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Liu Y, Sang G, Liu Z, Pan Y, Cheng J, Zhang Y. MPTN: A message-passing transformer network for drug repurposing from knowledge graph. Comput Biol Med 2024; 168:107800. [PMID: 38043469 DOI: 10.1016/j.compbiomed.2023.107800] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2023] [Revised: 11/09/2023] [Accepted: 11/29/2023] [Indexed: 12/05/2023]
Abstract
Drug repurposing (DR) based on knowledge graphs (KGs) is challenging, which uses knowledge graph reasoning models to predict new therapeutic pathways for existing drugs. With the rapid development of computing technology and the growing availability of validated biomedical data, various knowledge graph-based methods have been widely used to analyze and process complex and novel data to discover new indications for given drugs. However, existing methods need to be improved in extracting semantic information from contextual triples of biomedical entities. In this study, we propose a message-passing transformer network named MPTN based on knowledge graph for drug repurposing. Firstly, CompGCN is used as precoder to jointly aggregate entity and relation embeddings. Then, to fully capture the semantic information of entity context triples, the message propagating transformer module is designed. The module integrates the transformer into the message passing mechanism and incorporates the attention weight information of computing entity context triples into the entity embedding to update the entity embedding. Next, the residual connection is introduced to retain information as much as possible and improve prediction accuracy. Finally, MPTN utilizes the InteractE module as the decoder to obtain heterogeneous feature interactions in entity and relation representations and predict new pathways for drug treatment. Experiments on two datasets show that the model is superior to the existing knowledge graph embedding (KGE) learning methods.
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Affiliation(s)
- Yuanxin Liu
- School of Information Science and Technology, Dalian Maritime University, Dalian, 116026, Liaoning, China
| | - Guoming Sang
- School of Information Science and Technology, Dalian Maritime University, Dalian, 116026, Liaoning, China
| | - Zhi Liu
- School of Information Science and Technology, Dalian Maritime University, Dalian, 116026, Liaoning, China
| | - Yilin Pan
- School of Artificial Intelligence, Dalian Maritime University, Dalian, 116026, Liaoning, China
| | - Junkai Cheng
- School of Information Science and Technology, Dalian Maritime University, Dalian, 116026, Liaoning, China
| | - Yijia Zhang
- School of Information Science and Technology, Dalian Maritime University, Dalian, 116026, Liaoning, China.
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2
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Boudin M, Diallo G, Drancé M, Mougin F. The OREGANO knowledge graph for computational drug repurposing. Sci Data 2023; 10:871. [PMID: 38057380 PMCID: PMC10700660 DOI: 10.1038/s41597-023-02757-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2023] [Accepted: 11/16/2023] [Indexed: 12/08/2023] Open
Abstract
Drug repositioning is a faster and more affordable solution than traditional drug discovery approaches. From this perspective, computational drug repositioning using knowledge graphs is a very promising direction. Knowledge graphs constructed from drug data and information can be used to generate hypotheses (molecule/drug - target links) through link prediction using machine learning algorithms. However, it remains rare to have a holistically constructed knowledge graph using the broadest possible features and drug characteristics, which is freely available to the community. The OREGANO knowledge graph aims at filling this gap. The purpose of this paper is to present the OREGANO knowledge graph, which includes natural compounds related data. The graph was developed from scratch by retrieving data directly from the knowledge sources to be integrated. We therefore designed the expected graph model and proposed a method for merging nodes between the different knowledge sources, and finally, the data were cleaned. The knowledge graph, as well as the source codes for the ETL process, are openly available on the GitHub of the OREGANO project ( https://gitub.u-bordeaux.fr/erias/oregano ).
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Affiliation(s)
- Marina Boudin
- AHeaD team, Bordeaux Population Health Inserm U1219, Univ. Bordeaux, F-33000, Bordeaux, France.
