4.7 Article

Bridging the Gap between Preclinical and Clinical Studies Using Pharmacokinetic-Pharmacodynamic Modeling: An Analysis of GDC-0973, a MEK Inhibitor

Journal

CLINICAL CANCER RESEARCH
Volume 18, Issue 11, Pages 3090-3099

Publisher

AMER ASSOC CANCER RESEARCH
DOI: 10.1158/1078-0432.CCR-12-0445

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Purpose: GDC-0973 is a potent and selective mitogen-activated protein (MAP)/extracellular signal-regulated kinase (ERK) kinase (MEK) inhibitor. Pharmacokinetic-pharmacodynamic (PK-PD) modeling was used to relate GDC-0973 plasma and tumor concentrations, tumor pharmacodynamics and antitumor efficacy to establish pharmacokinetic endpoints and predict active doses in the clinic. Experimental Design: APK-PD model was used to characterize GDC-0973 tumor disposition and in vivo potency in WM-266-4 xenograft mice. Simulations were conducted using the PK-PD model along with human pharmacokinetics to identify a target plasma concentration and predict active doses. In vivo potency and antitumor efficacy were characterized in A375 melanoma xenograft mice, and a population-based integrated PK-PD-efficacy model was used to relate tumor pharmacodynamics (%pERK decrease) to antitumor activity. Results: GDC-0973 showed a sustained tumor pharmacodynamic response due to longer residence in tumor than in plasma. Following single doses of GDC-0973, estimated in vivo IC50 values of %pERK decrease based on tumor concentrations in xenograft mice were 0.78 (WM-266-4) and 0.52 mu mol/L (A375). Following multiple doses of GDC-0973, the estimated in vivo IC50 value in WM-266-4 increased (3.89 mmol/L). Human simulations predicted a minimum target plasma concentration of 83 nmol/L and an active dose range of 28 to 112 mg. The steep relationship between tumor pharmacodynamics (%pERK decrease) and antitumor efficacy suggests a pathway modulation threshold beyond which antitumor efficacy switches on. Conclusions: Clinical observations of %pERK decrease and antitumor activity were consistent with model predictions. This article illustrates how PK-PD modeling can improve the translation of preclinical data to humans by providing a means to integrate preclinical and early clinical data. Clin Cancer Res; 18(11); 3090-9. (C) 2012 AACR.

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