Repositioning of 8565 Existing Drugs for COVID-19

Benchmark of Popular Free Energy Approaches Revealing the Inhibitors Binding to SARS-CoV-2 Mpro

The COVID-19 pandemic has killed millions of people worldwide since its outbreak in December 2019. The pandemic is caused by the SARS-CoV-2 virus whose main protease (Mpro) is a promising drug target since it plays a key role in viral proliferation and replication. Currently, developing an effective therapy is an urgent task, which requires accurately estimating the ligand-binding free energy to SARS-CoV-2 Mpro. However, it should be noted that the accuracy of a free energy method probably depends on the protein target. A highly accurate approach for some targets may fail to produce a reasonable correlation with the experiment when a novel enzyme is considered as a drug target. Therefore, in this context, the ligand-binding affinity to SARS-CoV-2 Mpro was calculated via various approaches. The molecular docking approach was manipulated using Autodock Vina (Vina) and Autodock4 (AD4) protocols to preliminarily investigate the ligand-binding affinity and pose to SARS-CoV-2 Mpro. The binding free energy was then refined using the fast pulling of ligand (FPL), linear interaction energy (LIE), molecular mechanics-Poisson-Boltzmann surface area (MM-PBSA), and free energy perturbation (FEP) methods. The benchmark results indicated that for docking calculations, Vina is more accurate than AD4, and for free energy methods, FEP is the most accurate method, followed by LIE, FPL, and MM-PBSA (FEP > LIE ≈ FPL > MM-PBSA). Moreover, atomistic simulations revealed that the van der Waals interaction is the dominant factor. The residues Thr26, His41, Ser46, Asn142, Gly143, Cys145, His164, Glu166, and Gln189 are essential elements affecting the binding process. Our benchmark provides guidelines for further investigations using computational approaches.

Computational estimation of potential inhibitors from known drugs against the main protease of SARS-CoV-2

The coronavirus disease (COVID-19) pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has rapidly spread worldwide recently, leading to global social and economic disruption. Although the emergently approved vaccine programs against SARS-CoV-2 have been rolled out globally, the number of COVID-19 daily cases and deaths has remained significantly high. Here, we attempt to computationally screen for possible medications for COVID-19 via rapidly estimating the highly potential inhibitors from an FDA-approved drug database against the main protease (Mpro) of SARS-CoV-2. The approach combined molecular docking and fast pulling of ligand (FPL) simulations that were demonstrated to be accurate and suitable for quick prediction of SARS-CoV-2 Mpro inhibitors. The results suggested that twenty-seven compounds were capable of strongly associating with SARS-CoV-2 Mpro. Among them, the seven top leads are daclatasvir, teniposide, etoposide, levoleucovorin, naldemedine, cabozantinib, and irinotecan. The potential application of these drugs in COVID-19 therapy has thus been discussed.

Antivirals virtual screening to SARS-CoV-2 non-structural proteins

In March 2020, the World Health Organization (WHO) declared coronavirus disease-19 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), a pandemic. Since then, the search for a vaccine or drug for COVID-19 treatment has started worldwide. In this regard, a fast approach is the repurposing of drugs, primarily antiviral drugs. Herein, we performed a virtual screening using 22 antiviral drugs retrieved from the DrugBank repository, azithromycin (antibiotic), ivermectin (antinematode), and seven non-structural proteins (Nsps) of SARS-CoV-2, which are considered important targets for drugs, via molecular docking and molecular dynamics simulations. Drug-receptor binding energy was employed as the main descriptor. Based on the results, paritaprevir was predicted as a promising multi-target drug that favorably bound to all tested Nsps, mainly adipose differentiation-related protein (ADRP) (-36.2 kcal mol^-1) and coronavirus main proteinase (Mpro) (-32.2 kcal mol^-1). Moreover, the results suggest that simeprevir is a strong inhibitor of Mpro (-37.2 kcal mol^-1), which is an interesting finding because Mpro plays an important role in viral replication. In addition to drug-receptor affinity, hot spot residues were characterized to facilitate the design of new drug derivatives with improved biological responses.

