Prediction of In Vivo Pharmacokinetic Parameters and Time-Exposure Curves in Rats Using Machine Learning from the Chemical Structure.
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Authors
Martinsson, Anton
Whitehead, Tom
Mahmoud, Samar
Grabowski, Piotr
Irwin, Ben
Oprisiu, Ioana
Conduit, Gareth
Williamson, Beth
Greene, Nigel
Publication Date
2022-05-02Journal Title
Mol Pharm
ISSN
1543-8384
Publisher
American Chemical Society (ACS)
Number
acs.molpharmaceut.2c00027
Type
Article
This Version
AM
Metadata
Show full item recordCitation
Obrezanova, O., Martinsson, A., Whitehead, T., Mahmoud, S., Bender, A., Miljković, F., Grabowski, P., et al. (2022). Prediction of In Vivo Pharmacokinetic Parameters and Time-Exposure Curves in Rats Using Machine Learning from the Chemical Structure.. Mol Pharm, (acs.molpharmaceut.2c00027) https://doi.org/10.1021/acs.molpharmaceut.2c00027
Abstract
Animal pharmacokinetic (PK) data as well as human and animal in vitro systems are utilized in drug discovery to define the rate and route of drug elimination. Accurate prediction and mechanistic understanding of drug clearance and disposition in animals provide a degree of confidence for extrapolation to humans. In addition, prediction of in vivo properties can be used to improve design during drug discovery, help select compounds with better properties, and reduce the number of in vivo experiments. In this study, we generated machine learning models able to predict rat in vivo PK parameters and concentration-time PK profiles based on the molecular chemical structure and either measured or predicted in vitro parameters. The models were trained on internal in vivo rat PK data for over 3000 diverse compounds from multiple projects and therapeutic areas, and the predicted endpoints include clearance and oral bioavailability. We compared the performance of various traditional machine learning algorithms and deep learning approaches, including graph convolutional neural networks. The best models for PK parameters achieved R2 = 0.63 [root mean squared error (RMSE) = 0.26] for clearance and R2 = 0.55 (RMSE = 0.46) for bioavailability. The models provide a fast and cost-efficient way to guide the design of molecules with optimal PK profiles, to enable the prediction of virtual compounds at the point of design, and to drive prioritization of compounds for in vivo assays.
Sponsorship
Royal Society (RGF/EA/180034)
Royal Society (URF\R\201002)
Embargo Lift Date
2023-04-12
Identifiers
External DOI: https://doi.org/10.1021/acs.molpharmaceut.2c00027
This record's URL: https://www.repository.cam.ac.uk/handle/1810/336078
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