The Experts below are selected from a list of 5421 Experts worldwide ranked by ideXlab platform
Anil G Jegga - One of the best experts on this subject based on the ideXlab platform.
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Computational Drug Repositioning through heterogeneous network clustering
BMC Systems Biology, 2013Co-Authors: Chao Wu, Ranga Chandra Gudivada, Bruce J Aronow, Anil G JeggaAbstract:Background Given the costly and time consuming process and high attrition rates in Drug discovery and development, Drug Repositioning or Drug repurposing is considered as a viable strategy both to replenish the drying out Drug pipelines and to surmount the innovation gap. Although there is a growing recognition that mechanistic relationships from molecular to systems level should be integrated into Drug discovery paradigms, relatively few studies have integrated information about heterogeneous networks into computational Drug-Repositioning candidate discovery platforms. Results Using known disease-gene and Drug-target relationships from the KEGG database, we built a weighted disease and Drug heterogeneous network. The nodes represent Drugs or diseases while the edges represent shared gene, biological process, pathway, phenotype or a combination of these features. We clustered this weighted network to identify modules and then assembled all possible Drug-disease pairs (putative Drug Repositioning candidates) from these modules. We validated our predictions by testing their robustness and evaluated them by their overlap with Drug indications that were either reported in published literature or investigated in clinical trials. Conclusions Previous computational approaches for Drug Repositioning focused either on Drug-Drug and disease-disease similarity approaches whereas we have taken a more holistic approach by considering Drug-disease relationships also. Further, we considered not only gene but also other features to build the disease Drug networks. Despite the relative simplicity of our approach, based on the robustness analyses and the overlap of some of our predictions with Drug indications that are under investigation, we believe our approach could complement the current computational approaches for Drug Repositioning candidate discovery.
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Computational Drug Repositioning through heterogeneous network clustering.
BMC systems biology, 2013Co-Authors: Ranga Chandra Gudivada, Bruce J Aronow, Anil G JeggaAbstract:Given the costly and time consuming process and high attrition rates in Drug discovery and development, Drug Repositioning or Drug repurposing is considered as a viable strategy both to replenish the drying out Drug pipelines and to surmount the innovation gap. Although there is a growing recognition that mechanistic relationships from molecular to systems level should be integrated into Drug discovery paradigms, relatively few studies have integrated information about heterogeneous networks into computational Drug-Repositioning candidate discovery platforms. Using known disease-gene and Drug-target relationships from the KEGG database, we built a weighted disease and Drug heterogeneous network. The nodes represent Drugs or diseases while the edges represent shared gene, biological process, pathway, phenotype or a combination of these features. We clustered this weighted network to identify modules and then assembled all possible Drug-disease pairs (putative Drug Repositioning candidates) from these modules. We validated our predictions by testing their robustness and evaluated them by their overlap with Drug indications that were either reported in published literature or investigated in clinical trials. Previous computational approaches for Drug Repositioning focused either on Drug-Drug and disease-disease similarity approaches whereas we have taken a more holistic approach by considering Drug-disease relationships also. Further, we considered not only gene but also other features to build the disease Drug networks. Despite the relative simplicity of our approach, based on the robustness analyses and the overlap of some of our predictions with Drug indications that are under investigation, we believe our approach could complement the current computational approaches for Drug Repositioning candidate discovery.
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Computational Drug Repositioning through heterogeneous network clustering
BMC Systems Biology, 2013Co-Authors: Chao Wu, Ranga Chandra Gudivada, Bruce J Aronow, Anil G JeggaAbstract:Given the costly and time consuming process and high attrition rates in Drug discovery and development, Drug Repositioning or Drug repurposing is considered as a viable strategy both to replenish the drying out Drug pipelines and to surmount the innovation gap. Although there is a growing recognition that mechanistic relationships from molecular to systems level should be integrated into Drug discovery paradigms, relatively few studies have integrated information about heterogeneous networks into computational Drug-Repositioning candidate discovery platforms.
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Drug Repositioning for orphan diseases
Briefings in Bioinformatics, 2011Co-Authors: Divya Sardana, Ranga Chandra Gudivada, Minlu Zhang, Lun Yang, Anil G JeggaAbstract:The need and opportunity to discover therapeutics for rare or orphan diseases are enormous. Due to limited prevalence and/or commercial potential, of the approximately 6000 orphan diseases (defined by the FDA Orphan Drug Act as <200 000 US prevalence), only a small fraction (5%) is of interest to the biopharmaceutical industry. The fact that Drug development is complicated, time-consuming and expensive with extremely low success rates only adds to the low rate of therapeutics available for orphan diseases. An alternative and efficient strategy to boost the discovery of orphan disease therapeutics is to find connections between an existing Drug product and orphan disease. Drug Repositioning or Drug Repurposingcfinding a new indication for a Drugcis one way to maximize the potential of a Drug. The advantages of this approach are manifold, but rational Drug Repositioning for orphan diseases is not trivial and poses several formidable challengescpharmacologically and computationally. Most of the repositioned Drugs currently in the market are the result of serendipity. One reason the connection between Drug candidates and their potential new applications are not identified in an earlier or more systematic fashion is that the underlying mechanism ‘connecting’ them is either very intricate and unknown or indirect or dispersed and buried in an ever-increasing sea of information, much of which is emerging only recently and therefore is not well organized. In this study, we will review some of these issues and the current methodologies adopted or proposed to overcome them and translate chemical and biological discoveries into safe and effective orphan disease therapeutics.