Screening of drug databank against WT and mutant main protease of SARS-CoV-2: Towards finding potential compound for repurposing against COVID-19

Although several pharmacological agents are under investigation to be repurposed as therapeutic against COVID-19, not much success has been achieved yet. So, the search for an effective and active option for the treatment of COVID-19 is still a big challenge. The Spike protein (S), RNA-dependent RNA polymerase (RdRp), and Main protease (Mpro) are considered to be the primary therapeutic drug target for COVID-19. In this study we have screened the drugbank compound library against the Main Protease. But our search was not limited to just Mpro. Like other viruses, SARS-CoV-2, have also acquired unique mutations. These mutations within the active site of these target proteins may be an important factor hindering effective drug candidate development. In the present study we identified important active site mutations within the SARS-CoV-2 Mpro (Y54C, N142S, T190I and A191V). Further the drugbank database was computationally screened against Mpro and the selected mutants. Finally, we came up with the common molecules effective against the wild type (WT) and all the selected Mpro. The study found Imiglitazar, was found to be the most active compound against the wild type of Mpro. While PF-03715455 (Y54C), Salvianolic acid A (N142S and T190I), and Montelukast (A191V) were found to be most active against the other selected mutants. It was also found that some other compounds such as Acteoside, 4-Amino-N- {4-[2-(2,6-Dimethyl-Phenoxy)-Acetylamino]-3-Hydroxy-1-Isobutyl-5-Phenyl-Pentyl}-Benzamide, PF-00610355, 4-Amino-N-4-[2-(2,6-Dimethyl-Phenoxy)-Acetylamino]-3-Hydroxy-1-Isobutyl-5-Phenyl-Pentyl}-Benzamide and Atorvastatin were showing high efficacy against the WT as well as other selected mutants. We believe that these molecules will provide a better and effective option for the treatment of COVID-19 clinical manifestations.

Identification of Anti-SARS-CoV-2 Compounds from Food Using QSAR-Based Virtual Screening, Molecular Docking, and Molecular Dynamics Simulation Analysis

Due to the genetic similarity between SARS-CoV-2 and SARS-CoV, the present work endeavored to derive a balanced Quantitative Structure-Activity Relationship (QSAR) model, molecular docking, and molecular dynamics (MD) simulation studies to identify novel molecules having inhibitory potential against the main protease (Mpro) of SARS-CoV-2. The QSAR analysis developed on multivariate GA-MLR (Genetic Algorithm-Multilinear Regression) model with acceptable statistical performance (R2 = 0.898, Q2loo = 0.859, etc.). QSAR analysis attributed the good correlation with different types of atoms like non-ring Carbons and Nitrogens, amide Nitrogen, sp2-hybridized Carbons, etc. Thus, the QSAR model has a good balance of qualitative and quantitative requirements (balanced QSAR model) and satisfies the Organisation for Economic Co-operation and Development (OECD) guidelines. After that, a QSAR-based virtual screening of 26,467 food compounds and 360 heterocyclic variants of molecule 1 (benzotriazole-indole hybrid molecule) helped to identify promising hits. Furthermore, the molecular docking and molecular dynamics (MD) simulations of Mpro with molecule 1 recognized the structural motifs with significant stability. Molecular docking and QSAR provided consensus and complementary results. The validated analyses are capable of optimizing a drug/lead candidate for better inhibitory activity against the main protease of SARS-CoV-2.

