51
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Reese JT, Unni D, Callahan TJ, Cappelletti L, Ravanmehr V, Carbon S, Shefchek KA, Good BM, Balhoff JP, Fontana T, Blau H, Matentzoglu N, Harris NL, Munoz-Torres MC, Haendel MA, Robinson PN, Joachimiak MP, Mungall CJ. KG-COVID-19: A Framework to Produce Customized Knowledge Graphs for COVID-19 Response. PATTERNS (NEW YORK, N.Y.) 2021; 2:100155. [PMID: 33196056 PMCID: PMC7649624 DOI: 10.1016/j.patter.2020.100155] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/17/2020] [Revised: 10/02/2020] [Accepted: 11/05/2020] [Indexed: 02/06/2023]
Abstract
Integrated, up-to-date data about SARS-CoV-2 and COVID-19 is crucial for the ongoing response to the COVID-19 pandemic by the biomedical research community. While rich biological knowledge exists for SARS-CoV-2 and related viruses (SARS-CoV, MERS-CoV), integrating this knowledge is difficult and time-consuming, since much of it is in siloed databases or in textual format. Furthermore, the data required by the research community vary drastically for different tasks; the optimal data for a machine learning task, for example, is much different from the data used to populate a browsable user interface for clinicians. To address these challenges, we created KG-COVID-19, a flexible framework that ingests and integrates heterogeneous biomedical data to produce knowledge graphs (KGs), and applied it to create a KG for COVID-19 response. This KG framework also can be applied to other problems in which siloed biomedical data must be quickly integrated for different research applications, including future pandemics.
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Affiliation(s)
- Justin T. Reese
- Division of Environmental Genomics and Systems Biology, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | - Deepak Unni
- Division of Environmental Genomics and Systems Biology, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | - Tiffany J. Callahan
- Computational Bioscience Program, Department of Pharmacology, University of Colorado Anschutz School of Medicine, Aurora, CO 80045, USA
| | - Luca Cappelletti
- Department of Computer Science, University of Milano, 20122 Milan, Italy
| | - Vida Ravanmehr
- The Jackson Laboratory for Genomic Medicine, Farmington, CT 06032, USA
| | - Seth Carbon
- Division of Environmental Genomics and Systems Biology, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | - Kent A. Shefchek
- Linus Pauling Institute, Environmental and Molecular Toxicology, Oregon State University, Corvallis, OR 97331, USA
| | - Benjamin M. Good
- Division of Environmental Genomics and Systems Biology, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | - James P. Balhoff
- Renaissance Computing Institute, University of North Carolina at Chapel Hill, Chapel Hill, NC 27517, USA
| | - Tommaso Fontana
- Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, 20133 Milan, Italy
| | - Hannah Blau
- The Jackson Laboratory for Genomic Medicine, Farmington, CT 06032, USA
| | | | - Nomi L. Harris
- Division of Environmental Genomics and Systems Biology, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | - Monica C. Munoz-Torres
- Division of Environmental Genomics and Systems Biology, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
- Linus Pauling Institute, Environmental and Molecular Toxicology, Oregon State University, Corvallis, OR 97331, USA
| | - Melissa A. Haendel
- Linus Pauling Institute, Environmental and Molecular Toxicology, Oregon State University, Corvallis, OR 97331, USA
| | - Peter N. Robinson
- The Jackson Laboratory for Genomic Medicine, Farmington, CT 06032, USA
| | - Marcin P. Joachimiak
- Division of Environmental Genomics and Systems Biology, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | - Christopher J. Mungall
- Division of Environmental Genomics and Systems Biology, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
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52
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Kim H, Kim E, Lee I, Bae B, Park M, Nam H. Artificial Intelligence in Drug Discovery: A Comprehensive Review of Data-driven and Machine Learning Approaches. BIOTECHNOL BIOPROC E 2021; 25:895-930. [PMID: 33437151 PMCID: PMC7790479 DOI: 10.1007/s12257-020-0049-y] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2020] [Revised: 05/27/2020] [Accepted: 06/03/2020] [Indexed: 02/07/2023]
Abstract
As expenditure on drug development increases exponentially, the overall drug discovery process requires a sustainable revolution. Since artificial intelligence (AI) is leading the fourth industrial revolution, AI can be considered as a viable solution for unstable drug research and development. Generally, AI is applied to fields with sufficient data such as computer vision and natural language processing, but there are many efforts to revolutionize the existing drug discovery process by applying AI. This review provides a comprehensive, organized summary of the recent research trends in AI-guided drug discovery process including target identification, hit identification, ADMET prediction, lead optimization, and drug repositioning. The main data sources in each field are also summarized in this review. In addition, an in-depth analysis of the remaining challenges and limitations will be provided, and proposals for promising future directions in each of the aforementioned areas.
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Affiliation(s)
- Hyunho Kim
- School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology (GIST), Gwangju, 61005 Korea
| | - Eunyoung Kim
- School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology (GIST), Gwangju, 61005 Korea
| | - Ingoo Lee
- School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology (GIST), Gwangju, 61005 Korea
| | - Bongsung Bae
- School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology (GIST), Gwangju, 61005 Korea
| | - Minsu Park
- School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology (GIST), Gwangju, 61005 Korea
| | - Hojung Nam
- School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology (GIST), Gwangju, 61005 Korea
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53
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Bocci G, Bradfute SB, Ye C, Garcia MJ, Parvathareddy J, Reichard W, Surendranathan S, Bansal S, Bologa CG, Perkins DJ, Jonsson CB, Sklar LA, Oprea TI. Virtual and In Vitro Antiviral Screening Revive Therapeutic Drugs for COVID-19. ACS Pharmacol Transl Sci 2020; 3:1278-1292. [PMID: 33330842 PMCID: PMC7571299 DOI: 10.1021/acsptsci.0c00131] [Citation(s) in RCA: 32] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2020] [Indexed: 02/08/2023]
Abstract
The urgent need for a cure for early phase COVID-19 infected patients critically underlines drug repositioning strategies able to efficiently identify new and reliable treatments by merging computational, experimental, and pharmacokinetic expertise. Here we report new potential therapeutics for COVID-19 identified with a combined virtual and experimental screening strategy and selected among already approved drugs. We used hydroxychloroquine (HCQ), one of the most studied drugs in current clinical trials, as a reference template to screen for structural similarity against a library of almost 4000 approved drugs. The top-ranked drugs, based on structural similarity to HCQ, were selected for in vitro antiviral assessment. Among the selected drugs, both zuclopenthixol and nebivolol efficiently block SARS-CoV-2 infection with EC50 values in the low micromolar range, as confirmed by independent experiments. The anti-SARS-CoV-2 potential of ambroxol, amodiaquine, and its active metabolite (N-monodesethyl amodiaquine) is also discussed. In trying to understand the "hydroxychloroquine" mechanism of action, both pK a and the HCQ aromatic core may play a role. Further, we show that the amodiaquine metabolite and, to a lesser extent, zuclopenthixol and nebivolol are active in a SARS-CoV-2 titer reduction assay. Given the need for improved efficacy and safety, we propose zuclopenthixol, nebivolol, and amodiaquine as potential candidates for clinical trials against the early phase of the SARS-CoV-2 infection and discuss their potential use as adjuvant to the current (i.e., remdesivir and favipiravir) COVID-19 therapeutics.
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Affiliation(s)
- Giovanni Bocci
- Translational
Informatics Division, Department of Internal Medicine, University of New Mexico School of Medicine, Albuquerque, New Mexico 87131, United States
| | - Steven B. Bradfute
- Center
for Global Health, Department of Internal Medicine, University of New Mexico School of Medicine, Albuquerque, New Mexico 87131, United States
| | - Chunyan Ye
- Center
for Global Health, Department of Internal Medicine, University of New Mexico School of Medicine, Albuquerque, New Mexico 87131, United States
| | - Matthew J. Garcia
- UNM
Center for Molecular Discovery, University
of New Mexico School of Medicine, Albuquerque, New Mexico 87131, United States
| | - Jyothi Parvathareddy
- Department
of Microbiology, Immunology and Biochemistry, University of Tennessee Health Science Center, Memphis, Tennessee 3816, United States
| | - Walter Reichard
- Department
of Microbiology, Immunology and Biochemistry, University of Tennessee Health Science Center, Memphis, Tennessee 3816, United States
| | - Surekha Surendranathan
- Department
of Microbiology, Immunology and Biochemistry, University of Tennessee Health Science Center, Memphis, Tennessee 3816, United States
| | - Shruti Bansal
- Department
of Microbiology, Immunology and Biochemistry, University of Tennessee Health Science Center, Memphis, Tennessee 3816, United States
| | - Cristian G. Bologa
- Translational
Informatics Division, Department of Internal Medicine, University of New Mexico School of Medicine, Albuquerque, New Mexico 87131, United States
| | - Douglas J. Perkins
- Center
for Global Health, Department of Internal Medicine, University of New Mexico School of Medicine, Albuquerque, New Mexico 87131, United States
| | - Colleen B. Jonsson
- Department
of Microbiology, Immunology and Biochemistry, University of Tennessee Health Science Center, Memphis, Tennessee 3816, United States
| | - Larry A. Sklar
- UNM
Center for Molecular Discovery, University
of New Mexico School of Medicine, Albuquerque, New Mexico 87131, United States
| | - Tudor I. Oprea
- Translational
Informatics Division, Department of Internal Medicine, University of New Mexico School of Medicine, Albuquerque, New Mexico 87131, United States
- Department
of Rheumatology and Inflammation Research, Institute of Medicine, Sahlgrenska Academy at University of Gothenburg, Gothenburg, 413 90, Sweden
- Novo Nordisk
Foundation Center for Protein Research, Faculty of Health and Medical
Sciences, University of Copenhagen, Copenhagen, DK-2200, Denmark
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54
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Dafniet B, Cerisier N, Audouze K, Taboureau O. Drug-target-ADR Network and Possible Implications of Structural Variants in Adverse Events. Mol Inform 2020; 39:e2000116. [PMID: 32725965 PMCID: PMC8047896 DOI: 10.1002/minf.202000116] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2020] [Accepted: 07/28/2020] [Indexed: 12/19/2022]
Abstract
Adverse drug reactions (ADRs) are of major concern in drug safety. However, due to the biological complexity of human systems, understanding the underlying mechanisms involved in development of ADRs remains a challenging task. Here, we applied network sciences to analyze a tripartite network between 1000 drugs, 1407 targets, and 6164 ADRs. It allowed us to suggest drug targets susceptible to be associated to ADRs and organs, based on the system organ class (SOC). Furthermore, a score was developed to determine the contribution of a set of proteins to ADRs. Finally, we identified proteins that might increase the susceptibility of genes to ADRs, on the basis of knowledge about genomic structural variation in genes encoding proteins targeted by drugs. Such analysis should pave the way to individualize drug therapy and precision medicine.
