Computational approaches to DMPK: A realistic assessment of current methods and their practical impact. Part I: Physicochemical and in vitro properties

Koichi Handa; Mariko Hirano; Michiharu Kageyama; Andreas Bender

doi:10.1016/j.drudis.2025.104422

Computational approaches to DMPK: A realistic assessment of current methods and their practical impact. Part I: Physicochemical and in vitro properties

Drug Discov Today. 2025 Jun 30:104422. doi: 10.1016/j.drudis.2025.104422. Online ahead of print.

Authors

Koichi Handa¹, Mariko Hirano², Michiharu Kageyama², Andreas Bender³

Affiliations

¹ Axcelead Tokyo West Partners, Inc., Translational Science Group, 4-3-2 Asahigaoka, Hino-shi, Tokyo 191-0065, Japan. Electronic address: koichi.handa.0722@gmail.com.
² Axcelead Tokyo West Partners, Inc., Translational Science Group, 4-3-2 Asahigaoka, Hino-shi, Tokyo 191-0065, Japan.
³ College of Medicine and Health Sciences, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates; Centre for Molecular Informatics, Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, United Kingdom; STAR-UBB Institute, Babeş-Bolyai University, Cluj-Napoca, Romania. Electronic address: andreas.bender@ku.ac.ae.

PMID: 40602659
DOI: 10.1016/j.drudis.2025.104422

Abstract

Artificial intelligence and computational approaches have received considerable interest in recent years, and here we assess their real-world utility in drug discovery projects. We review recent in silico models in the area of drug metabolism and pharmacokinetics (DMPK), especially for physicochemical properties (pKa and logD) and in vitro assays [solubility (DMSO, Dried-DMSO, Powder), permeability (PAMPA, Caco-2, MDCK), metabolic stability (liver microsome, hepatocyte), and protein binding (plasma, microsome, brain)]. We discuss which are currently fit for purpose (and which are not), bridging both computational and experimental aspects in the early drug discovery stages. The review includes diverse aspects of obtaining data and model generation, as well as modeler/experimentalist interplay.

Keywords: ADME; DMPK; artificial intelligence; drug discovery; logD; machine learning; metabolic stability; pKa; permeability; protein binding; solubility.

Publication types

Review