OBJECTIVES: Our objective is to develop a quantitative, model-based protocol simulation approach for evaluating the clinical and economic effects of adverse drug outcomes related to genetic variation at early stages of drug or test development, using warfarin pharmacogenomics as a case-study. METHODS: We implemented a previously published (Hamberg et al. (2007)) population pharmacokinetic/pharmacodynamic (PK/PD) model of warfarin distribution and effect that incorporates the effects of genetic variation in the CYP2C9 and VKORC1 genes and other relevant demographic variables. We simulated outcomes (INR distribution) of a non-pharmacogenomic-based warfarin dosing protocol, and plan to simulate various pharmacogenomic-based dosing protocols and then integrate these results with pharmacoeconomic simulation models. RESULTS: INRs were modeled for 500 simulated patients using the same patient demographics (median and range) as those reported in the Hamberg analysis. The 5mg/daily INR nomogram of Kovacs et al. (2003) was simulated. Baseline INRs were uniformly distributed over a range of 0.9 to 1.3. The INR at day 6 after initiation of therapy ranged from 0.97 to 10.31 with a median of 3.61. Median INR grouped by CYP2C9 expression ranged from 3.17 for *1*1 patients to 5.29 for *3*3 patients. INR variations are linked to the risks of bleeding and stroke, and ultimately to the pharmacoeconomic outcomes of costs and quality-adjusted life years. CONCLUSION: ¢P-cubed¢ (P3) modeling will be feasible only when sufficient population PK/PD data are available and valid long-term linkages can be made. It may serve as a tool to explore the robustness of such linkages and probe alternative therapeutic scenarios. Although our findings are preliminary to date, P3-modeling may provide a useful quantitative framework to help inform pharmacogenomic trial design, regulatory decisions, and potentially clinical guidelines and reimbursement policies.