Zhichao Liu - One of the best experts on this subject based on the ideXlab platform.
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Computational Drug Repositioning for rare diseases in the era of precision medicine
Drug discovery today, 2017Co-Authors: Brian Delavan, Ruth A. Roberts, Ruili Huang, Wenjun Bao, Weida Tong, Zhichao LiuAbstract:There are tremendous unmet needs in Drug development for rare diseases. Computational Drug Repositioning is a promising approach and has been successfully applied to the development of treatments for diseases. However, how to utilize this knowledge and effectively conduct and implement computational Drug Repositioning approaches for rare disease therapies is still an open issue. Here, we focus on the means of utilizing accumulated genomic data for accelerating and facilitating Drug Repositioning for rare diseases. First, we summarize the current genome landscape of rare diseases. Second, we propose several promising bioinformatics approaches and pipelines for computational Drug Repositioning for rare diseases. Finally, we discuss recent regulatory incentives and other enablers in rare disease Drug development and outline the remaining challenges.
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In silico Drug Repositioning: what we need to know.
Drug discovery today, 2012Co-Authors: Zhichao Liu, Hong Fang, Kelly Reagan, Donna L Mendrick, William Slikker, Weida TongAbstract:Drug Repositioning, exemplified by sildenafil and thalidomide, is a promising way to explore alternative indications for existing Drugs. Recent research has shown that bioinformatics-based approaches have the potential to offer systematic insights into the complex relationships among Drugs, targets and diseases necessary for successful Repositioning. In this article, we propose the key bioinformatics steps essential for discovering valuable Repositioning methods. The proposed steps (repurposing with a purpose, repurposing with a strategy and repurposing with confidence) are aimed at providing a repurposing pipeline, with particular focus on the proposed Drugs of New Indications (DNI) database, which can be used alongside currently available resources to improve in silico Drug Repositioning.
Beste Turanli - One of the best experts on this subject based on the ideXlab platform.
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systems biology based Drug Repositioning for development of cancer therapy
Seminars in Cancer Biology, 2021Co-Authors: Beste Turanli, Ozlem Altay, Jan Boren, Hasan Turkez, Jens Nielsen, Mathias Uhlen, Kazim Yalcin Arga, Adil MardinogluAbstract:Drug Repositioning is a powerful method that can assists the conventional Drug discovery process by using existing Drugs for treatment of a disease rather than its original indication. The first examples of repurposed Drugs were discovered serendipitously, however data accumulated by high-throughput screenings and advancements in computational biology methods have paved the way for rational Drug Repositioning methods. As chemotherapeutic agents have notorious side effects that significantly reduce quality of life, Drug Repositioning promises repurposed noncancer Drugs with little or tolerable adverse effects for cancer patients. Here, we review current Drug-related data types and databases including some examples of web-based Drug Repositioning tools. Next, we describe systems biology approaches to be used in Drug Repositioning for effective cancer therapy. Finally, we highlight examples of mostly repurposed Drugs for cancer treatment and provide an overview of future expectations in the field for development of effective treatment strategies.
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Drug Repositioning Strategies to Explore New Candidates Treating Prostate Cancer
In Silico Drug Design, 2019Co-Authors: Beste Turanli, Kazim Yalcin ArgaAbstract:Abstract Drug discovery and development is a complex, resource-intensive, time-consuming, and highly regulated process. Drug Repositioning, also called Drug repurposing or Drug recycling, has appeared as an innovative strategy in the last decade and offers the promise of reducing Drug development time frames and costs. Here, we begin with the recent developments that led to the evolution of pharmacology over the last decades. A conceptual summary of different Drug Repositioning approaches has been given. Then, the publicly available web-based tools for Drug Repositioning are presented, and a comprehensive analysis is performed on the repurposed noncancer Drugs against prostate cancer through these tools. Finally, we discuss the gaps and future challenges for Drug Repositioning approaches and the concepts to propel the field forward for treating complex diseases as well as prostate cancer.
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Drug Repositioning for Effective Prostate Cancer Treatment.