| | - Gayo Diallo
- AHeaD team, Bordeaux Population Health Inserm U1219, Univ. Bordeaux, F-33000, Bordeaux, France
| | - Martin Drancé
- AHeaD team, Bordeaux Population Health Inserm U1219, Univ. Bordeaux, F-33000, Bordeaux, France
| | - Fleur Mougin
- AHeaD team, Bordeaux Population Health Inserm U1219, Univ. Bordeaux, F-33000, Bordeaux, France
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3
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A comprehensive review of Artificial Intelligence and Network based approaches to drug repurposing in Covid-19. Biomed Pharmacother 2022; 153:113350. [PMID: 35777222 PMCID: PMC9236981 DOI: 10.1016/j.biopha.2022.113350] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2022] [Revised: 06/22/2022] [Accepted: 06/24/2022] [Indexed: 11/26/2022] Open
Abstract
Conventional drug discovery and development is tedious and time-taking process; because of which it has failed to keep the required pace to mitigate threats and cater demands of viral and re-occurring diseases, such as Covid-19. The main reasons of this delay in traditional drug development are: high attrition rates, extensive time requirements, and huge financial investment with significant risk. The effective solution to de novo drug discovery is drug repurposing. Previous studies have shown that the network-based approaches and analysis are versatile platform for repurposing as the network biology is used to model the interactions between variety of biological concepts. Herein, we provide a comprehensive background of machine learning and deep learning in drug repurposing while specifically focusing on the applications of network-based approach to drug repurposing in Covid-19, data sources, and tools used. Furthermore, use of network proximity, network diffusion, and AI on network-based drug repurposing for Covid-19 is well-explained. Finally, limitations of network-based approaches in general and specific to network are stated along with future recommendations for better network-based models.
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4
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Guo F, Jiang C, Xi Y, Wang D, Zhang Y, Xie N, Guan Y, Zhang F, Yang H. Investigation of pharmacological mechanism of natural product using pathway fingerprints similarity based on "drug-target-pathway" heterogenous network. J Cheminform 2021; 13:68. [PMID: 34544480 PMCID: PMC8454151 DOI: 10.1186/s13321-021-00549-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2021] [Accepted: 09/03/2021] [Indexed: 02/05/2023] Open
Abstract
Natural products from traditional medicine inherit bioactivity from their source herbs. However, the pharmacological mechanism of natural products is often unclear and studied insufficiently. Pathway fingerprint similarity based on "drug-target-pathway" heterogeneous network provides new insight into Mechanism of Action (MoA) for natural products compared with reference drugs, which are selected approved drugs with similar bioactivity. Natural products with similar pathway fingerprints may have similar MoA to approved drugs. In our study, XYPI, an andrographolide derivative, had similar anti-inflammatory activity to Glucocorticoids (GCs) and non-steroidal anti-inflammatory drugs (NSAIDs), and GCs and NSAIDs have completely different MoA. Based on similarity evaluation, XYPI has similar pathway fingerprints as NSAIDs, but has similar target profile with GCs. The expression pattern of genes in LPS-activated macrophages after XYPI treatment is similar to that after NSAID but not GC treatment, and this experimental result is consistent with the computational prediction based on pathway fingerprints. These results imply that the pathway fingerprints of drugs have potential for drug similarity evaluation. This study used XYPI as an example to propose a new approach for investigating the pharmacological mechanism of natural products using pathway fingerprint similarity based on a "drug-target-pathway" heterogeneous network.
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Affiliation(s)
- Feifei Guo
- Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing, China
| | - Chunhong Jiang
- Joint Institute of Virology (Shantou University and The University of Hong Kong), Shantou University Medical College, Shantou, China
| | - Yujie Xi
- Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing, China
- Tianjin University of Traditional Chinese Medicine, Tianjin, China
| | - Dan Wang
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences-Beijing (PHOENIX Center), Beijing Institute of Lifeomics, Beijing, China
| | - Yi Zhang
- Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing, China
| | - Ning Xie
- State Key Laboratory of Innovative Natural Medicine and TCM Injections, Ganzhou, China
| | - Yi Guan
- Joint Institute of Virology (Shantou University and The University of Hong Kong), Shantou University Medical College, Shantou, China
| | - Fangbo Zhang
- Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing, China.
| | - Hongjun Yang
- Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing, China.
- Beijing Key Laboratory of Traditional Chinese Medicine Basic Research on Prevention and Treatment for Major Diseases, Experimental Research Center, China Academy of Chinese Medical Sciences, Beijing, China.
