Literature DB >> 32767930

Gene Therapy for Hemophilia A: Where We Stand.

Miaojin Zhou1, Zhiqing Hu1, Chunhua Zhang1, Lingqian Wu1, Zhuo Li1, Desheng Liang1.   

Abstract

Hemophilia A (HA) is a hereditary hemorrhagic disease caused by a deficiency of coagulation factor VIII (FVIII) in blood plasma. Patients with HA usually suffer from spontaneous and recurrent bleeding in joints and muscles, or even intracerebral hemorrhage, which might lead to disability or death. Although the disease is currently manageable via delivery of plasma-derived or recombinant FVIII, this approach is costly, and neutralizing antibodies may be generated in a large portion of patients, which render the regimens ineffective and inaccessible. Given the monogenic nature of HA and that a slight increase in FVIII can remarkably alleviate the phenotypes, HA has been considered to be a suitable target disease for gene therapy. Consequently, the introduction of a functional F8 gene copy into the appropriate target cells via viral or nonviral delivery vectors, including gene correction through genome editing approaches, could ultimately provide an effective therapeutic method for HA patients. In this review, we discuss the recent progress of gene therapy for HA with viral and nonviral delivery vectors, including piggyBac, lentiviral and adeno-associated viral vectors, as well as new raising issues involving liver toxicity, pre-existing neutralizing antibodies of viral approach, and the selection of the target cell type for nonviral delivery. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.

Entities:  

Keywords:  BDD-F8; Hemophilia A; adeno-associated viral; gene therapy; lentiviral; nonviral

Year:  2020        PMID: 32767930     DOI: 10.2174/1566523220666200806110849

Source DB:  PubMed          Journal:  Curr Gene Ther        ISSN: 1566-5232            Impact factor:   4.391


  2 in total

1.  Multiple Laplacian Regularized RBF Neural Network for Assessing Dry Weight of Patients With End-Stage Renal Disease.

Authors:  Xiaoyi Guo; Wei Zhou; Yan Yu; Yinghua Cai; Yuan Zhang; Aiyan Du; Qun Lu; Yijie Ding; Chao Li
Journal:  Front Physiol       Date:  2021-12-13       Impact factor: 4.566

2.  Online Diagnosis and Classification of CT Images Collected by Internet of Things Using Deep Learning.

Authors:  Qiufang Ma
Journal:  Comput Math Methods Med       Date:  2022-03-19       Impact factor: 2.238

  2 in total

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