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Shukla H, John D, Banerjee S, Tiwari AK. Drug repurposing for neurodegenerative diseases. PROGRESS IN MOLECULAR BIOLOGY AND TRANSLATIONAL SCIENCE 2024; 207:249-319. [PMID: 38942541 DOI: 10.1016/bs.pmbts.2024.03.035] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/30/2024]
Abstract
Neurodegenerative diseases (NDDs) are neuronal problems that include the brain and spinal cord and result in loss of sensory and motor dysfunction. Common NDDs include Alzheimer's disease (AD), Parkinson's disease (PD), Huntington's disease (HD), Multiple Sclerosis (MS), and Amyotrophic Lateral Sclerosis (ALS) etc. The occurrence of these diseases increases with age and is one of the challenging problems among elderly people. Though, several scientific research has demonstrated the key pathologies associated with NDDs still the underlying mechanisms and molecular details are not well understood and need to be explored and this poses a lack of effective treatments for NDDs. Several lines of evidence have shown that NDDs have a high prevalence and affect more than a billion individuals globally but still, researchers need to work forward in identifying the best therapeutic target for NDDs. Thus, several researchers are working in the directions to find potential therapeutic targets to alter the disease pathology and treat the diseases. Several steps have been taken to identify the early detection of the disease and drug repurposing for effective treatment of NDDs. Moreover, it is logical that current medications are being evaluated for their efficacy in treating such disorders; therefore, drug repurposing would be an efficient, safe, and cost-effective way in finding out better medication. In the current manuscript we discussed the utilization of drugs that have been repurposed for the treatment of AD, PD, HD, MS, and ALS.
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Affiliation(s)
- Halak Shukla
- Department of Biotechnology and Bioengineering, Institute of Advanced Research (IAR), Gandhinagar, Gujarat, India
| | - Diana John
- Department of Biotechnology and Bioengineering, Institute of Advanced Research (IAR), Gandhinagar, Gujarat, India
| | - Shuvomoy Banerjee
- Department of Biotechnology and Bioengineering, Institute of Advanced Research (IAR), Gandhinagar, Gujarat, India
| | - Anand Krishna Tiwari
- Genetics and Developmental Biology Laboratory, Department of Biotechnology and Bioengineering, Institute of Advanced Research (IAR), Gandhinagar, Gujarat, India.
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2
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Cai XS, Jiang H, Xiao J, Yan X, Xie P, Yu W, Lv WF, Wang J, Meng X, Chen CZ, Zhang M, Zhang Y, Yuan B, Zhang JB. Changes in bacterial community composition in the uterus of Holstein cow with endometritis before and after treatment with oxytetracycline. Sci Rep 2024; 14:9511. [PMID: 38664449 PMCID: PMC11045718 DOI: 10.1038/s41598-024-59674-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2023] [Accepted: 04/13/2024] [Indexed: 04/28/2024] Open
Abstract
It is important to study the bacteria that cause endometritis to identify effective therapeutic drugs for dairy cows. In this study, 20% oxytetracycline was used to treat Holstein cows (n = 6) with severe endometritis. Additional 10 Holstein cows (5 for healthy cows, 5 for cows with mild endometritis) were also selected. At the same time, changes in bacterial communities were monitored by high-throughput sequencing. The results show that Escherichia coli, Staphylococcus aureus and other common pathogenic bacteria could be detected by traditional methods in cows both with and without endometritis. However, 16S sequencing results show that changes in the abundance of these bacteria were not significant. Endometritis is often caused by mixed infections in the uterus. Oxytetracycline did not completely remove existing bacteria. However, oxytetracycline could effectively inhibit endometritis and had a significant inhibitory effect on the genera Bacteroides, Trueperella, Peptoniphilus, Parvimonas, Porphyromonas, and Fusobacterium but had no significant inhibitory effect on the bacterial genera Marinospirillum, Erysipelothrix, and Enteractinococcus. During oxytetracycline treatment, the cell motility, endocrine system, exogenous system, glycan biosynthesis and metabolism, lipid metabolism, metabolism of terpenoids, polyketides, cofactors and vitamins, signal transduction, and transport and catabolism pathways were affected.
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Affiliation(s)
- Xiao-Shi Cai
- College of Animal Sciences, Jilin University, Changchun, 130062, Jilin, China
| | - Hao Jiang
- College of Animal Sciences, Jilin University, Changchun, 130062, Jilin, China
| | - Jie Xiao
- College of Animal Sciences, Jilin University, Changchun, 130062, Jilin, China
- College of Animal Husbandry Engineering, Henan Vocational College of Agriculture, Zhengzhou, 450002, Henan, China
| | - Xiangmin Yan
- College of Animal Sciences, Jilin University, Changchun, 130062, Jilin, China
- Institute of Animal Husbandry, Xinjiang Academy of Animal Husbandry, Urumqi, 830001, Xinjiang, China
| | - Penggui Xie
- Yili Vocational and Technical College, Yili, 835000, Xinjiang, China
| | - Wenjie Yu
- College of Animal Sciences, Jilin University, Changchun, 130062, Jilin, China
| | - Wen-Fa Lv
- College of Animal Science and Technology, Jilin Agricultural University, Changchun, 130118, Jilin, China
| | - Jun Wang
- College of Animal Science and Technology, Jilin Agricultural University, Changchun, 130118, Jilin, China
| | - Xiangyu Meng
- Animal Husbandry Development Service Center of Tongyu County, Baicheng, 137200, Jilin, China
| | - Cheng-Zhen Chen
- College of Animal Sciences, Jilin University, Changchun, 130062, Jilin, China
| | - Mingjun Zhang
- College of Animal Sciences, Jilin University, Changchun, 130062, Jilin, China
| | - Yang Zhang
- College of Animal Sciences, Jilin University, Changchun, 130062, Jilin, China
- Institute of Animal Husbandry, Xinjiang Academy of Animal Husbandry, Urumqi, 830001, Xinjiang, China
| | - Bao Yuan
- College of Animal Sciences, Jilin University, Changchun, 130062, Jilin, China.
- , Changchun City, Jilin Province, China.
| | - Jia-Bao Zhang
- College of Animal Sciences, Jilin University, Changchun, 130062, Jilin, China.
- , Changchun City, Jilin Province, China.
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Chipofya M, Tayara H, Chong KT. Drug Therapeutic-Use Class Prediction and Repurposing Using Graph Convolutional Networks. Pharmaceutics 2021; 13:1906. [PMID: 34834320 PMCID: PMC8622176 DOI: 10.3390/pharmaceutics13111906] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2021] [Revised: 10/29/2021] [Accepted: 11/06/2021] [Indexed: 11/30/2022] Open
Abstract
An important stage in the process of discovering new drugs is when candidate molecules are tested of their efficacy. It is reported that testing drug efficacy empirically costs billions of dollars in the drug discovery pipeline. As a mechanism of expediting this process, researchers have resorted to using computational methods to predict the action of molecules in silico. Here, we present a way of predicting the therapeutic-use class of drugs from chemical structures only using graph convolutional networks. In comparison with existing methods which use fingerprints or images as training samples, our approach has yielded better results in all metrics under consideration. In particular, validation accuracy increased from 83-88% to 86-90% for single label tasks. Similarly, the model achieved an accuracy of over 88% on new test data. Finally, our multi-label classification model made new predictions which indicated that some of the drugs could have other therapeutic uses other than those indicated in the dataset. We performed a literature-based evaluation of these predictions and found evidence that validates them. This renders the model a potential tool to be used in search of drugs that are candidates for repurposing.
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Affiliation(s)
- Mapopa Chipofya
- Department of Electronics and Information Engineering, Jeonbuk National University, Jeonju 54896, Korea;
| | - Hilal Tayara
- School of International Engineering and Science, Jeonbuk National University, Jeonju 54896, Korea
| | - Kil To Chong
- Department of Electronics and Information Engineering, Jeonbuk National University, Jeonju 54896, Korea;
- Advanced Electronics and Information Research Center, Jeonbuk National University, Jeonju 54896, Korea
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4
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Sierra B, Magalhães AC, Soares D, Cavadas B, Perez AB, Alvarez M, Aguirre E, Bracho C, Pereira L, Guzman MG. Multi-Tissue Transcriptomic-Informed In Silico Investigation of Drugs for the Treatment of Dengue Fever Disease. Viruses 2021; 13:v13081540. [PMID: 34452405 PMCID: PMC8402662 DOI: 10.3390/v13081540] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2021] [Revised: 07/22/2021] [Accepted: 07/31/2021] [Indexed: 12/19/2022] Open
Abstract
Transcriptomics, proteomics and pathogen-host interactomics data are being explored for the in silico–informed selection of drugs, prior to their functional evaluation. The effectiveness of this kind of strategy has been put to the test in the current COVID-19 pandemic, and it has been paying off, leading to a few drugs being rapidly repurposed as treatment against SARS-CoV-2 infection. Several neglected tropical diseases, for which treatment remains unavailable, would benefit from informed in silico investigations of drugs, as performed in this work for Dengue fever disease. We analyzed transcriptomic data in the key tissues of liver, spleen and blood profiles and verified that despite transcriptomic differences due to tissue specialization, the common mechanisms of action, “Adrenergic receptor antagonist”, “ATPase inhibitor”, “NF-kB pathway inhibitor” and “Serotonin receptor antagonist”, were identified as druggable (e.g., oxprenolol, digoxin, auranofin and palonosetron, respectively) to oppose the effects of severe Dengue infection in these tissues. These are good candidates for future functional evaluation and clinical trials.
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Affiliation(s)
- Beatriz Sierra
- Virology Department, PAHO/WHO Collaborating Center for the Study of Dengue and its Vector, Pedro Kourí Institute of Tropical Medicine (IPK), Havana 11400, Cuba; (B.S.); (A.B.P.); (M.A.); (E.A.); (C.B.); (M.G.G.)
| | - Ana Cristina Magalhães
- i3S-Instituto de Investigação e Inovação em Saúde, Universidade do Porto, 4200-135 Porto, Portugal; (A.C.M.); (D.S.); (B.C.)
- IPATIMUP—Instituto de Patologia e Imunologia Molecular, Universidade do Porto, 4200-135 Porto, Portugal
- ICBAS—Instituto de Ciências Biomédicas Abel Salazar, Universidade do Porto, 4050-313 Porto, Portugal
| | - Daniel Soares
- i3S-Instituto de Investigação e Inovação em Saúde, Universidade do Porto, 4200-135 Porto, Portugal; (A.C.M.); (D.S.); (B.C.)
