Farmakometria

Farmakometria, or pharmacometrics, is a scientific discipline that uses mathematical models, statistics, and computational tools to quantify the relationship between drug exposure and pharmacological effect. The goal is to understand how drugs behave in the body (pharmacokinetics) and how those exposures translate into therapeutic or adverse effects (pharmacodynamics) and to use this understanding to optimize dosing.

Core activities include developing population PK/PD models that describe typical behavior and between-subject variability, identifying covariates that explain differences, and performing simulations to compare dosing regimens. Models are built from data across preclinical and clinical stages and can inform study design, dose selection, and adaptive strategies.

Methods and tools: nonlinear mixed-effects modeling, Bayesian estimation, and dose–response modeling are common. Software platforms such as NONMEM, Monolix, and Phoenix NLME are widely used, with newer tools like Pumas and Stan enabling Bayesian and hierarchical modeling.

Applications span drug development, regulatory submissions, and clinical practice. In development, pharmacometrics supports dose finding, regimen optimization, and extrapolation across populations or species. In clinical settings, it enables precision dosing by accounting for individual factors such as body weight, organ function, age, and genetics. Postmarketing and regulatory contexts increasingly rely on pharmacometric analyses for labeling, risk assessment, and real-world evidence.

Limitations include potential model misspecification, identifiability constraints, and data quality. Robust validation, external data, and transparent reporting are essential to ensure reliable predictions.