DeepCt: Predicting Pharmacokinetic Concentration-Time Curves and Compartmental Models from Chemical Structure Using Deep Learning.

ABSTRACT: After initial triaging using in vitro absorption, distribution, metabolism, and excretion (ADME) assays, pharmacokinetic (PK) studies are the first application of promising drug candidates in living mammals. Preclinical PK studies characterize the evolution of the compound's concentration over time, typically in rodents' blood or plasma. From this concentration-time (C-t) profiles, PK parameters such as total exposure or maximum concentration can be subsequently derived. An early estimation of compounds' PK offers the promise of reducing animal studies and cycle times by selecting and designing molecules with increased chances of success at the PK stage. Even though C-t curves are the major readout from a PK study, most machine learning-based prediction efforts

SUBMITTER: Beckers M 

PROVIDER: S-EPMC11615940 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature