ИСТИНА

Objectives: The growing need for QSP modeling in the industry and in communications with regulators necessitates a more standardized and seamless workflow for data structuring, model development and assessment, and reporting [1]. We selected five packages in MATLAB and R environments and evaluated their utility through a comparison of the corresponding workflows for three QSP models. Methods: We evaluated the performance and capabilities of five packages: the MATLAB-based IQM toolbox; R-based mrgsolve; RxODE; the AZR ODE solver with a set of MSDr functions; and IQR. Three different QSP models with mechanistic details and nonlinear features were developed in each of these packages, using study-level data. Results: All five packages use a highly flexible syntax and handle standardized ‘.csv’ datasets as inputs, to perform simulations of population and QSP models with varying degrees of complexity and design. The RxODE package was more than 10 times faster in terms of solver speed, as compared to the other four. However, lag times could not be incorporated in RxODE-type models, and only mrgsolve and IQR were able to incorporate regression parameters from the datasets. Both IQM and IQR packages incorporate parameter estimation tools which provided robust fitting against heterogeneous and complex study-level datasets, and automatically provide common model diagnostics, e.g., Observed vs. Predicted, Residuals and Time profiles plots. IQR enables the calculation of point-wise finite sample confidence intervals through likelihood profiling or via the Fisher Information Matrix. Conclusions: The five packages can handle identical model structures and dataset files, as well as a script-based workflows. While RxODE features the fastest solver, only IQR provided robust parameter estimations with simultaneous analysis of structural identifiability and estimation of 95% CI. References: [1] Gadkar K, et al. CPT Pharmacometrics Syst Pharmacol. 2016 May;5(5):235-49. doi: 10.1002/psp4.12071.