Pharmacovigilance in Cell and Gene Therapy: Evolving Challenges in Risk Management and Long-Term Follow-Up
Bibliographic record
Abstract
Cell and gene therapies, including CAR T-cells, CRISPR-based genome editing, and next-generation viral and non-viral delivery platforms, are transforming treatment paradigms across cancer, rare genetic disorders, immune dysregulation, and neurodegenerative disease. These therapies offer curative potential but also present safety challenges owing to prolonged biological activity, systemic immune engagement, and lasting genomic alterations. This review examines the range of related toxicities, including immune complications, genotoxicity, and organ-specific effects, with attention to atypical presentations, gaps in clinical trial safety capture, and disparities in global long-term follow-up infrastructure. Central to our analysis is a risk-adaptive, digitally enabled pharmacovigilance model that incorporates real-world data, artificial intelligence-based signal detection, and seamless pediatric-to-adult follow-up to proactively protect patients while supporting innovation. Integrated safety dashboards, pediatric transition roadmaps, and predictive monitoring tools are proposed as practical solutions to improve coordination among sponsors, regulators, and clinical sites. We also outline best practices for aligning risk evaluation and mitigation strategies with risk management plans and examine how wearable biosensors, electronic patient-reported outcomes, and multi-omics biomarkers contribute to near real-time safety surveillance. Ethical priorities such as informed consent, data privacy, and equitable access are addressed throughout. By positioning pharmacovigilance as a proactive and predictive foundation within the therapeutic landscape, this review offers a forward-looking blueprint to advance innovation while ensuring long-term patient safety.
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 | 0.057 | 0.130 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.004 | 0.009 |
| 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".