51
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Ceddia G, Pinoli P, Ceri S, Masseroli M. Matrix Factorization-based Technique for Drug Repurposing Predictions. IEEE J Biomed Health Inform 2020; 24:3162-3172. [PMID: 32365039 DOI: 10.1109/jbhi.2020.2991763] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Classical drug design methodologies are hugely costly and time-consuming, with approximately 85% of the new proposed molecules failing in the first three phases of the FDA drug approval process. Thus, strategies to find alternative indications for already approved drugs that leverage computational methods are of crucial relevance. We previously demonstrated the efficacy of the Non-negative Matrix Tri-Factorization, a method that allows exploiting both data integration and machine learning, to infer novel indications for approved drugs. In this work, we present an innovative enhancement of the NMTF method that consists of a shortest-path evaluation of drug-protein pairs using the protein-to-protein interaction network. This approach allows inferring novel protein targets that were never considered as drug targets before, increasing the information fed to the NMTF method. Indeed, this novel advance enables the investigation of drug-centric predictions, simultaneously identifying therapeutic classes, protein targets and diseases associated with a particular drug. To test our methodology, we applied the NMTF and shortest-path enhancement methods to an outdated collection of data and compared the predictions against the most updated version, obtaining very good performance, with an Average Precision Score of 0.82. The data enhancement strategy allowed increasing the number of putative protein targets from 3,691 to 15,295, while the predictive performance of the method is slightly increased. Finally, we also validated our top-scored predictions according to the literature, finding relevant confirmation of predicted interactions between drugs and protein targets, as well as of predicted annotations between drugs and both therapeutic classes and diseases.
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52
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Gysi DM, Nowick K. Construction, comparison and evolution of networks in life sciences and other disciplines. J R Soc Interface 2020; 17:20190610. [PMID: 32370689 PMCID: PMC7276545 DOI: 10.1098/rsif.2019.0610] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2019] [Accepted: 04/09/2020] [Indexed: 12/12/2022] Open
Abstract
Network approaches have become pervasive in many research fields. They allow for a more comprehensive understanding of complex relationships between entities as well as their group-level properties and dynamics. Many networks change over time, be it within seconds or millions of years, depending on the nature of the network. Our focus will be on comparative network analyses in life sciences, where deciphering temporal network changes is a core interest of molecular, ecological, neuropsychological and evolutionary biologists. Further, we will take a journey through different disciplines, such as social sciences, finance and computational gastronomy, to present commonalities and differences in how networks change and can be analysed. Finally, we envision how borrowing ideas from these disciplines could enrich the future of life science research.
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Affiliation(s)
- Deisy Morselli Gysi
- Department of Computer Science, Interdisciplinary Center of Bioinformatics, University of Leipzig, 04109 Leipzig, Germany
- Swarm Intelligence and Complex Systems Group, Faculty of Mathematics and Computer Science, University of Leipzig, 04109 Leipzig, Germany
- Center for Complex Networks Research, Northeastern University, 177 Huntington Avenue, Boston, MA 02115, USA
| | - Katja Nowick
- Human Biology Group, Institute for Biology, Faculty of Biology, Chemistry, Pharmacy, Freie Universität Berlin, Königin-Luise-Straβe 1-3, 14195 Berlin, Germany
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Bellera CL, Alberca LN, Sbaraglini ML, Talevi A. In Silico Drug Repositioning for Chagas Disease. Curr Med Chem 2020; 27:662-675. [PMID: 31622200 DOI: 10.2174/0929867326666191016114839] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2017] [Revised: 09/12/2019] [Accepted: 09/23/2019] [Indexed: 12/18/2022]
Abstract
Chagas disease is an infectious tropical disease included within the group of neglected tropical diseases. Though historically endemic to Latin America, it has lately spread to high-income countries due to human migration. At present, there are only two available drugs, nifurtimox and benznidazole, approved for this treatment, both with considerable side-effects (which often result in treatment interruption) and limited efficacy in the chronic stage of the disease in adults. Drug repositioning involves finding novel therapeutic indications for known drugs, including approved, withdrawn, abandoned and investigational drugs. It is today a broadly applied approach to develop innovative medications, since indication shifts are built on existing safety, ADME and manufacturing information, thus greatly shortening development timeframes. Drug repositioning has been signaled as a particularly interesting strategy to search for new therapeutic solutions for neglected and rare conditions, which traditionally present limited commercial interest and are mostly covered by the public sector and not-for-profit initiatives and organizations. Here, we review the applications of computer-aided technologies as systematic approaches to drug repositioning in the field of Chagas disease. In silico screening represents the most explored approach, whereas other rational methods such as network-based and signature-based approximations have still not been applied.
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Affiliation(s)
- Carolina L Bellera
- Laboratory of Bioactive Research and Development (LIDeB), Faculty of Exact Sciences, University of La Plata (UNLP), La Plata, Argentina
| | - Lucas N Alberca
- Laboratory of Bioactive Research and Development (LIDeB), Faculty of Exact Sciences, University of La Plata (UNLP), La Plata, Argentina
| | - María L Sbaraglini
- Laboratory of Bioactive Research and Development (LIDeB), Faculty of Exact Sciences, University of La Plata (UNLP), La Plata, Argentina
| | - Alan Talevi
- Laboratory of Bioactive Research and Development (LIDeB), Faculty of Exact Sciences, University of La Plata (UNLP), La Plata, Argentina
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54
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Hao M, Bryant SH, Wang Y. Open-source chemogenomic data-driven algorithms for predicting drug-target interactions. Brief Bioinform 2020; 20:1465-1474. [PMID: 29420684 DOI: 10.1093/bib/bby010] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2017] [Revised: 01/18/2018] [Indexed: 12/25/2022] Open
Abstract
While novel technologies such as high-throughput screening have advanced together with significant investment by pharmaceutical companies during the past decades, the success rate for drug development has not yet been improved prompting researchers looking for new strategies of drug discovery. Drug repositioning is a potential approach to solve this dilemma. However, experimental identification and validation of potential drug targets encoded by the human genome is both costly and time-consuming. Therefore, effective computational approaches have been proposed to facilitate drug repositioning, which have proved to be successful in drug discovery. Doubtlessly, the availability of open-accessible data from basic chemical biology research and the success of human genome sequencing are crucial to develop effective in silico drug repositioning methods allowing the identification of potential targets for existing drugs. In this work, we review several chemogenomic data-driven computational algorithms with source codes publicly accessible for predicting drug-target interactions (DTIs). We organize these algorithms by model properties and model evolutionary relationships. We re-implemented five representative algorithms in R programming language, and compared these algorithms by means of mean percentile ranking, a new recall-based evaluation metric in the DTI prediction research field. We anticipate that this review will be objective and helpful to researchers who would like to further improve existing algorithms or need to choose appropriate algorithms to infer potential DTIs in the projects. The source codes for DTI predictions are available at: https://github.com/minghao2016/chemogenomicAlg4DTIpred.
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55
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Masuda T, Tsuruda Y, Matsumoto Y, Uchida H, Nakayama KI, Mimori K. Drug repositioning in cancer: The current situation in Japan. Cancer Sci 2020; 111:1039-1046. [PMID: 31957175 PMCID: PMC7156828 DOI: 10.1111/cas.14318] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2019] [Revised: 01/03/2020] [Accepted: 01/09/2020] [Indexed: 12/13/2022] Open
Abstract
Cancer is a leading cause of death worldwide, and the incidence continues to increase. Despite major research aimed at discovering and developing novel and effective anticancer drugs, oncology drug development is a lengthy and costly process, with high attrition rates. Drug repositioning (DR, also referred to as drug repurposing), the process of finding new uses for approved noncancer drugs, has been gaining popularity in the past decade. DR has become a powerful alternative strategy for discovering and developing novel anticancer drug candidates from the existing approved drug space. Indeed, the availability of several large established libraries of clinical drugs and rapid advances in disease biology, genomics/transcriptomics/proteomics and bioinformatics has accelerated the pace of activity‐based, literature‐based and in silico DR, thereby improving safety and reducing costs. However, DR still faces financial obstacles in clinical trials, which could limit its practical use in the clinic. Here, we provide a brief review of DR in cancer and discuss difficulties in the development of DR for clinical use. Furthermore, we introduce some promising DR candidates for anticancer therapy in Japan.
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Affiliation(s)
- Takaaki Masuda
- Department of Surgery, Kyushu University Beppu Hospital, Beppu, Japan
| | - Yusuke Tsuruda
- Department of Surgery, Kyushu University Beppu Hospital, Beppu, Japan
| | | | - Hiroki Uchida
- Department of Surgery, Kyushu University Beppu Hospital, Beppu, Japan
| | - Keiichi I Nakayama
- Department of Molecular and Cellular Biology, Medical Institute of Bioregulation, Kyushu University, Fukuoka, Japan
| | - Koshi Mimori
- Department of Surgery, Kyushu University Beppu Hospital, Beppu, Japan
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56
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Chen S, Li X, Ma S, Xing X, Wang X, Zhu Z. Chemogenomics analysis of drug targets for the treatment of acute promyelocytic leukemia. Ann Hematol 2020; 99:753-763. [PMID: 32016577 DOI: 10.1007/s00277-019-03888-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2018] [Accepted: 12/05/2019] [Indexed: 12/13/2022]
Abstract
The main challenges in treating acute promyelocytic leukemia (APL) are currently early mortality, relapse, refractory disease after induction therapy, and drug resistance to ATRA and ATO. In this study, a computational chemogenomics approach was used to identify new molecular targets and drugs for APL treatment. The transcriptional profiles induced by APL were compared with those induced by genetic or chemical perturbations. The genes that can reverse the transcriptional profiles induced by APL when perturbed were considered to be potential therapeutic targets for APL. Drugs targeting these genes or proteins are predicted to be able to treat APL if they can reverse the APL-induced transcriptional profiles. To improve the target identification accuracy of the above correlation method, we plotted the functional protein association networks of the predicted targets by STRING. The results determined PML, RARA, SPI1, HDAC3, CEBPA, NPM1, ABL1, BCR, PTEN, FOS, PDGFRB, FGFR1, NUP98, AFF1, and MEIS1 to be top candidates. Interestingly, the functions of PML, RARA, HDAC3, CEBPA, NPM1, ABL, and BCR in APL have been previously reported in the literature. This is the first chemogenomics analysis predicting potential APL drug targets, and the findings could be used to guide the design of new drugs targeting refractory and recurrent APL.
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MESH Headings
- Antineoplastic Agents/pharmacology
- Antineoplastic Agents/therapeutic use
- Cheminformatics
- Datasets as Topic
- Drug Design
- Drug Development
- Gene Expression Profiling
- Gene Expression Regulation, Leukemic/drug effects
- Gene Expression Regulation, Leukemic/radiation effects
- Gene Targeting
- Genes, Neoplasm
- Humans
- Leukemia, Promyelocytic, Acute/drug therapy
- Molecular Targeted Therapy
- Neoplasm Proteins/antagonists & inhibitors
- Neoplasm Proteins/genetics
- Nucleophosmin
- Oncogene Proteins, Fusion/antagonists & inhibitors
- Oncogene Proteins, Fusion/genetics
- Protein Interaction Mapping
- RNA, Messenger/biosynthesis
- RNA, Messenger/genetics
- RNA, Neoplasm/biosynthesis
- RNA, Neoplasm/genetics
- Transcriptome
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Affiliation(s)
- Si Chen
- Department of Pharmacy, 967th Hospital of the Chinese People's Liberation Army, 80 Shengli Road, Xigang district, Dalian, 116011, Liaoning, China
- School of Medicine, Shanghai University, Shanghai, China
| | - Xiang Li
- School of Pharmacy, Second Military Medical University, 325 Guohe road, Yangpu district, Shanghai, 200433, China
| | - Shifan Ma
- School of Pharmacy, University of Pittsburgh, Pittsburgh, PA, USA
| | - Xinrui Xing
- School of Pharmacy, Second Military Medical University, 325 Guohe road, Yangpu district, Shanghai, 200433, China
| | - Xiaobo Wang
- Department of Pharmacy, 967th Hospital of the Chinese People's Liberation Army, 80 Shengli Road, Xigang district, Dalian, 116011, Liaoning, China.
| | - Zhenyu Zhu
- School of Pharmacy, Second Military Medical University, 325 Guohe road, Yangpu district, Shanghai, 200433, China.
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57
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De novo generation of hit-like molecules from gene expression signatures using artificial intelligence. Nat Commun 2020; 11:10. [PMID: 31900408 PMCID: PMC6941972 DOI: 10.1038/s41467-019-13807-w] [Citation(s) in RCA: 170] [Impact Index Per Article: 42.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2018] [Accepted: 11/27/2019] [Indexed: 01/20/2023] Open
Abstract
Finding new molecules with a desired biological activity is an extremely difficult task. In this context, artificial intelligence and generative models have been used for molecular de novo design and compound optimization. Herein, we report a generative model that bridges systems biology and molecular design, conditioning a generative adversarial network with transcriptomic data. By doing so, we can automatically design molecules that have a high probability to induce a desired transcriptomic profile. As long as the gene expression signature of the desired state is provided, this model is able to design active-like molecules for desired targets without any previous target annotation of the training compounds. Molecules designed by this model are more similar to active compounds than the ones identified by similarity of gene expression signatures. Overall, this method represents an alternative approach to bridge chemistry and biology in the long and difficult road of drug discovery. High quality hit identification remains a considerable challenge in de novo drug design. Here, the authors train a generative adversarial network with transcriptome profiles induced by a large set of compounds, enabling it to design molecules that are likely to induce desired expression profiles.
