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Saunders A, Harrington PDB. Advances in Activity/Property Prediction from Chemical Structures. Crit Rev Anal Chem 2024; 54:135-147. [PMID: 35482792 DOI: 10.1080/10408347.2022.2066461] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Abstract
Recent technological advancement in AI modeling of molecular property databases has significantly expanded the opportunities for drug design and development. Quantitative structure-activity relationships (QSARs) are shown to provide more accurate predictions with regards to biological activity as well as toxicological assessment. By using a combination of in-silico models or by combining disparate structure-activity databases, researchers have been able to improve accuracy for a variety of drug discovery and analysis methods, generating viable compounds, which in certain cases, can be synthesized and further studied in vitro to find candidates for potential development. Additionally, the development of compounds of determined toxicology can be discontinued earlier, allowing alternative routes to be evaluated, preventing wasted time and resources. Although the progress that has been made is tremendous, expert review is still necessary for most in-silico generated predictions. Regardless, the scientific community continues to move ever closer to completely automated drug discovery and evaluation.
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Affiliation(s)
- Arianne Saunders
- Department of Chemistry and Biochemistry, Ohio University, Athens, Ohio, USA
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2
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Hasselgren C, Kenyon M, Anger LT, Cornwell P, Watt E, Bercu J. Analysis of non-mutagenic substances in the context of drug impurity assessment - Few are potent toxicants. Regul Toxicol Pharmacol 2024; 150:105645. [PMID: 38761967 DOI: 10.1016/j.yrtph.2024.105645] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2024] [Revised: 05/07/2024] [Accepted: 05/13/2024] [Indexed: 05/20/2024]
Abstract
ICH Q3A/B guidelines provide qualification thresholds for impurities or degradation products in new drug substances and products. However, the guidelines note that certain impurities/degradation products may warrant further safety evaluation for being unusually potent or toxic. The purpose of this study was to confirm that especially toxic non-mutagenic compounds are rare and to identify classes of compounds that could warrant lower qualification thresholds. A total of 2815 compounds were evaluated, of which 2213 were assessed as non-mutagenic. For the purpose of this analysis, compounds were considered potent when the point of departure was ≤0.2 mg/kg/day based on the qualification threshold (1 mg/day or 0.02 mg/kg/day for a 50 kg human) in a new drug substance, with an additional 10-fold margin. Only 54 of the entire set (2.4%) would be considered potent based on this conservative potency analysis, confirming that the existing ICH Q3A/B qualification thresholds are appropriate for the majority of impurities. If the Q3A/B threshold, without the additional 10-fold margin is used, 14 compounds (0.6%) are considered "highly potent". Very few non-mutagenic structural classes were identified, including organothiophosphates and derivatives, polychlorinated benzenes and polychlorinated polycyclic aliphatics, that correlate with potential high potency, consistent with prior publications.
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Affiliation(s)
- Catrin Hasselgren
- Department of Safety Assessment, Genentech, Inc., South San Francisco, CA, 94080, USA.
