scholarly journals The Anatomy of the SARS-CoV-2 Biomedical Literature: Introducing the CovidX Network Algorithm for Drug Repurposing Recommendation

10.2196/21169 ◽  
2020 ◽  
Vol 22 (8) ◽  
pp. e21169
Author(s):  
Lyndsey Elaine Gates ◽  
Ahmed Abdeen Hamed
Keyword(s):  
Clinical Trials ◽  
Language Processing ◽  
Antiviral Drug ◽  
Drug Repurposing ◽  
Biomedical Literature ◽  
Drug Candidates ◽  
Fast Development ◽  
Drug Knowledge ◽  
Biomedical Publications ◽  
Multiple Drugs

Background Driven by the COVID-19 pandemic and the dire need to discover an antiviral drug, we explored the landscape of the SARS-CoV-2 biomedical publications to identify potential treatments. Objective The aims of this study are to identify off-label drugs that may have benefits for the coronavirus disease pandemic, present a novel ranking algorithm called CovidX to recommend existing drugs for potential repurposing, and validate the literature-based outcome with drug knowledge available in clinical trials. Methods To achieve such objectives, we applied natural language processing techniques to identify drugs and linked entities (eg, disease, gene, protein, chemical compounds). When such entities are linked, they form a map that can be further explored using network science tools. The CovidX algorithm was based upon a notion that we called “diversity.” A diversity score for a given drug was calculated by measuring how “diverse” a drug is calculated using various biological entities (regardless of the cardinality of actual instances in each category). The algorithm validates the ranking and awards those drugs that are currently being investigated in open clinical trials. The rationale behind the open clinical trial is to provide a validating mechanism of the PubMed results. This ensures providing up to date evidence of the fast development of this disease. Results From the analyzed biomedical literature, the algorithm identified 30 possible drug candidates for repurposing, ranked them accordingly, and validated the ranking outcomes against evidence from clinical trials. The top 10 candidates according to our algorithm are hydroxychloroquine, azithromycin, chloroquine, ritonavir, losartan, remdesivir, favipiravir, methylprednisolone, rapamycin, and tilorone dihydrochloride. Conclusions The ranking shows both consistency and promise in identifying drugs that can be repurposed. We believe, however, the full treatment to be a multifaceted, adjuvant approach where multiple drugs may need to be taken at the same time.

scholarly journals The Anatomy of SARS-CoV-2 BioMedical Literature: Introducing the CovidX Network Algorithm for Drug Repurposing Recommendation (Preprint)

2020 ◽  
Author(s):  
Lyndsey Elaine Gates ◽  
Ahmed Abdeen Hamed
Keyword(s):  
Clinical Trials ◽  
Language Processing ◽  
Antiviral Drug ◽  
Drug Repurposing ◽  
Biomedical Literature ◽  
Drug Candidates ◽  
Fast Development ◽  
Drug Knowledge ◽  
Biomedical Publications ◽  
Multiple Drugs

BACKGROUND Driven by the COVID-19 pandemic and the dire need to discover an antiviral drug to save our fellow humans, we explored the landscape of the SARS-CoV-2 biomedical publications to satisfy our objectives. OBJECTIVE The aims of this study are to identify off-label drugs that may have benefits for the coronavirus disease pandemic, present a novel ranking algorithm called CovidX to recommend existing drugs for potential repurposing, and validate the literature-based outcome with drug knowledge available in clinical trials. METHODS To achieve such objectives, we applied natural language processing techniques to identify drugs and linked entities (eg, disease, gene, protein, chemical compounds). When such entities are linked, they form a map that can be further explored using network science tools. The CovidX algorithm was based upon a notion that we called “diversity.” A diversity score for a given drug was calculated by measuring how “diverse” a drug is calculated using various biological entities (regardless of the cardinality of actual instances in each category). The algorithm validates the ranking and awards those drugs that are currently being investigated in open clinical trials. The rationale behind the open clinical trial is to provide a validating mechanism of the PubMed results. This ensures providing up to date evidence of the fast development of this disease. RESULTS From the analyzed biomedical literature, the algorithm identified 30 possible drug candidates for repurposing, ranked them accordingly, and validated the ranking outcomes against evidence from clinical trials. CONCLUSIONS The ranking shows both consistency and promise in identifying drugs that can be repurposed. We believe, however, the full treatment to be a multifaceted, adjuvant approach where multiple drugs may need to be taken at the same time.