Machine learning and applications in microbiology

To understand the intricacies of microorganisms at the molecular level requires making sense of copious volumes of data such that it may now be humanly impossible to detect insightful data patterns without an artificial intelligence application called machine learning. Applying machine learning to address biological problems is expected to grow at an unprecedented rate, yet it is perceived by the uninitiated as a mysterious and daunting entity entrusted to the domain of mathematicians and computer scientists. The aim of this review is to identify key points required to start the journey of becoming an effective machine learning practitioner. These key points are further reinforced with an evaluation of how machine learning has been applied so far in a broad scope of real-life microbiology examples. This includes predicting drug targets or vaccine candidates, diagnosing microorganisms causing infectious diseases, classifying drug resistance against antimicrobial medicines, predicting disease outbreaks and exploring microbial interactions. Our hope is to inspire microbiologists and other related researchers to join the emerging machine learning revolution.

Electrostatic features for nucleocapsid proteins of SARS-CoV and SARS-CoV-2

COVID-19 is increasingly affecting human health and global economy. Understanding the fundamental mechanisms of Severe Acute Respiratory Syndrome CoronaVirus 2 (SARS-CoV-2) is highly demanded to develop treatments for COVID-19. SARS-CoV and SARS-CoV-2 share 92.06% identity in their N protein RBDs' sequences, which results in very similar structures. However, the SARS-CoV-2 is more easily to spread. Utilizing multi-scale computational approaches, this work studied the fundamental mechanisms of the nucleocapsid (N) proteins of SARS-CoV and SARS-CoV-2, including their stabilities and binding strengths with RNAs at different pH values. Electrostatic potential on the surfaces of N proteins show that both the N proteins of SARS-CoV and SARS-CoV-2 have dominantly positive potential to attract RNAs. The binding forces between SARS-CoV N protein and RNAs at different distances are similar to that of SARS-CoV-2, both in directions and magnitudes. The electric filed lines between N proteins and RNAs are also similar for both SARS-CoV and SARS-CoV-2. The folding energy and binding energy dependence on pH revealed that the best environment for N proteins to perform their functions with RNAs is the weak acidic environment.

AI drug discovery screening for COVID-19 reveals zafirlukast as a repurposing candidate

AIMS

Over the past few years, AI has been considered as potential important area for improving drug development and in the current urgent need to fight the global COVID-19 pandemic new technologies are even more in focus with the hope to speed up this process. The purpose of our study was to identify the best repurposing candidates among FDA-approved drugs, based on their predicted antiviral activity against SARS-CoV-2.

MATERIALS AND METHODS

This article describes a drug discovery screening based on a supervised machine learning model, trained on in vitro data encoded in chemical fingerprints, representing particular molecular substructures. Predictive performance of our model has been evaluated using so-called scaffold splits offering a state-of-the-art setup for assessing model's ability to generalize to new chemical spaces, critical for drug repurposing applications.

KEY FINDINGS

Our study identified zafirlukast as the best repurposing candidate for COVID-19.

SIGNIFICANCE

Zafirlukast could be potent against COVID-19 both due to its predicted antiviral properties and its ability to attenuate the so called cytokine storm. Thus, these two critical mechanisms of action may be combined in one drug as a novel and promising pharmacotherapy in the current pandemic.

Artificial intelligence and machine learning could support drug development for hepatitis A virus internal ribosomal entry sites

Hepatitis A virus (HAV) infection is still an important health issue worldwide. Although several effective HAV vaccines are available, it is difficult to perform universal vaccination in certain countries. Therefore, it may be better to develop antivirals against HAV for the prevention of severe hepatitis A. We found that several drugs potentially inhibit HAV internal ribosomal entry site-dependent translation and HAV replication. Artificial intelligence and machine learning could also support screening of anti-HAV drugs, using drug repositioning and drug rescue approaches.

