Computational Drug Repurposing: Current Trends

2019 ◽  
Vol 26 (28) ◽  
pp. 5389-5409 ◽  
Author(s):  
Berin Karaman ◽  
Wolfgang Sippl
Keyword(s):  
Systems Biology ◽  
Large Scale ◽  
Drug Repurposing ◽  
Protein Targets ◽  
Structure Information ◽  
Drug Candidates ◽  
Large Scale Data ◽  
Challenges And Opportunities ◽  
Disease Signatures ◽  
Novel Drug

: Biomedical discovery has been reshaped upon the exploding digitization of data which can be retrieved from a number of sources, ranging from clinical pharmacology to cheminformatics-driven databases. Now, supercomputing platforms and publicly available resources such as biological, physicochemical, and clinical data, can all be integrated to construct a detailed map of signaling pathways and drug mechanisms of action in relation to drug candidates. Recent advancements in computer-aided data mining have facilitated analyses of ‘big data’ approaches and the discovery of new indications for pre-existing drugs has been accelerated. Linking gene-phenotype associations to predict novel drug-disease signatures or incorporating molecular structure information of drugs and protein targets with other kinds of data derived from systems biology provide great potential to accelerate drug discovery and improve the success of drug repurposing attempts. In this review, we highlight commonly used computational drug repurposing strategies, including bioinformatics and cheminformatics tools, to integrate large-scale data emerging from the systems biology, and consider both the challenges and opportunities of using this approach. Moreover, we provide successful examples and case studies that combined various in silico drug-repurposing strategies to predict potential novel uses for known therapeutics.

Large-scale Data Integration for Facilities Analytics: Challenges and Opportunities

2020 ◽  
Author(s):  
Balaje T. Thumati ◽  
Halasya Siva Subramania ◽  
Rajeev Shastri ◽  
Karthik Kalyana Kumar ◽  
Nicole Hessner ◽  
...  
Keyword(s):  
Data Integration ◽  
Large Scale ◽  
Large Scale Data ◽  
Challenges And Opportunities ◽  
Scale 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>

Systems Biology of the Vasopressin V2 Receptor: New Tools for Discovery of Molecular Actions of a GPCR

2021 ◽  
Vol 62 (1) ◽  
Author(s):  
Lihe Chen ◽  
Hyun Jun Jung ◽  
Arnab Datta ◽  
Euijung Park ◽  
Brian G. Poll ◽  
...  
Keyword(s):  
Systems Biology ◽  
Large Scale ◽  
Molecular Mechanisms ◽  
Water Channel ◽  
Peptide Hormone ◽  
Annual Review ◽  
Publication Date ◽  
Large Scale Data ◽  
V2 Receptor ◽  
Scale Data

Systems biology can be defined as the study of a biological process in which all of the relevant components are investigated together in parallel to discover the mechanism. Although the approach is not new, it has come to the forefront as a result of genome sequencing projects completed in the first few years of the current century. It has elements of large-scale data acquisition (chiefly next-generation sequencing–based methods and protein mass spectrometry) and large-scale data analysis (big data integration and Bayesian modeling). Here we discuss these methodologies and show how they can be applied to understand the downstream effects of GPCR signaling, specifically looking at how the neurohypophyseal peptide hormone vasopressin, working through the V2 receptor and PKA activation, regulates the water channel aquaporin-2. The emerging picture provides a detailed framework for understanding the molecular mechanisms involved in water balance disorders, pointing the way to improved treatment of both polyuric disorders and water-retention disorders causing dilutional hyponatremia. Expected final online publication date for the Annual Review of Pharmacology and Toxicology, Volume 62 is January 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

scholarly journals Design and development of drugs for Alzheimer’s dementia as a protein misfolding disorder

2015 ◽  
Vol 42 (S1) ◽  
pp. S16-S16
Author(s):  
C Barden ◽  
F Meier-Stephenson ◽  
MD Carter ◽  
S Banfield ◽  
EC Diez ◽  
...  
Keyword(s):  
Transgenic Mouse ◽  
Protein Misfolding ◽  
Large Scale ◽  
Beta Amyloid ◽  
Protein Oligomerization ◽  
Drug Candidates ◽  
Pharmacokinetic Properties ◽  
Disease Modifying Agents ◽  
Novel Drug

