A machine-learning model that incorporates CD45 surface expression predicts hematopoietic progenitor cell recovery after freeze-thaw.

BACKGROUND AIMS: Sufficient doses of viable CD34+ (vCD34) hematopoietic progenitor cells (HPCs) are crucial for engraftment. Additional-day apheresis collections can compensate for potential loss during cryopreservation but incur high cost and additional risk. To aid predicting such losses for clinical decision support, we developed a machine-learning model using variables obtainable on the day of collection. METHODS: In total, 370 consecutive autologous HPCs, apheresis-collected since 2014 at the Children's Hospital of Philadelphia, were retrospectively reviewed. Flow cytometry was used to assess vCD34% on fresh products and thawed quality control vials. The ratio of vCD34% thawed to fresh, which we call "post-thaw index," was used as an outcome measure, with a "poor" post-thaw index defined as <70%. HPC CD45 normalized mean fluorescence intensity (MFI) was calculated by dividing CD45 MFI of HPCs to the CD45 MFI of lymphocytes in the same sample. We trained XGBoost, k-nearest neighbor and random forest models for the prediction and calibrated the best model to minimize falsely-reassuring predictions. RESULTS: In total, 63 of 370 (17%) products had a poor post-thaw index. The best model was XGBoost, with an area under the receiver operator curve of 0.83 evaluated on an independent test data set. The most important predictor for a poor post-thaw index was the HPC CD45 normalized MFI. Transplants after 2015, based on the lower of the two vCD34% values, showed faster engraftment than older transplants, which were based on fresh vCD34% only (average 10.6 vs 11.7 days, P = 0.0006). CONCLUSIONS: Transplants taking into account post-thaw vCD34% improved engraftment time in our patients; however, it came at the cost of unnecessary multi-day collections. The results from applying our predictive algorithm retrospectively to our data suggest that more than one-third of additional-day collections could have been avoided. Our investigation also identified CD45 nMFI as a novel marker for assessing HPC health post-thaw.

Chimeric Antigen Receptor T Cell Manufacturing on an Automated Cell Processor.

Chimeric antigen receptor (CAR)-T cells represent a promising immunotherapeutic approach for the treatment of various malignant and non-malignant diseases. CAR-T cells are genetically modified T cells that express a chimeric protein that recognizes and binds to a cell surface target, resulting in the killing of the target cell. Traditional CAR-T cell manufacturing methods are labor-intensive, expensive, and may carry the risk of contamination. The CliniMACS Prodigy, an automated cell processor, allows for manufacturing cell therapy products at a clinical scale in a closed system, minimizing the risk of contamination. Processing occurs semi-automatically under the control of a computer and thus minimizes human involvement in the process, which saves time and reduces variability and errors. This manuscript and video describes the T cell transduction (TCT) process for manufacturing CAR-T cells using this processor. The TCT process involves CD4+/CD8+ T cell enrichment, activation, transduction with a viral vector, expansion, and harvest. Using the Activity Matrix, a functionality that allows ordering and timing of these steps, the TCT process can be customized extensively. We provide a walk-through of CAR-T cell manufacturing in compliance with current Good Manufacturing Practice (cGMP) and discuss required release testing and preclinical experiments that will support an Investigational New Drug (IND) application. We demonstrate the feasibility and discuss the advantages and disadvantages of using a semi-automatic process for clinical CAR-T cell manufacturing. Finally, we describe an ongoing investigator-initiated clinical trial that targets pediatric B-cell malignancies [NCT05480449] as an example of how this manufacturing process can be applied in a clinical setting.