Literature DB >> 24060671

Quantitative prediction of formulation-specific food effects and their population variability from in vitro data with the physiologically-based ADAM model: a case study using the BCS/BDDCS Class II drug nifedipine.

Nikunjkumar Patel1, Sebastian Polak2, Masoud Jamei3, Amin Rostami-Hodjegan4, David B Turner3.   

Abstract

Quantitative prediction of food effects (FE) upon drug pharmacokinetics, including population variability, in advance of human trials may help with trial design by optimising the number of subjects and sampling times when a clinical study is warranted or by negating the need for conduct of clinical studies. Classification and rule-based systems such as the BCS and BDDCS and statistical QSARs are widely used to anticipate the nature of FE in early drug development. However, their qualitative rather than quantitative nature makes them less appropriate for assessing the magnitude of FE. Moreover, these approaches are based upon drug properties alone and are not appropriate for estimating potential formulation-specific FE on modified or controlled release products. In contrast, physiologically-based mechanistic models can consider the scope and interplay of a range of physiological changes after food intake and, in combination with appropriate in vitro drug- and formulation-specific data, can make quantitative predictions of formulation-specific FE including the inter-individual variability of such effects. Herein the Advanced Dissolution, Absorption and Metabolism (ADAM) model is applied to the prediction of formulation-specific FE for BCS/BDDCS Class II drug and CYP3A4 substrate nifedipine using as far as possible only in vitro data. Predicted plasma concentration profiles of all three studied formulations under fasted and fed states are within 2-fold of clinically observed profiles. The % prediction error (%PE) in fed-to-fasted ratio of Cmax and AUC were less than 5% for all formulations except for the Cmax of Nifedicron (%PE=-29.6%). This successful case study should help to improve confidence in the use of mechanistic physiologically-based models coupled with in vitro data for the anticipation of FE in advance of in vivo studies. However, it is acknowledged that further studies with drugs/formulations exhibiting a wide range of properties are required to further validate this methodology.
Copyright © 2013 Elsevier B.V. All rights reserved.

Entities:  

Keywords:  Food effects; In vitro-in vivo extrapolation; Mechanistic modelling; Oral formulation; PBPK; Simcyp ADAM model

Mesh:

Substances:

Year:  2013        PMID: 24060671     DOI: 10.1016/j.ejps.2013.09.006

Source DB:  PubMed          Journal:  Eur J Pharm Sci        ISSN: 0928-0987            Impact factor:   4.384


  12 in total

Review 1.  Physiologically Based Pharmacokinetic (PBPK) Modeling and Simulation Approaches: A Systematic Review of Published Models, Applications, and Model Verification.

Authors:  Jennifer E Sager; Jingjing Yu; Isabelle Ragueneau-Majlessi; Nina Isoherranen
Journal:  Drug Metab Dispos       Date:  2015-08-21       Impact factor: 3.922

2.  General Pharmacokinetic Model for Topically Administered Ocular Drug Dosage Forms.

Authors:  Feng Deng; Veli-Pekka Ranta; Heidi Kidron; Arto Urtti
Journal:  Pharm Res       Date:  2016-07-18       Impact factor: 4.200

3.  Incorporation of the Time-Varying Postprandial Increase in Splanchnic Blood Flow into a PBPK Model to Predict the Effect of Food on the Pharmacokinetics of Orally Administered High-Extraction Drugs.

Authors:  Rachel H Rose; David B Turner; Sibylle Neuhoff; Masoud Jamei
Journal:  AAPS J       Date:  2017-05-19       Impact factor: 4.009

4.  Towards Bridging Translational Gap in Cardiotoxicity Prediction: an Application of Progressive Cardiac Risk Assessment Strategy in TdP Risk Assessment of Moxifloxacin.

Authors:  Nikunjkumar Patel; Oliver Hatley; Alexander Berg; Klaus Romero; Barbara Wisniowska; Debra Hanna; David Hermann; Sebastian Polak
Journal:  AAPS J       Date:  2018-03-14       Impact factor: 4.009

Review 5.  Lipid-associated oral delivery: Mechanisms and analysis of oral absorption enhancement.

Authors:  Oljora Rezhdo; Lauren Speciner; Rebecca Carrier
Journal:  J Control Release       Date:  2016-08-09       Impact factor: 9.776

6.  Physiologically Based Absorption Modeling to Explore the Impact of Food and Gastric pH Changes on the Pharmacokinetics of Alectinib.

Authors:  Neil J Parrott; Li J Yu; Ryusuke Takano; Mikiko Nakamura; Peter N Morcos
Journal:  AAPS J       Date:  2016-07-22       Impact factor: 4.009

7.  Translating Human Effective Jejunal Intestinal Permeability to Surface-Dependent Intrinsic Permeability: a Pragmatic Method for a More Mechanistic Prediction of Regional Oral Drug Absorption.

Authors:  Andrés Olivares-Morales; Hans Lennernäs; Leon Aarons; Amin Rostami-Hodjegan
Journal:  AAPS J       Date:  2015-05-19       Impact factor: 4.009

8.  Applications of linking PBPK and PD models to predict the impact of genotypic variability, formulation differences, differences in target binding capacity and target site drug concentrations on drug responses and variability.

Authors:  Manoranjenni Chetty; Rachel H Rose; Khaled Abduljalil; Nikunjkumar Patel; Gaohua Lu; Theresa Cain; Masoud Jamei; Amin Rostami-Hodjegan
Journal:  Front Pharmacol       Date:  2014-11-26       Impact factor: 5.810

Review 9.  Predictive Performance of Physiologically Based Pharmacokinetic Models for the Effect of Food on Oral Drug Absorption: Current Status.

Authors:  Mengyao Li; Ping Zhao; Yuzhuo Pan; Christian Wagner
Journal:  CPT Pharmacometrics Syst Pharmacol       Date:  2017-11-23

Review 10.  Top-down, Bottom-up and Middle-out Strategies for Drug Cardiac Safety Assessment via Modeling and Simulations.

Authors:  Zofia Tylutki; Sebastian Polak; Barbara Wiśniowska
Journal:  Curr Pharmacol Rep       Date:  2016-04-05
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