Background: There are no disease modifying agents for the treatment of Alzheimer’s disease (AD). Pathologically, AD is associated with the misfolding of two peptides: beta-amyloid (plaques) and tau (tangles). Methods: Using large-scale computer simulations, we modelled the misfolding of both beta-amyloid and tau, identifying a common conformational motif (CCM; i.e. an abnormal peptide shape), present in both beta-amyloid and tau, that promotes their misfolding. We screened a library of 11.8 million compounds against this in silico model of protein misfolding, identifying three novel molecular classes of putative therapeutics as anti-protein misfolding agents. We synthesized approximately 400 new chemical entity drug-like molecules in each of these three classes (i.e. 1200 potential drug candidates). These were comprehensively screened in a battery of five in vitro protein oligomerization assays. Selected compounds were next evaluated in the APP/PS1 doubly transgenic mouse model of AD. Results: Two new classes of molecules were identified with the ability to block the oligomerization of both beta-amyloid and tau. These compounds are drug-like with good pharmacokinetic properties and are brain-penetrant. They exhibit excellent efficacy in transgenic mouse models. Conclusion: Computer aided drug design has enabled the discovery of novel drug-like molecules able to inhibit both tau and beta-amyloid misfolding.

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&ndash;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 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 Prioritization of Potential Drugs Targeting the SARS-CoV-2 Main Protease

2020 ◽  
Author(s):  
Yanjin Li ◽  
Yu Zhang ◽  
Yikai Han ◽  
Tengfei Zhang ◽  
Ranran Du
Keyword(s):  
Large Scale ◽  
Drug Repurposing ◽  
Drug Candidates ◽  
Neural Activities ◽  
Main Protease ◽  
Global Threat ◽  
Potential Inhibitors ◽  
Molecular Patterns ◽  
Effective Drugs ◽  
Large Scale Screening

<p> Since its outbreak in 2019, the acute respiratory syndrome caused by SARS-Cov-2 has become a severe global threat to human. The lack of effective drugs strongly limits the therapeutic treatment against this pandemic disease. Here we employed a computational approach to prioritize potential inhibitors that directly target the core enzyme of SARS-Cov-2, the main protease, which is responsible for processing the viral RNA-translated polyprotein into functional proteins for viral replication. Based on a large-scale screening of over 13, 000 drug-like molecules, we have identified the most potential drugs that may suffice drug repurposing for SARS-Cov-2. Importantly, the second top hit is Beclabuvir, a known replication inhibitor of hepatitis C virus (HCV), which is recently reported to inhibit SARS-Cov-2 as well. We also noted several neurotransmitter-related ligands among the top candidates, suggesting a novel molecular similarity between this respiratory syndrome and neural activities. Our approach not only provides a comprehensive list of prioritized drug candidates for SARS-Cov-2, but also reveals intriguing molecular patterns that are worth future explorations.</p>

scholarly journals Large Scale Profiling of SARS-CoV-2-Infected Patients Identified Potential Therapeutic Host Targets and Drug Candidates for COVID-19

2021 ◽  
Author(s):  
Sidharth Jain ◽  
Samantha Rego ◽  
Sivanesan Dakshanamurthy
Keyword(s):  
Host Cell ◽  
Large Scale ◽  
Therapeutic Targets ◽  
Interferon Response ◽  
Translational Machinery ◽  
Drug Candidates ◽  
Rapid Spread ◽  
Key Genes ◽  
Gene Expression Alterations ◽  
Disease Signatures

Given the rapid spread of SARS-CoV-2 and rising death toll of COVID-19 in the current absence of effective treatments, it is imperative that therapeutics are developed and made available to patients as quickly as possible. Publicly available COVID-19 patient data can be used to identify host therapeutic targets, tailoring treatments to the disease signatures observed in patients. In this study, we identify potential host therapeutic targets based on gene expression alterations observed in COVID-19 patients. We analyzed RNAseq data from airway samples of COVID-19 patients and healthy controls to detect significantly differentially expressed genes and pathways that present potential therapeutic targets. Our analysis revealed expression changes in key genes involved in activation of immune pathways, as well as genes targeted by SARS-CoV2 to interfere with normal host cell functioning. Critical changes were observed in a number of genes, including EIF2AK2, which was shown to play important roles in activating the interferon response and interfering with host cell translational machinery in SARS-CoV-2 infection, presenting a prospective therapeutic target. We also identified drugs with potential to modulate multiple therapeutic targets within the most significant pathways. Our results both validate key genes, pathways, and drug candidates that have been reported by other studies and suggest others that have not been well-characterized and warrant further investigation by future studies. Further investigation of these therapeutic targets and their drug interactions may lead to effective therapeutic strategies to combat the current COVID-19 pandemic and protect against future outbreaks.<br>

