Evaluating Pharmacokinetic and Age-Related Parameters in the Static Mechanistic Model to Enhance the Accuracy of Drug-Drug Interaction Prediction in the Geriatric Population

Drug-drug interactions (DDIs) can significantly impact drug efficacy, patient safety, and healthcare costs. While potentially life-threatening, DDIs are also predictable, manageable, and avoidable. Due to polypharmacy and age-related physiological changes, DDIs are most prevalent in the geriatric population. However, geriatric patients are often excluded from clinical trials for ethical and scientific reasons. DDI assessments in the geriatric population are typically replied on extrapolated from younger populations, which may not always be appropriate. This research investigates whether the computational model, particularly the static mechanistic model, can accurately and appropriately predict DDIs in the geriatric population by modifying pharmacokinetic and age-related parameters that would represent older patients. Using a quantitative analysis method, a causal-comparative analysis was conducted on secondary data from published clinical DDI studies involving young and geriatric patients. The accuracy and predictability were compared before and after parameter modifications in the static mechanistic model. Data analysis included both descriptive and inferential statistics. Results demonstrated that modifying PK parameters, specifically using organ exit concentration, significantly improved DDI prediction in both young and geriatric populations. On the other hand, changing age-related input, specifically using reduced blood flow, has a minimum impact on DDI prediction in geriatric populations. These findings offer important insights for predicting and managing DDI in older adults. Future research can use the same approach to predict DDIs in other population groups, such as pediatric or organ-impaired patient groups.