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Chiu K, Racz R, Burkhart K, Florian J, Ford K, Iveth Garcia M, Geiger RM, Howard KE, Hyland PL, Ismaiel OA, Kruhlak NL, Li Z, Matta MK, Prentice KW, Shah A, Stavitskaya L, Volpe DA, Weaver JL, Wu WW, Rouse R, Strauss DG. New science, drug regulation, and emergent public health issues: The work of FDA's division of applied regulatory science. Front Med (Lausanne) 2023; 9:1109541. [PMID: 36743666 PMCID: PMC9893027 DOI: 10.3389/fmed.2022.1109541] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2022] [Accepted: 12/13/2022] [Indexed: 01/20/2023] Open
Abstract
The U.S. Food and Drug Administration (FDA) Division of Applied Regulatory Science (DARS) moves new science into the drug review process and addresses emergent regulatory and public health questions for the Agency. By forming interdisciplinary teams, DARS conducts mission-critical research to provide answers to scientific questions and solutions to regulatory challenges. Staffed by experts across the translational research spectrum, DARS forms synergies by pulling together scientists and experts from diverse backgrounds to collaborate in tackling some of the most complex challenges facing FDA. This includes (but is not limited to) assessing the systemic absorption of sunscreens, evaluating whether certain drugs can convert to carcinogens in people, studying drug interactions with opioids, optimizing opioid antagonist dosing in community settings, removing barriers to biosimilar and generic drug development, and advancing therapeutic development for rare diseases. FDA tasks DARS with wide ranging issues that encompass regulatory science; DARS, in turn, helps the Agency solve these challenges. The impact of DARS research is felt by patients, the pharmaceutical industry, and fellow regulators. This article reviews applied research projects and initiatives led by DARS and conducts a deeper dive into select examples illustrating the impactful work of the Division.
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Affiliation(s)
- Kimberly Chiu
- Division of Applied Regulatory Science, Office of Clinical Pharmacology, Office of Translational Science, Center for Drug Evaluation and Research, United States Food and Drug Administration, Silver Spring, MD, United States
| | - Rebecca Racz
- Division of Applied Regulatory Science, Office of Clinical Pharmacology, Office of Translational Science, Center for Drug Evaluation and Research, United States Food and Drug Administration, Silver Spring, MD, United States
| | - Keith Burkhart
- Division of Applied Regulatory Science, Office of Clinical Pharmacology, Office of Translational Science, Center for Drug Evaluation and Research, United States Food and Drug Administration, Silver Spring, MD, United States
| | - Jeffry Florian
- Division of Applied Regulatory Science, Office of Clinical Pharmacology, Office of Translational Science, Center for Drug Evaluation and Research, United States Food and Drug Administration, Silver Spring, MD, United States
| | - Kevin Ford
- Division of Applied Regulatory Science, Office of Clinical Pharmacology, Office of Translational Science, Center for Drug Evaluation and Research, United States Food and Drug Administration, Silver Spring, MD, United States
| | - M. Iveth Garcia
- Division of Applied Regulatory Science, Office of Clinical Pharmacology, Office of Translational Science, Center for Drug Evaluation and Research, United States Food and Drug Administration, Silver Spring, MD, United States
| | - Robert M. Geiger
- Division of Applied Regulatory Science, Office of Clinical Pharmacology, Office of Translational Science, Center for Drug Evaluation and Research, United States Food and Drug Administration, Silver Spring, MD, United States
| | - Kristina E. Howard
- Division of Applied Regulatory Science, Office of Clinical Pharmacology, Office of Translational Science, Center for Drug Evaluation and Research, United States Food and Drug Administration, Silver Spring, MD, United States
| | - Paula L. Hyland
- Division of Applied Regulatory Science, Office of Clinical Pharmacology, Office of Translational Science, Center for Drug Evaluation and Research, United States Food and Drug Administration, Silver Spring, MD, United States
| | - Omnia A. Ismaiel
- Division of Applied Regulatory Science, Office of Clinical Pharmacology, Office of Translational Science, Center for Drug Evaluation and Research, United States Food and Drug Administration, Silver Spring, MD, United States
| | - Naomi L. Kruhlak
- Division of Applied Regulatory Science, Office of Clinical Pharmacology, Office of Translational Science, Center for Drug Evaluation and Research, United States Food and Drug Administration, Silver Spring, MD, United States
| | - Zhihua Li
- Division of Applied Regulatory Science, Office of Clinical Pharmacology, Office of Translational Science, Center for Drug Evaluation and Research, United States Food and Drug Administration, Silver Spring, MD, United States
| | - Murali K. Matta
- Division of Applied Regulatory Science, Office of Clinical Pharmacology, Office of Translational Science, Center for Drug Evaluation and Research, United States Food and Drug Administration, Silver Spring, MD, United States
| | - Kristin W. Prentice
- Division of Applied Regulatory Science, Office of Clinical Pharmacology, Office of Translational Science, Center for Drug Evaluation and Research, United States Food and Drug Administration, Silver Spring, MD, United States,Booz Allen Hamilton, McLean, VA, United States
| | - Aanchal Shah
- Division of Applied Regulatory Science, Office of Clinical Pharmacology, Office of Translational Science, Center for Drug Evaluation and Research, United States Food and Drug Administration, Silver Spring, MD, United States,Booz Allen Hamilton, McLean, VA, United States
| | - Lidiya Stavitskaya
- Division of Applied Regulatory Science, Office of Clinical Pharmacology, Office of Translational Science, Center for Drug Evaluation and Research, United States Food and Drug Administration, Silver Spring, MD, United States
| | - Donna A. Volpe
- Division of Applied Regulatory Science, Office of Clinical Pharmacology, Office of Translational Science, Center for Drug Evaluation and Research, United States Food and Drug Administration, Silver Spring, MD, United States
| | - James L. Weaver
- Division of Applied Regulatory Science, Office of Clinical Pharmacology, Office of Translational Science, Center for Drug Evaluation and Research, United States Food and Drug Administration, Silver Spring, MD, United States
| | - Wendy W. Wu
- Division of Applied Regulatory Science, Office of Clinical Pharmacology, Office of Translational Science, Center for Drug Evaluation and Research, United States Food and Drug Administration, Silver Spring, MD, United States
| | - Rodney Rouse
- Division of Applied Regulatory Science, Office of Clinical Pharmacology, Office of Translational Science, Center for Drug Evaluation and Research, United States Food and Drug Administration, Silver Spring, MD, United States
| | - David G. Strauss
- Division of Applied Regulatory Science, Office of Clinical Pharmacology, Office of Translational Science, Center for Drug Evaluation and Research, United States Food and Drug Administration, Silver Spring, MD, United States,*Correspondence: David G. Strauss,
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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. Comput Toxicol 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] [What about the content of this article? (0)] [Affiliation(s)] [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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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] [What about the content of this article? (0)] [Affiliation(s)] [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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4
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Landry C, Kim MT, Kruhlak NL, Cross KP, Saiakhov R, Chakravarti S, Stavitskaya L. Transitioning to composite bacterial mutagenicity models in ICH M7 (Q)SAR analyses. Regul Toxicol Pharmacol 2019; 109:104488. [PMID: 31586682 PMCID: PMC6919322 DOI: 10.1016/j.yrtph.2019.104488] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2019] [Revised: 09/26/2019] [Accepted: 09/30/2019] [Indexed: 12/15/2022]
Abstract
The International Council on Harmonisation (ICH) M7(R1) guideline describes the use of complementary (quantitative) structure-activity relationship ((Q)SAR) models to assess the mutagenic potential of drug impurities in new and generic drugs. Historically, the CASE Ultra and Leadscope software platforms used two different statistical-based models to predict mutations at G-C (guanine-cytosine) and A-T (adenine-thymine) sites, to comprehensively assess bacterial mutagenesis. In the present study, composite bacterial mutagenicity models covering multiple mutation types were developed. These new models contain more than double the number of chemicals (n = 9,254 and n = 13,514) than the corresponding non-composite models and show better toxicophore coverage. Additionally, the use of a single composite bacterial mutagenicity model simplifies impurity analysis in an ICH M7 (Q)SAR workflow by reducing the number of model outputs requiring review. An external validation set of 388 drug impurities representing proprietary pharmaceutical chemical space showed performance statistics ranging from of 66-82% in sensitivity, 91-95% in negative predictivity and 96% in coverage. This effort represents a major enhancement to these (Q)SAR models and their use under ICH M7(R1), leading to improved patient safety through greater predictive accuracy, applicability, and efficiency when assessing the bacterial mutagenic potential of drug impurities.