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58
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Talevi A, Carrillo C, Comini M. The Thiol-polyamine Metabolism of Trypanosoma cruzi: Molecular Targets and Drug Repurposing Strategies. Curr Med Chem 2019; 26:6614-6635. [PMID: 30259812 DOI: 10.2174/0929867325666180926151059] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2018] [Revised: 07/23/2018] [Accepted: 09/10/2018] [Indexed: 12/18/2022]
Abstract
Chagas´ disease continues to be a challenging and neglected public health problem in many American countries. The etiologic agent, Trypanosoma cruzi, develops intracellularly in the mammalian host, which hinders treatment efficacy. Progress in the knowledge of parasite biology and host-pathogen interaction has not been paralleled by the development of novel, safe and effective therapeutic options. It is then urgent to seek for novel therapeutic candidates and to implement drug discovery strategies that may accelerate the discovery process. The most appealing targets for pharmacological intervention are those essential for the pathogen and, whenever possible, absent or significantly different from the host homolog. The thiol-polyamine metabolism of T. cruzi offers interesting candidates for a rational design of selective drugs. In this respect, here we critically review the state of the art of the thiolpolyamine metabolism of T. cruzi and the pharmacological potential of its components. On the other hand, drug repurposing emerged as a valid strategy to identify new biological activities for drugs in clinical use, while significantly shortening the long time and high cost associated with de novo drug discovery approaches. Thus, we also discuss the different drug repurposing strategies available with a special emphasis in their applications to the identification of drug candidates targeting essential components of the thiol-polyamine metabolism of T. cruzi.
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Affiliation(s)
- Alan Talevi
- Medicinal Chemistry, Department of Biological Sciences, Faculty of Exact Sciences, University of La Plata, La Plata, Argentina
| | - Carolina Carrillo
- Instituto de Ciencias y Tecnología Dr. César Milstein (ICT Milstein) - CONICET. Ciudad Autónoma de Buenos Aires, Argentina
| | - Marcelo Comini
- Institut Pasteur de Montevideo, Mataojo 2020, Montevideo 11400, Uruguay
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59
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Arakelyan A, Nersisyan L, Nikoghosyan M, Hakobyan S, Simonyan A, Hopp L, Loeffler-Wirth H, Binder H. Transcriptome-Guided Drug Repositioning. Pharmaceutics 2019; 11:E677. [PMID: 31842375 PMCID: PMC6969900 DOI: 10.3390/pharmaceutics11120677] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2019] [Revised: 11/17/2019] [Accepted: 12/11/2019] [Indexed: 02/06/2023] Open
Abstract
Drug repositioning can save considerable time and resources and significantly speed up the drug development process. The increasing availability of drug action and disease-associated transcriptome data makes it an attractive source for repositioning studies. Here, we have developed a transcriptome-guided approach for drug/biologics repositioning based on multi-layer self-organizing maps (ml-SOM). It allows for analyzing multiple transcriptome datasets by segmenting them into layers of drug action- and disease-associated transcriptome data. A comparison of expression changes in clusters of functionally related genes across the layers identifies "drug target" spots in disease layers and evaluates the repositioning possibility of a drug. The repositioning potential for two approved biologics drugs (infliximab and brodalumab) confirmed the drugs' action for approved diseases (ulcerative colitis and Crohn's disease for infliximab and psoriasis for brodalumab). We showed the potential efficacy of infliximab for the treatment of sarcoidosis, but not chronic obstructive pulmonary disease (COPD). Brodalumab failed to affect dysregulated functional gene clusters in Crohn's disease (CD) and systemic juvenile idiopathic arthritis (SJIA), clearly indicating that it may not be effective in the treatment of these diseases. In conclusion, ml-SOM offers a novel approach for transcriptome-guided drug repositioning that could be particularly useful for biologics drugs.
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Affiliation(s)
- Arsen Arakelyan
- Institute of Biomedicine and Pharmacy, Russian-Armenian University, 0051 Yerevan, Armenia; (M.N.); (A.S.)
| | - Lilit Nersisyan
- Group of Bioinformatics, Institute of Molecular Biology NAS RA, 0014 Yerevan, Armenia; (L.N.); (S.H.)
| | - Maria Nikoghosyan
- Institute of Biomedicine and Pharmacy, Russian-Armenian University, 0051 Yerevan, Armenia; (M.N.); (A.S.)
- Group of Bioinformatics, Institute of Molecular Biology NAS RA, 0014 Yerevan, Armenia; (L.N.); (S.H.)
| | - Siras Hakobyan
- Group of Bioinformatics, Institute of Molecular Biology NAS RA, 0014 Yerevan, Armenia; (L.N.); (S.H.)
| | - Arman Simonyan
- Institute of Biomedicine and Pharmacy, Russian-Armenian University, 0051 Yerevan, Armenia; (M.N.); (A.S.)
- Group of Bioinformatics, Institute of Molecular Biology NAS RA, 0014 Yerevan, Armenia; (L.N.); (S.H.)
| | - Lydia Hopp
- Interdisciplinary Centre for Bioinformatics, University of Leipzig, D-04107 Leipzig, Germany; (L.H.); (H.L.-W.)
| | - Henry Loeffler-Wirth
- Interdisciplinary Centre for Bioinformatics, University of Leipzig, D-04107 Leipzig, Germany; (L.H.); (H.L.-W.)
| | - Hans Binder
- Interdisciplinary Centre for Bioinformatics, University of Leipzig, D-04107 Leipzig, Germany; (L.H.); (H.L.-W.)
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60
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Moridi M, Ghadirinia M, Sharifi-Zarchi A, Zare-Mirakabad F. The assessment of efficient representation of drug features using deep learning for drug repositioning. BMC Bioinformatics 2019; 20:577. [PMID: 31726977 PMCID: PMC6854697 DOI: 10.1186/s12859-019-3165-y] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2019] [Accepted: 10/21/2019] [Indexed: 12/15/2022] Open
Abstract
BACKGROUND De novo drug discovery is a time-consuming and expensive process. Nowadays, drug repositioning is utilized as a common strategy to discover a new drug indication for existing drugs. This strategy is mostly used in cases with a limited number of candidate pairs of drugs and diseases. In other words, they are not scalable to a large number of drugs and diseases. Most of the in-silico methods mainly focus on linear approaches while non-linear models are still scarce for new indication predictions. Therefore, applying non-linear computational approaches can offer an opportunity to predict possible drug repositioning candidates. RESULTS In this study, we present a non-linear method for drug repositioning. We extract four drug features and two disease features to find the semantic relations between drugs and diseases. We utilize deep learning to extract an efficient representation for each feature. These representations reduce the dimension and heterogeneity of biological data. Then, we assess the performance of different combinations of drug features to introduce a pipeline for drug repositioning. In the available database, there are different numbers of known drug-disease associations corresponding to each combination of drug features. Our assessment shows that as the numbers of drug features increase, the numbers of available drugs decrease. Thus, the proposed method with large numbers of drug features is as accurate as small numbers. CONCLUSION Our pipeline predicts new indications for existing drugs systematically, in a more cost-effective way and shorter timeline. We assess the pipeline to discover the potential drug-disease associations based on cross-validation experiments and some clinical trial studies.
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Affiliation(s)
- Mahroo Moridi
- Department of Mathematics and Computer Science, Amirkabir University of Technology, (Tehran Polytechnic), Tehran, Iran
| | - Marzieh Ghadirinia
- Department of Computer Engineering, Sharif University of Technology, Tehran, Iran
| | - Ali Sharifi-Zarchi
- Department of Computer Engineering, Sharif University of Technology, Tehran, Iran
| | - Fatemeh Zare-Mirakabad
- Department of Mathematics and Computer Science, Amirkabir University of Technology, (Tehran Polytechnic), Tehran, Iran.
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61
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Yokoyama S, Sugimoto Y, Nakagawa C, Hosomi K, Takada M. Integrative analysis of clinical and bioinformatics databases to identify anticancer properties of digoxin. Sci Rep 2019; 9:16597. [PMID: 31719612 PMCID: PMC6851125 DOI: 10.1038/s41598-019-53392-y] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2019] [Accepted: 10/31/2019] [Indexed: 01/14/2023] Open
Abstract
Cardiac glycosides, such as digoxin, inhibit Na+/K+-ATPases and cause secondary activation of Na+/Ca2+ exchangers. Preclinical investigations have suggested that digoxin may have anticancer properties. In order to clarify the functional mechanisms of digoxin in cancer, we performed an integrative analysis of clinical and bioinformatics databases. The US Food and Drug Administration Adverse Event Reporting System and the Japan Medical Data Center claims database were used as clinical databases to evaluate reporting odds ratios and adjusted sequence ratios, respectively. The BaseSpace Correlation Engine and Connectivity Map bioinformatics databases were used to investigate molecular pathways related to digoxin anticancer mechanisms. Clinical database analyses suggested an inverse association between digoxin and four cancers: gastric, colon, prostate and haematological malignancy. The bioinformatics database analysis suggested digoxin may exert an anticancer effect via peroxisome proliferator-activated receptor α and apoptotic caspase cascade pathways. Our integrative analysis revealed the possibility of digoxin as a drug repositioning candidate for cancers.
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Affiliation(s)
- Satoshi Yokoyama
- Division of Clinical Drug Informatics, School of Pharmacy, Kindai University, 3-4-1 Kowakae, Higashiosaka City, Osaka, 577-8502, Japan.
| | - Yasuhiro Sugimoto
- Division of Clinical Drug Informatics, School of Pharmacy, Kindai University, 3-4-1 Kowakae, Higashiosaka City, Osaka, 577-8502, Japan
| | - Chihiro Nakagawa
- Division of Clinical Drug Informatics, School of Pharmacy, Kindai University, 3-4-1 Kowakae, Higashiosaka City, Osaka, 577-8502, Japan
| | - Kouichi Hosomi
- Division of Clinical Drug Informatics, School of Pharmacy, Kindai University, 3-4-1 Kowakae, Higashiosaka City, Osaka, 577-8502, Japan
| | - Mitsutaka Takada
- Division of Clinical Drug Informatics, School of Pharmacy, Kindai University, 3-4-1 Kowakae, Higashiosaka City, Osaka, 577-8502, Japan
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62
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Du Y, Li K, Wang X, Kaushik AC, Junaid M, Wei D. Identification of chlorprothixene as a potential drug that induces apoptosis and autophagic cell death in acute myeloid leukemia cells. FEBS J 2019; 287:1645-1665. [PMID: 31625692 DOI: 10.1111/febs.15102] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2019] [Revised: 07/16/2019] [Accepted: 10/16/2019] [Indexed: 01/07/2023]
Abstract
Although acute myeloid leukemia (AML) is a highly heterogeneous malignance, the common molecular mechanisms shared by different AML subtypes play critical roles in AML development. It is possible to identify new drugs that are effective for various AML subtypes based on the common molecular mechanisms. Therefore, we developed a hypothesis-driven bioinformatic drug screening framework by integrating multiple omics data. In this study, we identified that chlorprothixene, a dopamine receptor antagonist, could effectively inhibit growth of AML cells from different subtypes. RNA-seq analysis suggested that chlorprothixene perturbed a series of crucial biological processes such as cell cycle, apoptosis, and autophagy in AML cells. Further investigations indicated that chlorprothixene could induce both apoptosis and autophagy in AML cells, and apoptosis and autophagy could act as partners to induce cell death cooperatively. Remarkably, chlorprothixene was found to inhibit tumor growth and induce in situ leukemic cell apoptosis in the murine xenograft model. Furthermore, chlorprothixene treatment could reduce the level of oncofusion proteins PML-RARα and AML1-ETO, thus elevate the expression of apoptosis-related genes, and lead to AML cell death. Our results provided new insights for drug repositioning of AML therapy and confirmed that chlorprothixene might be a potential candidate for treatment of different subtypes of AML by reducing expression of oncofusion proteins. DATABASE: RNA-seq data are available in GEO database under the accession number GSE124316.
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Affiliation(s)
- Yuxin Du
- State Key Laboratory of Microbial Metabolism, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, China.,State Key Laboratory of Medical Genomics, Shanghai Institute of Hematology, Rui-Jin Hospital, Shanghai Jiao Tong University, China
| | - Kening Li
- State Key Laboratory of Microbial Metabolism, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, China.,State Key Laboratory of Medical Genomics, Shanghai Institute of Hematology, Rui-Jin Hospital, Shanghai Jiao Tong University, China
| | - Xiangeng Wang
- State Key Laboratory of Microbial Metabolism, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, China
| | - Aman Chandra Kaushik
- State Key Laboratory of Microbial Metabolism, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, China
| | - Muhammad Junaid
- State Key Laboratory of Microbial Metabolism, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, China
| | - Dongqing Wei
- State Key Laboratory of Microbial Metabolism, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, China
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63
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David L, Arús-Pous J, Karlsson J, Engkvist O, Bjerrum EJ, Kogej T, Kriegl JM, Beck B, Chen H. Applications of Deep-Learning in Exploiting Large-Scale and Heterogeneous Compound Data in Industrial Pharmaceutical Research. Front Pharmacol 2019; 10:1303. [PMID: 31749705 PMCID: PMC6848277 DOI: 10.3389/fphar.2019.01303] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2019] [Accepted: 10/14/2019] [Indexed: 12/21/2022] Open
Abstract
In recent years, the development of high-throughput screening (HTS) technologies and their establishment in an industrialized environment have given scientists the possibility to test millions of molecules and profile them against a multitude of biological targets in a short period of time, generating data in a much faster pace and with a higher quality than before. Besides the structure activity data from traditional bioassays, more complex assays such as transcriptomics profiling or imaging have also been established as routine profiling experiments thanks to the advancement of Next Generation Sequencing or automated microscopy technologies. In industrial pharmaceutical research, these technologies are typically established in conjunction with automated platforms in order to enable efficient handling of screening collections of thousands to millions of compounds. To exploit the ever-growing amount of data that are generated by these approaches, computational techniques are constantly evolving. In this regard, artificial intelligence technologies such as deep learning and machine learning methods play a key role in cheminformatics and bio-image analytics fields to address activity prediction, scaffold hopping, de novo molecule design, reaction/retrosynthesis predictions, or high content screening analysis. Herein we summarize the current state of analyzing large-scale compound data in industrial pharmaceutical research and describe the impact it has had on the drug discovery process over the last two decades, with a specific focus on deep-learning technologies.