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5
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Sonaye HV, Sheikh RY, Doifode CA. Drug repurposing: Iron in the fire for older drugs. Biomed Pharmacother 2021; 141:111638. [PMID: 34153846 DOI: 10.1016/j.biopha.2021.111638] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2020] [Revised: 04/19/2021] [Accepted: 04/19/2021] [Indexed: 12/22/2022] Open
Abstract
Repositioning or "repurposing" of existing therapies for indications of alternative disease is an attractive approach that can generate lower costs and require a shorter approval time than developing a de novo drug. The development of experimental drugs is time-consuming, expensive, and limited to a fairly small number of targets. The incorporation of separate and complementary data should be used, as each type of data set exposes a specific feature of organism knowledge Drug repurposing opportunities are often focused on sporadic findings or on time-consuming pre-clinical drug tests which are often not guided by hypothesis. In comparison, repurposing in-silico drugs is a new, hypothesis-driven method that takes advantage of big-data use. Nonetheless, the widespread use of omics technology, enhanced data storage, data sense, machine learning algorithms, and computational modeling all give unparalleled knowledge of the methods of action of biological processes and drugs, providing wide availability, for both disease-related data and drug-related data. This review has taken an in-depth look at the current state, possibilities, and limitations of further progress in the field of drug repositioning.
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Affiliation(s)
- H V Sonaye
- Shri Sachhidanand Shikshan Santh's Taywade College of Pharmacy, Nagpur 441111, India.
| | - R Y Sheikh
- K.E.M. Hospital Research Centre, Pune 411011, India.
| | - C A Doifode
- Shri Sachhidanand Shikshan Santh's Taywade College of Pharmacy, Nagpur 441111, India.
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6
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Shi W, Chen X, Deng L. A Review of Recent Developments and Progress in Computational Drug Repositioning. Curr Pharm Des 2021; 26:3059-3068. [PMID: 31951162 DOI: 10.2174/1381612826666200116145559] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2019] [Accepted: 01/09/2020] [Indexed: 12/27/2022]
Abstract
Computational drug repositioning is an efficient approach towards discovering new indications for existing drugs. In recent years, with the accumulation of online health-related information and the extensive use of biomedical databases, computational drug repositioning approaches have achieved significant progress in drug discovery. In this review, we summarize recent advancements in drug repositioning. Firstly, we explicitly demonstrated the available data source information which is conducive to identifying novel indications. Furthermore, we provide a summary of the commonly used computing approaches. For each method, we briefly described techniques, case studies, and evaluation criteria. Finally, we discuss the limitations of the existing computing approaches.
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Affiliation(s)
- Wanwan Shi
- School of Computer Science and Engineering, Central South University, Changsha, China
| | - Xuegong Chen
- School of Computer Science and Engineering, Central South University, Changsha, China
| | - Lei Deng
- School of Computer Science and Engineering, Central South University, Changsha, China
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7
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Kumar R, Harilal S, Gupta SV, Jose J, Thomas Parambi DG, Uddin MS, Shah MA, Mathew B. Exploring the new horizons of drug repurposing: A vital tool for turning hard work into smart work. Eur J Med Chem 2019; 182:111602. [PMID: 31421629 PMCID: PMC7127402 DOI: 10.1016/j.ejmech.2019.111602] [Citation(s) in RCA: 34] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2019] [Revised: 08/07/2019] [Accepted: 08/07/2019] [Indexed: 02/07/2023]
Abstract
Drug discovery and development are long and financially taxing processes. On an average it takes 12-15 years and costs 1.2 billion USD for successful drug discovery and approval for clinical use. Many lead molecules are not developed further and their potential is not tapped to the fullest due to lack of resources or time constraints. In order for a drug to be approved by FDA for clinical use, it must have excellent therapeutic potential in the desired area of target with minimal toxicities as supported by both pre-clinical and clinical studies. The targeted clinical evaluations fail to explore other potential therapeutic applications of the candidate drug. Drug repurposing or repositioning is a fast and relatively cheap alternative to the lengthy and expensive de novo drug discovery and development. Drug repositioning utilizes the already available clinical trials data for toxicity and adverse effects, at the same time explores the drug's therapeutic potential for a different disease. This review addresses recent developments and future scope of drug repositioning strategy.