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Affiliation(s)
- Curran Landry
- US Food and Drug Administration, Center for Drug Evaluation and Research, 10903 New Hampshire Ave, Silver Spring, MD, 20993, USA
| | - Marlene T Kim
- US Food and Drug Administration, Center for Drug Evaluation and Research, 10903 New Hampshire Ave, Silver Spring, MD, 20993, USA
| | - Naomi L Kruhlak
- US Food and Drug Administration, Center for Drug Evaluation and Research, 10903 New Hampshire Ave, Silver Spring, MD, 20993, USA
| | - Kevin P Cross
- Leadscope Inc., 1393 Dublin Road, Columbus, OH, 43215, USA
| | - Roustem Saiakhov
- Multicase Inc., 23811 Chagrin Boulevard, Suite 305, Beachwood, OH, 44122, USA
| | - Suman Chakravarti
- Multicase Inc., 23811 Chagrin Boulevard, Suite 305, Beachwood, OH, 44122, USA
| | - Lidiya Stavitskaya
- US Food and Drug Administration, Center for Drug Evaluation and Research, 10903 New Hampshire Ave, Silver Spring, MD, 20993, USA.
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Hasselgren C, Ahlberg E, Akahori Y, Amberg A, Anger LT, Atienzar F, Auerbach S, Beilke L, Bellion P, Benigni R, Bercu J, Booth ED, Bower D, Brigo A, Cammerer Z, Cronin MTD, Crooks I, Cross KP, Custer L, Dobo K, Doktorova T, Faulkner D, Ford KA, Fortin MC, Frericks M, Gad-McDonald SE, Gellatly N, Gerets H, Gervais V, Glowienke S, Van Gompel J, Harvey JS, Hillegass J, Honma M, Hsieh JH, Hsu CW, Barton-Maclaren TS, Johnson C, Jolly R, Jones D, Kemper R, Kenyon MO, Kruhlak NL, Kulkarni SA, Kümmerer K, Leavitt P, Masten S, Miller S, Moudgal C, Muster W, Paulino A, Lo Piparo E, Powley M, Quigley DP, Reddy MV, Richarz AN, Schilter B, Snyder RD, Stavitskaya L, Stidl R, Szabo DT, Teasdale A, Tice RR, Trejo-Martin A, Vuorinen A, Wall BA, Watts P, White AT, Wichard J, Witt KL, Woolley A, Woolley D, Zwickl C, Myatt GJ. Genetic toxicology in silico protocol. Regul Toxicol Pharmacol 2019; 107:104403. [PMID: 31195068 PMCID: PMC7485926 DOI: 10.1016/j.yrtph.2019.104403] [Citation(s) in RCA: 40] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2019] [Revised: 05/20/2019] [Accepted: 06/05/2019] [Indexed: 01/23/2023]
Abstract
In silico toxicology (IST) approaches to rapidly assess chemical hazard, and usage of such methods is increasing in all applications but especially for regulatory submissions, such as for assessing chemicals under REACH as well as the ICH M7 guideline for drug impurities. There are a number of obstacles to performing an IST assessment, including uncertainty in how such an assessment and associated expert review should be performed or what is fit for purpose, as well as a lack of confidence that the results will be accepted by colleagues, collaborators and regulatory authorities. To address this, a project to develop a series of IST protocols for different hazard endpoints has been initiated and this paper describes the genetic toxicity in silico (GIST) protocol. The protocol outlines a hazard assessment framework including key effects/mechanisms and their relationships to endpoints such as gene mutation and clastogenicity. IST models and data are reviewed that support the assessment of these effects/mechanisms along with defined approaches for combining the information and evaluating the confidence in the assessment. This protocol has been developed through a consortium of toxicologists, computational scientists, and regulatory scientists across several industries to support the implementation and acceptance of in silico approaches.
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Affiliation(s)
| | - Ernst Ahlberg
- Predictive Compound ADME & Safety, Drug Safety & Metabolism, AstraZeneca IMED Biotech Unit, Mölndal, Sweden
| | - Yumi Akahori
- Chemicals Evaluation and Research Institute, 1-4-25 Kouraku, Bunkyo-ku, Tokyo, 112-0004, Japan
| | - Alexander Amberg
- Sanofi, R&D Preclinical Safety Frankfurt, Industriepark Hoechst, D-65926, Frankfurt am Main, Germany
| | - Lennart T Anger
- Sanofi, R&D Preclinical Safety Frankfurt, Industriepark Hoechst, D-65926, Frankfurt am Main, Germany
| | - Franck Atienzar
- UCB BioPharma SPRL, Chemin du Foriest, B-1420 Braine-l'Alleud, Belgium
| | - Scott Auerbach
- The National Institute of Environmental Health Sciences, Division of the National Toxicology Program, Research Triangle Park, NC, 27709, USA
| | - Lisa Beilke
- Toxicology Solutions Inc., San Diego, CA, USA
| | | | | | - Joel Bercu
- Gilead Sciences, 333 Lakeside Drive, Foster City, CA, USA
| | - Ewan D Booth
- Syngenta, Product Safety Department, Jealott's Hill International Research Centre, Bracknell, Berkshire, RG42 6EY, UK
| | - Dave Bower
- Leadscope, Inc, 1393 Dublin Rd, Columbus, OH, 43215, USA
| | - Alessandro Brigo
- Roche Pharmaceutical Research & Early Development, Pharmaceutical Sciences, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd, Grenzacherstrasse 124, 4070, Basel, Switzerland
| | - Zoryana Cammerer
- Janssen Research & Development, 1400 McKean Road, Spring House, PA, 19477, USA
| | - Mark T D Cronin
- School of Pharmacy and Biomolecular Sciences, Liverpool John Moores University, Liverpool, L3 3AF, UK
| | - Ian Crooks
- British American Tobacco, Research and Development, Regents Park Road, Southampton, Hampshire, SO15 8TL, UK
| | - Kevin P Cross
- Leadscope, Inc, 1393 Dublin Rd, Columbus, OH, 43215, USA
| | - Laura Custer
- Bristol-Myers Squibb, Drug Safety Evaluation, 1 Squibb Dr, New Brunswick, NJ, 08903, USA
| | - Krista Dobo
- Pfizer Global Research & Development, 558 Eastern Point Road, Groton, CT, 06340, USA
| | - Tatyana Doktorova
- Douglas Connect GmbH, Technology Park Basel, Hochbergerstrasse 60C, CH-4057, Basel / Basel-Stadt, Switzerland
| | - David Faulkner
- Lawrence Berkeley National Laboratory, One Cyclotron Road, MS 70A-1161A, Berkeley, CA, 947020, USA
| | - Kevin A Ford
- Global Blood Therapeutics, 171 Oyster Point Boulevard, South San Francisco, CA, 94080, USA
| | - Marie C Fortin
- Jazz Pharmaceuticals, Inc., 200 Princeton South Corporate Center, Suite 180, Ewing, NJ, 08628, USA; Department of Pharmacology and Toxicology, Ernest Mario School of Pharmacy, Rutgers University, 170 Frelinghuysen Rd, Piscataway, NJ, 08855, USA
| | | | | | - Nichola Gellatly
- National Centre for the Replacement, Refinement and Reduction of Animals in Research (NC3Rs), Gibbs Building, 215 Euston Road, London, NW1 2BE, UK
| | - Helga Gerets
- UCB BioPharma SPRL, Chemin du Foriest, B-1420, Braine-l'Alleud, Belgium
| | | | - Susanne Glowienke
- Novartis Pharma AG, Pre-Clinical Safety, Werk Klybeck, CH, 4057, Basel, Switzerland
| | - Jacky Van Gompel
- Janssen Pharmaceutical Companies of Johnson & Johnson, 2340, Beerse, Belgium
| | - James S Harvey
- GlaxoSmithKline Pre-Clinical Development, Park Road, Ware, Hertfordshire, SG12 0DP, UK
| | - Jedd Hillegass
- Bristol-Myers Squibb, Drug Safety Evaluation, 1 Squibb Dr, New Brunswick, NJ, 08903, USA
| | - Masamitsu Honma
- Division of Genetics and Mutagenesis, National Institute of Health Sciences, Kanagawa, 210-9501, Japan
| | - Jui-Hua Hsieh
- Kelly Government Solutions, Research Triangle Park, NC, 27709, USA
| | - Chia-Wen Hsu
- FDA Center for Drug Evaluation and Research, Silver Spring, MD, USA
| | | | | | - Robert Jolly
- Toxicology Division, Eli Lilly and Company, Indianapolis, IN, USA
| | - David Jones
- Medicines and Healthcare Products Regulatory Agency, 10 South Colonnade, Canary Wharf, London, E14 4PU, UK
| | - Ray Kemper
- Vertex Pharmaceuticals Inc., Predictive and Investigative Safety Assessment, 50 Northern Ave, Boston, MA, USA
| | - Michelle O Kenyon
- Pfizer Global Research & Development, 558 Eastern Point Road, Groton, CT, 06340, USA
| | - Naomi L Kruhlak
- FDA Center for Drug Evaluation and Research, Silver Spring, MD, USA
| | - Sunil A Kulkarni
- Existing Substances Risk Assessment Bureau, Health Canada, Ottawa, ON, K1A 0K9, Canada
| | - Klaus Kümmerer
- Institute for Sustainable and Environmental Chemistry, Leuphana University Lüneburg, Scharnhorststraße 1/C13.311b, 21335, Lüneburg, Germany
| | - Penny Leavitt
- Bristol-Myers Squibb, Drug Safety Evaluation, 1 Squibb Dr, New Brunswick, NJ, 08903, USA
| | - Scott Masten