scholarly journals Identifying Protein Features Responsible for Improved Drug Repurposing Accuracies Using The CANDO Platform: Implications for Drug Design

2018 ◽  
Author(s):  
William Mangione ◽  
Ram Samudrala
Keyword(s):  
Drug Design ◽  
Drug Repurposing ◽  
Biomedical Literature ◽  
Learning Approaches ◽  
Drug Candidates ◽  
Protein Library ◽  
Fold Reduction ◽  
Ligand Interactions ◽  
Novel Drug

Drug repurposing is a valuable tool for combating the slowing rates of novel therapeutic discovery. The Computational Analysis of Novel Drug Opportunities (CANDO) platform performs shotgun repurposing of 2030 indications/diseases using 3733 drugs/compounds to predict interactions with 46,784 proteins and relating them via proteomic interaction signatures. An accuracy is calculated by comparing interaction similarities of drugs approved for the same indications. We performed a unique subset analysis by breaking down the full protein library into smaller subsets and then recombining the best performing subsets into larger supersets. Up to 14% improvement in accuracy is seen upon benchmarking the supersets, representing a 100–1000 fold reduction in the number of proteins considered relative to the full library. Further analysis revealed that libraries comprised of proteins with more equitably diverse ligand interactions are important for describing compound behavior. Using one of these libraries to generate putative drug candidates against malaria results in more drugs that could be validated in the biomedical literature than the list suggested by the full protein library. Our work elucidates the role of particular protein subsets and corresponding ligand interactions that play a role in drug repurposing, with implications for drug design and machine learning approaches to improve the CANDO platform.

scholarly journals In silico Drug Repurposing for COVID-19: Targeting SARS-CoV-2 Proteins through Docking and Quantum Mechanical Scoring

2020 ◽  
Author(s):  
Claudio Cavasotto ◽  
Juan Di Filippo
Keyword(s):  
Clinical Trials ◽  
Antiviral Drug ◽  
Drug Repurposing ◽  
Rational Drug Design ◽  
World Health ◽  
Quantum Mechanical ◽  
S Protein ◽  
Compound Screening ◽  
Health Organization ◽  
Approved Drugs

In December 2019, an infectious disease caused by the coronavirus SARS-CoV-2 appeared in Wuhan, China. This disease (COVID-19) spread rapidly worldwide, and on March 2020 was declared a pandemic by the World Health Organization (WHO). Today, almost 1,5 million people have been infected, with more than 85,000 casualties. Today, no vaccine nor antiviral drug is available. While the development of a vaccine might take at least a year, and for a novel drug, even longer; finding a new use to an old drug (drug repurposing) could be the most effective strategy. We present a docking-based screening using a quantum mechanical scoring of a library built from approved drugs and compounds undergoing clinical trials, against three SARS-CoV-2 target proteins: the spike or S-protein, and two proteases, the main protease and the papain-like<br>protease. The S-protein binds directly to the Angiotensin Converting Enzyme 2 receptor of the human host cell surface, while the two proteases process viral polyproteins.<br>Following the anaylysis of our structure-based compound screening, we propose several structurally diverse compounds (either FDA-approved or in clinical trials) that could display antiviral activity against SARS-CoV-2. Clearly, these compounds should be further evaluated in experimental assays and clinical trials to confirm their actual activity against the disease. We hope that these findings may contribute to the rational drug design against COVID-19.

