Quantitative Drug Benefit-Risk Assessment: Utility of Modeling and Simulation to Optimize Drug Safety in Older Adults

Background: More than 50 % of individuals affected by adverse drug events (ADEs) are older adults. Establishing a drug dosing regimen that balances benefit and risk, and minimizes ADEs in older populations can be challenging. Objective: The aim of this study is to evaluate the use of modeling, simulation, and risk-benefit acceptability methods to establish a drug dosing regimen that balances benefit and risk. Methods: The study population comprised nondiabetic patients with schizophrenia from the Clinical Antipsychotic Trials of Intervention Effectiveness (CATIE) ≥50 years old, who had been on oral olanzapine for ≥2 weeks. We used mixed-effects modeling based on a preexisting pharmacokinetic model to derive clearance estimates, which were then used to determine the olanzapine area under the concentration-time curve (AUC). Subsequently, with multivariate regression and Monte Carlo simulation, we estimated the olanzapine dose corresponding to the benefit-risk AUC breakpoint. Results: The study population (n = 34) was predominantly male (82.3%) and white (67.6%), with a mean age of 54.4 years and treatment duration of 361.8 days. The mean AUC was 747.6 ng h/mL (95% CI = 524.5, 970.7) for the benefit group (n = 16) and 754.1 (95% CI = 505.9, 1002.4) for the risk group (n = 15). The benefit-risk AUC breakpoint was 524.5 ng h/mL and the corresponding oral olanzapine dose that optimizes benefit-risk balance was 17.8 mg/d. Conclusions: Our study introduces a real-world approach for finding the safe drug dosing regimen without extensive exposure of a vulnerable and older population to drugs. Further studies into the use of modeling, simulation, and risk-benefit acceptability methods to enhance geriatric drug safety are needed.