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Cited by 32

The DOI matches its Crossref record; 44 of the 44 references it cites were found in a registry or an index.

Gene therapy with helper-dependent adenoviral vectors: lessons from studies in large animal models

Brunetti-Pierri, Nicola, Ng, Philip·2017·Virus Genes

Abstract

Helper-dependent adenoviral vectors (HDAd) are deleted of all viral genes and they can efficiently transduce a wide variety of dividing and non-dividing cells to mediate high transgene expression levels. Unlike early generation adenoviral vectors, the absence of viral genes in HDAd results in long-term transgene expression without chronic toxicity and permits a large cloning capacity of 36 kb. Moreover, HDAd genomes exist extra-chromosomally thus minimizing the risks of germline transmission and insertional mutagenesis. For these reasons, HDAd offers tremendous potential for in vivo gene therapy. This chapter reviews preclinical studies using HDAd in large animal models to assess safety and efficacy in a wide variety of gene therapy applications.

Overview

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Registry

Crossref · 10.1007/s11262-017-1471-x

Cites

44 of 44 references verified0 citation errors · 0 not checkable

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Citations

Cites

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Metadata

Authors
Brunetti-Pierri, N., & Ng, P.
Venue
Virus Genes
Published
2017-06-07
Volume
53
Issue
5
Pages
684-691
ISSN
  • 0920-8569
  • 1572-994X
Cited by
32
References
44
Language
en

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This page
CoChat. (2026). Citation check: Gene therapy with helper-dependent adenoviral vectors: lessons from studies in large animal models. https://app.cochat.ai/papers/10.1007/s11262-017-1471-x
The paper
Brunetti-Pierri, N., & Ng, P. (2017). Gene therapy with helper-dependent adenoviral vectors: lessons from studies in large animal models. Virus Genes. https://doi.org/10.1007/s11262-017-1471-x

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.