| | - Michelle Kenyon
- Drug Safety Research and Development, Pfizer Research and Development, Groton, CT, 06340, USA
| | - Lennart T Anger
- Department of Safety Assessment, Genentech, Inc., South San Francisco, CA, 94080, USA
| | - Paul Cornwell
- Nonclinical Safety Assessment, Eli Lilly & Co, Indianapolis, IN, 46285, USA
| | - Eric Watt
- Drug Safety Research and Development, Pfizer Research and Development, Groton, CT, 06340, USA
| | - Joel Bercu
- Gilead Sciences, Inc., Nonclinical Safety and Pathobiology (NSP), Foster City, CA, 94404, USA
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3
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Wu X, Su QZ, Yue X, Li H, Yang J, Wu S, Zhong HN, Li D, Jianguo Z, Chen S, Dong B. Occurrence and prioritization of non-volatile substances in recycled PET flakes produced in China. CHEMOSPHERE 2024; 352:141508. [PMID: 38387658 DOI: 10.1016/j.chemosphere.2024.141508] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/23/2023] [Revised: 12/18/2023] [Accepted: 02/19/2024] [Indexed: 02/24/2024]
Abstract
Recycled PET (rPET) is gaining popularity for use in the production of new food contact materials (FCMs) under the context of circular economy. However, the limited information on contaminants in rPET from China and concerns about their potential risk are major obstacles to their use in FCM in China. Fifty-five non-volatile compounds were tentatively identified in 126 batches of hot-washed rPET flakes aimed for food packaging applications in China. Although the 55 substances are not necessarily migratable and may not end up in the contacting media, their presence indicates a need for proper management and control across the value chain. For this reason, the 55 substances prioritized on the basis of level of concerns and in-silico genotoxicity profiler. Among them, dimethoxyethyl phthalate, dibutyl phthalate, bis(2-ethylhexyl) phthalate were classified as level V substances, and Michler's ketone and 4-nitrophenol were both categorized as level V substances and had the genotoxic structure alert, while 2,4,5-trimethylaniline was specified with genotoxic structure alert. The above substances have high priority and may pose a potential risk to human health, therefore special attention should be paid to their migration from rPET. Aside from providing valuable information on non-volatile contaminants present in hot-washed rPET flakes coming from China, this article proposed a prioritization workflow that can be of great help to identify priority substances deserving special attention across the value chain.
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Affiliation(s)
- Xuefeng Wu
- National Reference Laboratory for Food Contact Material (Guangdong), Guangzhou Customs Technology Center, Guangzhou 510075, China
| | - Qi-Zhi Su
- National Reference Laboratory for Food Contact Material (Guangdong), Guangzhou Customs Technology Center, Guangzhou 510075, China
| | - Xin Yue
- Danone open science research center (OSRC), Shanghai, 201204, China
| | - Hanke Li
- National Reference Laboratory for Food Contact Material (Guangdong), Guangzhou Customs Technology Center, Guangzhou 510075, China
| | - Jinghua Yang
- Danone open science research center (OSRC), Shanghai, 201204, China
| | - Siliang Wu
- National Reference Laboratory for Food Contact Material (Guangdong), Guangzhou Customs Technology Center, Guangzhou 510075, China
| | - Huai-Ning Zhong
- National Reference Laboratory for Food Contact Material (Guangdong), Guangzhou Customs Technology Center, Guangzhou 510075, China; State Key Laboratory of Pulp and Paper Engineering, South China University of Technology, Guangzhou, 510640, China.
| | - Dan Li
- National Reference Laboratory for Food Contact Material (Guangdong), Guangzhou Customs Technology Center, Guangzhou 510075, China
| | - Zheng Jianguo
- National Reference Laboratory for Food Contact Material (Guangdong), Guangzhou Customs Technology Center, Guangzhou 510075, China
| | - Sheng Chen
- National Reference Laboratory for Food Contact Material (Guangdong), Guangzhou Customs Technology Center, Guangzhou 510075, China
| | - Ben Dong
- National Reference Laboratory for Food Contact Material (Guangdong), Guangzhou Customs Technology Center, Guangzhou 510075, China.
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4
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Sayyed FH, Rathod N, Mishra VK, Nalawade V, Roy B. Identification, trace level quantification, and in silico assessment of potential genotoxic impurity in Famotidine. Drug Chem Toxicol 2024:1-9. [PMID: 38425309 DOI: 10.1080/01480545.2024.2321941] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2023] [Accepted: 02/16/2024] [Indexed: 03/02/2024]
Abstract
Potential genotoxic impurities in medications are an increasing concern in the pharmaceutical industry and regulatory bodies because of the risk of human carcinogenesis. To prevent the emergence of these impurities, it is crucial to carefully examine not only the final product but also the intermediates and key starting material (KSM) used in drug synthesis. During the related substances analysis of KSM of Famotidine, an unknown impurity in the range of 0.5-1.0% was found prompting the need for isolation and characterization due to the possibility of its to infiltrate into the final product. In this study, the impurity was isolated and characterized as 5-(2-chloroethyl)-3,3-dimethyl-3,4-dihydro-2H-1,2,4,6-thiatriazine 1,1-dioxide using multiple instrumental analysis, uncovering a structural alert that raises concern. Considering the potential impact of impurity on human health, an in silico genotoxicity assessment was established using Derek and Sarah tool in accordance with ICH M7 guideline. Furthermore, molecular docking and molecular dynamics simulation were performed to evaluate the specific interaction of the impurity with DNA. The findings reveal consistent interaction of the impurity with the dG-rich region of the DNA duplex and binding at the minor groove. Both in silico prediction and molecular dynamic study confirmed the genotoxic character of the impurity. The newly discovered impurity in famotidine has not been reported previously, and there is currently no analytical method available for its identification and control. A highly sensitive HPLC-UV method was developed and validated in accordance with ICH requirements, enabling quantification of the impurity at trace level in famotidine ensuring its safe release.
