scholarly journals Chemical and Textual Embeddings for Drug Repurposing

2020 ◽  
Vol 34 (08) ◽  
pp. 13338-13343
Author(s):  
Galia Nordon ◽  
Levi Gottlieb ◽  
Kira Radinsky
Keyword(s):  
Drug Repositioning ◽  
Drug Repurposing ◽  
Drug Approval ◽  
Major Health ◽  
Embedding Technique ◽  
Alternative Techniques ◽  
Approved Drugs ◽  
Chemical Groups ◽  
Novel Drug ◽  
Expensive Process

Drug approval is a long and expensive process, that can take 10-15 years and more than 2 billion dollars. Therefore alternative techniques, such as drug repositioning, to identify new uses for approved drugs, has been gaining increasing attention. We examine the employment of different drug embeddings to predict successful drug repositioning. We study the employment of drug molecular structure and show that using larger chemical construct, such as large functional chemical groups, is much more effective than small sub-structures. We then study embeddings that are based on textual medical publications and compare them with the chemical-structure-based embeddings. We eventually present a novel embedding technique to combine the merit of the textual and chemical-based approaches. We provide empirical results on a repositioning benchmark set. Additionally, we present an application of such embedding as part of an ongoing repositioning research conducted with a major health care supplier, and identify a novel drug and indication. The pair has been verified on a corpus of 1.5 million patient EHR data.

scholarly journals Potential 2019-nCoV 3C-like Protease Inhibitors Designed Using Generative Deep Learning Approaches

2020 ◽  
Author(s):  
Alex Zhavoronkov ◽  
Vladimir Aladinskiy ◽  
Alexander Zhebrak ◽  
Bogdan Zagribelnyy ◽  
Victor Terentiev ◽  
...  
Keyword(s):  
Homology Modelling ◽  
Drug Repurposing ◽  
Hiv Protease ◽  
Learning Approaches ◽  
Protein Targets ◽  
Chemical Libraries ◽  
Internal Resources ◽  
Novel Coronavirus ◽  
Approved Drugs ◽  
Novel Drug

<div> <div> <div> <p>The emergence of the 2019 novel coronavirus (2019-nCoV), for which there is no vaccine or any known effective treatment created a sense of urgency for novel drug discovery approaches. One of the most important 2019-nCoV protein targets is the 3C-like protease for which the crystal structure is known. Most of the immediate efforts are focused on drug repurposing of known clinically-approved drugs and virtual screening for the molecules available from chemical libraries that may not work well. For example, the IC50 of lopinavir, an HIV protease inhibitor, against the 3C-like protease is approximately 50 micromolar. In an attempt to address this challenge, on January 28th, 2020 Insilico Medicine decided to utilize a part of its generative chemistry pipeline to design novel drug-like inhibitors of 2019-nCoV and started generation on January 30th. It utilized three of its previously validated generative chemistry approaches: crystal-derived pocked- based generator, homology modelling-based generation, and ligand-based generation. Novel druglike compounds generated using these approaches are being published at www.insilico.com/ncov-sprint/ and will be continuously updated. Several molecules will be synthesized and tested using the internal resources; however, the team is seeking collaborations to synthesize, test, and, if needed, optimize the published molecules. </p> </div> </div> </div>

scholarly journals Using systems medicine to identify a therapeutic agent with potential for repurposing in inflammatory bowel disease

2020 ◽  
Vol 13 (11) ◽  
pp. dmm044040 ◽  
Author(s):  
Katie Lloyd ◽  
Stamatia Papoutsopoulou ◽  
Emily Smith ◽  
Philip Stegmaier ◽  
Francois Bergey ◽  
...  
Keyword(s):  
Drug Discovery ◽  
In Silico ◽  
Nuclear Translocation ◽  
Drug Repositioning ◽  
Drug Repurposing ◽  
Peritoneal Macrophages ◽  
Bowel Diseases ◽  
Inflammatory Bowel ◽  
Aseptic Conditions ◽  
Novel Drug

