1
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Brennan RJ, Jenkinson S, Brown A, Delaunois A, Dumotier B, Pannirselvam M, Rao M, Ribeiro LR, Schmidt F, Sibony A, Timsit Y, Sales VT, Armstrong D, Lagrutta A, Mittlestadt SW, Naven R, Peri R, Roberts S, Vergis JM, Valentin JP. The state of the art in secondary pharmacology and its impact on the safety of new medicines. Nat Rev Drug Discov 2024; 23:525-545. [PMID: 38773351 DOI: 10.1038/s41573-024-00942-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/05/2024] [Indexed: 05/23/2024]
Abstract
Secondary pharmacology screening of investigational small-molecule drugs for potentially adverse off-target activities has become standard practice in pharmaceutical research and development, and regulatory agencies are increasingly requesting data on activity against targets with recognized adverse effect relationships. However, the screening strategies and target panels used by pharmaceutical companies may vary substantially. To help identify commonalities and differences, as well as to highlight opportunities for further optimization of secondary pharmacology assessment, we conducted a broad-ranging survey across 18 companies under the auspices of the DruSafe leadership group of the International Consortium for Innovation and Quality in Pharmaceutical Development. Based on our analysis of this survey and discussions and additional research within the group, we present here an overview of the current state of the art in secondary pharmacology screening. We discuss best practices, including additional safety-associated targets not covered by most current screening panels, and present approaches for interpreting and reporting off-target activities. We also provide an assessment of the safety impact of secondary pharmacology screening, and a perspective on opportunities and challenges in this rapidly developing field.
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Affiliation(s)
| | | | | | | | | | | | - Mohan Rao
- Janssen Research & Development, San Diego, CA, USA
- Neurocrine Biosciences, San Diego, CA, USA
| | - Lyn Rosenbrier Ribeiro
- UCB Biopharma, Braine-l'Alleud, Belgium
- AstraZeneca, Cambridge, UK
- Grunenthal, Berkshire, UK
| | | | | | - Yoav Timsit
- Novartis Biomedical Research, Cambridge, MA, USA
- Blueprint Medicines, Cambridge, MA, USA
| | | | - Duncan Armstrong
- Novartis Biomedical Research, Cambridge, MA, USA
- Armstrong Pharmacology, Macclesfield, UK
| | | | | | - Russell Naven
- Takeda Pharmaceuticals, Cambridge, MA, USA
- Novartis Biomedical Research, Cambridge, MA, USA
| | - Ravikumar Peri
- Takeda Pharmaceuticals, Cambridge, MA, USA
- Alexion Pharmaceuticals, Wilmington, DE, USA
| | - Sonia Roberts
- Roche Pharma Research and Early Development, Roche Innovation Center, Basel, Switzerland
| | - James M Vergis
- Faegre Drinker Biddle and Reath, LLP, Washington, DC, USA
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2
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Schieferdecker S, Vock E. Development of Pharmacophore Models for the Important Off-Target 5-HT 2B Receptor. J Med Chem 2023; 66:1509-1521. [PMID: 36621987 DOI: 10.1021/acs.jmedchem.2c01679] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
Abstract
Toxicity is a major cause of attrition in the development of pharmaceuticals, and the off-target effects are a frequent contributor. The 5-HT2B receptor agonism is known to be responsible for a variety of safety concerns including valvular heart disease which was the cause for the withdrawal of several compounds from the market. An early detection of potential binding to this receptor is thus desirable. Herein, we present the identification of key amino acid residues in the active site of 5-HT2B by molecular dynamics simulations, the development of pharmacophore models and their performance on in-house data, and a structurally highly diverse subset of Enamine REAL labeled for 5-HT2B activity by a machine learning model. These models may be used as filters employed on screening compound sets for the early filtration of compounds with potential 5-HT2B off-target liabilities.
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Affiliation(s)
- Sebastian Schieferdecker
- Department of Nonclinical Drug Safety, Germany, Boehringer-Ingelheim Pharma GmbH & Co. KG, Biberach88397, Germany
| | - Esther Vock
- Department of Nonclinical Drug Safety, Germany, Boehringer-Ingelheim Pharma GmbH & Co. KG, Biberach88397, Germany
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3
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Bajorath J, Chávez-Hernández AL, Duran-Frigola M, Fernández-de Gortari E, Gasteiger J, López-López E, Maggiora GM, Medina-Franco JL, Méndez-Lucio O, Mestres J, Miranda-Quintana RA, Oprea TI, Plisson F, Prieto-Martínez FD, Rodríguez-Pérez R, Rondón-Villarreal P, Saldívar-Gonzalez FI, Sánchez-Cruz N, Valli M. Chemoinformatics and artificial intelligence colloquium: progress and challenges in developing bioactive compounds. J Cheminform 2022; 14:82. [PMID: 36461094 PMCID: PMC9716667 DOI: 10.1186/s13321-022-00661-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2022] [Accepted: 11/25/2022] [Indexed: 12/03/2022] Open
Abstract
We report the main conclusions of the first Chemoinformatics and Artificial Intelligence Colloquium, Mexico City, June 15-17, 2022. Fifteen lectures were presented during a virtual public event with speakers from industry, academia, and non-for-profit organizations. Twelve hundred and ninety students and academics from more than 60 countries. During the meeting, applications, challenges, and opportunities in drug discovery, de novo drug design, ADME-Tox (absorption, distribution, metabolism, excretion and toxicity) property predictions, organic chemistry, peptides, and antibiotic resistance were discussed. The program along with the recordings of all sessions are freely available at https://www.difacquim.com/english/events/2022-colloquium/ .
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Affiliation(s)
- Jürgen Bajorath
- grid.10388.320000 0001 2240 3300Department of Life Science Informatics, B-IT, LIMES Program Unit Chemical Biology and Medicinal Chemistry, Rheinische Friedrich-Wilhelms-Universität, Friedrich-Hirzebruch-Allee 5/6, 53113 Bonn, Germany
| | - Ana L. Chávez-Hernández
- grid.9486.30000 0001 2159 0001DIFACQUIM Research Group, Department of Pharmacy, School of Chemistry, National Autonomous University of Mexico, 04510 Mexico City, Mexico
| | - Miquel Duran-Frigola
- Ersilia Open Source Initiative, Cambridge, UK ,grid.7722.00000 0001 1811 6966Joint IRB-BSC-CRG Programme in Computational Biology, Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology, Barcelona, Catalonia Spain
| | - Eli Fernández-de Gortari
- grid.420330.60000 0004 0521 6935Nanosafety Laboratory, International Iberian Nanotechnology Laboratory, 4715-330 Braga, Portugal
| | - Johann Gasteiger
- grid.5330.50000 0001 2107 3311Computer-Chemie-Centrum, University of Erlangen-Nuremberg, Erlangen, Germany
| | - Edgar López-López
- grid.9486.30000 0001 2159 0001DIFACQUIM Research Group, Department of Pharmacy, School of Chemistry, National Autonomous University of Mexico, 04510 Mexico City, Mexico ,grid.512574.0Department of Pharmacology, Center for Research and Advanced Studies of the National Polytechnic Institute (CINVESTAV), 07360 Mexico City, Mexico
| | - Gerald M. Maggiora
- grid.134563.60000 0001 2168 186XBIO5 Institute, University of Arizona, Tucson, AZ 85721 USA
| | - José L. Medina-Franco
- grid.9486.30000 0001 2159 0001DIFACQUIM Research Group, Department of Pharmacy, School of Chemistry, National Autonomous University of Mexico, 04510 Mexico City, Mexico
| | | | - Jordi Mestres
- grid.5841.80000 0004 1937 0247Chemotargets SL, Baldiri Reixac 4, Parc Cientific de Barcelona (PCB), 08028 Barcelona, Catalonia Spain ,grid.20522.370000 0004 1767 9005Research Group on Systems Pharmacology, Research Program on Biomedical Informatics (GRIB), IMIM Hospital del Mar Medical Research Institute and University Pompeu Fabra, Parc de Recerca Biomedica (PRBB), 08003 Barcelona, Catalonia Spain
| | | | - Tudor I. Oprea
- grid.266832.b0000 0001 2188 8502Department of Internal Medicine, University of New Mexico School of Medicine, Albuquerque, NM 87131 USA ,grid.8761.80000 0000 9919 9582Department of Rheumatology and Inflammation Research, Institute of Medicine, Sahlgrenska Academy at Gothenburg University, 40530 Gothenburg, Sweden ,grid.5254.60000 0001 0674 042XNovo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, 2200 Copenhagen, Denmark ,Present Address: Roivant Discovery Sciences, Inc., 451 D Street, Boston, MA 02210 USA
| | - Fabien Plisson
- grid.512574.0Department of Biotechnology and Biochemistry, Center for Research and Advanced Studies of the National Polytechnic Institute (CINVESTAV-IPN), Irapuato Unit, 36824 Irapuato, Gto Mexico
| | - Fernando D. Prieto-Martínez
- grid.9486.30000 0001 2159 0001Chemistry Institute, National Autonomous University of Mexico, 04510 Mexico City, Mexico
| | - Raquel Rodríguez-Pérez
- grid.419481.10000 0001 1515 9979Novartis Institutes for Biomedical Research, 4002 Basel, Switzerland
| | - Paola Rondón-Villarreal
- grid.442204.40000 0004 0486 1035Universidad de Santander, Facultad de Ciencias Médicas y de la Salud, Instituto de Investigación Masira, Calle 70 No. 55-210, 680003 Santander, Bucaramanga Colombia
| | - Fernanda I. Saldívar-Gonzalez
- grid.9486.30000 0001 2159 0001DIFACQUIM Research Group, Department of Pharmacy, School of Chemistry, National Autonomous University of Mexico, 04510 Mexico City, Mexico
| | - Norberto Sánchez-Cruz
- grid.5841.80000 0004 1937 0247Chemotargets SL, Baldiri Reixac 4, Parc Cientific de Barcelona (PCB), 08028 Barcelona, Catalonia Spain ,grid.9486.30000 0001 2159 0001Instituto de Química, Unidad Mérida, Universidad Nacional Autónoma de México, Carretera Mérida-Tetiz Km. 4.5, Yucatán, 97357 Ucú, Mexico
| | - Marilia Valli
- grid.410543.70000 0001 2188 478XNuclei of Bioassays, Biosynthesis and Ecophysiology of Natural Products (NuBBE), Department of Organic Chemistry, Institute of Chemistry, São Paulo State University-UNESP, Araraquara, Brazil
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4
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Zahoránszky-Kőhalmi G, Siramshetty VB, Kumar P, Gurumurthy M, Grillo B, Mathew B, Metaxatos D, Backus M, Mierzwa T, Simon R, Grishagin I, Brovold L, Mathé EA, Hall MD, Michael SG, Godfrey AG, Mestres J, Jensen LJ, Oprea TI. A Workflow of Integrated Resources to Catalyze Network Pharmacology Driven COVID-19 Research. J Chem Inf Model 2022; 62:718-729. [PMID: 35057621 PMCID: PMC10790216 DOI: 10.1021/acs.jcim.1c00431] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
In the event of an outbreak due to an emerging pathogen, time is of the essence to contain or to mitigate the spread of the disease. Drug repositioning is one of the strategies that has the potential to deliver therapeutics relatively quickly. The SARS-CoV-2 pandemic has shown that integrating critical data resources to drive drug-repositioning studies, involving host-host, host-pathogen, and drug-target interactions, remains a time-consuming effort that translates to a delay in the development and delivery of a life-saving therapy. Here, we describe a workflow we designed for a semiautomated integration of rapidly emerging data sets that can be generally adopted in a broad network pharmacology research setting. The workflow was used to construct a COVID-19 focused multimodal network that integrates 487 host-pathogen, 63 278 host-host protein, and 1221 drug-target interactions. The resultant Neo4j graph database named "Neo4COVID19" is made publicly accessible via a web interface and via API calls based on the Bolt protocol. Details for accessing the database are provided on a landing page (https://neo4covid19.ncats.io/). We believe that our Neo4COVID19 database will be a valuable asset to the research community and will catalyze the discovery of therapeutics to fight COVID-19.