- The National Institute of Environmental Health Sciences, Division of the National Toxicology Program, Research Triangle Park, NC, 27709, USA
| | - Scott Miller
- Leadscope, Inc, 1393 Dublin Rd, Columbus, OH, 43215, USA
| | | | - Wolfgang Muster
- Roche Pharmaceutical Research & Early Development, Pharmaceutical Sciences, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd, Grenzacherstrasse 124, 4070, Basel, Switzerland
| | | | | | - Mark Powley
- Merck Research Laboratories, West Point, PA, 19486, USA
| | | | | | | | | | - Ronald D Snyder
- RDS Consulting Services, 2936 Wooded Vista Ct, Mason, OH, 45040, USA
| | | | | | | | | | | | | | | | - Brian A Wall
- Colgate-Palmolive Company, Piscataway, NJ, 08854, USA
| | - Pete Watts
- Bibra, Cantium House, Railway Approach, Wallington, Surrey, SM6 0DZ, UK
| | - Angela T White
- GlaxoSmithKline Pre-Clinical Development, Park Road, Ware, Hertfordshire, SG12 0DP, UK
| | - Joerg Wichard
- Bayer AG, Pharmaceuticals Division, Investigational Toxicology, Muellerstr. 178, D-13353, Berlin, Germany
| | - Kristine L Witt
- The National Institute of Environmental Health Sciences, Division of the National Toxicology Program, Research Triangle Park, NC, 27709, USA
| | - Adam Woolley
- ForthTox Limited, PO Box 13550, Linlithgow, EH49 7YU, UK
| | - David Woolley
- ForthTox Limited, PO Box 13550, Linlithgow, EH49 7YU, UK
| | - Craig Zwickl
- Transendix LLC, 1407 Moores Manor, Indianapolis, IN, 46229, USA
| | - Glenn J Myatt
- Leadscope, Inc, 1393 Dublin Rd, Columbus, OH, 43215, USA
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6
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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] [What about the content of this article? (0)] [Affiliation(s)] [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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7
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Amberg A, Anger LT, Bercu J, Bower D, Cross KP, Custer L, Harvey JS, Hasselgren C, Honma M, Johnson C, Jolly R, Kenyon MO, Kruhlak NL, Leavitt P, Quigley DP, Miller S, Snodin D, Stavitskaya L, Teasdale A, Trejo-Martin A, White AT, Wichard J, Myatt GJ. Extending (Q)SARs to incorporate proprietary knowledge for regulatory purposes: is aromatic N-oxide a structural alert for predicting DNA-reactive mutagenicity? Mutagenesis 2019; 34:67-82. [PMID: 30189015 DOI: 10.1093/mutage/gey020] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2018] [Revised: 07/02/2018] [Accepted: 07/28/2018] [Indexed: 11/13/2022] Open
Abstract
(Quantitative) structure-activity relationship or (Q)SAR predictions of DNA-reactive mutagenicity are important to support both the design of new chemicals and the assessment of impurities, degradants, metabolites, extractables and leachables, as well as existing chemicals. Aromatic N-oxides represent a class of compounds that are often considered alerting for mutagenicity yet the scientific rationale of this structural alert is not clear and has been questioned. Because aromatic N-oxide-containing compounds may be encountered as impurities, degradants and metabolites, it is important to accurately predict mutagenicity of this chemical class. This article analysed a series of publicly available aromatic N-oxide data in search of supporting information. The article also used a previously developed structure-activity relationship (SAR) fingerprint methodology where a series of aromatic N-oxide substructures was generated and matched against public and proprietary databases, including pharmaceutical data. An assessment of the number of mutagenic and non-mutagenic compounds matching each substructure across all sources was used to understand whether the general class or any specific subclasses appear to lead to mutagenicity. This analysis resulted in a downgrade of the general aromatic N-oxide alert. However, it was determined there were enough public and proprietary data to assign the quindioxin and related chemicals as well as benzo[c][1,2,5]oxadiazole 1-oxide subclasses as alerts. The overall results of this analysis were incorporated into Leadscope's expert-rule-based model to enhance its predictive accuracy.
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Affiliation(s)
- Alexander Amberg
- Sanofi, R&D Preclinical Safety Frankfurt, Industriepark Höchst, Frankfurt am Main, Germany
| | - Lennart T Anger
- Sanofi, R&D Preclinical Safety Frankfurt, Industriepark Höchst, Frankfurt am Main, Germany
| | - Joel Bercu
- Gilead Sciences, Nonclinical Safety and Pathobiology, Foster City, CA, USA
| | | | | | - Laura Custer
- Bristol-Myers Squibb, Drug Safety Evaluation, New Brunswick, NJ, USA
| | - James S Harvey
- GlaxoSmithKline Pre-Clinical Development, Ware, Hertfordshire, UK
| | | | - Masamitsu Honma
- National Institute of Health Sciences, Division of Genetics & Mutagenesis, Kamiyoga, Setagaya-ku, Tokyo, Japan
| | | | - Robert Jolly
- Toxicology Division, Eli Lilly and Company, Indianapolis, IN, USA
| | - Michelle O Kenyon
- Pfizer Worldwide Research and Development, Drug Safety, Genetic Toxicology, Groton, CT, USA
| | - Naomi L Kruhlak
- U.S. Food and Drug Administration, Center for Drug Evaluation and Research, Silver Spring, MD, USA
| | - Penny Leavitt
- Bristol-Myers Squibb, Drug Safety Evaluation, New Brunswick, NJ, USA
| | | | | | | | - Lidiya Stavitskaya
- U.S. Food and Drug Administration, Center for Drug Evaluation and Research, Silver Spring, MD, USA
| | - Andrew Teasdale
- AstraZeneca, Pharmaceutical Technology and Development, Macclesfield, Cheshire, UK
| | | | - Angela T White
- GlaxoSmithKline Pre-Clinical Development, Ware, Hertfordshire, UK
| | - Joerg Wichard
- Bayer AG, Pharmaceuticals Division, Investigational Toxicology, Muellerstr, Berlin, Germany
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8
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Hsu CW, Hewes KP, Stavitskaya L, Kruhlak NL. Construction and application of (Q)SAR models to predict chemical-induced in vitro chromosome aberrations. Regul Toxicol Pharmacol 2018; 99:274-288. [PMID: 30278198 DOI: 10.1016/j.yrtph.2018.09.026] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2018] [Revised: 09/24/2018] [Accepted: 09/26/2018] [Indexed: 12/23/2022]
Abstract
In drug development, genetic toxicology studies are conducted using in vitro and in vivo assays to identify potential mutagenic and clastogenic effects, as outlined in the International Council for Harmonisation (ICH) S2 regulatory guideline. (Quantitative) structure-activity relationship ((Q)SAR) models that predict assay outcomes can be used as an early screen to prioritize pharmaceutical candidates, or later during product development to evaluate safety when experimental data are unavailable or inconclusive. In the current study, two commercial QSAR platforms were used to build models for in vitro chromosomal aberrations in Chinese hamster lung (CHL) and Chinese hamster ovary (CHO) cells. Cross-validated CHL model predictive performance showed sensitivity of 80 and 82%, and negative predictivity of 75 and 76% based on 875 training set compounds. For CHO, sensitivity of 61 and 67% and negative predictivity of 68 and 74% was achieved based on 817 training set compounds. The predictive performance of structural alerts in a commercial expert rule-based SAR software was also investigated and showed positive predictivity of 48-100% for selected alerts. Case studies examining incorrectly-predicted compounds, non-DNA-reactive clastogens, and recently-approved pharmaceuticals are presented, exploring how an investigational approach using similarity searching and expert knowledge can improve upon individual (Q)SAR predictions of the clastogenicity of drugs.
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Affiliation(s)
- Chia-Wen Hsu
- US Food and Drug Administration, Center for Drug Evaluation and Research, Silver Spring, MD, USA
| | - Kurt P Hewes
- US Food and Drug Administration, Center for Drug Evaluation and Research, Silver Spring, MD, USA
| | - Lidiya Stavitskaya
- US Food and Drug Administration, Center for Drug Evaluation and Research, Silver Spring, MD, USA
| | - Naomi L Kruhlak
- US Food and Drug Administration, Center for Drug Evaluation and Research, Silver Spring, MD, USA.