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Affiliation(s)
- Faiz Hussain Sayyed
- Amity School of Applied Sciences (ASAS), Amity University Mumbai, Mumbai - Pune Expressway Bhatan, Mumbai, Maharashtra, India
| | - Nitin Rathod
- IPCA Laboratories, Chemical Research Division, Mumbai, India
| | - Vipin Kumar Mishra
- Amity School of Applied Sciences (ASAS), Amity University Mumbai, Mumbai - Pune Expressway Bhatan, Mumbai, Maharashtra, India
| | - Vighnesh Nalawade
- Amity School of Applied Sciences (ASAS), Amity University Mumbai, Mumbai - Pune Expressway Bhatan, Mumbai, Maharashtra, India
| | - Bappaditya Roy
- Amity School of Applied Sciences (ASAS), Amity University Mumbai, Mumbai - Pune Expressway Bhatan, Mumbai, Maharashtra, India
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5
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Basoccu F, Cuccu F, Porcheddu A. Mechanochemistry for Healthcare: Revealing the Nitroso Derivatives Genesis in the Solid State. CHEMSUSCHEM 2024; 17:e202301034. [PMID: 37818785 DOI: 10.1002/cssc.202301034] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/18/2023] [Revised: 09/27/2023] [Accepted: 10/10/2023] [Indexed: 10/13/2023]
Abstract
Nitroso derivatives with unique characteristics have been extensively studied in various fields, including biology and clinical research. Although there has been substantial investigation of "nitrosable" components in many drugs and commonly consumed nutrients, there is still a need for a higher awareness about their formation and characterization. This study demonstrates how these derivatives can be produced through a mechanochemical procedure under solid-state conditions. The results include synthesizing previously unknown compounds with potential biological and pharmaceutical applications, such as a nitrosamine derived from a Diclofenac-like structure.
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Affiliation(s)
- Francesco Basoccu
- Department of Chemical and Geological Sciences, University of Cagliari, Str. interna Policlinico Universitario, 09042, Monserrato CA, Italy
| | - Federico Cuccu
- Department of Chemical and Geological Sciences, University of Cagliari, Str. interna Policlinico Universitario, 09042, Monserrato CA, Italy
| | - Andrea Porcheddu
- Department of Chemical and Geological Sciences, University of Cagliari, Str. interna Policlinico Universitario, 09042, Monserrato CA, Italy
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Cayley AN, Foster RS, Brigo A, Muster W, Musso A, Kenyon MO, Parris P, White AT, Cohen-Ohana M, Nudelman R, Glowienke S. Assessing the utility of common arguments used in expert review of in silico predictions as part of ICH M7 assessments. Regul Toxicol Pharmacol 2023; 144:105490. [PMID: 37659712 DOI: 10.1016/j.yrtph.2023.105490] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2023] [Revised: 08/24/2023] [Accepted: 08/30/2023] [Indexed: 09/04/2023]
Abstract
Expert review of two predictions, made by complementary (quantitative) structure-activity relationship models, to an overall conclusion is a key component of using in silico tools to assess the mutagenic potential of impurities as part of the ICH M7 guideline. In lieu of a specified protocol, numerous publications have presented best practise guides, often indicating the occurrence of common prediction scenarios and the evidence required to resolve them. A semi-automated expert review tool has been implemented in Lhasa Limited's Nexus platform following collation of these common arguments and assignment to the associated prediction scenarios made by Derek Nexus and Sarah Nexus. Using datasets primarily donated by pharmaceutical companies, an automated analysis of the frequency these prediction scenarios occur, and the likelihood of the associated arguments assigning the correct resolution, could then be conducted. This article highlights that a relatively small number of common arguments may be used to accurately resolve many prediction scenarios to a single conclusion. The use of a standardised method of argumentation and assessment of evidence for a given impurity is proposed to improve the efficiency and consistency of expert review as part of an ICH M7 submission.