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Affiliation(s)
- Rajesh Kumar
- Department of Pharmacy, Kerala University of Health Sciences, Thrissur, Kerala, India
| | - Seetha Harilal
- Department of Pharmacy, Kerala University of Health Sciences, Thrissur, Kerala, India
| | - Sheeba Varghese Gupta
- Department of Pharmaceutical Sciences, College of Pharmacy, University of South Florida, Tampa, FL, 33612, USA
| | - Jobin Jose
- Department of Pharmaceutics, NGSM Institute of Pharmaceutical Science, NITTE Deemed to be University, Manglore, 575018, India
| | - Della Grace Thomas Parambi
- Department of Pharmaceutical Chemistry, College of Pharmacy, Jouf University, Sakaka, Al Jouf, 2014, Saudi Arabia
| | - Md Sahab Uddin
- Department of Pharmacy, Southeast University, Dhaka, Bangladesh; Pharmakon Neuroscience Research Network, Dhaka, Bangladesh
| | - Muhammad Ajmal Shah
- Department of Pharmacogonosy, Faculty of Pharmaceutical Sciences, Government College University, Faisalabad, Pakistan
| | - Bijo Mathew
- Division of Drug Design and Medicinal Chemistry Research Lab, Department of Pharmaceutical Chemistry, Ahalia School of Pharmacy, Palakkad, 678557, Kerala, India.
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8
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Sabetian S, Shamsir MS. Computer aided analysis of disease linked protein networks. Bioinformation 2019; 15:513-522. [PMID: 31485137 PMCID: PMC6704336 DOI: 10.6026/97320630015513] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2019] [Revised: 04/16/2019] [Accepted: 04/17/2019] [Indexed: 12/26/2022] Open
Abstract
Proteins can interact in various ways, ranging from direct physical relationships to indirect interactions in a formation of protein-protein
interaction network. Diagnosis of the protein connections is critical to identify various cellular pathways. Today constructing and
analyzing the protein interaction network is being developed as a powerful approach to create network pharmacology toward detecting
unknown genes and proteins associated with diseases. Discovery drug targets regarding therapeutic decisions are exciting outcomes of
studying disease networks. Protein connections may be identified by experimental and recent new computational approaches. Due to
difficulties in analyzing in-vivo proteins interactions, many researchers have encouraged improving computational methods to design
protein interaction network. In this review, the experimental and computational approaches and also advantages and disadvantages of
these methods regarding the identification of new interactions in a molecular mechanism have been reviewed. Systematic analysis of
complex biological systems including network pharmacology and disease network has also been discussed in this review.
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Affiliation(s)
- Soudabeh Sabetian
- Department of Biological and Health Sciences, Faculty of Bioscience and Medical Engineering, Universiti Teknologi Malaysia, 81310 Johor, Malaysia.,Infertility Research Center, Shiraz University, Shiraz 71454, Iran, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Mohd Shahir Shamsir
- Department of Biological and Health Sciences, Faculty of Bioscience and Medical Engineering, Universiti Teknologi Malaysia, 81310 Johor, Malaysia
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9
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Fukuoka Y. Machine Learning Approach for Predicting New Uses of Existing Drugs and Evaluation of Their Reliabilities. METHODS IN MOLECULAR BIOLOGY (CLIFTON, N.J.) 2019; 1903:269-279. [PMID: 30547448 DOI: 10.1007/978-1-4939-8955-3_16] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
In this chapter, a new method to evaluate the reliability of predicting new uses of existing drugs was proposed. The prediction was performed with a support vector machine (SVM) using various data. Because the reliability of prediction could not be evaluated based on the output of an SVM, which was binary, the proposed method evaluated the reliability as a product of a distance from the separating hyperplane of the SVM and a similarity between the disease targeted by the drug and a candidate disease. A validation using real data revealed that the performance of the proposed method was promising.
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10
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Mining heterogeneous network for drug repositioning using phenotypic information extracted from social media and pharmaceutical databases. Artif Intell Med 2019; 96:80-92. [DOI: 10.1016/j.artmed.2019.03.003] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2018] [Revised: 02/24/2019] [Accepted: 03/05/2019] [Indexed: 01/09/2023]
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11
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Lotfi Shahreza M, Ghadiri N, Mousavi SR, Varshosaz J, Green JR. A review of network-based approaches to drug repositioning. Brief Bioinform 2019; 19:878-892. [PMID: 28334136 DOI: 10.1093/bib/bbx017] [Citation(s) in RCA: 149] [Impact Index Per Article: 29.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2016] [Indexed: 01/17/2023] Open
Abstract
Experimental drug development is time-consuming, expensive and limited to a relatively small number of targets. However, recent studies show that repositioning of existing drugs can function more efficiently than de novo experimental drug development to minimize costs and risks. Previous studies have proven that network analysis is a versatile platform for this purpose, as the biological networks are used to model interactions between many different biological concepts. The present study is an attempt to review network-based methods in predicting drug targets for drug repositioning. For each method, the preferred type of data set is described, and their advantages and limitations are discussed. For each method, we seek to provide a brief description, as well as an evaluation based on its performance metrics.We conclude that integrating distinct and complementary data should be used because each type of data set reveals a unique aspect of information about an organism. We also suggest that applying a standard set of evaluation metrics and data sets would be essential in this fast-growing research domain.