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Affiliation(s)
| | - Vishal B. Siramshetty
- National Center for Advancing Translational Sciences, Rockville, 9800 Medical Center Dr., MD 20850, USA
| | - Praveen Kumar
- Department of Internal Medicine, University of New Mexico School of Medicine, 1 University of New Mexico, Albuquerque, NM 87131, USA
- Department of Computer Science, University of New Mexico, 1 University of New Mexico Albuquerque, NM 87131, USA
| | - Manideep Gurumurthy
- National Center for Advancing Translational Sciences, Rockville, 9800 Medical Center Dr., MD 20850, USA
| | - Busola Grillo
- National Center for Advancing Translational Sciences, Rockville, 9800 Medical Center Dr., MD 20850, USA
| | - Biju Mathew
- National Center for Advancing Translational Sciences, Rockville, 9800 Medical Center Dr., MD 20850, USA
| | - Dimitrios Metaxatos
- National Center for Advancing Translational Sciences, Rockville, 9800 Medical Center Dr., MD 20850, USA
| | - Mark Backus
- National Center for Advancing Translational Sciences, Rockville, 9800 Medical Center Dr., MD 20850, USA
| | - Tim Mierzwa
- National Center for Advancing Translational Sciences, Rockville, 9800 Medical Center Dr., MD 20850, USA
| | - Reid Simon
- National Center for Advancing Translational Sciences, Rockville, 9800 Medical Center Dr., MD 20850, USA
| | - Ivan Grishagin
- National Center for Advancing Translational Sciences, Rockville, 9800 Medical Center Dr., MD 20850, USA
- Rancho BioSciences LLC., 16955 Via Del Campo Suite 200, San Diego, CA 92127, USA
| | - Laura Brovold
- Rancho BioSciences LLC., 16955 Via Del Campo Suite 200, San Diego, CA 92127, USA
| | - Ewy A. Mathé
- National Center for Advancing Translational Sciences, Rockville, 9800 Medical Center Dr., MD 20850, USA
| | - Matthew D. Hall
- National Center for Advancing Translational Sciences, Rockville, 9800 Medical Center Dr., MD 20850, USA
| | - Samuel G. Michael
- National Center for Advancing Translational Sciences, Rockville, 9800 Medical Center Dr., MD 20850, USA
| | - Alexander G. Godfrey
- National Center for Advancing Translational Sciences, Rockville, 9800 Medical Center Dr., MD 20850, USA
| | - Jordi Mestres
- Research Group on Systems Pharmacology, Research Program on Biomedical Informatics (GRIB), IMIM Hospital del Mar Medical Research Institute and University Pompeu Fabra, Doctor Aiguader 88, 08003 Barcelona, Catalonia, Spain
| | - Lars J. Jensen
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences,University of Copenhagen, Blegdamsvej 3B, 2200 Copenhagen N, Denmark
| | - Tudor I. Oprea
- Department of Internal Medicine, University of New Mexico School of Medicine, 1 University of New Mexico, Albuquerque, NM 87131, USA
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences,University of Copenhagen, Blegdamsvej 3B, 2200 Copenhagen N, Denmark
- UNM Comprehensive Cancer Center, 1201 Camino de Salud NE, Albuquerque, NM 87102, USA
- Department of Rheumatology and Inflammation Research, Institute of Medicine, Sahlgrenska Academy at University of Gothenburg, Box 480, 40530 Gothenburg, Sweden
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5
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Gray B, Baruteau AE, Antolin AA, Pittman A, Sarganas G, Molokhia M, Blom MT, Bastiaenen R, Bardai A, Priori SG, Napolitano C, Weeke PE, Shakir SA, Haverkamp W, Mestres J, Winkel BG, Witney AA, Chis-Ster I, Sangaralingam A, Camm AJ, Tfelt-Hansen J, Roden DM, Tan HL, Garbe E, Sturkenboom M, Behr ER. Rare Variation in Drug Metabolism and Long QT Genes and the Genetic Susceptibility to Acquired Long QT Syndrome. CIRCULATION. GENOMIC AND PRECISION MEDICINE 2022; 15:e003391. [PMID: 35113648 DOI: 10.1161/circgen.121.003391] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
BACKGROUND Acquired long QT syndrome (aLQTS) is a serious unpredictable adverse drug reaction. Pharmacogenomic markers may predict risk. METHODS Among 153 aLQTS patients (mean age 58 years [range, 14-88], 98.7% White, 85.6% symptomatic), computational methods identified proteins interacting most significantly with 216 QT-prolonging drugs. All cases underwent sequencing of 31 candidate genes arising from this analysis or associating with congenital LQTS. Variants were filtered using a minor allele frequency <1% and classified for susceptibility for aLQTS. Gene-burden analyses were then performed comparing the primary cohort to control exomes (n=452) and an independent replication aLQTS exome sequencing cohort. RESULTS In 25.5% of cases, at least one rare variant was identified: 22.2% of cases carried a rare variant in a gene associated with congenital LQTS, and in 4% of cases that variant was known to be pathogenic or likely pathogenic for congenital LQTS; 7.8% cases carried a cytochrome-P450 (CYP) gene variant. Of 12 identified CYP variants, 11 (92%) were in an enzyme known to metabolize at least one culprit drug to which the subject had been exposed. Drug-drug interactions that affected culprit drug metabolism were found in 19% of cases. More than one congenital LQTS variant, CYP gene variant, or drug interaction was present in 7.8% of cases. Gene-burden analyses of the primary cohort compared to control exomes (n=452), and an independent replication aLQTS exome sequencing cohort (n=67) and drug-tolerant controls (n=148) demonstrated an increased burden of rare (minor allele frequency<0.01) variants in CYP genes but not LQTS genes. CONCLUSIONS Rare susceptibility variants in CYP genes are emerging as potentially important pharmacogenomic risk markers for aLQTS and could form part of personalized medicine approaches in the future.
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Affiliation(s)
- Belinda Gray
- Cardiology Clinical Academic Group, Molecular & Clinical Sciences Research Institute, St George's, University of London & St George's University Hospitals NHS Foundation Trust, London, United Kingdom (B.G., A.-E.B., R.B., A.S., A.J.C., E.R.B.)
| | - Alban-Elouen Baruteau
- Cardiology Clinical Academic Group, Molecular & Clinical Sciences Research Institute, St George's, University of London & St George's University Hospitals NHS Foundation Trust, London, United Kingdom (B.G., A.-E.B., R.B., A.S., A.J.C., E.R.B.)
- L'institut du thorax, INSERM, CNRS, UNIV Nantes, CHU Nantes, Nantes, France (A.-E.B.)
| | - Albert A Antolin
- Systems Pharmacology, Research Program on Biomedical Informatics (GRIB), IMIM Hospital del Mar Medical Research Institute & University Pompeu Fabra, Parc de Recerca Biomedica, Barcelona, Catalonia, Spain (A.A.A., M.J.M.)
| | - Alan Pittman
- Genetics Research Centre (A.P.), St George's University of London, United Kingdom
| | - Giselle Sarganas
- Clinical Pharmacology & Toxicology, Charite Universitaetsmedizin, Berlin, Germany (G.S.)
| | - Mariam Molokhia
- Department of Population Health Sciences, King's College London, United Kingdom (M.M.)
| | - Marieke T Blom
- Heart Centre AMC, Department of Experimental & Clinical Cardiology, Academic Medical Center, Amsterdam, the Netherlands (M.T.B., A.B., H.L.T.)
| | - Rachel Bastiaenen
- Cardiology Clinical Academic Group, Molecular & Clinical Sciences Research Institute, St George's, University of London & St George's University Hospitals NHS Foundation Trust, London, United Kingdom (B.G., A.-E.B., R.B., A.S., A.J.C., E.R.B.)
| | - Abdenasser Bardai
- Heart Centre AMC, Department of Experimental & Clinical Cardiology, Academic Medical Center, Amsterdam, the Netherlands (M.T.B., A.B., H.L.T.)
| | - Silvia G Priori
- Molecular Cardiology, IRCCS ICS Maugeri, Pavia, Italy (S.G.P., C.N.)
- Department of Molecular Medicine, University of Pavia, Italy (S.G.P., C.N.)
| | - Carlo Napolitano
- Molecular Cardiology, IRCCS ICS Maugeri, Pavia, Italy (S.G.P., C.N.)
- Department of Molecular Medicine, University of Pavia, Italy (S.G.P., C.N.)
| | - Peter E Weeke
- L'institut du thorax, INSERM, CNRS, UNIV Nantes, CHU Nantes, Nantes, France (A.-E.B.)
- Departments of Medicine, Pharmacology & Biomedical Informatics Vanderbilt University Medical Centre (P.E.W., D.M.R.)
| | - Saad A Shakir
- Drug Safety Research Unit, Bursledon Hall, Blundell Lane, Southampton, United Kingdom (S.A.S.)