In Silico Screening of the DrugBank Database to Search for Possible Drugs against SARS-CoV-2

Coronavirus desease 2019 (COVID-19) is responsible for more than 1.80 M deaths worldwide. A Quantitative Structure-Activity Relationships (QSAR) model is developed based on experimental pIC50 values reported for a structurally diverse dataset. A robust model with only five descriptors is found, with values of R2 = 0.897, Q2LOO = 0.854, and Q2ext = 0.876 and complying with all the parameters established in the validation Tropsha's test. The analysis of the applicability domain (AD) reveals coverage of about 90% for the external test set. Docking and molecular dynamic analysis are performed on the three most relevant biological targets for SARS-CoV-2: main protease, papain-like protease, and RNA-dependent RNA polymerase. A screening of the DrugBank database is executed, predicting the pIC50 value of 6664 drugs, which are IN the AD of the model (coverage = 79%). Fifty-seven possible potent anti-COVID-19 candidates with pIC50 values > 6.6 are identified, and based on a pharmacophore modelling analysis, four compounds of this set can be suggested as potent candidates to be potential inhibitors of SARS-CoV-2. Finally, the biological activity of the compounds was related to the frontier molecular orbitals shapes.

In Silico Study of Coumarins and Quinolines Derivatives as Potent Inhibitors of SARS-CoV-2 Main Protease

The pandemic that started in Wuhan (China) in 2019 has caused a large number of deaths, and infected people around the world due to the absence of effective therapy against coronavirus 2 of the severe acute respiratory syndrome (SARS-CoV-2). Viral maturation requires the activity of the main viral protease (Mpro), so its inhibition stops the progress of the disease. To evaluate possible inhibitors, a computational model of the SARS-CoV-2 enzyme Mpro was constructed in complex with 26 synthetic ligands derived from coumarins and quinolines. Analysis of simulations of molecular dynamics and molecular docking of the models show a high affinity for the enzyme (∆E binding between -5.1 and 7.1 kcal mol-1). The six compounds with the highest affinity show K d between 6.26 × 10-6 and 17.2 × 10-6, with binding affinity between -20 and -25 kcal mol-1, with ligand efficiency less than 0.3 associated with possible inhibitory candidates. In addition to the high affinity of these compounds for SARS-CoV-2 Mpro, low toxicity is expected considering the Lipinski, Veber and Pfizer rules. Therefore, this novel study provides candidate inhibitors that would allow experimental studies which can lead to the development of new treatments for SARS-CoV-2.

Binding of inhibitors to the monomeric and dimeric SARS-CoV-2 Mpro

SARS-CoV-2 rapidly infects millions of people worldwide since December 2019. There is still no effective treatment for the virus, resulting in the death of more than one million patients. Inhibiting the activity of SARS-CoV-2 main protease (Mpro), 3C-like protease (3CLP), is able to block the viral replication and proliferation. In this context, our study has revealed that in silico screening for inhibitors of SARS-CoV-2 Mpro can be reliably done using the monomeric structure of the Mpro instead of the dimeric one. Docking and fast pulling of ligand (FPL) simulations for both monomeric and dimeric forms correlate well with the corresponding experimental binding affinity data of 24 compounds. The obtained results were also confirmed via binding pose and noncovalent contact analyses. Our study results show that it is possible to speed up computer-aided drug design for SARS-CoV-2 Mpro by focusing on the monomeric form instead of the larger dimeric one.

Role of Machine Learning Techniques to Tackle the COVID-19 Crisis: Systematic Review

BACKGROUND

SARS-CoV-2, the novel coronavirus responsible for COVID-19, has caused havoc worldwide, with patients presenting a spectrum of complications that have pushed health care experts to explore new technological solutions and treatment plans. Artificial Intelligence (AI)-based technologies have played a substantial role in solving complex problems, and several organizations have been swift to adopt and customize these technologies in response to the challenges posed by the COVID-19 pandemic.

OBJECTIVE

The objective of this study was to conduct a systematic review of the literature on the role of AI as a comprehensive and decisive technology to fight the COVID-19 crisis in the fields of epidemiology, diagnosis, and disease progression.

METHODS

A systematic search of PubMed, Web of Science, and CINAHL databases was performed according to PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analysis) guidelines to identify all potentially relevant studies published and made available online between December 1, 2019, and June 27, 2020. The search syntax was built using keywords specific to COVID-19 and AI.

