期刊
EUROPEAN JOURNAL OF PHARMACEUTICAL SCIENCES
卷 96, 期 -, 页码 626-642出版社
ELSEVIER SCIENCE BV
DOI: 10.1016/j.ejps.2016.09.037
关键词
Physiologically-based pharmacokinetics (PBPK); modelling and simulation (M&S); absorption; oral bioavailability (F-oral); biopharmaceutics; drug database
资金
- Medical Research Council [MC_G1100157] Funding Source: Medline
- Medical Research Council [MC_G1100157] Funding Source: researchfish
Three Physiologically Based Pharmacokinetic software packages (GI-Sim, Simcyp (R) Simulator, and GastroPlus (TM)) were evaluated as part of the Innovative Medicine Initiative Oral Biopharmaceutics Tools project (OrBiTo) during a blinded bottom-up anticipation of human pharmacokinetics. After data analysis of the predicted vs. measured pharmacokinetics parameters, it was found that oral bioavailability (F-oral) was underpredicted for compounds with low permeability, suggesting improper estimates of intestinal surface area, colonic absorption and/or lack of intestinal transporter information. Foralwas also underpredicted for acidic compounds, suggesting overestimation of impact of ionisation on permeation, lack of information on intestinal transporters, or underestimation of solubilisation of weak acids due to less than optimal intestinal model pH settings or underestimation of bile micelle contribution. F-oral was overpredicted for weak bases, suggesting inadequate models for precipitation or lack of in vitro precipitation information to build informed models. Relative bioavailability was underpredicted for both high logP compounds as well as poorly water-soluble compounds, suggesting inadequate models for solubility/dissolution, underperforming bile enhancement models and/or lack of biorelevant solubility measurements. These results indicate areas for improvement in model software, modelling approaches, and generation of applicable input data. However, caution is required when interpreting the impact of drug-specific properties in this exercise, as the availability of input parameters was heterogeneous and highly variable, and the modellers generally used the data as is in this blinded bottom-up prediction approach. (C) 2016 Elsevier B.V. All rights reserved.
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