Repurposing Therapeutics for COVID-19: Rapid Prediction of Commercially available drugs through Machine Learning and Docking

Mahapatra, Sovesh; Nath, Prathul; Chatterjee, Manisha; Das, Neeladrisingha; Kalita, Deepjyoti; Roy, Partha; Satapathi, Soumitra

doi:10.1101/2020.04.05.20054254

Cited by 25 publications

(17 citation statements)

References 36 publications

(34 reference statements)

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“…As the outbreak of COVID-19 caused a worldwide public health emergency, drug repurposing was immediately employed to combat this disease [ 60 , 61 , 62 ]. Several existing antivirals for SARS-CoV, MERS, malaria, and human immunodeficiency virus (HIV) were tested to treat COVID-19 [ 63 , 64 , 65 , 66 , 67 ], and some drugs have entered into clinical studies [ 10 , 63 ]. The following strategies were applied to identify drugs that can be used to treat COVID-19 by targeting 3CL pro .…”

Section: Strategies Applied In Developing Protease Inhibitormentioning

confidence: 99%

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

Kang

2020

Microorganisms

105

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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.

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Section: Strategies Applied In Developing Protease Inhibitormentioning

confidence: 99%

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

Kang

2020

Microorganisms

105

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“…An anti-retroviral drug Atazanavir, a known HIV-protease inhibitor, was found to be the most effective drug. The authors claimed that Atazanavir might be a potential candidate for COVID-19 treatment and warranted a further clinical trial (Mahapatra et al, 2020). In a similar vein, S protein, N protein and 2 0 -o-ribose methyltransferase protein of SARS-Cov-2 were targeted by a combination of virtual drug screening, molecular docking and supervised ML techniques.…”

Section: Ai Integrated Molecular Docking Simulations For Drug Repurpomentioning

confidence: 99%

Emerging role of artificial intelligence in therapeutics for COVID-19: a systematic review

Kaushal

Sarma

Rana

et al. 2020

Journal of Biomolecular Structure and Dynamics

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To elucidate the role of artificial intelligence (AI) in therapeutics for coronavirus disease 2019 (COVID-19). Five databases were searched (December 2019–May 2020). We included both published and pre-print original articles in English that applied AI, machine learning or deep learning in drug repurposing, novel drug discovery, vaccine and antibody development for COVID-19. Out of 31 studies included, 16 studies applied AI for drug repurposing, whereas 10 studies utilized AI for novel drug discovery. Only four studies used AI technology for vaccine development, whereas one study generated stable antibodies against SARS-CoV-2. Approx. 50% of studies exclusively targeted 3CLpro of SARS-CoV-2, and only two studies targeted ACE/TMPSS2 for inhibiting host viral interactions. Around 16% of the identified drugs are in different phases of clinical evaluation against COVID-19. AI has emerged as a promising solution of COVID-19 therapeutics. During this current pandemic, many of the researchers have used AI-based strategies to process large databases in a more customized manner leading to the faster identification of several potential targets, novel/repurposing of drugs and vaccine candidates. A number of these drugs are either approved or are in a late-stage clinical trial and are potentially effective against SARS-CoV2 indicating validity of the methodology. However, as the use of AI-based screening program is currently in budding stage, sole reliance on such algorithms is not advisable at this current point of time and an evidence based approach is warranted to confirm their usefulness against this life-threatening disease. Communicated by Ramaswamy H. Sarma

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“… 23 , 24 , 25 , 26 Machine learning (ML) represents one means by which the likely effectiveness of specific treatments in a given individual may be predicted. 27 While ML has been applied to diverse tasks related to COVID-19, 28 within the context of therapeutics this technology has overwhelming been used to identify novel and repurposed drugs which may be effective in treating COVID-19, 28 , 29 , 30 , 31 , 32 , 33 , 34 rather than to identify which patients are most likely to experience a survival benefit from available treatments. To fill this gap, we present a pair of machine learning algorithms (MLAs) to encourage precision in the use of remdesivir or dexamethasone and related corticosteroids to treat COVID-19 patients using readily available data derived from electronic health records (EHRs).…”

Section: Introductionmentioning

confidence: 99%

Machine Learning as a Precision-Medicine Approach to Prescribing COVID-19 Pharmacotherapy with Remdesivir or Corticosteroids

Lam

Siefkas

Zelin

et al. 2021

Clinical Therapeutics

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Purpose : Coronavirus Disease 2019 ( COVID-19) continues to be a global threat and remains a significant cause of hospitalizations. Recent clinical guidelines have supported the use of corticosteroids and remdesivir in the treatment of COVID-19. However, uncertainty remains about which patients are most likely to benefit from treatment with either drug; such knowledge is crucial for avoiding preventable side effects, minimizing costs, and effectively allocating resources. This study presents a machine learning system capable of identifying patients for whom treatment with corticosteroids or remdesivir is associated with improved survival time. Methods : Gradient boosted decision tree models to predict treatment benefit were trained and tested on data from patients hospitalized at 10 hospitals in the United States between December 18, 2019 and October 18, 2020. 893 patients were treated with remdesivir, and 1,471 were treated with corticosteroids. Models were evaluated for their ability to identify patients that exhibited longer survival times when treated with corticosteroids or remdesivir. Fine and Gray models for the proportional hazard were evaluated comparing treated and untreated patients in the full COVID-19 population, in patients receiving supplemental oxygen, and in patients identified by the algorithm. Inverse probability of treatment weights were used to adjust for confounding. Models for each treatment were trained and tested separately. Findings : .Adult patients (age ≥ 18) were included in this study, with men comprising slightly more than 50% of the sample. After adjusting for confounding, neither corticosteroids nor remdesivir were associated with increased survival time in the full hospitalized COVID-19 population or in the population receiving supplemental oxygen. However, in the populations identified by the algorithms, both corticosteroids and remdesivir were significantly associated with an increase in survival time, with hazard ratios of 0.56 (p = 0.04) and 0.40 (p = 0.04), respectively Implications : Machine learning methods are capable of identifying hospitalized COVID-19 patients for whom treatment with corticosteroids or remdesivir is associated with an increase in survival time. These methods may help improve patient outcomes and allocate resources during the COVID-19 crisis.

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Repurposing Therapeutics for COVID-19: Rapid Prediction of Commercially available drugs through Machine Learning and Docking

Cited by 25 publications

References 36 publications

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

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

Emerging role of artificial intelligence in therapeutics for COVID-19: a systematic review

Machine Learning as a Precision-Medicine Approach to Prescribing COVID-19 Pharmacotherapy with Remdesivir or Corticosteroids

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