- IPATIMUP—Instituto de Patologia e Imunologia Molecular, Universidade do Porto, 4200-135 Porto, Portugal
| | - Bruno Cavadas
- i3S-Instituto de Investigação e Inovação em Saúde, Universidade do Porto, 4200-135 Porto, Portugal; (A.C.M.); (D.S.); (B.C.)
- IPATIMUP—Instituto de Patologia e Imunologia Molecular, Universidade do Porto, 4200-135 Porto, Portugal
| | - Ana B. Perez
- Virology Department, PAHO/WHO Collaborating Center for the Study of Dengue and its Vector, Pedro Kourí Institute of Tropical Medicine (IPK), Havana 11400, Cuba; (B.S.); (A.B.P.); (M.A.); (E.A.); (C.B.); (M.G.G.)
| | - Mayling Alvarez
- Virology Department, PAHO/WHO Collaborating Center for the Study of Dengue and its Vector, Pedro Kourí Institute of Tropical Medicine (IPK), Havana 11400, Cuba; (B.S.); (A.B.P.); (M.A.); (E.A.); (C.B.); (M.G.G.)
| | - Eglis Aguirre
- Virology Department, PAHO/WHO Collaborating Center for the Study of Dengue and its Vector, Pedro Kourí Institute of Tropical Medicine (IPK), Havana 11400, Cuba; (B.S.); (A.B.P.); (M.A.); (E.A.); (C.B.); (M.G.G.)
| | - Claudia Bracho
- Virology Department, PAHO/WHO Collaborating Center for the Study of Dengue and its Vector, Pedro Kourí Institute of Tropical Medicine (IPK), Havana 11400, Cuba; (B.S.); (A.B.P.); (M.A.); (E.A.); (C.B.); (M.G.G.)
| | - Luisa Pereira
- i3S-Instituto de Investigação e Inovação em Saúde, Universidade do Porto, 4200-135 Porto, Portugal; (A.C.M.); (D.S.); (B.C.)
- IPATIMUP—Instituto de Patologia e Imunologia Molecular, Universidade do Porto, 4200-135 Porto, Portugal
- Correspondence: ; Tel.: +351-22-607-4900
| | - Maria G. Guzman
- Virology Department, PAHO/WHO Collaborating Center for the Study of Dengue and its Vector, Pedro Kourí Institute of Tropical Medicine (IPK), Havana 11400, Cuba; (B.S.); (A.B.P.); (M.A.); (E.A.); (C.B.); (M.G.G.)
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Jaiswal S, Kumar M, Mandeep, Sunita, Singh Y, Shukla P. Systems Biology Approaches for Therapeutics Development Against COVID-19. Front Cell Infect Microbiol 2020; 10:560240. [PMID: 33194800 PMCID: PMC7655984 DOI: 10.3389/fcimb.2020.560240] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2020] [Accepted: 09/29/2020] [Indexed: 12/13/2022] Open
Abstract
Understanding the systems biology approaches for promoting the development of new therapeutic drugs is attaining importance nowadays. The threat of COVID-19 outbreak needs to be vanished for global welfare, and every section of research is focusing on it. There is an opportunity for finding new, quick, and accurate tools for developing treatment options, including the vaccine against COVID-19. The review at this moment covers various aspects of pathogenesis and host factors for exploring the virus target and developing suitable therapeutic solutions through systems biology tools. Furthermore, this review also covers the extensive details of multiomics tools i.e., transcriptomics, proteomics, genomics, lipidomics, immunomics, and in silico computational modeling aiming towards the study of host-virus interactions in search of therapeutic targets against the COVID-19.
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Affiliation(s)
- Shweta Jaiswal
- Enzyme Technology and Protein Bioinformatics Laboratory, Department of Microbiology, Maharshi Dayanand University, Rohtak, India
| | - Mohit Kumar
- Soil Microbial Ecology and Environmental Toxicology Laboratory, Department of Zoology, University of Delhi, Delhi, India
- Department of Zoology, Hindu College, University of Delhi, Delhi, India
| | - Mandeep
- Enzyme Technology and Protein Bioinformatics Laboratory, Department of Microbiology, Maharshi Dayanand University, Rohtak, India
| | - Sunita
- Enzyme Technology and Protein Bioinformatics Laboratory, Department of Microbiology, Maharshi Dayanand University, Rohtak, India
- Bacterial Pathogenesis Laboratory, Department of Zoology, University of Delhi, Delhi, India
| | - Yogendra Singh
- Bacterial Pathogenesis Laboratory, Department of Zoology, University of Delhi, Delhi, India
| | - Pratyoosh Shukla
- Enzyme Technology and Protein Bioinformatics Laboratory, Department of Microbiology, Maharshi Dayanand University, Rohtak, India
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6
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Hendrickx JO, van Gastel J, Leysen H, Martin B, Maudsley S. High-dimensionality Data Analysis of Pharmacological Systems Associated with Complex Diseases. Pharmacol Rev 2020; 72:191-217. [PMID: 31843941 DOI: 10.1124/pr.119.017921] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022] Open
Abstract
It is widely accepted that molecular reductionist views of highly complex human physiologic activity, e.g., the aging process, as well as therapeutic drug efficacy are largely oversimplifications. Currently some of the most effective appreciation of biologic disease and drug response complexity is achieved using high-dimensionality (H-D) data streams from transcriptomic, proteomic, metabolomics, or epigenomic pipelines. Multiple H-D data sets are now common and freely accessible for complex diseases such as metabolic syndrome, cardiovascular disease, and neurodegenerative conditions such as Alzheimer's disease. Over the last decade our ability to interrogate these high-dimensionality data streams has been profoundly enhanced through the development and implementation of highly effective bioinformatic platforms. Employing these computational approaches to understand the complexity of age-related diseases provides a facile mechanism to then synergize this pathologic appreciation with a similar level of understanding of therapeutic-mediated signaling. For informative pathology and drug-based analytics that are able to generate meaningful therapeutic insight across diverse data streams, novel informatics processes such as latent semantic indexing and topological data analyses will likely be important. Elucidation of H-D molecular disease signatures from diverse data streams will likely generate and refine new therapeutic strategies that will be designed with a cognizance of a realistic appreciation of the complexity of human age-related disease and drug effects. We contend that informatic platforms should be synergistic with more advanced chemical/drug and phenotypic cellular/tissue-based analytical predictive models to assist in either de novo drug prioritization or effective repurposing for the intervention of aging-related diseases. SIGNIFICANCE STATEMENT: All diseases, as well as pharmacological mechanisms, are far more complex than previously thought a decade ago. With the advent of commonplace access to technologies that produce large volumes of high-dimensionality data (e.g., transcriptomics, proteomics, metabolomics), it is now imperative that effective tools to appreciate this highly nuanced data are developed. Being able to appreciate the subtleties of high-dimensionality data will allow molecular pharmacologists to develop the most effective multidimensional therapeutics with effectively engineered efficacy profiles.
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Affiliation(s)
- Jhana O Hendrickx
- Receptor Biology Laboratory, Department of Biomedical Research (J.O.H., J.v.G., H.L., S.M.) and Faculty of Pharmacy, Biomedical and Veterinary Sciences (J.O.H., J.v.G., H.L., B.M., S.M.), University of Antwerp, Antwerp, Belgium
| | - Jaana van Gastel
- Receptor Biology Laboratory, Department of Biomedical Research (J.O.H., J.v.G., H.L., S.M.) and Faculty of Pharmacy, Biomedical and Veterinary Sciences (J.O.H., J.v.G., H.L., B.M., S.M.), University of Antwerp, Antwerp, Belgium
| | - Hanne Leysen
- Receptor Biology Laboratory, Department of Biomedical Research (J.O.H., J.v.G., H.L., S.M.) and Faculty of Pharmacy, Biomedical and Veterinary Sciences (J.O.H., J.v.G., H.L., B.M., S.M.), University of Antwerp, Antwerp, Belgium
| | - Bronwen Martin
- Receptor Biology Laboratory, Department of Biomedical Research (J.O.H., J.v.G., H.L., S.M.) and Faculty of Pharmacy, Biomedical and Veterinary Sciences (J.O.H., J.v.G., H.L., B.M., S.M.), University of Antwerp, Antwerp, Belgium
| | - Stuart Maudsley
- Receptor Biology Laboratory, Department of Biomedical Research (J.O.H., J.v.G., H.L., S.M.) and Faculty of Pharmacy, Biomedical and Veterinary Sciences (J.O.H., J.v.G., H.L., B.M., S.M.), University of Antwerp, Antwerp, Belgium
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7
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Deng L, Cai Y, Zhang W, Yang W, Gao B, Liu H. Pathway-Guided Deep Neural Network toward Interpretable and Predictive Modeling of Drug Sensitivity. J Chem Inf Model 2020; 60:4497-4505. [PMID: 32804489 DOI: 10.1021/acs.jcim.0c00331] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
To efficiently save cost and reduce risk in drug research and development, there is a pressing demand to develop in silico methods to predict drug sensitivity to cancer cells. With the exponentially increasing number of multi-omics data derived from high-throughput techniques, machine learning-based methods have been applied to the prediction of drug sensitivities. However, these methods have drawbacks either in the interpretability of the mechanism of drug action or limited performance in modeling drug sensitivity. In this paper, we presented a pathway-guided deep neural network (DNN) model to predict the drug sensitivity in cancer cells. Biological pathways describe a group of molecules in a cell that collaborates to control various biological functions like cell proliferation and death, thereby abnormal function of pathways can result in disease. To take advantage of the excellent predictive ability of DNN and the biological knowledge of pathways, we reshaped the canonical DNN structure by incorporating a layer of pathway nodes and their connections to input gene nodes, which makes the DNN model more interpretable and predictive compared to canonical DNN. We have conducted extensive performance evaluations on multiple independent drug sensitivity data sets and demonstrated that our model significantly outperformed the canonical DNN model and eight other classical regression models. Most importantly, we observed a remarkable activity decrease in disease-related pathway nodes during forward propagation upon inputs of drug targets, which implicitly corresponds to the inhibition effect of disease-related pathways induced by drug treatment on cancer cells. Our empirical experiments showed that our method achieves pharmacological interpretability and predictive ability in modeling drug sensitivity in cancer cells. The web server, the processed data sets, and source codes for reproducing our work are available at http://pathdnn.denglab.org.