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Affiliation(s)
- Maryam Lotfi Shahreza
- Department of Electrical and Computer Engineering, Isfahan University of Technology, Isfahan, Iran
| | - Nasser Ghadiri
- Department of Electrical and Computer Engineering, Isfahan University of Technology, Isfahan, Iran
| | | | - Jaleh Varshosaz
- Drug Delivery Systems Research Center of Isfahan University of Medical Sciences
| | - James R Green
- Department of Electrical and Computer Engineering, Isfahan University of Technology, Isfahan, Iran
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12
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Clustering based drug-drug interaction networks for possible repositioning of drugs against EGFR mutations: Clustering based DDI networks for EGFR mutations. Comput Biol Chem 2018; 75:24-31. [DOI: 10.1016/j.compbiolchem.2018.04.011] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2017] [Revised: 03/30/2018] [Accepted: 04/15/2018] [Indexed: 12/11/2022]
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13
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Abstract
The development of an intervention to slow or halt disease progression remains the greatest unmet therapeutic need in Parkinson's disease. Given the number of failures of various novel interventions in disease-modifying clinical trials in combination with the ever-increasing costs and lengthy processes for drug development, attention is being turned to utilizing existing compounds approved for other indications as novel treatments in Parkinson's disease. Advances in rational and systemic drug repurposing have identified a number of drugs with potential benefits for Parkinson's disease pathology and offer a potentially quicker route to drug discovery. Here, we review the safety and potential efficacy of the most promising candidates repurposed as potential disease-modifying treatments for Parkinson's disease in the advanced stages of clinical testing.
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Affiliation(s)
- Dilan Athauda
- Department of Clinical and Movement Neurosciences, UCL Institute of Neurology and National Hospital for Neurology & Neurosurgery, Queen Square, London, UK
| | - Thomas Foltynie
- Department of Clinical and Movement Neurosciences, UCL Institute of Neurology and National Hospital for Neurology & Neurosurgery, Queen Square, London, UK.
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14
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Colucci S, Donini FM, Di Sciascio E. Logical comparison over RDF resources in bio-informatics. J Biomed Inform 2017; 76:87-101. [PMID: 29127041 DOI: 10.1016/j.jbi.2017.11.004] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2017] [Revised: 09/21/2017] [Accepted: 11/05/2017] [Indexed: 11/18/2022]
Abstract
Comparison of resources is a frequent task in different bio-informatics applications, including drug-target interaction, drug repositioning and mechanism of action understanding, among others. This paper proposes a general method for the logical comparison of resources modeled in Resource Description Framework and shows its distinguishing features with reference to the comparison of drugs. In particular, the method returns a description of the commonalities between resources, rather than a numerical value estimating their similarity and/or relatedness. The approach is domain-independent and may be flexibly adapted to heterogeneous use cases, according to a process for setting parameters which is completely explicit. The paper also presents an experiment using the dataset Bioportal as knowledge source; the experiment is fully reproducible, thanks to the elicitation of criteria and values for parameter customization.
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Affiliation(s)
- S Colucci
- Politecnico di Bari, Via Orabona 4, 70125 Bari, Italy.
| | - F M Donini
- Università della Tuscia, Via S. Maria in Gradi 4, 01100 Viterbo, Italy.
| | - E Di Sciascio
- Politecnico di Bari, Via Orabona 4, 70125 Bari, Italy.