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Affiliation(s)
- Laurianne David
- Hit Discovery, Discovery Sciences, Biopharmaceutical R&D, AstraZeneca, Gothenburg, Sweden
- Department of Life Science Informatics, B-IT, Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn, Germany
| | - Josep Arús-Pous
- Hit Discovery, Discovery Sciences, Biopharmaceutical R&D, AstraZeneca, Gothenburg, Sweden
- Department of Chemistry and Biochemistry, University of Bern, Bern, Switzerland
| | - Johan Karlsson
- Quantitative Biology, Discovery Sciences, Biopharmaceutical R&D, AstraZeneca, Gothenburg, Sweden
| | - Ola Engkvist
- Hit Discovery, Discovery Sciences, Biopharmaceutical R&D, AstraZeneca, Gothenburg, Sweden
| | - Esben Jannik Bjerrum
- Hit Discovery, Discovery Sciences, Biopharmaceutical R&D, AstraZeneca, Gothenburg, Sweden
| | - Thierry Kogej
- Hit Discovery, Discovery Sciences, Biopharmaceutical R&D, AstraZeneca, Gothenburg, Sweden
| | - Jan M. Kriegl
- Department of Medicinal Chemistry, Boehringer Ingelheim Pharma GmbH & Co. KG, Biberach an der Riss, Germany
| | - Bernd Beck
- Department of Medicinal Chemistry, Boehringer Ingelheim Pharma GmbH & Co. KG, Biberach an der Riss, Germany
| | - Hongming Chen
- Hit Discovery, Discovery Sciences, Biopharmaceutical R&D, AstraZeneca, Gothenburg, Sweden
- Chemistry and Chemical Biology Centre, Guangzhou Regenerative Medicine and Health – Guangdong Laboratory, Guangzhou, China
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64
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Toro-Domínguez D, Lopez-Domínguez R, García Moreno A, Villatoro-García JA, Martorell-Marugán J, Goldman D, Petri M, Wojdyla D, Pons-Estel BA, Isenberg D, Morales-Montes de Oca G, Trejo-Zambrano MI, García González B, Rosetti F, Gómez-Martín D, Romero-Díaz J, Carmona-Sáez P, Alarcón-Riquelme ME. Differential Treatments Based on Drug-induced Gene Expression Signatures and Longitudinal Systemic Lupus Erythematosus Stratification. Sci Rep 2019; 9:15502. [PMID: 31664045 PMCID: PMC6820741 DOI: 10.1038/s41598-019-51616-9] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2019] [Accepted: 09/29/2019] [Indexed: 01/23/2023] Open
Abstract
Systemic lupus erythematosus (SLE) is a heterogeneous disease with unpredictable patterns of activity. Patients with similar activity levels may have different prognosis and molecular abnormalities. In this study, we aimed to measure the main differences in drug-induced gene expression signatures across SLE patients and to evaluate the potential for clinical data to build a machine learning classifier able to predict the SLE subset for individual patients. SLE transcriptomic data from two cohorts were compared with drug-induced gene signatures from the CLUE database to compute a connectivity score that reflects the capability of a drug to revert the patient signatures. Patient stratification based on drug connectivity scores revealed robust clusters of SLE patients identical to the clusters previously obtained through longitudinal gene expression data, implying that differential treatment depends on the cluster to which patients belongs. The best drug candidates found, mTOR inhibitors or those reducing oxidative stress, showed stronger cluster specificity. We report that drug patterns for reverting disease gene expression follow the cell-specificity of the disease clusters. We used 2 cohorts to train and test a logistic regression model that we employed to classify patients from 3 independent cohorts into the SLE subsets and provide a clinically useful model to predict subset assignment and drug efficacy.
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Affiliation(s)
- Daniel Toro-Domínguez
- Centro de Genómica e Investigaciones Oncológicas Pfizer-Universidad de Granada-Junta de Andalucía (GENYO), Granada, Spain
| | - Raúl Lopez-Domínguez
- Centro de Genómica e Investigaciones Oncológicas Pfizer-Universidad de Granada-Junta de Andalucía (GENYO), Granada, Spain
| | - Adrián García Moreno
- Centro de Genómica e Investigaciones Oncológicas Pfizer-Universidad de Granada-Junta de Andalucía (GENYO), Granada, Spain
| | - Juan A Villatoro-García
- Centro de Genómica e Investigaciones Oncológicas Pfizer-Universidad de Granada-Junta de Andalucía (GENYO), Granada, Spain
| | - Jordi Martorell-Marugán
- Centro de Genómica e Investigaciones Oncológicas Pfizer-Universidad de Granada-Junta de Andalucía (GENYO), Granada, Spain
| | - Daniel Goldman
- Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Michelle Petri
- Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | | | | | - David Isenberg
- Centre for Rheumatology, Division of Medicine University College London, London, United Kingdom
| | - Gabriela Morales-Montes de Oca
- Department of Immunology and Rheumatology, Instituto Nacional de Ciencias Médicas y Nutrición "Salvador Zubirán", Mexico City, Mexico
| | - María Isabel Trejo-Zambrano
- Department of Immunology and Rheumatology, Instituto Nacional de Ciencias Médicas y Nutrición "Salvador Zubirán", Mexico City, Mexico
| | - Benjamín García González
- Department of Immunology and Rheumatology, Instituto Nacional de Ciencias Médicas y Nutrición "Salvador Zubirán", Mexico City, Mexico
| | - Florencia Rosetti
- Department of Immunology and Rheumatology, Instituto Nacional de Ciencias Médicas y Nutrición "Salvador Zubirán", Mexico City, Mexico
| | - Diana Gómez-Martín
- Department of Immunology and Rheumatology, Instituto Nacional de Ciencias Médicas y Nutrición "Salvador Zubirán", Mexico City, Mexico
| | - Juanita Romero-Díaz
- Department of Immunology and Rheumatology, Instituto Nacional de Ciencias Médicas y Nutrición "Salvador Zubirán", Mexico City, Mexico
| | - Pedro Carmona-Sáez
- Centro de Genómica e Investigaciones Oncológicas Pfizer-Universidad de Granada-Junta de Andalucía (GENYO), Granada, Spain.
| | - Marta E Alarcón-Riquelme
- Centro de Genómica e Investigaciones Oncológicas Pfizer-Universidad de Granada-Junta de Andalucía (GENYO), Granada, Spain. .,Unit of Chronic Inflammation, Institute for Environmental Medicine, Karolinska Institutet, Stockholm, Sweden.
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65
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Karaman B, Sippl W. Computational Drug Repurposing: Current Trends. Curr Med Chem 2019; 26:5389-5409. [DOI: 10.2174/0929867325666180530100332] [Citation(s) in RCA: 37] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2018] [Revised: 05/06/2018] [Accepted: 05/14/2018] [Indexed: 01/31/2023]
Abstract
:
Biomedical discovery has been reshaped upon the exploding digitization of data
which can be retrieved from a number of sources, ranging from clinical pharmacology to
cheminformatics-driven databases. Now, supercomputing platforms and publicly available
resources such as biological, physicochemical, and clinical data, can all be integrated to construct
a detailed map of signaling pathways and drug mechanisms of action in relation to drug
candidates. Recent advancements in computer-aided data mining have facilitated analyses of
‘big data’ approaches and the discovery of new indications for pre-existing drugs has been
accelerated. Linking gene-phenotype associations to predict novel drug-disease signatures or
incorporating molecular structure information of drugs and protein targets with other kinds of
data derived from systems biology provide great potential to accelerate drug discovery and
improve the success of drug repurposing attempts. In this review, we highlight commonly
used computational drug repurposing strategies, including bioinformatics and cheminformatics
tools, to integrate large-scale data emerging from the systems biology, and consider both
the challenges and opportunities of using this approach. Moreover, we provide successful examples
and case studies that combined various in silico drug-repurposing strategies to predict
potential novel uses for known therapeutics.
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Affiliation(s)
- Berin Karaman
- Biruni University - Department of Pharmaceutical Chemistry, Istanbul, Turkey
| | - Wolfgang Sippl
- Martin-Luther University of Halle-Wittenberg - Institute of Pharmacy, Halle (Saale), Germany
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Turanli B, Altay O, Borén J, Turkez H, Nielsen J, Uhlen M, Arga KY, Mardinoglu A. Systems biology based drug repositioning for development of cancer therapy. Semin Cancer Biol 2019; 68:47-58. [PMID: 31568815 DOI: 10.1016/j.semcancer.2019.09.020] [Citation(s) in RCA: 47] [Impact Index Per Article: 9.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2019] [Revised: 09/23/2019] [Accepted: 09/24/2019] [Indexed: 01/20/2023]
Abstract
Drug repositioning is a powerful method that can assists the conventional drug discovery process by using existing drugs for treatment of a disease rather than its original indication. The first examples of repurposed drugs were discovered serendipitously, however data accumulated by high-throughput screenings and advancements in computational biology methods have paved the way for rational drug repositioning methods. As chemotherapeutic agents have notorious side effects that significantly reduce quality of life, drug repositioning promises repurposed noncancer drugs with little or tolerable adverse effects for cancer patients. Here, we review current drug-related data types and databases including some examples of web-based drug repositioning tools. Next, we describe systems biology approaches to be used in drug repositioning for effective cancer therapy. Finally, we highlight examples of mostly repurposed drugs for cancer treatment and provide an overview of future expectations in the field for development of effective treatment strategies.
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Affiliation(s)
- Beste Turanli
- Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm SE-17121, Sweden; Department of Bioengineering, Marmara University, Istanbul, Turkey; Department of Bioengineering, Istanbul Medeniyet University, Istanbul, Turkey
| | - Ozlem Altay
- Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm SE-17121, Sweden
| | - Jan Borén
- Department of Molecular and Clinical Medicine, University of Gothenburg and Sahlgrenska University Hospital Gothenburg, Sweden
| | - Hasan Turkez
- Department of Molecular Biology and Genetics, Erzurum Technical University, Erzurum 25240, Turkey
| | - Jens Nielsen
- Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, Sweden
| | - Mathias Uhlen
- Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm SE-17121, Sweden
| | | | - Adil Mardinoglu
- Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm SE-17121, Sweden; Centre for Host-Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King's College London, London SE1 9RT, United Kingdom.
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67
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Noh H, Shoemaker JE, Gunawan R. Network perturbation analysis of gene transcriptional profiles reveals protein targets and mechanism of action of drugs and influenza A viral infection. Nucleic Acids Res 2019; 46:e34. [PMID: 29325153 PMCID: PMC5887474 DOI: 10.1093/nar/gkx1314] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2017] [Accepted: 12/22/2017] [Indexed: 12/12/2022] Open
Abstract
Genome-wide transcriptional profiling provides a global view of cellular state and how this state changes under different treatments (e.g. drugs) or conditions (e.g. healthy and diseased). Here, we present ProTINA (Protein Target Inference by Network Analysis), a network perturbation analysis method for inferring protein targets of compounds from gene transcriptional profiles. ProTINA uses a dynamic model of the cell-type specific protein-gene transcriptional regulation to infer network perturbations from steady state and time-series differential gene expression profiles. A candidate protein target is scored based on the gene network's dysregulation, including enhancement and attenuation of transcriptional regulatory activity of the protein on its downstream genes, caused by drug treatments. For benchmark datasets from three drug treatment studies, ProTINA was able to provide highly accurate protein target predictions and to reveal the mechanism of action of compounds with high sensitivity and specificity. Further, an application of ProTINA to gene expression profiles of influenza A viral infection led to new insights of the early events in the infection.
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Affiliation(s)
- Heeju Noh
- Institute for Chemical and Bioengineering, ETH Zurich, Zurich 8093, Switzerland.,Swiss Institute of Bioinformatics, Lausanne 1015, Switzerland
| | - Jason E Shoemaker
- Department of Chemical and Petroleum Engineering, University of Pittsburgh, Pittsburgh, PA 15261, USA.,Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, PA 15261, USA
| | - Rudiyanto Gunawan
- Institute for Chemical and Bioengineering, ETH Zurich, Zurich 8093, Switzerland.,Swiss Institute of Bioinformatics, Lausanne 1015, Switzerland
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Pulley JM, Rhoads JP, Jerome RN, Challa AP, Erreger KB, Joly MM, Lavieri RR, Perry KE, Zaleski NM, Shirey-Rice JK, Aronoff DM. Using What We Already Have: Uncovering New Drug Repurposing Strategies in Existing Omics Data. Annu Rev Pharmacol Toxicol 2019; 60:333-352. [PMID: 31337270 DOI: 10.1146/annurev-pharmtox-010919-023537] [Citation(s) in RCA: 31] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
The promise of drug repurposing is to accelerate the translation of knowledge to treatment of human disease, bypassing common challenges associated with drug development to be more time- and cost-efficient. Repurposing has an increased chance of success due to the previous validation of drug safety and allows for the incorporation of omics. Hypothesis-generating omics processes inform drug repurposing decision-making methods on drug efficacy and toxicity. This review summarizes drug repurposing strategies and methodologies in the context of the following omics fields: genomics, epigenomics, transcriptomics, proteomics, metabolomics, microbiomics, phenomics, pregomics, and personomics. While each omics field has specific strengths and limitations, incorporating omics into the drug repurposing landscape is integral to its success.