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Affiliation(s)
- Lei Deng
- School of Computer Science and Engineering, Central South University, 410075 Changsha, China
| | - Yideng Cai
- School of Computer Science and Engineering, Central South University, 410075 Changsha, China
| | - Wenhao Zhang
- Aliyun School of Big Data, Changzhou University, 213164 Changzhou, China
| | - Wenyi Yang
- School of Computer Science and Engineering, Central South University, 410075 Changsha, China
| | - Bo Gao
- Department of Rheumatology, The Affiliated Changzhou No.2 People's Hospital of Nanjing Medical University, 213164 Changzhou, China
| | - Hui Liu
- Aliyun School of Big Data, Changzhou University, 213164 Changzhou, China
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Shameer K, Glicksberg BS, Hodos R, Johnson KW, Badgeley MA, Readhead B, Tomlinson MS, O’Connor T, Miotto R, Kidd BA, Chen R, Ma’ayan A, Dudley JT. Systematic analyses of drugs and disease indications in RepurposeDB reveal pharmacological, biological and epidemiological factors influencing drug repositioning. Brief Bioinform 2018; 19:656-678. [PMID: 28200013 PMCID: PMC6192146 DOI: 10.1093/bib/bbw136] [Citation(s) in RCA: 56] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2016] [Revised: 11/29/2016] [Indexed: 12/22/2022] Open
Abstract
Increase in global population and growing disease burden due to the emergence of infectious diseases (Zika virus), multidrug-resistant pathogens, drug-resistant cancers (cisplatin-resistant ovarian cancer) and chronic diseases (arterial hypertension) necessitate effective therapies to improve health outcomes. However, the rapid increase in drug development cost demands innovative and sustainable drug discovery approaches. Drug repositioning, the discovery of new or improved therapies by reevaluation of approved or investigational compounds, solves a significant gap in the public health setting and improves the productivity of drug development. As the number of drug repurposing investigations increases, a new opportunity has emerged to understand factors driving drug repositioning through systematic analyses of drugs, drug targets and associated disease indications. However, such analyses have so far been hampered by the lack of a centralized knowledgebase, benchmarking data sets and reporting standards. To address these knowledge and clinical needs, here, we present RepurposeDB, a collection of repurposed drugs, drug targets and diseases, which was assembled, indexed and annotated from public data. RepurposeDB combines information on 253 drugs [small molecules (74.30%) and protein drugs (25.29%)] and 1125 diseases. Using RepurposeDB data, we identified pharmacological (chemical descriptors, physicochemical features and absorption, distribution, metabolism, excretion and toxicity properties), biological (protein domains, functional process, molecular mechanisms and pathway cross talks) and epidemiological (shared genetic architectures, disease comorbidities and clinical phenotype similarities) factors mediating drug repositioning. Collectively, RepurposeDB is developed as the reference database for drug repositioning investigations. The pharmacological, biological and epidemiological principles of drug repositioning identified from the meta-analyses could augment therapeutic development.
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Affiliation(s)
- Khader Shameer
- Institute of Next Generation Healthcare, Mount Sinai Health System, New York,
NY, USA
| | - Benjamin S Glicksberg
- Icahn School of Medicine at Mount Sinai, Mount Sinai Health System, New York,
NY, USA
| | - Rachel Hodos
- Icahn School of Medicine at Mount Sinai, Mount Sinai Health System, New York,
NY, USA
- New York University, New York, NY, USA
| | - Kipp W Johnson
- Icahn School of Medicine at Mount Sinai, Mount Sinai Health System, New York,
NY, USA
| | - Marcus A Badgeley
- Icahn School of Medicine at Mount Sinai, Mount Sinai Health System, New York,
NY, USA
| | - Ben Readhead
- Institute of Next Generation Healthcare, Mount Sinai Health System, New York,
NY, USA
| | - Max S Tomlinson
- Institute of Next Generation Healthcare, Mount Sinai Health System, New York,
NY, USA
| | | | - Riccardo Miotto
- Institute of Next Generation Healthcare, Mount Sinai Health System, New York,
NY, USA
| | - Brian A Kidd
- Institute of Next Generation Healthcare, Mount Sinai Health System, New York,
NY, USA
| | - Rong Chen
- Clinical Genome Informatics, Icahn Institute of Genetics and Multiscale
Biology, Mount Sinai Health System, New York, NY
| | - Avi Ma’ayan
- Mount Sinai Center for Bioinformatics, Mount Sinai Health System, New York,
NY
| | - Joel T Dudley
- Institute of Next Generation Healthcare, Mount Sinai Health System, New York,
NY, USA
- Department of Genetics and Genomic Sciences, Mount Sinai Health System, New
York, NY, USA
- Department of Population Health Science and Policy, Mount Sinai Health System,
New York, NY, USA
- Director of Biomedical Informatics, Icahn School of Medicine at Mount Sinai,
Mount Sinai Health System, New York, NY
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9
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Soufan O, Ba-Alawi W, Magana-Mora A, Essack M, Bajic VB. DPubChem: a web tool for QSAR modeling and high-throughput virtual screening. Sci Rep 2018; 8:9110. [PMID: 29904147 PMCID: PMC6002400 DOI: 10.1038/s41598-018-27495-x] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2018] [Accepted: 05/31/2018] [Indexed: 01/01/2023] Open
Abstract
High-throughput screening (HTS) performs the experimental testing of a large number of chemical compounds aiming to identify those active in the considered assay. Alternatively, faster and cheaper methods of large-scale virtual screening are performed computationally through quantitative structure-activity relationship (QSAR) models. However, the vast amount of available HTS heterogeneous data and the imbalanced ratio of active to inactive compounds in an assay make this a challenging problem. Although different QSAR models have been proposed, they have certain limitations, e.g., high false positive rates, complicated user interface, and limited utilization options. Therefore, we developed DPubChem, a novel web tool for deriving QSAR models that implement the state-of-the-art machine-learning techniques to enhance the precision of the models and enable efficient analyses of experiments from PubChem BioAssay database. DPubChem also has a simple interface that provides various options to users. DPubChem predicted active compounds for 300 datasets with an average geometric mean and F1 score of 76.68% and 76.53%, respectively. Furthermore, DPubChem builds interaction networks that highlight novel predicted links between chemical compounds and biological assays. Using such a network, DPubChem successfully suggested a novel drug for the Niemann-Pick type C disease. DPubChem is freely available at www.cbrc.kaust.edu.sa/dpubchem .
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Affiliation(s)
- Othman Soufan
- Institute of Parasitology, McGill University, Montreal, QC, H9X 3V9, Canada
| | - Wail Ba-Alawi
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON, M5G 1L7, Canada.,Department of Medical Biophysics, University of Toronto, Toronto, ON, M5G 1L7, Canada
| | - Arturo Magana-Mora
- Computational Bio Big-Data Open Innovation Laboratory (CBBD-OIL), National Institute of Advanced Industrial Science and Technology (AIST), Tokyo, 135-0064, Japan
| | - Magbubah Essack
- Computational Bioscience Research Center, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
| | - Vladimir B Bajic
- Computational Bioscience Research Center, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia.
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10
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Differential gene expression in disease: a comparison between high-throughput studies and the literature. BMC Med Genomics 2017; 10:59. [PMID: 29020950 PMCID: PMC5637346 DOI: 10.1186/s12920-017-0293-y] [Citation(s) in RCA: 38] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2017] [Accepted: 10/02/2017] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Differential gene expression is important to understand the biological differences between healthy and diseased states. Two common sources of differential gene expression data are microarray studies and the biomedical literature. METHODS With the aid of text mining and gene expression analysis we have examined the comparative properties of these two sources of differential gene expression data. RESULTS The literature shows a preference for reporting genes associated to higher fold changes in microarray data, rather than genes that are simply significantly differentially expressed. Thus, the resemblance between the literature and microarray data increases when the fold-change threshold for microarray data is increased. Moreover, the literature has a reporting preference for differentially expressed genes that (1) are overexpressed rather than underexpressed; (2) are overexpressed in multiple diseases; and (3) are popular in the biomedical literature at large. Additionally, the degree to which diseases are similar depends on whether microarray data or the literature is used to compare them. Finally, vaguely-qualified reports of differential expression magnitudes in the literature have only small correlation with microarray fold-change data. CONCLUSIONS Reporting biases of differential gene expression in the literature can be affecting our appreciation of disease biology and of the degree of similarity that actually exists between different diseases.
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11
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Bioinformatics in translational drug discovery. Biosci Rep 2017; 37:BSR20160180. [PMID: 28487472 PMCID: PMC6448364 DOI: 10.1042/bsr20160180] [Citation(s) in RCA: 50] [Impact Index Per Article: 7.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2016] [Revised: 05/04/2017] [Accepted: 05/08/2017] [Indexed: 12/31/2022] Open
Abstract
Bioinformatics approaches are becoming ever more essential in translational drug discovery both in academia and within the pharmaceutical industry. Computational exploitation of the increasing volumes of data generated during all phases of drug discovery is enabling key challenges of the process to be addressed. Here, we highlight some of the areas in which bioinformatics resources and methods are being developed to support the drug discovery pipeline. These include the creation of large data warehouses, bioinformatics algorithms to analyse 'big data' that identify novel drug targets and/or biomarkers, programs to assess the tractability of targets, and prediction of repositioning opportunities that use licensed drugs to treat additional indications.