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15
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Huang CH, Chang PMH, Hsu CW, Huang CYF, Ng KL. Drug repositioning for non-small cell lung cancer by using machine learning algorithms and topological graph theory. BMC Bioinformatics 2016; 17 Suppl 1:2. [PMID: 26817825 PMCID: PMC4895785 DOI: 10.1186/s12859-015-0845-0] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
Abstract
Background Non-small cell lung cancer (NSCLC) is one of the leading causes of death globally, and research into NSCLC has been accumulating steadily over several years. Drug repositioning is the current trend in the pharmaceutical industry for identifying potential new uses for existing drugs and accelerating the development process of drugs, as well as reducing side effects. Results This work integrates two approaches - machine learning algorithms and topological parameter-based classification - to develop a novel pipeline of drug repositioning to analyze four lung cancer microarray datasets, enriched biological processes, potential therapeutic drugs and targeted genes for NSCLC treatments. A total of 7 (8) and 11 (12) promising drugs (targeted genes) were discovered for treating early- and late-stage NSCLC, respectively. The effectiveness of these drugs is supported by the literature, experimentally determined in-vitro IC50 and clinical trials. This work provides better drug prediction accuracy than competitive research according to IC50 measurements. Conclusions With the novel pipeline of drug repositioning, the discovery of enriched pathways and potential drugs related to NSCLC can provide insight into the key regulators of tumorigenesis and the treatment of NSCLC. Based on the verified effectiveness of the targeted drugs predicted by this pipeline, we suggest that our drug-finding pipeline is effective for repositioning drugs.
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Affiliation(s)
- Chien-Hung Huang
- Department of Computer Science and Information Engineering, National Formosa University, Hu-Wei, 63205, Taiwan.
| | - Peter Mu-Hsin Chang
- Division of Hematology and Oncology, Department of Medicine, Taipei Veterans General Hospital; Faculty of Medicine, National Yang Ming University, Taipei, 112, Taiwan.
| | - Chia-Wei Hsu
- Department of Computer Science and Information Engineering, National Formosa University, Hu-Wei, 63205, Taiwan.
| | - Chi-Ying F Huang
- Institute of Biopharmaceutical Sciences, National Yang-Ming University, Taipei, 112, Taiwan.
| | - Ka-Lok Ng
- Department of Bioinformatics and Medical Engineering, Asia University, Taichung, 41354, Taiwan. .,Department of Medical Research, China Medical University Hospital, China Medical University, Taichung, 40402, Taiwan.
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16
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Yu H, Choo S, Park J, Jung J, Kang Y, Lee D. Prediction of drugs having opposite effects on disease genes in a directed network. BMC SYSTEMS BIOLOGY 2016; 10 Suppl 1:2. [PMID: 26818006 PMCID: PMC4895308 DOI: 10.1186/s12918-015-0243-2] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
Background Developing novel uses of approved drugs, called drug repositioning, can reduce costs and times in traditional drug development. Network-based approaches have presented promising results in this field. However, even though various types of interactions such as activation or inhibition exist in drug-target interactions and molecular pathways, most of previous network-based studies disregarded this information. Methods We developed a novel computational method, Prediction of Drugs having Opposite effects on Disease genes (PDOD), for identifying drugs having opposite effects on altered states of disease genes. PDOD utilized drug-drug target interactions with ‘effect type’, an integrated directed molecular network with ‘effect type’ and ‘effect direction’, and disease genes with regulated states in disease patients. With this information, we proposed a scoring function to discover drugs likely to restore altered states of disease genes using the path from a drug to a disease through the drug-drug target interactions, shortest paths from drug targets to disease genes in molecular pathways, and disease gene-disease associations. Results We collected drug-drug target interactions, molecular pathways, and disease genes with their regulated states in the diseases. PDOD is applied to 898 drugs with known drug-drug target interactions and nine diseases. We compared performance of PDOD for predicting known therapeutic drug-disease associations with the previous methods. PDOD outperformed other previous approaches which do not exploit directional information in molecular network. In addition, we provide a simple web service that researchers can submit genes of interest with their altered states and will obtain drugs seeming to have opposite effects on altered states of input genes at http://gto.kaist.ac.kr/pdod/index.php/main. Conclusions Our results showed that ‘effect type’ and ‘effect direction’ information in the network based approaches can be utilized to identify drugs having opposite effects on diseases. Our study can offer a novel insight into the field of network-based drug repositioning. Electronic supplementary material The online version of this article (doi:10.1186/s12918-015-0243-2) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Hasun Yu
- Department of Bio and Brain Engineering, KAIST, 291 Daehak-ro, Yuseong-gu, Daejeon, 305-701, Republic of Korea. .,Bio-Synergy Research Center, 291 Daehak-ro, Yuseong-gu, Daejeon, 305-701, Republic of Korea.
