Prediction of drug-resistance in HIV-1 subtype C based on protease sequences from ART naive and first-line treatment failures in North India using genotypic and docking analysis

Assessment of a Computational Approach to Predict Drug Resistance Mutations for HIV, HBV and SARS-CoV-2

Viral resistance is a worldwide problem mitigating the effectiveness of antiviral drugs. Mutations in the drug-targeting proteins are the primary mechanism for the emergence of drug resistance. It is essential to identify the drug resistance mutations to elucidate the mechanism of resistance and to suggest promising treatment strategies to counter the drug resistance. However, experimental identification of drug resistance mutations is challenging, laborious and time-consuming. Hence, effective and time-saving computational structure-based approaches for predicting drug resistance mutations are essential and are of high interest in drug discovery research. However, these approaches are dependent on accurate estimation of binding free energies which indirectly correlate to the computational cost. Towards this goal, we developed a computational workflow to predict drug resistance mutations for any viral proteins where the structure is known. This approach can qualitatively predict the change in binding free energies due to mutations through residue scanning and Prime MM-GBSA calculations. To test the approach, we predicted resistance mutations in HIV-RT selected by (-)-FTC and demonstrated accurate identification of the clinical mutations. Furthermore, we predicted resistance mutations in HBV core protein for GLP-26 and in SARS-CoV-2 3CLpro for nirmatrelvir. Mutagenesis experiments were performed on two predicted resistance and three predicted sensitivity mutations in HBV core protein for GLP-26, corroborating the accuracy of the predictions.

Prediction and molecular field view of drug resistance in HIV-1 protease mutants

Conquering the mutational drug resistance is a great challenge in anti-HIV drug development and therapy. Quantitatively predicting the mutational drug resistance in molecular level and elucidating the three dimensional structure-resistance relationships for anti-HIV drug targets will help to improve the understanding of the drug resistance mechanism and aid the design of resistance evading inhibitors. Here the MB-QSAR (Mutation-dependent Biomacromolecular Quantitative Structure Activity Relationship) method was employed to predict the molecular drug resistance of HIV-1 protease mutants towards six drugs, and to depict the structure resistance relationships in HIV-1 protease mutants. MB-QSAR models were constructed based on a published data set of K_i values for HIV-1 protease mutants against drugs. Reliable MB-QSAR models were achieved and these models display both well internal and external prediction abilities. Interpreting the MB-QSAR models supplied structural information related to the drug resistance as well as the guidance for the design of resistance evading drugs. This work showed that MB-QSAR method can be employed to predict the resistance of HIV-1 protease caused by polymorphic mutations, which offer a fast and accurate method for the prediction of other drug target within the context of 3D structures.

Predicting, Diagnosing, and Treating Acute and Early HIV Infection in a Public Sector Facility in Eswatini

BACKGROUND

The lack of acute and early HIV infection (AEHI) diagnosis and care contributes to high HIV incidence in resource-limited settings. We aimed to assess the yield of AEHI, predict and diagnose AEHI, and describe AEHI care outcomes in a public sector setting in Eswatini.

SETTING

This study was conducted in Nhlangano outpatient department from March 2019 to March 2020.

METHODS

Adults at risk of AEHI underwent diagnostic testing for AEHI with the quantitative Xpert HIV-1 viral load (VL) assay. AEHI was defined as the detection of HIV-1 VL on Xpert and either an HIV-seronegative or HIV-serodiscordant third-generation antibody-based rapid diagnostic test (RDT) result. First, the cross-sectional analysis obtained the yield of AEHI and established a predictor risk score for the prediction of AEHI using Lasso logistic regression. Second, diagnostic accuracy statistics described the ability of the fourth-generation antibody/p24 antigen-based Alere HIV-Combo RDT to diagnose AEHI (vs Xpert VL testing). Third, we described acute HIV infection care outcomes of AEHI-positive patients using survival analysis.

RESULTS

Of 795 HIV-seronegative/HIV-serodiscordant outpatients recruited, 30 (3.8%, 95% confidence interval: 2.6% to 5.3%) had AEHI. The predictor risk score contained several factors (HIV-serodiscordant RDT, women, feeling at risk of HIV, swollen glands, and fatigue) and had sensitivity and specificity of 83.3% and 65.8%, respectively, to predict AEHI. The HIV-Combo RDT had sensitivity and specificity of 86.2% and 99.9%, respectively, to diagnose AEHI. Of 30 AEHI-positive patients, the 1-month cumulative treatment initiation was 74% (95% confidence interval: 57% to 88%), and the 3-month viral suppression (<1000 copies/mL) was 87% (67% to 98%).

CONCLUSION

AEHI diagnosis and care seem possible in resource-limited settings.

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

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.

