A novel predictive algorithm to personalize autologous T-cell harvest for chimeric antigen receptor T-cell manufacture.


Journal

Cytotherapy
ISSN: 1477-2566
Titre abrégé: Cytotherapy
Pays: England
ID NLM: 100895309

Informations de publication

Date de publication:
03 2023
Historique:
received: 03 05 2022
revised: 23 10 2022
accepted: 24 10 2022
pubmed: 14 12 2022
medline: 8 2 2023
entrez: 13 12 2022
Statut: ppublish

Résumé

The most widely accepted starting materials for chimeric antigen receptor T-cell manufacture are autologous CD3+ T cells obtained via the process of leukapheresis, also known as T-cell harvest. As this treatment modality gains momentum and apheresis units struggle to meet demand for harvest slots, strategies to streamline this critical step are warranted. This retrospective review of 262 T-cell harvests, with a control cohort of healthy donors, analyzed the parameters impacting CD3+ T-cell yield in adults with B-cell malignancies. The overall aim was to design a novel predictive algorithm to guide the required processed blood volume (PBV) (L) on the apheresis machine to achieve a specific CD3+ target yield. Factors associated with CD3+ T-cell yield on multivariate analysis included peripheral blood CD3+ count (natural log, ×10 The authors propose a transferrable model that incorporates clinical and laboratory variables accessible pre-harvest for use across the field of T-cell therapy. Pending further validation, such a model may be used to generate an individual leukapheresis plan and streamline the process of cell harvest, a well-recognized bottleneck in the industry.

Sections du résumé

BACKGROUND AIMS
The most widely accepted starting materials for chimeric antigen receptor T-cell manufacture are autologous CD3+ T cells obtained via the process of leukapheresis, also known as T-cell harvest. As this treatment modality gains momentum and apheresis units struggle to meet demand for harvest slots, strategies to streamline this critical step are warranted.
METHODS
This retrospective review of 262 T-cell harvests, with a control cohort of healthy donors, analyzed the parameters impacting CD3+ T-cell yield in adults with B-cell malignancies. The overall aim was to design a novel predictive algorithm to guide the required processed blood volume (PBV) (L) on the apheresis machine to achieve a specific CD3+ target yield.
RESULTS
Factors associated with CD3+ T-cell yield on multivariate analysis included peripheral blood CD3+ count (natural log, ×10
CONCLUSIONS
The authors propose a transferrable model that incorporates clinical and laboratory variables accessible pre-harvest for use across the field of T-cell therapy. Pending further validation, such a model may be used to generate an individual leukapheresis plan and streamline the process of cell harvest, a well-recognized bottleneck in the industry.

Identifiants

pubmed: 36513573
pii: S1465-3249(22)01008-8
doi: 10.1016/j.jcyt.2022.10.012
pii:
doi:

Substances chimiques

Receptors, Chimeric Antigen 0

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

323-329

Informations de copyright

Copyright © 2022 International Society for Cell & Gene Therapy. Published by Elsevier Inc. All rights reserved.

Auteurs

Maeve A O'Reilly (MA)

University College London Cancer Institute, London, UK; Department of Hematology, University College London Hospital, London, UK. Electronic address: maeve.o'reilly@ucl.ac.uk.

Aman Malhi (A)

Cancer Research UK & University College London Cancer Trials Center, University College London, London, UK.

Kathleen P L Cheok (KPL)

Department of Hematology, University College London Hospital, London, UK.

Stuart Ings (S)

Department of Hematology, University College London Hospital, London, UK.

Carmen Balsa (C)

Department of Hematology, University College London Hospital, London, UK.

Helen Keane (H)

Department of Hematology, University College London Hospital, London, UK.

Katarzyna Jalowiec (K)

Department of Hematology, University College London Hospital, London, UK.

Lorna Neill (L)

Department of Hematology, University College London Hospital, London, UK.

Karl S Peggs (KS)

University College London Cancer Institute, London, UK; Department of Hematology, University College London Hospital, London, UK.

Claire Roddie (C)

University College London Cancer Institute, London, UK; Department of Hematology, University College London Hospital, London, UK.

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Classifications MeSH