scholarly journals Separating Wheat from Chaff: Joining Biomedical Knowledge and Patient Data for Repurposing Medications

2019 ◽  
Vol 33 ◽  
pp. 9565-9572 ◽  
Author(s):  
Galia Nordon ◽  
Gideon Koren ◽  
Varda Shalev ◽  
Eric Horvitz ◽  
Kira Radinsky
Keyword(s):  
Clinical Trials ◽  
Medical Records ◽  
Large Scale ◽  
Medical Literature ◽  
Medical Knowledge ◽  
Drug Repurposing ◽  
Biomedical Literature ◽  
Biomedical Knowledge ◽  
Health Records ◽  
Clinical Expert

We present a system that jointly harnesses large-scale electronic health records data and a concept graph mined from the medical literature to guide drug repurposing—the process of applying known drugs in new ways to treat diseases. Our study is unique in methods and scope, per the scale of the concept graph and the quantity of data. We harness 10 years of nation-wide medical records of more than 1.5 million people and extract medical knowledge from all of PubMed, the world’s largest corpus of online biomedical literature. We employ links on the concept graph to provide causal signals to prioritize candidate influences between medications and target diseases. We show results of the system on studies of drug repurposing for hypertension and diabetes. In both cases, we present drug families identified by the algorithm which were previously unknown. We verify the results via clinical expert opinion and by prospective clinical trials on hypertension.

scholarly journals In silico Drug Repurposing for COVID-19: Targeting SARS-CoV-2 Proteins through Docking and Quantum Mechanical Scoring

2020 ◽  
Author(s):  
Claudio Cavasotto ◽  
Juan Di Filippo
Keyword(s):  
Clinical Trials ◽  
Antiviral Drug ◽  
Drug Repurposing ◽  
Rational Drug Design ◽  
World Health ◽  
Quantum Mechanical ◽  
Chemical Library ◽  
S Protein ◽  
Health Organization ◽  
Approved Drugs

In December 2019, an infectious disease caused by the coronavirus SARS-CoV-2 appeared in Wuhan, China. This disease (COVID-19) spread rapidly worldwide, and on March 2020 was declared a pandemic by the World Health Organization (WHO). Today, more than 4.7 million people have been infected, with almost 320,000 casualties, while no vaccine nor antiviral drug is in sight. The development of a vaccine might take at least a year, and even longer for a novel drug; thus, finding a new use to an old drug (drug repurposing) could be the most effective strategy. We present a high-throughput docking approach using a novel quantum mechanical scoring for screening a chemical library of ~11,500 molecules built from FDA-approved drugs and compounds undergoing clinical trials, against three SARS-CoV-2 target proteins: the spike or S-protein, and two proteases, the main protease and the papain-like protease. The S-protein binds directly to the Angiotensin Converting Enzyme 2 receptor of the human host cell surface, while the two proteases process viral polyproteins. Following the analysis of our structure-based virtual screening, we propose several structurally diverse compounds that could display antiviral activity against SARS-CoV-2. Clearly, these compounds should be further evaluated in experimental assays and clinical trials to confirm their actual activity against the disease. We hope that these findings may contribute to the rational drug design against COVID-19.

scholarly journals A graph-based method for reconstructing entities from coordination ellipsis in medical text

2020 ◽  
Vol 27 (9) ◽  
pp. 1364-1373 ◽  
Author(s):  
Chi Yuan ◽  
Yongli Wang ◽  
Ning Shang ◽  
Ziran Li ◽  
Ruxin Zhao ◽  
...  
Keyword(s):  
Clinical Trials ◽  
Global Optimization ◽  
Concept Mapping ◽  
Biomedical Literature ◽  
Eligibility Criteria ◽  
Medical Text ◽  
Representation Model ◽  
Biomedical Publications ◽  
Linguistic Phenomenon

