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Targeting endosomal acidification by chloroquine analogs as a promising strategy for the treatment of emerging viral diseases
Open accessAbstract
Emerging viruses such as HIV, dengue, influenza A, SARS coronavirus, Ebola, and other viruses pose a significant threat to human health. Majority of these viruses are responsible for the outbreaks of pathogenic lethal infections. To date, there are no effective therapeutic strategies available for the prophylaxis and treatment of these infections. Chloroquine analogs have been used for decades as the primary and most successful drugs against malaria. Concomitant with the emergence of chloroquine-resistant Plasmodium strains and a subsequent decrease in the use as antimalarial drugs, other applications of the analogs have been investigated. Since the analogs have interesting biochemical properties, these drugs are found to be effective against a wide variety of viral infections. As antiviral action, the analogs have been shown to inhibit acidification of endosome during the events of replication and infection. Moreover, immunomodulatory effects of analogs have been beneficial to patients with severe inflammatory complications of several viral diseases. Interestingly, one of the successful targeting strategies is the inhibition of HIV replication by the analogs in vitro which are being tested in several clinical trials. This review focuses on the potentialities of chloroquine analogs for the treatment of endosomal low pH dependent emerging viral diseases.
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Metadata
- Authors
- Al-Bari, M. A. A.
- Venue
- Pharmacology Research & Perspectives
- Publisher
- Wiley
- Published
- 2017-01-23
- Volume
- 5
- Issue
- 1
- Pages
- e00293
- ISSN
- 2052-1707 (ISSN-L)
- OpenAlex
- W2580356750
- Cited by
- 382
- References
- 90
- FWCI
- 25.36
- Type
- journal article
- Language
- en
- Open access
- gold
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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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.