Anti-HIV Agents: Current Status and Recent Trends

Geronikaki, Athina; Eleftheriou, Phaedra; Poroikov, Vladimir

doi:10.1007/7355_2015_5001

Cited by 14 publications

(9 citation statements)

References 234 publications

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“…Current antiretroviral therapy does not provide the complete cure of the infected patients, exhibits severe adverse effects, and leads to the appearance of resistant strains [10]. Thus, the discovery of new anti-HIV medicines remains an essential task.…”

Section: Introductionmentioning

confidence: 99%

(Q)SAR Models of HIV-1 Protein Inhibition by Drug-Like Compounds

et al. 2019

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Despite the achievements of antiretroviral therapy, discovery of new anti-HIV medicines remains an essential task because the existing drugs do not provide a complete cure for the infected patients, exhibit severe adverse effects, and lead to the appearance of resistant strains. To predict the interaction of drug-like compounds with multiple targets for HIV treatment, ligand-based drug design approach is widely applied. In this study, we evaluated the possibilities and limitations of (Q)SAR analysis aimed at the discovery of novel antiretroviral agents inhibiting the vital HIV enzymes. Local (Q)SAR models are based on the analysis of structure–activity relationships for molecules from the same chemical class, which significantly restrict their applicability domain. In contrast, global (Q)SAR models exploit data from heterogeneous sets of drug-like compounds, which allows their application to databases containing diverse structures. We compared the information for HIV-1 integrase, protease and reverse transcriptase inhibitors available in the EBI ChEMBL, NIAID HIV/OI/TB Therapeutics, and Clarivate Analytics Integrity databases as the sources for (Q)SAR training sets. Using the PASS and GUSAR software, we developed and validated a variety of (Q)SAR models, which can be further used for virtual screening of new antiretrovirals in the SAVI library. The developed models are implemented in the freely available web resource AntiHIV-Pred.

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Section: Introductionmentioning

confidence: 99%

(Q)SAR Models of HIV-1 Protein Inhibition by Drug-Like Compounds

et al. 2019

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“…The appearance of HIV-1 resistance, associated with amino acids substitutions in RT, requires the development of novel approaches that explain the relationships between a particular conformation of HIV-1 RT and drug resistance. These data can be applied for further development of new HIV-1 RT inhibitors and/or prediction of the HIV-1 resistance to antiretroviral drugs [ 8 ].…”

Section: Introductionmentioning

confidence: 99%

Molecular Docking Studies of HIV-1 Resistance to Reverse Transcriptase Inhibitors: Mini-Review

2018

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Currently, millions of people are living with human immunodeficiency virus type 1 (HIV-1), which causes acquired immunodeficiency syndrome. However, the spread of the HIV-1 resistance to antiviral agents is the major problem in the antiretroviral therapy and medical management of HIV-infected patients. HIV-1 reverse transcriptase (RT) is one of the key viral targets for HIV-1 inhibition. Therefore, the studies on the combatting the HIV resistance that occurs due to the structural changes in RT, are in great demand. This work aims to provide an overview of the state-of-the-art molecular docking approaches applied to the studies of the HIV-1 resistance, associated with RT structure changes. We have reviewed recent studies using molecular docking with mutant forms of RT. The work discusses the modifications of molecular docking, which have been developed to find the novel molecules active against resistance mutants of RT and/or recombinant strains of HIV-1. The perspectives of the existing algorithms of molecular docking to the studies on molecular mechanisms of resistance and selection of the correct binding poses for the reverse transcriptase inhibitors are discussed.

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“…HIV reverse transcriptase (RT) is part of HAART and one of the most attractive targets for the HIV inhibition [ 1 , 2 ]. The antiretroviral drugs that inhibit HIV RT only allow decreasing the HIV-1 replication but do not provide the full elimination of the virus [ 3 ]. Therefore, the development of new antiretroviral drugs is still of high interest due to the issues of safety and efficacy of the drugs, which are currently used in clinical practice [ 4 ].…”

Section: Introductionmentioning

confidence: 99%

HIV Resistance Prediction to Reverse Transcriptase Inhibitors: Focus on Open Data

Tarasova

Poroikov

2018

Molecules

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Research and development of new antiretroviral agents are in great demand due to issues with safety and efficacy of the antiretroviral drugs. HIV reverse transcriptase (RT) is an important target for HIV treatment. RT inhibitors targeting early stages of the virus-host interaction are of great interest for researchers. There are a lot of clinical and biochemical data on relationships between the occurring of the single point mutations and their combinations in the pol gene of HIV and resistance of the particular variants of HIV to nucleoside and non-nucleoside reverse transcriptase inhibitors. The experimental data stored in the databases of HIV sequences can be used for development of methods that are able to predict HIV resistance based on amino acid or nucleotide sequences. The data on HIV sequences resistance can be further used for (1) development of new antiretroviral agents with high potential for HIV inhibition and elimination and (2) optimization of antiretroviral therapy. In our communication, we focus on the data on the RT sequences and HIV resistance, which are available on the Internet. The experimental methods, which are applied to produce the data on HIV-1 resistance, the known data on their concordance, are also discussed.

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Anti-HIV Agents: Current Status and Recent Trends

Cited by 14 publications

References 234 publications

(Q)SAR Models of HIV-1 Protein Inhibition by Drug-Like Compounds

(Q)SAR Models of HIV-1 Protein Inhibition by Drug-Like Compounds

Molecular Docking Studies of HIV-1 Resistance to Reverse Transcriptase Inhibitors: Mini-Review

HIV Resistance Prediction to Reverse Transcriptase Inhibitors: Focus on Open Data

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