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Affiliation(s)
- Jill M Pulley
- Vanderbilt Institute for Clinical and Translational Research, Vanderbilt University Medical Center, Nashville, Tennessee 37203, USA
| | - Jillian P Rhoads
- Vanderbilt Institute for Clinical and Translational Research, Vanderbilt University Medical Center, Nashville, Tennessee 37203, USA
| | - Rebecca N Jerome
- Vanderbilt Institute for Clinical and Translational Research, Vanderbilt University Medical Center, Nashville, Tennessee 37203, USA
| | - Anup P Challa
- Vanderbilt Institute for Clinical and Translational Research, Vanderbilt University Medical Center, Nashville, Tennessee 37203, USA
| | - Kevin B Erreger
- Vanderbilt Institute for Clinical and Translational Research, Vanderbilt University Medical Center, Nashville, Tennessee 37203, USA
| | - Meghan M Joly
- Vanderbilt Institute for Clinical and Translational Research, Vanderbilt University Medical Center, Nashville, Tennessee 37203, USA
| | - Robert R Lavieri
- Vanderbilt Institute for Clinical and Translational Research, Vanderbilt University Medical Center, Nashville, Tennessee 37203, USA
| | - Kelly E Perry
- Vanderbilt Institute for Clinical and Translational Research, Vanderbilt University Medical Center, Nashville, Tennessee 37203, USA
| | - Nicole M Zaleski
- Vanderbilt Institute for Clinical and Translational Research, Vanderbilt University Medical Center, Nashville, Tennessee 37203, USA
| | - Jana K Shirey-Rice
- Vanderbilt Institute for Clinical and Translational Research, Vanderbilt University Medical Center, Nashville, Tennessee 37203, USA
| | - David M Aronoff
- Department of Medicine, Division of Infectious Diseases, Vanderbilt University School of Medicine, Nashville, Tennessee 37232, USA.,Departments of Obstetrics and Gynecology, and Pathology, Microbiology, and Immunology, Vanderbilt University Medical Center, Nashville, Tennessee 37232, USA;
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69
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Kim IW, Kim JH, Oh JM. Screening of Drug Repositioning Candidates for Castration Resistant Prostate Cancer. Front Oncol 2019; 9:661. [PMID: 31396486 PMCID: PMC6664029 DOI: 10.3389/fonc.2019.00661] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2019] [Accepted: 07/05/2019] [Indexed: 12/20/2022] Open
Abstract
Purpose: Most prostate cancers (PCs) initially respond to androgen deprivation therapy (ADT), but eventually many PC patients develop castration resistant PC (CRPC). Currently, available drugs that have been approved for the treatment of CRPC patients are limited. Computational drug repositioning methods using public databases represent a promising and efficient tool for discovering new uses for existing drugs. The purpose of the present study is to predict drug candidates that can treat CRPC using a computational method that integrates publicly available gene expression data of tumors from CRPC patients, drug-induced gene expression data and drug response activity data. Methods: Gene expression data from tumoral and normal or benign prostate tissue samples in CRPC patients were downloaded from the Gene Expression Omnibus (GEO) and differentially expressed genes (DEGs) in CRPC were determined with a meta-signature analysis by a metaDE R package. Additionally, drug activity data were downloaded from the ChEMBL database. Furthermore, the drug-induced gene expression data were downloaded from the LINCS database. The reversal relationship between the CRPC and drug gene expression signatures as the Reverse Gene Expression Scores (RGES) were computed. Drug candidates to treat CRPC were predicted using summarized scores (sRGES). Additionally, synergic effects of drug combinations were predicted with a Target Inhibition interaction using the Minimization and Maximization Averaging (TIMMA) algorithm. Results: The drug candidates of sorafenib, olaparib, elesclomol, tanespimycin, and ponatinib were predicted to be active for the treatment of CRPC. Meanwhile, CRPC-related genes, in this case MYL9, E2F2, APOE, and ZFP36, were identified as having gene expression data that can be reversed by these drugs. Additionally, lenalidomide in combination with pazopanib was predicted to be most potent for CRPC. Conclusion: These findings support the use of a computational reversal gene expression approach to identify new drug and drug combination candidates that can be used to treat CRPC.
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Affiliation(s)
- In-Wha Kim
- College of Pharmacy and Research Institute of Pharmaceutical Sciences, Seoul, South Korea
| | - Jae Hyun Kim
- College of Pharmacy and Research Institute of Pharmaceutical Sciences, Seoul, South Korea
| | - Jung Mi Oh
- College of Pharmacy and Research Institute of Pharmaceutical Sciences, Seoul, South Korea
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70
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Gazerani P. Identification of novel analgesics through a drug repurposing strategy. Pain Manag 2019; 9:399-415. [DOI: 10.2217/pmt-2018-0091] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022] Open
Abstract
The identification of new indications for approved or failed drugs is a process called drug repositioning or drug repurposing. The motivation includes overcoming the productivity gap that exists in drug development, which is a high-cost–high-risk process. Repositioning also includes rescuing drugs that have safely entered the market but have failed to demonstrate sufficient efficiency for the initial clinical indication. Considering the high prevalence of chronic pain, the lack of sufficient efficacy and the safety issues of current analgesics, repositioning seems to be an attractive approach. This review presents example of drugs that already have been repositioned and highlights new technologies that are available for the identification of additional compounds to stimulate the curiosity of readers for further exploration.
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Affiliation(s)
- Parisa Gazerani
- Biomedicine, Department of Health Science & Technology, Aalborg University, Frederik Bajers Vej 3 B, 9220 Aalborg East, Denmark
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71
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Park K. A review of computational drug repurposing. Transl Clin Pharmacol 2019; 27:59-63. [PMID: 32055582 PMCID: PMC6989243 DOI: 10.12793/tcp.2019.27.2.59] [Citation(s) in RCA: 102] [Impact Index Per Article: 20.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2019] [Revised: 06/23/2019] [Accepted: 06/24/2019] [Indexed: 12/21/2022] Open
Abstract
Although sciences and technology have progressed rapidly, de novo drug development has been a costly and time-consuming process over the past decades. In view of these circumstances, ‘drug repurposing’ (or ‘drug repositioning’) has appeared as an alternative tool to accelerate drug development process by seeking new indications for already approved drugs rather than discovering de novo drug compounds, nowadays accounting for 30% of newly marked drugs in the U.S. In the meantime, the explosive and large-scale growth of molecular, genomic and phenotypic data of pharmacological compounds is enabling the development of new area of drug repurposing called computational drug repurposing. This review provides an overview of recent progress in the area of computational drug repurposing. First, it summarizes available repositioning strategies, followed by computational methods commonly used. Then, it describes validation techniques for repurposing studies. Finally, it concludes by discussing the remaining challenges in computational repurposing.
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Affiliation(s)
- Kyungsoo Park
- Department of Pharmacology, Yonsei University College of Medicine, Seoul 03722, Korea
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72
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Bentea E, Depasquale EA, O’Donovan SM, Sullivan CR, Simmons M, Meador-Woodruff JH, Zhou Y, Xu C, Bai B, Peng J, Song H, Ming GL, Meller J, Wen Z, McCullumsmith RE. Kinase network dysregulation in a human induced pluripotent stem cell model of DISC1 schizophrenia. Mol Omics 2019; 15:173-188. [PMID: 31106784 PMCID: PMC6563817 DOI: 10.1039/c8mo00173a] [Citation(s) in RCA: 26] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
Protein kinases orchestrate signal transduction pathways involved in central nervous system functions ranging from neurodevelopment to synaptic transmission and plasticity. Abnormalities in kinase-mediated signaling are involved in the pathophysiology of neurological disorders, including neuropsychiatric disorders. Here, we expand on the hypothesis that kinase networks are dysregulated in schizophrenia. We investigated changes in serine/threonine kinase activity in cortical excitatory neurons differentiated from induced pluripotent stem cells (iPSCs) from a schizophrenia patient presenting with a 4 bp mutation in the disrupted in schizophrenia 1 (DISC1) gene and a corresponding control. Using kinome peptide arrays, we demonstrate large scale abnormalities in DISC1 cells, including a global depression of serine/threonine kinase activity, and changes in activity of kinases, including AMP-activated protein kinase (AMPK), extracellular signal-regulated kinases (ERK), and thousand-and-one amino acid (TAO) kinases. Using isogenic cell lines in which the DISC1 mutation is either introduced in the control cell line, or rescued in the schizophrenia cell line, we ascribe most of these changes to a direct effect of the presence of the DISC1 mutation. Investigating the gene expression signatures downstream of the DISC1 kinase network, and mapping them on perturbagen signatures obtained from the Library of Integrated Network-based Cellular Signatures (LINCS) database, allowed us to propose novel drug targets able to reverse the DISC1 kinase dysregulation gene expression signature. Altogether, our findings provide new insight into abnormalities of kinase networks in schizophrenia and suggest possible targets for disease intervention.
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Affiliation(s)
- Eduard Bentea
- Center for Neurosciences (C4N), Department of Pharmaceutical Biotechnology and Molecular Biology, Vrije Universiteit Brussel, Brussels, Belgium
| | - Erica A.K. Depasquale
- Department of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
| | - Sinead M. O’Donovan
- Department of Neurosciences, University of Toledo College of Medicine, Toledo, OH, USA
| | | | - Micah Simmons
- Department of Psychiatry and Behavioral Neurobiology, University of Alabama at Birmingham, Birmingham, AL, USA
| | - James H. Meador-Woodruff
- Department of Psychiatry and Behavioral Neurobiology, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Ying Zhou
- Departments of Psychiatry and Behavioral Sciences, Cell Biology, and Neurology, Emory University School of Medicine, Atlanta, GA, USA
| | - Chongchong Xu
- Departments of Psychiatry and Behavioral Sciences, Cell Biology, and Neurology, Emory University School of Medicine, Atlanta, GA, USA
| | - Bing Bai
- Departments of Structural Biology and Developmental Neurobiology, Center for Proteomics and Metabolomics, St. Jude Children’s Research Hospital, Memphis, TN, USA
- Department of Laboratory Medicine, Nanjing Drum Tower Hospital, Nanjing University Medical School, Nanjing, P. R. China
| | - Junmin Peng
- Departments of Structural Biology and Developmental Neurobiology, Center for Proteomics and Metabolomics, St. Jude Children’s Research Hospital, Memphis, TN, USA
| | - Hongjun Song
- Department of Neuroscience and Mahoney Institute for Neurosciences, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Cell and Developmental Biology, University of Pennsylvania, Philadelphia, PA 19104, USA
- Institute for Regenerative Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Guo-li Ming
- Department of Neuroscience and Mahoney Institute for Neurosciences, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Cell and Developmental Biology, University of Pennsylvania, Philadelphia, PA 19104, USA
- Institute for Regenerative Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Jarek Meller
- Department of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
- Departments of Environmental Health, Electrical Engineering & Computing Systems and Biomedical Informatics, University of Cincinnati College of Medicine, Cincinnati, OH, USA
| | - Zhexing Wen
- Departments of Psychiatry and Behavioral Sciences, Cell Biology, and Neurology, Emory University School of Medicine, Atlanta, GA, USA
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Sullivan CR, Mielnik CA, O'Donovan SM, Funk AJ, Bentea E, DePasquale EA, Alganem K, Wen Z, Haroutunian V, Katsel P, Ramsey AJ, Meller J, McCullumsmith RE. Connectivity Analyses of Bioenergetic Changes in Schizophrenia: Identification of Novel Treatments. Mol Neurobiol 2019; 56:4492-4517. [PMID: 30338483 PMCID: PMC7584383 DOI: 10.1007/s12035-018-1390-4] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2018] [Accepted: 10/11/2018] [Indexed: 01/21/2023]
Abstract
We utilized a cell-level approach to examine glycolytic pathways in the DLPFC of subjects with schizophrenia (n = 16) and control (n = 16) and found decreased mRNA expression of glycolytic enzymes in pyramidal neurons, but not astrocytes. To replicate these novel bioenergetic findings, we probed independent datasets for bioenergetic targets and found similar abnormalities. Next, we used a novel strategy to build a schizophrenia bioenergetic profile by a tailored application of the Library of Integrated Network-Based Cellular Signatures data portal (iLINCS) and investigated connected cellular pathways, kinases, and transcription factors using Enrichr. Finally, with the goal of identifying drugs capable of "reversing" the bioenergetic schizophrenia signature, we performed a connectivity analysis with iLINCS and identified peroxisome proliferator-activated receptor (PPAR) agonists as promising therapeutic targets. We administered a PPAR agonist to the GluN1 knockdown model of schizophrenia and found it improved long-term memory. Taken together, our findings suggest that tailored bioinformatics approaches, coupled with the LINCS library of transcriptional signatures of chemical and genetic perturbagens, may be employed to identify novel treatment strategies for schizophrenia and related diseases.
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Affiliation(s)
| | - Catharine A Mielnik
- Department of Pharmacology and Toxicology, University of Toronto, Toronto, ON, M5S 1A8, Canada
| | | | - Adam J Funk
- Department of Neuroscience, University of Toledo, Toledo, OH, USA
| | - Eduard Bentea
- Neurosciences TA Biology, UCB BioPharma SPRL, Braine-l'Alleud, Belgium
| | - Erica A DePasquale
- Department of Biomedical Informatics, University of Cincinnati College of Medicine, Cincinnati, OH, USA
| | - Khaled Alganem
- Department of Neuroscience, University of Toledo, Toledo, OH, USA
| | - Zhexing Wen
- Department of Psychiatry and Behavioral Sciences, Emory University, Atlanta, GA, USA
| | - Vahram Haroutunian
- Department of Psychiatry and Neuroscience, The Icahn School of Medicine at Mount Sinai, Bronx, NY, USA
| | - Pavel Katsel
- Department of Psychiatry and Neuroscience, The Icahn School of Medicine at Mount Sinai, Bronx, NY, USA
| | - Amy J Ramsey
- Department of Pharmacology and Toxicology, University of Toronto, Toronto, ON, M5S 1A8, Canada
- Department of Physiology, University of Toronto, Toronto, ON, M5S 1A8, Canada
| | - Jarek Meller
- Department of Biomedical Informatics, University of Cincinnati College of Medicine, Cincinnati, OH, USA
- Department of Electrical Engineering and Computer Science, University of Cincinnati College of Medicine, Cincinnati, OH, USA
- Department of Informatics, Nicolaus Copernicus University, Torun, Poland
- Department of Environmental Health, University of Cincinnati College of Medicine, Cincinnati, OH, USA
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74
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Vásquez-Bochm LX, Velázquez-Paniagua M, Castro-Vázquez SS, Guerrero-Rodríguez SL, Mondragon-Peralta A, De La Fuente-Granada M, Pérez-Tapia SM, González-Arenas A, Velasco-Velázquez MA. Transcriptome-based identification of lovastatin as a breast cancer stem cell-targeting drug. Pharmacol Rep 2019; 71:535-544. [DOI: 10.1016/j.pharep.2019.02.011] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2018] [Revised: 01/27/2019] [Accepted: 02/15/2019] [Indexed: 12/12/2022]
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75
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Mining heterogeneous network for drug repositioning using phenotypic information extracted from social media and pharmaceutical databases. Artif Intell Med 2019; 96:80-92. [DOI: 10.1016/j.artmed.2019.03.003] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2018] [Revised: 02/24/2019] [Accepted: 03/05/2019] [Indexed: 01/09/2023]
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76
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Computational Drug Repositioning for Gastric Cancer using Reversal Gene Expression Profiles. Sci Rep 2019; 9:2660. [PMID: 30804389 PMCID: PMC6389943 DOI: 10.1038/s41598-019-39228-9] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2018] [Accepted: 01/21/2019] [Indexed: 12/16/2022] Open
Abstract
Treatment of gastric cancer (GC) often produces poor outcomes. Moreover, predicting which GC treatments will be effective remains challenging. Computational drug repositioning using public databases is a promising and efficient tool for discovering new uses for existing drugs. Here we used a computational reversal of gene expression approach based on effects on gene expression signatures by GC disease and drugs to explore new GC drug candidates. Gene expression profiles for individual GC tumoral and normal gastric tissue samples were downloaded from the Gene Expression Omnibus (GEO) and differentially expressed genes (DEGs) in GC were determined with a meta-signature analysis. Profiles drug activity and drug-induced gene expression were downloaded from the ChEMBL and the LINCS databases, respectively. Candidate drugs to treat GC were predicted using reversal gene expression score (RGES). Drug candidates including sorafenib, olaparib, elesclomol, tanespimycin, selumetinib, and ponatinib were predicted to be active for treatment of GC. Meanwhile, GC-related genes such as PLOD3, COL4A1, UBE2C, MIF, and PRPF5 were identified as having gene expression profiles that can be reversed by drugs. These findings support the use of a computational reversal gene expression approach to identify new drug candidates that can be used to treat GC.
