Integrative approaches in HIV ‐1 non‐nucleoside reverse transcriptase inhibitor design

A Review on Parallel Virtual Screening Softwares for High-Performance Computers

Drug discovery is the most expensive, time-demanding, and challenging project in biopharmaceutical companies which aims at the identification and optimization of lead compounds from large-sized chemical libraries. The lead compounds should have high-affinity binding and specificity for a target associated with a disease, and, in addition, they should have favorable pharmacodynamic and pharmacokinetic properties (grouped as ADMET properties). Overall, drug discovery is a multivariable optimization and can be carried out in supercomputers using a reliable scoring function which is a measure of binding affinity or inhibition potential of the drug-like compound. The major problem is that the number of compounds in the chemical spaces is huge, making the computational drug discovery very demanding. However, it is cheaper and less time-consuming when compared to experimental high-throughput screening. As the problem is to find the most stable (global) minima for numerous protein-ligand complexes (on the order of 106 to 1012), the parallel implementation of in silico virtual screening can be exploited to ensure drug discovery in affordable time. In this review, we discuss such implementations of parallelization algorithms in virtual screening programs. The nature of different scoring functions and search algorithms are discussed, together with a performance analysis of several docking softwares ported on high-performance computing architectures.

HIV-1 Reverse Transcriptase Inhibition by Major Compounds in a Kenyan Multi-Herbal Composition (CareVid™): In Vitro and In Silico Contrast

CareVid is a multi-herbal product used in southwest Kenya as an immune booster and health tonic and has been anecdotally described as improving the condition of HIV-positive patients. The product is made up of roots, barks and whole plant of 14 African medicinal plants: Acacia nilotica (L.) Willd. ex Delile (currently, Vachelia nilotica (L.) P.J.H Hurter & Mabb.), Adenia gummifera (Harv.) Harms, Anthocleista grandiflora Gilg, Asparagus africanus Lam., Bersama abyssinica Fresen., Clematis hirsuta Guill. & Perr., Croton macrostachyus Hochst. ex Delile, Clutia robusta Pax (accepted as Clutia kilimandscharica Engl.), Dovyalis abyssinica (A. Rich.) Warb, Ekebergia capensis Sparm., Periploca linearifolia Quart.-Dill. & A. Rich., Plantago palmata Hook.f., Prunus africana Hook.f. Kalkman and Rhamnus prinoides L’Her. The objective of this study was to determine the major chemical constituents of CareVid solvent extracts and screen them for in vitro and in silico activity against the HIV-1 reverse transcriptase enzyme. To achieve this, CareVid was separately extracted using CH₂Cl₂, MeOH, 80% EtOH in H₂O, cold H₂O, hot H₂O and acidified H₂O (pH 1.5–3.5). The extracts were analysed using HPLC–MS equipped with UV diode array detection. HIV-1 reverse transcriptase inhibition was performed in vitro and compared to in silico HIV-1 reverse transcriptase inhibition, with the latter carried out using MOE software, placing the docking on the hydrophobic pocket in the subdomain of p66, the NNRTI pocket. The MeOH and 80% EtOH extracts showed strong in vitro HIV-1 reverse transcriptase inhibition, with an EC₅₀ of 7 μg·mL⁻¹. The major components were identified as sucrose, citric acid, ellagic acid, catechin 3-hexoside, epicatechin 3-hexoside, procyanidin B, hesperetin O-rutinoside, pellitorine, mangiferin, isomangiferin, 4-O-coumaroulquinic acid, ellagic acid, ellagic acid O-pentoside, crotepoxide, oleuropein, magnoflorine, tremulacin and an isomer of dammarane tetrol. Ellagic acid and procyanidin B inhibited the HIV-1 reverse transcription process at 15 and 3.2 µg/mL⁻¹, respectively. Docking studies did not agree with in vitro results because the best scoring ligand was crotepoxide (ΔG = −8.55 kcal/mol), followed by magnoflorine (ΔG = −8.39 kcal/mol). This study showed that CareVid has contrasting in vitro and in silico activity against HIV-1 reverse transcriptase. However, the strongest in vitro inhibitors were ellagic acid and procyanidin B.