- Associate Department of the School of Pharmacy & Biomedical Sciences, University of Portsmouth, United Kingdom (S.A.S.)
| | - Wilhelm Haverkamp
- Charité-Campus Virchow-Klinikum (CVK), Department of Cardiology, Berlin, Germany (W.H.)
| | - Jordi Mestres
- Systems Pharmacology, Research Program on Biomedical Informatics (GRIB), IMIM Hospital del Mar Medical Research Institute & University Pompeu Fabra, Parc de Recerca Biomedica, Barcelona, Catalonia, Spain (A.A.A., M.J.M.)
| | - Bo Gregers Winkel
- Department of Forensic Medicine, Faculty of Medical Sciences, University of Copenhagen, Denmark (B.W., J.T.-H.)
- Department of Cardiology, The Heart Centre, Copenhagen University Hospital, Rigshospitalet, Denmark (P.E.W., B.W., J.T.-H.)
| | - Adam A Witney
- Institute of Infection & Immunity (A.A.W., I.C.-S.), St George's University of London, United Kingdom
| | - Irina Chis-Ster
- Institute of Infection & Immunity (A.A.W., I.C.-S.), St George's University of London, United Kingdom
| | - Ajanthah Sangaralingam
- Cardiology Clinical Academic Group, Molecular & Clinical Sciences Research Institute, St George's, University of London & St George's University Hospitals NHS Foundation Trust, London, United Kingdom (B.G., A.-E.B., R.B., A.S., A.J.C., E.R.B.)
| | - A John Camm
- Cardiology Clinical Academic Group, Molecular & Clinical Sciences Research Institute, St George's, University of London & St George's University Hospitals NHS Foundation Trust, London, United Kingdom (B.G., A.-E.B., R.B., A.S., A.J.C., E.R.B.)
| | - Jacob Tfelt-Hansen
- Department of Cardiology, The Heart Centre, Copenhagen University Hospital, Rigshospitalet, Denmark (P.E.W., B.W., J.T.-H.)
- Department of Forensic Medicine, Faculty of Medical Sciences, University of Copenhagen, Denmark (B.W., J.T.-H.)
| | - Dan M Roden
- Departments of Medicine, Pharmacology & Biomedical Informatics Vanderbilt University Medical Centre (P.E.W., D.M.R.)
| | - Hanno L Tan
- Heart Centre AMC, Department of Experimental & Clinical Cardiology, Academic Medical Center, Amsterdam, the Netherlands (M.T.B., A.B., H.L.T.)
| | - Edeltraut Garbe
- Leibniz Institute for Prevention Research & Epidemiology - BIPS, Bremen, Germany (E.G.)
| | - Miriam Sturkenboom
- Julius Global Health, University Medical Center Utrecht, the Netherlands (M.S.)
| | - Elijah R Behr
- Cardiology Clinical Academic Group, Molecular & Clinical Sciences Research Institute, St George's, University of London & St George's University Hospitals NHS Foundation Trust, London, United Kingdom (B.G., A.-E.B., R.B., A.S., A.J.C., E.R.B.)
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6
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Tice RR, Bassan A, Amberg A, Anger LT, Beal MA, Bellion P, Benigni R, Birmingham J, Brigo A, Bringezu F, Ceriani L, Crooks I, Cross K, Elespuru R, Faulkner DM, Fortin MC, Fowler P, Frericks M, Gerets HHJ, Jahnke GD, Jones DR, Kruhlak NL, Lo Piparo E, Lopez-Belmonte J, Luniwal A, Luu A, Madia F, Manganelli S, Manickam B, Mestres J, Mihalchik-Burhans AL, Neilson L, Pandiri A, Pavan M, Rider CV, Rooney JP, Trejo-Martin A, Watanabe-Sailor KH, White AT, Woolley D, Myatt GJ. In Silico Approaches In Carcinogenicity Hazard Assessment: Current Status and Future Needs. COMPUTATIONAL TOXICOLOGY (AMSTERDAM, NETHERLANDS) 2021; 20. [PMID: 35368437 DOI: 10.1016/j.comtox.2021.100191] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
Historically, identifying carcinogens has relied primarily on tumor studies in rodents, which require enormous resources in both money and time. In silico models have been developed for predicting rodent carcinogens but have not yet found general regulatory acceptance, in part due to the lack of a generally accepted protocol for performing such an assessment as well as limitations in predictive performance and scope. There remains a need for additional, improved in silico carcinogenicity models, especially ones that are more human-relevant, for use in research and regulatory decision-making. As part of an international effort to develop in silico toxicological protocols, a consortium of toxicologists, computational scientists, and regulatory scientists across several industries and governmental agencies evaluated the extent to which in silico models exist for each of the recently defined 10 key characteristics (KCs) of carcinogens. This position paper summarizes the current status of in silico tools for the assessment of each KC and identifies the data gaps that need to be addressed before a comprehensive in silico carcinogenicity protocol can be developed for regulatory use.
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Affiliation(s)
- Raymond R Tice
- RTice Consulting, Hillsborough, North Carolina, 27278, USA
| | | | - Alexander Amberg
- Sanofi Preclinical Safety, Industriepark Höchst, 65926 Frankfurt, Germany
| | - Lennart T Anger
- Genentech, Inc., South San Francisco, California, 94080, USA
| | - Marc A Beal
- Healthy Environments and Consumer Safety Branch, Health Canada, Government of Canada, Ottawa, Ontario, Canada K1A 0K9
| | | | | | - Jeffrey Birmingham
- GlaxoSmithKline, David Jack Centre for R&D, Ware, Hertfordshire, SG12 0DP, United Kingdom
| | - Alessandro Brigo
- Roche Pharmaceutical Research & Early Development, Pharmaceutical Sciences, Roche Innovation, Center Basel, F. Hoffmann-La Roche Ltd, CH-4070, Basel, Switzerland
| | | | - Lidia Ceriani
- Humane Society International, 1000 Brussels, Belgium
| | - Ian Crooks
- British American Tobacco (Investments) Ltd, GR&D Centre, Southampton, SO15 8TL, United Kingdom
| | | | - Rosalie Elespuru
- Food and Drug Administration, Center for Devices and Radiological Health, Silver Spring, Maryland, 20993, USA
| | - David M Faulkner
- Chemical Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, California, USA
| | - Marie C Fortin
- Department of Pharmacology and Toxicology, Ernest Mario School of Pharmacy, Rutgers University, Piscataway, New Jersey, 08855, USA
| | - Paul Fowler
- FSTox Consulting (Genetic Toxicology), Northamptonshire, United Kingdom
| | | | | | - Gloria D Jahnke
- Division of the National Toxicology Program, National Institute of Environmental Health Sciences, Research Triangle Park, North Carolina, 27709, USA
| | | | - Naomi L Kruhlak
- Food and Drug Administration, Center for Drug Evaluation and Research, Silver Spring, Maryland, 20993, USA
| | - Elena Lo Piparo
- Chemical Food Safety Group, Nestlé Research, CH-1000 Lausanne 26, Switzerland
| | - Juan Lopez-Belmonte
- Cuts Ice Ltd Chemical Food Safety Group, Nestlé Research, CH-1000 Lausanne 26, Switzerland
| | - Amarjit Luniwal
- North American Science Associates (NAMSA) Inc., Minneapolis, Minnesota, 55426, USA
| | - Alice Luu
- Healthy Environments and Consumer Safety Branch, Health Canada, Government of Canada, Ottawa, Ontario, Canada K1A 0K9
| | - Federica Madia
- European Commission, Joint Research Centre (JRC), Ispra, Italy
| | - Serena Manganelli
- Chemical Food Safety Group, Nestlé Research, CH-1000 Lausanne 26, Switzerland
| | | | - Jordi Mestres
- IMIM Institut Hospital Del Mar d'Investigacions Mèdiques and Universitat Pompeu Fabra, Doctor Aiguader 88, Parc de Recerca Biomèdica, 08003 Barcelona, Spain; and Chemotargets SL, Baldiri Reixac 4, Parc Científic de Barcelona, 08028, Barcelona, Spain
| | | | - Louise Neilson
- Broughton Nicotine Services, Oak Tree House, Earby, Lancashire, BB18 6JZ United Kingdom
| | - Arun Pandiri
- Division of the National Toxicology Program, National Institute of Environmental Health Sciences, Research Triangle Park, North Carolina, 27709, USA
| | | | - Cynthia V Rider
- Division of the National Toxicology Program, National Institute of Environmental Health Sciences, Research Triangle Park, North Carolina, 27709, USA
| | - John P Rooney
- Integrated Laboratory Systems, LLC., Morrisville, North Carolina, 27560, USA
| | | | - Karen H Watanabe-Sailor
- School of Mathematical and Natural Sciences, Arizona State University, West Campus, Glendale, Arizona, 85306, USA
| | - Angela T White
- GlaxoSmithKline, David Jack Centre for R&D, Ware, Hertfordshire, SG12 0DP, United Kingdom
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7
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Zahoránszky-Kőhalmi G, Siramshetty VB, Kumar P, Gurumurthy M, Grillo B, Mathew B, Metaxatos D, Backus M, Mierzwa T, Simon R, Grishagin I, Brovold L, Mathé EA, Hall MD, Michael SG, Godfrey AG, Mestres J, Jensen LJ, Oprea TI. A Workflow of Integrated Resources to Catalyze Network Pharmacology Driven COVID-19 Research. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2020:2020.11.04.369041. [PMID: 33173863 PMCID: PMC7654851 DOI: 10.1101/2020.11.04.369041] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
MOTIVATION In the event of an outbreak due to an emerging pathogen, time is of the essence to contain or to mitigate the spread of the disease. Drug repositioning is one of the strategies that has the potential to deliver therapeutics relatively quickly. The SARS-CoV-2 pandemic has shown that integrating critical data resources to drive drug-repositioning studies, involving host-host, hostpathogen and drug-target interactions, remains a time-consuming effort that translates to a delay in the development and delivery of a life-saving therapy. RESULTS Here, we describe a workflow we designed for a semi-automated integration of rapidly emerging datasets that can be generally adopted in a broad network pharmacology research setting. The workflow was used to construct a COVID-19 focused multimodal network that integrates 487 host-pathogen, 74,805 host-host protein and 1,265 drug-target interactions. The resultant Neo4j graph database named "Neo4COVID19" is accessible via a web interface and via API calls based on the Bolt protocol. We believe that our Neo4COVID19 database will be a valuable asset to the research community and will catalyze the discovery of therapeutics to fight COVID-19. AVAILABILITY https://neo4covid19.ncats.io.