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77
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Wang L, Ma S, Hu Z, McGuire TF, Xie XQS. Chemogenomics Systems Pharmacology Mapping of Potential Drug Targets for Treatment of Traumatic Brain Injury. J Neurotrauma 2019; 36:565-575. [PMID: 30014763 DOI: 10.1089/neu.2018.5757] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022] Open
Abstract
Traumatic brain injury (TBI) is associated with high mortality and morbidity. Though the death rate of initial trauma has dramatically decreased, no drug has been developed to effectively limit the progression of the secondary injury caused by TBI. TBI appears to be a predisposing risk factor for Alzheimer's disease (AD), whereas the molecular mechanisms remain unknown. In this study, we have conducted a research investigation of computational chemogenomics systems pharmacology (CSP) to identify potential drug targets for TBI treatment. TBI-induced transcriptional profiles were compared with those induced by genetic or chemical perturbations, including drugs in clinical trials for TBI treatment. The protein-protein interaction network of these predicted targets were then generated for further analyses. Some protein targets when perturbed, exhibit inverse transcriptional profiles in comparison with the profiles induced by TBI, and they were recognized as potential therapeutic targets for TBI. Drugs acting on these targets are predicted to have the potential for TBI treatment if they can reverse the TBI-induced transcriptional profiles that lead to secondary injury. In particular, our results indicated that TRPV4, NEUROD1, and HPRT1 were among the top therapeutic target candidates for TBI, which are congruent with literature reports. Our analyses also suggested the strong associations between TBI and AD, as perturbations on AD-related genes, such as APOE, APP, PSEN1, and MAPT, can induce similar gene expression patterns as those of TBI. To the best of our knowledge, this is the first CSP-based gene expression profile analyses for predicting TBI-related drug targets, and the findings could be used to guide the design of new drugs targeting the secondary injury caused by TBI.
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Affiliation(s)
- Lirong Wang
- 1 Department of Pharmaceutical Sciences and Computational Chemical Genomics Screening Center, School of Pharmacy, University of Pittsburgh , Pittsburgh, Pennsylvania.,2 NIH National Center of Excellence for Computational Drug Abuse Research, University of Pittsburgh , Pittsburgh, Pennsylvania
| | - Shifan Ma
- 1 Department of Pharmaceutical Sciences and Computational Chemical Genomics Screening Center, School of Pharmacy, University of Pittsburgh , Pittsburgh, Pennsylvania.,2 NIH National Center of Excellence for Computational Drug Abuse Research, University of Pittsburgh , Pittsburgh, Pennsylvania
| | - Ziheng Hu
- 1 Department of Pharmaceutical Sciences and Computational Chemical Genomics Screening Center, School of Pharmacy, University of Pittsburgh , Pittsburgh, Pennsylvania.,2 NIH National Center of Excellence for Computational Drug Abuse Research, University of Pittsburgh , Pittsburgh, Pennsylvania
| | - Terence Francis McGuire
- 1 Department of Pharmaceutical Sciences and Computational Chemical Genomics Screening Center, School of Pharmacy, University of Pittsburgh , Pittsburgh, Pennsylvania.,2 NIH National Center of Excellence for Computational Drug Abuse Research, University of Pittsburgh , Pittsburgh, Pennsylvania
| | - Xiang-Qun Sean Xie
- 1 Department of Pharmaceutical Sciences and Computational Chemical Genomics Screening Center, School of Pharmacy, University of Pittsburgh , Pittsburgh, Pennsylvania.,2 NIH National Center of Excellence for Computational Drug Abuse Research, University of Pittsburgh , Pittsburgh, Pennsylvania.,3 Drug Discovery Institute, University of Pittsburgh , Pittsburgh, Pennsylvania.,4 Departments of Computational Biology and Structural Biology, School of Medicine, University of Pittsburgh , Pittsburgh, Pennsylvania
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78
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Brueggeman L, Sturgeon ML, Martin RM, Grossbach AJ, Nagahama Y, Zhang A, Howard MA, Kawasaki H, Wu S, Cornell RA, Michaelson JJ, Bassuk AG. Drug repositioning in epilepsy reveals novel antiseizure candidates. Ann Clin Transl Neurol 2019; 6:295-309. [PMID: 30847362 PMCID: PMC6389756 DOI: 10.1002/acn3.703] [Citation(s) in RCA: 26] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2018] [Revised: 10/24/2018] [Accepted: 10/26/2018] [Indexed: 01/22/2023] Open
Abstract
Objective Epilepsy treatment falls short in ~30% of cases. A better understanding of epilepsy pathophysiology can guide rational drug development in this difficult to treat condition. We tested a low-cost, drug-repositioning strategy to identify candidate epilepsy drugs that are already FDA-approved and might be immediately tested in epilepsy patients who require new therapies. Methods Biopsies of spiking and nonspiking hippocampal brain tissue from six patients with unilateral mesial temporal lobe epilepsy were analyzed by RNA-Seq. These profiles were correlated with transcriptomes from cell lines treated with FDA-approved drugs, identifying compounds which were tested for therapeutic efficacy in a zebrafish seizure assay. Results In spiking versus nonspiking biopsies, RNA-Seq identified 689 differentially expressed genes, 148 of which were previously cited in articles mentioning seizures or epilepsy. Differentially expressed genes were highly enriched for protein-protein interactions and formed three clusters with associated GO-terms including myelination, protein ubiquitination, and neuronal migration. Among the 184 compounds, a zebrafish seizure model tested the therapeutic efficacy of doxycycline, metformin, nifedipine, and pyrantel tartrate, with metformin, nifedipine, and pyrantel tartrate all showing efficacy. Interpretation This proof-of-principle analysis suggests our powerful, rapid, cost-effective approach can likely be applied to other hard-to-treat diseases.
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Affiliation(s)
- Leo Brueggeman
- Department of PsychiatryCarver College of MedicineUniversity of IowaIowa CityIowa
| | - Morgan L. Sturgeon
- The Interdisciplinary Graduate Program in Molecular MedicineCarver College of MedicineUniversity of IowaIowa CityIowa
| | | | | | | | - Angela Zhang
- Department of BiostatisticsUniversity of WashingtonSeattleWashington
| | | | | | - Shu Wu
- Department of PediatricsUniversity of IowaIowa CityIowa
| | - Robert A. Cornell
- Department of Anatomy and Cell BiologyUniversity of IowaIowa CityIowa
| | - Jacob J. Michaelson
- Department of PsychiatryCarver College of MedicineUniversity of IowaIowa CityIowa
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79
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Wu X, Xie S, Wang L, Fan P, Ge S, Xie XQ, Wu W. A computational strategy for finding novel targets and therapeutic compounds for opioid dependence. PLoS One 2018; 13:e0207027. [PMID: 30403753 PMCID: PMC6221321 DOI: 10.1371/journal.pone.0207027] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2018] [Accepted: 10/22/2018] [Indexed: 12/13/2022] Open
Abstract
Opioids are widely used for treating different types of pains, but overuse and abuse of prescription opioids have led to opioid epidemic in the United States. Besides analgesic effects, chronic use of opioid can also cause tolerance, dependence, and even addiction. Effective treatment of opioid addiction remains a big challenge today. Studies on addictive effects of opioids focus on striatum, a main component in the brain responsible for drug dependence and addiction. Some transcription regulators have been associated with opioid addiction, but relationship between analgesic effects of opioids and dependence behaviors mediated by them at the molecular level has not been thoroughly investigated. In this paper, we developed a new computational strategy that identifies novel targets and potential therapeutic molecular compounds for opioid dependence and addiction. We employed several statistical and machine learning techniques and identified differentially expressed genes over time which were associated with dependence-related behaviors after exposure to either morphine or heroin, as well as potential transcription regulators that regulate these genes, using time course gene expression data from mouse striatum. Moreover, our findings revealed that some of these dependence-associated genes and transcription regulators are known to play key roles in opioid-mediated analgesia and tolerance, suggesting that an intricate relationship between opioid-induce pain-related pathways and dependence may develop at an early stage during opioid exposure. Finally, we determined small compounds that can potentially target the dependence-associated genes and transcription regulators. These compounds may facilitate development of effective therapy for opioid dependence and addiction. We also built a database (http://daportals.org) for all opioid-induced dependence-associated genes and transcription regulators that we discovered, as well as the small compounds that target those genes and transcription regulators.
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Affiliation(s)
- Xiaojun Wu
- Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America
| | - Siwei Xie
- Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America
| | - Lirong Wang
- Department of Pharmaceutical Sciences and Computational Chemical Genomics Screening Center, School of Pharmacy, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
- Drug Discovery Institute, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
| | - Peihao Fan
- Department of Pharmaceutical Sciences and Computational Chemical Genomics Screening Center, School of Pharmacy, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
| | - Songwei Ge
- School of Information, Renmin University of China, Beijing, China
| | - Xiang-Qun Xie
- Department of Pharmaceutical Sciences and Computational Chemical Genomics Screening Center, School of Pharmacy, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
- Drug Discovery Institute, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
- Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
- Department of Structural Biology, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
| | - Wei Wu
- Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America
- * E-mail:
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80
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Drug Repurposing Prediction for Immune-Mediated Cutaneous Diseases using a Word-Embedding-Based Machine Learning Approach. J Invest Dermatol 2018; 139:683-691. [PMID: 30342048 DOI: 10.1016/j.jid.2018.09.018] [Citation(s) in RCA: 37] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2018] [Revised: 09/04/2018] [Accepted: 09/06/2018] [Indexed: 11/22/2022]
Abstract
Immune-mediated diseases affect more than 20% of the population, and many autoimmune diseases affect the skin. Drug repurposing (or repositioning) is a cost-effective approach for finding drugs that can be used to treat diseases for which they are currently not prescribed. We implemented an efficient bioinformatics approach using word embedding to summarize drug information from more than 20 million articles and applied machine learning to model the drug-disease relationship. We trained our drug repurposing approach separately on nine cutaneous diseases (including psoriasis, atopic dermatitis, and alopecia areata) and eight other immune-mediated diseases and obtained a mean area under the receiver operating characteristic of 0.93 in cross-validation. Focusing in particular on psoriasis, a chronic inflammatory condition of skin that affects more than 100 million people worldwide, we were able to confirm drugs that are known to be effective for psoriasis and to identify potential candidates used to treat other diseases. Furthermore, the targets of drug candidates predicted by our approach were significantly enriched among genes differentially expressed in psoriatic lesional skin from a large-scale RNA sequencing cohort. Although our algorithm cannot be used to determine clinical efficacy, our work provides an approach for suggesting drugs for repurposing to immune-mediated cutaneous diseases.
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81
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82
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Musa A, Tripathi S, Kandhavelu M, Dehmer M, Emmert-Streib F. Harnessing the biological complexity of Big Data from LINCS gene expression signatures. PLoS One 2018; 13:e0201937. [PMID: 30157183 PMCID: PMC6114505 DOI: 10.1371/journal.pone.0201937] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2018] [Accepted: 07/24/2018] [Indexed: 01/02/2023] Open
Abstract
Gene expression profiling using transcriptional drug perturbations are useful for many biomedical discovery studies including drug repurposing and elucidation of drug mechanisms (MoA) and many other pharmacogenomic applications. However, limited data availability across cell types has severely hindered our capacity to progress in these areas. To fill this gap, recently, the LINCS program generated almost 1.3 million profiles for over 40,000 drug and genetic perturbations for over 70 different human cell types, including meta information about the experimental conditions and cell lines. Unfortunately, Big Data like the ones generated from the ongoing LINCS program do not enable easy insights from the data but possess considerable challenges toward their analysis. In this paper, we address some of these challenges. Specifically, first, we study the gene expression signature profiles from all cell lines and their perturbagents in order to obtain insights in the distributional characteristics of available conditions. Second, we investigate the differential expression of genes for all cell lines obtaining an understanding of condition dependent differential expression manifesting the biological complexity of perturbagents. As a result, our analysis helps the experimental design of follow-up studies, e.g., by selecting appropriate cell lines.
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Affiliation(s)
- Aliyu Musa
- Predictive Medicine and Data Analytics Lab, Department of Signal Processing, Tampere University of Technology, Tampere, Finland
- Molecular Signaling Lab, Faculty of Biomedical Sciences and Engineering, Tampere University of Technology, Tampere, Finland
| | - Shailesh Tripathi
- Predictive Medicine and Data Analytics Lab, Department of Signal Processing, Tampere University of Technology, Tampere, Finland
- University of Applied Sciences Upper Austria, Steyr, Austria
| | - Meenakshisundaram Kandhavelu
- Molecular Signaling Lab, Faculty of Biomedical Sciences and Engineering, Tampere University of Technology, Tampere, Finland
- BioMediTech Institute, Tampere University of Technology, Tampere, Finland
| | - Matthias Dehmer
- University of Applied Sciences Upper Austria, Steyr, Austria
- Institute for Bioinformatics and Translational Research, UMIT- The Health and Life Sciences University, Hall in Tyrol, Austria
| | - Frank Emmert-Streib
- Predictive Medicine and Data Analytics Lab, Department of Signal Processing, Tampere University of Technology, Tampere, Finland
- Molecular Signaling Lab, Faculty of Biomedical Sciences and Engineering, Tampere University of Technology, Tampere, Finland
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83
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Khalid Z, Sezerman OU. Computational drug repurposing to predict approved and novel drug-disease associations. J Mol Graph Model 2018; 85:91-96. [PMID: 30130693 DOI: 10.1016/j.jmgm.2018.08.005] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2018] [Revised: 08/07/2018] [Accepted: 08/10/2018] [Indexed: 11/24/2022]
Abstract
The Drug often binds to more than one targets defined as polypharmacology, one application of which is drug repurposing also referred as drug repositioning or therapeutic switching. The traditional drug discovery and development is a high-priced and tedious process, thus making drug repurposing a popular alternate strategy. We proposed an integrative method based on similarity scheme that predicts approved and novel Drug targets with new disease associations. We combined PPI, biological pathways, binding site structural similarities and disease-disease similarity measures. The results showed 94% Accuracy with 0.93 Recall and 0.94 Precision measure in predicting the approved and novel targets surpassing the existing methods. All these parameters help in elucidating the unknown associations between drug and diseases for finding the new uses for old drugs.
