Quantitative drug benefit-risk assessment: utility of modeling and simulation to optimize drug safety in older adults

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.

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.

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.

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.

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.