ArticleBiotechnology progress
A digital shadow of CAR T cell expansion in a perfusion bioreactor: Informing optimal harvest times for autologous cell therapy.
Article in Biotechnology progress. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Mechanistic model for HEK293 viral vector processes and its application in a digital shadow framework.Bioprocess and biosystems engineering · 2026Article
- In Vivo T-Cell Engineering: Revolution in Delivery Strategies and Clinical Translation.BioDrugs : clinical immunotherapeutics, biopharmaceuticals and gene therapy · 2026Review
- Are we there yet? The road to faster and more efficient CAR T cell manufacturing.Journal of biological engineering · 2026Review
- Process Systems Engineering in Precision Medicine: Opportunities in Autologous CAR-T Therapy.Engineering in life sciences · 2026Review
- A digital shadow of CAR T cell expansion in a perfusion bioreactor: Informing optimal harvest times for autologous cell therapy.Biotechnology progressArticle
Corrections and comments
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Authors and funding
12 authors.
Funding
Abstract
Chimeric antigen receptor (CAR) T cell therapy has tremendous potential for the treatment of cancer and other diseases. To manufacture cells of the desired quantity and quality, it is important to expand the CAR T cells ex vivo for an optimal duration. However, identifying the optimal harvest time requires knowledge of the cell concentration during the expansion period. To address this challenge, we have developed a digital shadow of CAR T cell expansion that provides a soft sensor of cell concentration in real-time. Specifically, a novel mechanistic mathematical model of cell growth within a proportional-integral-derivative (PID) controlled perfusion bioreactor has been developed using nonlinear ordinary differential equations. The model is fitted to data generated via bioreactor runs of the Aglaris FACER, in which both donor and patient cells have been expanded in two different media. Off-line data includes the initial and final cell concentrations, and online data includes the glucose and lactate concentrations as well as the perfusion rate. Training the digital shadow utilizes all the off-line and online data for each run. In contrast, real-time testing utilizes only the initial cell concentration and the available online data at the time of model fitting. Real-time testing shows that with at least 2.5 days of online data, the final cell concentration up to 2.5 days later is predicted with a mean relative error of 13% (standard deviation ≈ 6%). Informative real-time predictions of cell concentration via the digital shadow can guide decisions regarding the optimal harvest time of CAR T cells.
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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.