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9
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Ellis CR, Kruhlak NL, Kim MT, Hawkins EG, Stavitskaya L. Predicting opioid receptor binding affinity of pharmacologically unclassified designer substances using molecular docking. PLoS One 2018; 13:e0197734. [PMID: 29795628 PMCID: PMC5967713 DOI: 10.1371/journal.pone.0197734] [Citation(s) in RCA: 41] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2017] [Accepted: 05/08/2018] [Indexed: 01/16/2023] Open
Abstract
Opioids represent a highly-abused and highly potent class of drugs that have become a significant threat to public safety. Often there are little to no pharmacological and toxicological data available for new, illicitly used and abused opioids, and this has resulted in a growing number of serious adverse events, including death. The large influx of new synthetic opioids permeating the street-drug market, including fentanyl and fentanyl analogs, has generated the need for a fast and effective method to evaluate the risk a substance poses to public safety. In response, the US FDA’s Center for Drug Evaluation and Research (CDER) has developed a rapidly-deployable, multi-pronged computational approach to assess a drug’s risk to public health. A key component of this approach is a molecular docking model to predict the binding affinity of biologically uncharacterized fentanyl analogs to the mu opioid receptor. The model was validated by correlating the docking scores of structurally diverse opioids with experimentally determined binding affinities. Fentanyl derivatives with sub-nanomolar binding affinity at the mu receptor (e.g. carfentanil and lofentanil) have significantly lower binding scores, while less potent fentanyl derivatives have increased binding scores. The strong correlation between the binding scores and the experimental binding affinities suggests that this approach can be used to accurately predict the binding strength of newly identified fentanyl analogs at the mu receptor in the absence of in vitro data and may assist in the temporary scheduling of those substances that pose a risk to public safety.
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Affiliation(s)
- Christopher R. Ellis
- Center for Drug Evaluation and Research, United States Food and Drug Administration, Silver Spring, Maryland, United States of America
| | - Naomi L. Kruhlak
- Center for Drug Evaluation and Research, United States Food and Drug Administration, Silver Spring, Maryland, United States of America
| | - Marlene T. Kim
- Center for Drug Evaluation and Research, United States Food and Drug Administration, Silver Spring, Maryland, United States of America
| | - Edward G. Hawkins
- Center for Drug Evaluation and Research, United States Food and Drug Administration, Silver Spring, Maryland, United States of America
| | - Lidiya Stavitskaya
- Center for Drug Evaluation and Research, United States Food and Drug Administration, Silver Spring, Maryland, United States of America
- * E-mail:
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10
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Myatt GJ, Ahlberg E, Akahori Y, Allen D, Amberg A, Anger LT, Aptula A, Auerbach S, Beilke L, Bellion P, Benigni R, Bercu J, Booth ED, Bower D, Brigo A, Burden N, Cammerer Z, Cronin MTD, Cross KP, Custer L, Dettwiler M, Dobo K, Ford KA, Fortin MC, Gad-McDonald SE, Gellatly N, Gervais V, Glover KP, Glowienke S, Van Gompel J, Gutsell S, Hardy B, Harvey JS, Hillegass J, Honma M, Hsieh JH, Hsu CW, Hughes K, Johnson C, Jolly R, Jones D, Kemper R, Kenyon MO, Kim MT, Kruhlak NL, Kulkarni SA, Kümmerer K, Leavitt P, Majer B, Masten S, Miller S, Moser J, Mumtaz M, Muster W, Neilson L, Oprea TI, Patlewicz G, Paulino A, Lo Piparo E, Powley M, Quigley DP, Reddy MV, Richarz AN, Ruiz P, Schilter B, Serafimova R, Simpson W, Stavitskaya L, Stidl R, Suarez-Rodriguez D, Szabo DT, Teasdale A, Trejo-Martin A, Valentin JP, Vuorinen A, Wall BA, Watts P, White AT, Wichard J, Witt KL, Woolley A, Woolley D, Zwickl C, Hasselgren C. In silico toxicology protocols. Regul Toxicol Pharmacol 2018; 96:1-17. [PMID: 29678766 DOI: 10.1016/j.yrtph.2018.04.014] [Citation(s) in RCA: 111] [Impact Index Per Article: 18.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2017] [Revised: 03/16/2018] [Accepted: 04/16/2018] [Indexed: 10/17/2022]
Abstract
The present publication surveys several applications of in silico (i.e., computational) toxicology approaches across different industries and institutions. It highlights the need to develop standardized protocols when conducting toxicity-related predictions. This contribution articulates the information needed for protocols to support in silico predictions for major toxicological endpoints of concern (e.g., genetic toxicity, carcinogenicity, acute toxicity, reproductive toxicity, developmental toxicity) across several industries and regulatory bodies. Such novel in silico toxicology (IST) protocols, when fully developed and implemented, will ensure in silico toxicological assessments are performed and evaluated in a consistent, reproducible, and well-documented manner across industries and regulatory bodies to support wider uptake and acceptance of the approaches. The development of IST protocols is an initiative developed through a collaboration among an international consortium to reflect the state-of-the-art in in silico toxicology for hazard identification and characterization. A general outline for describing the development of such protocols is included and it is based on in silico predictions and/or available experimental data for a defined series of relevant toxicological effects or mechanisms. The publication presents a novel approach for determining the reliability of in silico predictions alongside experimental data. In addition, we discuss how to determine the level of confidence in the assessment based on the relevance and reliability of the information.
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Affiliation(s)
- Glenn J Myatt
- Leadscope, Inc., 1393 Dublin Rd, Columbus, OH 43215, USA.
| | - Ernst Ahlberg
- Predictive Compound ADME & Safety, Drug Safety & Metabolism, AstraZeneca IMED Biotech Unit, Mölndal, Sweden
| | - Yumi Akahori
- Chemicals Evaluation and Research Institute, 1-4-25 Kouraku, Bunkyo-ku, Tokyo 112-0004 Japan
| | - David Allen
- Integrated Laboratory Systems, Inc., Research Triangle Park, NC, USA
| | - Alexander Amberg
- Sanofi, R&D Preclinical Safety Frankfurt, Industriepark Hoechst, D-65926 Frankfurt am Main, Germany
| | - Lennart T Anger
- Sanofi, R&D Preclinical Safety Frankfurt, Industriepark Hoechst, D-65926 Frankfurt am Main, Germany
| | - Aynur Aptula
- Unilever, Safety and Environmental Assurance Centre, Colworth, Beds, UK
| | - Scott Auerbach
- The National Institute of Environmental Health Sciences, Division of the National Toxicology Program, Research Triangle Park, NC 27709, USA
| | - Lisa Beilke
- Toxicology Solutions Inc., San Diego, CA, USA
| | | | | | - Joel Bercu
- Gilead Sciences, 333 Lakeside Drive, Foster City, CA, USA
| | - Ewan D Booth
- Syngenta, Product Safety Department, Jealott's Hill International Research Centre, Bracknell, Berkshire, RG42 6EY, UK
| | - Dave Bower
- Leadscope, Inc., 1393 Dublin Rd, Columbus, OH 43215, USA
| | - Alessandro Brigo
- Roche Pharmaceutical Research & Early Development, Pharmaceutical Sciences, Roche Innovation Center Basel, Switzerland
| | - Natalie Burden
- National Centre for the Replacement, Refinement and Reduction of Animals in Research (NC3Rs), Gibbs Building, 215 Euston Road, London NW1 2BE, UK
| | - Zoryana Cammerer
- Janssen Research & Development, 1400 McKean Road, Spring House, PA 19477, USA
| | - Mark T D Cronin
- School of Pharmacy and Chemistry, Liverpool John Moores University, Liverpool, L3 3AF, UK
| | - Kevin P Cross
- Leadscope, Inc., 1393 Dublin Rd, Columbus, OH 43215, USA
| | - Laura Custer
- Bristol-Myers Squibb, Drug Safety Evaluation, 1 Squibb Dr, New Brunswick, NJ 08903, USA
| | | | - Krista Dobo
- Pfizer Global Research & Development, 558 Eastern Point Road, Groton, CT 06340, USA
| | - Kevin A Ford
- Global Blood Therapeutics, South San Francisco, CA 94080, USA
| | - Marie C Fortin
- Department of Pharmacology and Toxicology, Ernest Mario School of Pharmacy, Rutgers, The State University of New Jersey, 170 Frelinghuysen Rd, Piscataway, NJ 08855, USA
