A Computational Approach for the Prediction of Treatment History and the Effectiveness or Failure of Antiretroviral Therapy

Tarasova, O.; Biziukova, Nadezhda; Kireev, Dmitry; Lagunin, Alexey; Ivanov, Sergey; Filimonov, Dmitry; Poroikov, Vladimir

doi:10.3390/ijms21030748

Cited by 14 publications

(6 citation statements)

References 35 publications

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“…The Prediction of Activity Spectra for Substances (PASS) tool was used to characterize the biological activity of the compounds using their structures in the SMILES file format [ 58 , 59 ]. The anti-EBOV inhibition efficiency was predicted using the SDF files of the compounds via a random forest-based model [ 60 ].…”

Section: Methodsmentioning

confidence: 99%

Computational Study on Potential Novel Anti-Ebola Virus Protein VP35 Natural Compounds

et al. 2021

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Ebola virus (EBOV) is one of the most lethal pathogens that can infect humans. The Ebola viral protein VP35 (EBOV VP35) inhibits host IFN-α/β production by interfering with host immune responses to viral invasion and is thus considered as a plausible drug target. The aim of this study was to identify potential novel lead compounds against EBOV VP35 using computational techniques in drug discovery. The 3D structure of the EBOV VP35 with PDB ID: 3FKE was used for molecular docking studies. An integrated library of 7675 African natural product was pre-filtered using ADMET risk, with a threshold of 7 and, as a result, 1470 ligands were obtained for the downstream molecular docking using AutoDock Vina, after an energy minimization of the protein via GROMACS. Five known inhibitors, namely, amodiaquine, chloroquine, gossypetin, taxifolin and EGCG were used as standard control compounds for this study. The area under the curve (AUC) value, evaluating the docking protocol obtained from the receiver operating characteristic (ROC) curve, generated was 0.72, which was considered to be acceptable. The four identified potential lead compounds of NANPDB4048, NANPDB2412, ZINC000095486250 and NANPDB2476 had binding affinities of −8.2, −8.2, −8.1 and −8.0 kcal/mol, respectively, and were predicted to possess desirable antiviral activity including the inhibition of RNA synthesis and membrane permeability, with the probable activity (Pa) being greater than the probable inactivity (Pi) values. The predicted anti-EBOV inhibition efficiency values (IC50), found using a random forest classifier, ranged from 3.35 to 11.99 μM, while the Ki values ranged from 0.97 to 1.37 μM. The compounds NANPDB4048 and NANPDB2412 had the lowest binding energy of −8.2 kcal/mol, implying a higher binding affinity to EBOV VP35 which was greater than those of the known inhibitors. The compounds were predicted to possess a low toxicity risk and to possess reasonably good pharmacological profiles. Molecular dynamics (MD) simulations of the protein–ligand complexes, lasting 50 ns, and molecular mechanisms Poisson-Boltzmann surface area (MM-PBSA) calculations corroborated the binding affinities of the identified compounds and identified novel critical interacting residues. The antiviral potential of the molecules could be confirmed experimentally, while the scaffolds could be optimized for the design of future novel anti-EBOV chemotherapeutics.

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Section: Methodsmentioning

confidence: 99%

Computational Study on Potential Novel Anti-Ebola Virus Protein VP35 Natural Compounds

et al. 2021

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“…The RHIVDB database information provides basis for the selection of the most effective treatment schema and for building models of treatment effectiveness based on clinical data (CD4+ cell count, viral load). The data on the amino acid sequences can be used along with treatment and clinical data to predict drug exposure or treatment effectiveness (Tarasova et al, 2020). The amino acid residue of the consensus HIV reverse transcriptase sequence is provided in bold.…”