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Affiliation(s)
- Bryan Dafniet
- Université de ParisINSERM U1133, CNRS UMR 825175006ParisFrance
| | | | - Karine Audouze
- Université de ParisT3S, INSERM UMR S-112475006ParisFrance
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55
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Montalvo-Casimiro M, González-Barrios R, Meraz-Rodriguez MA, Juárez-González VT, Arriaga-Canon C, Herrera LA. Epidrug Repurposing: Discovering New Faces of Old Acquaintances in Cancer Therapy. Front Oncol 2020; 10:605386. [PMID: 33312959 PMCID: PMC7708379 DOI: 10.3389/fonc.2020.605386] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2020] [Accepted: 10/15/2020] [Indexed: 12/13/2022] Open
Abstract
Gene mutations are strongly associated with tumor progression and are well known in cancer development. However, recently discovered epigenetic alterations have shown the potential to greatly influence tumoral response to therapy regimens. Such epigenetic alterations have proven to be dynamic, and thus could be restored. Due to their reversible nature, the promising opportunity to improve chemotherapy response using epigenetic therapy has arisen. Beyond helping to understand the biology of the disease, the use of modern clinical epigenetics is being incorporated into the management of the cancer patient. Potential epidrug candidates can be found through a process known as drug repositioning or repurposing, a promising strategy for the discovery of novel potential targets in already approved drugs. At present, novel epidrug candidates have been identified in preclinical studies and some others are currently being tested in clinical trials, ready to be repositioned. This epidrug repurposing could circumvent the classic paradigm where the main focus is the development of agents with one indication only, while giving patients lower cost therapies and a novel precision medical approach to optimize treatment efficacy and reduce toxicity. This review focuses on the main approved epidrugs, and their druggable targets, that are currently being used in cancer therapy. Also, we highlight the importance of epidrug repurposing by the rediscovery of known chemical entities that may enhance epigenetic therapy in cancer, contributing to the development of precision medicine in oncology.
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Affiliation(s)
- Michel Montalvo-Casimiro
- Unidad de Investigación Biomédica en Cáncer, Instituto Nacional de Cancerología-Instituto de Investigaciones Biomédicas, UNAM, Mexico City, Mexico
| | - Rodrigo González-Barrios
- Unidad de Investigación Biomédica en Cáncer, Instituto Nacional de Cancerología-Instituto de Investigaciones Biomédicas, UNAM, Mexico City, Mexico
| | - Marco Antonio Meraz-Rodriguez
- Unidad de Investigación Biomédica en Cáncer, Instituto Nacional de Cancerología-Instituto de Investigaciones Biomédicas, UNAM, Mexico City, Mexico
| | | | - Cristian Arriaga-Canon
- Unidad de Investigación Biomédica en Cáncer, Instituto Nacional de Cancerología-Instituto de Investigaciones Biomédicas, UNAM, Mexico City, Mexico
| | - Luis A. Herrera
- Unidad de Investigación Biomédica en Cáncer, Instituto Nacional de Cancerología-Instituto de Investigaciones Biomédicas, UNAM, Mexico City, Mexico
- Instituto Nacional de Medicina Genómica, Mexico City, Mexico
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56
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Reese JT, Unni D, Callahan TJ, Cappelletti L, Ravanmehr V, Carbon S, Shefchek KA, Good BM, Balhoff JP, Fontana T, Blau H, Matentzoglu N, Harris NL, Munoz-Torres MC, Haendel MA, Robinson PN, Joachimiak MP, Mungall CJ. KG-COVID-19: A Framework to Produce Customized Knowledge Graphs for COVID-19 Response. PATTERNS (NEW YORK, N.Y.) 2020; 2:100155. [PMID: 33196056 PMCID: PMC7649624 DOI: 10.2196/13803.100155 10.1016/j.patter.2020.100155] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Integrated, up-to-date data about SARS-CoV-2 and COVID-19 is crucial for the ongoing response to the COVID-19 pandemic by the biomedical research community. While rich biological knowledge exists for SARS-CoV-2 and related viruses (SARS-CoV, MERS-CoV), integrating this knowledge is difficult and time-consuming, since much of it is in siloed databases or in textual format. Furthermore, the data required by the research community vary drastically for different tasks; the optimal data for a machine learning task, for example, is much different from the data used to populate a browsable user interface for clinicians. To address these challenges, we created KG-COVID-19, a flexible framework that ingests and integrates heterogeneous biomedical data to produce knowledge graphs (KGs), and applied it to create a KG for COVID-19 response. This KG framework also can be applied to other problems in which siloed biomedical data must be quickly integrated for different research applications, including future pandemics.
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Affiliation(s)
- Justin T. Reese
- Division of Environmental Genomics and Systems Biology, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA,Corresponding author
| | - Deepak Unni
- Division of Environmental Genomics and Systems Biology, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | - Tiffany J. Callahan
- Computational Bioscience Program, Department of Pharmacology, University of Colorado Anschutz School of Medicine, Aurora, CO 80045, USA
| | - Luca Cappelletti
- Department of Computer Science, University of Milano, 20122 Milan, Italy
| | - Vida Ravanmehr
- The Jackson Laboratory for Genomic Medicine, Farmington, CT 06032, USA
| | - Seth Carbon
- Division of Environmental Genomics and Systems Biology, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | - Kent A. Shefchek
- Linus Pauling Institute, Environmental and Molecular Toxicology, Oregon State University, Corvallis, OR 97331, USA
| | - Benjamin M. Good
- Division of Environmental Genomics and Systems Biology, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | - James P. Balhoff
- Renaissance Computing Institute, University of North Carolina at Chapel Hill, Chapel Hill, NC 27517, USA
| | - Tommaso Fontana
- Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, 20133 Milan, Italy
| | - Hannah Blau
- The Jackson Laboratory for Genomic Medicine, Farmington, CT 06032, USA
| | | | - Nomi L. Harris
- Division of Environmental Genomics and Systems Biology, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | - Monica C. Munoz-Torres
- Division of Environmental Genomics and Systems Biology, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA,Linus Pauling Institute, Environmental and Molecular Toxicology, Oregon State University, Corvallis, OR 97331, USA
| | - Melissa A. Haendel
- Linus Pauling Institute, Environmental and Molecular Toxicology, Oregon State University, Corvallis, OR 97331, USA
| | - Peter N. Robinson
- The Jackson Laboratory for Genomic Medicine, Farmington, CT 06032, USA
| | - Marcin P. Joachimiak
- Division of Environmental Genomics and Systems Biology, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | - Christopher J. Mungall
- Division of Environmental Genomics and Systems Biology, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
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Matschinske J, Salgado-Albarrán M, Sadegh S, Bongiovanni D, Baumbach J, Blumenthal DB. Individuating Possibly Repurposable Drugs and Drug Targets for COVID-19 Treatment Through Hypothesis-Driven Systems Medicine Using CoVex. Assay Drug Dev Technol 2020; 18:348-355. [PMID: 33164550 PMCID: PMC7703307 DOI: 10.1089/adt.2020.1010] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022] Open
Abstract
Coronavirus disease 2019 (COVID-19), caused by the SARS-CoV-2 virus, has developed into a pandemic causing major disruptions and hundreds of thousands of deaths in wide parts of the world. As of July 3, 2020, neither vaccines nor approved drugs for effective treatment are available. In this article, we showcase how to individuate drug targets and potentially repurposable drugs in silico using CoVex a recently presented systems medicine platform for COVID-19 drug repurposing. Starting from initial hypotheses, CoVex leverages network algorithms to individuate host proteins involved in COVID-19 disease mechanisms, as well as existing drugs targeting these potential drug targets. Our analysis reveals GLA, PLAT, and GGCX as potential drug targets, and urokinase, argatroban, dabigatran etexilate, betrixaban, ximelagatran and anisindione as potentially repurposable drugs.
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Affiliation(s)
- Julian Matschinske
- Chair of Experimental Bioinformatics, Technical University of Munich, Freising, Germany
| | - Marisol Salgado-Albarrán
- Chair of Experimental Bioinformatics, Technical University of Munich, Freising, Germany
- Natural Sciences Department, Universidad Autónoma Metropolitana-Cuajimalpa, Mexico City, Mexico
| | - Sepideh Sadegh
- Chair of Experimental Bioinformatics, Technical University of Munich, Freising, Germany
| | - Dario Bongiovanni
- Department of Internal Medicine I, School of Medicine, University Hospital Rechts der Isar, Technical University of Munich, Munich, Germany
- German Center for Cardiovascular Research (DZHK), Partner Site Munich Heart Alliance, Munich, Germany
- Department of Cardiovascular Medicine, Humanitas Clinical and Research Center IRCCS, Humanitas University, Rozzano, Milan, Italy
| | - Jan Baumbach
- Chair of Experimental Bioinformatics, Technical University of Munich, Freising, Germany
| | - David B. Blumenthal
- Chair of Experimental Bioinformatics, Technical University of Munich, Freising, Germany
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58
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Fang J, Pieper AA, Nussinov R, Lee G, Bekris L, Leverenz JB, Cummings J, Cheng F. Harnessing endophenotypes and network medicine for Alzheimer's drug repurposing. Med Res Rev 2020; 40:2386-2426. [PMID: 32656864 PMCID: PMC7561446 DOI: 10.1002/med.21709] [Citation(s) in RCA: 48] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2019] [Revised: 06/23/2020] [Accepted: 06/27/2020] [Indexed: 12/16/2022]
Abstract
Following two decades of more than 400 clinical trials centered on the "one drug, one target, one disease" paradigm, there is still no effective disease-modifying therapy for Alzheimer's disease (AD). The inherent complexity of AD may challenge this reductionist strategy. Recent observations and advances in network medicine further indicate that AD likely shares common underlying mechanisms and intermediate pathophenotypes, or endophenotypes, with other diseases. In this review, we consider AD pathobiology, disease comorbidity, pleiotropy, and therapeutic development, and construct relevant endophenotype networks to guide future therapeutic development. Specifically, we discuss six main endophenotype hypotheses in AD: amyloidosis, tauopathy, neuroinflammation, mitochondrial dysfunction, vascular dysfunction, and lysosomal dysfunction. We further consider how this endophenotype network framework can provide advances in computational and experimental strategies for drug-repurposing and identification of new candidate therapeutic strategies for patients suffering from or at risk for AD. We highlight new opportunities for endophenotype-informed, drug discovery in AD, by exploiting multi-omics data. Integration of genomics, transcriptomics, radiomics, pharmacogenomics, and interactomics (protein-protein interactions) are essential for successful drug discovery. We describe experimental technologies for AD drug discovery including human induced pluripotent stem cells, transgenic mouse/rat models, and population-based retrospective case-control studies that may be integrated with multi-omics in a network medicine methodology. In summary, endophenotype-based network medicine methodologies will promote AD therapeutic development that will optimize the usefulness of available data and support deep phenotyping of the patient heterogeneity for personalized medicine in AD.
