Identification of Novel HIV-1 Integrase Inhibitors Using Shape-Based Screening, QSAR, and Docking Approach

Machine learning-based predictive models for identifying high active compounds against HIV-1 integrase

HIV-integrase is an important drug target because it catalyzes chromosomal integration of proviral DNA towards establishing latent infection. Computer-aided drug design has immensely contributed to identifying and developing novel antiviral drugs. We have developed various machine learning-based predictive models for identifying high activity compounds against HIV-integrase. Multiclass models were built using support vector machine with reasonable accuracy on the test and evaluation sets. The developed models were evaluated by rigorous validation approaches and the best features were selected by Boruta method. As compared to the model developed from all descriptors set, a slight improvement was observed among the selected descriptors. Validated models were further used for virtual screening of potential compounds from ChemBridge library. Of the six high active compounds predicted from selected models, compounds 9103124, 6642917 and 9082952 showed the most reasonable binding-affinity and stable-interaction with HIV-integrase active-site residues Asp64, Glu152 and Asn155. This was in agreement with previous reports on the essentiality of these residues against a wide range of inhibitors. We therefore highlight the rigorosity of validated classification models for accurate prediction and ranking of high active lead drugs against HIV-integrase.

Repurposing FDA-approved drugs as HIV-1 integrase inhibitors: an in silico investigation

The Human Immunodeficiency Virus (HIV) infection is a global pandemic that has claimed 33 million lives to-date. One of the most efficacious treatments for naïve or pretreated HIV patients is the HIV integrase strand transfer inhibitors (INSTIs). However, given that HIV treatment is life-long, the emergence of HIV strains resistant to INSTIs is an imminent challenge. In this work, we showed two best regression QSAR models that were constructed using a boosted Random Forest algorithm (r² = 0.998, q²_10CV = 0.721, q²_{external_test} = 0.754) and a boosted K* algorithm (r² = 0.987, q²_10CV = 0.721, q²_{external_test} = 0.758) to predict the pIC₅₀ values of INSTIs. Subsequently, the regression QSAR models were deployed against the Drugbank database for drug repositioning. The top-ranked compounds were further evaluated for their target engagement activity using molecular docking studies and accelerated Molecular Dynamics simulation. Lastly, their potential as INSTIs were also evaluated from our literature search. Our study offers the first example of a large-scale regression QSAR modelling effort for discovering highly active INSTIs to combat HIV infection.Communicated by Ramaswamy H. Sarma.

Lvsiyujins A–G, new sesquiterpenoids, from Curcuma phaeocaulis Valeton root tuber and their preliminary pharmacological property assessment based on ADME evaluation, molecular docking and in vitro experiments

Seven new sesquiterpenoids were isolated from the root tuber of C. phaeocaulis. A combination of calculations and experiments was used in structural analysis and biological activity exploration.

An Overview of Antiretroviral Agents for Treating HIV Infection in Paediatric Population

Paediatric Acquired ImmunoDeficiency Syndrome (AIDS) is a life-threatening and infectious disease in which the Human Immunodeficiency Virus (HIV) is mainly transmitted through Mother-To- Child Transmission (MTCT) during pregnancy, labour and delivery, or breastfeeding. This review provides an overview of the distinct therapeutic alternatives to abolish the systemic viral replication in paediatric HIV-1 infection. Numerous classes of antiretroviral agents have emerged as therapeutic tools for downregulation of different steps in the HIV replication process. These classes encompass Non- Nucleoside Analogue Reverse Transcriptase Inhibitors (NNRTIs), Nucleoside/Nucleotide Analogue Reverse Transcriptase Inhibitors (NRTIs/NtRTIs), INtegrase Inhibitors (INIs), Protease Inhibitors (PIs), and Entry Inhibitors (EIs). Co-administration of certain antiretroviral drugs with Pharmacokinetic Enhancers (PEs) may boost the effectiveness of the primary therapeutic agent. The combination of multiple antiretroviral drug regimens (Highly Active AntiRetroviral Therapy - HAART) is currently the standard therapeutic approach for HIV infection. So far, the use of HAART offers the best opportunity for prolonged and maximal viral suppression, and preservation of the immune system upon HIV infection. Still, the frequent administration of high doses of multiple drugs, their inefficient ability to reach the viral reservoirs in adequate doses, the development of drug resistance, and the lack of patient compliance compromise the complete HIV elimination. The development of nanotechnology-based drug delivery systems may enable targeted delivery of antiretroviral agents to inaccessible viral reservoir sites at therapeutic concentrations. In addition, the application of Computer-Aided Drug Design (CADD) approaches has provided valuable tools for the development of anti-HIV drug candidates with favourable pharmacodynamics and pharmacokinetic properties.

Identification of Selective Inhibitors of LdDHFR Enzyme Using Pharmacoinformatic Methods

Dihydrofolate reductase (DHFR) is a well-known enzyme of the folate metabolic pathway and it is a validated drug target for leishmaniasis. However, only a few leads are reported against Leishmania donovani DHFR (LdDHFR), and thus, there is a need to identify new inhibitors. In this article, pharmacoinformatic tools such as molecular docking, virtual screening, absorption, distribution, metabolism, excretion, and toxicity (ADMET) profiling, and molecular dynamics (MD) simulations were utilized to identify potential LdDHFR inhibitors. Initially, a natural DHFR substrate (dihydrofolate), a classical DHFR inhibitor (methotrexate), and a potent LdDHFR inhibitor, that is, "5-(3-(octyloxy)benzyl)pyrimidine-2,4-diamine" (LEAD) were docked in the active site of the LdDHFR and MD simulated to understand the binding mode characteristics of the substrates/inhibitors in the LdDHFR. The shape of the LEAD molecule was used as a query for shape-based virtual screening, while the three-dimensional structure of LdDHFR was utilized for docking-based virtual screening. In silico ADMET factors were also considered during virtual screening. These two screening processes yielded 25 suitable hits, which were further validated for their selectivity toward LdDHFR using molecular docking and prime molecular mechanics/generalized born surface area analysis in the human DHFR (HsDHFR). Best six hits, which were selective and energetically favorable for the LdDHFR, were chosen for MD simulations. The MD analysis showed that four of the hits exhibited very good binding affinity for LdDHFR with respect to HsDHFR, and two hits were found to be more selective than the reported potent LdDHFR inhibitor. The present study thus identifies hits that can be further designed and modified as potent LdDHFR inhibitors.

