ReviewEBioMedicine2025
Computational modelling of CAR T-cell therapy: from cellular kinetics to patient-level predictions.
Review in EBioMedicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 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
14 citing papers in PubMed.
- Optimizing Clinical Study Designs for CAR-T Cell Therapy: Development of an Efficient Sampling Strategy Through Optimal Experimental Design.CPT: pharmacometrics & systems pharmacology · 2026Article
- Advances in targeted and cellular therapies for relapsed/refractory mantle cell lymphoma: immunotherapeutic strategies and challenges.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2026Review
- A hybrid multiscale model for predicting CAR-T therapy outcomes in solid tumors.Scientific reports · 2026Article
- Review
- MISSTE: a multiscale integrative spatial simulator for understanding the mechanisms underlying tissue ecosystems.bioRxiv : the preprint server for biology · 2026Article
- Modeling and Simulation in Development and Regulatory Approval of Chimeric Antigen Receptor T-Cell Therapies: An Analysis of European Medicines Agency Centralized Procedures From 2018 to 2024.CPT: pharmacometrics & systems pharmacology · 2026Article
- Advancing adoptive T cell therapy in ovarian cancer: barriers, innovations, and emerging platforms.Journal for immunotherapy of cancer · 2026Review
- Data-driven computational modeling of CAR-T cell function.Frontiers in immunology · 2026Article
- CAR-T cell signaling dynamics, rational design principles and artificial intelligence for next-generation chimeric antigen receptors.Frontiers in immunology · 2026Review
- Harnessing the Power of CAR-NK Cells for Solid Tumors: Challenges, Innovations, and Future Frontiers in Immunotherapy.Cancer communications (London, England) · 2026Review
- Plying potency assays for immunotherapy of solid tumors.Frontiers in immunology · 2026Review
- Mathematical Oncology: How Modeling Is Transforming Clinical Decision-Making.Cancer research · 2025Review
- Functionalized mesenchymal stem cells for enhanced bone regeneration: advances and challenges.Stem cell research & therapy · 2025Review
- Immune effector cell-associated neurotoxicity syndrome: integrative mechanisms, predictive biomarkers, and translational pathways for prevention in CAR T-cell therapy.Frontiers in neurologyReview
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Chimeric Antigen Receptor (CAR) T-cell therapy is characterised by the heterogeneous cellular kinetic profile seen across patients. Unlike traditional chemotherapy, which displays predictable dose-exposure relationships resulting from well-understood pharmacokinetic processes, CAR T-cell dynamics rely on complex biologic factors that condition treatment response. Computational approaches hold potential to explore the intricate cellular dynamics arising from CAR T therapy, yet their ability to improve cancer treatment remains elusive. Here we present a comprehensive framework through which to understand, construct, and classify CAR T-cell kinetics models. Current approaches often rely on adapted empirical pharmacokinetic methods that overlook dynamics emerging from cellular interactions, or intricate theoretical multi-population models with limited clinical applicability. Our review shows that the utility of a model does not depend on the complexity of its design but on the strategic selection of its biological constituents, implementation of suitable mathematical tools, and the availability of biological measures from which to fit the model.
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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.