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Affiliation(s)
| | | | - Praveen Kumar
- Department of Internal Medicine, University of New Mexico School of Medicine, Albuquerque, NM, USA
- Department of Computer Science, University of New Mexico, Albuquerque, New Mexico, USA
| | | | - Busola Grillo
- National Center for Advancing Translational Sciences, Rockville, MD, USA
| | - Biju Mathew
- National Center for Advancing Translational Sciences, Rockville, MD, USA
| | | | - Mark Backus
- National Center for Advancing Translational Sciences, Rockville, MD, USA
| | - Tim Mierzwa
- National Center for Advancing Translational Sciences, Rockville, MD, USA
| | - Reid Simon
- National Center for Advancing Translational Sciences, Rockville, MD, USA
| | - Ivan Grishagin
- National Center for Advancing Translational Sciences, Rockville, MD, USA
- Rancho BioSciences LLC., San Diego, CA USA
| | | | - Ewy A. Mathé
- National Center for Advancing Translational Sciences, Rockville, MD, USA
| | - Matthew D. Hall
- National Center for Advancing Translational Sciences, Rockville, MD, USA
| | - Samuel G. Michael
- National Center for Advancing Translational Sciences, Rockville, MD, USA
| | | | - Jordi Mestres
- Research Group on Systems Pharmacology, Research Program on Biomedical Informatics (GRIB), IMIM Hospital del Mar Medical Research Institute and University Pompeu Fabra, Barcelona, Catalonia, Spain
| | - Lars J. Jensen
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Tudor I. Oprea
- Department of Internal Medicine, University of New Mexico School of Medicine, Albuquerque, NM, USA
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- UNM Comprehensive Cancer Center, Albuquerque, NM, USA
- Department of Rheumatology and Inflammation Research, Institute of Medicine, Sahlgrenska Academy at University of Gothenburg, Gothenburg, Sweden
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8
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Saik AYH, Lim YY, Stanslas J, Choo WS. Biosynthesis of Quercetin Palmitate Esters and Evaluation of their Physico‐Chemical Properties and Stability. J AM OIL CHEM SOC 2020. [DOI: 10.1002/aocs.12404] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/03/2023]
Affiliation(s)
- Amy Yi Hsan Saik
- Department of Pre‐clinical Sciences, Faculty of Medicine and Health Sciences Universiti Tunku Abdul Rahman Selangor 43000 Malaysia
| | - Yau Yan Lim
- School of Science Monash University Malaysia Selangor 47500 Malaysia
| | - Johnson Stanslas
- Department of Medicine, Faculty of Medicine and Health Sciences Universiti Putra Malaysia Selangor 43400 Malaysia
| | - Wee Sim Choo
- School of Science Monash University Malaysia Selangor 47500 Malaysia
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Melnikov F, Geohagen BC, Gavin T, LoPachin RM, Anastas PT, Coish P, Herr DW. Application of the hard and soft, acids and bases (HSAB) theory as a method to predict cumulative neurotoxicity. Neurotoxicology 2020; 79:95-103. [PMID: 32380191 DOI: 10.1016/j.neuro.2020.04.009] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2019] [Revised: 04/07/2020] [Accepted: 04/22/2020] [Indexed: 12/14/2022]
Abstract
Xenobiotic electrophiles can form covalent adducts that may impair protein function, damage DNA, and may lead a range of adverse effects. Cumulative neurotoxicity is one adverse effect that has been linked to covalent protein binding as a Molecular Initiating Event (MIE). This paper describes a mechanistic in silico chemical screening approach for neurotoxicity based on Hard and Soft Acids and Bases (HSAB) theory. We evaluated the applicability of HSAB-based electrophilicity screening protocol for neurotoxicity on 19 positive and 19 negative reference chemicals. These reference chemicals were identified from the literature, using available information on mechanisms of neurotoxicity whenever possible. In silico screening was based on structural alerts for protein binding motifs and electrophilicity index in the range of known neurotoxicants. The approach demonstrated both a high positive prediction rate (82-90 %) and specificity (90 %). The overall sensitivity was relatively lower (47 %). However, when predicting the toxicity of chemicals known or suspected of acting via non-specific adduct formation mechanism, the HSAB approach identified 7/8 (sensitivity 88 %) of positive control chemicals correctly. Consequently, the HSAB-based screening is a promising approach of identifying possible neurotoxins with adduct formation molecular initiating events. While the approach must be expanded over time to capture a wider range of MIEs involved in neurotoxicity, the mechanistic nature of the screen allows users to flag chemicals for possible adduct formation MIEs. Thus, the HSAB based toxicity screening is a promising strategy for toxicity assessment and chemical prioritization in neurotoxicology and other health endpoints that involve adduct formation.
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Affiliation(s)
- Fjodor Melnikov
- School of Forestry and Environmental Studies, Yale University, New Haven, CT, 06511, United States.
| | - Brian C Geohagen
- Department of Anesthesiology, Montefiore Medical Center, Albert Einstein College of Medicine, 111 E. 210th St, Bronx, NY, 10467, United States.
| | - Terrence Gavin
- Department of Chemistry, Iona College, 402 North Avenue, New Rochelle, NY, 10804, United States.
| | - Richard M LoPachin
- Department of Anesthesiology, Montefiore Medical Center, Albert Einstein College of Medicine, 111 E. 210th St, Bronx, NY, 10467, United States.
| | - Paul T Anastas
- School of Forestry and Environmental Science, School of Public Health, Yale University, New Haven, CT 06511, United States.
| | - Phillip Coish
- School of Forestry and Environmental Science, Yale University, New Haven, CT 06511, United States.
| | - David W Herr
- Center for Public Health and Environmental Assessment, U.S. Environmental Protection Agency, Research Triangle Park, NC, 27711, United States.
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10
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Hemmerich J, Ecker GF. In silico toxicology: From structure–activity relationships towards deep learning and adverse outcome pathways. WILEY INTERDISCIPLINARY REVIEWS-COMPUTATIONAL MOLECULAR SCIENCE 2020; 10:e1475. [PMID: 35866138 PMCID: PMC9286356 DOI: 10.1002/wcms.1475] [Citation(s) in RCA: 52] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/07/2020] [Revised: 03/09/2020] [Accepted: 03/10/2020] [Indexed: 12/18/2022]
Abstract
In silico toxicology is an emerging field. It gains increasing importance as research is aiming to decrease the use of animal experiments as suggested in the 3R principles by Russell and Burch. In silico toxicology is a means to identify hazards of compounds before synthesis, and thus in very early stages of drug development. For chemical industries, as well as regulatory agencies it can aid in gap‐filling and guide risk minimization strategies. Techniques such as structural alerts, read‐across, quantitative structure–activity relationship, machine learning, and deep learning allow to use in silico toxicology in many cases, some even when data is scarce. Especially the concept of adverse outcome pathways puts all techniques into a broader context and can elucidate predictions by mechanistic insights. This article is categorized under:Structure and Mechanism > Computational Biochemistry and Biophysics Data Science > Chemoinformatics
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Affiliation(s)
- Jennifer Hemmerich
- Department of Pharmaceutical Chemistry University of Vienna Vienna Austria
| | - Gerhard F. Ecker
- Department of Pharmaceutical Chemistry University of Vienna Vienna Austria
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11
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Ellis CR, Racz R, Kruhlak NL, Kim MT, Zakharov AV, Southall N, Hawkins EG, Burkhart K, Strauss DG, Stavitskaya L. Evaluating kratom alkaloids using PHASE. PLoS One 2020; 15:e0229646. [PMID: 32126112 PMCID: PMC7053747 DOI: 10.1371/journal.pone.0229646] [Citation(s) in RCA: 34] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2019] [Accepted: 02/11/2020] [Indexed: 01/01/2023] Open
Abstract
Kratom is a botanical substance that is marketed and promoted in the US for pharmaceutical opioid indications despite having no US Food and Drug Administration approved uses. Kratom contains over forty alkaloids including two partial agonists at the mu opioid receptor, mitragynine and 7-hydroxymitragynine, that have been subjected to the FDA's scientific and medical evaluation. However, pharmacological and toxicological data for the remaining alkaloids are limited. Therefore, we applied the Public Health Assessment via Structural Evaluation (PHASE) protocol to generate in silico binding profiles for 25 kratom alkaloids to facilitate the risk evaluation of kratom. PHASE demonstrates that kratom alkaloids share structural features with controlled opioids, indicates that several alkaloids bind to the opioid, adrenergic, and serotonin receptors, and suggests that mitragynine and 7-hydroxymitragynine are the strongest binders at the mu opioid receptor. Subsequently, the in silico binding profiles of a subset of the alkaloids were experimentally verified at the opioid, adrenergic, and serotonin receptors using radioligand binding assays. The verified binding profiles demonstrate the ability of PHASE to identify potential safety signals and provide a tool for prioritizing experimental evaluation of high-risk compounds.
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MESH Headings
- Animals
- Binding Sites
- HEK293 Cells
- Humans
- In Vitro Techniques
- Mitragyna/chemistry
- Molecular Docking Simulation
- Plants, Medicinal/chemistry
- Radioligand Assay
- Receptors, Adrenergic/drug effects
- Receptors, Adrenergic/metabolism
- Receptors, Opioid/drug effects
- Receptors, Opioid/metabolism
- Receptors, Opioid, mu/drug effects
- Receptors, Opioid, mu/metabolism
- Receptors, Serotonin/drug effects
- Receptors, Serotonin/metabolism
- Secologanin Tryptamine Alkaloids/chemistry
- Secologanin Tryptamine Alkaloids/pharmacokinetics
- Secologanin Tryptamine Alkaloids/pharmacology
- Structure-Activity Relationship
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Affiliation(s)
- Christopher R. Ellis
- Division of Applied Regulatory Science, Office of Clinical Pharmacology, Office of Translational Sciences, Center for Drug Evaluation and Research, United States Food and Drug Administration, Silver Spring, Maryland, United States of America
| | - Rebecca Racz
- Division of Applied Regulatory Science, Office of Clinical Pharmacology, Office of Translational Sciences, Center for Drug Evaluation and Research, United States Food and Drug Administration, Silver Spring, Maryland, United States of America
| | - Naomi L. Kruhlak
- Division of Applied Regulatory Science, Office of Clinical Pharmacology, Office of Translational Sciences, Center for Drug Evaluation and Research, United States Food and Drug Administration, Silver Spring, Maryland, United States of America
| | - Marlene T. Kim
- Division of Applied Regulatory Science, Office of Clinical Pharmacology, Office of Translational Sciences, Center for Drug Evaluation and Research, United States Food and Drug Administration, Silver Spring, Maryland, United States of America
| | - Alexey V. Zakharov
- National Center for Advancing Translational Sciences, National Institutes of Health, Rockville, Maryland, United States of America
| | - Noel Southall
- National Center for Advancing Translational Sciences, National Institutes of Health, Rockville, Maryland, United States of America
| | - Edward G. Hawkins
- Controlled Substances Staff, Center for Drug Evaluation and Research, United States Food and Drug Administration, Silver Spring, Maryland, United States of America
| | - Keith Burkhart
- Division of Applied Regulatory Science, Office of Clinical Pharmacology, Office of Translational Sciences, Center for Drug Evaluation and Research, United States Food and Drug Administration, Silver Spring, Maryland, United States of America
| | - David G. Strauss
- Division of Applied Regulatory Science, Office of Clinical Pharmacology, Office of Translational Sciences, Center for Drug Evaluation and Research, United States Food and Drug Administration, Silver Spring, Maryland, United States of America
| | - Lidiya Stavitskaya
- Division of Applied Regulatory Science, Office of Clinical Pharmacology, Office of Translational Sciences, Center for Drug Evaluation and Research, United States Food and Drug Administration, Silver Spring, Maryland, United States of America
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12
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Olivés J, Mestres J. Closing the Gap Between Therapeutic Use and Mode of Action in Remedial Herbs. Front Pharmacol 2019; 10:1132. [PMID: 31632273 PMCID: PMC6785637 DOI: 10.3389/fphar.2019.01132] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2019] [Accepted: 08/30/2019] [Indexed: 12/17/2022] Open
Abstract
The ancient tradition of taking parts of a plant or preparing plant extracts for treating certain discomforts and maladies has long been lacking a scientific rationale to support its preparation and still widespread use in several parts of the world. In an attempt to address this challenge, we collected and integrated data connecting metabolites, plants, diseases, and proteins. A mechanistic hypothesis is generated when a metabolite is known to be present in a given plant, that plant is known to be used to treat a certain disease, that disease is known to be linked to the function of a given protein, and that protein is finally known or predicted to interact with the original metabolite. The construction of plant–protein networks from mutually connected metabolites and diseases facilitated the identification of plausible mechanisms of action for plants being used to treat analgesia, hypercholesterolemia, diarrhea, catarrh, and cough. Additional concrete examples using both experimentally known and computationally predicted, and subsequently experimentally confirmed, metabolite–protein interactions to close the connection circle between metabolites, plants, diseases, and proteins offered further proof of concept for the validity and scope of the approach to generate mode of action hypotheses for some of the therapeutic uses of remedial herbs.