Frontiers in physiology, 2018Co-Authors: Beste Turanli, Jan Boren, Jens Nielsen, Mathias Uhlen, Kazim Yalcin Arga, Morten Grøtli, Adil MardinogluAbstract:Drug Repositioning has gained attention from both academia and pharmaceutical companies as an auxiliary process to conventional Drug discovery. Chemotherapeutic agents have notorious adverse effects that drastically reduce the life quality of cancer patients so Drug Repositioning is a promising strategy to identify non-cancer Drugs which have anti-cancer activity as well as tolerable adverse effects for human health. There are various strategies for discovery and validation of repurposed Drugs. In this review, 25 repurposed Drug candidates are presented as result of different strategies, 15 of which are already under clinical investigation for treatment of prostate cancer (PCa). To date, zoledronic acid is the only repurposed, clinically used, and approved non-cancer Drug for PCa. Anti-cancer activities of existing Drugs presented in this review cover diverse and also known mechanisms such as inhibition of mTOR and VEGFR2 signaling, inhibition of PI3K/Akt signaling, COX and selective COX-2 inhibition, NF-κB inhibition, Wnt/β-Catenin pathway inhibition, DNMT1 inhibition, and GSK-3β inhibition. In addition to monotherapy option, combination therapy with current anti-cancer Drugs may also increase Drug efficacy and reduce adverse effects. Thus, Drug Repositioning may become a key approach for Drug discovery in terms of time- and cost-efficiency comparing to conventional Drug discovery and development process.
Lianyin Jia - One of the best experts on this subject based on the ideXlab platform.
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Drug Repositioning based on individual bi-random walks on a heterogeneous network
BMC Bioinformatics, 2019Co-Authors: Yuehui Wang, Maozu Guo, Yazhou Ren, Lianyin JiaAbstract:Traditional Drug research and development is high cost, time-consuming and risky. Computationally identifying new indications for existing Drugs, referred as Drug Repositioning, greatly reduces the cost and attracts ever-increasing research interests. Many network-based methods have been proposed for Drug Repositioning and most of them apply random walk on a heterogeneous network consisted with disease and Drug nodes. However, these methods generally adopt the same walk-length for all nodes, and ignore the different contributions of different nodes. In this study, we propose a Drug Repositioning approach based on individual bi-random walks (DR-IBRW) on the heterogeneous network. DR-IBRW firstly quantifies the individual work-length of random walks for each node based on the network topology and knowledge that similar Drugs tend to be associated with similar diseases. To account for the inner structural difference of the heterogeneous network, it performs bi-random walks with the quantified walk-lengths, and thus to identify new indications for approved Drugs. Empirical study on public datasets shows that DR-IBRW achieves a much better Drug Repositioning performance than other related competitive methods. Using individual random walk-lengths for different nodes of heterogeneous network indeed boosts the Repositioning performance. DR-IBRW can be easily generalized to prioritize links between nodes of a network.
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Drug Repositioning based on individual bi-random walks on a heterogeneous network
BMC Bioinformatics, 2019Co-Authors: Yuehui Wang, Maozu Guo, Yazhou Ren, Lianyin JiaAbstract:Abstract Background Traditional Drug research and development is high cost, time-consuming and risky. Computationally identifying new indications for existing Drugs, referred as Drug Repositioning, greatly reduces the cost and attracts ever-increasing research interests. Many network-based methods have been proposed for Drug Repositioning and most of them apply random walk on a heterogeneous network consisted with disease and Drug nodes. However, these methods generally adopt the same walk-length for all nodes, and ignore the different contributions of different nodes. Results In this study, we propose a Drug Repositioning approach based on individual bi-random walks (DR-IBRW) on the heterogeneous network. DR-IBRW firstly quantifies the individual work-length of random walks for each node based on the network topology and knowledge that similar Drugs tend to be associated with similar diseases. To account for the inner structural difference of the heterogeneous network, it performs bi-random walks with the quantified walk-lengths, and thus to identify new indications for approved Drugs. Empirical study on public datasets shows that DR-IBRW achieves a much better Drug Repositioning performance than other related competitive methods. Conclusions Using individual random walk-lengths for different nodes of heterogeneous network indeed boosts the Repositioning performance. DR-IBRW can be easily generalized to prioritize links between nodes of a network.
Ranga Chandra Gudivada - One of the best experts on this subject based on the ideXlab platform.
