Literature DB >> 22449410

Prediction of the human oral bioavailability by using in vitro and in silico drug related parameters in a physiologically based absorption model.

Paulo Paixão1, Luís F Gouveia, José A G Morais.   

Abstract

Estimates of the human oral absolute bioavailability were made by using a physiological-based pharmacokinetic model of absorption and the drug solubility at the gastrointestinal pH range 1.5-7.5, the apparent permeability (P(app)) in Caco-2 cells and the intrinsic clearance (Cl(int)) in human hepatocytes suspensions as major drug related parameters. The predictive ability of this approach was tested in 164 drugs divided in four levels of input data: (i) in vitro data for both P(app) and Cl(int); (ii) in vitro data for Cl(int) only; (iii) in vitro data for P(app) only and (iv) in silico data for both P(app) and Cl(int). In all scenarios, solubility was estimated in silico. Excellent predictive abilities were observed when in vitro data for both P(app) and Cl(int) were used, with 84% of drugs with oral bioavailability predictions within a±20% interval of the correct value. This predictive ability is reduced with the introduction of the in silico estimated parameters, particularly when Cl(int) is used. Performance of the model using only in silico data provided 53% of drugs with bioavailability predictions within a±20% acceptance interval. However, 74% of drugs in the same scenario resulted in bioavailability predictions within a±35% interval, which indicates that a qualitative prediction of the absolute bioavailability is still possible. This approach is a valuable way to estimate a fundamental pharmacokinetic parameter, using data typically collected in the drug discovery and early development phases, providing also mechanistic information of the limiting bioavailability steps of the drug.
Copyright © 2012 Elsevier B.V. All rights reserved.

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Year:  2012        PMID: 22449410     DOI: 10.1016/j.ijpharm.2012.03.019

Source DB:  PubMed          Journal:  Int J Pharm        ISSN: 0378-5173            Impact factor:   5.875


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