Identification and validation of Triamcinolone and Gallopamil as treatments for early COVID-19 via an in silico repurposing pipeline

SARS-CoV-2, the causative virus of COVID-19 continues to cause an ongoing global pandemic. Therapeutics are still needed to treat mild and severe COVID-19. Drug repurposing provides an opportunity to deploy drugs for COVID-19 more rapidly than developing novel therapeutics. Some existing drugs have shown promise for treating COVID-19 in clinical trials. This in silico study uses structural similarity to clinical trial drugs to identify two drugs with potential applications to treat early COVID-19. We apply in silico validation to suggest a possible mechanism of action for both. Triamcinolone is a corticosteroid structurally similar to Dexamethasone. Gallopamil is a calcium channel blocker structurally similar to Verapamil. We propose that both these drugs could be useful to treat early COVID-19 infection due to the proximity of their targets within a SARS-CoV-2-induced protein-protein interaction network to kinases active in early infection, and the APOA1 protein which is linked to the spread of COVID-19.Comment: 32 pages, 4 figure

In Silico Analysis of the Multi-Targeted Mode of Action of Ivermectin and Related Compounds

Some clinical studies have indicated activity of ivermectin, a macrocyclic lactone, against COVID-19, but a biological mechanism initially proposed for this anti-viral effect is not applicable at physiological concentrations. This in silico investigation explores potential modes of action of ivermectin and 14 related compounds, by which the infectivity and morbidity of the SARS-CoV-2 virus may be limited. Binding affinity computations were performed for these agents on several docking sites each for models of (1) the spike glycoprotein of the virus, (2) the CD147 receptor, which has been identified as a secondary attachment point for the virus, and (3) the alpha-7 nicotinic acetylcholine receptor (α7nAChr), an indicated point of viral penetration of neuronal tissue as well as an activation site for the cholinergic anti-inflammatory pathway controlled by the vagus nerve. Binding affinities were calculated for these multiple docking sites and binding modes of each compound. Our results indicate the high affinity of ivermectin, and even higher affinities for some of the other compounds evaluated, for all three of these molecular targets. These results suggest biological mechanisms by which ivermectin may limit the infectivity and morbidity of the SARS-CoV-2 virus and stimulate an α7nAChr-mediated anti-inflammatory pathway that could limit cytokine production by immune cells

Computationally prioritized drugs inhibit SARS-CoV-2 infection and syncytia formation

The pharmacological arsenal against the COVID-19 pandemic is largely based on generic anti-inflammatory strategies or poorly scalable solutions. Moreover, as the ongoing vaccination campaign is rolling slower than wished, affordable and effective therapeutics are needed. To this end, there is increasing attention toward computational methods for drug repositioning and de novo drug design. Here, multiple data-driven computational approaches are systematically integrated to perform a virtual screening and prioritize candidate drugs for the treatment of COVID-19. From the list of prioritized drugs, a subset of representative candidates to test in human cells is selected. Two compounds, 7-hydroxystaurosporine and bafetinib, show synergistic antiviral effects in vitro and strongly inhibit viral-induced syncytia formation. Moreover, since existing drug repositioning methods provide limited usable information for de novo drug design, the relevant chemical substructures of the identified drugs are extracted to provide a chemical vocabulary that may help to design new effective drugs.Peer reviewe

Computational methods in drug repurposing and natural product based drug discovery