RESULTS

The search strategy resulted in 419 articles published and made available online during the aforementioned period. Of these, 130 publications were selected for further analyses. These publications were classified into 3 themes based on AI applications employed to combat the COVID-19 crisis: Computational Epidemiology, Early Detection and Diagnosis, and Disease Progression. Of the 130 studies, 71 (54.6%) focused on predicting the COVID-19 outbreak, the impact of containment policies, and potential drug discoveries, which were classified under the Computational Epidemiology theme. Next, 40 of 130 (30.8%) studies that applied AI techniques to detect COVID-19 by using patients' radiological images or laboratory test results were classified under the Early Detection and Diagnosis theme. Finally, 19 of the 130 studies (14.6%) that focused on predicting disease progression, outcomes (ie, recovery and mortality), length of hospital stay, and number of days spent in the intensive care unit for patients with COVID-19 were classified under the Disease Progression theme.

CONCLUSIONS

In this systematic review, we assembled studies in the current COVID-19 literature that utilized AI-based methods to provide insights into different COVID-19 themes. Our findings highlight important variables, data types, and available COVID-19 resources that can assist in facilitating clinical and translational research.

In-silico drug repurposing study: Amprenavir, enalaprilat, and plerixafor, potential drugs for destabilizing the SARS-CoV-2 S-protein-angiotensin-converting enzyme 2 complex

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) that leads to coronavirus disease (COVID-19) has put public health at risk in 2020. The spike protein (SP) in SARS-CoV-2 is primarily responsible for the attachment and entry of the virus into the cell, which binds to the angiotensin-converting enzyme 2 (ACE2). Owing to the lack of an effective therapy, drug repositioning is an opportunity to search for molecules with pharmacological potential for the treatment of COVID-19. In this study, three candidates with the potential to destabilize the SP-ACE2 complex are reported. Through molecular docking, 147 drugs were evaluated and their possible binding sites in the interface region of the SP-ACE2 complex and the SP of SARS-CoV-2 were identified. The five best candidate molecules were selected for molecular dynamics studies to observe changes in interactions between SP-ACE2 and ligands with the SP-ACE2 complex. Using umbrella sampling molecular dynamics simulations, the binding energy of SP with ACE2 (−29.58 kcal/mol) without ligands, and in complex with amprenavir (−20.13 kcal/mol), enalaprilat (–23.84 kcal/mol), and plerixafor (−19.72 kcal/mol) were calculated. These drugs are potential candidates for the treatment of COVID-19 as they destabilize the SP-ACE2 complex; the binding energy of SP is decreased in the presence of these drugs and may prevent the virus from entering the cell. Plerixafor is the drug with the greatest potential to destabilize the SP-ACE2 complex, followed by amprenavir and enalaprilat; thus, these three drugs are proposed for future in vitro and in vivo evaluations.

Rapid prediction of possible inhibitors for SARS-CoV-2 main protease using docking and FPL simulations

Originating for the first time in Wuhan, China, the outbreak of SARS-CoV-2 has caused a serious global health issue. An effective treatment for SARS-CoV-2 is still unavailable. Therefore, in this study, we have tried to predict a list of potential inhibitors for SARS-CoV-2 main protease (Mpro) using a combination of molecular docking and fast pulling of ligand (FPL) simulations. The approaches were initially validated over a set of eleven available inhibitors. Both Autodock Vina and FPL calculations produced consistent results with the experiments with correlation coefficients of R Dock = 0.72 ± 0.14 and R W = -0.76 ± 0.10, respectively. The combined approaches were then utilized to predict possible inhibitors that were selected from a ZINC15 sub-database for SARS-CoV-2 Mpro. Twenty compounds were suggested to be able to bind well to SARS-CoV-2 Mpro. Among them, five top-leads are periandrin V, penimocycline, cis-p-Coumaroylcorosolic acid, glycyrrhizin, and uralsaponin B. The obtained results could probably lead to enhance the COVID-19 therapy.