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Affiliation(s)
- Alex N Cayley
- Lhasa Limited, Granary Wharf House, 2 Canal Wharf, Leeds, LS11 5PS, UK
| | - Robert S Foster
- Lhasa Limited, Granary Wharf House, 2 Canal Wharf, Leeds, LS11 5PS, UK.
| | - Alessandro Brigo
- Roche Pharmaceutical Research & Early Development, Pharmaceutical Sciences, Roche Innovation Center Basel, Grenzacherstrasse 124, 4070, Basel, Switzerland
| | - Wolfgang Muster
- Roche Pharmaceutical Research & Early Development, Pharmaceutical Sciences, Roche Innovation Center Basel, Grenzacherstrasse 124, 4070, Basel, Switzerland
| | - Alyssa Musso
- Pfizer Global Research and Development, Drug Safety Research and Development, Eastern Point Road, MS 8274/1317, Groton, CT, 06340, USA
| | - Michelle O Kenyon
- Pfizer Global Research and Development, Drug Safety Research and Development, Eastern Point Road, MS 8274/1317, Groton, CT, 06340, USA
| | - Patricia Parris
- Pfizer Worldwide Research and Development, Drug Safety Research and Development, Ramsgate Road, Sandwich, Kent, CT13 9NJ, UK
| | - Angela T White
- GlaxoSmithKline Pre-Clinical Development, Park Road, Ware, Hertfordshire, SG12 0DP, UK
| | - Mirit Cohen-Ohana
- Teva Pharmaceutical Industries Ltd, Dvora HaNevi'a Street 124, Tel Aviv-Yafo, 6944020, Israel
| | - Raphael Nudelman
- Teva Pharmaceutical Industries Ltd, Dvora HaNevi'a Street 124, Tel Aviv-Yafo, 6944020, Israel
| | - Susanne Glowienke
- Novartis AG, NIBR, Pre-clinical Safety, Fabrikstrasse 16, CH-405, Basel, Switzerland
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7
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Mutagenic potential and structural alerts of phytotoxins. Food Chem Toxicol 2023; 173:113562. [PMID: 36563927 DOI: 10.1016/j.fct.2022.113562] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2022] [Revised: 12/09/2022] [Accepted: 12/14/2022] [Indexed: 12/25/2022]
Abstract
Toxic plant-produced chemicals, so-called phytotoxins, constitute a category of natural compounds belonging to a diversity of chemical classes. Some of them (e.g., alkaloids, terpenes, saponins) are associated with high toxic potency, while for many of others no toxicological data is available. In this study, the mutagenic potential of 1586 phytotoxins, as obtained from a publicly available database, was investigated applying different in silico approaches. (Q)SAR models (including statistical-based and rule-based systems) were used for the prediction of bacterial in vitro mutagenicity (Ames test) and the results from multiple tools were combined to assign consensus predicted values (i.e., positive, negative, inconclusive). The overall consensus outcome was then employed to investigate relationships between structural features of classes of phytotoxins and potential mutagenicity, allowing the identification of structural alerts raising a specific concern. The results highlighted that about 10% of the screened compounds were predicted to have mutagenic potential and the critical classes of concern, such as alkaloids, were further investigated in terms of subclasses (e.g., indole alkaloids, isoquinoline alkaloids), getting a deeper insight into the mutagenic potential of possible naturally occurring chemicals in plant materials and their structural alerts.