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Affiliation(s)
- Jiansong Fang
- Science and Technology Innovation Center, Guangzhou University of Chinese Medicine, Guangzhou, Guangdong 510006, China
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA
| | - Andrew A Pieper
- Harrington Discovery Institute, University Hospital Case Medical Center; Department of Psychiatry, Case Western Reserve University, Geriatric Research Education and Clinical Centers, Louis Stokes Cleveland VAMC, Cleveland, OH 44106, USA
| | - Ruth Nussinov
- Cancer and Inflammation Program, Leidos Biomedical Research, Inc., Frederick National Laboratory for Cancer Research, National Cancer Institute at Frederick, Frederick, MD 21702, USA
- Department of Human Molecular Genetics and Biochemistry, Sackler School of Medicine, Tel Aviv University, Tel Aviv 69978, Israel
| | - Garam Lee
- Cleveland Clinic Lou Ruvo Center for Brain Health, Las Vegas, NV 89106, USA
| | - Lynn Bekris
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA
- Department of Molecular Medicine, Cleveland Clinic Lerner College of Medicine, Case Western Reserve University, Cleveland, OH 44195, USA
| | - James B. Leverenz
- Lou Ruvo Center for Brain Health, Neurological Institute, Cleveland Clinic, Cleveland, OH 44195, USA
| | - Jeffrey Cummings
- Cleveland Clinic Lou Ruvo Center for Brain Health, Las Vegas, NV 89106, USA
- Department of Brain Health, School of Integrated Health Sciences, UNLV, Las Vegas, NV 89154, USA
| | - Feixiong Cheng
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA
- Department of Molecular Medicine, Cleveland Clinic Lerner College of Medicine, Case Western Reserve University, Cleveland, OH 44195, USA
- Case Comprehensive Cancer Center, Case Western Reserve University School of Medicine, Cleveland, Ohio 44106, USA
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59
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Essegian D, Khurana R, Stathias V, Schürer SC. The Clinical Kinase Index: A Method to Prioritize Understudied Kinases as Drug Targets for the Treatment of Cancer. CELL REPORTS MEDICINE 2020; 1:100128. [PMID: 33205077 PMCID: PMC7659504 DOI: 10.1016/j.xcrm.2020.100128] [Citation(s) in RCA: 37] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/29/2019] [Revised: 04/25/2020] [Accepted: 09/24/2020] [Indexed: 02/07/2023]
Abstract
The approval of the first kinase inhibitor, Gleevec, ushered in a paradigm shift for oncological treatment-the use of genomic data for targeted, efficacious therapies. Since then, over 48 additional small-molecule kinase inhibitors have been approved, solidifying the case for kinases as a highly druggable and attractive target class. Despite the role deregulated kinase activity plays in cancer, only 8% of the kinome has been effectively "drugged." Moreover, 24% of the 634 human kinases are understudied. We have developed a comprehensive scoring system that utilizes differential gene expression, pathological parameters, overall survival, and mutational hotspot analysis to rank and prioritize clinically relevant kinases across 17 solid tumor cancers from The Cancer Genome Atlas. We have developed the clinical kinase index (CKI) app (http://cki.ccs.miami.edu) to facilitate interactive analysis of all kinases in each cancer. Collectively, we report that understudied kinases have potential clinical value as biomarkers or drug targets that warrant further study.
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Affiliation(s)
- Derek Essegian
- Department of Pharmacology, Miller School of Medicine, University of Miami, Miami, USA
| | - Rimpi Khurana
- Department of Pharmacology, Miller School of Medicine, University of Miami, Miami, USA
| | - Vasileios Stathias
- Department of Pharmacology, Miller School of Medicine, University of Miami, Miami, USA.,Sylvester Comprehensive Cancer Center, University of Miami, Miami, USA
| | - Stephan C Schürer
- Department of Pharmacology, Miller School of Medicine, University of Miami, Miami, USA.,Sylvester Comprehensive Cancer Center, University of Miami, Miami, USA.,Institute for Data Science & Computing, University of Miami, Miami, USA
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60
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Govinda KC, Bocci G, Verma S, Hassan M, Holmes J, Yang JJ, Sirimulla S, Oprea TI. REDIAL-2020: A suite of machine learning models to estimate Anti-SARS-CoV-2 activities. CHEMRXIV : THE PREPRINT SERVER FOR CHEMISTRY 2020:12915779. [PMID: 33200119 PMCID: PMC7668752 DOI: 10.26434/chemrxiv.12915779] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Revised: 09/16/2020] [Indexed: 11/09/2022]
Abstract
Strategies for drug discovery and repositioning are an urgent need with respect to COVID-19. We developed "REDIAL-2020", a suite of machine learning models for estimating small molecule activity from molecular structure, for a range of SARS-CoV-2 related assays. Each classifier is based on three distinct types of descriptors (fingerprint, physicochemical, and pharmacophore) for parallel model development. These models were trained using high throughput screening data from the NCATS COVID19 portal (https://opendata.ncats.nih.gov/covid19/index.html), with multiple categorical machine learning algorithms. The "best models" are combined in an ensemble consensus predictor that outperforms single models where external validation is available. This suite of machine learning models is available through the DrugCentral web portal (http://drugcentral.org/Redial). Acceptable input formats are: drug name, PubChem CID, or SMILES; the output is an estimate of anti-SARS-CoV-2 activities. The web application reports estimated activity across three areas (viral entry, viral replication, and live virus infectivity) spanning six independent models, followed by a similarity search that displays the most similar molecules to the query among experimentally determined data. The ML models have 60% to 74% external predictivity, based on three separate datasets. Complementing the NCATS COVID19 portal, REDIAL-2020 can serve as a rapid online tool for identifying active molecules for COVID-19 treatment. The source code and specific models are available through Github (https://github.com/sirimullalab/redial-2020), or via Docker Hub (https://hub.docker.com/r/sirimullalab/redial-2020) for users preferring a containerized version.
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Affiliation(s)
- KC Govinda
- Computational Science Program, The University of Texas at El Paso, Texas 79968, USA
- Department of Pharmaceutical Sciences, School of Pharmacy, The University of Texas at El Paso, Texas 79902, USA
| | - Giovanni Bocci
- Translational Informatics Division, Department of Internal Medicine; University of New Mexico Health Sciences Center, Albuquerque, NM, USA
| | - Srijan Verma
- Department of Pharmaceutical Sciences, School of Pharmacy, The University of Texas at El Paso, Texas 79902, USA
- Department of Pharmacy, Birla Institute of Technology and Science, Pilani, Pilani Campus, Rajasthan, 333031, India
| | - Mahmudulla Hassan
- Department of Computer Science, The University of Texas at El Paso, Texas 79968, USA
| | - Jayme Holmes
- Translational Informatics Division, Department of Internal Medicine; University of New Mexico Health Sciences Center, Albuquerque, NM, USA
| | - Jeremy J. Yang
- Translational Informatics Division, Department of Internal Medicine; University of New Mexico Health Sciences Center, Albuquerque, NM, USA
| | - Suman Sirimulla
- Computational Science Program, The University of Texas at El Paso, Texas 79968, USA
- Department of Pharmaceutical Sciences, School of Pharmacy, The University of Texas at El Paso, Texas 79902, USA
- Department of Computer Science, The University of Texas at El Paso, Texas 79968, USA
| | - Tudor I. Oprea
- Translational Informatics Division, Department of Internal Medicine; University of New Mexico Health Sciences Center, Albuquerque, NM, USA
- Autophagy Inflammation and Metabolism Center of Biomedical Research Excellence, University of New Mexico Health Sciences Center, Albuquerque, NM, USA
- Department of Rheumatology and Inflammation Research, Institute of Medicine, Sahlgrenska Academy at University of Gothenburg, Gothenburg, Sweden
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
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Abstract
Drug repositioning aims to reuse "old" drugs to treat diseases outside their approved indication(s). Composition-of-matter patents and FDA exclusivities can hinder the immediate availability of some drugs to be repositioned (repurposed). Here, we analyze data from the FDA Orange Book and use current on-market patent validity and exclusivities to classify drugs into on-patent (ONP), off-patent (OFP), and off-market (OFM) sets. In the absence of an unanimously accepted definition for small molecules, these sets include organic molecules and peptides with molecular weight between 100 and 1250, which resulted in 237 ONP drugs, 320 OFM, and 996 OFP drugs, respectively. We discuss the differences between the three categories in terms of primary molecular properties, chemical diversity, mechanism-of-action target classes, and therapeutic areas and comment on the enrichment of OFP drugs in the near future. Given the intellectual property landscape, and in the absence of specific property rights, we suggest that drugs should be prioritized as follows, to improve the repositioning strategy: (i) OFP, (ii) OFM, and (iii) ONP, respectively.
