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Proteochemometric modeling of HIV protease susceptibility
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences, Pharmaceutical Pharmacology. (Proteochemometric group)
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences, Pharmaceutical Pharmacology. (Proteochemometric group)
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences, Pharmaceutical Pharmacology. (Proteochemometric group)ORCID iD: 0000-0002-8083-2864
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences, Pharmaceutical Pharmacology. (Proteochemometric group)
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2008 (English)In: BMC Bioinformatics, ISSN 1471-2105, E-ISSN 1471-2105, Vol. 9, 181- p.Article in journal (Refereed) Published
Abstract [en]

BACKGROUND

A major obstacle in treatment of HIV is the ability of the virus to mutate rapidly into drug-resistant variants. A method for predicting the susceptibility of mutated HIV strains to antiviral agents would provide substantial clinical benefit as well as facilitate the development of new candidate drugs. Therefore, we used proteochemometrics to model the susceptibility of HIV to protease inhibitors in current use, utilizing descriptions of the physico-chemical properties of mutated HIV proteases and 3D structural property descriptions for the protease inhibitors. The descriptions were correlated to the susceptibility data of 828 unique HIV protease variants for seven protease inhibitors in current use; the data set comprised 4792 protease-inhibitor combinations.

RESULTS

The model provided excellent predictability (R2 = 0.92, Q2 = 0.87) and identified general and specific features of drug resistance. The model's predictive ability was verified by external prediction in which the susceptibilities to each one of the seven inhibitors were omitted from the data set, one inhibitor at a time, and the data for the six remaining compounds were used to create new models. This analysis showed that the over all predictive ability for the omitted inhibitors was Q2 inhibitors = 0.72.

CONCLUSION

Our results show that a proteochemometric approach can provide generalized susceptibility predictions for new inhibitors. Our proteochemometric model can directly analyze inhibitor-protease interactions and facilitate treatment selection based on viral genotype. The model is available for public use, and is located at HIV Drug Research Centre.

Place, publisher, year, edition, pages
2008. Vol. 9, 181- p.
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Pharmaceutical Sciences
Identifiers
URN: urn:nbn:se:uu:diva-104215DOI: 10.1186/1471-2105-9-181ISI: 000256035700001PubMedID: 18402661OAI: oai:DiVA.org:uu-104215DiVA: diva2:219545
Available from: 2009-05-27 Created: 2009-05-27 Last updated: 2017-12-13Bibliographically approved

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Lapins, MarisEklund, MartinSpjuth, OlaWikberg, Jarl E S

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