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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] [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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Chou CY, Lin P, Kim J, Wang SS, Wang CC, Tung CW. Ensemble learning for predicting ex vivo human placental barrier permeability. BMC Bioinformatics 2022; 22:629. [PMID: 36138350 PMCID: PMC9502578 DOI: 10.1186/s12859-022-04937-y] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2022] [Accepted: 09/16/2022] [Indexed: 11/10/2022] Open
Abstract
Background The placental barrier protects the fetus from exposure to some toxicants and is vital for drug development and risk assessment of environmental chemicals. However, in vivo experiments for assessing the placental barrier permeability of chemicals is not ethically acceptable. Although ex vivo placental perfusion methods provide good alternatives for the assessment of placental barrier permeability, the application to a large number of test chemicals could be time- and resource-consuming. Computational prediction models for ex vivo placental barrier permeability are therefore desirable. Methods A total of 87 chemicals and corresponding 1444 physicochemical properties were divided into training and test datasets. Three types of algorithms including linear regression, random forest, and ensemble models were applied to develop prediction models for ex vivo placental barrier permeability. Results Among the tested models, the ensemble model integrating the previous two methods performed best for predicting ex vivo human placental barrier permeability with correlation coefficients of 0.887 and 0.825 when considering the applicability domain. An additional test on seven newly curated chemicals from the literature showed a good correlation coefficient of 0.879 which was further improved to 0.921 by considering the variation of experiments. Conclusion In this study, the first valid predicting model for ex vivo human placental barrier permeability was developed following the OECD guideline. The model is expected to be useful for assessing the human placental barrier permeability and can be integrated with developmental toxicity prediction models for investigating the toxic effects of chemicals on the fetus. Supplementary Information The online version contains supplementary material available at 10.1186/s12859-022-04937-y.
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Affiliation(s)
- Che-Yu Chou
- Graduate Institute of Data Science, Taipei Medical University, Taipei, Taiwan
| | - Pinpin Lin
- National Institute of Environmental Health Sciences, National Health Research Institutes, Miaoli County, Taiwan
| | - Jongwoon Kim
- Chemical Safety Research Center, Korea Research Institute of Chemical Technology (KRICT), Daejeon, Republic of Korea
| | - Shan-Shan Wang
- Graduate Institute of Data Science, Taipei Medical University, Taipei, Taiwan
| | - Chia-Chi Wang
- Department and Graduate Institute of Veterinary Medicine, School of Veterinary Medicine, National Taiwan University, Taipei, Taiwan.
| | - Chun-Wei Tung
- Graduate Institute of Data Science, Taipei Medical University, Taipei, Taiwan. .,Institute of Biotechnology and Pharmaceutical Research, National Health Research Institutes, Miaoli County, Taiwan.
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Determination of Three Alkyl Camphorsulfonates as Potential Genotoxic Impurities Using GC-FID and GC-MS by Analytical QbD. SEPARATIONS 2022. [DOI: 10.3390/separations9090246] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Abstract
Camphorsulfonic acid salts are commonly used in the manufacturing production of active pharmaceutical ingredients (APIs) and have the potential to form alkyl camphorsulfonates, which can be considered as potential genotoxic impurities (PGIs). Alkyl camphorsulfonates should be controlled using the Threshold of Toxicological Concern (TTC) when detected in APIs due to their genotoxicity. An in silico study utilizing the ICH M7 guideline was performed in order to classify the alkyl camphorsulfonates that can be produced from the reaction of camphorsulfonic acid salts with methanol, ethanol, and isopropyl alcohol, which are commonly used solvents in API manufacturing processes. Two sensitive, reproducible, and accurate analytical methods using GC-FID and GC-MS were developed using the analytical Quality By Design (QbD) approaches for the quantitation of three alkyl camphorsulfonates in APIs satisfying the control limit of PGIs according to the TTC. The detection limits of the GC-FID method were found to be between 1.5 to 1.9 ppm, and the detection limits of the GC-MS method were found to be between 0.055 to 0.102 ppm. The method was validated in terms of accuracy, linearity, precision, detection limit, quantitation limit, specificity and robustness.
