Mapping the Clinical Development Trajectory of Cell and Gene Therapy Products
Bibliographic record
Abstract
While cell and gene therapies (CGTs) have emerged as promising modalities to treat conditions with limited therapeutic options, their unconventional development is fraught with uncertainty, rendering them high-risk assets for many pharmaceutical companies. Here, we assess the clinical development trajectories of CGT products by estimating probabilities of successful clinical trial phase transitions and the likelihood of achieving regulatory approval. We included all CGT products entering clinical development from 1993 to 2023 and intended for marketing in the United States, Europe, Japan, Canada, and Switzerland. Associations between product success and characteristics were investigated. In sub-analyses, we examined the clinical trajectories of two promising product types, chimeric antigen receptor T (CAR T) cell therapies and adeno-associated viral (AAV) vector-based gene therapies. We identified 995 CGT products corresponding to 1,961 development programs. A total of 44 CGTs secured at least one regulatory approval, corresponding to an overall likelihood of approval of 5.3% (95% CI 4.0-6.9). Development programs with an orphan designation had a higher likelihood of approval than those without (9.4%, 95% CI 6.6-13.3 vs. 3.2%, 95% CI 2.0-4.9), while programs for oncology indications had a lower likelihood of approval compared to those for non-oncology indications (3.2%, 95% CI 1.6-5.1 vs. 8.0%, 95% CI 5.7-11.1). CAR T cells and AAV gene therapies had a similar overall likelihood of approval of 13.6% (95% CI 7.3, 23.9) and 13.6% (95% CI 6.4, 26.7), respectively. In conclusion, CGT products have a low overall likelihood of approval with variability based on orphan status, therapeutic area, and product type.
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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 | 0.011 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".