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84
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Yella JK, Yaddanapudi S, Wang Y, Jegga AG. Changing Trends in Computational Drug Repositioning. Pharmaceuticals (Basel) 2018; 11:E57. [PMID: 29874824 PMCID: PMC6027196 DOI: 10.3390/ph11020057] [Citation(s) in RCA: 105] [Impact Index Per Article: 17.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2018] [Revised: 06/01/2018] [Accepted: 06/02/2018] [Indexed: 12/12/2022] Open
Abstract
Efforts to maximize the indications potential and revenue from drugs that are already marketed are largely motivated by what Sir James Black, a Nobel Prize-winning pharmacologist advocated-"The most fruitful basis for the discovery of a new drug is to start with an old drug". However, rational design of drug mixtures poses formidable challenges because of the lack of or limited information about in vivo cell regulation, mechanisms of genetic pathway activation, and in vivo pathway interactions. Hence, most of the successfully repositioned drugs are the result of "serendipity", discovered during late phase clinical studies of unexpected but beneficial findings. The connections between drug candidates and their potential adverse drug reactions or new applications are often difficult to foresee because the underlying mechanism associating them is largely unknown, complex, or dispersed and buried in silos of information. Discovery of such multi-domain pharmacomodules-pharmacologically relevant sub-networks of biomolecules and/or pathways-from collection of databases by independent/simultaneous mining of multiple datasets is an active area of research. Here, while presenting some of the promising bioinformatics approaches and pipelines, we summarize and discuss the current and evolving landscape of computational drug repositioning.
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Affiliation(s)
- Jaswanth K Yella
- Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, 240 Albert Sabin Way MLC 7024, Cincinnati, OH 45229, USA.
| | - Suryanarayana Yaddanapudi
- Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, 240 Albert Sabin Way MLC 7024, Cincinnati, OH 45229, USA.
| | - Yunguan Wang
- Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, 240 Albert Sabin Way MLC 7024, Cincinnati, OH 45229, USA.
| | - Anil G Jegga
- Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, 240 Albert Sabin Way MLC 7024, Cincinnati, OH 45229, USA.
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH 45267, USA.
- Department of Computer Science, University of Cincinnati College of Engineering, Cincinnati, OH 45219, USA.
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85
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Lippmann C, Kringel D, Ultsch A, Lötsch J. Computational functional genomics-based approaches in analgesic drug discovery and repurposing. Pharmacogenomics 2018; 19:783-797. [DOI: 10.2217/pgs-2018-0036] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022] Open
Abstract
Persistent pain is a major healthcare problem affecting a fifth of adults worldwide with still limited treatment options. The search for new analgesics increasingly includes the novel research area of functional genomics, which combines data derived from various processes related to DNA sequence, gene expression or protein function and uses advanced methods of data mining and knowledge discovery with the goal of understanding the relationship between the genome and the phenotype. Its use in drug discovery and repurposing for analgesic indications has so far been performed using knowledge discovery in gene function and drug target-related databases; next-generation sequencing; and functional proteomics-based approaches. Here, we discuss recent efforts in functional genomics-based approaches to analgesic drug discovery and repurposing and highlight the potential of computational functional genomics in this field including a demonstration of the workflow using a novel R library ‘dbtORA’.
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Affiliation(s)
- Catharina Lippmann
- Fraunhofer Institute of Molecular Biology & Applied Ecology – Project Group Translational Medicine & Pharmacology (IME–TMP), Theodor-Stern-Kai 7, 60590 Frankfurt am Main, Germany
| | - Dario Kringel
- Institute of Clinical Pharmacology, Goethe-University, Theodor-Stern-Kai 7, 60590 Frankfurt am Main, Germany
| | - Alfred Ultsch
- DataBionics Research Group, University of Marburg, Hans-Meerwein-Straße 6, 35032 Marburg, Germany
| | - Jörn Lötsch
- Fraunhofer Institute of Molecular Biology & Applied Ecology – Project Group Translational Medicine & Pharmacology (IME–TMP), Theodor-Stern-Kai 7, 60590 Frankfurt am Main, Germany
- Institute of Clinical Pharmacology, Goethe-University, Theodor-Stern-Kai 7, 60590 Frankfurt am Main, Germany
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86
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Joachim RB, Altschuler GM, Hutchinson JN, Wong HR, Hide WA, Kobzik L. The relative resistance of children to sepsis mortality: from pathways to drug candidates. Mol Syst Biol 2018; 14:e7998. [PMID: 29773677 PMCID: PMC5974511 DOI: 10.15252/msb.20177998] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
Abstract
Attempts to develop drugs that address sepsis based on leads developed in animal models have failed. We sought to identify leads based on human data by exploiting a natural experiment: the relative resistance of children to mortality from severe infections and sepsis. Using public datasets, we identified key differences in pathway activity (Pathprint) in blood transcriptome profiles of septic adults and children. To find drugs that could promote beneficial (child) pathways or inhibit harmful (adult) ones, we built an in silico pathway drug network (PDN) using expression correlation between drug, disease, and pathway gene signatures across 58,475 microarrays. Specific pathway clusters from children or adults were assessed for correlation with drug‐based signatures. Validation by literature curation and by direct testing in an endotoxemia model of murine sepsis of the most correlated drug candidates demonstrated that the Pathprint‐PDN methodology is more effective at generating positive drug leads than gene‐level methods (e.g., CMap). Pathway‐centric Pathprint‐PDN is a powerful new way to identify drug candidates for intervention against sepsis and provides direct insight into pathways that may determine survival.
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Affiliation(s)
- Rose B Joachim
- Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Gabriel M Altschuler
- Department of Neuroscience, Sheffield Institute for Translational Neurosciences, University of Sheffield, Sheffield, UK
| | - John N Hutchinson
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Hector R Wong
- Division of Critical Care Medicine, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
| | - Winston A Hide
- Department of Neuroscience, Sheffield Institute for Translational Neurosciences, University of Sheffield, Sheffield, UK .,Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Lester Kobzik
- Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, MA, USA .,Department of Pathology, Brigham & Women's Hospital, Boston, MA, USA
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87
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Musa A, Ghoraie LS, Zhang SD, Glazko G, Yli-Harja O, Dehmer M, Haibe-Kains B, Emmert-Streib F. A review of connectivity map and computational approaches in pharmacogenomics. Brief Bioinform 2018; 19:506-523. [PMID: 28069634 PMCID: PMC5952941 DOI: 10.1093/bib/bbw112] [Citation(s) in RCA: 93] [Impact Index Per Article: 15.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022] Open
Abstract
Large-scale perturbation databases, such as Connectivity Map (CMap) or Library of Integrated Network-based Cellular Signatures (LINCS), provide enormous opportunities for computational pharmacogenomics and drug design. A reason for this is that in contrast to classical pharmacology focusing at one target at a time, the transcriptomics profiles provided by CMap and LINCS open the door for systems biology approaches on the pathway and network level. In this article, we provide a review of recent developments in computational pharmacogenomics with respect to CMap and LINCS and related applications.
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Affiliation(s)
- Aliyu Musa
- Predictive Medicine and Analytics Lab, Department of Signal Processing, Tampere University of Technology, Tampere, Finland
| | - Laleh Soltan Ghoraie
- Bioinformatics and Computational Genomics Laboratory, Princess Margaret Cancer Center, University Health Network, Toronto, ON, Canada
| | - Shu-Dong Zhang
- Northern Ireland Centre for Stratified Medicine, Biomedical Sciences Research Institute, University of Ulster, C-TRIC Building, Altnagelvin Area Hospital, Glenshane Road, Derry/Londonderry, Northern Ireland, UK
| | - Galina Glazko
- University of Rochester Department of Biostatistics and Computational Biology, Rochester, New York, USA
| | - Olli Yli-Harja
- Computational Systems Biology, Department of Signal Processing, Tampere University of Technology, Tampere, Finland
| | - Matthias Dehmer
- Institute for Bioinformatics and Translational Research, UMIT- The Health and Life Sciences University, Eduard Wallnoefer Zentrum 1, Hall in Tyrol, Austria
| | - Benjamin Haibe-Kains
- Bioinformatics and Computational Genomics Laboratory, Princess Margaret Cancer Center, University Health Network, Toronto, ON, Canada
- Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada
- Department of Computer Science, University of Toronto, Toronto, ON, Canada
- Ontario Institute of Cancer Research, Toronto, ON, Canada
| | - Frank Emmert-Streib
- Predictive Medicine and Analytics Lab, Department of Signal Processing, Tampere University of Technology, Tampere, Finland
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88
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Musa A, Ghoraie LS, Zhang SD, Glazko G, Yli-Harja O, Dehmer M, Haibe-Kains B, Emmert-Streib F. A review of connectivity map and computational approaches in pharmacogenomics. Brief Bioinform 2018. [PMID: 28069634 DOI: 10.1093/bib] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/14/2023] Open
Abstract
Large-scale perturbation databases, such as Connectivity Map (CMap) or Library of Integrated Network-based Cellular Signatures (LINCS), provide enormous opportunities for computational pharmacogenomics and drug design. A reason for this is that in contrast to classical pharmacology focusing at one target at a time, the transcriptomics profiles provided by CMap and LINCS open the door for systems biology approaches on the pathway and network level. In this article, we provide a review of recent developments in computational pharmacogenomics with respect to CMap and LINCS and related applications.
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Affiliation(s)
- Aliyu Musa
- Predictive Medicine and Analytics Lab, Department of Signal Processing, Tampere University of Technology, Tampere, Finland
| | - Laleh Soltan Ghoraie
- Bioinformatics and Computational Genomics Laboratory, Princess Margaret Cancer Center, University Health Network, Toronto, ON, Canada
| | - Shu-Dong Zhang
- Northern Ireland Centre for Stratified Medicine, Biomedical Sciences Research Institute, University of Ulster, C-TRIC Building, Altnagelvin Area Hospital, Glenshane Road, Derry/Londonderry BT47 6SB, Northern Ireland, UK
| | - Galina Glazko
- University of Rochester Department of Biostatistics and Computational Biology, Rochester, New York 14642, USA
| | - Olli Yli-Harja
- Computational Systems Biology, Department of Signal Processing, Tampere University of Technology, Tampere, Finland
| | - Matthias Dehmer
- Institute for Bioinformatics and Translational Research, UMIT- The Health and Life Sciences University, Eduard Wallnoefer Zentrum 1, 6060 Hall in Tyrol, Austria
| | - Benjamin Haibe-Kains
- Bioinformatics and Computational Genomics Laboratory, Princess Margaret Cancer Center, University Health Network, Toronto, ON, Canada
- Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada
- Department of Computer Science, University of Toronto, Toronto, ON, Canada
- Ontario Institute of Cancer Research, Toronto, ON, Canada
| | - Frank Emmert-Streib
- Predictive Medicine and Analytics Lab, Department of Signal Processing, Tampere University of Technology, Tampere, Finland
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Ferguson LB, Ozburn AR, Ponomarev I, Metten P, Reilly M, Crabbe JC, Harris RA, Mayfield RD. Genome-Wide Expression Profiles Drive Discovery of Novel Compounds that Reduce Binge Drinking in Mice. Neuropsychopharmacology 2018; 43:1257-1266. [PMID: 29251283 PMCID: PMC5916369 DOI: 10.1038/npp.2017.301] [Citation(s) in RCA: 30] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/12/2017] [Revised: 11/06/2017] [Accepted: 11/24/2017] [Indexed: 12/18/2022]
Abstract
Transcriptome-based drug discovery has identified new treatments for some complex diseases, but has not been applied to alcohol use disorder (AUD) or other psychiatric diseases, where there is a critical need for improved pharmacotherapies. High Drinking in the Dark (HDID-1) mice are a genetic model of AUD risk that have been selectively bred (from the HS/Npt line) to achieve intoxicating blood alcohol levels (BALs) after binge-like drinking. We compared brain gene expression of HDID-1 and HS/Npt mice, to determine a molecular signature for genetic risk for high intensity, binge-like drinking. Using multiple computational methods, we queried LINCS-L1000 (Library of Integrated Network-Based Cellular Signatures), a database containing gene expression signatures of thousands of compounds, to predict candidate drugs with the greatest potential to decrease alcohol consumption. Our analyses predicted novel compounds for testing, many with anti-inflammatory properties, providing further support for a neuroimmune mechanism of excessive alcohol drinking. We validated the top 2 candidates in vivo as a proof-of-concept. Terreic acid (a Bruton's tyrosine kinase inhibitor) and pergolide (a dopamine and serotonin receptor agonist) robustly reduced alcohol intake and BALs in HDID-1 mice, providing the first evidence for transcriptome-based drug discovery to target an addiction trait. Effective drug treatments for many psychiatric diseases are lacking, and the emerging tools and approaches outlined here offer researchers studying complex diseases renewed opportunities to discover new or repurpose existing compounds and expedite treatment options.