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11
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Trejo-Martin A, Bercu JP, Thresher A, Tennant RE, Thomas RF, Cross K, Czich A, Waese K, Nicolette JJ, Murray J, Sonders P, Kondratiuk A, Cheung JR, Thomas D, Lynch A, Harvey J, Glowienke S, Custer L, Escobar PA. Use of the bacterial reverse mutation assay to predict carcinogenicity of N-nitrosamines. Regul Toxicol Pharmacol 2022; 135:105247. [PMID: 35998738 DOI: 10.1016/j.yrtph.2022.105247] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2022] [Revised: 08/08/2022] [Accepted: 08/12/2022] [Indexed: 11/29/2022]
Abstract
Under ICH M7, impurities are assessed using the bacterial reverse mutation assay (i.e., Ames test) when predicted positive using in silico methodologies followed by expert review. N-Nitrosamines (NAs) have been of recent concern as impurities in pharmaceuticals, mainly because of their potential to be highly potent mutagenic carcinogens in rodent bioassays. The purpose of this analysis was to determine the sensitivity of the Ames assay to predict the carcinogenic outcome with curated proprietary Vitic (n = 131) and Leadscope (n = 70) databases. NAs were selected if they had corresponding rodent carcinogenicity assays. Overall, the sensitivity/specificity of the Ames assay was 93-97% and 55-86%, respectively. The sensitivity of the Ames assay was not significantly impacted by plate incorporation (84-89%) versus preincubation (82-89%). Sensitivity was not significantly different between use of rat and hamster liver induced S9 (80-93% versus 77-96%). The sensitivity of the Ames is high when using DMSO as a solvent (87-88%). Based on the analysis of these databases, the Ames assay conducted under OECD 471 guidelines is highly sensitive for detecting the carcinogenic hazards of NAs.
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Affiliation(s)
- Alejandra Trejo-Martin
- Gilead Sciences, Inc., Nonclinical Safety and Pathobiology (NSP), Foster City, CA, 94404, USA.
| | - Joel P Bercu
- Gilead Sciences, Inc., Nonclinical Safety and Pathobiology (NSP), Foster City, CA, 94404, USA
| | - Andrew Thresher
- Lhasa Limited, Granary Wharf House, 2 Canal Wharf, Leeds, West Yorkshire, LS11 5PS, UK
| | - Rachael E Tennant
- Lhasa Limited, Granary Wharf House, 2 Canal Wharf, Leeds, West Yorkshire, LS11 5PS, UK
| | - Robert F Thomas
- Lhasa Limited, Granary Wharf House, 2 Canal Wharf, Leeds, West Yorkshire, LS11 5PS, UK
| | - Kevin Cross
- Leadscope, Inc., an Instem Company, Columbus, OH, 43215, USA
| | - Andreas Czich
- Sanofi, R&D Preclinical Safety, D-65926, Frankfurt, Germany
| | - Kerstin Waese
- Sanofi, R&D Preclinical Safety, D-65926, Frankfurt, Germany
| | - John J Nicolette
- Janssen Pharmaceuticals, Global Toxicology, Raritan, New Jersey, USA
| | - Joel Murray
- AbbVie, Inc., Pre-clinical Safety, North Chicago, IL, USA
| | - Paul Sonders
- AbbVie, Inc., Pre-clinical Safety, North Chicago, IL, USA
| | | | - Jennifer R Cheung
- Pfizer Worldwide Research and Development, Genetic Toxicology, Eastern Point Road, Groton, CT, USA
| | - Dean Thomas
- GlaxoSmithKline R&D, Park Road, Ware, Hertfordshire, SG12 0DP, UK
| | - Anthony Lynch
- GlaxoSmithKline R&D, Park Road, Ware, Hertfordshire, SG12 0DP, UK
| | - James Harvey
- GlaxoSmithKline R&D, Park Road, Ware, Hertfordshire, SG12 0DP, UK
| | - Susanne Glowienke
- Novartis AG, NIBR, Pre-clinical Safety, WSJ-340, CH-4002 Basel, Switzerland
| | - Laura Custer
- Bristol-Myers Squibb, Nonclinical Safety, 1 Squibb Dr, New Brunswick, NJ, 08903, USA
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12
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Puppala RK, Subbaiah N, Maheswaramma KS. Trace level detection and quantification of genotoxic impurities 3‐amino‐4‐methylbenzoate, 3‐amino‐4‐methylbenzoic acid, and 3‐(4‐methyl‐1H‐imidazol‐1‐yl)‐5‐(trifluoromethyl) aniline in Nilotinib dihydrochloride active pharmaceutical ingredient using liquid chromatography‐tandem mass spectrometry. SEPARATION SCIENCE PLUS 2022. [DOI: 10.1002/sscp.202200047] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Affiliation(s)
