ArticleNPJ systems biology and applications2024
"Digital twins elucidate critical role of T
Article in NPJ systems biology and applications, 2024. 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, 19 citations in OpenAlex.
- Technical review of artificial intelligence in TCR-T therapy.Journal of the National Cancer Center · 2026Review
- What Is a Digital Twin in QSP, and Are We Doing It Right?CPT: pharmacometrics & systems pharmacology · 2026Article
- Process Systems Engineering in Precision Medicine: Opportunities in Autologous CAR-T Therapy.Engineering in life sciences · 2026Review
- Leveraging AI for cell biology discovery.Biochemical Society transactions · 2026Review
- Mapping the future of medicine through digital twins.Frontiers in molecular medicine · 2026Review
- A Step-by-Step Workflow for Performing In Silico Clinical Trials With Nonlinear Mixed Effects Models.CPT: pharmacometrics & systems pharmacology · 2025Article
- Enhancing randomized clinical trials with digital twins.NPJ systems biology and applications · 2025Review
- A scoping review of human digital twins in healthcare applications and usage patterns.NPJ digital medicine · 2025Article
- The Potential Use of Digital Twin Technology for Advancing CAR-T Cell Therapy.Current issues in molecular biology · 2025Review
- From virtual to reality: innovative practices of digital twins in tumor therapy.Journal of translational medicine · 2025Review
- Harnessing the AI/ML in Drug and Biological Products Discovery and Development: The Regulatory Perspective.Pharmaceuticals (Basel, Switzerland) · 2025Review
- Smart CAR-T Nanosymbionts: archetypes and proto-models.Frontiers in immunology · 2025Review
- Future Directions for Quantitative Systems Pharmacology.Handbook of experimental pharmacology · 2025Review
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Despite recent progress in adoptive T cell therapy for cancer, understanding and predicting the kinetics of infused T cells remains a challenge. Multiple factors can impact the distribution, expansion, and decay or persistence of infused T cells in patients. We have developed a novel quantitative systems pharmacology (QSP) model of TCR-transgenic T cell therapy in patients with solid tumors to describe the kinetics of endogenous T cells and multiple memory subsets of engineered T cells after infusion. These T cells undergo lymphodepletion, proliferation, trafficking, differentiation, and apoptosis in blood, lymph nodes, tumor site, and other peripheral tissues. Using the model, we generated patient-matched digital twins that recapitulate the circulating T cell kinetics reported from a clinical trial of TCR-engineered T cells targeting E7 in patients with metastatic HPV-associated epithelial cancers. Analyses of key parameters influencing cell kinetics and differences among digital twins identify stem cell-like memory T cells (T
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What OpenQuestion holds
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.