(M-033) babelmixr2 and PopED: Quick Conversion of NONMEM, Monolix and nlmixr2/rxode2 Models to PopED Optimal Design Analysis
Monday, November 11, 2024
7:00 AM – 5:00 PM MST
William Denney, PhD – Chief Scientist, Humnan Predictions; John Harrold, PhD – nlmixr2 team member, nlmixr2 team; Richard Hooijmaijers, BSc – Consultant PK-PD, LAP&P; Theodoros Papathanasiou, MPharm, PhD, GCSRT – Associate Director of Clinical Pharmacology Modeling and Simulation, GSK; Rik Schoemaker, PhD – Owner and senior consultant PK/PD modelling, founder and partner, Occams; Max Taubert, PhD – Sr. Principle Pharmacometrics Scientist, Novartis; Mirjam Trame, PhD – Vice President, Integrated Drug Development, US Northeast Regional Lead Pharmacometrics, Cetera; Justin Wilkins, PhD – Owner and Senior Consultant at Occams, Fellow and past president of the International Society of Pharmacometrics (ISoP), Occams
Director Novartis Fort Worth, Texas, United States
Disclosure(s):
Matthew L. Fidler, M.Stat., Ph.D.: No financial relationships to disclose
Introduction: nlmixr2 (www.nlmixr2.org) is an open-source R package, freely available on CRAN[1] and GitHub[2]. It builds on rxode2[3], an R package for simulation of nonlinear mixed effect models using ordinary differential equations (ODEs), providing an efficient and versatile way to specify pharmacometric models and dosing scenarios, with rapid execution due to compilation in C. PopED[4] is an open-source R package available on CRAN[4] and GitHub[5], used for computing optimal experimental designs based on nonlinear mixed-effect models. The babelmixr2[6,7] package facilitates the importing of nlmixr2 models to PopED via a new “poped” method.
Objectives: The objective of this project is to ease the transition of common model outputs (including NONMEM[8], Monolix[9], nlmixr2[1] and rxode2[3]) to the database structure used in computing optimal designs with the PopED package.
Methods: A new optimal design method, “poped”, was created in the babelmixr2 package. This takes an rxode2 functional model, including endpoints, and converts it to the required model structure for a PopED database (leveraging the internal R parsing tree). It uses a design-based event table to optimize doses, and sampling times. By combining these two elements with a standard nlmixr2 call, a poped database can be created. Many of the standard elements in a PopED database can be modified with the poped control options available in the `popedControl()` function. Notable exceptions are the sampling times and models, which are imported from the design event table and rxode2 endpoint model (which itself can be imported from other software with either the nonmem2rx[10] or monolix2rx[11] packages).
Results: Using the model described in the PopED vignette and using different ODE solvers, the problem had faster evaluation times than all other ODE solving methods used in the original vignette. Still, the ease of translation from NONMEM, Monolix or nlmixr2/rxode2 to PopED’s native format is the true benefit of this new extension inside the babelmixr2 package. While testing, optimal design models with multiple endpoints were found to work well.
Conclusions: The poped method inside of babelmixr2 is an easy and convenient way to transition previously-developed pharmacometric models to PopED format to help in study design and evaluation, thus supporting the design of leaner and more informative clinical trials.