Computational investigation on Andrographis paniculata phytochemicals to evaluate their potency against SARS-CoV-2 in comparison to known antiviral compounds in drug trials

The outbreak due to SARS-CoV-2 (or Covid-19) is spreading alarmingly and number of deaths due to infection is aggressively increasing every day. Due to the rapid human to human transmission of Covid-19, we are in need to find a potent drug at the earliest by ruling-out the traditional time-consuming approach of drug development. This is only possible if we use reliable computational approaches for screening compounds from chemical space or by drug repurposing or by finding the phytochemicals and nutraceuticals from plants as they can be immediately used without the need for carrying out drug-trials to test safety and efficacy. A number of plant products were routinely suggested as drugs in traditional Indian and Chinese medicine. Here using molecular docking approach, and combined molecular dynamics and MM-GBSA based free energy calculations approach, we study the potency of the four selected phytochemicals namely andrographolide (AGP1), 14-deoxy 11,12-didehydro andrographolide (AGP2), neoandrographolide (AGP3) and 14-deoxy andrographolide (AGP4) from A. paniculata plant against the four key targets including three non-structural proteins (3 L main protease (3CLpro), Papain-like proteinase (PLpro) and RNA-directed RNA polymerase (RdRp)) and a structural protein (spike protein (S)) of the virus which are responsible for replication, transcription and host cell recognition. The therapeutic potential of the selected phytochemicals against Covid-19 were also evaluated in comparison with a few commercially available drugs. The binding free energy data suggest that AGP3 could be used as a cost-effective drug-analog for treating covid-19 infection in developing countries.Communicated by Ramaswamy H. Sarma.

Design, synthesis and biological evaluation of quinoxaline compounds as anti-HIV agents targeting reverse transcriptase enzyme

Infection by human immunodeficiency virus still represents a continuous serious concern and a global threat to human health. Due to appearance of multi-resistant virus strains and the serious adverse side effects of the antiretroviral therapy administered, there is an urgent need for the development of new treatment agents, more active, less toxic and with increased tolerability to mutations. Quinoxaline derivatives are an emergent class of heterocyclic compounds with a wide spectrum of biological activities and therapeutic applications. These types of compounds have also shown high potency in the inhibition of HIV reverse transcriptase and HIV replication in cell culture. For these reasons we propose, in this work, the design, synthesis and biological evaluation of quinoxaline derivatives targeting HIV reverse transcriptase enzyme. For this, we first carried out a structure-based development of target-specific compound virtual chemical library of quinoxaline derivatives. The rational construction of the virtual chemical library was based on previously assigned pharmacophore features. This library was processed by a virtual screening protocol employing molecular docking and 3D-QSAR. Twenty-five quinoxaline compounds were selected for synthesis in the basis of their docking and 3D-QSAR scores and chemical synthetic simplicity. They were evaluated as inhibitors of the recombinant wild-type reverse transcriptase enzyme. Finally, the anti-HIV activity and cytotoxicity of the synthesized quinoxaline compounds with highest reverse transcriptase inhibitory capabilities was evaluated. This simple screening strategy led to the discovery of two selective and potent quinoxaline reverse transcriptase inhibitors with high selectivity index.

Multioutput Perturbation-Theory Machine Learning (PTML) Model of ChEMBL Data for Antiretroviral Compounds

Retroviral infections, such as HIV, are, until now, diseases with no cure. Medicine and pharmaceutical chemistry need and consider it a huge goal to define target proteins of new antiretroviral compounds. ChEMBL manages Big Data features with a complex data set, which is hard to organize. This makes information difficult to analyze due to a big number of characteristics described in order to predict new drug candidates for retroviral infections. For this reason, we propose to develop a new predictive model combining perturbation theory (PT) bases and machine learning (ML) modeling to create a new tool that can take advantage of all the available information. The PTML model proposed in this work for the ChEMBL data set preclinical experimental assays for antiretroviral compounds consists of a linear equation with four variables. The PT operators used are founded on multicondition moving averages, combining different features and simplifying the difficulty to manage all data. More than 140 000 preclinical assays for 56 105 compounds with different characteristics or experimental conditions have been carried out and can be found in ChEMBL database, covering combinations with 359 biological activity parameters (c₀), 55 protein accessions (c₁), 83 cell lines (c₂), 64 organisms of assay (c₃), and 773 subtypes or strains. We have included 150 148 preclinical experimental assays for HIV virus, 1188 for HTLV virus, 84 for simian immunodeficiency virus, 370 for murine leukemia virus, 119 for Rous sarcoma virus, 1581 for MMTV, etc. We also included 5277 assays for hepatitis B virus. The developed PTML model reached considerable values in sensibility (73.05% for training and 73.10% for validation), specificity (86.61% for training and 87.17% for validation), and accuracy (75.84% for training and 75.98% for validation). We also compared alternative PTML models with different PT operators such as covariance, moments, and exponential terms. Finally, we made a comparison between literature ML models with our PTML model and also artificial neural network (ANN) nonlinear models. We conclude that this PTML model is the first one to consider multiple characteristics of preclinical experimental antiretroviral assays combined, generating a simple, useful, and adaptable instrument, which could reduce time and costs in antiretroviral drugs research.

