Interpret animal PK data, select compounds, and connect PK to efficacy in an oral oncology kinase inhibitor project
11 topics
In vitro-in vivo extrapolation (IVIVE), predicting animal PK from lab assays using physiological scaling. Translating microsomal and hepatocyte CLint into predicted hepatic clearance via the well-stirred model, fumic correction, and MPPGL scaling.
Using Caco-2 permeability, PAMPA, efflux ratios, and solubility data to predict oral absorption (Fa) and bioavailability (F = Fa x Fg x Fh). Ranking compounds from in vitro data before the in vivo study.
Designing the in vivo PK experiment: selecting compounds from IVIVE predictions, choosing IV and PO dose levels, time-point sampling strategy, bioanalytical method (LC-MS/MS), formulation considerations, and interpreting the study output.
Deriving key PK parameters from IV bolus data: systemic clearance (CL), volume of distribution (V_(dss)), elimination half-life (t_(1/2)), mean residence time (MRT), and their interrelationships. Introduction to unbound clearance and why half-life is a derived, not intrinsic, parameter.
Interpreting oral PK data from the rat study: calculating bioavailability (F) from IV and PO AUC, understanding Cmax and Tmax, the three barriers to oral bioavailability (Fa, Fg, Fh), and comparing oral exposure across the Project Orion compound series.
The free drug principle: only unbound drug drives pharmacological activity. Plasma protein binding (PPB), fraction unbound in plasma (fup), unbound clearance, unbound AUC, and the common misconceptions about protein binding in drug discovery.
Constructing the complete PK scorecard for Project Orion: integrating clearance, bioavailability, exposure, unbound parameters, and potency with traffic-light cutoffs and IVIVC assessment to select the candidate compound.
How to select the best compound from a series: minimum PK criteria for oral oncology drugs, trade-off analysis between potency and PK, multi-parameter ranking, and building a compelling selection rationale for the project team.
Sources of pharmacokinetic variability in preclinical studies: inter-animal variability, food effects, dose-dependency, time-dependent PK, formulation effects, and common oncology PK pitfalls.
Linking pharmacokinetics to pharmacodynamics: PK/PD modelling, target coverage, maintaining free drug above IC50, Cmin/IC50 ratios, and time above threshold as drivers of efficacy.
The candidate nomination package: assembling potency, selectivity, ADME, PK, safety pharmacology, and toxicology data into a compelling case for advancing a molecule into IND-enabling (Investigational New Drug, filed with the FDA to obtain permission for first human trials) studies.