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Computational Drug Repositioning through heterogeneous network clustering
BMC Systems Biology, 2013Co-Authors: Chao Wu, Ranga Chandra Gudivada, Bruce J Aronow, Anil G JeggaAbstract:Background Given the costly and time consuming process and high attrition rates in Drug discovery and development, Drug Repositioning or Drug repurposing is considered as a viable strategy both to replenish the drying out Drug pipelines and to surmount the innovation gap. Although there is a growing recognition that mechanistic relationships from molecular to systems level should be integrated into Drug discovery paradigms, relatively few studies have integrated information about heterogeneous networks into computational Drug-Repositioning candidate discovery platforms. Results Using known disease-gene and Drug-target relationships from the KEGG database, we built a weighted disease and Drug heterogeneous network. The nodes represent Drugs or diseases while the edges represent shared gene, biological process, pathway, phenotype or a combination of these features. We clustered this weighted network to identify modules and then assembled all possible Drug-disease pairs (putative Drug Repositioning candidates) from these modules. We validated our predictions by testing their robustness and evaluated them by their overlap with Drug indications that were either reported in published literature or investigated in clinical trials. Conclusions Previous computational approaches for Drug Repositioning focused either on Drug-Drug and disease-disease similarity approaches whereas we have taken a more holistic approach by considering Drug-disease relationships also. Further, we considered not only gene but also other features to build the disease Drug networks. Despite the relative simplicity of our approach, based on the robustness analyses and the overlap of some of our predictions with Drug indications that are under investigation, we believe our approach could complement the current computational approaches for Drug Repositioning candidate discovery.
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Computational Drug Repositioning through heterogeneous network clustering.
BMC systems biology, 2013Co-Authors: Ranga Chandra Gudivada, Bruce J Aronow, Anil G JeggaAbstract:Given the costly and time consuming process and high attrition rates in Drug discovery and development, Drug Repositioning or Drug repurposing is considered as a viable strategy both to replenish the drying out Drug pipelines and to surmount the innovation gap. Although there is a growing recognition that mechanistic relationships from molecular to systems level should be integrated into Drug discovery paradigms, relatively few studies have integrated information about heterogeneous networks into computational Drug-Repositioning candidate discovery platforms. Using known disease-gene and Drug-target relationships from the KEGG database, we built a weighted disease and Drug heterogeneous network. The nodes represent Drugs or diseases while the edges represent shared gene, biological process, pathway, phenotype or a combination of these features. We clustered this weighted network to identify modules and then assembled all possible Drug-disease pairs (putative Drug Repositioning candidates) from these modules. We validated our predictions by testing their robustness and evaluated them by their overlap with Drug indications that were either reported in published literature or investigated in clinical trials. Previous computational approaches for Drug Repositioning focused either on Drug-Drug and disease-disease similarity approaches whereas we have taken a more holistic approach by considering Drug-disease relationships also. Further, we considered not only gene but also other features to build the disease Drug networks. Despite the relative simplicity of our approach, based on the robustness analyses and the overlap of some of our predictions with Drug indications that are under investigation, we believe our approach could complement the current computational approaches for Drug Repositioning candidate discovery.
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Computational Drug Repositioning through heterogeneous network clustering
BMC Systems Biology, 2013Co-Authors: Chao Wu, Ranga Chandra Gudivada, Bruce J Aronow, Anil G JeggaAbstract:Given the costly and time consuming process and high attrition rates in Drug discovery and development, Drug Repositioning or Drug repurposing is considered as a viable strategy both to replenish the drying out Drug pipelines and to surmount the innovation gap. Although there is a growing recognition that mechanistic relationships from molecular to systems level should be integrated into Drug discovery paradigms, relatively few studies have integrated information about heterogeneous networks into computational Drug-Repositioning candidate discovery platforms.
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Drug Repositioning for orphan diseases
Briefings in Bioinformatics, 2011Co-Authors: Divya Sardana, Ranga Chandra Gudivada, Minlu Zhang, Lun Yang, Anil G JeggaAbstract:The need and opportunity to discover therapeutics for rare or orphan diseases are enormous. Due to limited prevalence and/or commercial potential, of the approximately 6000 orphan diseases (defined by the FDA Orphan Drug Act as <200 000 US prevalence), only a small fraction (5%) is of interest to the biopharmaceutical industry. The fact that Drug development is complicated, time-consuming and expensive with extremely low success rates only adds to the low rate of therapeutics available for orphan diseases. An alternative and efficient strategy to boost the discovery of orphan disease therapeutics is to find connections between an existing Drug product and orphan disease. Drug Repositioning or Drug Repurposingcfinding a new indication for a Drugcis one way to maximize the potential of a Drug. The advantages of this approach are manifold, but rational Drug Repositioning for orphan diseases is not trivial and poses several formidable challengescpharmacologically and computationally. Most of the repositioned Drugs currently in the market are the result of serendipity. One reason the connection between Drug candidates and their potential new applications are not identified in an earlier or more systematic fashion is that the underlying mechanism ‘connecting’ them is either very intricate and unknown or indirect or dispersed and buried in an ever-increasing sea of information, much of which is emerging only recently and therefore is not well organized. In this study, we will review some of these issues and the current methodologies adopted or proposed to overcome them and translate chemical and biological discoveries into safe and effective orphan disease therapeutics.