For a few decades now, computation methods have been widely used in drug discovery or drug repurposing process, especially when saving time and money are important factors. Development of bioinformatics, chemoinformatics, molecular modelling techniques and machine or deep learning tools, as well as availability of various biological and chemical databases, have had a significant impact on improving the process of obtaining successful drug candidates. This dissertation describes the role of natural products in drug discovery, as well as presents several computational methods used in drug discovery and drug repurposing. Application of these methods is presented with the example of searching for potential drug treatment options for the COVID-19 disease. The disease is caused by the novel coronavirus SARS-CoV-2, which was first discovered in December 2019 and has caused the death of more than 5.6 million people worldwide (until January 2022). Findings from two research projects, which aimed to identify potential inhibitors of main protease of SARS-CoV-2, are presented in this work. Moreover, a summary on COVID-19 treatment possibilities has been included. In the first project, a ligand-based virtual screening of around 360,000 compounds from natural products databases, as well as approved and withdrawn drugs databases was conducted, followed by molecular docking and molecular dynamics simulations. Moreover, computational predictions of toxicity and cytochrome activity profiles for selected candidates were provided. Twelve candidates as SARS-CoV-2 main protease inhibitors were identified - among them novel drug candidates, as well as existing drugs. The second project was focused on finding potential inhibitors from plants (Reynoutria japonica and Reynoutria sachalinensis) and was based on molecular docking studies, followed by in vitro studies of the activity of selected compounds, extract, and fractions from those plants against the enzyme. Several natural compounds were identified as promising candidates for SARS-CoV-2 main protease inhibitors. Additionally, butanol fraction of Ryenoutria rhizomes extracts also showed inhibitory activity on the enzyme. Suggested drugs, natural compounds and plant extracts should be further investigated to confirm their potential as COVID-19 therapeutic options. Presented workflow could be used for investigation of compounds for other biological targets and different diseases in the future research projects.Seit einigen Jahrzehnten werden bei der Entwicklung und Repositionierung von Arzneimitteln rechenintensive computergestützte Methoden eingesetzt, insbesondere da Zeit- und Kostenersparnis wichtige Faktoren sind. Die Weiterentwicklung der Bioinformatik und Chemoinformatik und die damit einhergehende Optimierung von molekularen Modellierungstechniken und Tools für maschinelles sowie tiefes Lernen ermöglicht die Verarbeitung von großen biologischen und chemischen Datenbanken und hat einen erheblichen Einfluss auf die Verbesserung des Prozesses zur Gewinnung erfolgreicher Arzneimittelkandidaten. In dieser Dissertation wird die Rolle von Naturstoffen bei der Entwicklung von Arzneimitteln beschrieben, und es werden verschiedene computergestützte Methoden vorgestellt, die bei der Entdeckung von Arzneimitteln und der Repositionierung von Arzneimitteln eingesetzt werden. Die Anwendung dieser Methoden wird am Beispiel der Suche nach potenziellen medikamentösen Behandlungsmöglichkeiten für die Krankheit COVID-19 vorgestellt. Die Krankheit wird durch das neuartige Coronavirus SARS-CoV-2 ausgelöst, das erst im Dezember 2019 entdeckt wurde und bisher (bis Januar 2022) weltweit mehr als 5,6 Millionen Menschen das Leben gekostet hat. In dieser Arbeit werden Ergebnisse aus zwei Forschungsprojekten vorgestellt, die darauf abzielten, potenzielle Hemmstoffe der Hauptprotease von SARS-CoV-2 zu identifizieren. Außerdem wird ein Überblick über die Behandlungsmöglichkeiten von COVID-19 gegeben. Im ersten Projekt wurde ein ligandenbasiertes virtuelles Screening von rund 360.000 Kleinstrukturen aus Naturstoffdatenbanken sowie aus Datenbanken für zugelassene und zurückgezogene Arzneimittel durchgeführt, gefolgt von molekularem Docking und Molekulardynamiksimulationen. Darüber hinaus wurden für ausgewählte Kandidaten rechnerische Vorhersagen zur Toxizität und zu Cytochrom-P450-Aktivitätsprofilen erstellt. Es wurden zwölf Kandidaten als SARS-CoV-2-Hauptproteaseinhibitoren identifiziert - darunter sowohl neuartige als auch bereits vorhandene Arzneimittel. Das zweite Projekt konzentrierte sich auf die Suche nach potenziellen Inhibitoren aus Pflanzen (Reynoutria japonica und Reynoutria sachalinensis) und basierte auf molekularen Docking-Studien, gefolgt von In-vitro-Studien der Aktivität ausgewählter Verbindungen, Extrakte und Fraktionen aus diesen Pflanzen gegen das Enzym. Mehrere Naturstoffe wurden als vielversprechende Kandidaten für SARS-CoV-2- Hauptproteaseinhibitoren identifiziert. Außerdem zeigte die Butanolfraktion von Ryenoutria Rhizomextrakten ebenfalls eine hemmende Wirkung auf das Enzym. Die vorgeschlagenen Arzneimittel, Naturstoffe und Pflanzenextrakte sollten weiter untersucht werden, um ihr Potenzial als COVID-19-Therapieoptionen zu bestätigen. Der vorgestellte Arbeitsablauf könnte in zukünftigen Forschungsprojekten zur Untersuchung von Verbindungen für andere biologische Ziele und verschiedene Krankheiten verwendet werden

A Comprehensive Review of Detection Methods for SARS-CoV-2

Recently, the outbreak of the coronavirus disease 2019 (COVID-19), caused by the SARSCoV-2 virus, in China and its subsequent spread across the world has caused numerous infections and deaths and disrupted normal social activity. Presently, various techniques are used for the diagnosis of SARS-CoV-2 infection, with various advantages and weaknesses to each. In this paper, we summarize promising methods, such as reverse transcription-polymerase chain reaction (RT-PCR), serological testing, point-of-care testing, smartphone surveillance of infectious diseases, nanotechnology-based approaches, biosensors, amplicon-based metagenomic sequencing, smartphone, and wastewaterbased epidemiology (WBE) that can also be utilized for the detection of SARS-CoV-2. In addition, we discuss principles, advantages, and disadvantages of these detection methods, and highlight the potential methods for the development of additional techniques and products for early and fast detection of SARS-CoV-2

Statistical Explorations and Univariate Timeseries Analysis on COVID-19 Datasets to Understand the Trend of Disease Spreading and Death

publishedVersio

An innovative strategy to investigate microbial protein modifications in a reliable fast and sensitive way: A therapy oriented proof of concept based on UV-C irradiation of SARS-CoV-2 spike protein

: The characterization of modifications of microbial proteins is of primary importance to dissect pathogen lifecycle mechanisms and could be useful in identifying therapeutic targets. Attempts to solve this issue yielded only partial and non-exhaustive results. We developed a multidisciplinary approach by coupling in vitro infection assay, mass spectrometry (MS), protein 3D modelling, and surface plasma resonance (SPR). As a proof of concept, the effect of low UV-C (273 nm) irradiation on SARS-CoV-2 spike (S) protein was investigated. Following UV-C exposure, MS analysis identified, among other modifications, the disruption of a disulphide bond within the conserved S2 subunit of S protein. Computational analyses revealed that this bond breakage associates with an allosteric effect resulting in the generation of a closed conformation with a reduced ability to bind the ACE2 receptor. The UV-C-induced reduced affinity of S protein for ACE2 was further confirmed by SPR analyses and in vitro infection assays. This comprehensive approach pinpoints the S2 domain of S protein as a potential therapeutic target to prevent SARS-CoV-2 infection. Notably, this workflow could be used to screen a wide variety of microbial protein domains, resulting in a precise molecular fingerprint and providing new insights to adequately address future epidemics