Adaptive dosing of sunitinib in a metastatic renal cell carcinoma patient: when in silico modeling helps to go quicker to the point.

PURPOSE: Adaptive dosing strategy with oral targeted therapies in oncology is mostly based upon clinical signs. Using pharmacokinetics (PK) models to customize dosing could help saving time, i.e., by predicting clinical outcome through early monitoring of drug levels. CASE REPORT: We present the case of a metastatic renal cell carcinoma patient treated with standard Sunitinib dosing (i.e., 50 mg QD). Clinical signs suggested lack of efficacy. Therapeutic Drug Monitoring (TDM) confirmed that exposure was below the expected target exposure. PK modeling suggested that dosing could be increased safely to 75 mg QD. Sunitinib dosing was instead changed empirically to 62.5 mg only, increasing drug exposure to the lower part of the therapeutic window. Resolution of bone pains plus Stable Disease were observed. Even though further modeling suggested to increase Sunitinib dosing to 75 mg again, the intermediate dosing was maintained for the subsequent cycles to preserve the safety. Unfortunately, severe pains plus degradation of the general state were reported and imaging showed Progressive Disease. The patient was finally switched to alternative therapy, without being treated at the 75 mg level of Suntitinib. CONCLUSIONS AND DISCUSSION: This case suggests that model-based adaptive dosing could have allowed to reach quicker the best dosing with Sunitinib, thus possibly ensuring a better management of this patient. Model-informed dosing should be used instead of empirical search for the most appropriate dosing to ensure a good benefit/risk ratio with Sunitinib, especially in the context of such aggressive disease.