scholarly journals Drug repositoning or target repositioning: a structural perspective of drug-target-indication relationship for available repurposed drugs

2019 ◽  
Author(s):  
Daniele Parisi ◽  
Melissa F. Adasme ◽  
Anastasia Sveshnikova ◽  
Yves Moreau ◽  
Michael Schroeder
Keyword(s):  
Drug Development ◽  
Drug Target ◽  
Drug Repositioning ◽  
Close Relation ◽  
Full Potential ◽  
Development Cost ◽  
Computational Power ◽  
Future Developments ◽  
Current Drug ◽  
Repurposed Drugs

ABSTRACTDrug repositioning aims to find new indications for existing drugs, in order to reduce drug development cost and time. Currently, numerous successful stories of drug repositioning have been reported and many drugs are already available on the market. Although many of those cases are products of serendipitous findings, repositioning opportunities can be uncovered systematically by following either a disease-centric approach, as a result of a close relation between an old and new indication, a target-centric one, which links a known target and its established drug to a new indication, or a drug-centric approach, which connects a known drug to a new target and its associated indication. The three approaches differ in their complexity, potential, and limits, and most important the necessary starting information and computational power. Which one is predominant in current drug repositioning and what does this imply for future developments? To address these questions, we systematically evaluated over 100 drugs, 200 targets structures and over 300 indications from the Drug Repositioning Database. Each of the analysed cases has been classified based on one of the three repositioning approaches, showing that the majority, more than 60%, falls within the disease-centric definition, around 30% within the target-centric, and only less than 10% within the drug-centric. We therefore concluded that so far repositioning has mainly been disease and target repositioning and not, as drug repositioning, as expected by definition. We discuss the reasons and suggest directions to exploit the full potential of techniques useful for drug-centric in order to sustain future rationale repositioning pipelines.

scholarly journals Drug target gene-based analyses of drug repositionability in rare and intractable diseases

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Ryuichi Sakate ◽  
Tomonori Kimura
Keyword(s):  
Drug Development ◽  
Drug Target ◽  
Target Gene ◽  
Target Genes ◽  
Drug Information ◽  
Drug Repositioning ◽  
Clinical Trial Data ◽  
Insufficient Information ◽  
Disease Pair ◽  
Low Prevalence

AbstractDrug development for rare and intractable diseases has been challenging for decades due to the low prevalence and insufficient information on these diseases. Drug repositioning is increasingly being used as a promising option in drug development. We aimed to analyze the trend of drug repositioning and inter-disease drug repositionability among rare and intractable diseases. We created a list of rare and intractable diseases based on the designated diseases in Japan. Drug information extracted from clinical trial data were integrated with information of drug target genes, which represent the mechanism of drug action. We obtained 753 drugs and 551 drug target genes from 8307 clinical trials for 189 diseases or disease groups. Trend analysis of drug sharing between a disease pair revealed that 1676 drug repositioning events occurred in 4401 disease pairs. A score, Rgene, was invented to investigate the proportion of drug target genes shared between a disease pair. Annual changes of Rgene corresponded to the trend of drug repositioning and predicted drug repositioning events occurring within a year or two. Drug target gene-based analyses well visualized the drug repositioning landscape. This approach facilitates drug development for rare and intractable diseases.

scholarly journals Drug repositioning or target repositioning: A structural perspective of drug-target-indication relationship for available repurposed drugs

2020 ◽  
Vol 18 ◽  
pp. 1043-1055 ◽  
Author(s):  
Daniele Parisi ◽  
Melissa F. Adasme ◽  
Anastasia Sveshnikova ◽  
Sarah Naomi Bolz ◽  
Yves Moreau ◽  
...  
Keyword(s):  
Drug Target ◽  
Drug Repositioning ◽  
Repurposed Drugs ◽  
Structural Perspective

Drug Repurposing: An Emerging Tool for Drug Reuse, Recycling and Discovery

2021 ◽  
Vol 13 ◽  
Author(s):  
Supriya Roy ◽  
Suneela Dhaneshwar ◽  
Bhavya Bhasin
Keyword(s):  
Drug Development ◽  
Drug Repositioning ◽  
Drug Repurposing ◽  
Therapeutic Agents ◽  
Medical Disorders ◽  
Therapeutic Uses ◽  
Original Product ◽  
Pharmaceutical Industries ◽  
Repurposed Drugs ◽  
The Cost