| | | | - Nichola Gellatly
- National Centre for the Replacement, Refinement and Reduction of Animals in Research (NC3Rs), Gibbs Building, 215 Euston Road, London NW1 2BE, UK
| | | | - Kyle P Glover
- Defense Threat Reduction Agency, Edgewood Chemical Biological Center, Aberdeen Proving Ground, MD 21010, USA
| | - Susanne Glowienke
- Novartis Pharma AG, Pre-Clinical Safety, Werk Klybeck, CH-4057, Basel, Switzerland
| | - Jacky Van Gompel
- Janssen Pharmaceutical Companies of Johnson & Johnson, 2340 Beerse, Belgium
| | - Steve Gutsell
- Unilever, Safety and Environmental Assurance Centre, Colworth, Beds, UK
| | - Barry Hardy
- Douglas Connect GmbH, Technology Park Basel, Hochbergerstrasse 60C, CH-4057 Basel / Basel-Stadt, Switzerland
| | - James S Harvey
- GlaxoSmithKline Pre-Clinical Development, Park Road, Ware, Hertfordshire, SG12 0DP, UK
| | - Jedd Hillegass
- Bristol-Myers Squibb, Drug Safety Evaluation, 1 Squibb Dr, New Brunswick, NJ 08903, USA
| | | | - Jui-Hua Hsieh
- Kelly Government Solutions, Research Triangle Park, NC 27709, USA
| | - Chia-Wen Hsu
- FDA Center for Drug Evaluation and Research, Silver Spring, MD 20993, USA
| | - Kathy Hughes
- Existing Substances Risk Assessment Bureau, Health Canada, Ottawa, ON, K1A 0K9, Canada
| | | | - Robert Jolly
- Toxicology Division, Eli Lilly and Company, Indianapolis, IN, USA
| | - David Jones
- Medicines and Healthcare Products Regulatory Agency, 151 Buckingham Palace Road, London, SW1W 9SZ, UK
| | - Ray Kemper
- Vertex Pharmaceuticals Inc., Discovery and Investigative Toxicology, 50 Northern Ave, Boston, MA, USA
| | - Michelle O Kenyon
- Pfizer Global Research & Development, 558 Eastern Point Road, Groton, CT 06340, USA
| | - Marlene T Kim
- FDA Center for Drug Evaluation and Research, Silver Spring, MD 20993, USA
| | - Naomi L Kruhlak
- FDA Center for Drug Evaluation and Research, Silver Spring, MD 20993, USA
| | - Sunil A Kulkarni
- Existing Substances Risk Assessment Bureau, Health Canada, Ottawa, ON, K1A 0K9, Canada
| | - Klaus Kümmerer
- Institute for Sustainable and Environmental Chemistry, Leuphana University Lüneburg, Scharnhorststraße 1/C13.311b, 21335 Lüneburg, Germany
| | - Penny Leavitt
- Bristol-Myers Squibb, Drug Safety Evaluation, 1 Squibb Dr, New Brunswick, NJ 08903, USA
| | | | - Scott Masten
- The National Institute of Environmental Health Sciences, Division of the National Toxicology Program, Research Triangle Park, NC 27709, USA
| | - Scott Miller
- Leadscope, Inc., 1393 Dublin Rd, Columbus, OH 43215, USA
| | - Janet Moser
- Chemical Security Analysis Center, Department of Homeland Security, 3401 Ricketts Point Road, Aberdeen Proving Ground, MD 21010-5405, USA; Battelle Memorial Institute, 505 King Avenue, Columbus, OH 43210, USA
| | - Moiz Mumtaz
- Agency for Toxic Substances and Disease Registry, US Department of Health and Human Services, Atlanta, GA, USA
| | - Wolfgang Muster
- Roche Pharmaceutical Research & Early Development, Pharmaceutical Sciences, Roche Innovation Center Basel, Switzerland
| | - Louise Neilson
- British American Tobacco, Research and Development, Regents Park Road, Southampton, Hampshire, SO15 8TL, UK
| | - Tudor I Oprea
- Translational Informatics Division, Department of Internal Medicine, Health Sciences Center, The University of New Mexico, NM, USA
| | - Grace Patlewicz
- U.S. Environmental Protection Agency, National Center for Computational Toxicology, Research Triangle Park, NC 27711, USA
| | - Alexandre Paulino
- SAPEC Agro, S.A., Avenida do Rio Tejo, Herdade das Praias, 2910-440 Setúbal, Portugal
| | - Elena Lo Piparo
- Chemical Food Safety Group, Nestlé Research Center, Lausanne, Switzerland
| | - Mark Powley
- FDA Center for Drug Evaluation and Research, Silver Spring, MD 20993, USA
| | | | | | - Andrea-Nicole Richarz
- European Commission, Joint Research Centre, Directorate for Health, Consumers and Reference Materials, Chemical Safety and Alternative Methods Unit, Via Enrico Fermi 2749, 21027 Ispra, VA, Italy
| | - Patricia Ruiz
- Agency for Toxic Substances and Disease Registry, US Department of Health and Human Services, Atlanta, GA, USA
| | - Benoit Schilter
- Chemical Food Safety Group, Nestlé Research Center, Lausanne, Switzerland
| | | | - Wendy Simpson
- Unilever, Safety and Environmental Assurance Centre, Colworth, Beds, UK
| | - Lidiya Stavitskaya
- FDA Center for Drug Evaluation and Research, Silver Spring, MD 20993, USA
| | | | | | - David T Szabo
- RAI Services Company, 950 Reynolds Blvd., Winston-Salem, NC 27105, USA
| | | | | | | | | | - Brian A Wall
- Colgate-Palmolive Company, Piscataway, NJ 08854, USA
| | - Pete Watts
- Bibra, Cantium House, Railway Approach, Wallington, Surrey, SM6 0DZ, UK
| | - Angela T White
- GlaxoSmithKline Pre-Clinical Development, Park Road, Ware, Hertfordshire, SG12 0DP, UK
| | - Joerg Wichard
- Bayer Pharma AG, Investigational Toxicology, Muellerstr. 178, D-13353 Berlin, Germany
| | - Kristine L Witt
- The National Institute of Environmental Health Sciences, Division of the National Toxicology Program, Research Triangle Park, NC 27709, USA
| | - Adam Woolley
- ForthTox Limited, PO Box 13550, Linlithgow, EH49 7YU, UK
| | - David Woolley
- ForthTox Limited, PO Box 13550, Linlithgow, EH49 7YU, UK
| | - Craig Zwickl
- Transendix LLC, 1407 Moores Manor, Indianapolis, IN 46229, USA
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11
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Slavov SH, Wilkes JG, Buzatu DA, Kruhlak NL, Willard JM, Hanig JP, Beger RD. Computational identification of a phospholipidosis toxicophore using 13 C and 15 N NMR-distance based fingerprints. Bioorg Med Chem 2014; 22:6706-6714. [DOI: 10.1016/j.bmc.2014.08.021] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2014] [Revised: 08/13/2014] [Accepted: 08/18/2014] [Indexed: 10/24/2022]
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12
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Zhu X, Kruhlak NL. Construction and analysis of a human hepatotoxicity database suitable for QSAR modeling using post-market safety data. Toxicology 2014; 321:62-72. [PMID: 24721472 DOI: 10.1016/j.tox.2014.03.009] [Citation(s) in RCA: 49] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2014] [Accepted: 03/28/2014] [Indexed: 12/23/2022]
Abstract
Drug-induced liver injury (DILI) is one of the most common drug-induced adverse events (AEs) leading to life-threatening conditions such as acute liver failure. It has also been recognized as the single most common cause of safety-related post-market withdrawals or warnings. Efforts to develop new predictive methods to assess the likelihood of a drug being a hepatotoxicant have been challenging due to the complexity and idiosyncrasy of clinical manifestations of DILI. The FDA adverse event reporting system (AERS) contains post-market data that depict the morbidity of AEs. Here, we developed a scalable approach to construct a hepatotoxicity database using post-market data for the purpose of quantitative structure-activity relationship (QSAR) modeling. A set of 2029 unique and modelable drug entities with 13,555 drug-AE combinations was extracted from the AERS database using 37 hepatotoxicity-related query preferred terms (PTs). In order to determine the optimal classification scheme to partition positive from negative drugs, a manually-curated DILI calibration set composed of 105 negatives and 177 positives was developed based on the published literature. The final classification scheme combines hepatotoxicity-related PT data with supporting information that optimize the predictive performance across the calibration set. Data for other toxicological endpoints related to liver injury such as liver enzyme abnormalities, cholestasis, and bile duct disorders, were also extracted and classified. Collectively, these datasets can be used to generate a battery of QSAR models that assess a drug's potential to cause DILI.
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Affiliation(s)
- Xiao Zhu
- U.S. Food and Drug Administration, Center for Drug Evaluation and Research, 10903 New Hampshire Avenue, Silver Spring, MD 20993, United States
| | - Naomi L Kruhlak
- U.S. Food and Drug Administration, Center for Drug Evaluation and Research, 10903 New Hampshire Avenue, Silver Spring, MD 20993, United States.
