Literature DB >> 24970349

Physiologically based absorption modelling to predict the impact of drug properties on pharmacokinetics of bitopertin.

Neil Parrott1, Dominik Hainzl, Emmanuel Scheubel, Siegfried Krimmer, Christophe Boetsch, Elena Guerini, Meret Martin-Facklam.   

Abstract

Bitopertin (RG1678) is a glycine reuptake inhibitor in phase 3 trials for treatment of schizophrenia. Its clinical oral pharmacokinetics is sensitive to changes in drug substance particle size and dosage form. Physiologically based pharmacokinetic (PBPK) absorption model simulations of the impact of changes in particle size and dosage form (either capsules, tablets, or an aqueous suspension) on oral pharmacokinetics was verified by comparison to measured plasma concentrations. Then, a model parameter sensitivity analysis was applied to set limits on the particle sizes included in tablets for the market. The model was also used to explore the in vitro to in vivo correlation. Simulated changes in oral pharmacokinetics caused by differences in particle size and dosage form were confirmed in two separate relative bioavailability studies. Model parameter sensitivity analyses predicted that AUCinf was hardly reduced as long as particle diameter (D50) remained smaller than 30 μm, and >20% reduced Cmax is anticipated only when particle diameter exceeds 15 μm. An exploration of the sensitivity to the presence of larger particles within a polydisperse distribution showed that simulated Cmax is again more affected than AUC but is less than 20% reduced as long as D50 is less than 8 μm and D90 is smaller than 56 μm. PBPK absorption modelling can contribute to a quality by design (QbD) approach for clinical formulation development and support the setting of biorelevant specifications for release of the product.

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Year:  2014        PMID: 24970349      PMCID: PMC4147056          DOI: 10.1208/s12248-014-9639-y

Source DB:  PubMed          Journal:  AAPS J        ISSN: 1550-7416            Impact factor:   4.009


  18 in total

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Authors:  B Agoram; W S Woltosz; M B Bolger
Journal:  Adv Drug Deliv Rev       Date:  2001-10-01       Impact factor: 15.470

Review 2.  The use of modeling tools to drive efficient oral product design.

Authors:  Neil R Mathias; John Crison
Journal:  AAPS J       Date:  2012-05-30       Impact factor: 4.009

3.  Pharmaceutical quality by design: product and process development, understanding, and control.

Authors:  Lawrence X Yu
Journal:  Pharm Res       Date:  2008-01-10       Impact factor: 4.200

4.  Applications of physiologically based absorption models in drug discovery and development.

Authors:  Neil Parrott; Thierry Lave
Journal:  Mol Pharm       Date:  2008-06-12       Impact factor: 4.939

5.  A strategy for preclinical formulation development using GastroPlus as pharmacokinetic simulation tool and a statistical screening design applied to a dog study.

Authors:  Martin Kuentz; Sonja Nick; Neil Parrott; Dieter Röthlisberger
Journal:  Eur J Pharm Sci       Date:  2005-10-10       Impact factor: 4.384

6.  Dissolution modeling: factors affecting the dissolution rates of polydisperse powders.

Authors:  A T Lu; M E Frisella; K C Johnson
Journal:  Pharm Res       Date:  1993-09       Impact factor: 4.200

7.  Glycine reuptake inhibitor RG1678: a pharmacologic characterization of an investigational agent for the treatment of schizophrenia.

Authors:  Daniela Alberati; Jean-Luc Moreau; Judith Lengyel; Nicole Hauser; Roland Mory; Edilio Borroni; Emmanuel Pinard; Frederic Knoflach; Götz Schlotterbeck; Dominik Hainzl; Joseph G Wettstein
Journal:  Neuropharmacology       Date:  2011-11-27       Impact factor: 5.250

8.  Physiologically based pharmacokinetic modelling to predict single- and multiple-dose human pharmacokinetics of bitopertin.