Section: Discussionmentioning

confidence: 99%

“…Data on the amino acid sequences of HIV proteins, including reverse transcriptase (RT), protease (PR), integrase (IN), and envelope protein (ENV), are important for the prediction of HIV drug resistance ( Liu and Shafer, 2006 ; Toor et al, 2011 ; Raposo and Nobre, 2017 ; Ramon et al, 2019 ; Steiner et al, 2020 ) and the so-called drug exposure, which is considered one of the features potentially associated with HIV drug resistance ( Pironti et al, 2017 ). With data from the (i) amino acid sequences of HIV proteins, (ii) drug combinations used to treat HIV-positive patients, and (iii) clinical data obtained from the patients, it is possible to build models predicting (a) drug exposure and HIV drug resistance and (b) therapeutic effectiveness based on the HIV sequence data and the treatment history ( Tarasova et al, 2020 ).…”

Section: Introductionmentioning

confidence: 99%

RHIVDB: A Freely Accessible Database of HIV Amino Acid Sequences and Clinical Data of Infected Patients

et al. 2021

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Human immunodeficiency virus (HIV) infection remains one of the most severe problems for humanity, particularly due to the development of HIV resistance. To evaluate an association between viral sequence data and drug combinations and to estimate an effect of a particular drug combination on the treatment results, collection of the most representative drug combinations used to cure HIV and the biological data on amino acid sequences of HIV proteins is essential. We have created a new, freely available web database containing 1,651 amino acid sequences of HIV structural proteins [reverse transcriptase (RT), protease (PR), integrase (IN), and envelope protein (ENV)], treatment history information, and CD4+ cell count and viral load data available by the user’s query. Additionally, the biological data on new HIV sequences and treatment data can be stored in the database by any user followed by an expert’s verification. The database is available on the web at http://www.way2drug.com/rhivdb.

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“…Various mathematical models have been calibrated using genotype-fold change data proposed in the Stanford HIV database to predict mutational effects on viral dynamics in the literature [25][26][27][28][29][30][31][32][33][34][35][36][37][38][39][40]. The life span of patients can be considerably extended by the construction of reliable mathematical models that accurately predict suitable drugs for existing isolates.…”

Section: Introductionmentioning

confidence: 99%

ANN-based Drug-isolate-fold-change model predicting the resistance profiles of HIV-1 protease inhibitors

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et al. 2022

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: Drug resistance is a primary barrier to effective treatments of HIV/AIDS. Calculating quantitative relations between genotype and phenotype observations for each inhibitor with cell-based assays requires time and money consuming experiments. Machine learning models are good options for tackling these problems by generalizing the available data with suitable linear or nonlinear mappings. The main aim of this paper is to construct drug isolate fold change (DIF)-based artificial neural network (ANN) models for estimating the resistance potential of molecules inhibiting the HIV-1 protease (PR) enzyme. Throughout the study, seven of eight protease inhibitors (PIs) have been included in the training set and the remaining ones in the test set. Using the 7-in 1-out procedure, eight ANN models have been produced to measure the learning capacity of models from the descriptors of the inhibitors. The mean value of eight ANN models for unseen inhibitors is and 95% confidence interval (CI) is Predicting the fold change resistance for hundreds of isolates allowed for robust comparison of drug pairs. These eight models have predicted the drug resistance tendencies of each inhibitor pair with the mean 2D correlation coefficient 0.933 and 95% CI A classification problem has been created to predict the ordered relationship of the PIs and the mean accuracy, sensitivity and specificity values are obtained as 0.954, 0.791 and 0.791, respectively. The currently derived ANN models can accurately predict the drug resistance tendencies of PI pairs, and this observation could help test new inhibitors with various isolates.

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A Computational Approach for the Prediction of Treatment History and the Effectiveness or Failure of Antiretroviral Therapy

Cited by 14 publications

References 35 publications

Computational Study on Potential Novel Anti-Ebola Virus Protein VP35 Natural Compounds

Computational Study on Potential Novel Anti-Ebola Virus Protein VP35 Natural Compounds

RHIVDB: A Freely Accessible Database of HIV Amino Acid Sequences and Clinical Data of Infected Patients

ANN-based Drug-isolate-fold-change model predicting the resistance profiles of HIV-1 protease inhibitors

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