / ER-MPH formulation.

[0698] Step 5. Internal validation: The in vitro dissolution data and the convolution model were used to predict the in vivo PK profiles of the three formulations. Peak plasma concentrations (Cmax) and areas under the plasma concentration-time curve between zero and infinity (AUC) were predicted using the model for each formulation and compared to the average observed values. AUC values ​​were estimated using the log-linear trapezoidal rule with extrapolation to infinity using the last measurable concentration and elimination rate constants for the observed and model-predicted data. Prediction errors (%PE) were calculated for each PK parameter using the following formula, where n is the number of formulations (Table 84):

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[0699] The criteria for assessing the level of predictability for each PK parameter were a mean %PE of 10% or less and no individual values ​​greater than 15% (USDepartment of Health and Human Services, Food and Drug Administration Center for Drug Evaluation and Research (CDER). Guidance for Industry. Extended Release Oral Dosage Forms: Development, Evaluation, and Application of In Vitro / In Vivo Correlations (September 1997), which is incorporated herein by reference).

[0700] Step 6. External Validation: External validation of the IVIVC model was performed using data from a previously described Phase 1, single-center, open-label study of 18 healthy adult volunteers receiving 100 mg DR / ER-MPH (Liu T et al., J Child Adolesc Psychopharmacol. 2019;29(3):181-191, which is incorporated herein by reference). In vitro dissolution data of the formulations used in the study were described by the same sigmoidal Emax model previously applied. Expected typical profiles of in vivo PK were predicted using IVIVC together with the dissolution release parameters from the study. Equation 16 was used to calculate the %PE from observed and predicted values ​​of Cmax and AUC.

[0701] Software. All analyses were performed in NONMEM® (Regents of the University of California Corporation) using the first-order conditional estimation with interactions (FOCE-I) method. Population PK analysis of IR MPH data was performed using the ADVAN14 subroutine. Convolution models were performed using the ADVAN6 subroutine.

[0702] The following results were obtained in this example:

[0703] Figure 86A is a graph reporting exemplary values ​​for plasma MPH concentration versus time (hours) that allow for visual predictive performance assessment (Step 1) for the immediate release methylphenidate (IR MPH) model. The dark solid line indicates the median curve, the shaded area indicates the 90% prediction interval, and the dots indicate individual participant data.

[0704] Step 1 is the excretion of MPH (k el , rate of excretion) and disposition (V, volume of distribution) values ​​were obtained.

[0705] Figure 86B is a graph reporting exemplary values ​​for the fraction (%) of in vitro released dose versus time (hours) for model-predicted in vitro delayed-release and extended-release methylphenidate (DR / ER-MPH) dissolution profile (step 2).The dots represent average dissolution values, and the solid line represents the model-predicted profile.

[0706] Step 2 yielded in vitro dissolution values ​​including EC (time to release 50% of the dose) and g a (a parameter that characterizes the shape of the curve).

[0707] Figure 86C is a graph reporting exemplary values ​​for the median model predicted PK curve (Step 3) of DR / ER-MPH.

[0708] Step 3 is k el and yielded values ​​of interindividual variation of V.

[0709] Figure 86D is a graph reporting exemplary values ​​for mean plasma MPH concentrations predicted by the convolution model (Step 4). The dots represent the mean observed PK concentrations and the solid line represents the model-predicted PK profile.

[0710] Evaluation and validation of the IVIVC model

[0711] In the IVIVC model, all three DR / ER-MPH formulations exhibited monophasic plasma time concentration profiles, with the fast formulation exhibiting a shorter delay before the initial MPH release, a larger Cmax, a faster time to peak absorption (Tmax), and a greater relative bioavailability compared to the medium formulation (Figure 86D). The same trends were seen between the medium and slow formulations.

[0712] The relative bioavailability of the rapid, intermediate, and slow DR / ER-MPH formulations with respect to the IR MPH formulation was 100%, 80%, and 32%, respectively.

[0713] Regression analysis, including time scaling correction, revealed a statistically significant correlation (P<0.001) between observed plasma MPH concentrations and convolution-predicted concentrations (predicted from in vitro dissolution data and the convolution model) (Figure 87). In Figure 87, dark solid line, regression analysis; points, individual participant data; shaded areas, 95% confidence intervals; dashed lines, 95% prediction intervals.