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Affiliation(s)
- Joaquim Olivés
- Research Group on Systems Pharmacology, Research Programme on Biomedical Informatics (GRIB), IMIM Hospital del Mar Medical Research Institute, Barcelona, Spain
| | - Jordi Mestres
- Research Group on Systems Pharmacology, Research Programme on Biomedical Informatics (GRIB), IMIM Hospital del Mar Medical Research Institute, Barcelona, Spain.,Department of Experimental and Health Sciences, University Pompeu Fabra, Barcelona, Spain
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13
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Bendels S, Bissantz C, Fasching B, Gerebtzoff G, Guba W, Kansy M, Migeon J, Mohr S, Peters JU, Tillier F, Wyler R, Lerner C, Kramer C, Richter H, Roberts S. Safety screening in early drug discovery: An optimized assay panel. J Pharmacol Toxicol Methods 2019; 99:106609. [DOI: 10.1016/j.vascn.2019.106609] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2019] [Revised: 06/29/2019] [Accepted: 07/01/2019] [Indexed: 12/20/2022]
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14
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Onay A, Onay M. A Drug Decision Support System for Developing a Successful Drug Candidate Using Machine Learning Techniques. Curr Comput Aided Drug Des 2019; 16:407-419. [PMID: 31438830 DOI: 10.2174/1573409915666190716143601] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2019] [Revised: 04/24/2019] [Accepted: 05/06/2019] [Indexed: 11/22/2022]
Abstract
BACKGROUND Virtual screening of candidate drug molecules using machine learning techniques plays a key role in pharmaceutical industry to design and discovery of new drugs. Computational classification methods can determine drug types according to the disease groups and distinguish approved drugs from withdrawn ones. INTRODUCTION Classification models developed in this study can be used as a simple filter in drug modelling to eliminate potentially inappropriate molecules in the early stages. In this work, we developed a Drug Decision Support System (DDSS) to classify each drug candidate molecule as potentially drug or non-drug and to predict its disease group. METHODS Molecular descriptors were identified for the determination of a number of rules in drug molecules. They were derived using ADRIANA.Code program and Lipinski's rule of five. We used Artificial Neural Network (ANN) to classify drug molecules correctly according to the types of diseases. Closed frequent molecular structures in the form of subgraph fragments were also obtained with Gaston algorithm included in ParMol Package to find common molecular fragments for withdrawn drugs. RESULTS We observed that TPSA, XlogP Natoms, HDon_O and TPSA are the most distinctive features in the pool of the molecular descriptors and evaluated the performances of classifiers on all datasets and found that classification accuracies are very high on all the datasets. Neural network models achieved 84.6% and 83.3% accuracies on test sets including cardiac therapy, anti-epileptics and anti-parkinson drugs with approved and withdrawn drugs for drug classification problems. CONCLUSION The experimental evaluation shows that the system is promising at determination of potential drug molecules to classify drug molecules correctly according to the types of diseases.
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Affiliation(s)
- Aytun Onay
- Department of Computer Engineering, Faculty of Engineering & Architecture, Kafkas University, Kars, 36100, Turkey
| | - Melih Onay
- Department of Environmental Engineering, Computational & Experimental Biochemistry Lab, Faculty of Engineering, Van Yuzuncu Yil University, 65100, Van, Turkey
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15
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Ellis CR, Racz R, Kruhlak NL, Kim MT, Hawkins EG, Strauss DG, Stavitskaya L. Assessing the Structural and Pharmacological Similarity of Newly Identified Drugs of Abuse to Controlled Substances Using Public Health Assessment via Structural Evaluation. Clin Pharmacol Ther 2019; 106:116-122. [PMID: 30957872 PMCID: PMC6617983 DOI: 10.1002/cpt.1418] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2018] [Accepted: 02/16/2019] [Indexed: 12/17/2022]
Abstract
The US Food and Drug Administration's Center for Drug Evaluation and Research (CDER) developed an investigational Public Health Assessment via Structural Evaluation (PHASE) methodology to provide a structure-based evaluation of a newly identified opioid's risk to public safety. PHASE utilizes molecular structure to predict biological function. First, a similarity metric quantifies the structural similarity of a new drug relative to drugs currently controlled in the Controlled Substances Act (CSA). Next, software predictions provide the primary and secondary biological targets of the new drug. Finally, molecular docking estimates the binding affinity at the identified biological targets. The multicomponent computational approach coupled with expert review provides a rapid, systematic evaluation of a new drug in the absence of in vitro or in vivo data. The information provided by PHASE has the potential to inform law enforcement agencies with vital information regarding newly emerging illicit opioids.
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Affiliation(s)
- Christopher R Ellis
- Division of Applied Regulatory Science, Office of Clinical Pharmacology, Office of Translational Sciences, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, Maryland, USA
| | - Rebecca Racz
- Division of Applied Regulatory Science, Office of Clinical Pharmacology, Office of Translational Sciences, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, Maryland, USA
| | - Naomi L Kruhlak
- Division of Applied Regulatory Science, Office of Clinical Pharmacology, Office of Translational Sciences, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, Maryland, USA
| | - Marlene T Kim
- Division of Applied Regulatory Science, Office of Clinical Pharmacology, Office of Translational Sciences, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, Maryland, USA
| | - Edward G Hawkins
- Controlled Substances Staff, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, Maryland, USA
| | - David G Strauss
- Division of Applied Regulatory Science, Office of Clinical Pharmacology, Office of Translational Sciences, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, Maryland, USA
| | - Lidiya Stavitskaya
- Division of Applied Regulatory Science, Office of Clinical Pharmacology, Office of Translational Sciences, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, Maryland, USA
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16
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Pérez-Sianes J, Pérez-Sánchez H, Díaz F. Virtual Screening Meets Deep Learning. Curr Comput Aided Drug Des 2019; 15:6-28. [PMID: 30338743 DOI: 10.2174/1573409914666181018141602] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2017] [Revised: 10/08/2018] [Accepted: 10/11/2018] [Indexed: 12/27/2022]
Abstract
BACKGROUND Automated compound testing is currently the de facto standard method for drug screening, but it has not brought the great increase in the number of new drugs that was expected. Computer- aided compounds search, known as Virtual Screening, has shown the benefits to this field as a complement or even alternative to the robotic drug discovery. There are different methods and approaches to address this problem and most of them are often included in one of the main screening strategies. Machine learning, however, has established itself as a virtual screening methodology in its own right and it may grow in popularity with the new trends on artificial intelligence. OBJECTIVE This paper will attempt to provide a comprehensive and structured review that collects the most important proposals made so far in this area of research. Particular attention is given to some recent developments carried out in the machine learning field: the deep learning approach, which is pointed out as a future key player in the virtual screening landscape.
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Affiliation(s)
| | - Horacio Pérez-Sánchez
- Bioinformatics and High Performance Computing Research Group (BIO-HPC), Computer Engineering Department, Universidad Católica San Antonio de Murcia (UCAM), Murcia, Spain
| | - Fernando Díaz
- Departamento de Informática, Escuela de Ingeniería Informática, University of Valladolid, Segovia, Spain
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17
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Donzanti BA. Pharmacovigilance is Everyone's Concern: Let's Work It Out Together. Clin Ther 2018; 40:1967-1972. [DOI: 10.1016/j.clinthera.2018.09.016] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2018] [Revised: 09/27/2018] [Accepted: 09/28/2018] [Indexed: 02/06/2023]
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18
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Huang R, Xia M, Sakamuru S, Zhao J, Lynch C, Zhao T, Zhu H, Austin CP, Simeonov A. Expanding biological space coverage enhances the prediction of drug adverse effects in human using in vitro activity profiles. Sci Rep 2018; 8:3783. [PMID: 29491351 PMCID: PMC5830476 DOI: 10.1038/s41598-018-22046-w] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2017] [Accepted: 02/15/2018] [Indexed: 12/11/2022] Open
Abstract
In vitro assay data have recently emerged as a potential alternative to traditional animal toxicity studies to aid in the prediction of adverse effects of chemicals on humans. Here we evaluate the data generated from a battery of quantitative high-throughput screening (qHTS) assays applied to a large and diverse collection of chemicals, including approved drugs, for their capacity in predicting human toxicity. Models were built with animal in vivo toxicity data, in vitro human cell-based assay data, as well as in combination with chemical structure and/or drug-target information to predict adverse effects observed for drugs in humans. Interestingly, we found that the models built with the human cell-based assay data performed close to those of the models based on animal in vivo toxicity data. Furthermore, expanding the biological space coverage of assays by including additional drug-target annotations was shown to significantly improve model performance. We identified a small set of targets, which, when added to the current suite of in vitro human cell-based assay data, result in models that greatly outperform those built with the existing animal toxicity data. Assays can be developed for this set of targets to screen compounds for construction of robust models for human toxicity prediction.