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Affiliation(s)
- Sorin Avram
- Department of Computational Chemistry, "Coriolan Dragulescu" Institute of Chemistry, 24 Mihai Viteazu Boulevard, Timişoara, Timiş 300223, România
| | - Ramona Curpan
- Department of Computational Chemistry, "Coriolan Dragulescu" Institute of Chemistry, 24 Mihai Viteazu Boulevard, Timişoara, Timiş 300223, România
| | - Liliana Halip
- Department of Computational Chemistry, "Coriolan Dragulescu" Institute of Chemistry, 24 Mihai Viteazu Boulevard, Timişoara, Timiş 300223, România
| | - Alina Bora
- Department of Computational Chemistry, "Coriolan Dragulescu" Institute of Chemistry, 24 Mihai Viteazu Boulevard, Timişoara, Timiş 300223, România
| | - Tudor I Oprea
- Translational Informatics Division, Department of Internal Medicine, University of New Mexico School of Medicine, Albuquerque, New Mexico 87131, United States.,Department of Rheumatology and Inflammation Research, Institute of Medicine, Sahlgrenska Academy at University of Gothenburg, 413 90 Gothenburg, Sweden.,Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, DK-2200 Copenhagen, Denmark
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Reese J, Unni D, Callahan TJ, Cappelletti L, Ravanmehr V, Carbon S, Fontana T, Blau H, Matentzoglu N, Harris NL, Munoz-Torres MC, Robinson PN, Joachimiak MP, Mungall CJ. KG-COVID-19: a framework to produce customized knowledge graphs for COVID-19 response. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2020:2020.08.17.254839. [PMID: 32839776 PMCID: PMC7444288 DOI: 10.1101/2020.08.17.254839] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
Integrated, up-to-date data about SARS-CoV-2 and coronavirus disease 2019 (COVID-19) is crucial for the ongoing response to the COVID-19 pandemic by the biomedical research community. While rich biological knowledge exists for SARS-CoV-2 and related viruses (SARS-CoV, MERS-CoV), integrating this knowledge is difficult and time consuming, since much of it is in siloed databases or in textual format. Furthermore, the data required by the research community varies drastically for different tasks - the optimal data for a machine learning task, for example, is much different from the data used to populate a browsable user interface for clinicians. To address these challenges, we created KG-COVID-19, a flexible framework that ingests and integrates biomedical data to produce knowledge graphs (KGs) for COVID-19 response. This KG framework can also be applied to other problems in which siloed biomedical data must be quickly integrated for different research applications, including future pandemics. BIGGER PICTURE An effective response to the COVID-19 pandemic relies on integration of many different types of data available about SARS-CoV-2 and related viruses. KG-COVID-19 is a framework for producing knowledge graphs that can be customized for downstream applications including machine learning tasks, hypothesis-based querying, and browsable user interface to enable researchers to explore COVID-19 data and discover relationships.
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63
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Poroikov VV. Computer-Aided Drug Design: from Discovery of Novel Pharmaceutical Agents to Systems Pharmacology. BIOCHEMISTRY (MOSCOW), SUPPLEMENT SERIES B: BIOMEDICAL CHEMISTRY 2020. [DOI: 10.1134/s1990750820030117] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/25/2023]
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64
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Singh N, Decroly E, Khatib AM, Villoutreix BO. Structure-based drug repositioning over the human TMPRSS2 protease domain: search for chemical probes able to repress SARS-CoV-2 Spike protein cleavages. Eur J Pharm Sci 2020; 153:105495. [PMID: 32730844 PMCID: PMC7384984 DOI: 10.1016/j.ejps.2020.105495] [Citation(s) in RCA: 32] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2020] [Revised: 07/16/2020] [Accepted: 07/27/2020] [Indexed: 12/28/2022]
Abstract
In December 2019, a new coronavirus was identified in the Hubei province of central china and named SARS-CoV-2. This new virus induces COVID-19, a severe respiratory disease with high death rate. A putative target to interfere with the virus is the host transmembrane serine protease family member II (TMPRSS2). This enzyme is critical for the entry of coronaviruses into human cells by cleaving and activating the spike protein (S) of SARS-CoV-2. Repositioning approved, investigational and experimental drugs on the serine protease domain of TMPRSS2 could thus be valuable. There is no experimental structure for TMPRSS2 but it is possible to develop quality structural models for the serine protease domain using comparative modeling strategies as such domains are highly structurally conserved. Beside the TMPRSS2 catalytic site, we predicted on our structural models a main exosite that could be important for the binding of protein partners and/or substrates. To block the catalytic site or the exosite of TMPRSS2 we used structure-based virtual screening computations and two different collections of approved, investigational and experimental drugs. We propose a list of 156 molecules that could bind to the catalytic site and 100 compounds that may interact with the exosite. These small molecules should now be tested in vitro to gain novel insights over the roles of TMPRSS2 or as starting point for the development of second generation analogs.
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Affiliation(s)
- Natesh Singh
- Univ. Lille, INSERM, Institut Pasteur de Lille, U1177, F-59000 Lille, France
| | | | - Abdel-Majid Khatib
- Univ. Bordeaux, Allée Geoffroy St Hilaire, 33615 Pessac, France
- INSERM, LAMC, UMR 1029, Allée Geoffroy St Hilaire, 33615 Pessac, France
- Corresponding authors.
| | - Bruno O. Villoutreix
- Univ. Lille, INSERM, Institut Pasteur de Lille, U1177, F-59000 Lille, France
- Corresponding authors.
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Sadegh S, Matschinske J, Blumenthal DB, Galindez G, Kacprowski T, List M, Nasirigerdeh R, Oubounyt M, Pichlmair A, Rose TD, Salgado-Albarrán M, Späth J, Stukalov A, Wenke NK, Yuan K, Pauling JK, Baumbach J. Exploring the SARS-CoV-2 virus-host-drug interactome for drug repurposing. Nat Commun 2020; 11:3518. [PMID: 32665542 PMCID: PMC7360763 DOI: 10.1038/s41467-020-17189-2] [Citation(s) in RCA: 113] [Impact Index Per Article: 28.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2020] [Accepted: 06/12/2020] [Indexed: 11/09/2022] Open
Abstract
Coronavirus Disease-2019 (COVID-19) is an infectious disease caused by the SARS-CoV-2 virus. Various studies exist about the molecular mechanisms of viral infection. However, such information is spread across many publications and it is very time-consuming to integrate, and exploit. We develop CoVex, an interactive online platform for SARS-CoV-2 host interactome exploration and drug (target) identification. CoVex integrates virus-human protein interactions, human protein-protein interactions, and drug-target interactions. It allows visual exploration of the virus-host interactome and implements systems medicine algorithms for network-based prediction of drug candidates. Thus, CoVex is a resource to understand molecular mechanisms of pathogenicity and to prioritize candidate therapeutics. We investigate recent hypotheses on a systems biology level to explore mechanistic virus life cycle drivers, and to extract drug repurposing candidates. CoVex renders COVID-19 drug research systems-medicine-ready by giving the scientific community direct access to network medicine algorithms. It is available at https://exbio.wzw.tum.de/covex/.
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Affiliation(s)
- Sepideh Sadegh
- Chair of Experimental Bioinformatics, TUM School of Life Sciences, Technical University of Munich, München, Germany
| | - Julian Matschinske
- Chair of Experimental Bioinformatics, TUM School of Life Sciences, Technical University of Munich, München, Germany
| | - David B Blumenthal
- Chair of Experimental Bioinformatics, TUM School of Life Sciences, Technical University of Munich, München, Germany
| | - Gihanna Galindez
- Chair of Experimental Bioinformatics, TUM School of Life Sciences, Technical University of Munich, München, Germany
| | - Tim Kacprowski
- Chair of Experimental Bioinformatics, TUM School of Life Sciences, Technical University of Munich, München, Germany
| | - Markus List
- Chair of Experimental Bioinformatics, TUM School of Life Sciences, Technical University of Munich, München, Germany
| | - Reza Nasirigerdeh
- Chair of Experimental Bioinformatics, TUM School of Life Sciences, Technical University of Munich, München, Germany
| | - Mhaned Oubounyt
- Chair of Experimental Bioinformatics, TUM School of Life Sciences, Technical University of Munich, München, Germany
| | - Andreas Pichlmair
- Institute of Virology, TUM School of Medicine, Technical University of Munich, München, Germany
| | - Tim Daniel Rose
- LipiTUM, Chair of Experimental Bioinformatics, TUM School of Life Sciences, Technical University of Munich, München, Germany
| | - Marisol Salgado-Albarrán
- Chair of Experimental Bioinformatics, TUM School of Life Sciences, Technical University of Munich, München, Germany
- Natural Sciences Department, Universidad Autónoma Metropolitana-Cuajimalpa (UAM-C), 05300, Mexico City, Mexico
| | - Julian Späth
- Chair of Experimental Bioinformatics, TUM School of Life Sciences, Technical University of Munich, München, Germany
| | - Alexey Stukalov
- Institute of Virology, TUM School of Medicine, Technical University of Munich, München, Germany
| | - Nina K Wenke
- Chair of Experimental Bioinformatics, TUM School of Life Sciences, Technical University of Munich, München, Germany
| | - Kevin Yuan
- Chair of Experimental Bioinformatics, TUM School of Life Sciences, Technical University of Munich, München, Germany
| | - Josch K Pauling
- LipiTUM, Chair of Experimental Bioinformatics, TUM School of Life Sciences, Technical University of Munich, München, Germany
| | - Jan Baumbach
- Chair of Experimental Bioinformatics, TUM School of Life Sciences, Technical University of Munich, München, Germany.
- Computational Biomedicine Lab, Department of Mathematics and Computer Science, University of Southern Denmark, Odense, Denmark.
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66
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Chemical, Physical and Biological Triggers of Evolutionary Conserved Bcl-xL-Mediated Apoptosis. Cancers (Basel) 2020; 12:cancers12061694. [PMID: 32630560 PMCID: PMC7352625 DOI: 10.3390/cancers12061694] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2020] [Revised: 06/23/2020] [Accepted: 06/24/2020] [Indexed: 11/18/2022] Open
Abstract
Background: The evidence that pan-Bcl-2 or Bcl-xL-specific inhibitors prematurely kill virus-infected or RNA/DNA-transfected cells provides rationale for investigating these apoptotic inducers further. We hypothesized that not only invasive RNA or DNA (biological factors) but also DNA/RNA-damaging chemical or physical factors could trigger apoptosis that have been sensitized with pan-Bcl-2 or Bcl-xL-specific agents; Methods: We tested chemical and physical factors plus Bcl-xL-specific inhibitor A-1155463 in cells of various origins and the small roundworms (C. elegans); Results: We show that combination of a A-1155463 along with a DNA-damaging agent, 4-nitroquinoline-1-oxide (4NQO), prematurely kills cells of various origins as well as C. elegans. The synergistic effect is p53-dependent and associated with the release of Bad and Bax from Bcl-xL, which trigger mitochondrial outer membrane permeabilization. Furthermore, we found that combining Bcl-xL-specific inhibitors with various chemical compounds or physical insults also induced cell death; Conclusions: Thus, we were able to identify several biological, chemical and physical triggers of the evolutionarily conserved Bcl-xL-mediated apoptotic pathway, shedding light on strategies and targets for novel drug development.
