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

PLoS ONE ◽  
2020 ◽  
Vol 15 (11) ◽  
pp. e0241543
Author(s):  
Sovesh Mohapatra ◽  
Prathul Nath ◽  
Manisha Chatterjee ◽  
Neeladrisingha Das ◽  
Deepjyoti Kalita ◽  
...  
Keyword(s):  
Machine Learning ◽  
Therapeutic Agents ◽  
The Novel ◽  
Epidemic Disease ◽  
Treatment Regimens ◽  
Antiretroviral Drug ◽  
Rapid Prediction ◽  
Bayes Algorithm ◽  
Novel Coronavirus ◽  
Smart Drug

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.

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

2020 ◽  
Author(s):  
Sovesh Mohapatra ◽  
Prathul Nath ◽  
Manisha Chatterjee ◽  
Neeladrisingha Das ◽  
Deepjyoti Kalita ◽  
...  
Keyword(s):  
Machine Learning ◽  
Development Process ◽  
Therapeutic Agents ◽  
List Type ◽  
Epidemic Disease ◽  
Antiretroviral Drug ◽  
The World ◽  
Bayes Algorithm ◽  
Novel Coronavirus ◽  
Effective Drugs

ABSTRACTBackgroundThe 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 findingsWe 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 Naïve 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 suggest that the antiretroviral drug Atazanavir (DrugBank ID – DB01072) would probably be one of the most effective drugs based on the selected criterions.ConclusionsOur 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.Author summaryWhy was this study done?The recent outbreak of novel coronavirus disease (COVID-19) is now considered to be a pandemic threat to the global population. The new coronavirus, 2019-nCoV has now affected more than 200 countries with over 17,83,941 cases confirmed and 1,09,312 deaths reported all over the world [as on 12 April 2020].There is an urgent need for the development of drugs or vaccine which can save people worldwide. However, the vast complexity in drug design and protocols regarding clinical trials often prohibit developing various new drug combinations for this epidemic disease. Recently, Artificial Intelligence (AI) technology have been utilized to revolutionize the drug development process. Can we use AI based repurposing of existing drugs for accelerated clinical trial in the treatment of COVID-19?What did the researchers do and find?Here, we report the Machine Learning (ML) model based on the Naïve 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 suggest that the antiretroviral drug Atazanavir (DrugBank ID – DB01072) would probably be one of the most effective drugs based on the selected criterions.What do these findings mean?The present approach will save a lot of resources and time for synthesizing novel drugs and thus will be useful for a vast majority of medical research community.

scholarly journals REVIEW ON PANDEMIC OUTBREAK OF CORONAVIRUS (COVID-19): VIROLOGY, AVAILABLE TREATMENT, PREVENTION, AND FUTURE PROJECTION

2020 ◽  
pp. 1-6
Author(s):  
Abhijit Mohan Kanavaje ◽  
Vipul Ajit Sansare
Keyword(s):  
Early Stage ◽  
Scale Up ◽  
Therapeutic Agents ◽  
Family Welfare ◽  
The Novel ◽  
Future Projection ◽  
Potential Threat ◽  
Treatment Regimens ◽  
Treatment And Prevention ◽  
Novel Coronavirus

Since the outbreak of the novel coronavirus disease COVID-19, caused by the SARS-CoV-2 virus, this disease has spread rapidly around the globe. On 11 March 2020, WHO declared Novel Coronavirus Disease (COVID-19) outbreak as a pandemic and reiterated the call for countries to take immediate actions and scale up the response to treat, detect and reduce transmission to save people’s lives. As of 3 April 2020, according to the Ministry of Health & Family Welfare (MoHFW), a total of 2301 COVID-19 cases (including 55 foreign nationals) have been reported in 29 states/union territories. These include 156 who have been cured/discharged,1 who has migrated, and 56 deaths in India. Considering the potential threat of a pandemic, scientists and physicians have been racing to understand this new virus and the pathophysiology of this disease to uncover possible treatment regimens and discover effective therapeutic agents and vaccines. The objective of this review article was to have a preliminary opinion about the disease, the ways of treatment, and prevention in this early stage of this outbreak.

scholarly journals Is Machine Learning a Better Way to Identify COVID-19 Patients Who Might Benefit from Hydroxychloroquine Treatment?—The IDENTIFY Trial