ABSTRACTInflammatory bowel diseases (IBDs) cause significant morbidity and mortality. Aberrant NF-κB signalling is strongly associated with these conditions, and several established drugs influence the NF-κB signalling network to exert their effect. This study aimed to identify drugs that alter NF-κB signalling and could be repositioned for use in IBD. The SysmedIBD Consortium established a novel drug-repurposing pipeline based on a combination of in silico drug discovery and biological assays targeted at demonstrating an impact on NF-κB signalling, and a murine model of IBD. The drug discovery algorithm identified several drugs already established in IBD, including corticosteroids. The highest-ranked drug was the macrolide antibiotic clarithromycin, which has previously been reported to have anti-inflammatory effects in aseptic conditions. The effects of clarithromycin effects were validated in several experiments: it influenced NF-κB-mediated transcription in murine peritoneal macrophages and intestinal enteroids; it suppressed NF-κB protein shuttling in murine reporter enteroids; it suppressed NF-κB (p65) DNA binding in the small intestine of mice exposed to lipopolysaccharide; and it reduced the severity of dextran sulphate sodium-induced colitis in C57BL/6 mice. Clarithromycin also suppressed NF-κB (p65) nuclear translocation in human intestinal enteroids. These findings demonstrate that in silico drug repositioning algorithms can viably be allied to laboratory validation assays in the context of IBD, and that further clinical assessment of clarithromycin in the management of IBD is required.This article has an associated First Person interview with the joint first authors of the paper.

scholarly journals A review on drug repurposing applicable to COVID-19

2020 ◽  
Author(s):  
Serena Dotolo ◽  
Anna Marabotti ◽  
Angelo Facchiano ◽  
Roberto Tagliaferri
Keyword(s):  
Artificial Intelligence ◽  
Drug Target ◽  
Lower Cost ◽  
Drug Repositioning ◽  
Drug Repurposing ◽  
Chemical Compounds ◽  
The Moment ◽  
New Applications ◽  
Novel Drug ◽  
Small Chemical

Abstract Drug repurposing involves the identification of new applications for existing drugs at a lower cost and in a shorter time. There are different computational drug-repurposing strategies and some of these approaches have been applied to the coronavirus disease 2019 (COVID-19) pandemic. Computational drug-repositioning approaches applied to COVID-19 can be broadly categorized into (i) network-based models, (ii) structure-based approaches and (iii) artificial intelligence (AI) approaches. Network-based approaches are divided into two categories: network-based clustering approaches and network-based propagation approaches. Both of them allowed to annotate some important patterns, to identify proteins that are functionally associated with COVID-19 and to discover novel drug–disease or drug–target relationships useful for new therapies. Structure-based approaches allowed to identify small chemical compounds able to bind macromolecular targets to evaluate how a chemical compound can interact with the biological counterpart, trying to find new applications for existing drugs. AI-based networks appear, at the moment, less relevant since they need more data for their application.

scholarly journals Drug REpurposing using AI/ML tools - for Rare Diseases (DREAM-RD): A case study with Fragile X Syndrome (FXS)

2020 ◽  
Author(s):  
Kavitha Agastheeswaramoorthy ◽  
Aarti Sevilimedu
Keyword(s):  
Fragile X Syndrome ◽  
Drug Repositioning ◽  
Fragile X ◽  
Drug Repurposing ◽  
Machine Learning Algorithms ◽  
Specific Gene ◽  
Drug Induced ◽  
Approved Drugs ◽  
Disease Specific

AbstractDrug repositioning is emerging as an increasingly relevant option for rare disease therapy and management. Various methods for identifying suitable drug candidates have been tried and range from clinical symptomatic repurposing to data driven strategies which are based on the disease-specific gene or protein expression, modification, signalling and physiological perturbation profiles. The use of Artificial Intelligence (AI) and machine learning algorithms (ML) allows one to combine diverse data sets, and extract disease-specific data profiles which may not be intuitive or apparent from a subset of data. In this case study with Fragile X syndrome and autism, we have used multiple computational methodologies to extract profiles, which are then combined to arrive at a comprehensive signature (disease DEG). This DEG was then used to interrogate the large collection of drug-induced perturbation profiles present in public databases, to find appropriate small molecules to reverse or mimic the disease-profiles. We have labelled this pipeline Drug Repurposing using AI/ML tools - for Rare Diseases (DREAM-RD). We have shortlisted over 100 FDA approved drugs using the aforementioned pipeline, which may potentially be useful to ameliorate autistic phenotypes associated with FXS.

scholarly journals Computational guided approach for drug repurposing against SARS-CoV-2

2021 ◽  
Author(s):  
Jigisha Anand ◽  
Tanmay Ghildiyal ◽  
Aakanksha Madhwal ◽  
Rishabh Bhatt ◽  
Devvret Verma ◽  
...  
Keyword(s):  
Drug Targets ◽  
De Novo ◽  
Drug Repositioning ◽  
Drug Repurposing ◽  
Computational Docking ◽  
Drug Candidates ◽  
Docking Tool ◽  
Inhibitory Potential ◽  
Approved Drugs ◽  
Tract Infections