Authors:  Neil Parrott; Dominik Hainzl; Daniela Alberati; Carsten Hofmann; Richard Robson; Bruno Boutouyrie; Meret Martin-Facklam
Journal:  Clin Pharmacokinet       Date:  2013-08       Impact factor: 6.447

9.  Evaluation of various dissolution media for predicting in vivo performance of class I and II drugs.

Authors:  E Galia; E Nicolaides; D Hörter; R Löbenberg; C Reppas; J B Dressman
Journal:  Pharm Res       Date:  1998-05       Impact factor: 4.200

Review 10.  Glutamate and schizophrenia: phencyclidine, N-methyl-D-aspartate receptors, and dopamine-glutamate interactions.

Authors:  Daniel C Javitt
Journal:  Int Rev Neurobiol       Date:  2007       Impact factor: 3.230

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  10 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.  Application of Absorption Modeling in Rational Design of Drug Product Under Quality-by-Design Paradigm.

Authors:  Filippos Kesisoglou; Amitava Mitra
Journal:  AAPS J       Date:  2015-05-22       Impact factor: 4.009

Review 3.  Physiologically-based pharmacokinetic models: approaches for enabling personalized medicine.

Authors:  Clara Hartmanshenn; Megerle Scherholz; Ioannis P Androulakis
Journal:  J Pharmacokinet Pharmacodyn       Date:  2016-09-19       Impact factor: 2.745

4.  Optimization of Spray-Drying Parameters for Formulation Development at Preclinical Scale.

Authors:  Marika Nespi; Robert Kuhn; Chun-Wan Yen; Joseph W Lubach; Dennis Leung
Journal:  AAPS PharmSciTech       Date:  2021-12-20       Impact factor: 3.246

Review 5.  The Use of Physiologically Based Pharmacokinetic Analyses-in Biopharmaceutics Applications -Regulatory and Industry Perspectives.

Authors:  Om Anand; Xavier J H Pepin; Vidula Kolhatkar; Paul Seo
Journal:  Pharm Res       Date:  2022-05-18       Impact factor: 4.580

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.  Effects of Cytochrome P450 3A4 Inhibitors-Ketoconazole and Erythromycin-on Bitopertin Pharmacokinetics and Comparison with Physiologically Based Modelling Predictions.

Authors:  Christophe Boetsch; Neil Parrott; Stephen Fowler; Agnes Poirier; Dominik Hainzl; Ludger Banken; Meret Martin-Facklam; Carsten Hofmann
Journal:  Clin Pharmacokinet       Date:  2016-02       Impact factor: 6.447

8.  Combining 'Bottom-Up' and 'Top-Down' Methods to Assess Ethnic Difference in Clearance: Bitopertin as an Example.

Authors:  Sheng Feng; Jun Shi; Neil Parrott; Pei Hu; Cornelia Weber; Meret Martin-Facklam; Tomohisa Saito; Richard Peck
Journal:  Clin Pharmacokinet       Date:  2016-07       Impact factor: 6.447

Review 9.  Progress in Prediction and Interpretation of Clinically Relevant Metabolic Drug-Drug Interactions: a Minireview Illustrating Recent Developments and Current Opportunities.

Authors:  Stephen Fowler; Peter N Morcos; Yumi Cleary; Meret Martin-Facklam; Neil Parrott; Michael Gertz; Li Yu
Journal:  Curr Pharmacol Rep       Date:  2017-02-01

10.  Use of Physiologically Based Pharmacokinetic (PBPK) Modeling for Predicting Drug-Food Interactions: an Industry Perspective.

Authors:  Arian Emami Riedmaier; Kevin DeMent; James Huckle; Phil Bransford; Cordula Stillhart; Richard Lloyd; Ravindra Alluri; Sumit Basu; Yuan Chen; Varsha Dhamankar; Stephanie Dodd; Priyanka Kulkarni; Andrés Olivares-Morales; Chi-Chi Peng; Xavier Pepin; Xiaojun Ren; Thuy Tran; Christophe Tistaert; Tycho Heimbach; Filippos Kesisoglou; Christian Wagner; Neil Parrott
Journal:  AAPS J       Date:  2020-09-27       Impact factor: 4.009

  10 in total

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