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12
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Matsui-Hasumi A, Sato Y, Uto-Konomi A, Yamashita S, Uehori J, Yoshimura A, Yamashita M, Asahara H, Suzuki S, Kubo M. E3 ubiquitin ligases SIAH1/2 regulate hypoxia-inducible factor-1 (HIF-1)-mediated Th17 cell differentiation. Int Immunol 2017; 29:133-143. [DOI: 10.1093/intimm/dxx014] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2017] [Accepted: 03/16/2017] [Indexed: 12/11/2022] Open
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13
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Aliper A, Plis S, Artemov A, Ulloa A, Mamoshina P, Zhavoronkov A. Deep Learning Applications for Predicting Pharmacological Properties of Drugs and Drug Repurposing Using Transcriptomic Data. Mol Pharm 2016; 13:2524-30. [PMID: 27200455 DOI: 10.1021/acs.molpharmaceut.6b00248] [Citation(s) in RCA: 264] [Impact Index Per Article: 33.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Deep learning is rapidly advancing many areas of science and technology with multiple success stories in image, text, voice and video recognition, robotics, and autonomous driving. In this paper we demonstrate how deep neural networks (DNN) trained on large transcriptional response data sets can classify various drugs to therapeutic categories solely based on their transcriptional profiles. We used the perturbation samples of 678 drugs across A549, MCF-7, and PC-3 cell lines from the LINCS Project and linked those to 12 therapeutic use categories derived from MeSH. To train the DNN, we utilized both gene level transcriptomic data and transcriptomic data processed using a pathway activation scoring algorithm, for a pooled data set of samples perturbed with different concentrations of the drug for 6 and 24 hours. In both pathway and gene level classification, DNN achieved high classification accuracy and convincingly outperformed the support vector machine (SVM) model on every multiclass classification problem, however, models based on pathway level data performed significantly better. For the first time we demonstrate a deep learning neural net trained on transcriptomic data to recognize pharmacological properties of multiple drugs across different biological systems and conditions. We also propose using deep neural net confusion matrices for drug repositioning. This work is a proof of principle for applying deep learning to drug discovery and development.
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Affiliation(s)
- Alexander Aliper
- Insilico Medicine, ETC, B301, Johns Hopkins University , Baltimore, Maryland 21218, United States
| | - Sergey Plis
- Datalytic Solutions , 1101 Yale Boulevard NE, Albuquerque, New Mexico 87106, United States.,The Mind Research Network , Albuquerque, New Mexico 87106, United States
| | - Artem Artemov
- Insilico Medicine, ETC, B301, Johns Hopkins University , Baltimore, Maryland 21218, United States
| | - Alvaro Ulloa
- The Mind Research Network , Albuquerque, New Mexico 87106, United States
| | - Polina Mamoshina
- Insilico Medicine, ETC, B301, Johns Hopkins University , Baltimore, Maryland 21218, United States
| | - Alex Zhavoronkov
- Insilico Medicine, ETC, B301, Johns Hopkins University , Baltimore, Maryland 21218, United States.,The Biogerontology Research Foundation , Oxford, U.K
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14
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Preston S, Jabbar A, Gasser RB. A perspective on genomic-guided anthelmintic discovery and repurposing using Haemonchus contortus. INFECTION GENETICS AND EVOLUTION 2016; 40:368-373. [DOI: 10.1016/j.meegid.2015.06.029] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/12/2015] [Revised: 06/28/2015] [Accepted: 06/29/2015] [Indexed: 02/02/2023]
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15
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Wu X, Chen X, Dan J, Cao Y, Gao S, Guo Z, Zerbe P, Chai Y, Diao Y, Zhang L. Characterization of anti-leukemia components from Indigo naturalis using comprehensive two-dimensional K562/cell membrane chromatography and in silico target identification. Sci Rep 2016; 6:25491. [PMID: 27150638 PMCID: PMC4858665 DOI: 10.1038/srep25491] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2015] [Accepted: 04/18/2016] [Indexed: 12/30/2022] Open
Abstract
Traditional Chinese Medicine (TCM) has been developed for thousands of years and has formed an integrated theoretical system based on a large amount of clinical practice. However, essential ingredients in TCM herbs have not been fully identified, and their precise mechanisms and targets are not elucidated. In this study, a new strategy combining comprehensive two-dimensional K562/cell membrane chromatographic system and in silico target identification was established to characterize active components from Indigo naturalis, a famous TCM herb that has been widely used for the treatment of leukemia in China, and their targets. Three active components, indirubin, tryptanthrin and isorhamnetin, were successfully characterized and their anti-leukemia effects were validated by cell viability and cell apoptosis assays. Isorhamnetin, with undefined cancer related targets, was selected for in silico target identification. Proto-oncogene tyrosine-protein kinase (Src) was identified as its membrane target and the dissociation constant (Kd) between Src and isorhamnetin was 3.81 μM. Furthermore, anti-leukemia effects of isorhamnetin were mediated by Src through inducing G2/M cell cycle arrest. The results demonstrated that the integrated strategy could efficiently characterize active components in TCM and their targets, which may bring a new light for a better understanding of the complex mechanism of herbal medicines.
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Affiliation(s)
- Xunxun Wu
- School of Biomedical Science, Institute of Molecular Medicine, Huaqiao University, Quanzhou 362021, PR China.,School of Pharmacy, Shanghai Changzheng Hospital, Second Military Medical University, Shanghai 200433, PR China
| | - Xiaofei Chen
- School of Pharmacy, Shanghai Changzheng Hospital, Second Military Medical University, Shanghai 200433, PR China
| | - Jia Dan
- School of Pharmacy, Shanghai Changzheng Hospital, Second Military Medical University, Shanghai 200433, PR China
| | - Yan Cao
- School of Pharmacy, Shanghai Changzheng Hospital, Second Military Medical University, Shanghai 200433, PR China
| | - Shouhong Gao
- School of Pharmacy, Shanghai Changzheng Hospital, Second Military Medical University, Shanghai 200433, PR China
| | - Zhiying Guo
- School of Biomedical Science, Institute of Molecular Medicine, Huaqiao University, Quanzhou 362021, PR China.,School of Pharmacy, Shanghai Changzheng Hospital, Second Military Medical University, Shanghai 200433, PR China
| | - Philipp Zerbe
- Department of Plant Biology, University of California, Davis, CA 95616, USA
| | - Yifeng Chai
- School of Pharmacy, Shanghai Changzheng Hospital, Second Military Medical University, Shanghai 200433, PR China
| | - Yong Diao
- School of Biomedical Science, Institute of Molecular Medicine, Huaqiao University, Quanzhou 362021, PR China
| | - Lei Zhang
- School of Pharmacy, Shanghai Changzheng Hospital, Second Military Medical University, Shanghai 200433, PR China
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16
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Fauteux F, Hill JJ, Jaramillo ML, Pan Y, Phan S, Famili F, O'Connor-McCourt M. Computational selection of antibody-drug conjugate targets for breast cancer. Oncotarget 2016; 7:2555-71. [PMID: 26700623 PMCID: PMC4823055 DOI: 10.18632/oncotarget.6679] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2015] [Accepted: 11/21/2015] [Indexed: 01/03/2023] Open
Abstract
The selection of therapeutic targets is a critical aspect of antibody-drug conjugate research and development. In this study, we applied computational methods to select candidate targets overexpressed in three major breast cancer subtypes as compared with a range of vital organs and tissues. Microarray data corresponding to over 8,000 tissue samples were collected from the public domain. Breast cancer samples were classified into molecular subtypes using an iterative ensemble approach combining six classification algorithms and three feature selection techniques, including a novel kernel density-based method. This feature selection method was used in conjunction with differential expression and subcellular localization information to assemble a primary list of targets. A total of 50 cell membrane targets were identified, including one target for which an antibody-drug conjugate is in clinical use, and six targets for which antibody-drug conjugates are in clinical trials for the treatment of breast cancer and other solid tumors. In addition, 50 extracellular proteins were identified as potential targets for non-internalizing strategies and alternative modalities. Candidate targets linked with the epithelial-to-mesenchymal transition were identified by analyzing differential gene expression in epithelial and mesenchymal tumor-derived cell lines. Overall, these results show that mining human gene expression data has the power to select and prioritize breast cancer antibody-drug conjugate targets, and the potential to lead to new and more effective cancer therapeutics.
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Affiliation(s)
- François Fauteux
- Information and Communication Technologies, National Research Council Canada, Ottawa, Ontario, Canada
| | - Jennifer J. Hill
- Human Health Therapeutics, National Research Council Canada, Ottawa, Ontario, Canada
| | - Maria L. Jaramillo
- Human Health Therapeutics, National Research Council Canada, Montreal, Quebec, Canada
| | - Youlian Pan
- Information and Communication Technologies, National Research Council Canada, Ottawa, Ontario, Canada
| | - Sieu Phan
- Information and Communication Technologies, National Research Council Canada, Ottawa, Ontario, Canada
| | - Fazel Famili
- Information and Communication Technologies, National Research Council Canada, Ottawa, Ontario, Canada
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Rodriguez-Esteban R. Biocuration with insufficient resources and fixed timelines. DATABASE-THE JOURNAL OF BIOLOGICAL DATABASES AND CURATION 2015; 2015:bav116. [PMID: 26708987 PMCID: PMC4691339 DOI: 10.1093/database/bav116] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/11/2015] [Accepted: 11/17/2015] [Indexed: 11/14/2022]
Abstract
Biological curation, or biocuration, is often studied from the perspective of creating and maintaining databases that have the goal of mapping and tracking certain areas of biology. However, much biocuration is, in fact, dedicated to finite and time-limited projects in which insufficient resources demand trade-offs. This typically more ephemeral type of curation is nonetheless of importance in biomedical research. Here, I propose a framework to understand such restricted curation projects from the point of view of return on curation (ROC), value, efficiency and productivity. Moreover, I suggest general strategies to optimize these curation efforts, such as the ‘multiple strategies’ approach, as well as a metric called overhead that can be used in the context of managing curation resources.
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Affiliation(s)
- Raul Rodriguez-Esteban
- Roche Pharmaceutical Research and Early Development, pRED Informatics, Roche Innovation Center Basel, Basel 4070, Switzerland
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18
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Kell DB, Goodacre R. Metabolomics and systems pharmacology: why and how to model the human metabolic network for drug discovery. Drug Discov Today 2014; 19:171-82. [PMID: 23892182 PMCID: PMC3989035 DOI: 10.1016/j.drudis.2013.07.014] [Citation(s) in RCA: 111] [Impact Index Per Article: 11.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2013] [Revised: 07/03/2013] [Accepted: 07/16/2013] [Indexed: 02/06/2023]
Abstract
Metabolism represents the 'sharp end' of systems biology, because changes in metabolite concentrations are necessarily amplified relative to changes in the transcriptome, proteome and enzyme activities, which can be modulated by drugs. To understand such behaviour, we therefore need (and increasingly have) reliable consensus (community) models of the human metabolic network that include the important transporters. Small molecule 'drug' transporters are in fact metabolite transporters, because drugs bear structural similarities to metabolites known from the network reconstructions and from measurements of the metabolome. Recon2 represents the present state-of-the-art human metabolic network reconstruction; it can predict inter alia: (i) the effects of inborn errors of metabolism; (ii) which metabolites are exometabolites, and (iii) how metabolism varies between tissues and cellular compartments. However, even these qualitative network models are not yet complete. As our understanding improves so do we recognise more clearly the need for a systems (poly)pharmacology.
