Article Text

Download PDFPDF

195 Overcoming the dose-response prediction limitation from bench to clinic for T-cell engagers: using quantitative systems pharmacology (QSP) modelling in the development of CDR404 for solid tumors
  1. Melissa Vrohlings1,
  2. Drew Marquis2,
  3. Scott Gruver2,
  4. Fei Hua2,
  5. Stephanie Jungmichel3,
  6. Nadia Sanchez4,
  7. Ivana Tosevski4,
  8. Leonardo Borras4 and
  9. Swethajit Biswas5
  1. 1CDR-Life, Schlieren, Switzerland
  2. 2Applied BioMath, LLC, Concord, MA, USA
  3. 3CDR-Life, Schlieren/Zurich, Zurich, Switzerland
  4. 4CDR-Life, Horgen, Zurich, Switzerland
  5. 5CDR-Life, Zurich, Switzerland
  • Journal for ImmunoTherapy of Cancer (JITC) preprint. The copyright holder for this preprint are the authors/funders, who have granted JITC permission to display the preprint. All rights reserved. No reuse allowed without permission.


Background T-cell engagers are dependent on crosslinking of tumor cells with T cells and the activation of endogenous T-cells for their mechanism-of-action (MoA). As a result, such dose-response relationships are difficult to predict before a phase 1 trial begins. To overcome this prediction limitation from the bench to the clinic, and to take into consideration the heterogeneity of multiple MoA factors in the human tumor microenvironment, innovative quantitative modeling approaches are required. Here, we describe a quantitative systems pharmacology (QSP) model developed to support the first-in-human trial design for CDR404, a highly potent and highly specific antibody-based T-cell engager that binds bivalently to a MAGE-A4 peptide displayed on HLA-A*02:01 on cancer cells and monovalently to CD3 on T-cells.

Methods The QSP model for CDR404 focused on describing the drug PK and dynamics of binding to MAGE-A4/HLA-A*02:01 and CD3 in the tumor compartment. Receptor turnover (synthesis and degradation) was also included in the model. Measured binding KDs as well as MAGE-A4 peptide/HLA-A*02:01 copy numbers on cancer cells were used to inform the model development.

Results By fitting the binding-related parameters, the model was able to describe the in vitro cytotoxicity dose responses for four PBMC donors at different effector-to-target ratios for two MAGE-A4+/HLA-A02:01+ human cancer cell lines (NCI-H1703: squamous lung cancer and A375: melanoma). Differences in target cell lysis from donors correlated with varying percentages of CD8 T cells among the donors. Trimer formation corresponding to in vitro IFNγ release was also predicted by the model. Following calibration to in vitro data, the model for cancer patients was developed. The impact of variability in MAGE-A4 expression levels in the tumor on trimer formation was explored through sensitivity analysis. Dose selection for the first in human study was based on doses that predicted to have similar trimer formation as the cytotoxicity assays. The model predicted Phase 1 trial starting dose for CDR404, and doses potentially associated with anti-tumor responses in patients will be presented at the meeting.

Conclusions QSP modeling is a powerful approach which guides the clinical trial design through integrating biophysics data, in vitro functional data, preclinical PK and MoA-related target biology. The developed QSP model will become an integrated part of the clinical development program and can be updated with emerging clinical data during the Phase 1 trial to facilitate discovery of a safe and therapeutic dose range for CDR404.

Ethics Approval Animal studies were performed in compliance with the recommendations of the Guide for Care and Use of Laboratory Animals with respect to restraint, husbandry, surgical procedures, feed and fluid regulation, and veterinary care. The animal care and use program is accredited by the Association for Assessment and Accreditation of Laboratory Animal Care International (AAALAC), which assures compliance with accepted standards for the care and use of laboratory animals.

This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See

Statistics from

Request Permissions

If you wish to reuse any or all of this article please use the link below which will take you to the Copyright Clearance Center’s RightsLink service. You will be able to get a quick price and instant permission to reuse the content in many different ways.