The Journey of HIV-1 Non-Nucleoside Reverse Transcriptase Inhibitors (NNRTIs) from Lab to Clinic

Human immunodeficiency virus (HIV) infection is now pandemic. Targeting HIV-1 reverse transcriptase (HIV-1 RT) has been considered as one of the most successful targets for the development of anti-HIV treatment. Among the HIV-1 RT inhibitors, non-nucleoside reverse transcriptase inhibitors (NNRTIs) have gained a definitive place due to their unique antiviral potency, high specificity, and low toxicity in antiretroviral combination therapies used to treat HIV. Until now, >50 structurally diverse classes of compounds have been reported as NNRTIs. Among them, six NNRTIs were approved for HIV-1 treatment, namely, nevirapine (NVP), delavirdine (DLV), efavirenz (EFV), etravirine (ETR), rilpivirine (RPV), and doravirine (DOR). In this perspective, we focus on the six NNRTIs and lessons learned from their journey through development to clinical studies. It demonstrates the obligatory need of understanding the physicochemical and biological principles (lead optimization), resistance mutations, synthesis, and clinical requirements for drugs.

Different Positron Emission Tomography Tau Tracers Bind to Multiple Binding Sites on the Tau Fibril: Insight from Computational Modeling

Using the recently reported cryo-EM structure for the tau fibril [ Fitzpatrick et al. (2017) Nature 547, 185-190 ], which is a potential target concerning Alzheimer's disease, we present the first molecular modeling studies on its interaction with various positron emission tomography (PET) tracers. Experimentally, based on the binding assay studies, at least three different high-affinity binding sites have been reported for tracers in the tau fibril. Herein, through integrated modeling using molecular docking, molecular dynamics, and binding free energy calculations, we provide insight into the binding patterns of various tracers to the tau fibril. We suggest that there are four different high-affinity binding sites available for many of the studied tracers showing varying binding affinity to different binding sites. Thus, PBB3 binds most strongly to site 4, and interestingly, this site is not a preferable site for any other tracers. For THK5351, our data show that it strongly binds to sites 3 and 1, the former one being more preferable. We also find that MK6240 and T807 bind to site 1 specifically. The modeling data also give some insight into whether a tracer bound to a specific site can be replaced by others or not. For example, the displacement of T807 by PBB3 as reported experimentally can also be explained and attributed to the larger binding affinity of the latter compound in all binding sites. The binding free energy results explain very well the small binding affinity of THK523 compared to all the aryl quinoline moieties containing THK tracers. The ability of certain tau tracers, like FDDNP and THK523, to bind to amyloid fibrils has also been investigated. Furthermore, such off-target interaction of tau tracers with amyloid beta fibrils has been validated using a quantum mechanical fragmentation approach.

A computational study for rational HIV-1 non-nucleoside reverse transcriptase inhibitor selection and the discovery of novel allosteric pockets for inhibitor design

HIV drug resistant mutations that render the current Highly Active Anti-Retroviral Therapy (HAART) cocktail drugs ineffective are increasingly reported. To study the mechanisms of these mutations in conferring drug resistance, we computationally analyzed 14 reverse transcriptase (RT) structures of HIV-1 on the following parameters: drug-binding pocket volume, allosteric effects caused by the mutations, and structural thermal stability. We constructed structural correlation-based networks of the mutant RT-drug complexes and the analyses support the use of efavirenz (EFZ) as the first-line drug, given that cross-resistance is least likely to develop from EFZ-resistant mutations. On the other hand, rilpivirine (RPV)-resistant mutations showed the highest cross-resistance to the other non-nucleoside RT inhibitors. With significant drug cross-resistance associated with the known allosteric drug-binding site, there is a need to identify new allosteric druggable sites in the structure of RT. Through computational analyses, we found such a novel druggable pocket on the HIV-1 RT structure that is comparable with the original allosteric drug site, opening the possibility to the design of new inhibitors.