2020 ◽  
Vol 9 (12) ◽  
pp. 3834
Author(s):  
Hoyt Burdick ◽  
Carson Lam ◽  
Samson Mataraso ◽  
Anna Siefkas ◽  
Gregory Braden ◽  
...  
Keyword(s):  
Machine Learning ◽  
Clinical Trial ◽  
Learning Algorithm ◽  
Pragmatic Trial ◽  
Therapeutic Agents ◽  
Study Endpoint ◽  
Machine Learning Algorithm ◽  
The Novel ◽  
Hazard Ratios ◽  
Novel Coronavirus

Therapeutic agents for the novel coronavirus disease 2019 (COVID-19) have been proposed, but evidence supporting their use is limited. A machine learning algorithm was developed in order to identify a subpopulation of COVID-19 patients for whom hydroxychloroquine was associated with improved survival; this population might be relevant for study in a clinical trial. A pragmatic trial was conducted at six United States hospitals. We enrolled COVID-19 patients that were admitted between 10 March and 4 June 2020. Treatment was not randomized. The study endpoint was mortality; discharge was a competing event. Hazard ratios were obtained on the entire population, and on the subpopulation indicated by the algorithm as suitable for treatment. A total of 290 patients were enrolled. In the subpopulation that was identified by the algorithm, hydroxychloroquine was associated with a statistically significant (p = 0.011) increase in survival (adjusted hazard ratio 0.29, 95% confidence interval (CI) 0.11–0.75). Adjusted survival among the algorithm indicated patients was 82.6% in the treated arm and 51.2% in the arm not treated. No association between treatment and mortality was observed in the general population. A 31% increase in survival at the end of the study was observed in a population of COVID-19 patients that were identified by a machine learning algorithm as having a better outcome with hydroxychloroquine treatment. Precision medicine approaches may be useful in identifying a subpopulation of COVID-19 patients more likely to be proven to benefit from hydroxychloroquine treatment in a clinical trial.

scholarly journals Analysis and Prediction of COVID-19 Using SIR, SEIQR, and Machine Learning Models: Australia, Italy, and UK Cases

Information ◽  
2021 ◽  
Vol 12 (3) ◽  
pp. 109 ◽  
Author(s):  
Iman Rahimi ◽  
Amir H. Gandomi ◽  
Panagiotis G. Asteris ◽  
Fang Chen
Keyword(s):  
Machine Learning ◽  
Logistic Function ◽  
Prediction Performance ◽  
Machine Learning Algorithms ◽  
Model Parameters ◽  
The Novel ◽  
Chinese City ◽  
Limited Memory ◽  
Increasing Trend ◽  
Novel Coronavirus

The novel coronavirus disease, also known as COVID-19, is a disease outbreak that was first identified in Wuhan, a Central Chinese city. In this report, a short analysis focusing on Australia, Italy, and UK is conducted. The analysis includes confirmed and recovered cases and deaths, the growth rate in Australia compared with that in Italy and UK, and the trend of the disease in different Australian regions. Mathematical approaches based on susceptible, infected, and recovered (SIR) cases and susceptible, exposed, infected, quarantined, and recovered (SEIQR) cases models are proposed to predict epidemiology in the above-mentioned countries. Since the performance of the classic forms of SIR and SEIQR depends on parameter settings, some optimization algorithms, namely Broyden–Fletcher–Goldfarb–Shanno (BFGS), conjugate gradients (CG), limited memory bound constrained BFGS (L-BFGS-B), and Nelder–Mead, are proposed to optimize the parameters and the predictive capabilities of the SIR and SEIQR models. The results of the optimized SIR and SEIQR models were compared with those of two well-known machine learning algorithms, i.e., the Prophet algorithm and logistic function. The results demonstrate the different behaviors of these algorithms in different countries as well as the better performance of the improved SIR and SEIQR models. Moreover, the Prophet algorithm was found to provide better prediction performance than the logistic function, as well as better prediction performance for Italy and UK cases than for Australian cases. Therefore, it seems that the Prophet algorithm is suitable for data with an increasing trend in the context of a pandemic. Optimization of SIR and SEIQR model parameters yielded a significant improvement in the prediction accuracy of the models. Despite the availability of several algorithms for trend predictions in this pandemic, there is no single algorithm that would be optimal for all cases.

scholarly journals Monitoring the Impact of Air Quality on the COVID-19 Fatalities in Delhi, India: Using Machine Learning Techniques

2020 ◽  
pp. 1-8
Author(s):  
Jasleen Kaur Sethi ◽  
Mamta Mittal
Keyword(s):  
Machine Learning ◽  
Air Quality ◽  
Air Pollutants ◽  
Machine Learning Techniques ◽  
Environmental Restoration ◽  
The Novel ◽  
Ozone Pollution ◽  
Learning Techniques ◽  
Novel Coronavirus ◽  
The Impact

