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Model‐Based Approach for Optimizing Study Design and Clinical Drug Performances of Extended‐Release Formulations of Methylphenidate for the Treatment of ADHD
Author(s) -
Gomeni R,
BressolleGomeni FMM,
Spencer TJ,
Faraone SV,
Fang L,
Babiskin A
Publication year - 2017
Publication title -
clinical pharmacology and therapeutics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.941
H-Index - 188
eISSN - 1532-6535
pISSN - 0009-9236
DOI - 10.1002/cpt.684
Subject(s) - methylphenidate , bioequivalence , pharmacology , immediate release , convolution (computer science) , attention deficit hyperactivity disorder , drug , in vivo , pharmacokinetics , computer science , medicine , machine learning , clinical psychology , microbiology and biotechnology , biology , artificial neural network
Methylphenidate (MPH) is currently used to treat children with attention deficit hyperactivity disorder (ADHD). Several extended‐release (ER) formulations characterized by a dual release process were developed to improve efficacy over an extended duration. In this study, a model‐based approach using literature data was developed to: 1) evaluate the most efficient pharmacokinetic (PK) model to characterize the complex PK profile of MPH ER formulations; 2) provide PK endpoint metrics for comparing ER formulations; 3) define criteria for optimizing development of ER formulations using a convolution‐based model linking in vitro release, in vivo release, and hour‐by‐hour behavioral ratings of ADHD symptoms; and 4) define an optimized trial design for assessing the activity of MPH in pediatric populations. The convolution‐based model accurately described the complex PK profiles of a variety of ER MPH products, providing a natural framework for establishing an in vitro / in vivo correlation and for defining criteria for assessing comparative bioequivalence of MPH ER products.