An Innovative Sequence-to-Structure-Based Approach to Drug Resistance Interpretation and Prediction: The Use of Molecular Interaction Fields to Detect HIV-1 Protease Binding-Site Dissimilarities

In silico methodologies have opened new avenues of research to understanding and predicting drug resistance, a pressing health issue that keeps rising at alarming pace. Sequence-based interpretation systems are routinely applied in clinical context in an attempt to predict mutation-based drug resistance and thus aid the choice of the most adequate antibiotic and antiviral therapy. An important limitation of approaches based on genotypic data exclusively is that mutations are not considered in the context of the three-dimensional (3D) structure of the target. Structure-based in silico methodologies are inherently more suitable to interpreting and predicting the impact of mutations on target-drug interactions, at the cost of higher computational and time demands when compared with sequence-based approaches. Herein, we present a fast, computationally inexpensive, sequence-to-structure-based approach to drug resistance prediction, which makes use of 3D protein structures encoded by input target sequences to draw binding-site comparisons with susceptible templates. Rather than performing atom-by-atom comparisons between input target and template structures, our workflow generates and compares Molecular Interaction Fields (MIFs) that map the areas of energetically favorable interactions between several chemical probe types and the target binding site. Quantitative, pairwise dissimilarity measurements between the target and the template binding sites are thus produced. The method is particularly suited to understanding changes to the 3D structure and the physicochemical environment introduced by mutations into the target binding site. Furthermore, the workflow relies exclusively on freeware, making it accessible to anyone. Using four datasets of known HIV-1 protease sequences as a case-study, we show that our approach is capable of correctly classifying resistant and susceptible sequences given as input. Guided by ROC curve analyses, we fined-tuned a dissimilarity threshold of classification that results in remarkable discriminatory performance (accuracy ≈ ROC AUC ≈ 0.99), illustrating the high potential of sequence-to-structure-, MIF-based approaches in the context of drug resistance prediction. We discuss the complementarity of the proposed methodology to existing prediction algorithms based on genotypic data. The present work represents a new step toward a more comprehensive and structurally-informed interpretation of the impact of genetic variability on the response to HIV-1 therapies.

The efficiency of ligand-receptor interaction information alone as new descriptors in QSAR modeling via random forest artificial neural network

A new approach is introduced for the construction of a predictive quantitative structure-activity relationship model in which only ligand-receptor (LR) interaction features are used as relevant descriptors. This approach combines the benefit of the random forest (RF) as a new variable selection method with the intrinsic capability of the artificial neural network (ANN). The interaction information of the ligand-receptor (LR) complex was used as molecular docking descriptors. The most relevant descriptors were selected using the RF technique and used as inputs of ANN. The proposed RF ANN (RF-LM-ANN) method was optimized and then evaluated by the prediction of pEC₅₀ for some of the azine derivatives as non-nucleoside reverse transcriptase inhibitors. RF-LM-ANN model under the optimal conditions was evaluated using internal (validation) and external test sets. The determination coefficients of the external test and validation sets were 0.88 and 0.89, respectively. The mean square deviation (MSE) values for the prediction of biological activities in the external test and validation sets were found to be 0.10 and 0.11, respectively. The results obtained demonstrated the good prediction ability and high generalizability of the proposed RF-LM-ANN model based on the MMDs alone.

Primary HIV Drug Resistance among Recently Infected Cases of HIV in North-West India

Background

Antiretroviral treatment may lead to the emergence of HIV drug resistance, which can be transmitted. HIV primary drug resistance (PDR) is of great public health concern because it has the potential to compromise the efficacy of antiretroviral therapy (ART) at the population level.

Objective

To estimate the level of primary drug resistance among recently infected cases of HIV in 6 ART centres of North-Western India from September 2014 to June 2016.

Methods

The level of primary drug resistance was studied among 37 recently infected HIV cases identified by Limiting antigen (Lag) avidity assay based on modified Recent Infection Testing Algorithm (RITA). The reverse transcriptase region of HIV-1 pol gene (1-268 codons) was genotyped. The sequences were analyzed using the Calibrated Population Resistance (CPR) tool of Stanford University HIV drug resistance (DR) database to identify drug resistance.

Results

Among 37 isolates studied, 6 (16.2%) samples showed primary drug resistance (PDR) against reverse transcriptase (RT) inhibitor. The proportion of primary drug resistance was 22.2% (2/9) among female sex workers, 14.3% (1/7) among men having sex with men, and 14.3% (3/21) among injecting drug users. Observed mutations were K219R, L74V, K219N, and Y181C. Injecting drug user (IDU) has showed resistance to either nucleoside/nucleotide reverse transcriptase inhibitors (NRTI) or nonnucleotide reverse transcriptase inhibitors (NNRTI).

Conclusion

Resistance to either NRTI or NNRTI among the recently is a new challenge that needs to be addressed. The fact that both Y181C isolates are IDUs is important and represents 2/21 (~10%) NNRTI drug resistance. Surveillance for primary drug resistance (PDR) needs to be integrated into next generation of HIV surveillance as access to ART is increasing due to introduction of test and treat policy.

