nlmixr is a free and open‐source R package for fitting nonlinear pharmacokinetic (PK), pharmacodynamic (PD), joint PK‐PD, and quantitative systems pharmacology mixed‐effects models. Currently, nlmixr is capable of fitting both traditional compartmental PK models as well as more complex models implemented using ordinary differential equations. We believe that, over time, it will become a capable, credible alternative to commercial software tools, such as NONMEM, Monolix, and Phoenix NLME.

The free and open‐source package nlmixr implements pharmacometric nonlinear mixed effects model parameter estimation in R. It provides a uniform language to define pharmacometric models using ordinary differential equations. Performances of the stochastic approximation expectation‐maximization (SAEM) and first order‐conditional estimation with interaction (FOCEI) algorithms in nlmixr were compared with those found in the industry standards, Monolix and NONMEM, using the following two scenarios: a simple model fit to 500 sparsely sampled data sets and a range of more complex compartmental models with linear and nonlinear clearance fit to data sets with rich sampling. Estimation results obtained from nlmixr for FOCEI and SAEM matched the corresponding output from NONMEM/FOCEI and Monolix/SAEM closely both in terms of parameter estimates and associated standard errors. These results indicate that nlmixr may provide a viable alternative to existing tools for pharmacometric parameter estimation.

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Establishing a dosing regimen that maximizes clinical benefit and minimizes side effects for novel therapeutics is a key objective for drug developers. Finding an optimal dose and schedule can be particularly challenging for compounds with a narrow therapeutic window such as in oncology. Modeling and simulation tools can be valuable to conduct in‐silico evaluations of various dosing scenarios with the goal to identify those that could minimize toxicities, avoid unscheduled dose‐interruptions, or minimize premature discontinuations, which all could limit the potential for therapeutic benefit.In this tutorial we present a stepwise development of an adaptive dose simulation framework that can be used for dose‐optimization simulations. The tutorial first describes the general workflow, followed by a technical description with basic to advanced practical examples of its implementation in mrgsolve and is concluded with examples on how to utilize this in decision making around dose and schedule optimization.The adaptive simulation framework is built with pharmacokinetic, pharmacodynamic (i.e., biomarkers, activity markers, target engagement markers, efficacy markers) and safety models that include evaluations of unexplained inter‐individual and intra‐individual variability and covariate impact, which can be replaced and expanded (e.g., combination setting, comparator setting) with user‐defined models. Subsequent adaptive simulations allow to investigate the impact of starting dose, dosing intervals and event‐driven (exposure or effect) dose modifications on any endpoint. The resulting simulation‐derived insights can be used in quantitatively proposing dose and regimens that better balance benefit and side effects for further evaluation, aiding dose selection discussions, and designing dose modification recommendations, among others.