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      Repurposing therapeutics for COVID-19: Rapid prediction of commercially available drugs through machine learning and docking

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          Abstract

          Background

          The outbreak of the novel coronavirus disease COVID-19, caused by the SARS-CoV-2 virus has spread rapidly around the globe during the past 3 months. As the virus infected cases and mortality rate of this disease is increasing exponentially, scientists and researchers all over the world are relentlessly working to understand this new virus along with possible treatment regimens by discovering active therapeutic agents and vaccines. So, there is an urgent requirement of new and effective medications that can treat the disease caused by SARS-CoV-2.

          Methods and findings

          We perform the study of drugs that are already available in the market and being used for other diseases to accelerate clinical recovery, in other words repurposing of existing drugs. The vast complexity in drug design and protocols regarding clinical trials often prohibit developing various new drug combinations for this epidemic disease in a limited time. Recently, remarkable improvements in computational power coupled with advancements in Machine Learning (ML) technology have been utilized to revolutionize the drug development process. Consequently, a detailed study using ML for the repurposing of therapeutic agents is urgently required. Here, we report the ML model based on the Naive Bayes algorithm, which has an accuracy of around 73% to predict the drugs that could be used for the treatment of COVID-19. Our study predicts around ten FDA approved commercial drugs that can be used for repurposing. Among all, we found that 3 of the drugs fulfils the criterions well among which the antiretroviral drug Amprenavir (DrugBank ID–DB00701) would probably be the most effective drug based on the selected criterions.

          Conclusions

          Our study can help clinical scientists in being more selective in identifying and testing the therapeutic agents for COVID-19 treatment. The ML based approach for drug discovery as reported here can be a futuristic smart drug designing strategy for community applications.

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          Most cited references46

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          Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China

          Chaolin Huang, Yeming Wang, Xingwang Li … (2020)
          Summary Background A recent cluster of pneumonia cases in Wuhan, China, was caused by a novel betacoronavirus, the 2019 novel coronavirus (2019-nCoV). We report the epidemiological, clinical, laboratory, and radiological characteristics and treatment and clinical outcomes of these patients. Methods All patients with suspected 2019-nCoV were admitted to a designated hospital in Wuhan. We prospectively collected and analysed data on patients with laboratory-confirmed 2019-nCoV infection by real-time RT-PCR and next-generation sequencing. Data were obtained with standardised data collection forms shared by WHO and the International Severe Acute Respiratory and Emerging Infection Consortium from electronic medical records. Researchers also directly communicated with patients or their families to ascertain epidemiological and symptom data. Outcomes were also compared between patients who had been admitted to the intensive care unit (ICU) and those who had not. Findings By Jan 2, 2020, 41 admitted hospital patients had been identified as having laboratory-confirmed 2019-nCoV infection. Most of the infected patients were men (30 [73%] of 41); less than half had underlying diseases (13 [32%]), including diabetes (eight [20%]), hypertension (six [15%]), and cardiovascular disease (six [15%]). Median age was 49·0 years (IQR 41·0–58·0). 27 (66%) of 41 patients had been exposed to Huanan seafood market. One family cluster was found. Common symptoms at onset of illness were fever (40 [98%] of 41 patients), cough (31 [76%]), and myalgia or fatigue (18 [44%]); less common symptoms were sputum production (11 [28%] of 39), headache (three [8%] of 38), haemoptysis (two [5%] of 39), and diarrhoea (one [3%] of 38). Dyspnoea developed in 22 (55%) of 40 patients (median time from illness onset to dyspnoea 8·0 days [IQR 5·0–13·0]). 26 (63%) of 41 patients had lymphopenia. All 41 patients had pneumonia with abnormal findings on chest CT. Complications included acute respiratory distress syndrome (12 [29%]), RNAaemia (six [15%]), acute cardiac injury (five [12%]) and secondary infection (four [10%]). 13 (32%) patients were admitted to an ICU and six (15%) died. Compared with non-ICU patients, ICU patients had higher plasma levels of IL2, IL7, IL10, GSCF, IP10, MCP1, MIP1A, and TNFα. Interpretation The 2019-nCoV infection caused clusters of severe respiratory illness similar to severe acute respiratory syndrome coronavirus and was associated with ICU admission and high mortality. Major gaps in our knowledge of the origin, epidemiology, duration of human transmission, and clinical spectrum of disease need fulfilment by future studies. Funding Ministry of Science and Technology, Chinese Academy of Medical Sciences, National Natural Science Foundation of China, and Beijing Municipal Science and Technology Commission.
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            A Novel Coronavirus from Patients with Pneumonia in China, 2019

