Related Experiment Video
Updated: Aug 16, 2025
Testing Cancer Immunotherapeutics in a Humanized Mouse Model Bearing Human Tumors
Published on: December 16, 2022
Sources of inter-individual variability leading to significant changes in anti-PD-1 and anti-PD-L1 efficacy
Jessica C Leete1,2, Michael G Zager3, Cynthia J Musante4
1Clinical Pharmacology, Early Clinical Development, Pfizer Inc., Cambridge, MA, United States.
Abstract:
While anti-PD-1 and anti-PD-L1 [anti-PD-(L)1] monotherapies are effective treatments for many types of cancer, high variability in patient responses is observed in clinical trials. Understanding the sources of response variability can help prospectively identify potential responsive patient populations. Preclinical data may offer insights to this point and, in combination with modeling, may be predictive of sources of variability and their impact on efficacy. Herein, a quantitative systems pharmacology (QSP) model of anti-PD-(L)1 was developed to account for the known pharmacokinetic properties of anti-PD-(L)1 antibodies, their impact on CD8+ T cell activation and influx into the tumor microenvironment, and subsequent anti-tumor effects in CT26 tumor syngeneic mouse model. The QSP model was sufficient to describe the variability inherent in the anti-tumor responses post anti-PD-(L)1 treatments. Local sensitivity analysis identified tumor cell proliferation rate, PD-1 expression on CD8+ T cells, PD-L1 expression on tumor cells, and the binding affinity of PD-1:PD-L1 as strong influencers of tumor growth. It also suggested that treatment-mediated tumor growth inhibition is sensitive to T cell properties including the CD8+ T cell proliferation half-life, CD8+ T cell half-life, cytotoxic T-lymphocyte (CTL)-mediated tumor cell killing rate, and maximum rate of CD8+ T cell influx into the tumor microenvironment. Each of these parameters alone could not predict anti-PD-(L)1 treatment response but they could shift an individual mouse's treatment response when perturbed. The presented preclinical QSP modeling framework provides a path to incorporate potential sources of response variability in human translation modeling of anti-PD-(L)1.
Insights
Quantitative systems pharmacology modeling identified key factors influencing anti-PD-(L)1 therapy response variability. Understanding these parameters can help predict patient responses to cancer immunotherapy.
Area of Science:
- Immunology
- Pharmacology
- Computational Biology
Background:
- Anti-programmed cell death protein 1 (anti-PD-1) and anti-programmed cell death ligand 1 (anti-PD-L1) immunotherapies show variable efficacy across cancer patients.
- Identifying sources of this response variability is crucial for predicting patient outcomes and optimizing treatment strategies.
Purpose of the Study:
- To develop a quantitative systems pharmacology (QSP) model to understand variability in anti-PD-(L)1 treatment response.
- To identify key biological parameters that influence the efficacy of anti-PD-(L)1 therapies in a preclinical cancer model.
Main Methods:
- A QSP model was constructed incorporating pharmacokinetics of anti-PD-(L)1 antibodies, CD8+ T cell dynamics, and anti-tumor effects.
- The model was validated using data from the CT26 tumor syngeneic mouse model.
- Local sensitivity analysis was performed to identify influential parameters.
Main Results:
- The QSP model successfully described response variability in anti-PD-(L)1 treatments.
- Tumor cell proliferation rate, PD-1/PD-L1 expression levels, and PD-1:PD-L1 binding affinity were identified as critical for tumor growth.
- CD8+ T cell properties, including proliferation and killing rates, and influx into the tumor microenvironment, significantly impacted treatment efficacy.
Conclusions:
- The QSP model provides a framework for analyzing anti-PD-(L)1 response variability.
- Key parameters identified can inform strategies for predicting patient response and improving immunotherapy outcomes.
- This preclinical modeling approach can aid in the translation of findings to human clinical trials.