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Affiliation(s)
- Douglas B Kell
- School of Chemistry and Manchester Institute of Biotechnology, The University of Manchester, 131 Princess Street, Manchester M1 7DN, UK.
| | - Royston Goodacre
- School of Chemistry and Manchester Institute of Biotechnology, The University of Manchester, 131 Princess Street, Manchester M1 7DN, UK
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19
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Identifying tinnitus-related genes based on a side-effect network analysis. CPT-PHARMACOMETRICS & SYSTEMS PHARMACOLOGY 2014; 3:e97. [PMID: 24477090 PMCID: PMC3910011 DOI: 10.1038/psp.2013.75] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/07/2013] [Accepted: 11/24/2013] [Indexed: 12/13/2022]
Abstract
Tinnitus, phantom sound perception, is a worldwide highly prevalent disorder for which no clear underlying pathology has been established and for which no approved drug is on the market. Thus, there is an urgent need for new approaches to understand this condition. We used a network pharmacology side-effect analysis to search for genes that are involved in tinnitus generation. We analyzed a network of 1,313 drug–target pairs, based on 275 compounds that elicit tinnitus as side effect and their targets reported in databases, and used a quantitative score to identify emergent significant targets that were more common than expected at random. Cyclooxigenase 1 and 2 were significant, which validates our approach, since salicylate is a known tinnitus generator. More importantly, we predict previously unknown tinnitus-related targets. The present results have important implications toward understanding tinnitus pathophysiology and might pave the way toward the design of novel pharmacotherapies.
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20
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Crommelin DJA, Sindelar RD, Meibohm B. Genomics, Other “Omic” Technologies, Personalized Medicine, and Additional Biotechnology-Related Techniques. PHARMACEUTICAL BIOTECHNOLOGY 2013. [PMCID: PMC7122419 DOI: 10.1007/978-1-4614-6486-0_8] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/02/2023]
Abstract
The products resulting for biotechnologies continue to grow at an exponential rate, and the expectations are that an even greater percentage of drug development will be in the area of the biologics. In 2011, worldwide there were over 800 new biotech drugs and treatments in development including 23 antisense, 64 cell therapy, 50 gene therapy, 300 monoclonal antibodies, 78 recombinant proteins, and 298 vaccines (PhRMA 2012). Pharmaceutical biotechnology techniques are at the core of most methodologies used today for drug discovery and development of both biologics and small molecules. While recombinant DNA technology and hybridoma techniques were the major methods utilized in pharmaceutical biotechnology through most of its historical timeline, our ever-widening understanding of human cellular function and disease processes and a wealth of additional and innovative biotechnologies have been, and will continue to be, developed in order to harvest the information found in the human genome. These technological advances will provide a better understanding of the relationship between genetics and biological function, unravel the underlying causes of disease, explore the association of genomic variation and drug response, enhance pharmaceutical research, and fuel the discovery and development of new and novel biopharmaceuticals. These revolutionary technologies and additional biotechnology-related techniques are improving the very competitive and costly process of drug development of new medicinal agents, diagnostics, and medical devices. Some of the technologies and techniques described in this chapter are both well established and commonly used applications of biotechnology producing potential therapeutic products now in development including clinical trials. New techniques are emerging at a rapid and unprecedented pace and their full impact on the future of molecular medicine has yet to be imagined.
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Affiliation(s)
- Daan J. A. Crommelin
- Department of Pharmaceutical Sciences, Utrecht University, Utrecht, Utrecht The Netherlands
| | - Robert D. Sindelar
- Department of Pharmaceutical Sciences and Department of Medicine, The University of British Columbia, Vancouver, British Columbia Canada
| | - Bernd Meibohm
- Department of Pharmaceutical Sciences, University of Tennessee Health Science Center, College of Pharmacy, Memphis, Tennessee USA
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21
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Abstract
BACKGROUND One of the primary pillars of drug discovery is the drug target, its relationship to both the drugs designed against it and the biological processes in which it is involved. Here we review the informatics approaches required to build a complete catalogue of known drug targets. OBJECTIVE Using Pfizer's internal target database as a narrative, we review the steps involved in the construction of an integrated, enterprise target-informatics system. We consider how compiling the drug target universe requires integration across several resources such as competitor intelligence and pharmacological activity databases, as well as input from techniques such as text-mining. In particular, we address data standards and the complexities of representing targets in a structured ontology as well as opportunities for future development. CONCLUSION Drug target-orientated databases address important areas of drug discovery such as chemogenomics, drug/candidate repurposing and business intelligence. As research in industry and academia drives continued expansion of the druggable genome, it is crucial that such systems be maintained to provide an accurate picture of the landscape. This power of this information stretches beyond drug discovery and into the wider scientific community where small molecule tool compounds can enable the dissection of complex cellular pathways.
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Affiliation(s)
- Lee Harland
- Pfizer Regenerative Medicine, Granta Park, Cambridge, UK +44 1304641575 ;
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22
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Abstract
The molecular pathways that govern human disease consist of molecular circuits that coalesce into complex, overlapping networks. These network pathways are presumably regulated in a coordinated fashion, but such regulation has been difficult to decipher using only reductionistic principles. The emerging paradigm of "network medicine" proposes to utilize insights garnered from network topology (eg, the static position of molecules in relation to their neighbors) as well as network dynamics (eg, the unique flux of information through the network) to understand better the pathogenic behavior of complex molecular interconnections that traditional methods fail to recognize. As methodologies evolve, network medicine has the potential to capture the molecular complexity of human disease while offering computational methods to discern how such complexity controls disease manifestations, prognosis, and therapy. This review introduces the fundamental concepts of network medicine and explores the feasibility and potential impact of network-based methods for predicting individual manifestations of human disease and designing rational therapies. Wherever possible, we emphasize the application of these principles to cardiovascular disease.
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Affiliation(s)
- Stephen Y Chan
- Division of Cardiovascular Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA
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Wu CC, D'Argenio D, Asgharzadeh S, Triche T. TARGETgene: a tool for identification of potential therapeutic targets in cancer. PLoS One 2012; 7:e43305. [PMID: 22952662 PMCID: PMC3432038 DOI: 10.1371/journal.pone.0043305] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2012] [Accepted: 07/18/2012] [Indexed: 11/19/2022] Open
Abstract
The vast array of in silico resources and data of high throughput profiling currently available in life sciences research offer the possibility of aiding cancer gene and drug discovery process. Here we propose to take advantage of these resources to develop a tool, TARGETgene, for efficiently identifying mutation drivers, possible therapeutic targets, and drug candidates in cancer. The simple graphical user interface enables rapid, intuitive mapping and analysis at the systems level. Users can find, select, and explore identified target genes and compounds of interest (e.g., novel cancer genes and their enriched biological processes), and validate predictions using user-defined benchmark genes (e.g., target genes detected in RNAi screens) and curated cancer genes via TARGETgene. The high-level capabilities of TARGETgene are also demonstrated through two applications in this paper. The predictions in these two applications were then satisfactorily validated by several ways, including known cancer genes, results of RNAi screens, gene function annotations, and target genes of drugs that have been used or in clinical trial in cancer treatments. TARGETgene is freely available from the Biomedical Simulations Resource web site (http://bmsr.usc.edu/Software/TARGET/TARGET.html).
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Affiliation(s)
- Chia-Chin Wu
- Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, Texas, United States of America.
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25
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Lee HS, Bae T, Lee JH, Kim DG, Oh YS, Jang Y, Kim JT, Lee JJ, Innocenti A, Supuran CT, Chen L, Rho K, Kim S. Rational drug repositioning guided by an integrated pharmacological network of protein, disease and drug. BMC SYSTEMS BIOLOGY 2012; 6:80. [PMID: 22748168 PMCID: PMC3443412 DOI: 10.1186/1752-0509-6-80] [Citation(s) in RCA: 64] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/05/2012] [Accepted: 05/31/2012] [Indexed: 12/21/2022]
Abstract
Background The process of drug discovery and development is time-consuming and costly, and the probability of success is low. Therefore, there is rising interest in repositioning existing drugs for new medical indications. When successful, this process reduces the risk of failure and costs associated with de novo drug development. However, in many cases, new indications of existing drugs have been found serendipitously. Thus there is a clear need for establishment of rational methods for drug repositioning. Results In this study, we have established a database we call “PharmDB” which integrates data associated with disease indications, drug development, and associated proteins, and known interactions extracted from various established databases. To explore linkages of known drugs to diseases of interest from within PharmDB, we designed the Shared Neighborhood Scoring (SNS) algorithm. And to facilitate exploration of tripartite (Drug-Protein-Disease) network, we developed a graphical data visualization software program called phExplorer, which allows us to browse PharmDB data in an interactive and dynamic manner. We validated this knowledge-based tool kit, by identifying a potential application of a hypertension drug, benzthiazide (TBZT), to induce lung cancer cell death. Conclusions By combining PharmDB, an integrated tripartite database, with Shared Neighborhood Scoring (SNS) algorithm, we developed a knowledge platform to rationally identify new indications for known FDA approved drugs, which can be customized to specific projects using manual curation. The data in PharmDB is open access and can be easily explored with phExplorer and accessed via BioMart web service (http://www.i-pharm.org/, http://biomart.i-pharm.org/).
