Use of the Optimata Virtual Patient (OVP) to predict effects of sunitinib malate in advanced pancreatic cancer.

Abstract

341 Background: Pancreatic cancer is considered to be incurable by available treatment modalities, with 5-year survival rate <4%. Sunitinib malate (Sutent) significantly increased progression-free survival in patients with advanced islet cell tumors (pancreatic); however, it demonstrated only modest single-agent effect in a phase II study in patients with metastatic pancreatic adenocarcinoma (PaC) that progressed after first-line therapy with gemcitabine. To improve the response of PaC patients to drugs, the interplay between biologic, pathologic, and pharmacologic processes underlying drug-patient interactions have been mathematically modeled, predicting efficacy responses, different toxicities, and long-term tolerability in clinical trials, allowing for improved dosing regimens and patient selection (OVP engine) (Agur Z., 2010). The OVP engine was used to predict Sunitinib malate single-agent activity in advanced PaC. Methods: OVP replicated the observed growth patterns of human PaC. Pharmacokinetics (PK) and pharmacodynamics (PD) of sunitinib malate were modeled based on literature (NDA 21-938). Effects of sunitinib malate on human PaC xenografts were scaled to model PK/PD in human. To evaluate drug efficacy in advanced PaC patient-population, a Virtual PaC Patient- Population was created by replacing the population averaged parameter values in the model by their distribution in the population. Using this procedure, a large set of virtual patients was generated. Model simulations with sunitinib malate therapy (50 mg QD/28 days; 14 days rest [1 cycle]) enabled the prediction and classification of patients' response according to RECIST criteria and compared with the actual clinical response (O'Reilly et al, 2008). Results: Simulations of the FDA-approved schedule, predicted a stable disease in 81% of the patients following one treatment cycle and 54% following two treatment cycles. Stable disease was predicted to be the best observed response, which was confirmed in the clinical study. Conclusions: Our model predictions are compatible with clinical results of a recent phase II trial with the same treatment regimen of sunitinib malate (O'Reilly et al, 2008) and further suggest the use of mathematical model during drug development. [Table: see text]