Prevalence and Spectrum of HIV-1 Resistance Mutations in the Siberian Federal District

Штрек, С. В.; Levakhina, Lidiya; Блох, А. И.; Pasechnik, Oksana; Пеньевская, Н. А.

doi:10.3390/v14102117

Cited by 1 publication

(1 citation statement)

References 9 publications

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“…One of the major challenges in the fight against HIV infection is the emergence of drug resistance, which poses a serious threat to public health. Several studies have shown an alarming increase in drug resistance to antiretroviral drugs, including acquired resistance, which complicates the effective treatment of the disease [1][2][3][4]. Indeed, the likelihood of developing resistance to at least one antiretroviral drug is high and can lead to treatment failure in about 65% of cases [5].…”

Section: Introductionmentioning

confidence: 99%

Web Service for HIV Drug Resistance Prediction Based on Analysis of Amino Acid Substitutions in Main Drug Targets

Paremskaia,

Rudik,

Filimonov

et al. 2023

Viruses

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Predicting viral drug resistance is a significant medical concern. The importance of this problem stimulates the continuous development of experimental and new computational approaches. The use of computational approaches allows researchers to increase therapy effectiveness and reduce the time and expenses involved when the prescribed antiretroviral therapy is ineffective in the treatment of infection caused by the human immunodeficiency virus type 1 (HIV-1). We propose two machine learning methods and the appropriate models for predicting HIV drug resistance related to amino acid substitutions in HIV targets: (i) k-mers utilizing the random forest and the support vector machine algorithms of the scikit-learn library, and (ii) multi-n-grams using the Bayesian approach implemented in MultiPASSR software. Both multi-n-grams and k-mers were computed based on the amino acid sequences of HIV enzymes: reverse transcriptase and protease. The performance of the models was estimated by five-fold cross-validation. The resulting classification models have a relatively high reliability (minimum accuracy for the drugs is 0.82, maximum: 0.94) and were used to create a web application, HVR, for the prediction of HIV drug resistance to protease inhibitors and nucleoside and non-nucleoside reverse transcriptase inhibitors based on the analysis of the amino acid sequences of the appropriate HIV proteins from clinical samples.

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