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Affiliation(s)
- Hee Sook Lee
- Medicinal Bioconvergence Research Center, College of Pharmacy, Seoul National University, Seoul, Korea
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van Mulligen EM, Fourrier-Reglat A, Gurwitz D, Molokhia M, Nieto A, Trifiro G, Kors JA, Furlong LI. The EU-ADR corpus: annotated drugs, diseases, targets, and their relationships. J Biomed Inform 2012; 45:879-84. [PMID: 22554700 DOI: 10.1016/j.jbi.2012.04.004] [Citation(s) in RCA: 55] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2011] [Revised: 02/02/2012] [Accepted: 04/11/2012] [Indexed: 11/25/2022]
Abstract
Corpora with specific entities and relationships annotated are essential to train and evaluate text-mining systems that are developed to extract specific structured information from a large corpus. In this paper we describe an approach where a named-entity recognition system produces a first annotation and annotators revise this annotation using a web-based interface. The agreement figures achieved show that the inter-annotator agreement is much better than the agreement with the system provided annotations. The corpus has been annotated for drugs, disorders, genes and their inter-relationships. For each of the drug-disorder, drug-target, and target-disorder relations three experts have annotated a set of 100 abstracts. These annotated relationships will be used to train and evaluate text-mining software to capture these relationships in texts.
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Affiliation(s)
- Erik M van Mulligen
- Dept. of Medical Informatics, Erasmus University Medical Center, Rotterdam, The Netherlands.
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Elgoyhen AB, Langguth B, Vanneste S, De Ridder D. Tinnitus: network pathophysiology-network pharmacology. Front Syst Neurosci 2012; 6:1. [PMID: 22291622 PMCID: PMC3265967 DOI: 10.3389/fnsys.2012.00001] [Citation(s) in RCA: 42] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2011] [Accepted: 01/11/2012] [Indexed: 01/12/2023] Open
Abstract
Tinnitus, the phantom perception of sound, is a prevalent disorder. One in 10 adults has clinically significant subjective tinnitus, and for one in 100, tinnitus severely affects their quality of life. Despite the significant unmet clinical need for a safe and effective drug targeting tinnitus relief, there is currently not a single Food and Drug Administration (FDA)-approved drug on the market. The search for drugs that target tinnitus is hampered by the lack of a deep knowledge of the underlying neural substrates of this pathology. Recent studies are increasingly demonstrating that, as described for other central nervous system (CNS) disorders, tinnitus is a pathology of brain networks. The application of graph theoretical analysis to brain networks has recently provided new information concerning their topology, their robustness and their vulnerability to attacks. Moreover, the philosophy behind drug design and pharmacotherapy in CNS pathologies is changing from that of "magic bullets" that target individual chemoreceptors or "disease-causing genes" into that of "magic shotguns," "promiscuous" or "dirty drugs" that target "disease-causing networks," also known as network pharmacology. In the present work we provide some insight into how this knowledge could be applied to tinnitus pathophysiology and pharmacotherapy.
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Affiliation(s)
- Ana B. Elgoyhen
- Instituto de Investigaciones en Ingeniería Genética y Biología Molecular, Consejo Nacional de Investigaciones Científicas y Técnicas and Tercera Cátedra de Farmacología, Facultad de Medicina, Universidad de Buenos AiresBuenos Aires, Argentina
| | - Berthold Langguth
- Interdisciplinary Tinnitus Clinic, Departments of Psychiatry and Psychotherapy, University of RegensburgRegensburg, Germany
| | - Sven Vanneste
- TRI, BRAIN and Department of Neurosurgery, University Hospital AntwerpEdegem, Belgium
| | - Dirk De Ridder
- TRI, BRAIN and Department of Neurosurgery, University Hospital AntwerpEdegem, Belgium
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AbdulHameed MDM, Chaudhury S, Singh N, Sun H, Wallqvist A, Tawa GJ. Exploring polypharmacology using a ROCS-based target fishing approach. J Chem Inf Model 2012; 52:492-505. [PMID: 22196353 DOI: 10.1021/ci2003544] [Citation(s) in RCA: 70] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Abstract
Polypharmacology has emerged as a new theme in drug discovery. In this paper, we studied polypharmacology using a ligand-based target fishing (LBTF) protocol. To implement the protocol, we first generated a chemogenomic database that links individual protein targets with a specified set of drugs or target representatives. Target profiles were then generated for a given query molecule by computing maximal shape/chemistry overlap between the query molecule and the drug sets assigned to each protein target. The overlap was computed using the program ROCS (Rapid Overlay of Chemical Structures). We validated this approach using the Directory of Useful Decoys (DUD). DUD contains 2950 active compounds, each with 36 property-matched decoys, against 40 protein targets. We chose a set of known drugs to represent each DUD target, and we carried out ligand-based virtual screens using data sets of DUD actives seeded into DUD decoys for each target. We computed Receiver Operator Characteristic (ROC) curves and associated area under the curve (AUC) values. For the majority of targets studied, the AUC values were significantly better than for the case of a random selection of compounds. In a second test, the method successfully identified off-targets for drugs such as rimantadine, propranolol, and domperidone that were consistent with those identified by recent experiments. The results from our ROCS-based target fishing approach are promising and have potential application in drug repurposing for single and multiple targets, identifying targets for orphan compounds, and adverse effect prediction.
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Affiliation(s)
- Mohamed Diwan M AbdulHameed
- Biotechnology High Performance Computing Software Applications Institute, Telemedicine and Advanced Technology Research Center, U.S. Army Medical Research and Materiel Command, Fort Detrick, Maryland 21702, USA.
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Yamada R, Cao X, Butkevich AN, Millard M, Odde S, Mordwinkin N, Gundla R, Zandi E, Louie SG, Petasis NA, Neamati N. Discovery and Preclinical Evaluation of a Novel Class of Cytotoxic Propynoic Acid Carbamoyl Methyl Amides (PACMAs). J Med Chem 2011; 54:2902-14. [DOI: 10.1021/jm101655d] [Citation(s) in RCA: 23] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2023]
Affiliation(s)
- Roppei Yamada
- Department of Pharmacology and Pharmaceutical Sciences, School of Pharmacy, ¶Department of Chemistry and Loker Hydrocarbon Research Institute, USC College, §Department of Clinical Pharmacy and Pharmaceutical Economics & Policy, School of Pharmacy, #Department of Molecular Microbiology and Immunology, Keck School of Medicine, and ‡Norris Comprehensive Cancer Center, Keck School of Medicine, University of Southern California, Los Angeles, California, 90089
| | - Xuefei Cao
- Department of Pharmacology and Pharmaceutical Sciences, School of Pharmacy, ¶Department of Chemistry and Loker Hydrocarbon Research Institute, USC College, §Department of Clinical Pharmacy and Pharmaceutical Economics & Policy, School of Pharmacy, #Department of Molecular Microbiology and Immunology, Keck School of Medicine, and ‡Norris Comprehensive Cancer Center, Keck School of Medicine, University of Southern California, Los Angeles, California, 90089
| | - Alexey N. Butkevich
- Department of Pharmacology and Pharmaceutical Sciences, School of Pharmacy, ¶Department of Chemistry and Loker Hydrocarbon Research Institute, USC College, §Department of Clinical Pharmacy and Pharmaceutical Economics & Policy, School of Pharmacy, #Department of Molecular Microbiology and Immunology, Keck School of Medicine, and ‡Norris Comprehensive Cancer Center, Keck School of Medicine, University of Southern California, Los Angeles, California, 90089
| | - Melissa Millard
- Department of Pharmacology and Pharmaceutical Sciences, School of Pharmacy, ¶Department of Chemistry and Loker Hydrocarbon Research Institute, USC College, §Department of Clinical Pharmacy and Pharmaceutical Economics & Policy, School of Pharmacy, #Department of Molecular Microbiology and Immunology, Keck School of Medicine, and ‡Norris Comprehensive Cancer Center, Keck School of Medicine, University of Southern California, Los Angeles, California, 90089
| | - Srinivas Odde
- Department of Pharmacology and Pharmaceutical Sciences, School of Pharmacy, ¶Department of Chemistry and Loker Hydrocarbon Research Institute, USC College, §Department of Clinical Pharmacy and Pharmaceutical Economics & Policy, School of Pharmacy, #Department of Molecular Microbiology and Immunology, Keck School of Medicine, and ‡Norris Comprehensive Cancer Center, Keck School of Medicine, University of Southern California, Los Angeles, California, 90089
| | - Nick Mordwinkin
- Department of Pharmacology and Pharmaceutical Sciences, School of Pharmacy, ¶Department of Chemistry and Loker Hydrocarbon Research Institute, USC College, §Department of Clinical Pharmacy and Pharmaceutical Economics & Policy, School of Pharmacy, #Department of Molecular Microbiology and Immunology, Keck School of Medicine, and ‡Norris Comprehensive Cancer Center, Keck School of Medicine, University of Southern California, Los Angeles, California, 90089
| | - Rambabu Gundla
- Department of Pharmacology and Pharmaceutical Sciences, School of Pharmacy, ¶Department of Chemistry and Loker Hydrocarbon Research Institute, USC College, §Department of Clinical Pharmacy and Pharmaceutical Economics & Policy, School of Pharmacy, #Department of Molecular Microbiology and Immunology, Keck School of Medicine, and ‡Norris Comprehensive Cancer Center, Keck School of Medicine, University of Southern California, Los Angeles, California, 90089
| | - Ebrahim Zandi
- Department of Pharmacology and Pharmaceutical Sciences, School of Pharmacy, ¶Department of Chemistry and Loker Hydrocarbon Research Institute, USC College, §Department of Clinical Pharmacy and Pharmaceutical Economics & Policy, School of Pharmacy, #Department of Molecular Microbiology and Immunology, Keck School of Medicine, and ‡Norris Comprehensive Cancer Center, Keck School of Medicine, University of Southern California, Los Angeles, California, 90089
| | - Stan G. Louie
- Department of Pharmacology and Pharmaceutical Sciences, School of Pharmacy, ¶Department of Chemistry and Loker Hydrocarbon Research Institute, USC College, §Department of Clinical Pharmacy and Pharmaceutical Economics & Policy, School of Pharmacy, #Department of Molecular Microbiology and Immunology, Keck School of Medicine, and ‡Norris Comprehensive Cancer Center, Keck School of Medicine, University of Southern California, Los Angeles, California, 90089
| | - Nicos A. Petasis
- Department of Pharmacology and Pharmaceutical Sciences, School of Pharmacy, ¶Department of Chemistry and Loker Hydrocarbon Research Institute, USC College, §Department of Clinical Pharmacy and Pharmaceutical Economics & Policy, School of Pharmacy, #Department of Molecular Microbiology and Immunology, Keck School of Medicine, and ‡Norris Comprehensive Cancer Center, Keck School of Medicine, University of Southern California, Los Angeles, California, 90089
| | - Nouri Neamati
- Department of Pharmacology and Pharmaceutical Sciences, School of Pharmacy, ¶Department of Chemistry and Loker Hydrocarbon Research Institute, USC College, §Department of Clinical Pharmacy and Pharmaceutical Economics & Policy, School of Pharmacy, #Department of Molecular Microbiology and Immunology, Keck School of Medicine, and ‡Norris Comprehensive Cancer Center, Keck School of Medicine, University of Southern California, Los Angeles, California, 90089
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Buchan NS, Rajpal DK, Webster Y, Alatorre C, Gudivada RC, Zheng C, Sanseau P, Koehler J. The role of translational bioinformatics in drug discovery. Drug Discov Today 2011; 16:426-34. [PMID: 21402166 DOI: 10.1016/j.drudis.2011.03.002] [Citation(s) in RCA: 37] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2010] [Revised: 01/25/2011] [Accepted: 03/07/2011] [Indexed: 12/11/2022]
Abstract
The application of translational approaches (e.g. from bed to bench and back) is gaining momentum in the pharmaceutical industry. By utilizing the rapidly increasing volume of data at all phases of drug discovery, translational bioinformatics is poised to address some of the key challenges faced by the industry. Indeed, computational analysis of clinical data and patient records has informed decision-making in multiple aspects of drug discovery and development. Here, we review key examples of translational bioinformatics approaches to emphasize its potential to enhance the quality of drug discovery pipelines, reduce attrition rates and, ultimately, lead to more effective treatments.
