IMI - Oral biopharmaceutics tools project - Evaluation of bottom-up PBPK prediction success part 4: Prediction accuracy and software comparisons with improved data and modelling strategies.

Autor: Ahmad A; University of Manchester, United Kingdom. Electronic address: amais.ahmed@manchester.ac.uk., Pepin X; AstraZeneca, United Kingdom., Aarons L; University of Manchester, United Kingdom., Wang Y; University of Manchester, United Kingdom., Darwich AS; Royal Institute of Technology, Stockholm, Sweden., Wood JM; AstraZeneca, United Kingdom., Tannergren C; AstraZeneca, United Kingdom., Karlsson E; AstraZeneca, United Kingdom., Patterson C; AstraZeneca, United Kingdom., Thörn H; AstraZeneca, United Kingdom., Ruston L; AstraZeneca, United Kingdom., Mattinson A; AstraZeneca, United Kingdom., Carlert S; AstraZeneca, United Kingdom., Berg S; AstraZeneca, United Kingdom., Murphy D; AstraZeneca, United Kingdom., Engman H; AstraZeneca, United Kingdom., Laru J; AstraZeneca, United Kingdom., Barker R; AstraZeneca, United Kingdom., Flanagan T; AstraZeneca, United Kingdom., Abrahamsson B; AstraZeneca, United Kingdom., Budhdeo S; AstraZeneca, United Kingdom., Franek F; AstraZeneca, United Kingdom., Moir A; AstraZeneca, United Kingdom., Hanisch G; AstraZeneca, United Kingdom., Pathak SM; Certara, Simcyp Division, United Kingdom., Turner D; Certara, Simcyp Division, United Kingdom., Jamei M; Certara, Simcyp Division, United Kingdom., Brown J; Bristol-Myers Squibb, United Kingdom., Good D; Bristol-Myers Squibb, United Kingdom., Vaidhyanathan S; Bristol-Myers Squibb, United Kingdom., Jackson C; Bristol-Myers Squibb, United Kingdom., Nicolas O; Sanofi, United States., Beilles S; Sanofi, United States., Nguefack JF; Sanofi, United States., Louit G; Sanofi, United States., Henrion L; Sanofi, United States., Ollier C; Sanofi, United States., Boulu L; Sanofi, United States., Xu C; Sanofi, United States., Heimbach T; Novartis, United States., Ren X; Novartis, United States., Lin W; Novartis, United States., Nguyen-Trung AT; Novartis, United States., Zhang J; Novartis, United States., He H; Novartis, United States., Wu F; Novartis, United States., Bolger MB; Simulation Plus, Inc., United States., Mullin JM; Simulation Plus, Inc., United States., van Osdol B; Simulation Plus, Inc., United States., Szeto K; Simulation Plus, Inc., United States., Korjamo T; Orion Pharma, Finland., Pappinen S; Orion Pharma, Finland., Tuunainen J; Orion Pharma, Finland., Zhu W; Merck Research Laboratories, Merck & Co., United States., Xia B; Merck Research Laboratories, Merck & Co., United States., Daublain P; Merck Research Laboratories, Merck & Co., United States., Wong S; Pfizer, United States., Varma MVS; Pfizer, United States., Modi S; Pfizer, United States., Schäfer KJ; Boehringer-ingelheim, Germany., Schmid K; Boehringer-ingelheim, Germany., Lloyd R; GlaxoSmithKline, United Kingdom., Patel A; GlaxoSmithKline, United Kingdom., Tistaert C; Janssen, Belgium., Bevernage J; Janssen, Belgium., Nguyen MA; Johannes Gutenberg University of Mainz, Germany., Lindley D; AbbVie, Germany., Carr R; AbbVie, Germany., Rostami-Hodjegan A; University of Manchester, United Kingdom; Certara, Simcyp Division, United Kingdom.
Jazyk: angličtina
Zdroj: European journal of pharmaceutics and biopharmaceutics : official journal of Arbeitsgemeinschaft fur Pharmazeutische Verfahrenstechnik e.V [Eur J Pharm Biopharm] 2020 Nov; Vol. 156, pp. 50-63. Date of Electronic Publication: 2020 Aug 14.
DOI: 10.1016/j.ejpb.2020.08.006
Abstrakt: Oral drug absorption is a complex process depending on many factors, including the physicochemical properties of the drug, formulation characteristics and their interplay with gastrointestinal physiology and biology. Physiological-based pharmacokinetic (PBPK) models integrate all available information on gastro-intestinal system with drug and formulation data to predict oral drug absorption. The latter together with in vitro-in vivo extrapolation and other preclinical data on drug disposition can be used to predict plasma concentration-time profiles in silico. Despite recent successes of PBPK in many areas of drug development, an improvement in their utility for evaluating oral absorption is much needed. Current status of predictive performance, within the confinement of commonly available in vitro data on drugs and formulations alongside systems information, were tested using 3 PBPK software packages (GI-Sim (ver.4.1), Simcyp® Simulator (ver.15.0.86.0), and GastroPlus™ (ver.9.0.00xx)). This was part of the Innovative Medicines Initiative (IMI) Oral Biopharmaceutics Tools (OrBiTo) project. Fifty eight active pharmaceutical ingredients (APIs) were qualified from the OrBiTo database to be part of the investigation based on a priori set criteria on availability of minimum necessary information to allow modelling exercise. The set entailed over 200 human clinical studies with over 700 study arms. These were simulated using input parameters which had been harmonised by a panel of experts across different software packages prior to conduct of any simulation. Overall prediction performance and software packages comparison were evaluated based on performance indicators (Fold error (FE), Average fold error (AFE) and absolute average fold error (AAFE)) of pharmacokinetic (PK) parameters. On average, PK parameters (Area Under the Concentration-time curve (AUC 0-tlast ), Maximal concentration (C max ), half-life (t 1/2 )) were predicted with AFE values between 1.11 and 1.97. Variability in FEs of these PK parameters was relatively high with AAFE values ranging from 2.08 to 2.74. Around half of the simulations were within the 2-fold error for AUC 0-tlast and around 90% of the simulations were within 10-fold error for AUC 0-tlast . Oral bioavailability (F oral ) predictions, which were limited to 19 APIs having intravenous (i.v.) human data, showed AFE and AAFE of values 1.37 and 1.75 respectively. Across different APIs, AFE of AUC 0-tlast predictions were between 0.22 and 22.76 with 70% of the APIs showing an AFE > 1. When compared across different formulations and routes of administration, AUC 0-tlast for oral controlled release and i.v. administration were better predicted than that for oral immediate release formulations. Average predictive performance did not clearly differ between software packages but some APIs showed a high level of variability in predictive performance across different software packages. This variability could be related to several factors such as compound specific properties, the quality and availability of information, and errors in scaling from in vitro and preclinical in vivo data to human in vivo behaviour which will be explored further. Results were compared with previous similar exercise when the input data selection was carried by the modeller rather than a panel of experts on each in vitro test. Overall, average predictive performance was increased as reflected in smaller AAFE value of 2.8 as compared to AAFE value of 3.8 in case of previous exercise.
(Copyright © 2020 Elsevier B.V. All rights reserved.)
Databáze: MEDLINE
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