The pharmacokinetic/pharmacodynamic pipeline: translating anticancer drug pharmacology to the clinic

Abstract

Progress in an understanding of the genetic basis of cancer coupled to molecular pharmacology of potential new anticancer drugs calls for new approaches that are able to address key issues in the drug development process, including pharmacokinetic (PK) and pharmacodynamic (PD) relationships. The incorporation of predictive preclinical PK/PD models into rationally designed early-stage clinical trials offers a promising way to relieve a significant bottleneck in the drug discovery pipeline. The aim of the current review is to discuss some considerations for how quantitative PK and PD analyses for anticancer drugs may be conducted and integrated into a global translational effort, and the importance of examining drug disposition and dynamics in target tissues to support the development of preclinical PK/PD models that can be subsequently extrapolated to predict pharmacologic characteristics in patients. In this article, we describe three different physiologically based (PB) PK modeling approaches, i.e., the whole-body PBPK model, the hybrid PBPK model, and the two-pore model for macromolecules, as well as their applications. General conclusions are that greater effort should be made to generate more clinical data that could validate scaled preclinical PB-PK/PD tumor-based models and, thus, stimulate a framework for preclinical to clinical translation. Finally, given the innovative techniques to measure tissue drug concentrations and associated biomarkers of drug responses, development of predictive PK/PD models will become a standard approach for drug discovery and development.

Fig. 1

a Schematic presentation of the hybrid PBPK model of TMZ in brain. The dose input for rats was IV, whereas for humans, it was oral. The structure of the brain model is the same in rats and human. b Human model-predicted plasma (dotted line), and CSF TMZ concentrations using naïve (solid line) and scaled (dashed line) model predictors. The CSF TMZ concentrations for these two predictors closely overlap. c Human model-predicted TMZ brain tumor concentrations comparing three different dosing regimens; standard (200 mg/m² daily × 5 days every 28 days), compressed (200 mg/m² every 8 h for 5 doses every 28 days), and extended (75 mg/m² daily × 21 days every 28 days). The simulations are shown to 120 h, the end of the standard dosing cycle (7)

Fig. 2

Equivalent PK/PD dosing based on model predictions. a Schematic representation of a hybrid PK/PD model consisting of a two-compartment systemic disposition model, a one-compartment tumor model, and a two-compartment target-response model. Model simulations of b tumor gefitinib concentration-time profiles and c corresponding tumor pERK inhibition-time profiles following 150 mg/kg p.o. daily × 15 day in LN229-wild-type EGFR tumor-bearing mice and 70 mg/kg p.o. daily × 15 day in LN229-EGFRvIII mutant tumor-bearing mice (39)

Fig. 3

Schematic representation of the two-pore model for describing the flux of MAb across the capillary wall. Q plasma flow rate, L _org lymph flow rate, α _S and α _L fractions of the hydraulic conductivity attributable to the small and large pore pathways respectively, J _S and J _L transcapillary fluid flow rate (from vascular to interstitial) via small and large pores respectively, J _iso fluid recirculation flow rate