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Affiliation(s)
- Natalie S Buchan
- GlaxoSmithKline, Computational Biology, Gunnels Wood Road, Stevenage, Hertfordshire SG1 2NY, UK
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Deftereos SN, Andronis C, Friedla EJ, Persidis A, Persidis A. Drug repurposing and adverse event prediction using high-throughput literature analysis. WILEY INTERDISCIPLINARY REVIEWS-SYSTEMS BIOLOGY AND MEDICINE 2011; 3:323-34. [PMID: 21416632 DOI: 10.1002/wsbm.147] [Citation(s) in RCA: 80] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
Abstract
Drug repurposing is the process of using existing drugs in indications other than the ones they were originally designed for. It is an area of significant recent activity due to the mounting costs of traditional drug development and scarcity of new chemical entities brought to the market by bio-pharmaceutical companies. By selecting drugs that already satisfy basic toxicity, ADME and related criteria, drug repurposing promises to deliver significant value at reduced cost and in dramatically shorter time frames than is normally the case for the drug development process. The same process that results in drug repurposing can also be used for the prediction of adverse events of known or novel drugs. The analytics method is based on the description of the mechanism of action of a drug, which is then compared to the molecular mechanisms underlying all known adverse events. This review will focus on those approaches to drug repurposing and adverse event prediction that are based on the biomedical literature. Such approaches typically begin with an analysis of the literature and aim to reveal indirect relationships among seemingly unconnected biomedical entities such as genes, signaling pathways, physiological processes, and diseases. Networks of associations of these entities allow the uncovering of the molecular mechanisms underlying a disease, better understanding of the biological effects of a drug and the evaluation of its benefit/risk profile. In silico results can be tested in relevant cellular and animal models and, eventually, in clinical trials.
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Abstract
There is a critical need for improving the level of chemistry awareness in systems biology. The data and information related to modulation of genes and proteins by small molecules continue to accumulate at the same time as simulation tools in systems biology and whole body physiologically based pharmacokinetics (PBPK) continue to evolve. We called this emerging area at the interface between chemical biology and systems biology systems chemical biology (SCB) (Nat Chem Biol 3: 447-450, 2007).The overarching goal of computational SCB is to develop tools for integrated chemical-biological data acquisition, filtering and processing, by taking into account relevant information related to interactions between proteins and small molecules, possible metabolic transformations of small molecules, as well as associated information related to genes, networks, small molecules, and, where applicable, mutants and variants of those proteins. There is yet an unmet need to develop an integrated in silico pharmacology/systems biology continuum that embeds drug-target-clinical outcome (DTCO) triplets, a capability that is vital to the future of chemical biology, pharmacology, and systems biology. Through the development of the SCB approach, scientists will be able to start addressing, in an integrated simulation environment, questions that make the best use of our ever-growing chemical and biological data repositories at the system-wide level. This chapter reviews some of the major research concepts and describes key components that constitute the emerging area of computational systems chemical biology.
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Affiliation(s)
- Tudor I Oprea
- Department of Biochemistry and Molecular Biology, School of Medicine, University of New Mexico, Albuquerque, NM, USA
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Hopkins AL, Bickerton GR, Carruthers IM, Boyer SK, Rubin H, Overington JP. Rapid analysis of pharmacology for infectious diseases. Curr Top Med Chem 2011; 11:1292-300. [PMID: 21401504 PMCID: PMC3182413 DOI: 10.2174/156802611795429130] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2010] [Revised: 08/03/2010] [Accepted: 11/15/2010] [Indexed: 11/26/2022]
Abstract
Pandemic, epidemic and endemic infectious diseases are united by a common problem: how do we rapidly and cost-effectively identify potential pharmacological interventions to treat infections? Given the large number of emerging and neglected infectious diseases and the fact that they disproportionately afflict the poorest members of the global society, new ways of thinking are required to developed high productivity discovery systems that can be applied to a larger number of pathogens. The growing availability of parasite genome data provides the basis for developing methods to prioritize, a priori, the potential drug target and pharmacological landscape of an infectious disease. Thus the overall objective of infectious disease informatics is to enable the rapid generation of plausible, novel medical hypotheses of testable pharmacological experiments, by uncovering undiscovered relationships in the wealth of biomedical literature and databases that were collected for other purposes. In particular our goal is to identify potential drug targets present in a pathogen genome and prioritize which pharmacological experiments are most likely to discover drug-like lead compounds rapidly against a pathogen (i.e. which specific compounds and drug targets should be screened, in which assays and where they can be sourced). An integral part of the challenge is the development and integration of methods to predict druggability, essentiality, synthetic lethality and polypharmacology in pathogen genomes, while simultaneously integrating the inevitable issues of chemical tractability and the potential for acquired drug resistance from the start.
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Affiliation(s)
- Andrew L Hopkins
- Division of Biological Chemistry and Drug Discovery, College of Life Sciences, University of Dundee, UK.
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Sefcik LS, Wilson JL, Papin JA, Botchwey EA. Harnessing systems biology approaches to engineer functional microvascular networks. TISSUE ENGINEERING PART B-REVIEWS 2010; 16:361-70. [PMID: 20121415 DOI: 10.1089/ten.teb.2009.0611] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/15/2023]
Abstract
Microvascular remodeling is a complex process that includes many cell types and molecular signals. Despite a continued growth in the understanding of signaling pathways involved in the formation and maturation of new blood vessels, approximately half of all compounds entering clinical trials will fail, resulting in the loss of much time, money, and resources. Most pro-angiogenic clinical trials to date have focused on increasing neovascularization via the delivery of a single growth factor or gene. Alternatively, a focus on the concerted regulation of whole networks of genes may lead to greater insight into the underlying physiology since the coordinated response is greater than the sum of its parts. Systems biology offers a comprehensive network view of the processes of angiogenesis and arteriogenesis that might enable the prediction of drug targets and whether or not activation of the targets elicits the desired outcome. Systems biology integrates complex biological data from a variety of experimental sources (-omics) and analyzes how the interactions of the system components can give rise to the function and behavior of that system. This review focuses on how systems biology approaches have been applied to microvascular growth and remodeling, and how network analysis tools can be utilized to aid novel pro-angiogenic drug discovery.
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Affiliation(s)
- Lauren S Sefcik
- Department of Chemical and Biomolecular Engineering, Lafayette College, Easton, Pennsylvania, USA
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Wang C, Jiang W, Li W, Lian B, Chen X, Hua L, Lin H, Li D, Li X, Liu Z. Topological properties of the drug targets regulated by microRNA in human protein–protein interaction network. J Drug Target 2010; 19:354-64. [DOI: 10.3109/1061186x.2010.504261] [Citation(s) in RCA: 32] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022]
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Wu CC, Asgharzadeh S, Triche TJ, D'Argenio DZ. Prediction of human functional genetic networks from heterogeneous data using RVM-based ensemble learning. ACTA ACUST UNITED AC 2010; 26:807-13. [PMID: 20134029 DOI: 10.1093/bioinformatics/btq044] [Citation(s) in RCA: 23] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
MOTIVATION Three major problems confront the construction of a human genetic network from heterogeneous genomics data using kernel-based approaches: definition of a robust gold-standard negative set, large-scale learning and massive missing data values. RESULTS The proposed graph-based approach generates a robust GSN for the training process of genetic network construction. The RVM-based ensemble model that combines AdaBoost and reduced-feature yields improved performance on large-scale learning problems with massive missing values in comparison to Naïve Bayes. CONTACT dargenio@bmsr.usc.edu SUPPLEMENTARY INFORMATION Supplementary material is available at Bioinformatics online.
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Affiliation(s)
- Chia-Chin Wu
- Department of Biomedical Engineering, University of Southern California, Los Angeles, 90089, USA
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Somps CJ, Greene N, Render JA, Aleo MD, Fortner JH, Dykens JA, Phillips G. A current practice for predicting ocular toxicity of systemically delivered drugs. Cutan Ocul Toxicol 2009; 28:1-18. [PMID: 19514919 DOI: 10.1080/15569520802618585] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Abstract
The ability to predict ocular side effects of systemically delivered drugs is an important issue for pharmaceutical companies. Although animal models involving standard clinical ophthalmic examinations and postmortem microscopic examinations of eyes are still used to identify ocular issues, these methods are being supplemented with additional in silico, in vitro, and in vivo techniques to identify potential safety issues and assess risk. The addition of these tests to a development plan for a potential new drug provides the opportunity to save time and money by detecting ocular issues earlier in the program. This review summarizes a current practice for minimizing the potential for systemically administered, new medicines to cause adverse effects in the eye.
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Affiliation(s)
- Chris J Somps
- Drug Safety Research & Development, Pfizer Global R & D, Groton, CT 06340, USA.