Characterizing early drug resistance-related events using geometric ensembles from HIV protease dynamics

The use of antiretrovirals (ARVs) has drastically improved the life quality and expectancy of HIV patients since their introduction in health care. Several millions are still afflicted worldwide by HIV and ARV resistance is a constant concern for both healthcare practitioners and patients, as while treatment options are finite, the virus constantly adapts via complex mutation patterns to select for resistant strains under the pressure of drug treatment. The HIV protease is a crucial enzyme for viral maturation and has been a game changing drug target since the first application. Due to similarities in protease inhibitor designs, drug cross-resistance is not uncommon across ARVs of the same class. It is known that resistance against protease inhibitors is associated with a wider active site, but results from our large scale molecular dynamics simulations combined with statistical tests and network analysis further show, for the first time, that there are regions of local expansions and compactions associated with high levels of resistance conserved across eight different protease inhibitors visible in their complexed form within closed receptor conformations. The observed conserved expansion sites may provide an alternative drug-targeting site. Further, the method developed here is novel, supplementary to methods of variation analysis at sequence level, and should be applicable in analysing the structural consequences of mutations in other contexts using molecular ensembles.

Machine Learning to Improve the Effectiveness of ANRS in Predicting HIV Drug Resistance

OBJECTIVES

Human immunodeficiency virus infection and acquired immune deficiency syndrome (HIV/AIDS) is one of the major burdens of disease in developing countries, and the standard-of-care treatment includes prescribing antiretroviral drugs. However, antiretroviral drug resistance is inevitable due to selective pressure associated with the high mutation rate of HIV. Determining antiretroviral resistance can be done by phenotypic laboratory tests or by computer-based interpretation algorithms. Computer-based algorithms have been shown to have many advantages over laboratory tests. The ANRS (Agence Nationale de Recherches sur le SIDA) is regarded as a gold standard in interpreting HIV drug resistance using mutations in genomes. The aim of this study was to improve the prediction of the ANRS gold standard in predicting HIV drug resistance.

METHODS

A genome sequence and HIV drug resistance measures were obtained from the Stanford HIV database (http://hivdb.stanford.edu/). Feature selection was used to determine the most important mutations associated with resistance prediction. These mutations were added to the ANRS rules, and the difference in the prediction ability was measured.

RESULTS

This study uncovered important mutations that were not associated with the original ANRS rules. On average, the ANRS algorithm was improved by 79% ± 6.6%. The positive predictive value improved by 28%, and the negative predicative value improved by 10%.

CONCLUSIONS

The study shows that there is a significant improvement in the prediction ability of ANRS gold standard.

Transfusion transmitted infections in thalassaemics: need for reappraisal of blood screening strategy in India

The aim of the study was to assess the blood safety in India through prevalence in thalassaemic population. Safety of the blood supply is a subject of great concern for all recipients. This review attempts to assess the relevance and format of tests for viruses in the context of transfusion transmitted infection (TTI) prevalence in India. Serological marker testing for human immunodeficiency virus-1/2 (HIV-1/2), hepatitis C virus (HCV) and hepatitis B virus (HBV) is mandatory in India. Numerous TTI incidents in the repeat recipients supported by results from nucleic acid technology (NAT) testing indicate the deficiencies in blood safety. The β-thalassaemic population (3-17%) in India has been used to reflect on blood safety. The prevalence of HIV-1/2, HCV and HBV in the Indian donor population, the limitations in accessing safe donors, quality of serological tests and the impact on repeat recipients is evaluated. The reports point to prevalence of ˜2% of viral diseases in the blood donor population, and the insufficiency of serology testing resulting in up to 45% TTIs in thalassaemics. The revelation by individual donation (ID) NAT testing, of 1 per 310 units being serology negative-NAT reactive is alarming. Extrapolating the serology negative NAT reactive yields, for an annual blood supply of 7.9 million units, 23,700 units or nearly 100,000 blood components are likely to be infectious. Though the cost for ID-NAT testing is considered unaffordable for a medium development country such as India, the enormity of TTIs will place an unmanageable cost burden on the society.

Emergence of drug resistance-associated mutations in HIV-1 subtype C protease gene in north India

A major cause of anti-retroviral therapy (ART) failure is the drug resistance-associated mutations in polymerase gene of HIV-1. Paucity of data regarding potential drug resistance to protease inhibitors (PIs) prompted us to carry out this study. Drug resistance (DR) genotyping of the entire protease gene was performed in 104 HIV-1 ART-naive and first-line ART-experienced patients. The DR pattern was analyzed using the Stanford HIV-DR database, International AIDS Society-USA mutation list and REGA algorithm version 8.0. Subtyping was done using Mega 4 and REGA HIV-1 subtyping tool-v2.01. Majority of our sequences 98 (96%) were subtype C and remaining four (3.92%) were subtype A1. In three (2.9%) DE patients, major DR-associated mutation at D30 N and M46I positions were detected. Approximately 70% polymorphisms as minor mutations were observed in protease gene, of which 14 distinct amino acids changes were linked to partial DR such as G16E, K20R, M36I, D60E, I62V, L63P, I64M, H69K, T74A/S, V77I, V82I, I85V, L89M, and I93L. The two major and several minor mutations detected in this study confer low/intermediate levels of resistance to most PIs independently or together. Our results conclude that resistance testing in HIV-1-infected patients should be performed before the initiation of PI therapy for better therapeutic outcome in this region. This information not only will shed light on the extent of current DR in HIV strains but also will aid in patient treatment and drug designing.