NONMEM

NONMEM, short for nonlinear mixed-effects modeling, is a widely used software tool for population pharmacokinetic and pharmacodynamic analysis. It enables the estimation of typical population parameters and the magnitude of between-subject and, in some models, between-occasion variability from sparse or unbalanced data. The method is designed to handle complex nonlinear relationships between doses, concentrations, and responses, and it supports covariate modeling to explain variability across individuals.

The modeling framework in NONMEM combines a structural model (for example, compartmental PK or PK/PD relationships) with statistical models that describe variability. Fixed effects (THETA) describe typical parameter values, random effects (ETA, ETAV, or OMEGA blocks) represent interindividual or inter-occasion variability, and residual error (SIGMA or EPS) accounts for unexplained measurement error. Likelihood-based estimation requires integrating over the random effects, typically using approaches such as the Laplace approximation, first-order methods like FO or FOCE, or more advanced stochastic methods. NONMEM also supports covariate effects, residual error models, and various data handling features to accommodate rich or sparse sampling designs.

A typical workflow involves preparing data in a standard format, writing a model control stream that defines the problem, data, model, and estimation method, and then running estimations to obtain parameter estimates and diagnostic outputs. Outputs include parameter estimates with standard errors, goodness-of-fit plots, and simulation capabilities for dose optimization and predictive checks. NONMEM is widely used in industry and academia for drug development, regulatory submissions, and methodological research in population PK/PD.