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‘OnePoint’ – combining OneNote and SharePoint to facilitate knowledge transfer. Drug Discov Today 2009; 14:845-50. [DOI: 10.1016/j.drudis.2009.06.015] [Citation(s) in RCA: 11] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2009] [Revised: 06/30/2009] [Accepted: 06/30/2009] [Indexed: 11/23/2022]
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Lowering industry firewalls: pre-competitive informatics initiatives in drug discovery. Nat Rev Drug Discov 2009; 8:701-8. [DOI: 10.1038/nrd2944] [Citation(s) in RCA: 72] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
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Chakrabarti D. A methods-based biotechnology course for undergraduates. BIOCHEMISTRY AND MOLECULAR BIOLOGY EDUCATION : A BIMONTHLY PUBLICATION OF THE INTERNATIONAL UNION OF BIOCHEMISTRY AND MOLECULAR BIOLOGY 2009; 37:227-231. [PMID: 21567741 DOI: 10.1002/bmb.20302] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/30/2023]
Abstract
This new course in biotechnology for upper division undergraduates provides a comprehensive overview of the process of drug discovery that is relevant to biopharmaceutical industry. The laboratory exercises train students in both cell-free and cell-based assays. Oral presentations by the students delve into recent progress in drug discovery. Combination of lectures, hands-on experiments, oral presentations, and accurate recording of laboratory data provides students a thorough training in biotechnology that better prepares them for the job market.
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Affiliation(s)
- Debopam Chakrabarti
- Department of Molecular Biology and Microbiology, Burnett School of Biomedical Sciences, University of Central Florida, Orlando, Florida 32826.
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Zhu M, Gao L, Li X, Liu Z, Xu C, Yan Y, Walker E, Jiang W, Su B, Chen X, Lin H. The analysis of the drug–targets based on the topological properties in the human protein–protein interaction network. J Drug Target 2009; 17:524-32. [DOI: 10.1080/10611860903046610] [Citation(s) in RCA: 68] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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Saxena SK, Mishra N, Saxena R. Advances in antiviral drug discovery and development: Part II: Advancements in antiviral drug development. Future Virol 2009. [DOI: 10.2217/fvl.09.1] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Affiliation(s)
- Shailendra K Saxena
- Centre for Cellular & Molecular Biology, Uppal Road, Hyderabad 500 007 (AP), India
| | - Niraj Mishra
- Centre for Cellular & Molecular Biology, Uppal Road, Hyderabad 500 007 (AP), India
| | - Rakhi Saxena
- Centre for Cellular & Molecular Biology, Uppal Road, Hyderabad 500 007 (AP), India
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Janga SC, Tzakos A. Structure and organization of drug-target networks: insights from genomic approaches for drug discovery. MOLECULAR BIOSYSTEMS 2009; 5:1536-48. [DOI: 10.1039/b908147j] [Citation(s) in RCA: 77] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
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Abstract
The dominant paradigm in drug discovery is the concept of designing maximally selective ligands to act on individual drug targets. However, many effective drugs act via modulation of multiple proteins rather than single targets. Advances in systems biology are revealing a phenotypic robustness and a network structure that strongly suggests that exquisitely selective compounds, compared with multitarget drugs, may exhibit lower than desired clinical efficacy. This new appreciation of the role of polypharmacology has significant implications for tackling the two major sources of attrition in drug development--efficacy and toxicity. Integrating network biology and polypharmacology holds the promise of expanding the current opportunity space for druggable targets. However, the rational design of polypharmacology faces considerable challenges in the need for new methods to validate target combinations and optimize multiple structure-activity relationships while maintaining drug-like properties. Advances in these areas are creating the foundation of the next paradigm in drug discovery: network pharmacology.
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Affiliation(s)
- Andrew L Hopkins
- Division of Biological Chemistry and Drug Discovery, College of Life Science, University of Dundee, Dundee, UK.
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Smith LL, Brent RL, Cohen SM, Doerrer NG, Goodman JI, Greim H, Holsapple MP, Lightfoot RM. Predicting Future Human and Environmental Health Challenges: The Health and Environmental Sciences Institute's Scientific Mapping Exercise. Crit Rev Toxicol 2008; 38:817-45. [DOI: 10.1080/10408440802486378] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023]
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Albiston AL, Morton CJ, Ng HL, Pham V, Yeatman HR, Ye S, Fernando RN, De Bundel D, Ascher DB, Mendelsohn FAO, Parker MW, Chai SY. Identification and characterization of a new cognitive enhancer based on inhibition of insulin‐regulated aminopeptidase. FASEB J 2008; 22:4209-17. [DOI: 10.1096/fj.08-112227] [Citation(s) in RCA: 86] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/29/2023]
Affiliation(s)
- Anthony L. Albiston
- Howard Florey Institute, Florey Neurosciences InstitutesParkvilleVictoriaAustralia
| | - Craig J. Morton
- St. Vincent's Institute of Medical ResearchFitzroyVictoriaAustralia
| | - Hooi Ling Ng
- St. Vincent's Institute of Medical ResearchFitzroyVictoriaAustralia
| | - Vi Pham
- Howard Florey Institute, Florey Neurosciences InstitutesParkvilleVictoriaAustralia
| | - Holly R. Yeatman
- Howard Florey Institute, Florey Neurosciences InstitutesParkvilleVictoriaAustralia
| | - Siying Ye
- Howard Florey Institute, Florey Neurosciences InstitutesParkvilleVictoriaAustralia
- Department of PhysiologyDartmouth Medical SchoolHanoverNHUSA
| | - Ruani N. Fernando
- Howard Florey Institute, Florey Neurosciences InstitutesParkvilleVictoriaAustralia
- Division of Molecular Neurobiology, Department of Medical Biochemistry and BiophysicsKarolinska InstituteStockholmSweden
| | - Dimitri De Bundel
- Howard Florey Institute, Florey Neurosciences InstitutesParkvilleVictoriaAustralia
- Research Group of Experimental Phar macology, Department of Pharmaceutical Chemistry, Drug Analysis and Drug InformationVrije UniversityBrusselBrusselsBelgium
| | - David B. Ascher
- St. Vincent's Institute of Medical ResearchFitzroyVictoriaAustralia
| | | | - Michael W. Parker
- Department of Biochemistry and Molecular BiologyBio21 Molecular Science and Biotechnology InstituteParkvilleVictoriaAustralia
- St. Vincent's Institute of Medical ResearchFitzroyVictoriaAustralia
| | - Siew Yeen Chai
- Howard Florey Institute, Florey Neurosciences InstitutesParkvilleVictoriaAustralia
- Centre for NeuroscienceUniversity of MelbourneParkvilleVictoriaAustralia
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Agarwal P, Searls DB. Literature mining in support of drug discovery. Brief Bioinform 2008; 9:479-92. [DOI: 10.1093/bib/bbn035] [Citation(s) in RCA: 56] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
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Rabinowitz JR, Goldsmith MR, Little SB, Pasquinelli MA. Computational molecular modeling for evaluating the toxicity of environmental chemicals: prioritizing bioassay requirements. ENVIRONMENTAL HEALTH PERSPECTIVES 2008; 116:573-7. [PMID: 18470285 PMCID: PMC2367647 DOI: 10.1289/ehp.11077] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/16/2007] [Accepted: 02/01/2008] [Indexed: 05/05/2023]
Abstract
BACKGROUND The human health risk from exposure to environmental chemicals often must be evaluated when relevant elements of the preferred data are unavailable. Therefore, strategies are needed that can predict this information and prioritize the outstanding data requirements for the risk evaluation. Many modes of molecular toxicity require the chemical or one of its biotransformation products to interact with specific biologic macromolecules (i.e., proteins and DNA). Molecular modeling approaches may be adapted to study the interactions of environmental chemicals with biomolecular targets. OBJECTIVE In this commentary we provide an overview of the challenges that arise from applying molecular modeling tools developed and commonly used for pharmaceutical discovery to the problem of predicting the potential toxicities of environmental chemicals. DISCUSSION The use of molecular modeling tools to predict the unintended health and environmental consequences of environmental chemicals differs strategically from the use of the same tools in the pharmaceutical discovery process in terms of the goals and potential applications. It also requires consideration of the greater diversity of chemical space and binding affinity domains than is covered by pharmaceuticals. CONCLUSION Molecular modeling methods offer one of several complementary approaches to evaluate the risk to human health and the environment as a result of exposure to environmental chemicals. These tools can streamline the hazard assessment process by simulating possible modes of action and providing virtual screening tools that can help prioritize bioassay requirements. Tailoring these strategies to the particular challenges presented by environmental chemical interactions make them even more effective.
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Affiliation(s)
| | | | | | - Melissa A. Pasquinelli
- Address correspondence to M.A. Pasquinelli, North Carolina State University, Fiber and Polymer Science/Department of TECS, Campus Box 8301, 2401 Research Dr., Raleigh, NC 27695 USA. Telephone: (919) 515-9426. Fax: (919) 515-6532. E-mail:
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Huang YH, Hoebe K, Sauer K. New therapeutic targets in immune disorders: ItpkB, Orai1 and UNC93B. Expert Opin Ther Targets 2008; 12:391-413. [PMID: 18348677 DOI: 10.1517/14728222.12.4.391] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
BACKGROUND Sequencing of the murine and human genomes has enabled large-scale functional genomics approaches to target identification. This holds the promise of drastically accelerating target discovery. Moreover, by providing an initial validation coincident with target identification, cell based cDNA or small interfering RNA (siRNA) screens and in particular genome-wide in vivo approaches, including forward or reverse genetics and analyses of natural gene polymorphisms, can move the relatively late step of target validation to the beginning of the process, reducing the risk of pursuing targets with little in vivo relevance. OBJECTIVE We critically discuss the value of combining functional genomics with traditional approaches for accelerating target identification and validation. METHODS We evaluate the potentials of inositol (1,4,5)trisphosphate 3-kinase B (ItpkB), Orai1 and UNC93B, three particularly interesting proteins that were recently identified through functional genomics, as targets in immune disorders. RESULTS/CONCLUSION Combining functional genomics with traditional approaches can accelerate target discovery and validation, but requires a follow-up platform that integrates and analyzes all relevant data for assessment of the clinical potential of the growing number of novel targets.
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Affiliation(s)
- Yina H Huang
- The Scripps Research Institute, Department of Immunology, 10550 North Torrey Pines Road, La Jolla, CA 92037, USA
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