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67
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Potential Antiviral Options against SARS-CoV-2 Infection. Viruses 2020; 12:v12060642. [PMID: 32545799 PMCID: PMC7354438 DOI: 10.3390/v12060642] [Citation(s) in RCA: 69] [Impact Index Per Article: 17.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2020] [Revised: 06/09/2020] [Accepted: 06/11/2020] [Indexed: 01/18/2023] Open
Abstract
As of June 2020, the number of people infected with severe acute respiratory coronavirus 2 (SARS-CoV-2) continues to skyrocket, with more than 6.7 million cases worldwide. Both the World Health Organization (WHO) and United Nations (UN) has highlighted the need for better control of SARS-CoV-2 infections. However, developing novel virus-specific vaccines, monoclonal antibodies and antiviral drugs against SARS-CoV-2 can be time-consuming and costly. Convalescent sera and safe-in-man broad-spectrum antivirals (BSAAs) are readily available treatment options. Here, we developed a neutralization assay using SARS-CoV-2 strain and Vero-E6 cells. We identified the most potent sera from recovered patients for the treatment of SARS-CoV-2-infected patients. We also screened 136 safe-in-man broad-spectrum antivirals against the SARS-CoV-2 infection in Vero-E6 cells and identified nelfinavir, salinomycin, amodiaquine, obatoclax, emetine and homoharringtonine. We found that a combination of orally available virus-directed nelfinavir and host-directed amodiaquine exhibited the highest synergy. Finally, we developed a website to disseminate the knowledge on available and emerging treatments of COVID-19.
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68
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Wu Q, Taboureau O, Audouze K. Development of an adverse drug event network to predict drug toxicity. Curr Res Toxicol 2020; 1:48-55. [PMID: 34345836 PMCID: PMC8320634 DOI: 10.1016/j.crtox.2020.06.001] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2020] [Revised: 05/31/2020] [Accepted: 06/04/2020] [Indexed: 11/28/2022] Open
Abstract
Despite of their therapeutic effects, drug's exposure may have negative effects on human health such as adverse drug reaction (ADR) and side effects (SE). Adverse drug events (ADEs), that correspond to an event occurring during the drug treatment (i.e. ADR and SE), is not necessarily caused by the drug itself, as this is the case with medical errors and social factors. Due to the complexity of the biological systems, not all ADEs are known for marketed drugs. Therefore, new and effective methods are needed to determine potential risks, including the development of computational strategies. We present an ADE association network based on 90,827 drug-ADE associations between 930 unique drug and 6221 unique ADE, on which we implemented a scoring system based on a pull-down approach for prediction of drug-ADE combination. Based on our network, ADEs proposed for three drugs, safinamide, sonidegib, rufinamide are further discussed. The model was able to identify, already known drug-ADE associations that are supported by the literature and FDA reports, and also to predict uncharacterized associations such as dopamine dysregulation syndrome, or nicotinic acid deficiency for the drugs safinamide and sonidegib respectively, illustrating the power of such integrative toxicological approach.
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Key Words
- ADE, adverse drug event
- ADR, adverse drug reaction
- AOP, adverse outcome pathway
- Adverse event network
- Computational toxicology
- FAERS, FDA Adverse Event Reporting System
- FDA, Food and Drug Administration
- HMS-PCI, high-throughput mass spectrometric protein complex identification
- LRT, Likelihood Ratio Test
- MedDRA, Medical Dictionary for Regulatory Activities
- Network science
- PPAN, protein-protein association network
- PT, Preferred Term
- Predictive toxicity
- QSAR, Quantitative structure-activity relationships
- SE, side effect
- SOC, System Organ Class
- System toxicology
- TAP–MS, tandem-affinity-purification method coupled to mass spectrometry
- pullS, pull-down score
- wS, weighted score
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Affiliation(s)
- Qier Wu
- Université de Paris, T3S, Inserm UMR S-1124, F-75006 Paris, France
| | - Olivier Taboureau
- Université de Paris, BFA, CNRS UMR 8251, ERL Inserm U1133, CNRS UMR 8251, F-75013 Paris, France
| | - Karine Audouze
- Université de Paris, T3S, Inserm UMR S-1124, F-75006 Paris, France
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69
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Stathias V, Turner J, Koleti A, Vidovic D, Cooper D, Fazel-Najafabadi M, Pilarczyk M, Terryn R, Chung C, Umeano A, Clarke DJB, Lachmann A, Evangelista JE, Ma’ayan A, Medvedovic M, Schürer SC. LINCS Data Portal 2.0: next generation access point for perturbation-response signatures. Nucleic Acids Res 2020; 48:D431-D439. [PMID: 31701147 PMCID: PMC7145650 DOI: 10.1093/nar/gkz1023] [Citation(s) in RCA: 88] [Impact Index Per Article: 22.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2019] [Revised: 10/17/2019] [Accepted: 11/04/2019] [Indexed: 12/21/2022] Open
Abstract
The Library of Integrated Network-Based Cellular Signatures (LINCS) is an NIH Common Fund program with the goal of generating a large-scale and comprehensive catalogue of perturbation-response signatures by utilizing a diverse collection of perturbations across many model systems and assay types. The LINCS Data Portal (LDP) has been the primary access point for the compendium of LINCS data and has been widely utilized. Here, we report the first major update of LDP (http://lincsportal.ccs.miami.edu/signatures) with substantial changes in the data architecture and APIs, a completely redesigned user interface, and enhanced curated metadata annotations to support more advanced, intuitive and deeper querying, exploration and analysis capabilities. The cornerstone of this update has been the decision to reprocess all high-level LINCS datasets and make them accessible at the data point level enabling users to directly access and download any subset of signatures across the entire library independent from the originating source, project or assay. Access to the individual signatures also enables the newly implemented signature search functionality, which utilizes the iLINCS platform to identify conditions that mimic or reverse gene set queries. A newly designed query interface enables global metadata search with autosuggest across all annotations associated with perturbations, model systems, and signatures.
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Affiliation(s)
- Vasileios Stathias
- Department of Molecular and Cellular Pharmacology, Miller School of Medicine, University of Miami, USA
- Center for Computational Science, University of Miami, USA
- BD2K-LINCS Data Coordination and Integration Center, USA
| | - John Turner
- Department of Molecular and Cellular Pharmacology, Miller School of Medicine, University of Miami, USA
- BD2K-LINCS Data Coordination and Integration Center, USA
| | - Amar Koleti
- Center for Computational Science, University of Miami, USA
- BD2K-LINCS Data Coordination and Integration Center, USA
| | - Dusica Vidovic
- Department of Molecular and Cellular Pharmacology, Miller School of Medicine, University of Miami, USA
- BD2K-LINCS Data Coordination and Integration Center, USA
| | - Daniel Cooper
- Department of Molecular and Cellular Pharmacology, Miller School of Medicine, University of Miami, USA
- BD2K-LINCS Data Coordination and Integration Center, USA
| | - Mehdi Fazel-Najafabadi
- BD2K-LINCS Data Coordination and Integration Center, USA
- Laboratory for Statistical Genomics and Systems Biology, Division of Biostatistics and Bioinformatics, Department of Environmental Health, University of Cincinnati College of Medicine, USA
| | - Marcin Pilarczyk
- BD2K-LINCS Data Coordination and Integration Center, USA
- Laboratory for Statistical Genomics and Systems Biology, Division of Biostatistics and Bioinformatics, Department of Environmental Health, University of Cincinnati College of Medicine, USA
| | - Raymond Terryn
- Department of Molecular and Cellular Pharmacology, Miller School of Medicine, University of Miami, USA
| | - Caty Chung
- BD2K-LINCS Data Coordination and Integration Center, USA
- Sylvester Comprehensive Cancer Center, Miller School of Medicine, University of Miami, USA
| | - Afoma Umeano
- Department of Molecular and Cellular Pharmacology, Miller School of Medicine, University of Miami, USA
| | - Daniel J B Clarke
- BD2K-LINCS Data Coordination and Integration Center, USA
- Department of Pharmacological Sciences, Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, New York, USA
| | - Alexander Lachmann
- BD2K-LINCS Data Coordination and Integration Center, USA
- Department of Pharmacological Sciences, Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, New York, USA
| | - John Erol Evangelista
- BD2K-LINCS Data Coordination and Integration Center, USA
- Department of Pharmacological Sciences, Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, New York, USA
| | - Avi Ma’ayan
- BD2K-LINCS Data Coordination and Integration Center, USA
- Department of Pharmacological Sciences, Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, New York, USA
| | - Mario Medvedovic
- BD2K-LINCS Data Coordination and Integration Center, USA
- Laboratory for Statistical Genomics and Systems Biology, Division of Biostatistics and Bioinformatics, Department of Environmental Health, University of Cincinnati College of Medicine, USA
| | - Stephan C Schürer
- Department of Molecular and Cellular Pharmacology, Miller School of Medicine, University of Miami, USA
- Center for Computational Science, University of Miami, USA
- BD2K-LINCS Data Coordination and Integration Center, USA
- Sylvester Comprehensive Cancer Center, Miller School of Medicine, University of Miami, USA
- To whom correspondence should be addressed. Tel: +1 305 243 6552;
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70
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Liu H, Zhang W, Zou B, Wang J, Deng Y, Deng L. DrugCombDB: a comprehensive database of drug combinations toward the discovery of combinatorial therapy. Nucleic Acids Res 2020; 48:D871-D881. [PMID: 31665429 PMCID: PMC7145671 DOI: 10.1093/nar/gkz1007] [Citation(s) in RCA: 50] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2019] [Revised: 10/14/2019] [Accepted: 10/17/2019] [Indexed: 01/09/2023] Open
Abstract
Drug combinations have demonstrated high efficacy and low adverse side effects compared to single drug administration in cancer therapies and thus have drawn intensive attention from researchers and pharmaceutical enterprises. Due to the rapid development of high-throughput screening (HTS), the number of drug combination datasets available has increased tremendously in recent years. Therefore, there is an urgent need for a comprehensive database that is crucial to both experimental and computational screening of synergistic drug combinations. In this paper, we present DrugCombDB, a comprehensive database devoted to the curation of drug combinations from various data sources: (i) HTS assays of drug combinations; (ii) manual curations from the literature; and (iii) FDA Orange Book and external databases. Specifically, DrugCombDB includes 448 555 drug combinations derived from HTS assays, covering 2887 unique drugs and 124 human cancer cell lines. In particular, DrugCombDB has more than 6000 000 quantitative dose responses from which we computed multiple synergy scores to determine the overall synergistic or antagonistic effects of drug combinations. In addition to the combinations extracted from existing databases, we manually curated 457 drug combinations from thousands of PubMed publications. To benefit the further experimental validation and development of computational models, multiple datasets that are ready to train prediction models for classification and regression analysis were constructed and other significant related data were gathered. A website with a user-friendly graphical visualization has been developed for users to access the wealth of data and download prebuilt datasets. Our database is available at http://drugcombdb.denglab.org/.