scholarly journals Recent Advances in In Silico Target Fishing

Molecules ◽  
2021 ◽  
Vol 26 (17) ◽  
pp. 5124 ◽  
Author(s):  
Salvatore Galati ◽  
Miriana Di Stefano ◽  
Elisa Martinelli ◽  
Giulio Poli ◽  
Tiziano Tuccinardi
Keyword(s):  
Drug Discovery ◽  
In Silico ◽  
Drug Targets ◽  
Biological Activities ◽  
Protein Structures ◽  
Drug Repurposing ◽  
Specific Protein ◽  
Protein Targets ◽  
Drug Candidates ◽  
Target Fishing

In silico target fishing, whose aim is to identify possible protein targets for a query molecule, is an emerging approach used in drug discovery due its wide variety of applications. This strategy allows the clarification of mechanism of action and biological activities of compounds whose target is still unknown. Moreover, target fishing can be employed for the identification of off targets of drug candidates, thus recognizing and preventing their possible adverse effects. For these reasons, target fishing has increasingly become a key approach for polypharmacology, drug repurposing, and the identification of new drug targets. While experimental target fishing can be lengthy and difficult to implement, due to the plethora of interactions that may occur for a single small-molecule with different protein targets, an in silico approach can be quicker, less expensive, more efficient for specific protein structures, and thus easier to employ. Moreover, the possibility to use it in combination with docking and virtual screening studies, as well as the increasing number of web-based tools that have been recently developed, make target fishing a more appealing method for drug discovery. It is especially worth underlining the increasing implementation of machine learning in this field, both as a main target fishing approach and as a further development of already applied strategies. This review reports on the main in silico target fishing strategies, belonging to both ligand-based and receptor-based approaches, developed and applied in the last years, with a particular attention to the different web tools freely accessible by the scientific community for performing target fishing studies.

scholarly journals Large Scale Profiling of SARS-CoV-2-Infected Patients Identified Potential Therapeutic Host Targets and Drug Candidates for COVID-19

2021 ◽  
Author(s):  
Sidharth Jain ◽  
Samantha Rego ◽  
Sivanesan Dakshanamurthy
Keyword(s):  
Host Cell ◽  
Large Scale ◽  
Therapeutic Targets ◽  
Interferon Response ◽  
Translational Machinery ◽  
Drug Candidates ◽  
Rapid Spread ◽  
Key Genes ◽  
Gene Expression Alterations ◽  
Disease Signatures

Given the rapid spread of SARS-CoV-2 and rising death toll of COVID-19 in the current absence of effective treatments, it is imperative that therapeutics are developed and made available to patients as quickly as possible. Publicly available COVID-19 patient data can be used to identify host therapeutic targets, tailoring treatments to the disease signatures observed in patients. In this study, we identify potential host therapeutic targets based on gene expression alterations observed in COVID-19 patients. We analyzed RNAseq data from airway samples of COVID-19 patients and healthy controls to detect significantly differentially expressed genes and pathways that present potential therapeutic targets. Our analysis revealed expression changes in key genes involved in activation of immune pathways, as well as genes targeted by SARS-CoV2 to interfere with normal host cell functioning. Critical changes were observed in a number of genes, including EIF2AK2, which was shown to play important roles in activating the interferon response and interfering with host cell translational machinery in SARS-CoV-2 infection, presenting a prospective therapeutic target. We also identified drugs with potential to modulate multiple therapeutic targets within the most significant pathways. Our results both validate key genes, pathways, and drug candidates that have been reported by other studies and suggest others that have not been well-characterized and warrant further investigation by future studies. Further investigation of these therapeutic targets and their drug interactions may lead to effective therapeutic strategies to combat the current COVID-19 pandemic and protect against future outbreaks.<br>

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