Abstract Objective Coordination ellipsis is a linguistic phenomenon abound in medical text and is challenging for concept normalization because of difficulty in recognizing elliptical expressions referencing 2 or more entities accurately. To resolve this bottleneck, we aim to contribute a generalizable method to reconstruct concepts from medical coordinated elliptical expressions in a variety of biomedical corpora. Materials and Methods We proposed a graph-based representation model and built a pipeline to reconstruct concepts from coordinated elliptical expressions in medical text (RECEEM). There are 4 modules: (1) identify all possible candidate conjunct pairs from original coordinated elliptical expressions, (2) calculate coefficients for candidate conjuncts using the embedding model, (3) select the most appropriate decompositions by global optimization, and (4) rebuild concepts based on a pathfinding algorithm. We evaluated the pipeline’s performance on 2658 coordinated elliptical expressions from 3 different medical corpora (ie, biomedical literature, clinical narratives, and eligibility criteria from clinical trials). Precision, recall, and F1 score were calculated. Results The F1 scores for biomedical publications, clinical narratives, and research eligibility criteria were 0.862, 0.721, and 0.870, respectively. RECEEM outperformed 2 previously released methods. By incorporating RECEEM into 2 existing NLP tools, the F1 scores increased from 0.248 to 0.460 and from 0.287 to 0.630 on concept mapping of 1125 coordination ellipses. Conclusions RECEEM improves concept normalization for medical coordinated elliptical expressions in a variety of biomedical corpora. It outperformed existing methods and significantly enhanced the performance of 2 notable NLP systems for mapping coordination ellipses in the evaluation. The algorithm is open sourced online (https://github.com/chiyuan1126/RECEEM).

scholarly journals A phenomics approach for antiviral drug discovery

BMC Biology ◽  
2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Jonne Rietdijk ◽  
Marianna Tampere ◽  
Aleksandra Pettke ◽  
Polina Georgiev ◽  
Maris Lapins ◽  
...  
Keyword(s):  
Drug Discovery ◽  
Viral Infection ◽  
Drug Screening ◽  
Viral Infections ◽  
Antiviral Drug ◽  
Drug Repurposing ◽  
Host Cells ◽  
Lung Fibroblasts ◽  
Morphological Properties ◽  
Drug Candidates

Abstract Background The emergence and continued global spread of the current COVID-19 pandemic has highlighted the need for methods to identify novel or repurposed therapeutic drugs in a fast and effective way. Despite the availability of methods for the discovery of antiviral drugs, the majority tend to focus on the effects of such drugs on a given virus, its constituent proteins, or enzymatic activity, often neglecting the consequences on host cells. This may lead to partial assessment of the efficacy of the tested anti-viral compounds, as potential toxicity impacting the overall physiology of host cells may mask the effects of both viral infection and drug candidates. Here we present a method able to assess the general health of host cells based on morphological profiling, for untargeted phenotypic drug screening against viral infections. Results We combine Cell Painting with antibody-based detection of viral infection in a single assay. We designed an image analysis pipeline for segmentation and classification of virus-infected and non-infected cells, followed by extraction of morphological properties. We show that this methodology can successfully capture virus-induced phenotypic signatures of MRC-5 human lung fibroblasts infected with human coronavirus 229E (CoV-229E). Moreover, we demonstrate that our method can be used in phenotypic drug screening using a panel of nine host- and virus-targeting antivirals. Treatment with effective antiviral compounds reversed the morphological profile of the host cells towards a non-infected state. Conclusions The phenomics approach presented here, which makes use of a modified Cell Painting protocol by incorporating an anti-virus antibody stain, can be used for the unbiased morphological profiling of virus infection on host cells. The method can identify antiviral reference compounds, as well as novel antivirals, demonstrating its suitability to be implemented as a strategy for antiviral drug repurposing and drug discovery.

scholarly journals A Comperehensive Drug Repurposing Study for Covid19 Treatment: Novel Putative DHODH Inhibitors Show Association to Serotonin-Dopamine Receptors