| | - Sungji Choo
- Department of Bio and Brain Engineering, KAIST, 291 Daehak-ro, Yuseong-gu, Daejeon, 305-701, Republic of Korea. .,Bio-Synergy Research Center, 291 Daehak-ro, Yuseong-gu, Daejeon, 305-701, Republic of Korea.
| | - Junseok Park
- Department of Bio and Brain Engineering, KAIST, 291 Daehak-ro, Yuseong-gu, Daejeon, 305-701, Republic of Korea. .,Bio-Synergy Research Center, 291 Daehak-ro, Yuseong-gu, Daejeon, 305-701, Republic of Korea.
| | - Jinmyung Jung
- Department of Bio and Brain Engineering, KAIST, 291 Daehak-ro, Yuseong-gu, Daejeon, 305-701, Republic of Korea. .,Bio-Synergy Research Center, 291 Daehak-ro, Yuseong-gu, Daejeon, 305-701, Republic of Korea.
| | - Yeeok Kang
- Department of Bio and Brain Engineering, KAIST, 291 Daehak-ro, Yuseong-gu, Daejeon, 305-701, Republic of Korea. .,Bio-Synergy Research Center, 291 Daehak-ro, Yuseong-gu, Daejeon, 305-701, Republic of Korea.
| | - Doheon Lee
- Department of Bio and Brain Engineering, KAIST, 291 Daehak-ro, Yuseong-gu, Daejeon, 305-701, Republic of Korea. .,Bio-Synergy Research Center, 291 Daehak-ro, Yuseong-gu, Daejeon, 305-701, Republic of Korea.
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Jamal S, Goyal S, Shanker A, Grover A. Checking the STEP-Associated Trafficking and Internalization of Glutamate Receptors for Reduced Cognitive Deficits: A Machine Learning Approach-Based Cheminformatics Study and Its Application for Drug Repurposing. PLoS One 2015; 10:e0129370. [PMID: 26066505 PMCID: PMC4466797 DOI: 10.1371/journal.pone.0129370] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2015] [Accepted: 05/07/2015] [Indexed: 01/08/2023] Open
Abstract
BACKGROUND Alzheimer's disease, a lethal neurodegenerative disorder that leads to progressive memory loss, is the most common form of dementia. Owing to the complexity of the disease, its root cause still remains unclear. The existing anti-Alzheimer's drugs are unable to cure the disease while the current therapeutic options have provided only limited help in restoring moderate memory and remain ineffective at restricting the disease's progression. The striatal-enriched protein tyrosine phosphatase (STEP) has been shown to be involved in the internalization of the receptor, N-methyl D-aspartate (NMDR) and thus is associated with the disease. The present study was performed using machine learning algorithms, docking protocol and molecular dynamics (MD) simulations to develop STEP inhibitors, which could be novel anti-Alzheimer's molecules. METHODS The present study deals with the generation of computational predictive models based on chemical descriptors of compounds using machine learning approaches followed by substructure fragment analysis. To perform this analysis, the 2D molecular descriptors were generated and machine learning algorithms (Naïve Bayes, Random Forest and Sequential Minimization Optimization) were utilized. The binding mechanisms and the molecular interactions between the predicted active compounds and the target protein were modelled using docking methods. Further, the stability of the protein-ligand complex was evaluated using MD simulation studies. The substructure fragment analysis was performed using Substructure fingerprint (SubFp), which was further explored using a predefined dictionary. RESULTS The present study demonstrates that the computational methodology used can be employed to examine the biological activities of small molecules and prioritize them for experimental screening. Large unscreened chemical libraries can be screened to identify potential novel hits and accelerate the drug discovery process. Additionally, the chemical libraries can be searched for significant substructure patterns as reported in the present study, thus possibly contributing to the activity of these molecules.
