ArticleNature communications2023
In silico cancer immunotherapy trials uncover the consequences of therapy-specific response patterns for clinical trial design and outcome.
Article in Nature communications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
22 citing papers in PubMed, 32 citations in OpenAlex.
- A hybrid multiscale model for predicting CAR-T therapy outcomes in solid tumors.Scientific reports · 2026Article
- Overcoming tumour resistance in cancer immunotherapy through predictive and pharmacodynamic biomarkers.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2026Review
- Beyond the mouse: 3R-guided alternative animal models transforming cancer research.Molecular cancer · 2026Review
- GAN-enhanced digital twins for biomarker-guided cancer immunotherapy and adoptive cell therapy: a mini review.Frontiers in medicine · 2026Review
- Beyond prediction: AI as a mechanistic microscope and digital twin for colorectal cancer immunotherapy.Frontiers in immunology · 2026Review
- Mathematical Oncology: How Modeling Is Transforming Clinical Decision-Making.Cancer research · 2025Review
- A Step-by-Step Workflow for Performing In Silico Clinical Trials With Nonlinear Mixed Effects Models.CPT: pharmacometrics & systems pharmacology · 2025Article
- Multiscale mathematical model-informed reinforcement learning optimizes combination treatment scheduling in glioblastoma evolution.Science advances · 2025Article
- The future of pharmaceuticals: Artificial intelligence in drug discovery and development.Journal of pharmaceutical analysis · 2025Review
- A Model of Intratumor and Interpatient Heterogeneity Explains Clinical Trials of Curative Combination Therapy for Lymphoma.Blood cancer discovery · 2025Article
- Advances and critical aspects in cancer treatment development using digital twins.Briefings in bioinformatics · 2025Review
- Harnessing the AI/ML in Drug and Biological Products Discovery and Development: The Regulatory Perspective.Pharmaceuticals (Basel, Switzerland) · 2025Review
- Phytochemical synergies in BK002: advanced molecular docking insights for targeted prostate cancer therapy.Frontiers in pharmacology · 2025Review
- Digital patient modeling identifies predictive biomarkers of regorafenib response in elderly metastatic colorectal cancer.Frontiers in systems biology · 2025Article
- The PERMIT guidelines for designing and implementing all stages of personalised medicine research.Scientific reports · 2024Article
- 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
- Critical Appraisal and Future Challenges of Artificial Intelligence and Anticancer Drug Development.Pharmaceuticals (Basel, Switzerland) · 2024Article
- Surrogacy and the Valuation of ATMPs: Taking Our Place in the Evidence Generation/Assessment Continuum.PharmacoEconomics · 2024Article
- Generating Counterfactual Patient Timelines from Real-World Data.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2024Article
- Development of bispecific T cell engagers: harnessing quantitative systems pharmacology.Trends in pharmacological sciences · 2023Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Late-stage cancer immunotherapy trials often lead to unusual survival curve shapes, like delayed curve separation or a plateauing curve in the treatment arm. It is critical for trial success to anticipate such effects in advance and adjust the design accordingly. Here, we use in silico cancer immunotherapy trials - simulated trials based on three different mathematical models - to assemble virtual patient cohorts undergoing late-stage immunotherapy, chemotherapy, or combination therapies. We find that all three simulation models predict the distinctive survival curve shapes commonly associated with immunotherapies. Considering four aspects of clinical trial design - sample size, endpoint, randomization rate, and interim analyses - we demonstrate how, by simulating various possible scenarios, the robustness of trial design choices can be scrutinized, and possible pitfalls can be identified in advance. We provide readily usable, web-based implementations of our three trial simulation models to facilitate their use by biomedical researchers, doctors, and trialists.
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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.