Progress in Developing Inhibitors of SARS-CoV-2 3C-Like Protease

Coronavirus disease 2019 (COVID-19) is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The viral outbreak started in late 2019 and rapidly became a serious health threat to the global population. COVID-19 was declared a pandemic by the World Health Organization in March 2020. Several therapeutic options have been adopted to prevent the spread of the virus. Although vaccines have been developed, antivirals are still needed to combat the infection of this virus. SARS-CoV-2 is an enveloped virus, and its genome encodes polyproteins that can be processed into structural and nonstructural proteins. Maturation of viral proteins requires cleavages by proteases. Therefore, the main protease (3 chymotrypsin-like protease (3CLpro) or Mpro) encoded by the viral genome is an attractive drug target because it plays an important role in cleaving viral polyproteins into functional proteins. Inhibiting this enzyme is an efficient strategy to block viral replication. Structural studies provide valuable insight into the function of this protease and structural basis for rational inhibitor design. In this review, we describe structural studies on the main protease of SARS-CoV-2. The strategies applied in developing inhibitors of the main protease of SARS-CoV-2 and currently available protein inhibitors are summarized. Due to the availability of high-resolution structures, structure-guided drug design will play an important role in developing antivirals. The availability of high-resolution structures, potent peptidic inhibitors, and diverse compound scaffolds indicate the feasibility of developing potent protease inhibitors as antivirals for COVID-19.

A multiscale model of virus pandemic: Heterogeneous interactive entities in a globally connected world

This paper is devoted to the multidisciplinary modelling of a pandemic initiated by an aggressive virus, specifically the so-called SARS-CoV-2 Severe Acute Respiratory Syndrome, corona virus n.2. The study is developed within a multiscale framework accounting for the interaction of different spatial scales, from the small scale of the virus itself and cells, to the large scale of individuals and further up to the collective behaviour of populations. An interdisciplinary vision is developed thanks to the contributions of epidemiologists, immunologists and economists as well as those of mathematical modellers. The first part of the contents is devoted to understanding the complex features of the system and to the design of a modelling rationale. The modelling approach is treated in the second part of the paper by showing both how the virus propagates into infected individuals, successfully and not successfully recovered, and also the spatial patterns, which are subsequently studied by kinetic and lattice models. The third part reports the contribution of research in the fields of virology, epidemiology, immune competition, and economy focussed also on social behaviours. Finally, a critical analysis is proposed looking ahead to research perspectives.

Explaining the experience of prenatal care and investigating the association between psychological factors with self-care in pregnant women during COVID-19 pandemic: a mixed method study protocol

BACKGROUND

Coronavirus disease 2019 (COVID-19) is a novel global public health emergency. Prenatal care (PNC) providing institutes should identify the needs and demands of pregnant women by optimizing the means of PNC services during the COVID-19 pandemic. The present study aims to: a) explain prenatal care experiences; b) assess the factors affecting self-care, and c) present a prenatal care guideline and Strategies to improve the PNC.

METHODS

This mixed-methods study with a sequential explanatory design consists of three phases. The first phase is a qualitative study exploring the prenatal care experiences among pregnant women. In this phase, the subjects will be selected through purposive sampling; moreover, in-depth individual interviewing will be used for data collection. Finally, the conventional content analysis approach will be employed for data analysis. The second phase is quantitative and will be used as a cross-sectional approach for assessing the association between psychological factors of self-care. In this regard, a multistage cluster sampling method will be used to select 215 subjects who will be visited in health care centers of Tabriz, Iran. The third phase will be focusing on developing a prenatal care guideline and Strategies, using the qualitative and quantitative results of the previous phases, a review of the related literature, and the nominal group technique will be performed among experts.

DISCUSSION

The present research is the first study to investigate the prenatal care experiences and factors influencing self-care among pregnant women during COVID-19 pandemic. For the purposes of the study, a mixed-methods approach will be used which aims to develop strategies for improving health care services. It is hoped that the strategy proposed in the current study could lead to improvements in this regard.

ETHICAL CODE

IR.TBZMED.REC.1399.003.