Professor and Chair of Pharmaceutical Sciences/President and CEO University at Buffalo, SUNY/Enhanced Pharmacodynamics, LLC Buffalo, New York
Biomarkers have been shown to be useful in confirming molecular recognition and drug-target interactions, selecting safe and effective dosing regimens, and identifying patient subpopulations more or less likely to experience drug efficacy or adverse events. The most useful biomarkers tend to reside within the causal pathways connecting drug target engagement and clinical response. The information such biomarkers provide can be further enhanced by leveraging them in the development and application of mechanism-based pharmacokinetic/pharmacodynamic (PK/PD) models. In this presentation, examples and cases of mechanism-based PK/PD models of biomarkers will be shown for informing the clinical development a small molecule drug, monoclonal antibodies, and a new allogeneic chimeric antigen receptor T-cell (CAR-T) therapy. The future of such models will likely incorporate quantitative systems pharmacology (QSP) representation of pathways, advancements in novel imaging and ‘omics-based analytics, and hybrid modeling approaches linking PK/PD and QSP models with machine learning algorithms.
Learning Objectives:
Contrast and compare the role of target-based biomarkers from those residing in the causal pathways of drug action (or mechanism-based).
Recognize how mechanism-based biomarkers can be leveraged in the development and application of PK/PD models to enhance research translation and clinical drug development.
Identify potential advances in biomarker PK/PD models coming from imaging and 'omics-based platforms, more complex systems pharmacology relationships, and hybrid modeling strategies.