[0714] The parameter estimates associated with Figure 87 were as shown in Table 83.

[0715] [Table 83]

[0716] In Table 83, "DF" is the degree of freedom, "MPH" is methylphenidate, and "Pr" is probability.

[0717] The established IVIVC model was able to predict the in vivo PK from the in vitro dissolution data with high accuracy for each formulation evaluated; the mean %PE for the slow, medium and fast formulations was 6.07% for Cmax and 1.64% for AUC (Table 84).

[0718] [Table 84]

[0719] In Table 84, " a " indicates the data source of the actual value, i.e., the LD200-111 clinical study report (Ironshore Pharmaceuticals & Development, Inc. internal data: HLD200-111), "AUC" indicates the area under the plasma concentration-time curve between 0 and infinity, "Cmax" indicates the peak plasma concentration, and "PE" indicates the prediction error.

[0720] The mean %PE for both AUC and Cmax was less than 10% and the %PE for each formulation was less than 15% (see Table 84), which met the FDA criteria for a successful IVIVC (USDepartment of Health and Human Services, Food and Drug Administration Center for Drug Evaluation and Research (CDER). Guidance for Industry. Extended Release Oral Dosage Forms: Development, Evaluation, and Application of In Vitro Drugs). Vitro / In Vivo Correlations (September 1997), which is incorporated herein by reference.

[0721] The estimated %PE for the external validation data was 0.79% for Cmax and 9.85% for AUC, demonstrating the external predictability of IVIVC (USDepartment of Health and Human Services, Food and Drug Administration Center for Drug Evaluation and Research (CDER). Guidance for Industry. Extended Release Oral Dosage Forms: Development, Evaluation, and Application of In Vitro / In Vivo Correlations (September 1997), which is incorporated herein by reference) (see Table 85).

[0722] [Table 85]

[0723] In Table 85, " a " indicates the source of the actual value (Liu T et al., J Child Adolesc Psychopharmacol. 2019;29(3):181-191, which is incorporated herein by reference), "AUC" indicates the area under the plasma concentration-time curve between 0 and infinity, "Cmax" indicates the peak plasma concentration, and "PE" indicates the prediction error.

[0724] In this example, a convolution-based Level A IVIVC model for DR / ER-MPH was developed and validated. In vitro dissolution data could predict the in vivo PK time course with high accuracy. The AUC and C for slow, medium, and fast formulations of DR / ER-MPH were max were less than 10% and 15% for the respective formulations, which met the FDA criteria for a successful Level A IVIVC correlation. IVIVC also met the FDA criteria for external predictability.

[0725] Example 51. Evaluation of IVIVC using a convolution modeling approach. This example further describes the assessment of IVIVC (Step 4 described in Example 50) using a convolutional modeling approach.

[0726] The convolution model used for the evaluation of the IVIVC relationship is shown in Figure 90. The convolution model shown in Figure 90 was used to fit the data of the three formulations independently using a non-linear mixed effects approach. In vivo drug release was estimated by fixing the mean values ​​of the volume (V) and the mean values ​​of the excretion rate (kel) to those estimated by analysis of the IR data. It was assumed that the PK time course of in vivo release of the different formulations was characterized by a sigmoidal Emax model.

[0727] The average data of the three formulations were fitted together. The following parameters were fixed to values ​​estimated in previous analyses: mean kel, and V (estimated from analysis of IR data in step 1), and variance (inter-individual variability) of kel and V (estimated in step 3), dissolution data estimated in step 2 (EC and GA for the three formulations). The convolution model was implemented in NONMEM using the ADVAN6 subroutine and the FOCE-I method.

[0728] The average data of the three formulations (slow, medium and fast) were fitted together 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_Med and F_Fast) and additive residual error (err). The f(t) function was estimated using a finite difference approach. The F_parameter represents the dose scaling value with respect to the reference IR formulation. The rationale for using this parameter is to allow for any differences that may exist in bioavailability between the reference formulation and the slow, medium and fast release formulations.

[0729] The time scaling parameters (a1, a2, b1, b2, and b3) were estimated as fixed effect parameters (without random effects) because these parameters were expected to take the same values ​​for all formulations. No random effects were associated with any of the parameters in the convolution model.

[0730] The estimated parameter values ​​are shown in Table 86.

[0731] [Table 86]

[0732] The mean observed and model predicted MPH in vivo concentrations for the slow, medium and fast release rate formulations are shown in Figure 86D.

[0733] The results of the analysis showed that the relative bioavailability of the fast, intermediate and slow release formulations to the IR formulation was 100%, 79.8% and 32.2%, respectively.

[0734] Analyses were performed to assess the correlation between the in vitro release and the in vivo release obtained from the IVIVC analysis, including time scaling correction.

[0735] The regression analysis is shown in Figure 87 and the results of the regression analysis are shown in Table 83. The analysis showed that a statistically significant correlation was found between the in vitro dissolution data and the in vivo absorption data (p<0.001).

[0736] Example 52. Comparison of models considered to describe the in vitro dissolution data of each formulation. Three different models have been considered to describe the in vitro dissolution data of the formulations, respectively. Table 87 shows the functional equations associated with these three models.

[0737] [Table 87]

[0738] Comparison of alternative models was performed using the log-likelihood ratio test for nested models or the Akaike Information Criterion (AIC) for unnested models. For nested models, alternative models were selected if the reduction in objective function value (OFV) associated with this model was ≥ 3.84 (χ for 1 degree of freedom (df)). 2 <0.05) was considered to be a significantly better descriptor of the data. For unnested models, the model with the lower AIC value was considered the preferred model. The AIC criterion was defined as AIC=-2LL+2*n p In the formula, n p is the total number of parameters in the model. Of the two models, the most informative one will be the one with the lowest AIC value.

[0739] A comparison of the performance of the single Weibull model and the double Weibull model was made using the log-likelihood ratio test because these two models were nested models (Table 88). A comparison of the performance of the double Weibull model and the sigmoid Emax model was made using the AIC criterion because these two models were not nested models (Table 89).

[0740] [Table 88]

[0741] [Table 89]

[0742] Comparing model performance, it was shown that the double Weibull model performed better than the single Weibull model, but the sigmoid Emax model performed better than the double Weibull model.Therefore, the sigmoid Emax model was retained as the preferred model.Figure 88 shows the measured dissolution data and model predicted dissolution data obtained from the analysis using the single Weibull model and the double Weibull model.Figure 89 shows the measured dissolution data and model predicted dissolution data obtained from the analysis using the sigmoid Emax model and the double Weibull model.

[0743] Example 53. Use of scaling parameters as sub-models to fit a single model for slow, medium and fast release formulations. This example relates to the use of scaling parameters as sub-models to fit a single model to three formulations (slow release formulation, intermediate release formulation and fast release formulation) that exhibit different PK profiles, e.g., different bioavailability and / or absorption sites.

[0744] Plots of fraction absorbed versus fraction dissolved and Levy plots (Tvivo vs. Tvitro) for slow, intermediate and fast release formulations are shown in Figures 91, 92 and 93, respectively.

[0745] For example, the Levy plots shown in Figures 91, 92 and 93 provide further evidence of the non-linearity of colonic absorption: fast release formulations (not absorbed in the colon) have an even distribution of points above and below the line of the Levy plot, whereas for the medium and slow formulations, which are absorbed more distally, the majority of the points fall below the line. **********

[0746] The subject matter disclosed above should be considered illustrative and not restrictive, and the appended claims are intended to encompass all such modifications, enhancements, and other implementations that fall within the true spirit and scope of the present disclosure. Accordingly, to the maximum extent permitted by law, the scope of the present disclosure should be determined by the broadest permissible interpretation of the following claims and their equivalents, and should not be limited or restricted by the foregoing detailed description.

[0747] 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 "plural" includes two or more referents unless the content 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. The present invention provides, for example, the following items. (Item 1) Methylphenidate or its pharmaceutical salt 1. A solid oral pharmaceutical composition comprising: The in vivo absorption model of the solid oral pharmaceutical composition comprises: (i) Single Weibull function:

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Claims

[Claim 1] The invention as depicted in the drawings.

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