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Affiliation(s)
- Salma Jamal
- Department of Bioscience and Biotechnology, Banasthali University, Tonk, Rajasthan, India
| | - Sukriti Goyal
- Department of Bioscience and Biotechnology, Banasthali University, Tonk, Rajasthan, India
| | - Asheesh Shanker
- Department of Bioscience and Biotechnology, Banasthali University, Tonk, Rajasthan, India
| | - Abhinav Grover
- School of Biotechnology, Jawaharlal Nehru University, New Delhi, India
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Rakshit H, Chatterjee P, Roy D. A bidirectional drug repositioning approach for Parkinson's disease through network-based inference. Biochem Biophys Res Commun 2015; 457:280-7. [PMID: 25576361 DOI: 10.1016/j.bbrc.2014.12.101] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2014] [Accepted: 12/22/2014] [Indexed: 10/24/2022]
Abstract
Parkinson's Disease (PD) is one of the most prevailing neurodegenerative disorders. Novel computational approaches are required to find new ways of using the existing drugs or drug repositioning, as currently there exists no cure for PD. We proposed a new bidirectional drug repositioning method that consists of Top-down and Bottom-up approaches and finally gives information about significant repositioning drug candidates. This method takes into account of the topological significance of drugs in the tripartite Indication-drug-target network (IDTN) as well the significance of their targets in the PD-specific protein-protein interaction network (PPIN). 9 non-Parkinsonian drugs have been proposed as the significant repositioning candidates for PD. In order to find out the efficiency of the repositioning candidates we introduced a parameter called the On-target ratio (OTR). The average OTR value of final repositioning candidates has been found to be higher than that of known PD specific drugs.
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Affiliation(s)
- Hindol Rakshit
- Department of Information Engineering, Computer Science and Mathematics, University of L'Aquila, 67100, Italy.
| | - Paulami Chatterjee
- Department of Biophysics, Bose Institute, Acharya J.C. Bose Centenary Building, P-1/12 C.I.T Scheme VII M, Kolkata 700054, India.
| | - Debjani Roy
- Department of Biophysics, Bose Institute, Acharya J.C. Bose Centenary Building, P-1/12 C.I.T Scheme VII M, Kolkata 700054, India.
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Festen EAM, Weersma RK. How will insights from genetics translate to clinical practice in inflammatory bowel disease? Best Pract Res Clin Gastroenterol 2014; 28:387-97. [PMID: 24913379 DOI: 10.1016/j.bpg.2014.04.002] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/28/2014] [Revised: 04/05/2014] [Accepted: 04/13/2014] [Indexed: 02/07/2023]
Abstract
Inflammatory bowel disease, consisting of Crohn's disease and ulcerative colitis, is a chronic inflammatory disease of the gut, which arises through an excessive immune response to the normal gut flora in a genetically susceptible host. The disease affects predominantly young adults and due to its chronic and relapsing nature gives rise to a high disease burden both financially, physically and psychologically. Current therapy still cannot prevent the need for surgical intervention in more than half of IBD patients. Consequently, advances in IBD therapy are of high importance. Recently, several new forms of targeted therapy have been introduced, which should improve surgery-free prognosis of IBD patients. Recent identification of genetic risk variants for IBD has led to new insights into the biological mechanisms of the disease, which will, in the future, lead to new targeted therapy. In the meantime repositioning of drugs from biologically similar diseases towards IBD might lead to new IBD therapies.
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Affiliation(s)
- E A M Festen
- University of Groningen, University Medical Centre Groningen, Department of Gastroenterology and Hepatology, Groningen, The Netherlands; University of Groningen, University Medical Centre Groningen, Department of Genetics, The Netherlands
| | - R K Weersma
- University of Groningen, University Medical Centre Groningen, Department of Gastroenterology and Hepatology, Groningen, The Netherlands.
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Abstract
Drug action can be rationalized as interaction of a molecule with proteins in a regulatory network of targets from a specific biological system. Both drug and side effects are often governed by interaction of the drug molecule with many, often unrelated, targets. Accordingly, arrays of protein–ligand interaction data from numerous in vitro profiling assays today provide growing evidence of polypharmacological drug interactions, even for marketed drugs. In vitro off-target profiling has therefore become an important tool in early drug discovery to learn about potential off-target liabilities, which are sometimes beneficial, but more often safety relevant. The rapidly developing field of in silico profiling approaches is complementing in vitro profiling. These approaches capitalize from large amounts of biochemical data from multiple sources to be exploited for optimizing undesirable side effects in pharmaceutical research. Therefore, current in silico profiling models are nowadays perceived as valuable tools in drug discovery, and promise a platform to support optimally informed decisions.
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Wu C, Gudivada RC, Aronow BJ, Jegga AG. Computational drug repositioning through heterogeneous network clustering. BMC SYSTEMS BIOLOGY 2013; 7 Suppl 5:S6. [PMID: 24564976 PMCID: PMC4029299 DOI: 10.1186/1752-0509-7-s5-s6] [Citation(s) in RCA: 74] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
Abstract
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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