            Na Zhu, Dingyu Zhang, Wenling Wang … (2020)
            Summary In December 2019, a cluster of patients with pneumonia of unknown cause was linked to a seafood wholesale market in Wuhan, China. A previously unknown betacoronavirus was discovered through the use of unbiased sequencing in samples from patients with pneumonia. Human airway epithelial cells were used to isolate a novel coronavirus, named 2019-nCoV, which formed a clade within the subgenus sarbecovirus, Orthocoronavirinae subfamily. Different from both MERS-CoV and SARS-CoV, 2019-nCoV is the seventh member of the family of coronaviruses that infect humans. Enhanced surveillance and further investigation are ongoing. (Funded by the National Key Research and Development Program of China and the National Major Project for Control and Prevention of Infectious Disease in China.)
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              Genomic characterisation and epidemiology of 2019 novel coronavirus: implications for virus origins and receptor binding

              Roujian Lu, Xiang Zhao, Juan Li … (2021)
              Summary Background In late December, 2019, patients presenting with viral pneumonia due to an unidentified microbial agent were reported in Wuhan, China. A novel coronavirus was subsequently identified as the causative pathogen, provisionally named 2019 novel coronavirus (2019-nCoV). As of Jan 26, 2020, more than 2000 cases of 2019-nCoV infection have been confirmed, most of which involved people living in or visiting Wuhan, and human-to-human transmission has been confirmed. Methods We did next-generation sequencing of samples from bronchoalveolar lavage fluid and cultured isolates from nine inpatients, eight of whom had visited the Huanan seafood market in Wuhan. Complete and partial 2019-nCoV genome sequences were obtained from these individuals. Viral contigs were connected using Sanger sequencing to obtain the full-length genomes, with the terminal regions determined by rapid amplification of cDNA ends. Phylogenetic analysis of these 2019-nCoV genomes and those of other coronaviruses was used to determine the evolutionary history of the virus and help infer its likely origin. Homology modelling was done to explore the likely receptor-binding properties of the virus. Findings The ten genome sequences of 2019-nCoV obtained from the nine patients were extremely similar, exhibiting more than 99·98% sequence identity. Notably, 2019-nCoV was closely related (with 88% identity) to two bat-derived severe acute respiratory syndrome (SARS)-like coronaviruses, bat-SL-CoVZC45 and bat-SL-CoVZXC21, collected in 2018 in Zhoushan, eastern China, but were more distant from SARS-CoV (about 79%) and MERS-CoV (about 50%). Phylogenetic analysis revealed that 2019-nCoV fell within the subgenus Sarbecovirus of the genus Betacoronavirus, with a relatively long branch length to its closest relatives bat-SL-CoVZC45 and bat-SL-CoVZXC21, and was genetically distinct from SARS-CoV. Notably, homology modelling revealed that 2019-nCoV had a similar receptor-binding domain structure to that of SARS-CoV, despite amino acid variation at some key residues. Interpretation 2019-nCoV is sufficiently divergent from SARS-CoV to be considered a new human-infecting betacoronavirus. Although our phylogenetic analysis suggests that bats might be the original host of this virus, an animal sold at the seafood market in Wuhan might represent an intermediate host facilitating the emergence of the virus in humans. Importantly, structural analysis suggests that 2019-nCoV might be able to bind to the angiotensin-converting enzyme 2 receptor in humans. The future evolution, adaptation, and spread of this virus warrant urgent investigation. Funding National Key Research and Development Program of China, National Major Project for Control and Prevention of Infectious Disease in China, Chinese Academy of Sciences, Shandong First Medical University.
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                Author and article information