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Affiliation(s)
- Hui Liu
- Lab of Information Management, Changzhou University, Changzhou 213164, China
| | - Wenhao Zhang
- Lab of Information Management, Changzhou University, Changzhou 213164, China
| | - Bo Zou
- School of Computer Science and Engineering, Central South University, Changsha 410075, China
| | - Jinxian Wang
- School of Computer Science and Engineering, Central South University, Changsha 410075, China
| | - Yuanyuan Deng
- School of Computer Science and Engineering, Central South University, Changsha 410075, China
| | - Lei Deng
- School of Computer Science and Engineering, Central South University, Changsha 410075, China.,School of Software, Xinjiang University, Urumqi 830008, China
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Agoni C, Olotu FA, Ramharack P, Soliman ME. Druggability and drug-likeness concepts in drug design: are biomodelling and predictive tools having their say? J Mol Model 2020; 26:120. [PMID: 32382800 DOI: 10.1007/s00894-020-04385-6] [Citation(s) in RCA: 38] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2019] [Accepted: 04/22/2020] [Indexed: 11/29/2022]
Abstract
The drug discovery process typically involves target identification and design of suitable drug molecules against these targets. Despite decades of experimental investigations in the drug discovery domain, about 96% overall failure rate has been recorded in drug development due to the "undruggability" of various identified disease targets, in addition to other challenges. Likewise, the high attrition rate of drug candidates in the drug discovery process has also become an enormous challenge for the pharmaceutical industry. To alleviate this negative outlook, new trends in drug discovery have emerged. By drifting away from experimental research methods, computational tools and big data are becoming valuable in the prediction of biological target druggability and the drug-likeness of potential therapeutic agents. These tools have proven to be useful in saving time and reducing research costs. As with any emerging technique, however, controversial opinions have been presented regarding the validation of predictive computational tools. To address the challenges associated with these varying opinions, this review attempts to highlight the principles of druggability and drug-likeness and their recent advancements in the drug discovery field. Herein, we present the different computational tools and their reliability of predictive analysis in the drug discovery domain. We believe that this report would serve as a comprehensive guide towards computational-oriented drug discovery research. Graphical abstract Highlights of methods for assessing the druggability of biological targets.
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Affiliation(s)
- Clement Agoni
- Molecular Bio-Computation & Drug Design Lab, School of Health Sciences, University of KwaZulu-Natal, Westville, Durban, 4000, South Africa
| | - Fisayo A Olotu
- Molecular Bio-Computation & Drug Design Lab, School of Health Sciences, University of KwaZulu-Natal, Westville, Durban, 4000, South Africa
| | - Pritika Ramharack
- Molecular Bio-Computation & Drug Design Lab, School of Health Sciences, University of KwaZulu-Natal, Westville, Durban, 4000, South Africa
| | - Mahmoud E Soliman
- Molecular Bio-Computation & Drug Design Lab, School of Health Sciences, University of KwaZulu-Natal, Westville, Durban, 4000, South Africa.
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72
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Zeng X, Zhu S, Hou Y, Zhang P, Li L, Li J, Huang LF, Lewis SJ, Nussinov R, Cheng F. Network-based prediction of drug-target interactions using an arbitrary-order proximity embedded deep forest. Bioinformatics 2020; 36:2805-2812. [PMID: 31971579 PMCID: PMC7203727 DOI: 10.1093/bioinformatics/btaa010] [Citation(s) in RCA: 79] [Impact Index Per Article: 19.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2019] [Revised: 12/24/2019] [Accepted: 01/17/2020] [Indexed: 11/14/2022] Open
Abstract
MOTIVATION Systematic identification of molecular targets among known drugs plays an essential role in drug repurposing and understanding of their unexpected side effects. Computational approaches for prediction of drug-target interactions (DTIs) are highly desired in comparison to traditional experimental assays. Furthermore, recent advances of multiomics technologies and systems biology approaches have generated large-scale heterogeneous, biological networks, which offer unexpected opportunities for network-based identification of new molecular targets among known drugs. RESULTS In this study, we present a network-based computational framework, termed AOPEDF, an arbitrary-order proximity embedded deep forest approach, for prediction of DTIs. AOPEDF learns a low-dimensional vector representation of features that preserve arbitrary-order proximity from a highly integrated, heterogeneous biological network connecting drugs, targets (proteins) and diseases. In total, we construct a heterogeneous network by uniquely integrating 15 networks covering chemical, genomic, phenotypic and network profiles among drugs, proteins/targets and diseases. Then, we build a cascade deep forest classifier to infer new DTIs. Via systematic performance evaluation, AOPEDF achieves high accuracy in identifying molecular targets among known drugs on two external validation sets collected from DrugCentral [area under the receiver operating characteristic curve (AUROC) = 0.868] and ChEMBL (AUROC = 0.768) databases, outperforming several state-of-the-art methods. In a case study, we showcase that multiple molecular targets predicted by AOPEDF are associated with mechanism-of-action of substance abuse disorder for several marketed drugs (such as aripiprazole, risperidone and haloperidol). AVAILABILITY AND IMPLEMENTATION Source code and data can be downloaded from https://github.com/ChengF-Lab/AOPEDF.
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Affiliation(s)
- Xiangxiang Zeng
- Department of Computer Science, College of Information Science and Engineering, Hunan University, Changsha, Hunan 410082, China
| | - Siyi Zhu
- Department of Computer Science, Xiamen University, Xiamen 361005, China
| | - Yuan Hou
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA
| | - Pengyue Zhang
- Department of Biomedical Informatics, Ohio State University, Columbus, OH 43210, USA
| | - Lang Li
- Department of Biomedical Informatics, Ohio State University, Columbus, OH 43210, USA
| | - Jing Li
- Department of Computer and Data Sciences, Case Western Reserve University, Cleveland, OH 44106, USA
| | - L Frank Huang
- Division of Experimental Hematology and Cancer Biology, Brain Tumor Center, Cincinnati Children’s Hospital Medical Center
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH 45229, USA
| | - Stephen J Lewis
- Department of Pediatrics, Case Western Reserve University, Cleveland, OH 44106, USA
| | - Ruth Nussinov
- Computational Structural Biology Section, Basic Science Program, Frederick National Laboratory for Cancer Research, National Cancer Institute at Frederick, Frederick, MD 21702, USA
- Department of Human Molecular Genetics and Biochemistry, Sackler School of Medicine, Tel Aviv University, Tel Aviv 69978, Israel
| | - Feixiong Cheng
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA
- Department of Molecular Medicine, Cleveland Clinic Lerner College of Medicine, Case Western Reserve University, Cleveland, OH 44195, USA
- Case Comprehensive Cancer Center, Case Western Reserve University School of Medicine, Cleveland, OH 44106, USA
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73
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Poroikov VV. [Computer-aided drug design: from discovery of novel pharmaceutical agents to systems pharmacology]. BIOMEDIT︠S︡INSKAI︠A︡ KHIMII︠A︡ 2020; 66:30-41. [PMID: 32116224 DOI: 10.18097/pbmc20206601030] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
Abstract
New drug discovery is based on the analysis of public information about the mechanisms of the disease, molecular targets, and ligands, which interaction with the target could lead to the normalization of the pathological process. The available data on diseases, drugs, pharmacological effects, molecular targets, and drug-like substances, taking into account the combinatorics of the associative relations between them, correspond to the Big Data. To analyze such data, the application of computer-aided drug design methods is necessary. An overview of the studies in this area performed by the Laboratory for Structure-Function Based Drug Design of IBMC is presented. We have developed the approaches to identifying promising pharmacological targets, predicting several thousand types of biological activity based on the structural formula of the compound, analyzing protein-ligand interactions based on assessing local similarity of amino acid sequences, identifying likely molecular mechanisms of side effects of drugs, calculating the integral toxicity of drugs taking into account their metabolism, have been developed in the human body, predicting sustainable and sensitive options strains and evaluating the effectiveness of combinations of antiretroviral drugs in patients, taking into account the molecular genetic characteristics of the clinical isolates of HIV-1. Our computer programs are implemented as the web-services freely available on the Internet, which are used by thousands of researchers from many countries of the world to select the most promising substances for the synthesis and determine the priority areas for experimental testing of their biological activity.
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Affiliation(s)
- V V Poroikov
- Institute of Biomedical Chemistry, Moscow, Russia
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74
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Discovery and development of safe-in-man broad-spectrum antiviral agents. Int J Infect Dis 2020; 93:268-276. [PMID: 32081774 PMCID: PMC7128205 DOI: 10.1016/j.ijid.2020.02.018] [Citation(s) in RCA: 140] [Impact Index Per Article: 35.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2020] [Revised: 02/07/2020] [Accepted: 02/11/2020] [Indexed: 12/13/2022] Open
Abstract
We reviewed the discovery and development process of broad-spectrum antiviral agents. We summarized the information on 120 safe-in-man agents in a freely accessible database. Further studies will increase the number of broad-spectrum antivirals, expand the spectrum of their indications, and identify drug combinations for treatment of emerging and re-emerging viral infections.
Viral diseases are one of the leading causes of morbidity and mortality in the world. Virus-specific vaccines and antiviral drugs are the most powerful tools to combat viral diseases. However, broad-spectrum antiviral agents (BSAAs, i.e. compounds targeting viruses belonging to two or more viral families) could provide additional protection of the general population from emerging and re-emerging viral diseases, reinforcing the arsenal of available antiviral options. Here, we review discovery and development of BSAAs and summarize the information on 120 safe-in-man agents in a freely accessible database (https://drugvirus.info/). Future and ongoing pre-clinical and clinical studies will increase the number of BSAAs, expand the spectrum of their indications, and identify drug combinations for treatment of emerging and re-emerging viral infections as well as co-infections.
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75
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Bagherian M, Sabeti E, Wang K, Sartor MA, Nikolovska-Coleska Z, Najarian K. Machine learning approaches and databases for prediction of drug-target interaction: a survey paper. Brief Bioinform 2020; 22:247-269. [PMID: 31950972 PMCID: PMC7820849 DOI: 10.1093/bib/bbz157] [Citation(s) in RCA: 161] [Impact Index Per Article: 40.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2019] [Revised: 11/01/2019] [Accepted: 11/07/2019] [Indexed: 12/12/2022] Open
Abstract
The task of predicting the interactions between drugs and targets plays a key role in the process of drug discovery. There is a need to develop novel and efficient prediction approaches in order to avoid costly and laborious yet not-always-deterministic experiments to determine drug–target interactions (DTIs) by experiments alone. These approaches should be capable of identifying the potential DTIs in a timely manner. In this article, we describe the data required for the task of DTI prediction followed by a comprehensive catalog consisting of machine learning methods and databases, which have been proposed and utilized to predict DTIs. The advantages and disadvantages of each set of methods are also briefly discussed. Lastly, the challenges one may face in prediction of DTI using machine learning approaches are highlighted and we conclude by shedding some lights on important future research directions.
