Artificial Intelligence–Integrated Pharmacokinetic Modelling for Personalised Dose Prediction: A Data-Driven Approach

Variability in drug response among individuals continues to be a significant obstacle in clinical pharmacotherapy. Traditional pharmacokinetic (PK) models, although informative in terms of mechanisms, frequently do not adequately represent intricate nonlinear relationships between individual patient variables and drug exposure. This research introduces a data-driven framework that combines artificial intelligence (AI) with pharmacokinetic modelling to facilitate personalised dose forecasting. A hybrid approach integrating machine learning techniques with physiologically based pharmacokinetic (PBPK) concepts was created, utilising diverse clinical datasets. The suggested system showed enhanced predictive precision (R² = 0.93) and lowered dosing inaccuracies in comparison to conventional models. The results affirm that AI-enhanced PK modelling serves as an effective method for precise dosing and enhanced therapeutic results.