Pharmacometric models to support individual dosing

Individualized dosing is essential to optimize therapeutic outcomes while minimizing adverse drug reactions. Pharmacometric models—quantitative frameworks that describe the relationship between drug exposure, patient characteristics, and clinical response—offer a powerful approach to guide dose selection in clinical practice. These models integrate pharmacokinetic and pharmacodynamic principles with patient-specific covariates such as age, weight, organ function, and genetic variability. By accounting for inter- and intra-individual variability, pharmacometric approaches enable prediction of optimal dosing strategies beyond conventional population-based recommendations.

Applications range from model-informed precision dosing in oncology, infectious diseases, and pediatrics, to adaptive dosing strategies in critical care. Bayesian forecasting, in particular, allows real-time updating of individual dose requirements based on therapeutic drug monitoring data. Moreover, simulation tools based on population pharmacokinetic-pharmacodynamic models support the evaluation of alternative dosing regimens before implementation in clinical trials or practice.

The integration of pharmacometrics into clinical decision support systems represents a step toward personalized medicine, fostering evidence-based, data-driven dosing strategies. Challenges remain, including the need for robust validation, user-friendly software, and seamless integration into clinical workflows. Nevertheless, growing regulatory support and advances in digital health highlight the potential of pharmacometric models to transform drug therapy optimization.

In conclusion, pharmacometric modeling provides a scientific foundation for individualized dosing, improving efficacy and safety while supporting rational, patient-centered therapeutic decision-making.

A recording of the talk is available >here<