Prediction of In Vivo Pharmacokinetic Parameters and Time–Exposure Curves in Rats Using Machine Learning from the Chemical Structure

This article is a collaboration with Intellegens, the University of Cambridge and AstraZeneca. It provides a proof-of-concept study in which Cerella™ is used to predict rat in vivo pharmacokinetic (PK) parameters and concentration–time PK profiles.

Authors

Olga Obrezanova, Anton Martinsson, Tom Whitehead, Samar Mahmoud, Andreas Bender, Filip Miljković, Piotr Grabowski, Ben Irwin, Ioana Oprisiu, Gareth Conduit, Matt Segall, Graham F. Smith, Beth Williamson, Susanne Winiwarter, and Nigel Greene.

Summary

To uncover the rate and route of a potential drug’s elimination from the body, scientists assess and predict both in vivo animal pharmacokinetic (PK) data as well as human and animal in vitro systems. Computational prediction of in vivo properties can also 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, the researchers generated new machine learning models. These were able to predict rat in vivo PK parameters and concentration–time PK profiles based on a compound’s molecular structure and either measured or predicted in vitro parameters. They trained the models on internal in vivo rat PK data for over 3000 diverse compounds, with predicted endpoints including clearance and oral bioavailability, and compared various traditional machine learning algorithms and deep learning approaches for performance.

They found that the models provide a fast and cost-efficient way to guide the design of molecules with optimal PK profiles. This enables the prediction of virtual compounds at the point of design, and drives the prioritization of compounds for in vivo assays.

Citation details

O. Obrezanova, A. Martinsson, T. Whitehead, S. Mahmoud, A. Bender, F. Miljković, P. Grabowski, B. Irwin, I. Oprisiu, G. Conduit, M. Segall, G. F. Smith, B. Williamson, S. Winiwarter, and N. Greene Mol. Pharmaceutics, 2022, 19, 5, 1488–1504.

DOI: 10.1021/acs.molpharmaceut.2c00027

Find out more

Read the full article from the journal webpage via the button below. Additionally, for further detail on how Cerella can be used to predict PK parameters and curves, watch our webinar. It features three of the authors of this paper, Matt Segall (Optibrium), Tom Whitehead (Intellegens), Nigel Greene (AstraZeneca), discussing their work around Cerella and its applications.