Optimizing the commercial manufacturing of tisagenlecleucel for patients in Japan: A 4-year experiential journey
Bibliographic record
Abstract
Background: Tisagenlecleucel, an autologous CD19-directed chimeric antigen receptor T-cell therapy, was approved in Japan, in March 2019 for patients with relapsed/refractory (r/r) B-cell acute lymphoblastic leukamia and r/r diffuse large B-cell lymphoma, and in August 2022 for patients with r/r follicular lymphoma. Post-approval, a key goal has been to upscale and continuously improve manufacturing and shipment success rates (MSR and SSR, respectively) in the commercial setting to meet the needs of patients worldwide, including in Japan. Herein, we report accrued experience from a 4-year journey of commercial tisagenlecleucel manufacturing process optimization for patients in Japan. Methods: Data were collected from the Novartis tisagenlecleucel manufacturing database, and only commercial tisagenlecleucel orders between August 2019 and July 2023 were included. The data for cancellations after shipment were based on cancellation forms obtained from institutions. Results: Since the first approval of tisagenlecleucel in Japan, its MSR and SSR improved over 4 years. Particularly, the MSR increased from 85.6 % in the first year to 95.3 % in the year 4, while the SSR increased from 92.4 % to 98.4 % in the same period. The corresponding termination and out-of-specification (OOS) rates decreased from 6.8 % to 0.9 %, and from 7.6 % to 3.7 %, respectively. The MSR (87.9 % versus 94.6 %), SSR (93.0 % versus 97.5 %), termination (6.0 % versus 1.9 %), overall OOS rates (6.1 % versus 3.5 %), and viability OOS (2.8 % versus 0.2 %) have improved significantly since the implementation of manufacturing process improvements in 2022. Conclusion: Continuous improvements in the tisagenlecleucel manufacturing process over the years resulted in higher manufacturing success and increased product availability for Japanese patients.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".