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Affiliation(s)
- Ruili Huang
- Division of Pre-clinical Innovation, National Center for Advancing Translational Sciences (NCATS), National Institutes of Health (NIH), Rockville, MD, 20850, USA.
| | - Menghang Xia
- Division of Pre-clinical Innovation, National Center for Advancing Translational Sciences (NCATS), National Institutes of Health (NIH), Rockville, MD, 20850, USA
| | - Srilatha Sakamuru
- Division of Pre-clinical Innovation, National Center for Advancing Translational Sciences (NCATS), National Institutes of Health (NIH), Rockville, MD, 20850, USA
| | - Jinghua Zhao
- Division of Pre-clinical Innovation, National Center for Advancing Translational Sciences (NCATS), National Institutes of Health (NIH), Rockville, MD, 20850, USA
| | - Caitlin Lynch
- Division of Pre-clinical Innovation, National Center for Advancing Translational Sciences (NCATS), National Institutes of Health (NIH), Rockville, MD, 20850, USA
| | - Tongan Zhao
- Division of Pre-clinical Innovation, National Center for Advancing Translational Sciences (NCATS), National Institutes of Health (NIH), Rockville, MD, 20850, USA
| | - Hu Zhu
- Division of Pre-clinical Innovation, National Center for Advancing Translational Sciences (NCATS), National Institutes of Health (NIH), Rockville, MD, 20850, USA
| | - Christopher P Austin
- Division of Pre-clinical Innovation, National Center for Advancing Translational Sciences (NCATS), National Institutes of Health (NIH), Rockville, MD, 20850, USA
| | - Anton Simeonov
- Division of Pre-clinical Innovation, National Center for Advancing Translational Sciences (NCATS), National Institutes of Health (NIH), Rockville, MD, 20850, USA
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19
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Lazović B, Blazić I, Zlatković-Svenda M, Žugić V. Severe pneumonia caused by antipsihotic drugs: What does not suit, the patient or the drug? SANAMED 2018. [DOI: 10.24125/sanamed.v13i3.257] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022] Open
Abstract
Introduction: Antipsychotic drugs are generally categorized as typical antipsychotics (sometimes referred to as first-generation or conventional antipsychotics, or neuroleptics) and atypical antipsychotics; both are approved for the treatment of acute and chronic psychoses (i.e, schizophrenia), mania, agitation, and other psychiatric disorders. In 2005 the US Food and Drug Administration issued a warning about the increased risk of all-cause mortality associated with atypical antipsychotic use in elderly patients with dementia. Community acquired pneumonia (CAP) was one of the most frequently reported causes of death. The same warning was extended to typical antipsychotics in 2008 with extension to people with or without dementia. Case report: We present a 65-year-old Caucasian woman who was admitted to hospital due to massive pneumonia. She was suffered forschisophrenia 15-years and at moment of admission she was in remission. She had continuously high fever up to 40 degrees. All collected cultures (blood, sputum, urine, smear of aspirating catheter) were negative. She was treated with various antibiotics without improvement. After changing antipsychotic drugs, she showed slow improvement until total recovery after 3 months. Discussion and conclusion: Antipsychotic-associated CAP seems to be a clinically relevant issue in frail elderly patients, as consistently documented in several epidemiologic investigations. No clear evidence exists for an increased risk of pneumonia in younger patients treated with antipsychotics. In elderly populations, the increase in risk is dose-dependent, and is more pronounced in the early phases of treatment. Future studies should better define the mechanism underlying antipsychotic-induced pneumonia and identify subgroups of antipsychotic users at higher risk of developing pneumonia.
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Abstract
Following the elucidation of the human genome, chemogenomics emerged in the beginning of the twenty-first century as an interdisciplinary research field with the aim to accelerate target and drug discovery by making best usage of the genomic data and the data linkable to it. What started as a systematization approach within protein target families now encompasses all types of chemical compounds and gene products. A key objective of chemogenomics is the establishment, extension, analysis, and prediction of a comprehensive SAR matrix which by application will enable further systematization in drug discovery. Herein we outline future perspectives of chemogenomics including the extension to new molecular modalities, or the potential extension beyond the pharma to the agro and nutrition sectors, and the importance for environmental protection. The focus is on computational sciences with potential applications for compound library design, virtual screening, hit assessment, analysis of phenotypic screens, lead finding and optimization, and systems biology-based prediction of toxicology and translational research.
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Affiliation(s)
- Edgar Jacoby
- Janssen Research & Development, Beerse, Belgium.
| | - J B Brown
- Life Science Informatics Research Unit, Laboratory of Molecular Biosciences, Kyoto University Graduate School of Medicine, Kyoto, Japan
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21
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Abstract
In this review we address to what extent computational techniques can augment our ability to predict toxicity. The first section provides a brief history of empirical observations on toxicity dating back to the dawn of Sumerian civilization. Interestingly, the concept of dose emerged very early on, leading up to the modern emphasis on kinetic properties, which in turn encodes the insight that toxicity is not solely a property of a compound but instead depends on the interaction with the host organism. The next logical step is the current conception of evaluating drugs from a personalized medicine point of view. We review recent work on integrating what could be referred to as classical pharmacokinetic analysis with emerging systems biology approaches incorporating multiple omics data. These systems approaches employ advanced statistical analytical data processing complemented with machine learning techniques and use both pharmacokinetic and omics data. We find that such integrated approaches not only provide improved predictions of toxicity but also enable mechanistic interpretations of the molecular mechanisms underpinning toxicity and drug resistance. We conclude the chapter by discussing some of the main challenges, such as how to balance the inherent tension between the predicitive capacity of models, which in practice amounts to constraining the number of features in the models versus allowing for rich mechanistic interpretability, i.e., equipping models with numerous molecular features. This challenge also requires patient-specific predictions on toxicity, which in turn requires proper stratification of patients as regards how they respond, with or without adverse toxic effects. In summary, the transformation of the ancient concept of dose is currently successfully operationalized using rich integrative data encoded in patient-specific models.
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Biological substantiation of antipsychotic-associated pneumonia: Systematic literature review and computational analyses. PLoS One 2017; 12:e0187034. [PMID: 29077727 PMCID: PMC5659779 DOI: 10.1371/journal.pone.0187034] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2017] [Accepted: 10/12/2017] [Indexed: 02/07/2023] Open
Abstract
Introduction Antipsychotic (AP) safety has been widely investigated. However, mechanisms underlying AP-associated pneumonia are not well-defined. Aim The aim of this study was to investigate the known mechanisms of AP-associated pneumonia through a systematic literature review, confirm these mechanisms using an independent data source on drug targets and attempt to identify novel AP drug targets potentially linked to pneumonia. Methods A search was conducted in Medline and Web of Science to identify studies exploring the association between pneumonia and antipsychotic use, from which information on hypothesized mechanism of action was extracted. All studies had to be in English and had to concern AP use as an intervention in persons of any age and for any indication, provided that the outcome was pneumonia. Information on the study design, population, exposure, outcome, risk estimate and mechanism of action was tabulated. Public repositories of pharmacology and drug safety data were used to identify the receptor binding profile and AP safety events. Cytoscape was then used to map biological pathways that could link AP targets and off-targets to pneumonia. Results The literature search yielded 200 articles; 41 were included in the review. Thirty studies reported a hypothesized mechanism of action, most commonly activation/inhibition of cholinergic, histaminergic and dopaminergic receptors. In vitro pharmacology data confirmed receptor affinities identified in the literature review. Two targets, thromboxane A2 receptor (TBXA2R) and platelet activating factor receptor (PTAFR) were found to be novel AP target receptors potentially associated with pneumonia. Biological pathways constructed using Cytoscape identified plausible biological links potentially leading to pneumonia downstream of TBXA2R and PTAFR. Conclusion Innovative approaches for biological substantiation of drug-adverse event associations may strengthen evidence on drug safety profiles and help to tailor pharmacological therapies to patient risk factors.
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Saadeh HA, Khasawneh MA, Samadi A, El-Haty IA, Satała G, Bojarski AJ, Ismaili L, Bautista-Aguilera ÓM, Yañez M, Mestres J, Marco-Contelles J. Design, Synthesis and Biological Evaluation of Potent Antioxidant 1-(2,5-Dimethoxybenzyl)-4-arylpiperazines and N
-Azolyl Substituted 2-(4-Arylpiperazin-1-yl). ChemistrySelect 2017. [DOI: 10.1002/slct.201700397] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
Affiliation(s)
- Haythem A. Saadeh
- Department of Chemistry; College of Science; United Arab Emirates University; Al Ain 15551 UAE
- Department of Chemistry; Faculty of Science; The University of Jordan; Amman 11942 Jordan
| | - Mohammad A. Khasawneh
- Department of Chemistry; College of Science; United Arab Emirates University; Al Ain 15551 UAE
| | - Abdelouahid Samadi
- Department of Chemistry; College of Science; United Arab Emirates University; Al Ain 15551 UAE
| | - Ismail A. El-Haty
- Department of Chemistry; College of Science; United Arab Emirates University; Al Ain 15551 UAE
| | - Grzegorz Satała
- Institute of Pharmacology; Polish Academy of Sciences; 12 Smętna Street 31-343 Kraków Poland
| | - Andrzej J. Bojarski
- Institute of Pharmacology; Polish Academy of Sciences; 12 Smętna Street 31-343 Kraków Poland
| | - Lhassane Ismaili
- Neurosciences Intégratives et Cliniques, EA 481; Univ. Bourgogne Franche-Comté; Laboratoire de Chimie Organique et Thérapeutique, UFR SMP; 19, rue Ambroise Paré F-25000 Besançon France
| | - Óscar M. Bautista-Aguilera
- Neurosciences Intégratives et Cliniques, EA 481; Univ. Bourgogne Franche-Comté; Laboratoire de Chimie Organique et Thérapeutique, UFR SMP; 19, rue Ambroise Paré F-25000 Besançon France
| | - Matilde Yañez
- Facultad de Farmacia; Departamento de Farmacología; Universidad de Santiago de Compostela; Campus Vida, Santiago de Compostela La Coruña Spain
| | - Jordi Mestres
- Research Group on Systems Pharmacology; Research Program on Biomedical Informatics (GRIB); IMIM Hospital del Mar Institute of Medical Research; Universitat Pompeu Fabra; Doctor Aiguader 88 08003 Barcelona Spain
| | - José Marco-Contelles
- Laboratory of Medicinal Chemistry (IQOG, CSIC); C/ Juan de la Cierva 3 28006- Madrid Spain
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24
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Synthesis, pharmacological evaluation and molecular docking of pyranopyrazole-linked 1,4-dihydropyridines as potent positive inotropes. Mol Divers 2017; 21:533-546. [DOI: 10.1007/s11030-017-9738-7] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2016] [Accepted: 04/09/2017] [Indexed: 01/14/2023]
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25
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Hartung T, FitzGerald RE, Jennings P, Mirams GR, Peitsch MC, Rostami-Hodjegan A, Shah I, Wilks MF, Sturla SJ. Systems Toxicology: Real World Applications and Opportunities. Chem Res Toxicol 2017; 30:870-882. [PMID: 28362102 PMCID: PMC5396025 DOI: 10.1021/acs.chemrestox.7b00003] [Citation(s) in RCA: 68] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2017] [Indexed: 01/14/2023]
Abstract
Systems Toxicology aims to change the basis of how adverse biological effects of xenobiotics are characterized from empirical end points to describing modes of action as adverse outcome pathways and perturbed networks. Toward this aim, Systems Toxicology entails the integration of in vitro and in vivo toxicity data with computational modeling. This evolving approach depends critically on data reliability and relevance, which in turn depends on the quality of experimental models and bioanalysis techniques used to generate toxicological data. Systems Toxicology involves the use of large-scale data streams ("big data"), such as those derived from omics measurements that require computational means for obtaining informative results. Thus, integrative analysis of multiple molecular measurements, particularly acquired by omics strategies, is a key approach in Systems Toxicology. In recent years, there have been significant advances centered on in vitro test systems and bioanalytical strategies, yet a frontier challenge concerns linking observed network perturbations to phenotypes, which will require understanding pathways and networks that give rise to adverse responses. This summary perspective from a 2016 Systems Toxicology meeting, an international conference held in the Alps of Switzerland, describes the limitations and opportunities of selected emerging applications in this rapidly advancing field. Systems Toxicology aims to change the basis of how adverse biological effects of xenobiotics are characterized, from empirical end points to pathways of toxicity. This requires the integration of in vitro and in vivo data with computational modeling. Test systems and bioanalytical technologies have made significant advances, but ensuring data reliability and relevance is an ongoing concern. The major challenge facing the new pathway approach is determining how to link observed network perturbations to phenotypic toxicity.
