Translational pharmacokinetic-pharmacodynamic modeling from nonclinical to clinical development: a case study of anticancer drug, crizotinib

Abstract

Attrition risk related to efficacy is still a major reason why new chemical entities fail in clinical trials despite recently increased understanding of translational pharmacology. Pharmacokinetic-pharmacodynamic (PKPD) analysis is key to translating in vivo drug potency from nonclinical models to patients by providing a quantitative assessment of in vivo drug potency with mechanistic insight of drug action. The pharmaceutical industry is clearly moving toward more mechanistic and quantitative PKPD modeling to have a deeper understanding of translational pharmacology. This paper summarizes an anticancer drug case study describing the translational PKPD modeling of crizotinib, an orally available, potent small molecule inhibitor of multiple tyrosine kinases including anaplastic lymphoma kinase (ALK) and mesenchymal-epithelial transition factor (MET), from nonclinical to clinical development. Overall, the PKPD relationships among crizotinib systemic exposure, ALK or MET inhibition, and tumor growth inhibition (TGI) in human tumor xenograft models were well characterized in a quantitative manner using mathematical modeling: the results suggest that 50% ALK inhibition is required for >50% TGI whereas >90% MET inhibition is required for >50% TGI. Furthermore, >75% ALK inhibition and >95% MET inhibition in patient tumors were projected by PKPD modeling during the clinically recommended dosing regimen, twice daily doses of crizotinib 250 mg (500 mg/day). These simulation results of crizotinib-mediated ALK and MET inhibition appeared consistent with the currently reported clinical responses. In summary, the present paper presents an anticancer drug example to demonstrate that quantitative PKPD modeling can be used for predictive translational pharmacology from nonclinical to clinical development.

Fig. 1

Main work streams for setting the first-in-human starting dose and subsequent phase II dose/dosing regimen recommendation in cancer therapeutics. NOAEL, no observed adverse effect level; HNSTD, highest non-severely toxic dose; HED, human equivalent dose

Fig. 2

PKPD modeling summary of crizotinib-mediated target modulation and antitumor efficacy in human tumor xenograft models. C _p, plasma concentration; F, oral bioavailability; k _a, absorption rate constant; V, volume of distribution; k, elimination rate constant; t, time after dosing; C _e, effect-site concentration; k _e0, rate constant for equilibration with the effect site; E, biomarker response ratio to baseline (E ₀); EC ₅₀, concentration causing 50% of maximum effect (E _max); T, tumor volume; R, logistic function (1-T/T _ss), where T _ss is a maximum sustainable tumor volume (R = 1 for exponential growth model)

Fig. 3

Overview of the projection of crizotinib minimum efficacious concentration (C _eff) in patients based on the exposure-response relationships for ALK inhibition versus antitumor efficacy in nonclinical xenograft models. Concentration-response curves for crizotinib-mediated ALK inhibition and tumor growth inhibition (TGI) were simulated at the concentration range of 1 to 10,000 ng/mL with sigmoidal E _max model using the pharmacodynamic parameters (EC ₅₀, E _max and γ) obtained from a mouse xenograft model with H3122 NSCLC cells

Fig. 4

Overview of the projection of crizotinib minimum efficacious concentration (C _eff) in patients based on the exposure-response relationships for MET inhibition versus antitumor efficacy in nonclinical xenograft models. Concentration-response curves for crizotinib-mediated MET inhibition and tumor growth inhibition (TGI) were simulated at the concentration range of 1 to 10,000 ng/mL with sigmoidal E _max model using the pharmacodynamic parameters (EC ₅₀, E _max and γ) obtained from a mouse xenograft model with GTL16 GC cells

Fig. 5

Main work streams to predict clinically efficacious concentration and dose by PKPD modeling and simulation