Machine Learning in Personalized Medication Regimen Design for the Geriatric Population; Integrating Pharmacokinetic and Pharmacodynamic Modeling with Clinical Decision Making

Geriatric pharmacotherapy is usually challenged by physiological senescence. For instance, progressive declines in organ function and alterations in body composition can complicate drug disposition. However, conventional pharmacometrics models commonly have limited capacity to map these high-dimensional, non-linear relationships. In this review, we are examining the recent shift toward integrating Machine Learning (ML) with mechanistic Pharmacokinetic (PK)/Pharmacodynamic (PD) models to improve the accuracy and precision of dosing. Machine learning approaches like Random Forest and XGBoost consistently provided more accurate exposure predictions and significantly more efficient computational workflows than conventional methods. Nevertheless, concerns such as "black box" transparency and the potential of algorithmic bias toward specific patient demographics are challenging. It is important to incorporate explainability tools like SHAP, and adopting FAIR data principles is crucial for achieving professional trust and ensuring site-specific generalizability.