ReviewPharmaceutics2026
Artificial Intelligence and the Transformation of Cell and Gene Therapy Development.
Review in Pharmaceutics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Cell and Gene Therapy (CGT) represents a paradigm shift in medicine, offering curative potential for previously intractable diseases. However, the complexity, high cost, and manufacturing challenges inherent in developing, producing, and administering these therapies hinder their widespread accessibility. This review examines the critical and increasingly synergistic role of Artificial Intelligence (AI) and Machine Learning (ML) in overcoming these barriers across the entire CGT lifecycle, from discovery and construct design to smart manufacturing, clinical translation, and regulatory applications. We analyze how AI-driven approaches fundamentally differ from conventional methods, facilitating rapid construct optimization, generating highly predictive translational models, enabling the vision of autonomous, digital-twin-driven manufacturing, and establishing new paradigms for pharmacovigilance and regulatory oversight. The integration of AI is not merely an incremental improvement but a foundational transformation, positioning CGT to move from niche, bespoke treatments to scalable, accessible, and highly personalized medical modalities. We conclude by discussing current gaps, particularly data scarcity and regulatory uncertainty, and outlining a roadmap to realize the full potential of AI-enabled CGT.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.