QSAR Study on Antioxidant Tripeptides and the Antioxidant Activity of the Designed Tripeptides in Free Radical Systems

In this study, quantitative structure-activity relationship (QSAR) models were determined based on 91 antioxidant tripeptides. We firstly adopted the stepwise regression (SWR) method for selecting key variables without autocorrelation and then utilized multiple linear regression (MLR), support vector machine (SVM), random forest (RF), and partial least square regression (PLS) to develop predictive QSAR models based on the screened variables. The results demonstrated that all the established models have good reliability (R²_train > 0.86, Q²_train > 0.70) and relatively good predictability (R²_test > 0.88). The contribution of amino acid residues was calculated from the stepwise regression combined with multiple linear regression (SWR-MLR) method model that shows Trp, Tyr, or Cys at C-terminus is favorable for antioxidant activity of tripeptides. Nineteen antioxidant tripeptides were designed based on SWR-MLR models, and the antioxidant activity of these tripeptides were evaluated using three antioxidant assays in free radical systems (1,1-diphenyl-2-picrylhydrazyl (DPPH) radical scavenging capacity, trolox equivalent antioxidant capacity assay, and the ferric reducing antioxidant power assay). The experimental antioxidant activities of these tripeptides were higher than the calculated/predicted activity values of the QSAR models. The QSAR models established can be used to identify and screen novel antioxidant tripeptides with high activity.

3D QSAR studies, molecular docking and ADMET evaluation, using thiazolidine derivatives as template to obtain new inhibitors of PIM1 kinase

Proviral Integration site for Moloney murine leukemia virus-1 (PIM1) belongs to the serine/threonine kinase family of Ca²⁺-calmodulin-dependent protein kinase (CAMK) group, which is involved in cell survival and proliferation as well as a number of other signal transduction pathways. Thus, PIM1 is regarded as a promising target for treatment of cancers. In the present paper, a three-dimensional quantitative structure activity relationship (3D-QSAR) and molecular docking were performed to investigate the binding between PIM1 and thiazolidine inhibitors in order to design potent inhibitors. The comparative molecular similarity indices analysis (CoMSIA) was developed using twenty-six molecules having pIC₅₀ ranging from 8.854 to 6.011 (IC₅₀ in nM). The best CoMSIA model gave significant statistical quality. The determination coefficient (R²) and Leave-One-Out cross-validation coefficient (Q²) are 0.85 and 0.58, respectively. Furthermore, the predictive ability of this model was evaluated by external validation((n = 11, R²_test = 0.72, and MAE = 0.170 log units). The graphical contour maps could provide structural features to improve inhibitory activity. Furthermore, a good consistency between contour maps and molecular docking strongly demonstrates that the molecular modeling is reliable. Based on these satisfactory results, we designed several new potent PIM1 inhibitors and their inhibitory activities were predicted by the molecular models. Additionally, those newly designed inhibitors, showed promising results in the preliminary in silico ADMET evaluations, compared to the best inhibitor from the studied dataset. The results expand our understanding of thiazolidines as inhibitors of PIM1 and could be of great help in lead optimization for early drug discovery of highly potent inhibitors.

Effect of HIV-1 Subtype C integrase mutations implied using molecular modeling and docking data

The degree of sequence variation in HIV-1 integrase genes among infected patients and their impact on clinical response to Anti retroviral therapy (ART) is of interest. Therefore, we collected plasma samples from 161 HIV-1 infected individuals for subsequent integrase gene amplification (1087 bp). Thus, 102 complete integrase gene sequences identified as HIV-1 subtype-C was assembled. This sequence data was further used for sequence analysis and multiple sequence alignment (MSA) to assess position specific frequency of mutations within pol gene among infected individuals. We also used biophysical geometric optimization technique based molecular modeling and docking (Schrodinger suite) methods to infer differential function caused by position specific sequence mutations towards improved inhibitor selection. We thus identified accessory mutations (usually reduce susceptibility) leading to the resistance of some known integrase inhibitors in 14% of sequences in this data set. The Stanford HIV-1 drug resistance database provided complementary information on integrase resistance mutations to deduce molecular basis for such observation. Modeling and docking analysis show reduced binding by mutants for known compounds. The predicted binding values further reduced for models with combination of mutations among subtype C clinical strains. Thus, the molecular basis implied for the consequence of mutations in different variants of integrase genes of HIV-1 subtype C clinical strains from South India is reported. This data finds utility in the design, modification and development of a representative yet an improved inhibitor for HIV-1 integrase.

Development of HIV-1 integrase inhibitors: recent molecular modeling perspectives

Of the three viral enzymes essential to HIV replication, HIV-1 integrase (IN) is gaining popularity as a target for the antiviral therapy of AIDS. Substantial work focusing on IN has been done over the past three decades, which has facilitated and led to the approval of three drugs. Here, we discuss in detail the development of IN inhibitors between January 2012 and May 2014, with a particular focus on molecular simulation. We highlight controversial aspects of computational drug design and refer to alternative practices where appropriate. The analysis of these computational approaches provides some useful clues to the possible future discovery of novel IN inhibitors.