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13
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Orogo AM, Choi SS, Minnier BL, Kruhlak NL. Notification: Construction and Consensus Performance of (Q)SAR Models for Predicting Phospholipidosis Using a Dataset of 743 Compounds. Mol Inform 2013; 32:121. [DOI: 10.1002/minf.201380141] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
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14
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Orogo AM, Choi SS, Minnier BL, Kruhlak NL. Construction and Consensus Performance of (Q)SAR Models for Predicting Phospholipidosis Using a Dataset of 743 Compounds. Mol Inform 2012; 31:725-39. [DOI: 10.1002/minf.201200048] [Citation(s) in RCA: 28] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2012] [Accepted: 07/26/2012] [Indexed: 11/10/2022]
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15
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16
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Valerio LG, Arvidson KB, Busta E, Minnier BL, Kruhlak NL, Benz RD. Testing computational toxicology models with phytochemicals. Mol Nutr Food Res 2010; 54:186-94. [DOI: 10.1002/mnfr.200900259] [Citation(s) in RCA: 11] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/25/2023]
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17
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Valerio LG, Yang C, Arvidson KB, Kruhlak NL. A structural feature-based computational approach for toxicology predictions. Expert Opin Drug Metab Toxicol 2010; 6:505-18. [DOI: 10.1517/17425250903499286] [Citation(s) in RCA: 26] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
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18
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Ursem CJ, Kruhlak NL, Contrera JF, MacLaughlin PM, Benz RD, Matthews EJ. Identification of structure–activity relationships for adverse effects of pharmaceuticals in humans. Part A: Use of FDA post-market reports to create a database of hepatobiliary and urinary tract toxicities. Regul Toxicol Pharmacol 2009; 54:1-22. [DOI: 10.1016/j.yrtph.2008.12.009] [Citation(s) in RCA: 25] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
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Contrera JF, Matthews EJ, Kruhlak NL, Benz RD. In Silico Screening of Chemicals for Genetic Toxicity Using MDL-QSAR, Nonparametric Discriminant Analysis, E-State, Connectivity, and Molecular Property Descriptors. Toxicol Mech Methods 2008; 18:207-16. [DOI: 10.1080/15376510701857106] [Citation(s) in RCA: 15] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
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Matthews EJ, Kruhlak NL, Benz RD, Contrera JF, Marchant CA, Yang C. Combined Use of MC4PC, MDL-QSAR, BioEpisteme, Leadscope PDM, and Derek for Windows Software to Achieve High-Performance, High-Confidence, Mode of Action–Based Predictions of Chemical Carcinogenesis in Rodents. Toxicol Mech Methods 2008; 18:189-206. [DOI: 10.1080/15376510701857379] [Citation(s) in RCA: 44] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
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Kruhlak NL, Choi SS, Contrera JF, Weaver JL, Willard JM, Hastings KL, Sancilio LF. Development of a Phospholipidosis Database and Predictive Quantitative Structure-Activity Relationship (QSAR) Models. Toxicol Mech Methods 2008; 18:217-27. [DOI: 10.1080/15376510701857262] [Citation(s) in RCA: 33] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
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Contrera JF, Kruhlak NL, Matthews EJ, Benz RD. Comparison of MC4PC and MDL-QSAR rodent carcinogenicity predictions and the enhancement of predictive performance by combining QSAR models. Regul Toxicol Pharmacol 2007; 49:172-82. [PMID: 17703860 DOI: 10.1016/j.yrtph.2007.07.001] [Citation(s) in RCA: 38] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2007] [Revised: 06/07/2007] [Accepted: 07/06/2007] [Indexed: 10/23/2022]
Abstract
This report presents a comparison of the predictive performance of MC4PC and MDL-QSAR software as well as a method for combining the predictions from both programs to increase overall accuracy. The conclusions are based on 10 x 10% leave-many-out internal cross-validation studies using 1540 training set compounds with 2-year rodent carcinogenicity findings. The models were generated using the same weight of evidence scoring method previously developed [Matthews, E.J., Contrera, J.F., 1998. A new highly specific method for predicting the carcinogenic potential of pharmaceuticals in rodents using enhanced MCASE QSAR-ES software. Regul. Toxicol. Pharmacol. 28, 242-264.]. Although MC4PC and MDL-QSAR use different algorithms, their overall predictive performance was remarkably similar. Respectively, the sensitivity of MC4PC and MDL-QSAR was 61 and 63%, specificity was 71 and 75%, and concordance was 66 and 69%. Coverage for both programs was over 95% and receiver operator characteristic (ROC) intercept statistic values were above 2.00. The software programs had complimentary coverage with none of the 1540 compounds being uncovered by both MC4PC and MDL-QSAR. Merging MC4PC and MDL-QSAR predictions improved the overall predictive performance. Consensus sensitivity increased to 67%, specificity to 84%, concordance to 76%, and ROC to 4.31. Consensus rules can be tuned to reflect the priorities of the user, so that greater emphasis may be placed on predictions with high sensitivity/low false negative rates or high specificity/low false positive rates. Sensitivity was optimized to 75% by reclassifying all compounds predicted to be positive in MC4PC or MDL-QSAR as positive, and specificity was optimized to 89% by reclassifying all compounds predicted negative in MC4PC or MDL-QSAR as negative.
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Affiliation(s)
- Joseph F Contrera
- US Food and Drug Administration, Center for Drug Evaluation and Research, Office of Pharmaceutical Science, Informatics and Computational Safety Analysis Staff, 10903 New Hampshire Avenue, Silver Spring, MD 20993-0002, USA.
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Matthews EJ, Kruhlak NL, Daniel Benz R, Ivanov J, Klopman G, Contrera JF. A comprehensive model for reproductive and developmental toxicity hazard identification: II. Construction of QSAR models to predict activities of untested chemicals. Regul Toxicol Pharmacol 2007; 47:136-55. [DOI: 10.1016/j.yrtph.2006.10.001] [Citation(s) in RCA: 39] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2006] [Indexed: 11/28/2022]
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Kruhlak NL, Contrera JF, Benz RD, Matthews EJ. Progress in QSAR toxicity screening of pharmaceutical impurities and other FDA regulated products. Adv Drug Deliv Rev 2007; 59:43-55. [PMID: 17229485 DOI: 10.1016/j.addr.2006.10.008] [Citation(s) in RCA: 76] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2006] [Accepted: 10/25/2006] [Indexed: 11/24/2022]
Abstract
Active ingredients in pharmaceutical products undergo extensive testing to ensure their safety before being made available to the American public. A consideration during the regulatory review process is the safety of pharmaceutical contaminants and degradents which may be present in the drug product at low levels. Several published guidances are available that outline the criteria for further testing of these impurities to assess their toxic potential, where further testing is in the form of a battery of toxicology assays and the identification of known structural alerts. However, recent advances in the development of computational methods have made available additional resources for safety assessment such as structure similarity searching and quantitative structure-activity relationship (QSAR) models. These methods offer a rapid and cost-effective first-pass screening capability to assess toxicity when conventional toxicology data are limited or lacking, with the potential to identify compounds that would be appropriate for further testing. This article discusses some of the considerations when using computational toxicology methods for regulatory decision support and gives examples of how the technology is currently being applied at the US Food and Drug Administration.
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Affiliation(s)
- Naomi L Kruhlak
- US Food and Drug Administration, Center for Drug Evaluation and Research, Office of Pharmaceutical Science, Informatics and Computational Safety Analysis Staff, US Food and Drug Administration, 10903 New Hampshire Avenue, Silver Spring, MD 20993, USA
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Matthews EJ, Kruhlak NL, Daniel Benz R, Contrera JF. A comprehensive model for reproductive and developmental toxicity hazard identification: I. Development of a weight of evidence QSAR database. Regul Toxicol Pharmacol 2007; 47:115-35. [PMID: 17207562 DOI: 10.1016/j.yrtph.2006.11.002] [Citation(s) in RCA: 24] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2006] [Indexed: 10/23/2022]
Abstract
A weight of evidence (WOE) reproductive and developmental toxicology (reprotox) database was constructed that is suitable for quantitative structure-activity relationship (QSAR) modeling and human hazard identification of untested chemicals. The database was derived from multiple publicly available reprotox databases and consists of more than 10,000 individual rat, mouse, or rabbit reprotox tests linked to 2134 different organic chemical structures. The reprotox data were classified into seven general classes (male reproductive toxicity, female reproductive toxicity, fetal dysmorphogenesis, functional toxicity, mortality, growth, and newborn behavioral toxicity), and 90 specific categories as defined in the source reprotox databases. Each specific category contained over 500 chemicals, but the percentage of active chemicals was low, generally only 0.1-10%. The mathematical WOE model placed greater significance on confirmatory observations from repeat experiments, chemicals with multiple findings within a category, and the categorical relatedness of the findings. Using the weighted activity scores, statistical analyses were performed for specific data sets to identify clusters of categories that were correlated, containing similar profiles of active and inactive chemicals. The analysis revealed clusters of specific categories that contained chemicals that were active in two or more mammalian species (trans-species). Such chemicals are considered to have the highest potential risk to humans. In contrast, some specific categories exhibited only single species-specific activities. Results also showed that the rat and mouse were more susceptible to dysmorphogenesis than rabbits (6.1- and 3.6-fold, respectively).
