scholarly journals An integrated dataset for in silico drug discovery

2010 ◽  
Vol 7 (3) ◽  
Author(s):  
Simon J Cockell ◽  
Jochen Weile ◽  
Phillip Lord ◽  
Claire Wipat ◽  
Dmytro Andriychenko ◽  
...  
Keyword(s):  
Systems Biology ◽  
Drug Discovery ◽  
Drug Development ◽  
Data Integration ◽  
In Silico ◽  
Drug Repositioning ◽  
Integrated Systems ◽  
Integration Platform ◽  
Treatment Indications ◽  
New Treatment

SummaryDrug development is expensive and prone to failure. It is potentially much less risky and expensive to reuse a drug developed for one condition for treating a second disease, than it is to develop an entirely new compound. Systematic approaches to drug repositioning are needed to increase throughput and find candidates more reliably. Here we address this need with an integrated systems biology dataset, developed using the Ondex data integration platform, for the in silico discovery of new drug repositioning candidates. We demonstrate that the information in this dataset allows known repositioning examples to be discovered. We also propose a means of automating the search for new treatment indications of existing compounds.

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 DIPSBC - data integration platform for systems biology collaborations

2012 ◽  
Vol 13 (1) ◽  
Author(s):  
Felix Dreher ◽  
Thomas Kreitler ◽  
Christopher Hardt ◽  
Atanas Kamburov ◽  
Reha Yildirimman ◽  
...  
Keyword(s):  
Systems Biology ◽  
Data Integration ◽  
Integration Platform

Drug discovery in the age of systems biology: the rise of computational approaches for data integration

2012 ◽  
Vol 23 (4) ◽  
pp. 609-616 ◽  
Author(s):  
Murat Iskar ◽  
Georg Zeller ◽  
Xing-Ming Zhao ◽  
Vera van Noort ◽  
Peer Bork
Keyword(s):  
Systems Biology ◽  
Drug Discovery ◽  
Data Integration ◽  
Computational Approaches

scholarly journals In Silico Drug Repositioning Against Human NRP1 to Block SARS-CoV-2 Host Entry

2020 ◽  
Author(s):  
Seref Gul
Keyword(s):  
Drug Discovery ◽  
In Silico ◽  
Structural Information ◽  
Drug Repositioning ◽  
Experimental Studies ◽  
Md Simulations ◽  
Drug Binding ◽  
Discovery Process ◽  
Drug Discovery Process ◽  
S Protein

Despite COVID-19 turned into a pandemic, no approved drug for the treatment or globally available vaccine is out yet. In such a global emergency, drug repurposing approach that bypasses a costly and long-time demanding drug discovery process is an effective way in search of finding drugs for the COVID-19 treatment. Recent studies showed that SARS-CoV-2 uses neuropilin-1 (NRP1) for host entry. Here I took advantage of structural information of the NRP1 in complex with C-terminal of spike (S) protein of SARS-CoV-2 to identify drugs that may inhibit NRP1 and S protein interaction. U.S. Food and Drug Administration (FDA) approved drugs were screened using docking simulations. Among top drugs, well-tolerated drugs were selected for further analysis. Molecular dynamics (MD) simulations of drugs-NRP1 complexes were run for 100 ns to assess the persistency of binding. MM/GBSA calculations from MD simulations showed that eltrombopag, glimepiride, sitagliptin, dutasteride, and ergotamine stably and strongly bind to NRP1. In silico Alanine scanning analysis revealed that Tyr<sup>297</sup>, Trp<sup>301</sup>, and Tyr<sup>353</sup> amino acids of NRP1 are critical for drug binding. Validating the effect of drugs analyzed in this paper by experimental studies and clinical trials will expedite the drug discovery process for COVID-19.

scholarly journals Using systems medicine to identify a therapeutic agent with potential for repurposing in Inflammatory Bowel Disease

2019 ◽  
Author(s):  
Katie Lloyd ◽  
Stamatia Papoutsopoulou ◽  
Emily Smith ◽  
Philip Stegmaier ◽  
Francois Bergey ◽  
...  
Keyword(s):  
Inflammatory Bowel Disease ◽  
Drug Discovery ◽  
Bowel Disease ◽  
In Silico ◽  
Nuclear Translocation ◽  
Drug Repositioning ◽  
Drug Repurposing ◽  
Peritoneal Macrophages ◽  
Inflammatory Bowel ◽  
Aseptic Conditions

AbstractObjectiveInflammatory bowel diseases 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 which alter NF-κB signalling and may be repositioned for use in inflammatory bowel disease.DesignThe 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-kappaB signalling, and a murine model of IBD.ResultsThe 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.Clarithromycin’s 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 LPS, 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.ConclusionsThese findings demonstrate that in-silico drug repositioning algorithms can viably be allied to laboratory validation assays in the context of inflammatory bowel disease; and that further clinical assessment of clarithromycin in the management of inflammatory bowel disease is required.

scholarly journals Applications of Genome-Wide Screening and Systems Biology Approaches in Drug Repositioning

