An interactive translational pharmacometric case study for an antibody exhibiting TMDD

Monoclonal antibodies (mAbs) often display target-mediated drug disposition (TMDD), which causes the PK to be non-linear and possibly informative on target engagement. Published TMDD models allow the quantification of the PK and target engagement [Mager & Jusko 2001, Gibiansky et al., 2008, Koch et al., 2016]. Although readily available, TMDD models and their simplifications may be challenging to use and interpret in a real-life situation. Making the right assumptions and using the right techniques as a pharmacometrician is crucial to answer the question at hand.

What will you learn?

In this one-day course we will specifically discuss the importance of understanding the pharmacology of mAbs to make the right decisions in 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.

What to expect?

In this course, we will mimic a real-life example of a mAb with straightforward target engagement. 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. We will focus on both the technical challenges associated with applying these models, and on the right interpretation of the acquired results. In the morning we will start with the analysis of a preclinical dataset, after which we will proceed to the design of a first in human trial (e.g., dose and sampling selection). During the afternoon we’ll work on the analysis of the resulting clinical data.

For whom?

This course is intended for pharmacometricians and pharmacokineticists with basic modelling experience. Participants will be introduced into the basic concepts and application of TMDD models through ‘learning by doing’. Some population PK/PD modelling experience is required. An installation in R is required and installation instructions will be shared shortly before the course. No experience is required with other specific software as we will guide you through the use of nlmixr2 within a shinyMixR workflow [Fidler et al., 2019]. Scripts and code will be available to efficiently work on the case and for more information on these opensource packages please visit: https://nlmixr2.org/

Participants will be asked up front to provide some detail on their experience, such that we can suggest further reading in case needed. Due to the interactive nature of the course and the drug development focus, the total number of participants is set at 20, of which there is space for up to five people at reduced course fees.

Date: Tuesday, 27th June, 2023
Time: 09:00 - 17:00
Course instructors: Tamara van Steeg, Sven Hoefman and Richard Hooijmaijers
Fees: $600 for Industry, $300 for Academia/ PhD Students
Contact us at: info@lapp.nl for more information and please register via the form.

 

By submitting your name and email address you agree that this information will be stored until the actual course has been completed. This information will not be used for any other purposes except for registration and payment of the fees.

Registration form

    References

    Mager & Jusko 2001 General pharmacokinetic model for drugs exhibiting target-mediated drug disposition. Journal of Pharmacokinetics and Pharmacodynamics 28 (6): 507-32.

    Gibiansky et al., 2008 Approximations of the target-mediated drug disposition model and identifiability of model parameters. Journal of Pharmacokinetics and Pharmacodynamics 35 (5): 573-91.

    Koch et al., 2016 Target-mediated drug disposition with drug–drug interaction, Part I: single drug case in alternative formulations. Journal of Pharmacokinetics and Pharmacodynamics 44 (1): 17–26.

    Fidler et al., 2019 Nonlinear Mixed-Effects Model Development and Simulation Using nlmixr and Related R Open-Source Packages. CPT Pharmacometrics Systems Pharmacology 8(9): 621-633.