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Advances in Predictions of Oral Bioavailability of Candidate Drugs in Man with New Machine Learning Methodology

Authors :
Urban Fagerholm
Sven Hellberg
Ola Spjuth
Source :
Molecules, Vol 26, Iss 9, p 2572 (2021)
Publication Year :
2021
Publisher :
MDPI AG, 2021.

Abstract

Oral bioavailability (F) is an essential determinant for the systemic exposure and dosing regimens of drug candidates. F is determined by numerous processes, and computational predictions of human estimates have so far shown limited results. We describe a new methodology where F in humans is predicted directly from chemical structure using an integrated strategy combining 9 machine learning models, 3 sets of structural alerts, and 2 physiologically-based pharmacokinetic models. We evaluate the model on a benchmark dataset consisting of 184 compounds, obtaining a predictive accuracy (Q2) of 0.50, which is successful according to a pharmaceutical industry proposal. Twenty-seven compounds were found (beforehand) to be outside the main applicability domain for the model. We compare our results with interspecies correlations (rat, mouse and dog vs. human) using the same dataset, where animal vs. human-correlations (R2) were found to be 0.21 to 0.40 and maximum prediction errors were smaller than maximum interspecies differences. We conclude that our method has sufficient predictive accuracy to be practically useful with applications in human exposure and dose predictions, compound optimization and decision making, with potential to rationalize drug discovery and development and decrease failures and overexposures in early clinical trials with candidate drugs.

Details

Language :
English
ISSN :
14203049
Volume :
26
Issue :
9
Database :
Directory of Open Access Journals
Journal :
Molecules
Publication Type :
Academic Journal
Accession number :
edsdoj.55eb822846f44ada7f7a2fdd983b7b6
Document Type :
article
Full Text :
https://doi.org/10.3390/molecules26092572