- Ravi Kumar Puppala
- Department of Chemistry Jawaharlal Nehru Technological University Anantapur Ananthapuramu India
| | - Nelaturi Subbaiah
- Department of Chemistry Jawaharlal Nehru Technological University Anantapur Ananthapuramu India
| | - K Sesha Maheswaramma
- Department of Chemistry Jawaharlal Nehru Technological University Anantapur College of Engineering Pulivendula Kadapa India
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13
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Analytical Method Development for 19 Alkyl Halides as Potential Genotoxic Impurities by Analytical Quality by Design. Molecules 2022; 27:molecules27144437. [PMID: 35889310 PMCID: PMC9320377 DOI: 10.3390/molecules27144437] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2022] [Revised: 07/05/2022] [Accepted: 07/07/2022] [Indexed: 12/10/2022] Open
Abstract
Major issues in the pharmaceutical industry involve efficient risk management and control strategies of potential genotoxic impurities (PGIs). As a result, the development of an appropriate method to control these impurities is required. An optimally sensitive and simultaneous analytical method using gas chromatography with a mass spectrometry detector (GC–MS) was developed for 19 alkyl halides determined to be PGIs. These 19 alkyl halides were selected from 144 alkyl halides through an in silico study utilizing quantitative structure–activity relationship (Q-SAR) approaches via expert knowledge rule-based software and statistical-based software. The analytical quality by design (QbD) approach was adopted for the development of a sensitive and robust analytical method for PGIs. A limited number of literature studies have reviewed the analytical QbD approach in the PGI method development using GC–MS as the analytical instrument. A GC equipped with a single quadrupole mass spectrometry detector (MSD) and VF-624 ms capillary column was used. The developed method was validated in terms of specificity, the limit of detection, quantitation, linearity, accuracy, and precision, according to the ICH Q2 guideline.
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14
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Furukawa A, Ono S, Yamada K, Torimoto N, Asayama M, Muto S. A local QSAR model based on the stability of nitrenium ions to support the ICH M7 expert review on the mutagenicity of primary aromatic amines. Genes Environ 2022; 44:10. [PMID: 35313995 PMCID: PMC8935809 DOI: 10.1186/s41021-022-00238-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2021] [Accepted: 02/14/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Aromatic amines, often used as intermediates for pharmaceutical synthesis, may be mutagenic and therefore pose a challenge as metabolites or impurities in drug development. However, predicting the mutagenicity of aromatic amines using commercially available, quantitative structure-activity relationship (QSAR) tools is difficult and often requires expert review. In this study, we developed a shareable QSAR tool based on nitrenium ion stability. RESULTS The evaluation using in-house aromatic amine intermediates revealed that our model has prediction accuracy of aromatic amine mutagenicity comparable to that of commercial QSAR tools. The effect of changing the number and position of substituents on the mutagenicity of aromatic amines was successfully explained by the change in the nitrenium ion stability. Furthermore, case studies showed that our QSAR tool can support the expert review with quantitative indicators. CONCLUSIONS This local QSAR tool will be useful as a quantitative support tool to explain the substituent effects on the mutagenicity of primary aromatic amines. By further refinement through method sharing and standardization, our tool can support the International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use (ICH) M7 expert review with quantitative indicators.