Background: In the current SARS-CoV-2 outbreak, drug repositioning emerges as a promising approach to develop efficient therapeutics in comparison to de novo drug development. The present investigation screened 130 US FDA-approved drugs including hypertension, cardiovascular diseases, respiratory tract infections (RTI), antibiotics and antiviral drugs for their inhibitory potential against SARS-CoV-2. Materials & methods: The molecular drug targets against SARS-CoV-2 proteins were determined by the iGEMDOCK computational docking tool. The protein homology models were generated through SWISS Model workspace. The pharmacokinetics of all the ligands was determined by ADMET analysis. Results: The study identified 15 potent drugs exhibiting significant inhibitory potential against SARS-CoV-2. Conclusion: Our investigation has identified possible repurposed drug candidates to improve the current modus operandi of the treatment given to COVID-19 patients.

scholarly journals Potential COVID-2019 3C-like Protease Inhibitors Designed Using Generative Deep Learning Approaches

2020 ◽  
Author(s):  
Alex Zhavoronkov ◽  
Vladimir Aladinskiy ◽  
Alexander Zhebrak ◽  
Bogdan Zagribelnyy ◽  
Victor Terentiev ◽  
...  
Keyword(s):  
Homology Modelling ◽  
Drug Repurposing ◽  
Hiv Protease ◽  
Learning Approaches ◽  
Protein Targets ◽  
Chemical Libraries ◽  
Internal Resources ◽  
Novel Coronavirus ◽  
Approved Drugs ◽  
Novel Drug

<div> <div> <p>The emergence of the 2019 novel coronavirus (COVID-19), for which there is no vaccine or any known effective treatment created a sense of urgency for novel drug discovery approaches. One of the most important COVID-19 protein targets is the 3C-like protease for which the crystal structure is known. Most of the immediate efforts are focused on drug repurposing of known clinically-approved drugs and virtual screening for the molecules available from chemical libraries that may not work well. For example, the IC50 of lopinavir, an HIV protease inhibitor, against the 3C-like protease is approximately 50 micromolar, which is far from ideal. In an attempt to address this challenge, on January 28th, 2020 Insilico Medicine decided to utilize a part of its generative chemistry pipeline to design novel drug-like inhibitors of COVID-19 and started generation on January 30th. It utilized three of its previously validated generative chemistry approaches: crystal-derived pocked-based generator, homology modelling-based generation, and ligand-based generation. Novel druglike compounds generated using these approaches were published at <a href="http://www.insilico.com/ncov-sprint/">www.insilico.com/ncov-sprint/</a>. Several molecules will be synthesized and tested using the internal resources; however, the team is seeking collaborations to synthesize, test, and, if needed, optimize the published molecules. <br></p> </div> </div>

scholarly journals Identification of Potential Molecules Against COVID-19 Main Protease Through Structure-Guided Virtual Screening Approach

2020 ◽  
Author(s):  
Lovika Mittal ◽  
Anita Kumari ◽  
Mitul Srivastava ◽  
Mrityunjay Singh ◽  
Shailendra Asthana
Keyword(s):  
Virtual Screening ◽  
Design Method ◽  
Hot Spot ◽  
Drug Repurposing ◽  
Inhibitory Effect ◽  
Drug Molecules ◽  
Main Protease ◽  
Approved Drugs ◽  
Novel Drug

<p>In this work, computer-aided drug design method has been implemented to quickly identify promising drug repurposing candidates against COVID-19 main protease (M<sup>pro</sup>)<sup> </sup>. The world is facing an epidemic and in absence of vaccine or any effective treatment, it has created a sense of urgency for novel drug discovery approaches. We have made an immediate effort by performing virtual screening of clinically approved drugs or molecules under clinical trials against COVID-19 M<sup>pro</sup> to identify potential drug molecules. With given knowledge of this system, N3 and 13B compounds have shown inhibitory effect against COVID-19 M<sup>pro</sup>. Both the compounds were considered as control to filter out the screened molecules. Overall, we have identified six potential compounds, Leupeptin Hemisulphate, Pepstatin A, Nelfinavir , Birinapant, Lypression and Octeotide which have shown the docking energy > -8.0 kcal/mol and MMGBSA > -68.0 kcal/mol. The binding pattern of these compounds suggests that they interacted with key <i>hot-spot</i> residues. Also, their pharmacokinetic annotations and therapeutic importance have indicated that they possess drug-like properties and could pave their way for<i> in-vitro</i> studies. The findings of this work will be significant for structure and pharmacophore-based designing. </p>

scholarly journals Drug Repurposing: A Review

2021 ◽  
pp. 161-169
Author(s):  
Rani Teksinh Bhagat ◽  
Santosh Ramarao Butle
Keyword(s):  
Drug Development ◽  
Research And Development ◽  
Development Process ◽  
Drug Repositioning ◽  
Drug Repurposing ◽  
New Drugs ◽  
Preclinical Development ◽  
Therapeutic Uses ◽  
Original Application ◽  
Approved Drugs