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Affiliation(s)
- Edwin J Matthews
- US Food and Drug Administration, Center for Drug Evaluation and Research, Office of Pharmaceutical Science, Informatics and Computational Safety Analysis Staff, Silver Spring, MD 20993-0002, USA.
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Matthews EJ, Kruhlak NL, Cimino MC, Benz RD, Contrera JF. An analysis of genetic toxicity, reproductive and developmental toxicity, and carcinogenicity data: I. Identification of carcinogens using surrogate endpoints. Regul Toxicol Pharmacol 2006; 44:83-96. [PMID: 16386343 DOI: 10.1016/j.yrtph.2005.11.003] [Citation(s) in RCA: 111] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2005] [Indexed: 11/17/2022]
Abstract
A retrospective analysis of standard genetic toxicity (genetox) tests, reproductive and developmental toxicity (reprotox) studies, and rodent carcinogenicity bioassays (rcbioassay) was performed to identify the genetox and reprotox endpoints whose results best correlate with rcbioassay observations. A database of 7205 chemicals with genetox (n = 4961), reprotox (n = 2173), and rcbioassay (n = 1442) toxicity data was constructed; 1112 of the chemicals have both genetox and rcbioassay data and 721 chemicals have both reprotox and rcbioassay data. This study differed from previous studies by using conservative weight of evidence criteria to classify chemical carcinogens, data from 63 genetox and reprotox toxicological endpoints, and a new statistical parameter of correlation indicator (CI, the average of specificity and positive predictivity) to identify good surrogate endpoints for predicting carcinogenicity. Among 63 endpoints, results revealed that carcinogenicity was well correlated with certain tests for gene mutation (n = 8), in vivo clastogenicity (n = 2), unscheduled DNA synthesis assay (n = 1), and reprotox (n = 3). The current FDA regulatory battery of four genetox tests used to predict carcinogenicity includes two tests with good correlation (gene mutation in Salmonella and in vivo micronucleus) and two tests with poor correlation (mouse lymphoma gene mutation and in vitro chromosome aberrations) by our criteria.
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Affiliation(s)
- Edwin J Matthews
- US Food and Drug Administration, Center for Drug Evaluation and Research, Office of Pharmaceutical Science, Informatics and Computational Safety Analysis Staff (ICSAS), 10903 New Hampshire Avenue, Silver Spring, MD 20993, USA.
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Matthews EJ, Kruhlak NL, Cimino MC, Benz RD, Contrera JF. An analysis of genetic toxicity, reproductive and developmental toxicity, and carcinogenicity data: II. Identification of genotoxicants, reprotoxicants, and carcinogens using in silico methods. Regul Toxicol Pharmacol 2005; 44:97-110. [PMID: 16352383 DOI: 10.1016/j.yrtph.2005.10.004] [Citation(s) in RCA: 61] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2005] [Indexed: 10/25/2022]
Abstract
This study examined a novel method to identify carcinogens that employed expanded data sets composed of in silico data pooled with actual experimental genetic toxicity (genetox) and reproductive and developmental toxicity (reprotox) data. We constructed 21 modules using the MC4PC program including 13 of 14 (11 genetox and 3 reprotox) tests that we found correlated with results of rodent carcinogenicity bioassays (rcbioassays) [Matthews, E.J., Kruhlak, N.L., Cimino, M.C., Benz, R.D., Contrera, J.F., 2005b. An analysis of genetic toxicity, reproductive and developmental toxicity, and carcinogenicity data: I. Identification of carcinogens using surrogate endpoints. Regul. Toxicol. Pharmacol.]. Each of the 21 modules was evaluated by cross-validation experiments and those with high specificity (SP) and positive predictivity (PPV) were used to predict activities of the 1442 chemicals tested for carcinogenicity for which actual genetox or reprotox data were missing. The expanded data sets had approximately 70% in silico data pooled with approximately 30% experimental data. Based upon SP and PPV, the expanded data sets showed good correlation with carcinogenicity testing results and had correlation indicator (CI, the average of SP and PPV) values of 75.5-88.7%. Conversely, expanded data sets for 9 non-correlated test endpoints were shown not to correlate with carcinogenicity results (CI values <75%). Results also showed that when Salmonella mutagenic carcinogens were removed from the 12 correlated, expanded data sets, only 7 endpoints showed added value by detecting significantly more additional carcinogens than non-carcinogens.
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Affiliation(s)
- Edwin J Matthews
- US Food and Drug Administration, Center for Drug Evaluation and Research, Office of Pharmaceutical Science, Informatics and Computational Safety Analysis Staff (ICSAS), 10903 New Hampshire Avenue, Silver Spring, MD 20993, USA.
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Contrera JF, Matthews EJ, Kruhlak NL, Benz RD. In silico screening of chemicals for bacterial mutagenicity using electrotopological E-state indices and MDL QSAR software. Regul Toxicol Pharmacol 2005; 43:313-23. [PMID: 16242226 DOI: 10.1016/j.yrtph.2005.09.001] [Citation(s) in RCA: 44] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2005] [Indexed: 11/30/2022]
Abstract
Quantitative structure-activity relationship (QSAR) software offers a rapid, cost effective means of prioritizing the mutagenic potential of chemicals. MDL QSAR models were developed using atom-type E-state indices and non-parametric discriminant analysis. Models were developed for Salmonella typhimurium gene mutation, combining results from strains TA97, TA98, TA100, TA1535, TA1536, TA1537, and TA1538 (n=3228), and Escherichia coli gene mutation tests WP2, WP100, and polA (n=472). Composite microbial mutation models (n=3338) were developed combining all Salmonella, E. coli, and the Bacillus subtilis rec spot test study results. The datasets contained 74% non-pharmaceuticals and 26% pharmaceuticals. Salmonella and microbial mutagenesis external validation studies included a total of 1444 and 1485 compounds, respectively. The average specificity, sensitivity, positive predictivity, concordance, and coverage of Salmonella models was 76, 81, 73, 78, and 98%, respectively, with similar performance for the microbial mutagenesis models. MDL QSAR and discriminant analysis provides rapid and highly automated mutagenicity screening software with good specificity, sensitivity, and coverage that is simpler and requires less user intervention than other similar software. MDL QSAR modules for microbial mutagenicity can provide efficient and cost effective large scale screening of compounds for mutagenic potential for the chemical and pharmaceutical industry.
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Affiliation(s)
- Joseph F Contrera
- US Food and Drug Administration, Center for Drug Evaluation and Research, Office of Pharmaceutical Science, Informatics and Computational Safety Analysis Staff, 5600 Fishers Lane, Rockville, MD 20857, USA.
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Contrera JF, Matthews EJ, Kruhlak NL, Benz RD. Estimating the safe starting dose in phase I clinical trials and no observed effect level based on QSAR modeling of the human maximum recommended daily dose. Regul Toxicol Pharmacol 2005; 40:185-206. [PMID: 15546675 DOI: 10.1016/j.yrtph.2004.08.004] [Citation(s) in RCA: 62] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2004] [Indexed: 11/29/2022]
Abstract
Estimating the maximum recommended starting dose (MRSD) of a pharmaceutical for phase I human clinical trials and the no observed effect level (NOEL) for non-pharmaceuticals is currently based exclusively on an extrapolation of the results of animal toxicity studies. This process is inexact and requires the results of toxicity studies in multiple species (rat, dog, and monkey) to identify the no observed adverse effect level (NOAEL) and most sensitive test species. Multiple uncertainty (safety) factors are also necessary to compensate for incompatibility and uncertainty underlying the extrapolation of animal toxicity to humans. The maximum recommended daily dose for pharmaceuticals (MRDD) is empirically derived from human clinical trials. The MRDD is an estimated upper dose limit beyond which a drug's efficacy is not increased and/or undesirable adverse effects begin to outweigh beneficial effects. The MRDD is essentially equivalent to the NOAEL in humans, a dose beyond which adverse (toxicological) or undesirable pharmacological effects are observed. The NOAEL in test animals is currently used to estimate the safe starting dose in human clinical trials. MDL QSAR predictive modeling of the human MRDD may provide a better, simpler and more relevant estimation of the MRSD for pharmaceuticals and the toxic dose threshold of chemicals in humans than current animal extrapolation based risk assessment models and may be a useful addition to current methods. A database of the MRDD for over 1300 pharmaceuticals was compiled and modeled using MDL QSAR software and E-state and connectivity topological descriptors. MDL QSAR MRDD models were found to have good predictive performance with 74-78% of predicted MRDD values for 120 internal and 160 external validation compounds falling within a range of +/-10-fold the actual MRDD value. The predicted MRDD can be used to estimate the MRSD for pharmaceuticals in phase I clinical trials with the addition of a 10-fold safety factor. For non-pharmaceutical chemicals any compound-related effect can be considered an undesirable and adverse toxicological effect and the predicted MRDD can be used to estimate the NOEL with the addition of an appropriate safety factor.