ABSTRACT Objective: The focus of this study is to monitor the effect of lockdown on the various air pollutants due to the coronavirus disease (COVID-19) pandemic and identify the ones that affect COVID-19 fatalities so that measures to control the pollution could be enforced. Methods: Various machine learning techniques: Decision Trees, Linear Regression, and Random Forest have been applied to correlate air pollutants and COVID-19 fatalities in Delhi. Furthermore, a comparison between the concentration of various air pollutants and the air quality index during the lockdown period and last two years, 2018 and 2019, has been presented. Results: From the experimental work, it has been observed that the pollutants ozone and toluene have increased during the lockdown period. It has also been deduced that the pollutants that may impact the mortalities due to COVID-19 are ozone, NH3, NO2, and PM10. Conclusions: The novel coronavirus has led to environmental restoration due to lockdown. However, there is a need to impose measures to control ozone pollution, as there has been a significant increase in its concentration and it also impacts the COVID-19 mortality rate.

scholarly journals Nanotechnology against the novel coronavirus (severe acute respiratory syndrome coronavirus 2): diagnosis, treatment, therapy and future perspectives

Nanomedicine ◽  
2021 ◽  
Vol 16 (6) ◽  
pp. 497-516
Author(s):  
Hamid Rashidzadeh ◽  
Hossein Danafar ◽  
Hossein Rahimi ◽  
Faezeh Mozafari ◽  
Marziyeh Salehiabar ◽  
...  
Keyword(s):  
Infectious Disease ◽  
Therapeutic Agents ◽  
Rapid Diagnosis ◽  
Socioeconomic Impact ◽  
The Novel ◽  
Future Perspectives ◽  
Mortality And Morbidity ◽  
Self Assembling ◽  
Novel Coronavirus ◽  
Significant Mortality

COVID-19, as an emerging infectious disease, has caused significant mortality and morbidity along with socioeconomic impact. No effective treatment or vaccine has been approved yet for this pandemic disease. Cutting-edge tools, especially nanotechnology, should be strongly considered to tackle this virus. This review aims to propose several strategies to design and fabricate effective diagnostic and therapeutic agents against COVID-19 by the aid of nanotechnology. Polymeric, inorganic self-assembling materials and peptide-based nanoparticles are promising tools for battling COVID-19 as well as its rapid diagnosis. This review summarizes all of the exciting advances nanomaterials are making toward COVID-19 prevention, diagnosis and therapy.

scholarly journals Associations of clinical characteristics and antiviral drugs with viral RNA clearance in patients with COVID-19 in Guangzhou, China: a retrospective cohort study

2020 ◽  
Author(s):  
Xudan Chen ◽  
Yuying Zhang ◽  
Baoyi Zhu ◽  
Jianwen Zeng ◽  
Wenxin Hong ◽  
...  
Keyword(s):  
Clinical Characteristics ◽  
Time Lag ◽  
Treatment Strategies ◽  
Antiviral Drugs ◽  
Viral Rna ◽  
The Novel ◽  
Illness Onset ◽  
Treatment Regimens ◽  
Global Pandemic ◽  
Novel Coronavirus

AbstractBackgroundThe novel coronavirus disease 2019 (COVID-19) characterized by respiratory symptoms has become a global pandemic although factors influencing viral RNA clearance remained unclear to inform optimal isolation period and treatment strategies.MethodsIn this retrospective study, we included patients with confirmed COVID-19 admitted to Guangzhou Eighth People’s Hospital from 20th January 2020 to 15th March 2020. The associations of clinical characteristics and treatment regimens on time to viral RNA clearance were analyzed.ResultsWe examined 284 consecutive COVID-19 cases, accounting for 82% of confirmed cases in Guangzhou during this period. At the time of reporting (20th March 2020), 276 (97.2%) had recovered and were discharged from hospital with a median hospital stay of 18 days (interquartile range [IQR]:13-24). Overall, 280 patients achieved viral RNA clearance with a median length of 12 days (IQR: 8-16) after onset of illness. Amongst them, 66.1% had viral RNA cleared within 14 days, and 89.3% within 21 days. Older age, severity of disease, time lag from illness onset to hospital admission, high body temperature, and corticosteroid use were associated with delayed clearance of viral RNA. None of the antiviral regimens (chloroquine, oseltamivir, arbidol, and lopinavir/ritonavir) improved viral RNA clearance. The use of lopinavir/ritonavir was associated with delayed clearance of viral RNA after adjusting for confounders.ConclusionIn patients with COVID-19, isolation for a minimum of 21 days after onset of illness may be warranted, while the use of antiviral drugs does not enhance viral RNA clearance.Brief SummaryViral RNA was cleared in 89% of the COVID-19 patients within 21 days after illness onset. The use of antiviral drugs (chloroquine, oseltamivir, arbidol, and lopinavir/ritonavir) did not shorten viral RNA clearance, especially in non-serious cases.

