ReviewClinical pharmacology and therapeutics2021
Quantitative Systems Pharmacology Approaches for Immuno-Oncology: Adding Virtual Patients to the Development Paradigm.
Review in Clinical pharmacology and therapeutics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 56 papers, 1 of them a synthesis that pooled it.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
56 citing papers in PubMed, 1 synthesis or guideline pooled it, 70 citations in OpenAlex.
- A Systematic Review of the Efforts and Hindrances of Modeling and Simulation of CAR T-cell Therapy.The AAPS journal · 2021Pooled it
- Virtual clinical trials of anti-PD-1 and anti-CTLA-4 immunotherapy in advanced hepatocellular carcinoma using a quantitative systems pharmacology model.Journal for immunotherapy of cancer · 2022Trial
- Leveraging a QSP Platform for Prediction of Clinical Efficacy and Safety Biomarkers in Immuno-Oncology Combination Therapy.Clinical pharmacology and therapeutics · 2026Article
- Quantitative Systems Pharmacology (QSP): Bridging Biology and Mechanism with Clinical Drug Development Decisions.Journal of clinical pharmacology · 2026Review
- Model-Informed Dosing Strategies for Epcoritamab Monotherapy in Relapsed or Refractory Large B-Cell Lymphoma.Clinical pharmacology and therapeutics · 2026Article
- CellExLink: End-to-end cell-type recognition and normalization in biomedical text.PLoS computational biology · 2026Article
- Model-Informed Drug Development (MIDD) for Analgesics: Review of Applications, Regulatory Integration, and Emerging Directions.Journal of pain research · 2026Review
- Anti-CD19 CAR T-cell therapy for autoimmune diseases: from exploratory clinical trials to quantitative pharmacodynamic modeling development.Frontiers in immunology · 2026Review
- Mathematical Oncology: How Modeling Is Transforming Clinical Decision-Making.Cancer research · 2025Review
- Identification and characterization of virtual sub-populations through phenotype-guided filtering. The challenging case of nonidentifiable models in the context of therapeutic evaluation.Journal of pharmacokinetics and pharmacodynamics · 2025Article
- Implicit Runge-Kutta based sparse identification of governing equations in biologically motivated systems.Scientific reports · 2025Article
- A comparative computational analysis of IFN-alpha pharmacokinetics and its induced cellular response in mice and humans.PLoS computational biology · 2025Article
- A Unified Explanation for Drug Repurposing and Pharmacological Pleiotropy Based on Classical and Statistical Thermodynamics.Pharmacology research & perspectives · 2025Review
- A Reflection on the Evolution of Antibody-Based Therapies in Oncology: Trastuzumab as an Exemplar-Commentary on Dijkers et al.Clinical pharmacology and therapeutics · 2025Article
- An integrated quantitative systems pharmacology virtual population approach for calibration with oncology efficacy endpoints.CPT: pharmacometrics & systems pharmacology · 2025Article
- Systems immunology: When systems biology meets immunology.Frontiers in immunology · 2025Review
- Multi-physiology modeling of the immune system in the era of precision immunotherapy.Frontiers in immunology · 2025Review
- Quantitative Systems Pharmacology Modeling in Immuno-Oncology: Hypothesis Testing, Dose Optimization, and Efficacy Prediction.Handbook of experimental pharmacology · 2025Review
- Advancing cancer drug development with mechanistic mathematical modeling: bridging the gap between theory and practice.Journal of pharmacokinetics and pharmacodynamics · 2024Review
- Ten challenges and opportunities in computational immuno-oncology.Journal for immunotherapy of cancer · 2024Review
Corrections and comments
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Authors and funding
39 authors at 8 institutions in 4 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Drug development in oncology commonly exploits the tools of molecular biology to gain therapeutic benefit through reprograming of cellular responses. In immuno-oncology (IO) the aim is to direct the patient's own immune system to fight cancer. After remarkable successes of antibodies targeting PD1/PD-L1 and CTLA4 receptors in targeted patient populations, the focus of further development has shifted toward combination therapies. However, the current drug-development approach of exploiting a vast number of possible combination targets and dosing regimens has proven to be challenging and is arguably inefficient. In particular, the unprecedented number of clinical trials testing different combinations may no longer be sustainable by the population of available patients. Further development in IO requires a step change in selection and validation of candidate therapies to decrease development attrition rate and limit the number of clinical trials. Quantitative systems pharmacology (QSP) proposes to tackle this challenge through mechanistic modeling and simulation. Compounds' pharmacokinetics, target binding, and mechanisms of action as well as existing knowledge on the underlying tumor and immune system biology are described by quantitative, dynamic models aiming to predict clinical results for novel combinations. Here, we review the current QSP approaches, the legacy of mathematical models available to quantitative clinical pharmacologists describing interaction between tumor and immune system, and the recent development of IO QSP platform models. We argue that QSP and virtual patients can be integrated as a new tool in existing IO drug development approaches to increase the efficiency and effectiveness of the search for novel combination therapies.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.