Applications of R Programming in Pharmacometrics: A Simulation-Based Population Pharmacokinetic Case Study

Pharmacometrics is a core component of model-informed drug development, integrating pharmacokinetics, pharmacodynamics, and computational modelling to characterize drug disposition, quantify variability, and optimize dosing strategies. The growing use of simulation-based methods has strengthened the role of computational platforms such as R in pharmacometrics analysis, enabling robust modelling, stochastic simulation, and reproducible quantitative workflows. In this short communication, an R-based population pharmacokinetic simulation framework was applied to evaluate the impact of interindividual variability on drug exposure under repeated dosing conditions. Using a one-compartment model with first-order elimination, 200 virtual subjects were simulated with variability in elimination rate constants incorporated through a log-normal distribution. Variability in concentration–time profiles and exposure metrics, including Cmax and AUC, was assessed. The simulations demonstrated substantial variability in systemic exposure despite identical dosing, highlighting the influence of pharmacokinetic heterogeneity on therapeutic outcomes. These findings underscore the utility of simulation-driven pharmacometrics modelling and support the application of R in individualized dosing and model-informed precision therapeutics.