2020 ◽  
Author(s):  
Burak Berber ◽  
Osman Doluca
Keyword(s):  
Clinical Trials ◽  
Dopamine Receptors ◽  
Large Scale ◽  
Drug Repurposing ◽  
Dihydroorotate Dehydrogenase ◽  
Docking Analysis ◽  
Drug Candidates ◽  
Dehydrogenase Enzyme

Here we discuss the potential of targeting Dihydroorotate dehydrogenase enzyme to help treat Covid 19. Next, we present a very large scale of docking analysis using 7900 drug candidates and 20 Dihydroorotate dehydrogenase structures. Our findings not only identify 28-FDA approved candidate molecules, but also show common characteristics among the candidates, especially their association with serotonin-dopamine receptors. In continuation, we discuss the existing clinical trials for Covid 19 treatment of some of the drug candidates we have identified, supporting that the rest are good candidates.

scholarly journals Entrectinib - a SARS-CoV-2 inhibitor in Human Lung Tissue (HLT) cells

2021 ◽  
Author(s):  
Alejandro Peralta-Garcia ◽  
Mariona Torrens-Fontanals ◽  
Tomasz Maciej Stepniewski ◽  
Judit Grau-Expósito ◽  
David Perea ◽  
...  
Keyword(s):  
Lung Tissue ◽  
Human Lung ◽  
Antiviral Drug ◽  
Drug Repurposing ◽  
Pharmaceutical Companies ◽  
Research Groups ◽  
Specific Mechanism ◽  
Human Lung Tissue ◽  
Drug Candidates

Since the start of the COVID-19 outbreak, pharmaceutical companies and research groups have focused on the development of vaccines and antiviral drugs against SARS-CoV-2. Here, we apply a drug repurposing strategy to identify potential drug candidates that are able to block the entrance of the virus into human cells. By combining virtual screening with in vitro pseudovirus assays and antiviral assays in Human Lung Tissue (HLT) cells, we identify entrectinib as a promising antiviral drug. We found that part of the antiviral action of entrectinib is mediated by a non-specific mechanism, likely occurring at the viral membrane level. Such a profile could provide entrectinib with protection against the development of drug resistance by emerging SARS-CoV-2 variants.

scholarly journals In silico Drug Repurposing for COVID-19: Targeting SARS-CoV-2 Proteins through Docking and Quantum Mechanical Scoring

2020 ◽  
Author(s):  
Claudio Cavasotto ◽  
Juan Di Filippo
Keyword(s):  
Clinical Trials ◽  
Antiviral Drug ◽  
Drug Repurposing ◽  
Rational Drug Design ◽  
World Health ◽  
Quantum Mechanical ◽  
Chemical Library ◽  
S Protein ◽  
Health Organization ◽  
Approved Drugs

In December 2019, an infectious disease caused by the coronavirus SARS-CoV-2 appeared in Wuhan, China. This disease (COVID-19) spread rapidly worldwide, and on March 2020 was declared a pandemic by the World Health Organization (WHO). Today, more than 4.7 million people have been infected, with almost 320,000 casualties, while no vaccine nor antiviral drug is in sight. The development of a vaccine might take at least a year, and even longer for a novel drug; thus, finding a new use to an old drug (drug repurposing) could be the most effective strategy. We present a high-throughput docking approach using a novel quantum mechanical scoring for screening a chemical library of ~11,500 molecules built from FDA-approved drugs and compounds undergoing clinical trials, against three SARS-CoV-2 target proteins: the spike or S-protein, and two proteases, the main protease and the papain-like protease. The S-protein binds directly to the Angiotensin Converting Enzyme 2 receptor of the human host cell surface, while the two proteases process viral polyproteins. Following the analysis of our structure-based virtual screening, we propose several structurally diverse compounds that could display antiviral activity against SARS-CoV-2. Clearly, these compounds should be further evaluated in experimental assays and clinical trials to confirm their actual activity against the disease. We hope that these findings may contribute to the rational drug design against COVID-19.

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