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Affiliation(s)
- Thomas Hartung
- Center
for Alternatives to Animal Testing (CAAT), Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland 21205, United States
- University
of Konstanz, CAAT-Europe, 78457 Konstanz, Germany
| | - Rex E. FitzGerald
- Swiss
Centre for Applied Human Toxicology, University
of Basel, 4055 Basel, Switzerland
| | - Paul Jennings
- Division
of Physiology, Department of Physiology and Medical Physics, Medical University of Innsbruck, 6020 Innsbruck, Austria
| | - Gary R. Mirams
- Centre
for Mathematical Medicine & Biology, School of Mathematical Sciences, University of Nottingham, Nottingham NG7 2RD, U.K.
| | - Manuel C. Peitsch
- Department
of Research and Development, Philip Morris
International, 2000 Neuchâtel, Switzerland
| | - Amin Rostami-Hodjegan
- Centre
for Applied Pharmacokinetic Research, University
of Manchester, Manchester M13 9PL, U.K.
- Simcyp
Limited (a Certara Company), Blades Enterprise
Centre, Sheffield S2 4SU, U.K.
| | - Imran Shah
- National
Center for Computational Toxicology, Research Triangle Park, North Carolina 27711, United States
| | - Martin F. Wilks
- Swiss
Centre for Applied Human Toxicology, University
of Basel, 4055 Basel, Switzerland
| | - Shana J. Sturla
- Department
of Health Sciences and Technology, ETH Zurich, 8092 Zurich, Switzerland
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26
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Johnstone S, Albert JS. Pharmacological property optimization for allosteric ligands: A medicinal chemistry perspective. Bioorg Med Chem Lett 2017; 27:2239-2258. [PMID: 28408223 DOI: 10.1016/j.bmcl.2017.03.084] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2016] [Revised: 03/26/2017] [Accepted: 03/27/2017] [Indexed: 12/11/2022]
Abstract
New strategies to potentially improve drug safety and efficacy emerge with allosteric programs. Biased allosteric modulators can be designed with high subtype selectivity and defined receptor signaling endpoints, however, selecting the most meaningful parameters for optimization can be perplexing. Historically, "potency hunting" at the expense of physicochemical and pharmacokinetic optimization has led to numerous tool compounds with excellent pharmacological properties but no path to drug development. Conversely, extensive physicochemical and pharmacokinetic screening with only post hoc bias and allosteric characterization has led to inefficacious compounds or compounds with on-target toxicities. This field is rapidly evolving with new mechanistic understanding, changes in terminology, and novel opportunities. The intent of this digest is to summarize current understanding and debates within the field. We aim to discuss, from a medicinal chemistry perspective, the parameter choices available to drive SAR.
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Affiliation(s)
- Shawn Johnstone
- Department of Chemistry, IntelliSyn Pharma, 7171 Frederick-Banting, Montreal, Quebec H4S 1Z9, Canada.
| | - Jeffrey S Albert
- Department of Chemistry, IntelliSyn Pharma, 7171 Frederick-Banting, Montreal, Quebec H4S 1Z9, Canada; Department of Chemistry, AviSyn Pharma, 4275 Executive Square, Suite 200, La Jolla, CA 92037, United States.
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27
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Zaman S, Sarntivijai S, Abernethy DR. Use of Biomedical Ontologies for Integration of Biological Knowledge for Learning and Prediction of Adverse Drug Reactions. GENE REGULATION AND SYSTEMS BIOLOGY 2017; 11:1177625017696075. [PMID: 28469412 PMCID: PMC5398297 DOI: 10.1177/1177625017696075] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/29/2016] [Accepted: 02/04/2017] [Indexed: 12/26/2022]
Abstract
Drug-induced toxicity is a major public health concern that leads to patient morbidity and mortality. To address this problem, the Food and Drug Administration is working on the PredicTox initiative, a pilot research program on tyrosine kinase inhibitors, to build mechanistic and predictive models for drug-induced toxicity. This program involves integrating data acquired during preclinical studies and clinical trials within pharmaceutical company development programs that they have agreed to put in the public domain and in publicly available biological, pharmacological, and chemical databases. The integration process is accommodated by biomedical ontologies, a set of standardized vocabularies that define terms and logical relationships between them in each vocabulary. We describe a few programs that have used ontologies to address biomedical questions. The PredicTox effort is leveraging the experience gathered from these early initiatives to develop an infrastructure that allows evaluation of the hypothesis that having a mechanistic understanding underlying adverse drug reactions will improve the capacity to understand drug-induced clinical adverse drug reactions.
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Affiliation(s)
- Shadia Zaman
- Office of Clinical Pharmacology, Office of Translational Sciences, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, MD, USA
| | - Sirarat Sarntivijai
- European Bioinformatics Institute, European Molecular Biology Laboratory (EMBL-EBI), Wellcome Trust Genome Campus, Cambridge, UK
| | - Darrell R Abernethy
- Office of Clinical Pharmacology, Office of Translational Sciences, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, MD, USA
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28
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Papoian T, Jagadeesh G, Saulnier M, Simpson N, Ravindran A, Yang B, Laniyonu AA, Khan I, Szarfman A. Regulatory Forum Review*: Utility of in Vitro Secondary Pharmacology Data to Assess Risk of Drug-induced Valvular Heart Disease in Humans: Regulatory Considerations. Toxicol Pathol 2017; 45:381-388. [DOI: 10.1177/0192623317690609] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
Drug-induced valvular heart disease (VHD) is a serious side effect linked to long-term treatment with 5-hydroxytryptamine (serotonin) receptor 2B (5-HT2B) agonists. Safety assessment for off-target pharmacodynamic activity is a common approach used to screen drugs for this undesired property. Such studies include in vitro assays to determine whether the drug is a 5-HT2B agonist, a necessary pharmacological property for development of VHD. Measures of in vitro binding affinity (IC50, Ki) or cellular functional activity (EC50) are often compared to maximum therapeutic free plasma drug levels ( fCmax) from which safety margins (SMs) can be derived. However, there is no clear consensus on what constitutes an appropriate SM under various therapeutic conditions of use. The strengths and limitations of SM determinations and current risk assessment methodology are reviewed and evaluated. It is concluded that the use of SMs based on Ki values, or those relative to serotonin (5-HT), appears to be a better predictor than the use of EC50 or EC50/human fCmax values for determining whether known 5-HT2B agonists have resulted in VHD. It is hoped that such a discussion will improve efforts to reduce this preventable serious drug-induced toxicity from occurring and lead to more informed risk assessment strategies.
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Affiliation(s)
- Thomas Papoian
- Division of Cardiovascular and Renal Products, Center for Drug Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, Maryland, USA
| | - Gowraganahalli Jagadeesh
- Division of Cardiovascular and Renal Products, Center for Drug Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, Maryland, USA
| | - Muriel Saulnier
- Division of Cardiovascular and Renal Products, Center for Drug Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, Maryland, USA
| | - Natalie Simpson
- Division of Hematology Oncology Toxicology, Center for Drug Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, Maryland, USA
| | - Arippa Ravindran
- Division of Psychiatry Products, Center for Drug Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, Maryland, USA
| | - Baichun Yang
- Division of Cardiovascular and Renal Products, Center for Drug Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, Maryland, USA
| | - Adebayo A. Laniyonu
- Division of Medical Imaging Products, Center for Drug Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, Maryland, USA
| | - Imran Khan
- Division of Psychiatry Products, Center for Drug Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, Maryland, USA
| | - Ana Szarfman
- Division of Cardiovascular and Renal Products, Center for Drug Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, Maryland, USA
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29
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Wittwehr C, Aladjov H, Ankley G, Byrne HJ, de Knecht J, Heinzle E, Klambauer G, Landesmann B, Luijten M, MacKay C, Maxwell G, Meek MEB, Paini A, Perkins E, Sobanski T, Villeneuve D, Waters KM, Whelan M. How Adverse Outcome Pathways Can Aid the Development and Use of Computational Prediction Models for Regulatory Toxicology. Toxicol Sci 2017; 155:326-336. [PMID: 27994170 PMCID: PMC5340205 DOI: 10.1093/toxsci/kfw207] [Citation(s) in RCA: 112] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/16/2023] Open
Abstract
Efforts are underway to transform regulatory toxicology and chemical safety assessment from a largely empirical science based on direct observation of apical toxicity outcomes in whole organism toxicity tests to a predictive one in which outcomes and risk are inferred from accumulated mechanistic understanding. The adverse outcome pathway (AOP) framework provides a systematic approach for organizing knowledge that may support such inference. Likewise, computational models of biological systems at various scales provide another means and platform to integrate current biological understanding to facilitate inference and extrapolation. We argue that the systematic organization of knowledge into AOP frameworks can inform and help direct the design and development of computational prediction models that can further enhance the utility of mechanistic and in silico data for chemical safety assessment. This concept was explored as part of a workshop on AOP-Informed Predictive Modeling Approaches for Regulatory Toxicology held September 24-25, 2015. Examples of AOP-informed model development and its application to the assessment of chemicals for skin sensitization and multiple modes of endocrine disruption are provided. The role of problem formulation, not only as a critical phase of risk assessment, but also as guide for both AOP and complementary model development is described. Finally, a proposal for actively engaging the modeling community in AOP-informed computational model development is made. The contents serve as a vision for how AOPs can be leveraged to facilitate development of computational prediction models needed to support the next generation of chemical safety assessment.
