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Tutorial

Tutorial: Using Past Models to Bridge to Open Models and Open Science using nlmixr2

Thursday, November 14, 2024
8:00 AM – 12:00 PM MST
Location: Sonoran Sky Ballroom 1 - 4

    Chair(s)

  • MF

    Matthew L. Fidler, M.Stat., PhD (he/him/his)

    Director
    Novartis
    Fort Worth, Texas, United States

    Disclosure(s): Baylor Scott and White: Employment (Ongoing); Novartis: Employment (Ongoing)

    • Speaker and Panelist(s)

  • William S Denney, Ph.D. (he/him/his)

    Chief Scientist
    Human Predictions, Massachusetts, United States

    Disclosure(s): No financial relationships to disclose

  • Mirjam Trame, Ph.D (she/her/hers)

    Vice President Pharmacometrics
    Certara
    Boston, Massachusetts, United States

    Disclosure(s): No financial relationships to disclose

  • Justin J. Wilkins, PhD (he/him/his)

    Owner and Senior Consultant at Occams, Fellow and past president of ISoP
    Occams
    Werl, Nordrhein-Westfalen, Germany

    Disclosure(s): Genentech: Consultant/Advisory Board (Ongoing); Merck KGaA: Consultant/Advisory Board (Ongoing); Roche: Consultant/Advisory Board (Ongoing)

Disclosure(s):
Matthew L. Fidler, M.Stat., PhD: Baylor Scott and White: Employment (Ongoing); Novartis: Employment (Ongoing)
William S Denney, Ph.D.: No financial relationships to disclose
Mirjam Trame, Ph.D: No financial relationships to disclose
Justin J. Wilkins, PhD: Genentech: Consultant/Advisory Board (Ongoing); Merck KGaA: Consultant/Advisory Board (Ongoing); Roche: Consultant/Advisory Board (Ongoing)
Description: Open science is a movement to make science available to all levels of society. The science in much of population-based pharmacometrics in the past has been focused on developing nonlinear mixed effects models in proprietary tools like NONMEM and Monolix. This makes it necessary to have licenses of whatever tool is being used to be able to explore models with your data. Special populations in low to middle income countries may not have access to these tools which makes analysis of additional clinical data in these regions more challenging. This tutorial discusses automated methods to import NONMEM and Monolix into the open-source framework rxode2 and nlmixr2 using packages like nonmem2rx, monolix2rx and babelmixr2. Once converted, you can use these models to make patient-based adaptive dosing decisions, simulate other dosing scenarios and even use the model to analyze new data and explore any regional differences in drug effect.

Learning Objectives:

  • Upon completion, participants learn the basics of how a nlmixr2/rxode2 model is written and can write a model themselves. 2. Upon completion, participants will know how to import NONMEM and Monolix models into the nlmixr2/rxode2 model function using nonmem2rx and monolix2rx 3. Upon completion, participants will know how to simulate new dosing scenarios using rxode2 4. Upon completion, participants will know how to (re-)estimate new data using nlmixr2 5. Upon completion, participants learn how to individualize dosing clinically using posologyr