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Eur J Pharm Sci. 2017 Jan 1;96:626-642. doi: 10.1016/j.ejps.2016.09.037. Epub 2016 Sep 28.

IMI - Oral biopharmaceutics tools project - Evaluation of bottom-up PBPK prediction success part 3: Identifying gaps in system parameters by analysing In Silico performance across different compound classes.

Author information

1
University of Manchester, United Kingdom.
2
University of Manchester, United Kingdom. Electronic address: alison.margolskee@manchester.ac.uk.
3
AstraZeneca, United Kingdom; Sanofi, France.
4
University of Manchester, United Kingdom; Simcyp Ltd, United Kingdom.
5
AstraZeneca, Sweden.
6
AstraZeneca, United Kingdom.
7
Sanofi, France.
8
Sanofi, Germany.
9
Sanofi, United States.
10
Simcyp Ltd, United Kingdom.
11
Orion Pharma, Finland.
12
AstraZeneca, United Kingdom; Orion Pharma, Finland.
13
Goethe University Frankfurt am Main, Germany.
14
Novartis, United States.
15
AbbVie, Germany.
16
Simulations Plus, Inc., United States.
17
Pfizer, United States.
18
Pfizer, United Kingdom.
19
Merck Sharp & Dohme (MSD), United Kingdom.
20
Janssen, Belgium.
21
GlaxoSmithKline, United Kingdom.
22
Johannes Gutenberg University of Mainz, Germany.
23
Bristol-Myers Squibb, United Kingdom.
24
Uppsala University, Sweden.

Abstract

Three Physiologically Based Pharmacokinetic software packages (GI-Sim, Simcyp® Simulator, and GastroPlus™) 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 (Foral) was underpredicted for compounds with low permeability, suggesting improper estimates of intestinal surface area, colonic absorption and/or lack of intestinal transporter information. Foral was 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. Foral 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.

KEYWORDS:

Physiologically-based pharmacokinetics (PBPK); absorption; biopharmaceutics; drug database; modelling and simulation (M&S); oral bioavailability (F(oral))

PMID:
27693299
DOI:
10.1016/j.ejps.2016.09.037
[Indexed for MEDLINE]

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