In 2019, a new pandemic suddenly emerged in Wuhan, China. The new coronavirus, named SARS-CoV-2, spread globally and it was officially declared a pandemic by the World Health Organization on March 12, 2020. The immediate need for new vaccine development against the virus became a global health priority. However, the emergence of new variants has affected the immunization effectiveness, as some of these new variants have shown reduced sensitivity to vaccines and greater transmission capacity. In this scenario, the search for effective antiviral treatments complementary to the fight against these diseases becomes essential. To mitigate the severity of symptoms and control infections, pharmaceutical therapies play a fundamental role in protecting the population. The discovery of new medicines involves computational methods that assess various drug candidates, identifying promising molecules for future biological testing. Such techniques enable efficient and cost-effective evaluations, significantly accelerating the drug development process. During the pandemic period, ANVISA (National Health Surveillance Agency) and the FDA (Food and Drug Administration) approved the use of Remdesivir as a medication against the coronavirus. However, there have been reports of toxicity associated with the drug, as well as controversies regarding its potential contribution to the development of respiratory discomfort. This highlights the need to search for analogs with greater efficacy and safety. Thus, computational methods are promising for identifying Remdesivir analogs having potential antiviral action against SARS-CoV-2, contributing to safer treatments for COVID-19. The objective of this work is to identify, based on in silico protein-ligand interaction, analogs with antiviral activity through their similar pharmacophoric properties and subsequent evaluation of pharmacokinetic characteristics and toxicity. The protein-analog complexes were generated using QuickVina 2.1 software to dock Remdesivir analogs against 845 pockets from 437 SARS-CoV-2 target proteins downloaded from RCS-PDB. For this study, complexes with the lowest kcal/mol score were selected. Were chosen only pockets with druggability scores >= 0.5, as identified by fpocket 4.0. The methodology used for the study involves the following steps: conversion of files containing complexes from .pdbqt to .mol2 using OpenBABEL, Ligand-Based Virtual Screening (LBVS) and Structure-Based Virtual Screening (SBVS), selection of protein-analog complexes by the best pharmacophoric groups, energy minimization, and analysis of pharmacokinetic properties. In LBVS, analogs from best Remdesivir complex were downloaded from databases including CHEMBL, PubChem, ChemDiv, MCULE, MCULE-Ult, MolPort, ZINC, LabNetwork, NCI Open, and ChemSpace, available in Pharmit. In SBVS, the converted file was loaded and prepared for the SARS-CoV-2 target proteins, namely 7A93 and 7CAB, with parameters established for molecular mass (0 ? 500), rotatable bonds (0 ? 6), LogP (0 ? 6), hydrogen bond acceptors (0 ? 12), and hydrogen bond donors (0 ? 12). Subsequently, followed by docking of the analogs into these pockets. Molecules that exhibited affinity scores more negative than Remdesivir (-10.5 kcal/mol) were identified in their respective databases and subjected to ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) analysis in AdmetLab 2.0. To find analogs of these candidates with better scores, some displayed high toxicity. Therefore, searches were conducted for similar pharmacophoric groups with higher affinity and lower toxicity when compared to the reference drug. For the first complex (7A93), 47 results of compounds with affinities more negative than Remdesivir (-10.5), originating from three different molecular conformations, were analyzed for their ADMET properties. Twelve molecules demonstrated better absorption, metabolism, distribution, excretion, and lower toxicity compared to Remdesivir and were selected for further consideration. Similarly, for the second complex (7CAB), properties of 342 molecules with affinities more negative than Remdesivir, resulting from two different molecular conformations, were analyzed. Eighteen molecules exhibited improved properties compared to Remdesivir and were chosen for further evaluation. Remdesivir has unfavorable toxicity results, particularly respiratory toxicity, as well as drug-induced liver injury (DILI), AMES toxicity, and potential carcinogenicity. It is important to note that carcinogenic potential is not a criterion for discarding the analogs evaluated, as the treatment in question should not be recurrent or prolonged. Furthermore, Remdesivir exhibits a value classified as critical for respiratory toxicity, which should be carefully considered as a negative factor for drugs intended for COVID-19 treatment. Therefore, all molecules that demonstrated this toxicity were immediately discarded. As a result, all molecules presented as the best results obtained fewer toxicity points compared to the reference drug. In accordance with the literature, Remdesivir was identified as a possible model with pharmacological action potential against COVID-19. Studies were conducted and provided evidence pointing to its effectiveness against the virus, leading to the purpose of this study to create a library of compounds with chemical activity similar to Remdesivir. Subsequently, analogs of the reference drug went through virtual screening, energy minimization, and pharmacokinetic property analysis. In ADMET analysis, for the first complex (7A93), 12 top results were selected, of which two molecules (C2 and E1) exhibited either no toxicity or only minimal toxicity and no respiratory toxicity - for this complex, E1 was selected as the best molecule. For ADMET analysis of the second complex (7CAB), 18 top results were chosen, of which four molecules (A7 , B3, B6 and B7) showed either no toxicity or only minimal toxicity and no respiratory toxicity - for this complex, B7 was selected as the best molecule. These molecules were assigned alphanumeric nomenclature to preserve their identities for possible future work. All of them met the SAscore parameter, which evaluates the ease of analog synthesis. The effectiveness of the search strategy for pharmacophoric groups demonstrates significant interactions with the active sites of SPIKE in obtaining compounds with higher affinity and lower in silico toxicity compared to Remdesivir. The combination of these interactions with low toxicity makes these molecules candidates for further in vitro testing. We express our sincere gratitude for their structures and financial support to Federal University of Paraíba (UFPB), including Centro de Biotecnologia (CBIOTEC), Laboratório Multiusuário de Bioinformática e Análise de Dados (LAMBDA) and Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPQ) for their financial support that allowed the procedure of the study.
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Francisco Mendonça Junior
Pascal Marchand
Teresinha Gonçalves da Silva
Isabelle Orliac-Garnier
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Ricardo Olimpio de Moura
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