Development of an mPBPK machine learning framework for early target pharmacology assessment of biotherapeutics.

ABSTRACT: Development of antibodies often begins with the assessment and optimization of their physicochemical properties, and their efficient engagement with the target of interest. Decisions at the early optimization stage are critical for the success of the drug candidate but are constrained due to the limited knowledge of the antibody and target pharmacology. In the present work, we propose a machine learning-based target pharmacology assessment framework that utilizes minimal physiologically based pharmacokinetic (mPBPK) modeling and machine learning (ML) to infer optimal physicochemical properties of antibodies and their targets. We use a mPBPK model previously developed by our group that incorporates a multivariate quantitative relationship between antibodies' physicochemical properties such

SUBMITTER: Patidar K 

PROVIDER: S-EPMC11794604 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature