Using quantitative systems pharmacology modeling to optimize combination therapy of anti-PD-L1 checkpoint inhibitor
Samira Anbari1, Hanwen Wang1, Yu Zhang1
1Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, United States.
Abstract:
Although immune checkpoint blockade therapies have shown evidence of clinical effectiveness in many types of cancer, the outcome of clinical trials shows that very few patients with colorectal cancer benefit from treatments with checkpoint inhibitors. Bispecific T cell engagers (TCEs) are gaining popularity because they can improve patients' immunological responses by promoting T cell activation. The possibility of combining TCEs with checkpoint inhibitors to increase tumor response and patient survival has been highlighted by preclinical and clinical outcomes. However, identifying predictive biomarkers and optimal dose regimens for individual patients to benefit from combination therapy remains one of the main challenges. In this article, we describe a modular quantitative systems pharmacology (QSP) platform for immuno-oncology that includes specific processes of immune-cancer cell interactions and was created based on published data on colorectal cancer. We generated a virtual patient cohort with the model to conduct in silico virtual clinical trials for combination therapy of a PD-L1 checkpoint inhibitor (atezolizumab) and a bispecific T cell engager (cibisatamab). Using the model calibrated against the clinical trials, we conducted several virtual clinical trials to compare various doses and schedules of administration for two drugs with the goal of therapy optimization. Moreover, we quantified the score of drug synergy for these two drugs to further study the role of the combination therapy.
Insights
A new quantitative systems pharmacology platform models colorectal cancer immunotherapy. This approach optimizes combination therapy with checkpoint inhibitors and bispecific T cell engagers for improved patient outcomes.
Area of Science:
- Immuno-oncology
- Pharmacology
- Computational biology
Background:
- Immune checkpoint inhibitors (ICIs) show limited efficacy in colorectal cancer (CRC).
- Bispecific T cell engagers (TCEs) enhance T cell activation and anti-tumor responses.
- Combining ICIs and TCEs may improve CRC treatment outcomes, but optimal strategies are unknown.
Purpose of the Study:
- To develop a modular quantitative systems pharmacology (QSP) platform for immuno-oncology in CRC.
- To simulate virtual clinical trials for combination therapy of PD-L1 inhibitor (atezolizumab) and TCE (cibisatamab).
- To optimize dosing regimens and evaluate drug synergy for improved CRC treatment.
Main Methods:
- Developed a QSP model integrating immune-cancer cell interactions specific to CRC.
- Created a virtual patient cohort for *in silico* clinical trials.
- Calibrated the model using clinical trial data.
- Performed virtual trials to compare different doses and schedules of atezolizumab and cibisatamab.
- Quantified drug synergy scores for the combination therapy.
Main Results:
- The QSP platform successfully simulated virtual clinical trials for combination therapy.
- Various dosing regimens and schedules were evaluated for atezolizumab and cibisatamab.
- Drug synergy scores were quantified, providing insights into the combination's efficacy.
- The model facilitated *in silico* optimization of therapeutic strategies.
Conclusions:
- The developed QSP platform is a valuable tool for optimizing immuno-oncology combination therapies in CRC.
- Virtual clinical trials can guide the selection of optimal doses and schedules for improved patient response.
- Understanding drug synergy is crucial for maximizing the benefits of combining checkpoint inhibitors and TCEs in CRC treatment.
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