Evidence map›Paper›PMID 41900842›Full record

ReviewPharmaceutics2026

Artificial Intelligence and the Transformation of Cell and Gene Therapy Development.

Jared R Auclair, Jeewon Joung, Maya A Singh, Gaël Debauve, Rominder Singh

Abstract readReview
In one paragraph

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.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Jared R AuclairCollege of Professional Studies, Northeastern University, Boston, MA 02115, USA.ORCID 0000-0002-3094-1544
Jeewon JoungNational Institute of Food and Drug Safety Evaluation, Ministry of Food and Drug Safety, Cheongju-si 28159, Republic of Korea.ORCID 0009-0005-8651-7987
Maya A SinghDepartment of Bioengineering, University of Washington, Seattle, WA 98195, USA.ORCID 0000-0002-2025-5356
Gaël DebauveGene Therapy Analytical Sciences, UCB, 1070 Brussels, Belgium.
Rominder SinghCollege of Professional Studies, Northeastern University, Boston, MA 02115, USA.ORCID 0009-0001-8298-8176

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

artificial intelligencecell and gene therapyCGT developmentCGT manufacturingmachine learning

Identifiers

PMID41900842
PMCPMC13029694

What OpenQuestion holds

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Registered trials

None linked

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.