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Affiliation(s)
- Laura B Ferguson
- The Waggoner Center for Alcohol and Addiction Research, The University of Texas at Austin, Austin, TX, USA
| | - Angela R Ozburn
- Department of Behavioral Neuroscience, Oregon Health and Science University, Portland, OR, USA
- VA Portland Health Care System, Portland, OR, USA
| | - Igor Ponomarev
- The Waggoner Center for Alcohol and Addiction Research, The University of Texas at Austin, Austin, TX, USA
| | - Pamela Metten
- Department of Behavioral Neuroscience, Oregon Health and Science University, Portland, OR, USA
- VA Portland Health Care System, Portland, OR, USA
| | - Matthew Reilly
- Division of Neuroscience and Behavior, National Institute on Alcohol Abuse and Alcoholism, Bethesda, MD, USA
| | - John C Crabbe
- Department of Behavioral Neuroscience, Oregon Health and Science University, Portland, OR, USA
- VA Portland Health Care System, Portland, OR, USA
| | - R Adron Harris
- The Waggoner Center for Alcohol and Addiction Research, The University of Texas at Austin, Austin, TX, USA
| | - R Dayne Mayfield
- The Waggoner Center for Alcohol and Addiction Research, The University of Texas at Austin, Austin, TX, USA
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90
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KalantarMotamedi Y, Eastman RT, Guha R, Bender A. A systematic and prospectively validated approach for identifying synergistic drug combinations against malaria. Malar J 2018; 17:160. [PMID: 29642892 PMCID: PMC5896032 DOI: 10.1186/s12936-018-2294-5] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2017] [Accepted: 03/24/2018] [Indexed: 01/01/2023] Open
Abstract
Background Nearly half of the world’s population (3.2 billion people) were at risk of malaria in 2015, and resistance to current therapies is a major concern. While the standard of care includes drug combinations, there is a pressing need to identify new combinations that can bypass current resistance mechanisms. In the work presented here, a combined transcriptional drug repositioning/discovery and machine learning approach is proposed. Methods The integrated approach utilizes gene expression data from patient-derived samples, in combination with large-scale anti-malarial combination screening data, to predict synergistic compound combinations for three Plasmodium falciparum strains (3D7, DD2 and HB3). Both single compounds and combinations predicted to be active were prospectively tested in experiment. Results One of the predicted single agents, apicidin, was active with the AC50 values of 74.9, 84.1 and 74.9 nM in 3D7, DD2 and HB3 P. falciparum strains while its maximal safe plasma concentration in human is 547.6 ± 136.6 nM. Apicidin at the safe dose of 500 nM kills on average 97% of the parasite. The synergy prediction algorithm exhibited overall precision and recall of 83.5 and 65.1% for mild-to-strong, 48.8 and 75.5% for moderate-to-strong and 12.0 and 62.7% for strong synergies. Some of the prospectively predicted combinations, such as tacrolimus-hydroxyzine and raloxifene-thioridazine, exhibited significant synergy across the three P. falciparum strains included in the study. Conclusions Systematic approaches can play an important role in accelerating discovering novel combinational therapies for malaria as it enables selecting novel synergistic compound pairs in a more informed and cost-effective manner. Electronic supplementary material The online version of this article (10.1186/s12936-018-2294-5) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Yasaman KalantarMotamedi
- Centre for Molecular Informatics, Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge, CB2 1EW, UK
| | - Richard T Eastman
- Division of Preclinical Innovation, National Center for Advancing Translational Sciences, National Institutes of Health, 9800 Medical Center Drive, Rockville, MD, 20852, USA
| | - Rajarshi Guha
- Division of Preclinical Innovation, National Center for Advancing Translational Sciences, National Institutes of Health, 9800 Medical Center Drive, Rockville, MD, 20852, USA.
| | - Andreas Bender
- Centre for Molecular Informatics, Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge, CB2 1EW, UK.
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91
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Wang E, Zhu H, Wang X, Gower AC, Wallack M, Blusztajn JK, Kowall N, Qiu WQ. Amylin Treatment Reduces Neuroinflammation and Ameliorates Abnormal Patterns of Gene Expression in the Cerebral Cortex of an Alzheimer's Disease Mouse Model. J Alzheimers Dis 2018; 56:47-61. [PMID: 27911303 DOI: 10.3233/jad-160677] [Citation(s) in RCA: 31] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Our recent study has demonstrated that peripheral amylin treatment reduces the amyloid pathology in the brain of Alzheimer's disease (AD) mouse models, and improves their learning and memory. We hypothesized that the beneficial effects of amylin for AD was beyond reducing the amyloids in the brain, and have now directly tested the actions of amylin on other aspects of AD pathogenesis, especially neuroinflammation. A 10-week course of peripheral amylin treatment significantly reduced levels of cerebral inflammation markers, Cd68 and Iba1, in amyloid precursor protein (APP) transgenic mice. Mechanistic studies indicated the protective effect of amylin required interaction with its cognate receptor because silencing the amylin receptor expression blocked the amylin effect on Cd68 in microglia. Using weighted gene co-expression network analysis, we discovered that amylin treatment influenced two gene modules linked with amyloid pathology: 1) a module related to proinflammation and transport/vesicle process that included a hub gene of Cd68, and 2) a module related to mitochondria function that included a hub gene of Atp5b. Amylin treatment restored the expression of most genes in the APP cortex toward levels observed in the wild-type (WT) cortex in these two modules including Cd68 and Atp5b. Using a human dataset, we found that the expression levels of Cd68 and Atp5b were significantly correlated with the neurofibrillary tangle burden in the AD brain and with their cognition. These data suggest that amylin acts on the pathological cascade in animal models of AD, and further supports the therapeutic potential of amylin-type peptides for AD.
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Affiliation(s)
- Erming Wang
- Department of Pharmacology and Experimental Therapeutics, Boston University School of Medicine, Boston, MA, USA
| | - Haihao Zhu
- Department of Pharmacology and Experimental Therapeutics, Boston University School of Medicine, Boston, MA, USA
| | - Xiaofan Wang
- Department of Pharmacology and Experimental Therapeutics, Boston University School of Medicine, Boston, MA, USA
| | - Adam C Gower
- Clinical and Translational Science Institute, Boston University School of Medicine, Boston, MA, USA
| | - Max Wallack
- Department of Pharmacology and Experimental Therapeutics, Boston University School of Medicine, Boston, MA, USA
| | - Jan Krzysztof Blusztajn
- Department of Pathology and Laboratory Medicine, Boston University School of Medicine, Boston, MA, USA
| | - Neil Kowall
- Department of Neurology, Boston University School of Medicine, Boston, MA, USA.,Alzheimer's Disease Center, Boston University School of Medicine, Boston, MA, USA
| | - Wei Qiao Qiu
- Department of Pharmacology and Experimental Therapeutics, Boston University School of Medicine, Boston, MA, USA.,Alzheimer's Disease Center, Boston University School of Medicine, Boston, MA, USA.,Department of Psychiatry, Boston University School of Medicine, Boston, MA, USA
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92
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Computational drugs repositioning identifies inhibitors of oncogenic PI3K/AKT/P70S6K-dependent pathways among FDA-approved compounds. Oncotarget 2018; 7:58743-58758. [PMID: 27542212 PMCID: PMC5312272 DOI: 10.18632/oncotarget.11318] [Citation(s) in RCA: 31] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2016] [Accepted: 08/03/2016] [Indexed: 12/19/2022] Open
Abstract
The discovery of inhibitors for oncogenic signalling pathways remains a key focus in modern oncology, based on personalized and targeted therapeutics. Computational drug repurposing via the analysis of FDA-approved drug network is becoming a very effective approach to identify therapeutic opportunities in cancer and other human diseases. Given that gene expression signatures can be associated with specific oncogenic mutations, we tested whether a "reverse" oncogene-specific signature might assist in the computational repositioning of inhibitors of oncogenic pathways. As a proof of principle, we focused on oncogenic PI3K-dependent signalling, a molecular pathway frequently driving cancer progression as well as raising resistance to anticancer-targeted therapies. We show that implementation of "reverse" oncogenic PI3K-dependent transcriptional signatures combined with interrogation of drug networks identified inhibitors of PI3K-dependent signalling among FDA-approved compounds. This led to repositioning of Niclosamide (Niclo) and Pyrvinium Pamoate (PP), two anthelmintic drugs, as inhibitors of oncogenic PI3K-dependent signalling. Niclo inhibited phosphorylation of P70S6K, while PP inhibited phosphorylation of AKT and P70S6K, which are downstream targets of PI3K. Anthelmintics inhibited oncogenic PI3K-dependent gene expression and showed a cytostatic effect in vitro and in mouse mammary gland. Lastly, PP inhibited the growth of breast cancer cells harbouring PI3K mutations. Our data indicate that drug repositioning by network analysis of oncogene-specific transcriptional signatures is an efficient strategy for identifying oncogenic pathway inhibitors among FDA-approved compounds. We propose that PP and Niclo should be further investigated as potential therapeutics for the treatment of tumors or diseases carrying the constitutive activation of the PI3K/P70S6K signalling axis.
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93
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Talevi A. Drug repositioning: current approaches and their implications in the precision medicine era. EXPERT REVIEW OF PRECISION MEDICINE AND DRUG DEVELOPMENT 2018. [DOI: 10.1080/23808993.2018.1424535] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Affiliation(s)
- Alan Talevi
- Laboratory of Research and Development of Bioactive Compounds – Medicinal Chemistry, Department of Biological Sciences, Faculty of Exact Sciences, University of La Plata, La Plata, Argentina
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94
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Schubert M, Klinger B, Klünemann M, Sieber A, Uhlitz F, Sauer S, Garnett MJ, Blüthgen N, Saez-Rodriguez J. Perturbation-response genes reveal signaling footprints in cancer gene expression. Nat Commun 2018; 9:20. [PMID: 29295995 PMCID: PMC5750219 DOI: 10.1038/s41467-017-02391-6] [Citation(s) in RCA: 334] [Impact Index Per Article: 55.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2016] [Accepted: 11/24/2017] [Indexed: 12/18/2022] Open
Abstract
Aberrant cell signaling can cause cancer and other diseases and is a focal point of drug research. A common approach is to infer signaling activity of pathways from gene expression. However, mapping gene expression to pathway components disregards the effect of post-translational modifications, and downstream signatures represent very specific experimental conditions. Here we present PROGENy, a method that overcomes both limitations by leveraging a large compendium of publicly available perturbation experiments to yield a common core of Pathway RespOnsive GENes. Unlike pathway mapping methods, PROGENy can (i) recover the effect of known driver mutations, (ii) provide or improve strong markers for drug indications, and (iii) distinguish between oncogenic and tumor suppressor pathways for patient survival. Collectively, these results show that PROGENy accurately infers pathway activity from gene expression in a wide range of conditions.
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Affiliation(s)
- Michael Schubert
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Cambridge, CB10 1SD, UK
| | - Bertram Klinger
- Institute of Pathology, Charité Universitätsmedizin Berlin, Charitéplatz 1, 10117, Berlin, Germany
- IRI Life Sciences and Institute for Theoretical Biology, Humboldt University Berlin, Philippstr. 13/Haus 18, 10115, Berlin, Germany
| | - Martina Klünemann
- Institute of Pathology, Charité Universitätsmedizin Berlin, Charitéplatz 1, 10117, Berlin, Germany
- IRI Life Sciences and Institute for Theoretical Biology, Humboldt University Berlin, Philippstr. 13/Haus 18, 10115, Berlin, Germany
| | - Anja Sieber
- Institute of Pathology, Charité Universitätsmedizin Berlin, Charitéplatz 1, 10117, Berlin, Germany
- IRI Life Sciences and Institute for Theoretical Biology, Humboldt University Berlin, Philippstr. 13/Haus 18, 10115, Berlin, Germany
| | - Florian Uhlitz
- Institute of Pathology, Charité Universitätsmedizin Berlin, Charitéplatz 1, 10117, Berlin, Germany
- IRI Life Sciences and Institute for Theoretical Biology, Humboldt University Berlin, Philippstr. 13/Haus 18, 10115, Berlin, Germany
| | - Sascha Sauer
- Max Delbrück Center for Molecular Medicine (MDC), Berlin Institute for Medical Systems Biology/Berlin Institute of Health, Robert-Rössle-Str. 10, 13092, Berlin, Germany
| | - Mathew J Garnett
- Wellcome Trust Sanger Institute, Wellcome Genome Campus, Cambridge, CB10 1SA, UK
| | - Nils Blüthgen
- Institute of Pathology, Charité Universitätsmedizin Berlin, Charitéplatz 1, 10117, Berlin, Germany
- IRI Life Sciences and Institute for Theoretical Biology, Humboldt University Berlin, Philippstr. 13/Haus 18, 10115, Berlin, Germany
| | - Julio Saez-Rodriguez
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Cambridge, CB10 1SD, UK.
- RWTH Aachen University, Faculty of Medicine, Joint Research Centre for Computational Biomedicine, Aachen, 52057, Germany.
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95
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Predicting drug-disease interactions by semi-supervised graph cut algorithm and three-layer data integration. BMC Med Genomics 2017; 10:79. [PMID: 29297383 PMCID: PMC5751445 DOI: 10.1186/s12920-017-0311-0] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022] Open
Abstract
Background Prediction of drug-disease interactions is promising for either drug repositioning or disease treatment fields. The discovery of novel drug-disease interactions, on one hand can help to find novel indictions for the approved drugs; on the other hand can provide new therapeutic approaches for the diseases. Recently, computational methods for finding drug-disease interactions have attracted lots of attention because of their far more higher efficiency and lower cost than the traditional wet experiment methods. However, they still face several challenges, such as the organization of the heterogeneous data, the performance of the model, and so on. Methods In this work, we present to hierarchically integrate the heterogeneous data into three layers. The drug-drug and disease-disease similarities are first calculated separately in each layer, and then the similarities from three layers are linearly fused into comprehensive drug similarities and disease similarities, which can then be used to measure the similarities between two drug-disease pairs. We construct a novel weighted drug-disease pair network, where a node is a drug-disease pair with known or unknown treatment relation, an edge represents the node-node relation which is weighted with the similarity score between two pairs. Now that similar drug-disease pairs are supposed to show similar treatment patterns, we can find the optimal graph cut of the network. The drug-disease pair with unknown relation can then be considered to have similar treatment relation with that within the same cut. Therefore, we develop a semi-supervised graph cut algorithm, SSGC, to find the optimal graph cut, based on which we can identify the potential drug-disease treatment interactions. Results By comparing with three representative network-based methods, SSGC achieves the highest performances, in terms of both AUC score and the identification rates of true drug-disease pairs. The experiments with different integration strategies also demonstrate that considering several sources of data can improve the performances of the predictors. Further case studies on four diseases, the top-ranked drug-disease associations have been confirmed by KEGG, CTD database and the literature, illustrating the usefulness of SSGC. Conclusions The proposed comprehensive similarity scores from multi-views and multiple layers and the graph-cut based algorithm can greatly improve the prediction performances of drug-disease associations.