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Affiliation(s)
- Maryam Bagherian
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Elyas Sabeti
- Michigan Institute for Data Science, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Kai Wang
- Department of Biostatistics, School of Public Health, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Maureen A Sartor
- Department of Pathology, University of Michigan, Ann Arbor, MI, 48109, USA
| | | | - Kayvan Najarian
- Department of Electrical Engineering and Computer Science, College of Engineering, University of Michigan, Ann Arbor, MI, 48109, USA
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76
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Zeng X, Zhu S, Lu W, Liu Z, Huang J, Zhou Y, Fang J, Huang Y, Guo H, Li L, Trapp BD, Nussinov R, Eng C, Loscalzo J, Cheng F. Target identification among known drugs by deep learning from heterogeneous networks. Chem Sci 2020; 11:1775-1797. [PMID: 34123272 PMCID: PMC8150105 DOI: 10.1039/c9sc04336e] [Citation(s) in RCA: 165] [Impact Index Per Article: 41.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2019] [Accepted: 01/09/2020] [Indexed: 12/11/2022] Open
Abstract
Without foreknowledge of the complete drug target information, development of promising and affordable approaches for effective treatment of human diseases is challenging. Here, we develop deepDTnet, a deep learning methodology for new target identification and drug repurposing in a heterogeneous drug-gene-disease network embedding 15 types of chemical, genomic, phenotypic, and cellular network profiles. Trained on 732 U.S. Food and Drug Administration-approved small molecule drugs, deepDTnet shows high accuracy (the area under the receiver operating characteristic curve = 0.963) in identifying novel molecular targets for known drugs, outperforming previously published state-of-the-art methodologies. We then experimentally validate that deepDTnet-predicted topotecan (an approved topoisomerase inhibitor) is a new, direct inhibitor (IC50 = 0.43 μM) of human retinoic-acid-receptor-related orphan receptor-gamma t (ROR-γt). Furthermore, by specifically targeting ROR-γt, topotecan reveals a potential therapeutic effect in a mouse model of multiple sclerosis. In summary, deepDTnet offers a powerful network-based deep learning methodology for target identification to accelerate drug repurposing and minimize the translational gap in drug development.
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Affiliation(s)
- Xiangxiang Zeng
- College of Information Science and Engineering, Hunan University Changsha Hunan 410082 China
| | - Siyi Zhu
- Department of Computer Science, Xiamen University Xiamen Fujian 361005 China
| | - Weiqiang Lu
- Shanghai Key Laboratory of Regulatory Biology, Institute of Biomedical Sciences and School of Life Sciences, East China Normal University Shanghai 200241 China
| | - Zehui Liu
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology Shanghai 200237 China
| | - Jin Huang
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology Shanghai 200237 China
| | - Yadi Zhou
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic 9500 Euclid Avenue Cleveland OH 44106 USA +1-216-6361609 +1-216-4447654
| | - Jiansong Fang
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic 9500 Euclid Avenue Cleveland OH 44106 USA +1-216-6361609 +1-216-4447654
| | - Yin Huang
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic 9500 Euclid Avenue Cleveland OH 44106 USA +1-216-6361609 +1-216-4447654
- Key Laboratory of Drug Quality Control and Pharmacovigilance, China Pharmaceutical University Nanjing 210009 China
| | - Huimin Guo
- Key Laboratory of Drug Quality Control and Pharmacovigilance, China Pharmaceutical University Nanjing 210009 China
| | - Lang Li
- Department of Biomedical Informatics, College of Medicine, The Ohio State University Columbus OH 43210 USA
| | - Bruce D Trapp
- Department of Neurosciences, Lerner Research Institute, Cleveland Clinic Cleveland OH 44022 USA
| | - Ruth Nussinov
- Cancer and Inflammation Program, Leidos Biomedical Research, Inc., Frederick National Laboratory for Cancer Research, National Cancer Institute at Frederick Frederick MD 21702 USA
- Department of Human Molecular Genetics and Biochemistry, Sackler School of Medicine, Tel Aviv University Tel Aviv 69978 Israel
| | - Charis Eng
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic 9500 Euclid Avenue Cleveland OH 44106 USA +1-216-6361609 +1-216-4447654
- Department of Molecular Medicine, Cleveland Clinic Lerner College of Medicine, Case Western Reserve University Cleveland OH 44195 USA
- Case Comprehensive Cancer Center, Case Western Reserve University School of Medicine Cleveland Ohio 44106 USA
- Taussig Cancer Institute, Cleveland Clinic Cleveland Ohio 44195 USA
- Department of Genetics and Genome Sciences, Case Western Reserve University School of Medicine Cleveland Ohio 44106 USA
| | - Joseph Loscalzo
- Department of Medicine, Brigham and Women's Hospital, Harvard Medical School Boston MA 02115 USA
| | - Feixiong Cheng
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic 9500 Euclid Avenue Cleveland OH 44106 USA +1-216-6361609 +1-216-4447654
- Department of Molecular Medicine, Cleveland Clinic Lerner College of Medicine, Case Western Reserve University Cleveland OH 44195 USA
- Case Comprehensive Cancer Center, Case Western Reserve University School of Medicine Cleveland Ohio 44106 USA
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77
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Wang Y, Zhang S, Li F, Zhou Y, Zhang Y, Wang Z, Zhang R, Zhu J, Ren Y, Tan Y, Qin C, Li Y, Li X, Chen Y, Zhu F. Therapeutic target database 2020: enriched resource for facilitating research and early development of targeted therapeutics. Nucleic Acids Res 2020; 48:D1031-D1041. [PMID: 31691823 PMCID: PMC7145558 DOI: 10.1093/nar/gkz981] [Citation(s) in RCA: 371] [Impact Index Per Article: 92.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2019] [Revised: 10/10/2019] [Accepted: 10/12/2019] [Indexed: 12/12/2022] Open
Abstract
Knowledge of therapeutic targets and early drug candidates is useful for improved drug discovery. In particular, information about target regulators and the patented therapeutic agents facilitates research regarding druggability, systems pharmacology, new trends, molecular landscapes, and the development of drug discovery tools. To complement other databases, we constructed the Therapeutic Target Database (TTD) with expanded information about (i) target-regulating microRNAs and transcription factors, (ii) target-interacting proteins, and (iii) patented agents and their targets (structures and experimental activity values if available), which can be conveniently retrieved and is further enriched with regulatory mechanisms or biochemical classes. We also updated the TTD with the recently released International Classification of Diseases ICD-11 codes and additional sets of successful, clinical trial, and literature-reported targets that emerged since the last update. TTD is accessible at http://bidd.nus.edu.sg/group/ttd/ttd.asp. In case of possible web connectivity issues, two mirror sites of TTD are also constructed (http://db.idrblab.org/ttd/ and http://db.idrblab.net/ttd/).
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Affiliation(s)
- Yunxia Wang
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang 310058, China
| | - Song Zhang
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang 310058, China
| | - Fengcheng Li
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang 310058, China
| | - Ying Zhou
- Zhejiang Provincial Key Laboratory for Drug Clinical Research and Evaluation, The First Affiliated Hospital, Zhejiang University, Hangzhou, Zhejiang 310000, China
| | - Ying Zhang
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang 310058, China
| | - Zhengwen Wang
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang 310058, China
| | - Runyuan Zhang
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang 310058, China
| | - Jiang Zhu
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang 310058, China
| | - Yuxiang Ren
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang 310058, China
| | - Ying Tan
- State Key Laboratory of Chemical Oncogenomics, Key Laboratory of Chemical Biology, The Graduate School at Shenzhen, Tsinghua University, Shenzhen, Guangdong 518055, China
| | - Chu Qin
- Bioinformatics and Drug Design Group, Department of Pharmacy, and Center for Computational Science and Engineering, National University of Singapore 117543, Singapore
| | - Yinghong Li
- School of Pharmaceutical Sciences, Chongqing University, Chongqing 401331, China
| | - Xiaoxu Li
- School of Pharmaceutical Sciences, Chongqing University, Chongqing 401331, China
| | - Yuzong Chen
- Bioinformatics and Drug Design Group, Department of Pharmacy, and Center for Computational Science and Engineering, National University of Singapore 117543, Singapore
| | - Feng Zhu
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang 310058, China
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78
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Pershad Y, Guo M, Altman RB. Pathway and network embedding methods for prioritizing psychiatric drugs. PACIFIC SYMPOSIUM ON BIOCOMPUTING. PACIFIC SYMPOSIUM ON BIOCOMPUTING 2020; 25:671-682. [PMID: 31797637 PMCID: PMC6951442] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
One in five Americans experience mental illness, and roughly 75% of psychiatric prescriptions do not successfully treat the patient's condition. Extensive evidence implicates genetic factors and signaling disruption in the pathophysiology of these diseases. Changes in transcription often underlie this molecular pathway dysregulation; individual patient transcriptional data can improve the efficacy of diagnosis and treatment. Recent large-scale genomic studies have uncovered shared genetic modules across multiple psychiatric disorders - providing an opportunity for an integrated multi-disease approach for diagnosis. Moreover, network-based models informed by gene expression can represent pathological biological mechanisms and suggest new genes for diagnosis and treatment. Here, we use patient gene expression data from multiple studies to classify psychiatric diseases, integrate knowledge from expert-curated databases and publicly available experimental data to create augmented disease-specific gene sets, and use these to recommend disease-relevant drugs. From Gene Expression Omnibus, we extract expression data from 145 cases of schizophrenia, 82 cases of bipolar disorder, 190 cases of major depressive disorder, and 307 shared controls. We use pathway-based approaches to predict psychiatric disease diagnosis with a random forest model (78% accuracy) and derive important features to augment available drug and disease signatures. Using protein-protein-interaction networks and embedding-based methods, we build a pipeline to prioritize treatments for psychiatric diseases that achieves a 3.4-fold improvement over a background model. Thus, we demonstrate that gene-expression-derived pathway features can diagnose psychiatric diseases and that molecular insights derived from this classification task can inform treatment prioritization for psychiatric diseases.
