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Article

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

1
Prosilico AB, Lännavägen 7, SE-141 45 Huddinge, Sweden
2
Department of Pharmaceutical Biosciences and Science for Life Laboratory, Uppsala University, Box 591, SE-751 24 Uppsala, Sweden
*
Author to whom correspondence should be addressed.
Academic Editor: In-Soo Yoon
Molecules 2021, 26(9), 2572; https://doi.org/10.3390/molecules26092572
Received: 22 March 2021 / Revised: 20 April 2021 / Accepted: 22 April 2021 / Published: 28 April 2021
(This article belongs to the Section Medicinal Chemistry)
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. View Full-Text
Keywords: absorption; ADME; bioavailability; computational; in silico; PBPK; pharmacokinetics; prediction; QSAR absorption; ADME; bioavailability; computational; in silico; PBPK; pharmacokinetics; prediction; QSAR
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MDPI and ACS Style

Fagerholm, U.; Hellberg, S.; Spjuth, O. Advances in Predictions of Oral Bioavailability of Candidate Drugs in Man with New Machine Learning Methodology. Molecules 2021, 26, 2572. https://doi.org/10.3390/molecules26092572

AMA Style

Fagerholm U, Hellberg S, Spjuth O. Advances in Predictions of Oral Bioavailability of Candidate Drugs in Man with New Machine Learning Methodology. Molecules. 2021; 26(9):2572. https://doi.org/10.3390/molecules26092572

Chicago/Turabian Style

Fagerholm, Urban, Sven Hellberg, and Ola Spjuth. 2021. "Advances in Predictions of Oral Bioavailability of Candidate Drugs in Man with New Machine Learning Methodology" Molecules 26, no. 9: 2572. https://doi.org/10.3390/molecules26092572

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