Skip to main content
ACoP 2024 Main banner
Final Program


Conference Program
Icon Legend
This session is not in your schedule.
This session is in your schedule. Click again to remove it.
Back

Workshop

LAP&P course; An interactive translational pharmacometric case study for an antibody exhibiting TMDD, LAP&P Consultants BV

Friday, November 15, 2024
8:00 AM – 5:00 PM MST
Location: Lantana

    Speaker(s)

  • Tv

    Tamara van Steeg

    Principal Consultant Pharmacometrician, Director Research
    LAP&P Consultants BV, Netherlands

    Disclosure information not submitted.

  • SH

    Sven Hoefman

    Disclosure information not submitted.

  • RH

    Richard Hooijmaijers

    Disclosure information not submitted.

In this 1.5-day course we will specifically discuss the importance of understanding the pharmacology of monoclonal antibodies (mAbs) to make the right decisions in target-mediated drug disposition (TMDD) model development. If you want to learn more on 1) the relevance of mechanistic modelling for mAbs, 2) the considerations with regards to scaling the model for first in human (FIH) approaches, and 3) what TMDD models are, what approximations may be applicable and how to use these models for (non-) clinical data. After a short introduction, we will directly dive into a hands-on session, in which the participants work in small groups on TMDD modelling challenges using a nlmixr2 within a shinyMixR workflow.

Learning Objectives:

  • Apply TMDD models for a therapeutic biologic within translational drug development
  • Participants will be introduced into these concepts through ‘learning by doing’: during the greater part of the course, participants will work hands-on on a real-life example to support the development of a mAb from animal studies to the clinic.
  • Participants will learn to deal with both the technical challenges associated with applying TMDD models, and the right interpretation of the acquired results.
  • Learn to perform population PK/PD analyses within a platform that is fully open source, using R and the nlmixr2 and shinyMixR packages, whereby scripts and code will be available to efficiently work on the case.