Cancers ◽  
2020 ◽  
Vol 12 (9) ◽  
pp. 2694
Author(s):  
Elyas Mohammadi ◽  
Rui Benfeitas ◽  
Hasan Turkez ◽  
Jan Boren ◽  
Jens Nielsen ◽  
...  
Keyword(s):  
Systems Biology ◽  
Drug Discovery ◽  
High Throughput ◽  
De Novo ◽  
Drug Repositioning ◽  
Machine Learning Techniques ◽  
Success Rates ◽  
Biological Targets ◽  
Modes Of Action ◽  
Genome Wide

Modern drug discovery through de novo drug discovery entails high financial costs, low success rates, and lengthy trial periods. Drug repositioning presents a suitable approach for overcoming these issues by re-evaluating biological targets and modes of action of approved drugs. Coupling high-throughput technologies with genome-wide essentiality screens, network analysis, genome-scale metabolic modeling, and machine learning techniques enables the proposal of new drug–target signatures and uncovers unanticipated modes of action for available drugs. Here, we discuss the current issues associated with drug repositioning in light of curated high-throughput multi-omic databases, genome-wide screening technologies, and their application in systems biology/medicine approaches.

Chemogenomic Approaches for Revealing Drug target Interactions in Drug Discovery

2021 ◽  
Vol 22 ◽  
Author(s):  
Harshita Bhargava ◽  
Amita Sharma ◽  
Prashanth Suravajhala
Keyword(s):  
Drug Discovery ◽  
In Silico ◽  
Drug Target ◽  
Drug Repositioning ◽  
Personalised Medicine ◽  
Classification Problem ◽  
Target Interaction ◽  
Advantages And Disadvantages

: The drug discovery process has been a crucial and cost-intensive process. This cost is not only monetary but also involves risks, time, and labour that are incurred while introducing a drug in the market. In order to reduce this cost and the risks associated with the drugs that may result in severe side effects, the in silico methods have gained popularity in recent years. These methods have had a significant impact on not only drug discovery but also the related areas such as drug repositioning, drug-target interaction prediction, drug side effect prediction, personalised medicine, etc. Amongst these research areas predicting interactions between drugs and targets forms the basis for drug discovery. The availability of big data in the form of bioinformatics, genetic databases, along with computational methods, have further supported data-driven decision-making. The results obtained through these methods may be further validated using in vitro or in vivo experiments. This validation step can further justify the predictions resulting from in silico approaches, further increasing the accuracy of the overall result in subsequent stages. A variety of approaches are used in predicting drug-target interactions, including ligand-based, molecular docking based and chemogenomic-based approaches. This paper discusses the chemogenomic methods, considering drug target interaction as a classification problem on whether or not an interaction between a particular drug and target would serve as a basis for understanding drug discovery/drug repositioning. We present the advantages and disadvantages associated with their application.

Industry Watch

2008 ◽  
Vol 12 (03) ◽  
pp. 40-51 ◽  
Keyword(s):  
Cancer Research ◽  
Systems Biology ◽  
Drug Discovery ◽  
Infectious Diseases ◽  
Drug Development ◽  
Collaborative Research ◽  
Medical Equipment ◽  
Strategic Alliance ◽  
Equipment Manufacturer ◽  
Development Pipeline

Progen Expands Drug Development Pipeline through Acquisition of CellGate. Morphotek Announces Collaborative Research Agreement with Ludwig Institute for Cancer Research. Crucell Enters Agreement with Sanofi Pasteur for Biologicals against Rabies. Cellworks Announces Drug Discovery Collaboration with Orchid Based on Systems Biology Technology. ILS Partners with Wistar. CardioDynamics International Corporation (CDIC) Announces Strategic Alliance with Leading Medical Equipment Manufacturer in India. Biocon and IATRICa Partner to Develop Novel Immunoconjugate Therapeutics against Cancer and Infectious Diseases. GSK Launches 2 DTP Vaccines in India. BIOBASE Opens New Subsidiary Nihon BIOBASE KK in Yokohama.

scholarly journals Expression profiling of drug response--from genes to pathways

2006 ◽  
Vol 8 (3) ◽  
pp. 283-293
Keyword(s):  
Data Analysis ◽  
Systems Biology ◽  
Drug Discovery ◽  
Drug Development ◽  
Drug Response ◽  
Single Gene ◽  
Individual Response ◽  
New Paradigm ◽  
Major Bottleneck ◽  
The Individual

Understanding individual response to a drug-what determines its efficacy and tolerability-is the major bottleneck in current drug development and clinical trials. Intracellular response and metabolism, for example through cytochrome P-450 enzymes, may either enhance or decrease the effect of different drugs, dependent on the genetic variant. Microarrays offer the potential to screen the genetic composition of the individual patient. However, experiments are "noisy" and must be accompanied by solid and robust data analysis. Furthermore, recent research aims at the combination of high-throughput data with methods of mathematical modeling, enabling problem-oriented assistance in the drug discovery process. This article will discuss state-of-the-art DNA array technology platforms and the basic elements of data analysis and bioinformatics research in drug discovery. Enhancing single-gene analysis, we will present a new method for interpreting gene expression changes in the context of entire pathways. Furthermore, we will introduce the concept of systems biology as a new paradigm for drug development and highlight our recent research-the development of a modeling and simulation platform for biomedical applications. We discuss the potentials of systems biology for modeling the drug response of the individual patient.

Sign in / Sign up

Export Citation Format

Share Document