The drug development is a very time consuming and complex process. Drug development Process is Expensive. Success rate for the new drug development is very small. In recent years, decreases the new drugs development. The powerful tools are developed to support the research and development (R&D) process is essential. The Drug repurposing are helpful for research and development process. The drug re-purposing as an approach finds new therapeutic uses for current candidates or existing candidates or approved drugs, different from its original application. The main aimed of Drug repurposing is to reduce costs and research time investments in Research & Development. It is used for the diagnosis and treatment of various diseases. Repositioning is important over traditional approaches and need for effective therapies. Drug re-purposing identifies new application for already banned or existing drugs from market. In drug design, drug repurposing plays important role, because it helps to preclinical development. It reducing time efforts, expenses and failures in drug discovery process. It is also called as drug repositioning, drug redirecting, drug reprofiling.

scholarly journals Identification of Potential Molecules Against COVID-19 Main Protease Through Structure-Guided Virtual Screening Approach

2020 ◽  
Author(s):  
Lovika Mittal ◽  
Anita Kumari ◽  
Mitul Srivastava ◽  
Mrityunjay Singh ◽  
Shailendra Asthana
Keyword(s):  
Virtual Screening ◽  
Design Method ◽  
Hot Spot ◽  
Drug Repurposing ◽  
Inhibitory Effect ◽  
Drug Molecules ◽  
Main Protease ◽  
Approved Drugs ◽  
Novel Drug

<p>In this work, Computer-aided drug design method has been implemented to quickly identify promising drug repurposing candidates against COVID-19. The world is facing an epidemic and in absence of vaccine or any effective treatment, it has created a sense of urgency for novel drug discovery approaches. We have made an immediate effort by performing virtual screening of clinically approved drugs or molecules under clinical trials against COVID-19 to identify potential drug molecules.</p> <p>With given knowledge of this system, N3 and 13B compounds have shown inhibitory effect against COVD-19. Both the compounds were considered as control to filter out the screened molecules. Overall, we have identified six potential compounds, Leupeptin Hemisulphate, Pepstatin A, Nelfinavir , Birinapant, Lypression and Octeotide which have shown the docking energy > -8 kcal/mol and MMGBSA > -90 kcal/mol. The binding pattern of these compounds suggests that they interact with key <i>hot-spot</i> residues. Also, the pharmacokinetic annotations and their biological activity have indicated that they possess drug-like properties and pave their way for in vitro studies</p> <p>The findings of this work will be significant for structure and pharmacophore-based designing</p>

Harnessing Drug Repurposing for Exploration of New Diseases: An Insight to Strategies and Case Studies

2020 ◽  
Vol 20 ◽  
Author(s):  
Priti Jain ◽  
Shreyans K Jain ◽  
Munendra Jain
Keyword(s):  
Drug Discovery ◽  
Case Studies ◽  
Drug Repositioning ◽  
Pediatric Population ◽  
Clinical Status ◽  
Drug Repurposing ◽  
Discovery Research ◽  
Drug Discovery Research ◽  
Traditional Drug ◽  
Approved Drugs

Background: Traditional drug discovery is time consuming, costly, and risky process. Owing to the large investment, excessive attrition, and declined output; drug repurposing has become a blooming approach for the identification and development of new therapeutics. The method has gained momentum in the past few years and has resulted in many excellent discoveries. Industries are resurrecting the failed and shelved drugs to save time and cost. The process accounts for approximately 30% of the new US Food and Drug Administration approved drugs and vaccines in recent years. Methods: A systematic literature search using appropriate keywords were made to identify articles discussing the different strategies being adopted for repurposing and various drugs that have been/are being repurposed. Results: This review aims to describe the comprehensive data about the various strategies (Blinded search, computational approaches, and experimental approaches) used for the repurposing along with success case studies (treatment for orphan diseases, neglected tropical disease, neurodegenerative diseases, and drugs for pediatric population). It also inculcates an elaborated list of more than 100 drugs that have been repositioned, approaches adopted, and their present clinical status. We have also attempted to incorporate the different databases used for computational repurposing. Conclusion: The data presented is proof that drug repurposing is a prolific approach circumventing the issues poised by conventional drug discovery approaches. It is a highly promising approach and when combined with sophisticated computational tools it also carries high precision. The review would help researches in prioritizing the drug-repositioning method much needed to flourish the drug discovery research.

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