Individualizing apheresis processing volumes using a predictive algorithm: Analysis of the CAR T-cell collection protocol utilized in ACIT001/EXC002, a phase lb/ll trial of decentralized production of CAR T-cells for treatment of relapsed/refractory aggressive NHL and ALL
Notice bibliographique
Résumé
Abstract Introduction: Chimeric antigen receptor (CAR) T-cell therapy has transformed the treatment landscape for relapsed/refractory (R/R) B-cell malignancies. To manufacture CAR T-cells, autologous T-cell collection is performed via apheresis, which involves the standard processing of 2-4 times a patient's total blood volume (TBV). However, this often exceeds what is necessary for adequate cell yield, resulting in inefficient utilization of finite medical resources and an increased risk of patient complications. For institutions producing CAR T-cells in-house, a more individualized collection strategy is warranted. This study evaluates the performance of our institution's calculator for predicting adequate T-cell collection in patients undergoing apheresis for local CAR T-cell manufacturing. Methods: Adult patients with R/R non-Hodgkin lymphoma (NHL) or acute lymphoblastic leukemia (ALL) underwent T-cell collection as part of a clinical trial examining decentralized CAR T-cell production (NCT03938987). On the day of the apheresis procedure, each patient's absolute T-cell count was calculated by multiplying the baseline lymphocyte count by the percentage of CD3+ lymphocytes determined by flow cytometry of the peripheral blood. This number was then multiplied by the patient's TBV to determine the total potential T-cell count (TPT). The estimated number of T-cells collected per apheresis cycle (1 cycle = time to process 1 TBV) was calculated by multiplying the TPT by an assumed collection efficiency of 50%, less the T-cells collected in any previous cycles. The estimated number of apheresis cycles needed to reach a collection target of 1.0 x 109 CD3+ lymphocytes was then calculated to determine an estimated time on machine (TOM). Actual CD3+ counts were assessed at the midpoint and end of the estimated TOM. Results: A total of 29 patients with R/R NHL (69%) or ALL (31%) underwent T-cell collection. The median age at the time of apheresis was 58 years (range, 26–77), with 48% of patients aged ≥60 years. Patients were heavily pre-treated, with 72% of patients having received ≥4 prior lines of therapy and 14% of patients having undergone prior allogeneic stem cell transplantation. T-cell collection was well-tolerated in all patients, with no major adverse events reported during apheresis. Baseline laboratory evaluation revealed a median CD3+ lymphocyte count of 0.6 × 10⁹/L, within the normal reference range. In 21 of 29 procedures (72%), our calculation accurately predicted or overestimated the TOM required to reach the target collection. In the remaining 8 cases (28%), the midpoint assessment was essential to identify under-collection and adjust parameters to meet the target yield by the estimated TOM. Ultimately, the collection target was achieved in 93% of patients (27/29). We examined the characteristics of the patients who had a correct or overestimated TOM (Group A) and an underestimated TOM (Group B). Patients in Group B had a significantly smaller TBV compared to patients in Group A (4.41 L versus 4.95 L, p=0.04). Age, sex, underlying diagnosis, number of prior lines of treatment, and baseline CBC values were not significantly different between groups. The estimated number of TBVs required for adequate collection was lower in Group A than in Group B (1.50 versus 4.50), resulting in a shorter estimated TOM (2.28 hours versus 4.77 hours, p=0.02); this also translated to a shorter actual TOM (2.40 hours versus 3.93 hours, p=0.03). The median collection efficacy was higher in Group A than in Group B (21.79% versus 8.81%), although this difference was not significant (p=0.30). Conclusion: Our findings demonstrate that a straightforward calculation based on baseline CD3+ lymphocyte counts can be used to estimate apheresis times in heavily pretreated patients undergoing T-cell collection for CAR T-cell manufacturing. However, real-time midpoint analysis remains essential to ensure adequate T-cell collection, particularly in patients with lower TBVs. Further research in this area is needed to be able to further refine collection protocols, with the goal of more precisely predicting the required TOM for individual patients.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,005 | 0,009 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».