                Contributors
                Sovesh Mohapatra: Role: ConceptualizationRole: Formal analysisRole: Methodology
                Prathul Nath: Role: ConceptualizationRole: InvestigationRole: Methodology
                Manisha Chatterjee: Role: SoftwareRole: Validation
                Neeladrisingha Das: Role: MethodologyRole: Validation
                Deepjyoti Kalita: Role: Formal analysisRole: Validation
                Partha Roy: Role: ConceptualizationRole: InvestigationRole: MethodologyRole: Writing – review & editing
                Soumitra Satapathi:
                ORCID: https://orcid.org/0000-0001-9218-3796
                Role: ConceptualizationRole: Writing – original draft
                Pinyi Lu: Role: Editor
                Journal
                Journal ID (nlm-ta): PLoS One
                Journal ID (iso-abbrev): PLoS One
                Journal ID (publisher-id): plos
                Journal ID (pmc): plosone
                Title: PLoS ONE
                Publisher: Public Library of Science (San Francisco, CA USA )
                ISSN (Electronic): 1932-6203
                Publication date (Electronic): 12 November 2020
                Publication date Collection: 2020
                Publication date PMC-release: 12 November 2020
                Volume: 15
                Issue: 11
                Electronic Location Identifier: e0241543
                Affiliations
                [1 ] Department of Physics, Indian Institute of Technology, Roorkee, Haridwar, Uttarakhand, India
                [2 ] Department of Pharmacology, Subharti Medical College, Meerut, Uttar Pradesh, India
                [3 ] Department of Biotechnology, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand, India
                [4 ] Department of Microbiology, All India Institute of Medical Science, Rishikesh, Uttarakhand, India
                [5 ] Centre of Nanotechnology, Indian Institute of Technology, Roorkee, Haridwar, Uttarakhand, India
                Biotechnology HPC Software Applications Institute (BHSAI), UNITED STATES
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                Author information
                https://orcid.org/0000-0001-9218-3796
                Article
                Publisher ID: PONE-D-20-10793
                DOI: 10.1371/journal.pone.0241543
                PMC ID: 7660547
                PubMed ID: 33180803
                SO-VID: 669493c4-bb25-4cfd-9a59-78b904994c00
                Copyright © © 2020 Mohapatra et al
                License:

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                Date received : 17 April 2020
                Date accepted : 16 October 2020
                Page count
                Figures: 4, Tables: 1, Pages: 13
                Funding
                Ministry of Information Science and Technology, Government of India.
                Categories
                Subject: Research Article
                Subject: Medicine and Health Sciences
                Subject: Medical Conditions
                Subject: Infectious Diseases
                Subject: Viral Diseases
                Subject: Covid 19
                Subject: Biology and life sciences
                Subject: Organisms
                Subject: Viruses
                Subject: RNA viruses
                Subject: Coronaviruses
                Subject: SARS coronavirus
                Subject: Biology and life sciences
                Subject: Microbiology
                Subject: Medical microbiology
                Subject: Microbial pathogens
                Subject: Viral pathogens
                Subject: Coronaviruses
                Subject: SARS coronavirus
                Subject: Medicine and health sciences
                Subject: Pathology and laboratory medicine
                Subject: Pathogens
                Subject: Microbial pathogens
                Subject: Viral pathogens
                Subject: Coronaviruses
                Subject: SARS coronavirus
                Subject: Biology and life sciences
                Subject: Organisms
                Subject: Viruses
                Subject: Viral pathogens
                Subject: Coronaviruses
                Subject: SARS coronavirus
                Subject: Biology and life sciences
                Subject: Organisms
                Subject: Viruses
                Subject: RNA viruses
                Subject: Coronaviruses
                Subject: SARS coronavirus
                Subject: SARS CoV 2
                Subject: Biology and life sciences
                Subject: Microbiology
                Subject: Medical microbiology
                Subject: Microbial pathogens
                Subject: Viral pathogens
                Subject: Coronaviruses
                Subject: SARS coronavirus
                Subject: SARS CoV 2
                Subject: Medicine and health sciences
                Subject: Pathology and laboratory medicine
                Subject: Pathogens
                Subject: Microbial pathogens
                Subject: Viral pathogens
                Subject: Coronaviruses
                Subject: SARS coronavirus
                Subject: SARS CoV 2
                Subject: Biology and life sciences
                Subject: Organisms
                Subject: Viruses
                Subject: Viral pathogens
                Subject: Coronaviruses
                Subject: SARS coronavirus
                Subject: SARS CoV 2
                Subject: Medicine and Health Sciences
                Subject: Pharmaceutics
                Subject: Drug Therapy
                Subject: Physical Sciences
                Subject: Mathematics
                Subject: Applied Mathematics
                Subject: Algorithms
                Subject: Machine Learning Algorithms
                Subject: Research and Analysis Methods
                Subject: Simulation and Modeling
                Subject: Algorithms
                Subject: Machine Learning Algorithms
                Subject: Computer and Information Sciences
                Subject: Artificial Intelligence
                Subject: Machine Learning
                Subject: Machine Learning Algorithms
                Subject: Biology and Life Sciences
                Subject: Biochemistry
                Subject: Enzymology
                Subject: Enzymes
                Subject: Proteases
                Subject: Biology and Life Sciences
                Subject: Biochemistry
                Subject: Proteins
                Subject: Enzymes
                Subject: Proteases
                Subject: Computer and Information Sciences
                Subject: Artificial Intelligence
                Subject: Machine Learning
                Subject: Medicine and Health Sciences
                Subject: Pharmacology
                Subject: Drug Research and Development
                Subject: Drug Discovery
                Custom metadata
                Data Availability All relevant data are within the manuscript and its Supporting Information files.
                Outbreaks COVID-19

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