scholarly journals Severity of Coronavirus Disease 2019 in Patients with Cancer

2020 ◽  
Vol 4 (2) ◽  
pp. 119-126
Author(s):  
Zahraa Qusairy ◽  
Miran Rada
Keyword(s):  
Global Health ◽  
Treatment Options ◽  
Underlying Disease ◽  
Therapeutic Agents ◽  
The Novel ◽  
Supportive Treatment ◽  
Plasma Therapy ◽  
Patients With Cancer ◽  
Novel Coronavirus

The outbreak of the novel coronavirus disease 2019 (COVID-19) has appeared to be one of the biggest global health threats worldwide with no specific therapeutic agents. As of August 2020, over 22.4 million confirmed cases and more than 788,000 deaths have been reported globally, and the toll is expected to increase before the pandemic is over. Given the aggressive nature of their underlying disease, cancer patients seem to be more vulnerable to COVID-19 and various studies have confirmed this hypothesis. Herein, we review the current information regarding the role of cancer in SARS-CoV-2 infections. Moreover, we discuss the effective supportive treatment options for COVID-19 including Dexamethasone, Tocilizumab and Remdesivir and convalescent plasma therapy (CPT), as well as discuss their efficacy in COVID-19 patients with cancer.

How Does the Novel Coronavirus Kill? A Machine Learning Approach

2020 ◽  
Author(s):  
Logan Ryan ◽  
Huaqin Pan ◽  
Samson Mataraso ◽  
Anna Lynn-Palevsky ◽  
Emily Pellegrini ◽  
...  
Keyword(s):  
Machine Learning ◽  
Learning Approach ◽  
The Novel ◽  
Machine Learning Approach ◽  
Novel Coronavirus

Shape-based Machine Learning Models for the Potential Novel COVID-19 Protease Inhibitors Assisted by Molecular Dynamics Simulation

2020 ◽  
Vol 20 (24) ◽  
pp. 2146-2167 ◽  
Author(s):  
Anuraj Nayarisseri ◽  
Ravina Khandelwal ◽  
Maddala Madhavi ◽  
Chandrabose Selvaraj ◽  
Umesh Panwar ◽  
...  
Keyword(s):  
Machine Learning ◽  
Molecular Dynamics ◽  
Protease Inhibitors ◽  
Dynamics Simulation ◽  
The Novel ◽  
Public Health Services ◽  
Dynamic Simulations ◽  
Disease Patterns ◽  
Novel Coronavirus ◽  
Dynamics Simulations

Background: The vast geographical expansion of novel coronavirus and an increasing number of COVID-19 affected cases have overwhelmed health and public health services. Artificial Intelligence (AI) and Machine Learning (ML) algorithms have extended their major role in tracking disease patterns, and in identifying possible treatments. Objective: This study aims to identify potential COVID-19 protease inhibitors through shape-based Machine Learning assisted by Molecular Docking and Molecular Dynamics simulations. Methods: 31 Repurposed compounds have been selected targeting the main coronavirus protease (6LU7) and a machine learning approach was employed to generate shape-based molecules starting from the 3D shape to the pharmacophoric features of their seed compound. Ligand-Receptor Docking was performed with Optimized Potential for Liquid Simulations (OPLS) algorithms to identify highaffinity compounds from the list of selected candidates for 6LU7, which were subjected to Molecular Dynamic Simulations followed by ADMET studies and other analyses. Results: Shape-based Machine learning reported remdesivir, valrubicin, aprepitant, and fulvestrant as the best therapeutic agents with the highest affinity for the target protein. Among the best shape-based compounds, a novel compound identified was not indexed in any chemical databases (PubChem, Zinc, or ChEMBL). Hence, the novel compound was named 'nCorv-EMBS'. Further, toxicity analysis showed nCorv-EMBS to be suitable for further consideration as the main protease inhibitor in COVID-19. Conclusion: Effective ACE-II, GAK, AAK1, and protease 3C blockers can serve as a novel therapeutic approach to block the binding and attachment of the main COVID-19 protease (PDB ID: 6LU7) to the host cell and thus inhibit the infection at AT2 receptors in the lung. The novel compound nCorv- EMBS herein proposed stands as a promising inhibitor to be evaluated further for COVID-19 treatment.

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