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Affiliation(s)
- Yash Pershad
- Biomedical Informatics Program, Departments of Bioengineering, Genetics, & Medicine, Stanford University, Stanford, CA 94305, USA
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79
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Tambuyzer E, Vandendriessche B, Austin CP, Brooks PJ, Larsson K, Miller Needleman KI, Valentine J, Davies K, Groft SC, Preti R, Oprea TI, Prunotto M. Therapies for rare diseases: therapeutic modalities, progress and challenges ahead. Nat Rev Drug Discov 2019; 19:93-111. [PMID: 31836861 DOI: 10.1038/s41573-019-0049-9] [Citation(s) in RCA: 164] [Impact Index Per Article: 32.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/24/2019] [Indexed: 12/26/2022]
Abstract
Most rare diseases still lack approved treatments despite major advances in research providing the tools to understand their molecular basis, as well as legislation providing regulatory and economic incentives to catalyse the development of specific therapies. Addressing this translational gap is a multifaceted challenge, for which a key aspect is the selection of the optimal therapeutic modality for translating advances in rare disease knowledge into potential medicines, known as orphan drugs. With this in mind, we discuss here the technological basis and rare disease applicability of the main therapeutic modalities, including small molecules, monoclonal antibodies, protein replacement therapies, oligonucleotides and gene and cell therapies, as well as drug repurposing. For each modality, we consider its strengths and limitations as a platform for rare disease therapy development and describe clinical progress so far in developing drugs based on it. We also discuss selected overarching topics in the development of therapies for rare diseases, such as approval statistics, engagement of patients in the process, regulatory pathways and digital tools.
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Affiliation(s)
- Erik Tambuyzer
- BioPontis Alliance for Rare Diseases Foundation fup/son, Brussels, Belgium. .,BioPontis Alliance Rare Disease Foundation, Inc, Raleigh, NC, USA.
| | - Benjamin Vandendriessche
- Byteflies, Antwerp, Belgium.,Department of Electrical, Computer, and Systems Engineering (ECSE), Case Western Reserve University, Cleveland, OH, USA
| | - Christopher P Austin
- National Center for Advancing Translational Sciences, National Institutes of Health, Bethesda, MD, USA
| | - Philip J Brooks
- National Center for Advancing Translational Sciences, National Institutes of Health, Bethesda, MD, USA
| | - Kristina Larsson
- Orphan Medicines Office, European Medicines Agency, Amsterdam, Netherlands
| | | | | | - Kay Davies
- MDUK Oxford Neuromuscular Centre, Department of Physiology, Anatomy and Genetics, University of Oxford, Oxford, UK
| | - Stephen C Groft
- National Center for Advancing Translational Sciences, National Institutes of Health, Bethesda, MD, USA
| | - Robert Preti
- Hitachi Chemical Regenerative Medicine Business Sector, Allendale, NJ, USA
| | - Tudor I Oprea
- Translational Informatics Division, Department of Internal Medicine, University of New Mexico Albuquerque, Albuquerque, NM, USA.,UNM Comprehensive Cancer Center, University of New Mexico Health Science Center, Albuquerque, NM, USA
| | - Marco Prunotto
- School of Pharmaceutical Sciences, Institute of Pharmaceutical Sciences of Western Switzerland, University of Geneva, Geneva, Switzerland.
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80
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Bocci G, Benet LZ, Oprea TI. Can BDDCS illuminate targets in drug design? Drug Discov Today 2019; 24:2299-2306. [PMID: 31585170 DOI: 10.1016/j.drudis.2019.09.021] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2019] [Revised: 09/18/2019] [Accepted: 09/25/2019] [Indexed: 12/19/2022]
Abstract
The fact that pharmacokinetic (PK) properties of drugs influence their interaction with protein targets is a principle known for decades. The same cannot be said for the opposite, namely that targets influence the PK properties of drugs. Evidence confirming this possibility is introduced here for the first time, as we show that certain protein families have a clear preference for drugs with specific PK properties. We investigate this by cross-referencing 'druggable target' annotations for >1000 US Food and Drug Administration (FDA)-approved drugs with their PK profile, as defined by the Biopharmaceutics Drug Disposition Classification System (BDDCS) criteria, and then examine the BDDCS preference for several major target protein families and therapeutic categories. Our findings suggest a novel way to conduct drug discovery by focusing PK profiles at the very early stage of target selection.
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Affiliation(s)
- Giovanni Bocci
- Translational Informatics Division, Department of Internal Medicine, University of New Mexico, Albuquerque, NM, USA
| | - Leslie Z Benet
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, CA, USA
| | - Tudor I Oprea
- Translational Informatics Division, Department of Internal Medicine, University of New Mexico, Albuquerque, NM, USA; UNM Comprehensive Cancer Center, Albuquerque, NM, USA; Department of Rheumatology and Inflammation Research, Institute of Medicine, Sahlgrenska Academy at University of Gothenburg, Gothenburg, Sweden; Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.
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81
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Oprea TI. Exploring the dark genome: implications for precision medicine. Mamm Genome 2019; 30:192-200. [PMID: 31270560 DOI: 10.1007/s00335-019-09809-0] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2019] [Accepted: 06/15/2019] [Indexed: 01/08/2023]
Abstract
The increase in the number of both patients and healthcare practitioners who grew up using the Internet and computers (so-called "digital natives") is likely to impact the practice of precision medicine, and requires novel platforms for data integration and mining, as well as contextualized information retrieval. The "Illuminating the Druggable Genome Knowledge Management Center" (IDG KMC) quantifies data availability from a wide range of chemical, biological, and clinical resources, and has developed platforms that can be used to navigate understudied proteins (the "dark genome"), and their potential contribution to specific pathologies. Using the "Target Importance and Novelty Explorer" (TIN-X) highlights the role of LRRC10 (a dark gene) in dilated cardiomyopathy. Combining mouse and human phenotype data leads to increased strength of evidence, which is discussed for four additional dark genes: SLX4IP and its role in glucose metabolism, the role of HSF2BP in coronary artery disease, the involvement of ELFN1 in attention-deficit hyperactivity disorder and the role of VPS13D in mouse neural tube development and its confirmed role in childhood onset movement disorders. The workflow and tools described here are aimed at guiding further experimental research, particularly within the context of precision medicine.
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Affiliation(s)
- Tudor I Oprea
- Department of Internal Medicine, University of New Mexico School of Medicine, Albuquerque, NM, USA. .,UNM Comprehensive Cancer Center, Albuquerque, NM, USA. .,Department of Rheumatology and Inflammation Research, Institute of Medicine, Sahlgrenska Academy at University of Gothenburg, Gothenburg, Sweden. .,Faculty of Health and Medical Sciences, Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, Copenhagen, Denmark.
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82
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HyperFoods: Machine intelligent mapping of cancer-beating molecules in foods. Sci Rep 2019; 9:9237. [PMID: 31270435 PMCID: PMC6610092 DOI: 10.1038/s41598-019-45349-y] [Citation(s) in RCA: 32] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2019] [Accepted: 06/03/2019] [Indexed: 01/02/2023] Open
Abstract
Recent data indicate that up-to 30–40% of cancers can be prevented by dietary and lifestyle measures alone. Herein, we introduce a unique network-based machine learning platform to identify putative food-based cancer-beating molecules. These have been identified through their molecular biological network commonality with clinically approved anti-cancer therapies. A machine-learning algorithm of random walks on graphs (operating within the supercomputing DreamLab platform) was used to simulate drug actions on human interactome networks to obtain genome-wide activity profiles of 1962 approved drugs (199 of which were classified as “anti-cancer” with their primary indications). A supervised approach was employed to predict cancer-beating molecules using these ‘learned’ interactome activity profiles. The validated model performance predicted anti-cancer therapeutics with classification accuracy of 84–90%. A comprehensive database of 7962 bioactive molecules within foods was fed into the model, which predicted 110 cancer-beating molecules (defined by anti-cancer drug likeness threshold of >70%) with expected capacity comparable to clinically approved anti-cancer drugs from a variety of chemical classes including flavonoids, terpenoids, and polyphenols. This in turn was used to construct a ‘food map’ with anti-cancer potential of each ingredient defined by the number of cancer-beating molecules found therein. Our analysis underpins the design of next-generation cancer preventative and therapeutic nutrition strategies.
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Blucher AS, McWeeney SK, Stein L, Wu G. Visualization of drug target interactions in the contexts of pathways and networks with ReactomeFIViz. F1000Res 2019; 8:908. [PMID: 31372215 PMCID: PMC6644836 DOI: 10.12688/f1000research.19592.1] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 06/11/2019] [Indexed: 01/08/2023] Open
Abstract
The precision medicine paradigm is centered on therapies targeted to particular molecular entities that will elicit an anticipated and controlled therapeutic response. However, genetic alterations in the drug targets themselves or in genes whose products interact with the targets can affect how well a drug actually works for an individual patient. To better understand the effects of targeted therapies in patients, we need software tools capable of simultaneously visualizing patient-specific variations and drug targets in their biological context. This context can be provided using pathways, which are process-oriented representations of biological reactions, or biological networks, which represent pathway-spanning interactions among genes, proteins, and other biological entities. To address this need, we have recently enhanced the Reactome Cytoscape app, ReactomeFIViz, to assist researchers in visualizing and modeling drug and target interactions. ReactomeFIViz integrates drug-target interaction information with high quality manually curated pathways and a genome-wide human functional interaction network. Both the pathways and the functional interaction network are provided by Reactome, the most comprehensive open source biological pathway knowledgebase. We describe several examples demonstrating the application of these new features to the visualization of drugs in the contexts of pathways and networks. Complementing previous features in ReactomeFIViz, these new features enable researchers to ask focused questions about targeted therapies, such as drug sensitivity for patients with different mutation profiles, using a pathway or network perspective.
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Affiliation(s)
- Aurora S Blucher
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR, 97239, USA
| | - Shannon K McWeeney
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR, 97239, USA.,Division of Bioinformatics and Computational Biology, Department of Medical Informatics and Clinical Epidemiology, Oregon Health & Science University, Portland, OR, 97239, USA
| | - Lincoln Stein
- Ontario Institute for Cancer Research, Toronto, ON, M5G 0A3, Canada.,Department of Molecular Genetics, University of Toronto, Toronto, ON, M5S 1A1, Canada
| | - Guanming Wu
- Division of Bioinformatics and Computational Biology, Department of Medical Informatics and Clinical Epidemiology, Oregon Health & Science University, Portland, OR, 97239, USA
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