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Affiliation(s)
| | | | - Gerald Ankley
- US Environmental Protection Agency, Duluth, Minnesota 55804
| | | | - Joop de Knecht
- National Institute for Public Health and the Environment (RIVM), Bilthoven, MA 3721, The Netherlands
| | - Elmar Heinzle
- Universität des Saarlandes, 66123 Saarbrücken, Germany
| | | | | | - Mirjam Luijten
- National Institute for Public Health and the Environment (RIVM), Bilthoven, MA 3721, The Netherlands
| | - Cameron MacKay
- Unilever Safety and Environmenta Assurance Centre, Sharnbrook, MK44 1LQ, UK
| | - Gavin Maxwell
- Unilever Safety and Environmenta Assurance Centre, Sharnbrook, MK44 1LQ, UK
| | | | - Alicia Paini
- European Commission, Joint Research Centre, Ispra 21027, Italy
| | - Edward Perkins
- US Army Engineer Research and Development Center, Vicksburg, Mississippi 39180
| | | | - Dan Villeneuve
- US Environmental Protection Agency, Duluth, Minnesota 55804
| | - Katrina M Waters
- Pacific Northwest National Laboratory, Richland, Washington 99352
| | - Maurice Whelan
- European Commission, Joint Research Centre, Ispra 21027, Italy
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30
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Saik AYH, Lim YY, Stanslas J, Choo WS. Lipase-catalyzed acylation of quercetin with cinnamic acid. BIOCATAL BIOTRANSFOR 2016. [DOI: 10.1080/10242422.2016.1212844] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
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31
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O’Meara MJ, Ballouz S, Shoichet BK, Gillis J. Ligand Similarity Complements Sequence, Physical Interaction, and Co-Expression for Gene Function Prediction. PLoS One 2016; 11:e0160098. [PMID: 27467773 PMCID: PMC4965129 DOI: 10.1371/journal.pone.0160098] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2016] [Accepted: 07/13/2016] [Indexed: 12/13/2022] Open
Abstract
The expansion of protein-ligand annotation databases has enabled large-scale networking of proteins by ligand similarity. These ligand-based protein networks, which implicitly predict the ability of neighboring proteins to bind related ligands, may complement biologically-oriented gene networks, which are used to predict functional or disease relevance. To quantify the degree to which such ligand-based protein associations might complement functional genomic associations, including sequence similarity, physical protein-protein interactions, co-expression, and disease gene annotations, we calculated a network based on the Similarity Ensemble Approach (SEA: sea.docking.org), where protein neighbors reflect the similarity of their ligands. We also measured the similarity with functional genomic networks over a common set of 1,131 genes, and found that the networks had only small overlaps, which were significant only due to the large scale of the data. Consistent with the view that the networks contain different information, combining them substantially improved Molecular Function prediction within GO (from AUROC~0.63–0.75 for the individual data modalities to AUROC~0.8 in the aggregate). We investigated the boost in guilt-by-association gene function prediction when the networks are combined and describe underlying properties that can be further exploited.
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Affiliation(s)
- Matthew J. O’Meara
- Department of Pharmaceutical Chemistry, University of California San Francisco, San Francisco, California, 94158–2550, United States of America
| | - Sara Ballouz
- Cold Spring Harbor Laboratory, Stanley Institute for Cognitive Genomics, 500 Sunnyside Boulevard, Woodbury, NY, 11797, United States of America
| | - Brian K. Shoichet
- Department of Pharmaceutical Chemistry, University of California San Francisco, San Francisco, California, 94158–2550, United States of America
- * E-mail: (BKS); (JG)
| | - Jesse Gillis
- Cold Spring Harbor Laboratory, Stanley Institute for Cognitive Genomics, 500 Sunnyside Boulevard, Woodbury, NY, 11797, United States of America
- * E-mail: (BKS); (JG)
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32
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Humphreys WG, Will Y, Guengerich FP. Toxicology Strategies for Drug Discovery - Present and Future: Introduction. Chem Res Toxicol 2016; 29:437. [PMID: 27087588 DOI: 10.1021/acs.chemrestox.6b00049] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- W Griffith Humphreys
- Bristol-Myers Squibb Pharmaceutical Research Institute , Princeton, New Jersey 08543, United States
| | - Yvonne Will
- Drug Safety Research and Development, Pfizer , Eastern Point Road, Groton, Connecticut 06340, United States
| | - F Peter Guengerich
- Department of Biochemistry and Center in Molecular Toxicology, Vanderbilt University School of Medicine , 638 Robinson Research Building, 2200 Pierce Avenue, Nashville, Tennessee 37232-0146, United States
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33
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Remez N, Garcia-Serna R, Vidal D, Mestres J. The In Vitro Pharmacological Profile of Drugs as a Proxy Indicator of Potential In Vivo Organ Toxicities. Chem Res Toxicol 2016; 29:637-48. [PMID: 26952164 DOI: 10.1021/acs.chemrestox.5b00470] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
The potential of a drug to cause certain organ toxicities is somehow implicitly contained in its full pharmacological profile, provided the drug reaches and accumulates at the various organs where the different interacting proteins in its profile, both targets and off-targets, are expressed. Under this assumption, a computational approach was implemented to obtain a projected anatomical profile of a drug from its in vitro pharmacological profile linked to protein expression data across 47 organs. It was observed that the anatomical profiles obtained when using only the known primary targets of the drugs reflected roughly the intended organ targets. However, when both known and predicted secondary pharmacology was considered, the projected anatomical profiles of the drugs were able to clearly highlight potential organ off-targets. Accordingly, when applied to sets of drugs known to cause cardiotoxicity and hepatotoxicity, the approach is able to identify heart and liver, respectively, as the organs where the proteins in the pharmacological profile of the corresponding drugs are specifically expressed. When applied to a set of drugs linked to a risk of Torsades de Pointes, heart is again the organ clearly standing out from the rest and a potential protein profile hazard is proposed. The approach can be used as a proxy indicator of potential in vivo organ toxicities.
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Affiliation(s)
- Nikita Remez
- Systems Pharmacology, Research Program on Biomedical Informatics (GRIB), IMIM Hospital del Mar Medical Research Institute and University Pompeu Fabra, Parc de Recerca Biomèdica , Doctor Aiguader 88, 08003 Barcelona, Catalonia, Spain.,Chemotargets SL, Parc Científic de Barcelona, Baldiri Reixac 4 (TI-05A7), 08028 Barcelona, Catalonia, Spain
| | - Ricard Garcia-Serna
- Chemotargets SL, Parc Científic de Barcelona, Baldiri Reixac 4 (TI-05A7), 08028 Barcelona, Catalonia, Spain
| | - David Vidal
- Chemotargets SL, Parc Científic de Barcelona, Baldiri Reixac 4 (TI-05A7), 08028 Barcelona, Catalonia, Spain
| | - Jordi Mestres
- Systems Pharmacology, Research Program on Biomedical Informatics (GRIB), IMIM Hospital del Mar Medical Research Institute and University Pompeu Fabra, Parc de Recerca Biomèdica , Doctor Aiguader 88, 08003 Barcelona, Catalonia, Spain.,Chemotargets SL, Parc Científic de Barcelona, Baldiri Reixac 4 (TI-05A7), 08028 Barcelona, Catalonia, Spain
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Antolin AA, Workman P, Mestres J, Al-Lazikani B. Polypharmacology in Precision Oncology: Current Applications and Future Prospects. Curr Pharm Des 2016; 22:6935-6945. [PMID: 27669965 PMCID: PMC5403974 DOI: 10.2174/1381612822666160923115828] [Citation(s) in RCA: 57] [Impact Index Per Article: 7.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2016] [Accepted: 09/19/2016] [Indexed: 02/08/2023]
Abstract
Over the past decade, a more comprehensive, large-scale approach to studying cancer genetics and biology has revealed the challenges of tumor heterogeneity, adaption, evolution and drug resistance, while systems-based pharmacology and chemical biology strategies have uncovered a much more complex interaction between drugs and the human proteome than was previously anticipated. In this mini-review we assess the progress and potential of drug polypharmacology in biomarker-driven precision oncology. Polypharmacology not only provides great opportunities for drug repurposing to exploit off-target effects in a new single-target indication but through simultaneous blockade of multiple targets or pathways offers exciting opportunities to slow, overcome or even prevent inherent or adaptive drug resistance. We highlight the many challenges associated with exploiting known or desired polypharmacology in drug design and development, and assess computational and experimental methods to uncover unknown polypharmacology. A comprehensive understanding of the intricate links between polypharmacology, efficacy and safety is urgently needed if we are to tackle the enduring challenge of cancer drug resistance and to fully exploit polypharmacology for the ultimate benefit of cancer patients.
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Affiliation(s)
- Albert A. Antolin
- Cancer Research UK Cancer Therapeutics Unit, Division of Cancer Therapeutics, The Institute of Cancer Research, London, UK
- Systems Pharmacology, Research Program on Biomedical Informatics (GRIB), IMIM Hospital del Mar Medical Research Institute and University Pompeu Fabra, Parc de Recerca Biomèdica, Barcelona, Catalonia, Spain
| | - Paul Workman
- Cancer Research UK Cancer Therapeutics Unit, Division of Cancer Therapeutics, The Institute of Cancer Research, London, UK
| | - Jordi Mestres
- Systems Pharmacology, Research Program on Biomedical Informatics (GRIB), IMIM Hospital del Mar Medical Research Institute and University Pompeu Fabra, Parc de Recerca Biomèdica, Barcelona, Catalonia, Spain
| | - Bissan Al-Lazikani
- Cancer Research UK Cancer Therapeutics Unit, Division of Cancer Therapeutics, The Institute of Cancer Research, London, UK
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