Quantitative Systems Pharmacology Modeling in Immuno-Oncology: Hypothesis Testing, Dose Optimization, and Efficacy Prediction
Open accessAbstract
Despite an increasing number of clinical trials, cancer is one of the leading causes of death worldwide in the past decade. Among all complex diseases, clinical trials in oncology have among the lowest success rates, in part due to the high intra- and inter-tumoral heterogeneity. There are more than a thousand cancer drugs and treatment combinations being investigated in ongoing clinical trials for various cancer subtypes, germline mutations, metastasis, etc. Particularly, treatments relying on the (re)activation of the immune system have become increasingly present in the clinical trial pipeline. However, the complexities of the immune response and cancer-immune interactions pose a challenge to the development of these therapies. Quantitative systems pharmacology (QSP), as a computational approach to predict tumor response to treatments of interest, can be used to conduct in silico clinical trials with virtual patients (and emergent use of digital twins) in place of real patients, thus lowering the time and cost of clinical trials. In line with improved mechanistic understanding of the human immune system and promising results from recent cancer immunotherapy, QSP models can play critical roles in model-informed drug development in immuno-oncology. In this chapter, we discuss how QSP models were designed to serve different study objectives, including hypothesis testing, dose optimization, and efficacy prediction, via case studies in immuno-oncology.
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QSP model calibration requires data from diverse sources including targeted experimental studies and clinical endpoints.
“calibration requires data from diverse sources including targeted experimental studies and clinical endpoints [2, 3].”
From Structured Schemas for Provenance‐Rich, <scp>LLM</scp> ‐Assisted <scp>QSP</scp> Model Calibration (2026) · cites this paper as [2]
QSP calibration requires data from diverse sources including targeted experimental studies and clinical endpoints.
“calibration requires data from diverse sources including targeted experimental studies and clinical endpoints2,3.”
From Structured Schemas for Provenance-Rich, LLM-Assisted QSP Model Calibration (2026) · cites this paper as [2]
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Structured Schemas for Provenance-Rich, LLM-Assisted QSP Model Calibration
Eliason, J., & Popel, A. S. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.03.05.709623
“QSP models can comprise tens to hundreds of ordinary differen-38 tial equations, and calibration requires data from diverse sources including targeted experimental studies and clinical endpoints 2,3 .”
Self-citationStructured Schemas for Provenance‐Rich, <scp>LLM</scp> ‐Assisted <scp>QSP</scp> Model Calibration
Eliason, J., & Popel, A. S. (2026) · CPT: Pharmacometrics & Systems Pharmacology · doi:10.1002/psp4.70306
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- Authors
- Wang, H., Arulraj, T., Ippolito, A., & Popel, A. S.
- Affiliations
- Johns Hopkins University (US)
- Corresponding author
- Popel, Aleksander S.
- Venue
- Handbook of Experimental Pharmacology
- Publisher
- Springer Nature Switzerland
- Published
- 2024
- Volume
- 289
- Pages
- 261-284
- ISSN
- 0171-2004 (ISSN-L)
- 1865-0325
- PMC
- PMC12713570
- OpenAlex
- W4405632892
- Cited by
- 6
- References
- 85
- Citations by year
- 2026: 3 · 2025: 3
- FWCI
- 36.62
- Type
- book chapter
- Language
- en
- Open access
- Licence
- Locations
- Handbook of experimental pharmacology (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Institutes of Health
- National Cancer Institute (R01CA138264)
- Topics
- Physical Sciences › Mathematics › Modeling and Simulation › Mathematical Biology Tumor Growth
- Health Sciences › Medicine › Oncology › Cancer Immunotherapy and Biomarkers
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Clinical Trials
- Keywords
- quantitative systems pharmacology, cancer immunotherapy, hypothesis testing, dose optimization, efficacy prediction, in silico clinical trials, virtual patients, digital twin, model-informed drug development, antitumor immunity, tumor-immune interactions, tumor heterogeneity
- MeSH terms
- Network Pharmacology, Animals, Antineoplastic Agents, Dose-Response Relationship, Drug, Humans, Immunotherapy, Models, Biological, Neoplasms
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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