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The results of this meta-analysis indicate that increased exposure to sunitinib is associated with improved clinical outcomes (longer TTP, longer OS, greater chance of antitumor response), as well as some increased risk of adverse effects. A sunitinib 50-mg starting dose seems reasonable, providing clinical benefit with acceptably low risk of adverse events.

Purpose: Sunitinib malate is an oral multitargeted tyrosine kinase inhibitor approved for advanced renal cell carcinoma and imatinib-resistant or imatinib-intolerant gastrointestinal stromal tumor. Following administration, sunitinib is metabolized by cytochrome P450 3A4 to an active metabolite (SU12662). The objective of this analysis was to assess sunitinib and SU12662 pharmacokinetics and to identify covariates that might explain variability in exposure following oral administration. Experimental Design: Data from 590 subjects (73 volunteers and 517 patients) in 14 studies were analyzed. Plasma concentration-time data were analyzed using nonlinear mixed-effects modeling to estimate population pharmacokinetic parameters, as well as relationships between these parameters and gender, race, age, weight, creatinine clearance, Eastern Cooperative Oncology Group score, and tumor type. Simulations were done to determine the predicted effect of these covariates on exposure.Results: Separate models were developed for sunitinib and SU12662 (each a two-compartment model with first-order absorption and elimination). Sunitinib parameters were estimated as CL/F, 51.8 L/h andVd/F central , 2,030 liters. SU12662 parameters were estimated as CL/F, 29.6 L/h and Vd/F central , 3,080 liters. Tumor type (except acute myeloid leukemia), Asian race, gender, body weight, and elevated Eastern Cooperative Oncology Group score described a portion of the variability in CL/F for sunitinib and metabolite; gender and body weight explained some of the variability in Vd/F central for sunitinib and metabolite. Among patients, the predicted changes in sunitinib and metabolite AUC and C max as a result of the individual covariates ranged up to 17%. Conclusion: The magnitude of the predicted changes in exposure with the covariates studied minimizes the necessity for dose adjustment in any of these subpopulations.

The predictive value of longitudinal biomarker data (vascular endothelial growth factor (VEGF), soluble VEGF receptor (sVEGFR)-2, sVEGFR-3, and soluble stem cell factor receptor (sKIT)) for tumor response and survival was assessed based on data from 303 patients with imatinib-resistant gastrointestinal stromal tumors (GIST) receiving sunitinib and/or placebo treatment. The longitudinal tumor size data were well characterized by a tumor growth inhibition model, which included, as significant descriptors of tumor size change, the model-predicted relative changes from baseline over time for sKIT (most significant) and sVEGFR-3, in addition to sunitinib exposure. Survival time was best described by a parametric time-to-event model with baseline tumor size and relative change in sVEGFR-3 over time as predictive factors. Based on the proposed modeling framework to link longitudinal biomarker data with overall survival using pharmacokinetic–pharmacodynamic models, sVEGFR-3 demonstrated the greatest predictive potential for overall survival following sunitinib treatment in GIST.