: Drug repositioning or repurposing is a revolutionary breakthrough in drug development that focuses on rediscovering new uses for old therapeutic agents. Drug repositioning can be defined more precisely as the process of exploring new indications for an already approved drug while drug repurposing includes overall re-development approaches grounded in the identical chemical structure of the active drug moiety as in the original product The repositioning approach accelerates the drug development process, curtails the cost and risk inherent to drug development. The strategy focuses on the polypharmacology of drugs to unlocks novel opportunities for logically designing more efficient therapeutic agents for unmet medical disorders. Drug repositioning also expresses certain regulatory challenges that hamper its further utilization. The review outlines the eminent role of drug repositioning in new drug discovery, methods to predict the molecular targets of a drug molecule, advantages that the strategy offers to the pharmaceutical industries, explaining how the industrial collaborations with academics can assist in the discovering more repositioning opportunities. The focus of the review is to highlight the latest applications of drug repositioning in various disorders. The review also includes a comparison of old and new therapeutic uses of repurposed drugs, with the assessment of their novel mechanisms of action and pharmacological effects in the management of various disorders. Various restrictions and challenges that repurposed drugs come across during their development and regulatory phases are also highlighted.

scholarly journals NeoDTI: Neural integration of neighbor information from a heterogeneous network for discovering new drug-target interactions

2018 ◽  
Author(s):  
Fangping Wan ◽  
Lixiang Hong ◽  
An Xiao ◽  
Tao Jiang ◽  
Jianyang Zeng
Keyword(s):  
Drug Development ◽  
Biological Networks ◽  
Heterogeneous Network ◽  
Drug Target ◽  
Large Scale ◽  
Drug Repositioning ◽  
Supplementary Information ◽  
Functional Roles ◽  
Aggregation Techniques ◽  
Wide Range

AbstractMotivationAccurately predicting drug-target interactions (DTIs) in silico can guide the drug discovery process and thus facilitate drug development. Computational approaches for DTI prediction that adopt the systems biology perspective generally exploit the rationale that the properties of drugs and targets can be characterized by their functional roles in biological networks.ResultsInspired by recent advance of information passing and aggregation techniques that generalize the convolution neural networks (CNNs) to mine large-scale graph data and greatly improve the performance of many network-related prediction tasks, we develop a new nonlinear end-to-end learning model, called NeoDTI, that integrates diverse information from heterogeneous network data and automatically learns topology-preserving representations of drugs and targets to facilitate DTI prediction. The substantial prediction performance improvement over other state-of-the-art DTI prediction methods as well as several novel predicted DTIs with evidence supports from previous studies have demonstrated the superior predictive power of NeoDTI. In addition, NeoDTI is robust against a wide range of choices of hyperparameters and is ready to integrate more drug and target related information (e.g., compound-protein binding affinity data). All these results suggest that NeoDTI can offer a powerful and robust tool for drug development and drug repositioning.Availability and implementationThe source code and data used in NeoDTI are available at: https://github.com/FangpingWan/[email protected] informationSupplementary data are available at Bioinformatics online.

scholarly journals Artificial intelligence-based computational framework for drug-target prioritization and inference of novel repositionable drugs for Alzheimer’s disease

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Shingo Tsuji ◽  
Takeshi Hase ◽  
Ayako Yachie-Kinoshita ◽  
Taiko Nishino ◽  
Samik Ghosh ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Deep Learning ◽  
Drug Target ◽  
Target Genes ◽  
Drug Repositioning ◽  
Therapeutic Targets ◽  
Machine Learning Techniques ◽  
Computational Framework ◽  
Novel Therapeutic

Abstract Background Identifying novel therapeutic targets is crucial for the successful development of drugs. However, the cost to experimentally identify therapeutic targets is huge and only approximately 400 genes are targets for FDA-approved drugs. As a result, it is inevitable to develop powerful computational tools that can identify potential novel therapeutic targets. Fortunately, the human protein-protein interaction network (PIN) could be a useful resource to achieve this objective. Methods In this study, we developed a deep learning-based computational framework that extracts low-dimensional representations of high-dimensional PIN data. Our computational framework uses latent features and state-of-the-art machine learning techniques to infer potential drug target genes. Results We applied our computational framework to prioritize novel putative target genes for Alzheimer’s disease and successfully identified key genes that may serve as novel therapeutic targets (e.g., DLG4, EGFR, RAC1, SYK, PTK2B, SOCS1). Furthermore, based on these putative targets, we could infer repositionable candidate-compounds for the disease (e.g., tamoxifen, bosutinib, and dasatinib). Conclusions Our deep learning-based computational framework could be a powerful tool to efficiently prioritize new therapeutic targets and enhance the drug repositioning strategy.

Sign in / Sign up

Export Citation Format

Share Document