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Affiliation(s)
- Joseph F Contrera
- US Food and Drug Administration, Center for Drug Evaluation and Research (HFD-901), Office of Pharmaceutical Science, Informatics and Computational Safety Analysis Staff (ICSAS), 5600 Fishers Lane, Rockville, MD 20857, USA.
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Matthews EJ, Kruhlak NL, Weaver JL, Benz RD, Contrera JF. Assessment of the health effects of chemicals in humans: II. Construction of an adverse effects database for QSAR modeling. Curr Drug Discov Technol 2004; 1:243-54. [PMID: 16472241 DOI: 10.2174/1570163043334794] [Citation(s) in RCA: 25] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/06/2023]
Abstract
The FDA's Spontaneous Reporting System (SRS) database contains over 1.5 million adverse drug reaction (ADR) reports for 8620 drugs/biologics that are listed for 1191 Coding Symbols for Thesaurus of Adverse Reaction (COSTAR) terms of adverse effects. We have linked the trade names of the drugs to 1861 generic names and retrieved molecular structures for each chemical to obtain a set of 1515 organic chemicals that are suitable for modeling with commercially available QSAR software packages. ADR report data for 631 of these compounds were extracted and pooled for the first five years that each drug was marketed. Patient exposure was estimated during this period using pharmaceutical shipping units obtained from IMS Health. Significant drug effects were identified using a Reporting Index (RI), where RI = (# ADR reports / # shipping units) x 1,000,000. MCASE/MC4PC software was used to identify the optimal conditions for defining a significant adverse effect finding. Results suggest that a significant effect in our database is characterized by > or = 4 ADR reports and > or = 20,000 shipping units during five years of marketing, and an RI > or = 4.0. Furthermore, for a test chemical to be evaluated as active it must contain a statistically significant molecular structural alert, called a decision alert, in two or more toxicologically related endpoints. We also report the use of a composite module, which pools observations from two or more toxicologically related COSTAR term endpoints to provide signal enhancement for detecting adverse effects.
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Affiliation(s)
- Edwin J Matthews
- U.S. Food and Drug Administration, 5600 Fishers Lane (HFD-901), Rockville, Maryland 20857, USA.
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Matthews EJ, Kruhlak NL, Benz RD, Contrera JF. Assessment of the health effects of chemicals in humans: I. QSAR estimation of the maximum recommended therapeutic dose (MRTD) and no effect level (NOEL) of organic chemicals based on clinical trial data. Curr Drug Discov Technol 2004; 1:61-76. [PMID: 16472220 DOI: 10.2174/1570163043484789] [Citation(s) in RCA: 63] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/06/2023]
Abstract
The primary objective of this investigation was to develop a QSAR model to estimate the no effect level (NOEL) of chemicals in humans using data derived from pharmaceutical clinical trials and the MCASE software program. We believe that a NOEL model derived from human data provides a more specific estimate of the toxic dose threshold of chemicals in humans compared to current risk assessment models which extrapolate from animals to humans employing multiple uncertainty safety factors. A database of the maximum recommended therapeutic dose (MRTD) of marketed pharmaceuticals was compiled. Chemicals with low MRTDs were classified as high-toxicity compounds; chemicals with high MRTDs were classified as low-toxicity compounds. Two separate training data sets were constructed to identify specific structural alerts associated with high and low toxicity chemicals. A total of 134 decision alerts correlated with toxicity in humans were identified from 1309 training data set chemicals. An internal validation experiment showed that predictions for high- and low-toxicity chemicals were good (positive predictivity >92%) and differences between experimental and predicted MRTDs were small (0.27-0.70 log-fold). Furthermore, the model exhibited good coverage (89.9-93.6%) for three classes of chemicals (pharmaceuticals, direct food additives, and food contact substances). An additional investigation demonstrated that the maximum tolerated dose (MTD) of chemicals in rodents was poorly correlated with MRTD values in humans (R2 = 0.2005, n = 326). Finally, this report discusses experimental factors which influence the accuracy of test chemical predictions, potential applications of the model, and the advantages of this model over those that rely only on results of animal toxicology studies.
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Affiliation(s)
- Edwin J Matthews
- U.S. Food and Drug Administration, 5600 Fishers Lane (HFD-901), Rockville, Maryland 20857, USA. matthewse@ cder.fda.gov
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Kruhlak NL, Wang M, Boorman PM, Parvez M, McDonald R. Syntheses, structures, and stabilities of [PPh(4)][WS(3)(SR)](R=(i)Bu,(i)Pr,(i)Bu, benzyl, allyl) and [PPh(4)][MoS(3)(S(t)Bu)]. Inorg Chem 2001; 40:3141-8. [PMID: 11399185 DOI: 10.1021/ic000970n] [Citation(s) in RCA: 26] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Intermediates in the condensation process of [MS(4)](2)(-) (M = Mo, W) to polythiometalates, in the presence of alkyl halides, had not been reported prior to our communication of [PPh(4)][WS(3)(SEt)] (Boorman, P. M.; Wang, M.; Parvez, M. J. Chem. Soc., Chem. Commun. 1995, 999-1000). We now report the isolation of a range of related compounds, with 1 degrees, 2 degrees, and 3 degrees alkyl thiolate ligands, including one Mo example. [PPh(4)][WS(3)(SR)] (R = (i)Bu (1), (i)Pr (2), (t)Bu (3), benzyl (5), allyl (6)) and [PPh(4)][MoS(3)(S(t)Bu)] (4) have been isolated in fair to good yields from the reaction of [PPh(4)](2)[MS(4)] with the appropriate alkyl halide in acetonitrile and subjected to analysis by X-ray crystallography. Crystal data are as follows: for 1, triclinic space group P1 (No. 2), a = 11.0377(6) A, b = 11.1307(5) A, c = 13.6286(7) A, alpha = 82.941(1) degrees, beta = 84.877(1) degrees, gamma = 60.826(1) degrees, Z = 2; for 2, monoclinic space group P2(1)/c (No. 14), a = 9.499(6) A, b = 15.913(5) A, c = 18.582(6) A, beta = 99.29(4) degrees, Z = 4; for 3, monoclinic space group P2(1)/n (No. 14), a = 10.667(2) A, b = 17.578(2) A, c = 16.117(3) A, beta = 101.67(1) degrees, Z = 4; for 4, monoclinic space group P2(1)/n (No. 14), a = 10.558(3) A, b = 17.477(3) A, c = 15.954(3) A, beta = 101.18(2) degrees, Z = 4; for 5, monoclinic space group P2(1)/n (No. 14), a = 16.2111(9) A, b = 11.0080(6) A, c = 18.1339(10) A, beta = 111.722(1) degrees, Z = 4; for 6, triclinic space group P1 (No. 2), a = 9.4716(9) A, b = 10.4336(10) A, c = 14.4186(14) A, alpha = 100.183(2) degrees, beta = 90.457(2) degrees, gamma = 91.747(2) degrees, Z = 2. Structures 3 and 4 are isomorphous, and 1 exhibits disorder about the tertiary carbon. 6 has been shown to exhibit fluxionality in solution by variable-temperature (1)H NMR studies, and an allyl migration mechanism is implicated in this process. The kinetics for the reaction of [WS(4)](2)(-) and EtBr were measured and suggest an associative nucleophilic substitution (S(N)2) mechanism. The decomposition of the [WS(3)(SEt)](-) ion is shown to be second order with respect to this ion, suggesting the formation of a transient binuclear intermediate. M-S bond cleavage is the predominant step in decomposition of 1-6 to yield alkyl sulfides, alkyl thiols, and polythiometalates such as [PPh(4)](2)[M(3)S(9)]. In contrast, reactions of [PPh(4)](2)[WO(x)()S(4)(-)(x)()] (x = 1, 2) with (t)BuBr result in the additional decomposition product of isobutene, presumably by C-S bond cleavage and beta-hydrogen transfer. Interestingly, the reaction of [PPh(4)](2)[WOS(3)] with BzCl yields 5 as the only isolable W thiolate species.
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Affiliation(s)
- N L Kruhlak
- Department of Chemistry, University of Calgary, Calgary, Alberta, Canada, T2N 1N4
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