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96
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Thillaiyampalam G, Liberante F, Murray L, Cardwell C, Mills K, Zhang SD. An integrated meta-analysis approach to identifying medications with potential to alter breast cancer risk through connectivity mapping. BMC Bioinformatics 2017; 18:581. [PMID: 29268695 PMCID: PMC5740937 DOI: 10.1186/s12859-017-1989-x] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2017] [Accepted: 12/06/2017] [Indexed: 12/17/2022] Open
Abstract
BACKGROUND Gene expression connectivity mapping has gained much popularity in recent years with a number of successful applications in biomedical research testifying its utility and promise. A major application of connectivity mapping is the identification of small molecule compounds capable of inhibiting a disease state. In this study, we are additionally interested in small molecule compounds that may enhance a disease state or increase the risk of developing that disease. Using breast cancer as a case study, we aim to develop and test a methodology for identifying commonly prescribed drugs that may have a suppressing or inducing effect on the target disease (breast cancer). RESULTS We obtained from public data repositories a collection of breast cancer gene expression datasets with over 7000 patients. An integrated meta-analysis approach to gene expression connectivity mapping was developed, which involved unified processing and normalization of raw gene expression data, systematic removal of batch effects, and multiple runs of balanced sampling for differential expression analysis. Differentially expressed genes stringently selected were used to construct multiple non-joint gene signatures representing the same biological state. Remarkably these non-joint gene signatures retrieved from connectivity mapping separate lists of candidate drugs with significant overlaps, providing high confidence in their predicted effects on breast cancers. Of particular note, among the top 26 compounds identified as inversely connected to the breast cancer gene signatures, 14 of them are known anti-cancer drugs. CONCLUSIONS A few candidate drugs with potential to enhance breast cancer or increase the risk of the disease were also identified; further investigation on a large population is required to firmly establish their effects on breast cancer risks. This work thus provides a novel approach and an applicable example for identifying medications with potential to alter cancer risks through gene expression connectivity mapping.
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Affiliation(s)
| | - Fabio Liberante
- Centre for Cancer Research and Cell Biology (CCRCB), Queen’s University Belfast, Belfast, UK
| | - Liam Murray
- Centre for Public Health, Queen’s University Belfast, Belfast, UK
| | - Chris Cardwell
- Centre for Public Health, Queen’s University Belfast, Belfast, UK
| | - Ken Mills
- Centre for Cancer Research and Cell Biology (CCRCB), Queen’s University Belfast, Belfast, UK
| | - Shu-Dong Zhang
- Centre for Cancer Research and Cell Biology (CCRCB), Queen’s University Belfast, Belfast, UK
- Northern Ireland Centre for Stratified Medicine, Biomedical Sciences Research Institute, University of Ulster, C-TRIC Building, Altnagelvin Area Hospital, Glenshane Road, L/Derry, Northern Ireland, BT47 6SB UK
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97
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Abbruzzese C, Matteoni S, Signore M, Cardone L, Nath K, Glickson JD, Paggi MG. Drug repurposing for the treatment of glioblastoma multiforme. JOURNAL OF EXPERIMENTAL & CLINICAL CANCER RESEARCH : CR 2017; 36:169. [PMID: 29179732 PMCID: PMC5704391 DOI: 10.1186/s13046-017-0642-x] [Citation(s) in RCA: 49] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 11/09/2017] [Accepted: 11/17/2017] [Indexed: 01/07/2023]
Abstract
Background Glioblastoma Multiforme is the deadliest type of brain tumor and is characterized by very poor prognosis with a limited overall survival. Current optimal therapeutic approach has essentially remained unchanged for more than a decade, consisting in maximal surgical resection followed by radiotherapy plus temozolomide. Main body Such a dismal patient outcome represents a compelling need for innovative and effective therapeutic approaches. Given the development of new drugs is a process presently characterized by an immense increase in costs and development time, drug repositioning, finding new uses for existing approved drugs or drug repurposing, re-use of old drugs when novel molecular findings make them attractive again, are gaining significance in clinical pharmacology, since it allows faster and less expensive delivery of potentially useful drugs from the bench to the bedside. This is quite evident in glioblastoma, where a number of old drugs is now considered for clinical use, often in association with the first-line therapeutic intervention. Interestingly, most of these medications are, or have been, widely employed for decades in non-neoplastic pathologies without relevant side effects. Now, the refinement of their molecular mechanism(s) of action through up-to-date technologies is paving the way for their use in the therapeutic approach of glioblastoma as well as other cancer types. Short conclusion The spiraling costs of new antineoplastic drugs and the long time required for them to reach the market demands a profoundly different approach to keep lifesaving therapies affordable for cancer patients. In this context, repurposing can represent a relatively inexpensive, safe and fast approach to glioblastoma treatment. To this end, pros and cons must be accurately considered.
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Affiliation(s)
- Claudia Abbruzzese
- Department of Research, Advanced Diagnostics and Technological Innovation, Unit of Cellular Networks and Therapeutic Targets, Proteomics Area, Regina Elena National Cancer Institute, IRCCS, Via Elio Chianesi, 53, Rome, Italy
| | - Silvia Matteoni
- Department of Research, Advanced Diagnostics and Technological Innovation, Unit of Cellular Networks and Therapeutic Targets, Proteomics Area, Regina Elena National Cancer Institute, IRCCS, Via Elio Chianesi, 53, Rome, Italy
| | - Michele Signore
- RPPA Unit, Proteomics Area, Core Facilities, Istituto Superiore di Sanità, Rome, Italy
| | - Luca Cardone
- Department of Research, Advanced Diagnostics and Technological Innovation, Unit of Cellular Networks and Therapeutic Targets, Regina Elena National Cancer Institute, IRCCS, Rome, Italy
| | - Kavindra Nath
- Laboratory of Molecular Imaging, Department of Radiology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Jerry D Glickson
- Laboratory of Molecular Imaging, Department of Radiology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Marco G Paggi
- Department of Research, Advanced Diagnostics and Technological Innovation, Unit of Cellular Networks and Therapeutic Targets, Proteomics Area, Regina Elena National Cancer Institute, IRCCS, Via Elio Chianesi, 53, Rome, Italy.
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Aliper A, Jellen L, Cortese F, Artemov A, Karpinsky-Semper D, Moskalev A, Swick AG, Zhavoronkov A. Towards natural mimetics of metformin and rapamycin. Aging (Albany NY) 2017; 9:2245-2268. [PMID: 29165314 PMCID: PMC5723685 DOI: 10.18632/aging.101319] [Citation(s) in RCA: 62] [Impact Index Per Article: 8.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2017] [Accepted: 11/02/2017] [Indexed: 12/14/2022]
Abstract
Aging is now at the forefront of major challenges faced globally, creating an immediate need for safe, widescale interventions to reduce the burden of chronic disease and extend human healthspan. Metformin and rapamycin are two FDA-approved mTOR inhibitors proposed for this purpose, exhibiting significant anti-cancer and anti-aging properties beyond their current clinical applications. However, each faces issues with approval for off-label, prophylactic use due to adverse effects. Here, we initiate an effort to identify nutraceuticals-safer, naturally-occurring compounds-that mimic the anti-aging effects of metformin and rapamycin without adverse effects. We applied several bioinformatic approaches and deep learning methods to the Library of Integrated Network-based Cellular Signatures (LINCS) dataset to map the gene- and pathway-level signatures of metformin and rapamycin and screen for matches among over 800 natural compounds. We then predicted the safety of each compound with an ensemble of deep neural network classifiers. The analysis revealed many novel candidate metformin and rapamycin mimetics, including allantoin and ginsenoside (metformin), epigallocatechin gallate and isoliquiritigenin (rapamycin), and withaferin A (both). Four relatively unexplored compounds also scored well with rapamycin. This work revealed promising candidates for future experimental validation while demonstrating the applications of powerful screening methods for this and similar endeavors.
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Affiliation(s)
- Alexander Aliper
- Insilico Medicine, Inc, Research Department, Baltimore, MD 21218, USA
| | - Leslie Jellen
- Insilico Medicine, Inc, Research Department, Baltimore, MD 21218, USA
| | - Franco Cortese
- Biogerontology Research Foundation, Research Department, Oxford, United Kingdom
- Department of Biomedical and Molecular Science, Queen's University School of Medicine, Queen's University, Kingston, ON K7L 3N6, Canada
| | - Artem Artemov
- Insilico Medicine, Inc, Research Department, Baltimore, MD 21218, USA
| | | | - Alexey Moskalev
- Laboratory of Molecular Radiobiology and Gerontology, Institute of Biology of Komi Science Center of Ural Branch of Russian Academy of Sciences, Syktyvkar, 167982, Russia
| | | | - Alex Zhavoronkov
- Insilico Medicine, Inc, Research Department, Baltimore, MD 21218, USA
- Biogerontology Research Foundation, Research Department, Oxford, United Kingdom
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99
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Safikhani Z, Smirnov P, Thu KL, Silvester J, El-Hachem N, Quevedo R, Lupien M, Mak TW, Cescon D, Haibe-Kains B. Gene isoforms as expression-based biomarkers predictive of drug response in vitro. Nat Commun 2017; 8:1126. [PMID: 29066719 PMCID: PMC5655668 DOI: 10.1038/s41467-017-01153-8] [Citation(s) in RCA: 40] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2016] [Accepted: 08/23/2017] [Indexed: 01/09/2023] Open
Abstract
Next-generation sequencing technologies have recently been used in pharmacogenomic studies to characterize large panels of cancer cell lines at the genomic and transcriptomic levels. Among these technologies, RNA-sequencing enable profiling of alternatively spliced transcripts. Given the high frequency of mRNA splicing in cancers, linking this feature to drug response will open new avenues of research in biomarker discovery. To identify robust transcriptomic biomarkers for drug response across studies, we develop a meta-analytical framework combining the pharmacological data from two large-scale drug screening datasets. We use an independent pan-cancer pharmacogenomic dataset to test the robustness of our candidate biomarkers across multiple cancer types. We further analyze two independent breast cancer datasets and find that specific isoforms of IGF2BP2, NECTIN4, ITGB6, and KLHDC9 are significantly associated with AZD6244, lapatinib, erlotinib, and paclitaxel, respectively. Our results support isoform expressions as a rich resource for biomarkers predictive of drug response.
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Affiliation(s)
- Zhaleh Safikhani
- Princess Margaret Cancer Centre, University Health Network, 101 College Street, Toronto, ON, Canada, M5G1L7
- Department of Medical Biophysics, University of Toronto, 101 College Street, Toronto, ON, Canada, M5G1L7
| | - Petr Smirnov
- Princess Margaret Cancer Centre, University Health Network, 101 College Street, Toronto, ON, Canada, M5G1L7
| | - Kelsie L Thu
- Princess Margaret Cancer Centre, University Health Network, 101 College Street, Toronto, ON, Canada, M5G1L7
- Institut de Recherches Cliniques de Montréal, 110 Pine Avenue West, Montreal, QC, Canada, H2W 1R7
| | - Jennifer Silvester
- Princess Margaret Cancer Centre, University Health Network, 101 College Street, Toronto, ON, Canada, M5G1L7
- Institut de Recherches Cliniques de Montréal, 110 Pine Avenue West, Montreal, QC, Canada, H2W 1R7
| | - Nehme El-Hachem
- Institut de Recherches Cliniques de Montréal, 110 Pine Avenue West, Montreal, QC, Canada, H2W 1R7
| | - Rene Quevedo
- Princess Margaret Cancer Centre, University Health Network, 101 College Street, Toronto, ON, Canada, M5G1L7
- Department of Medical Biophysics, University of Toronto, 101 College Street, Toronto, ON, Canada, M5G1L7
| | - Mathieu Lupien
- Princess Margaret Cancer Centre, University Health Network, 101 College Street, Toronto, ON, Canada, M5G1L7
- Department of Medical Biophysics, University of Toronto, 101 College Street, Toronto, ON, Canada, M5G1L7
| | - Tak W Mak
- Princess Margaret Cancer Centre, University Health Network, 101 College Street, Toronto, ON, Canada, M5G1L7
- Department of Medical Biophysics, University of Toronto, 101 College Street, Toronto, ON, Canada, M5G1L7
- Campbell Family Institute for Breast Cancer Research, 620 University Avenue, Toronto, ON, Canada, M5G2C1
| | - David Cescon
- Princess Margaret Cancer Centre, University Health Network, 101 College Street, Toronto, ON, Canada, M5G1L7
- Campbell Family Institute for Breast Cancer Research, 620 University Avenue, Toronto, ON, Canada, M5G2C1
- Division of Medical Oncology and Hematology, Department of Medicine, University of Toronto, 27 King's College Circle, Toronto, ON, Canada, M5S 1A1
| | - Benjamin Haibe-Kains
- Princess Margaret Cancer Centre, University Health Network, 101 College Street, Toronto, ON, Canada, M5G1L7.
- Department of Medical Biophysics, University of Toronto, 101 College Street, Toronto, ON, Canada, M5G1L7.
- Department of Computer Science, University of Toronto, 10 King's College Road, Toronto, ON, Canada, M5S 3G4.
- Ontario Institute of Cancer Research, 661 University Avenue, Suite 510, Toronto, ON, Canada, M5G 0A3.
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Sam E, Athri P. Web-based drug repurposing tools: a survey. Brief Bioinform 2017; 20:299-316. [DOI: 10.1093/bib/bbx125] [Citation(s) in RCA: 30] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2017] [Indexed: 12/15/2022] Open
Affiliation(s)
- Elizabeth Sam
- Department of Computer Science & Engineering Amrita, University Bengaluru, India
| | - Prashanth Athri
- Department of Computer Science & Engineering Amrita, University Bengaluru, India
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