Evidence map›Paper›PMID 33986759›Full record

ArticleFrontiers in immunology2021

Overcoming Immunological Challenges Limiting Capsid-Mediated Gene Therapy With Machine Learning.

Anna Z Wec, Kathy S Lin, Jamie C Kwasnieski, Sam Sinai, Jeff Gerold, Eric D Kelsic

Open access · goldAbstract read
In one paragraph

Article in Frontiers in immunology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
23citing papers in PubMed, 1 pooled it
3.1field-weighted citation impact, top 8% of its field
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

23 citing papers in PubMed, 1 synthesis or guideline pooled it, 26 citations in OpenAlex.

  1. Pooled it
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  9. Artificial Intelligence-Based Approaches for AAV Vector Engineering.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025
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  13. Neurosurgical gene therapy for central nervous system diseases.Neurotherapeutics : the journal of the American Society for Experimental NeuroTherapeutics · 2024
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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

6 authors.

Anna Z WecApplied Biology, Dyno Therapeutics Inc, Cambridge, MA, United States.
Kathy S LinData Science, Dyno Therapeutics Inc, Cambridge, MA, United States.
Jamie C KwasnieskiApplied Biology, Dyno Therapeutics Inc, Cambridge, MA, United States.
Sam SinaiData Science, Dyno Therapeutics Inc, Cambridge, MA, United States.
Jeff GeroldData Science, Dyno Therapeutics Inc, Cambridge, MA, United States.
Eric D KelsicApplied Biology, Dyno Therapeutics Inc, Cambridge, MA, United States.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

A key hurdle to making adeno-associated virus (AAV) capsid mediated gene therapy broadly beneficial to all patients is overcoming pre-existing and therapy-induced immune responses to these vectors. Recent advances in high-throughput DNA synthesis, multiplexing and sequencing technologies have accelerated engineering of improved capsid properties such as production yield, packaging efficiency, biodistribution and transduction efficiency. Here we outline how machine learning, advances in viral immunology, and high-throughput measurements can enable engineering of a new generation of de-immunized capsids beyond the antigenic landscape of natural AAVs, towards expanding the therapeutic reach of gene therapy.

Indexed as

Machine LearningAnimalsCapsidDependovirusGenetic TherapyGenetic VectorsHumansAAV capsid designgene therapyimmune evasionmachine learningprotein engineering

Identifiers

PMID33986759
PMCPMC8112259
OpenAlexW3157577579

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.