ArticleFrontiers in pharmacology2023
Using quantitative systems pharmacology modeling to optimize combination therapy of anti-PD-L1 checkpoint inhibitor and T cell engager.
Article in Frontiers in pharmacology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Quantitative Systems Pharmacology (QSP): Bridging Biology and Mechanism with Clinical Drug Development Decisions.Journal of clinical pharmacology · 2026Review
- Mechanistic learning to predict and understand minimal residual disease.bioRxiv : the preprint server for biology · 2026Article
- An Overview on T-Cell Engagers: The Current Position in Both the Biological and Mathematical Context.Computational and structural biotechnology journal · 2026Review
- T-Cell Engager Therapy in Prostate Cancer: Molecular Insights into a New Frontier in Immunotherapy.Cancers · 2025Review
- Clinical Pharmacology of Cytokine Release Syndrome with T-Cell-Engaging Bispecific Antibodies: Current Insights and Drug Development Strategies.Clinical cancer research : an official journal of the American Association for Cancer Research · 2025Review
- Quantitative Systems Pharmacology Modeling in Immuno-Oncology: Hypothesis Testing, Dose Optimization, and Efficacy Prediction.Handbook of experimental pharmacology · 2025Review
- Virtual patient analysis identifies strategies to improve the performance of predictive biomarkers for PD-1 blockade.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Identifying biomarkers for treatment of uveal melanoma by T cell engager using a QSP model.NPJ systems biology and applications · 2024Article
- Integration of Clinical Trial Spatial Multiomics Analysis and Virtual Clinical Trials Enables Immunotherapy Response Prediction and Biomarker Discovery.Cancer research · 2024Article
- Computational methods and biomarker discovery strategies for spatial proteomics: a review in immuno-oncology.Briefings in bioinformatics · 2024Review
- From virtual patients to digital twins in immuno-oncology: lessons learned from mechanistic quantitative systems pharmacology modeling.NPJ digital medicine · 2024Review
- Leveraging multi-omics data to empower quantitative systems pharmacology in immuno-oncology.Briefings in bioinformatics · 2024Article
- Informing virtual clinical trials of hepatocellular carcinoma with spatial multi-omics analysis of a human neoadjuvant immunotherapy clinical trial.bioRxiv : the preprint server for biology · 2023Article
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Authors and funding
8 authors.
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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
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