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Affiliation(s)
- Ayaka Furukawa
- Safety Research Laboratories, Innovative Research Division, Mitsubishi Tanabe Pharma Corporation, 26-1, Muraoka-Higashi 2-chome, Fujisawa, Kanagawa, 251-8555, Japan.
| | - Satoshi Ono
- Discovery Technology Laboratories, Innovative Research Division, Mitsubishi Tanabe Pharma Corporation, 1000, Kamoshida-cho, Aoba-ku, Yokohama, Kanagawa, 227-0033, Japan.
| | - Katsuya Yamada
- Safety Research Laboratories, Innovative Research Division, Mitsubishi Tanabe Pharma Corporation, 26-1, Muraoka-Higashi 2-chome, Fujisawa, Kanagawa, 251-8555, Japan
| | - Nao Torimoto
- Discovery Technology Laboratories, Innovative Research Division, Mitsubishi Tanabe Pharma Corporation, 1000, Kamoshida-cho, Aoba-ku, Yokohama, Kanagawa, 227-0033, Japan
| | - Mahoko Asayama
- Safety Research Laboratories, Innovative Research Division, Mitsubishi Tanabe Pharma Corporation, 26-1, Muraoka-Higashi 2-chome, Fujisawa, Kanagawa, 251-8555, Japan
| | - Shigeharu Muto
- Safety Research Laboratories, Innovative Research Division, Mitsubishi Tanabe Pharma Corporation, 26-1, Muraoka-Higashi 2-chome, Fujisawa, Kanagawa, 251-8555, Japan
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15
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Coppi A, Davies R, Wegesser T, Ishida K, Karmel J, Han J, Aiello F, Xie Y, Corbett MT, Parsons AT, Monticello TM, Minocherhomji S. Characterization of false positive, contaminant-driven mutagenicity in impurities associated with the sotorasib drug substance. Regul Toxicol Pharmacol 2022; 131:105162. [DOI: 10.1016/j.yrtph.2022.105162] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2021] [Revised: 02/14/2022] [Accepted: 03/17/2022] [Indexed: 10/18/2022]
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16
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Moorthy MK, Ali SM, Reddy GVS. A new liquid chromatography–quadrupole time of flight‐tandem mass spectrometry method development and validation for identification and ultra‐trace level quantification of genotoxic impurity 1,3‐diacetoxy‐2‐(acetoxymethoxy) propane in valganciclovir hydrochloride active pharmaceutical ingredient. SEPARATION SCIENCE PLUS 2022. [DOI: 10.1002/sscp.202100076] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Affiliation(s)
- Manchuri Krishna Moorthy
- Department of Chemistry Jawaharlal Nehru Technological University Anantapur Ananthapuramu Andhra Pradesh India
| | - Shaik Mahammad Ali
- Department of Chemistry Jawaharlal Nehru Technological University Anantapur Ananthapuramu Andhra Pradesh India
| | - Gopireddy Venkata Subba Reddy
- Department of Chemistry Jawaharlal Nehru Technological University Anantapur, College of Engineering, Pulivendula Kadapa Andhra Pradesh India
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17
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Myatt GJ, Bassan A, Bower D, Johnson C, Miller S, Pavan M, Cross KP. Implementation of in silico toxicology protocols within a visual and interactive hazard assessment platform. COMPUTATIONAL TOXICOLOGY (AMSTERDAM, NETHERLANDS) 2022; 21:100201. [PMID: 35036665 PMCID: PMC8754399 DOI: 10.1016/j.comtox.2021.100201] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/03/2023]
Abstract
Mechanistically-driven alternative approaches to hazard assessment invariably require a battery of tests, including both in silico models and experimental data. The decision-making process, from selection of the methods to combining the information based on the weight-of-evidence, is ideally described in published guidelines or protocols. This ensures that the application of such approaches is defendable to reviewers within regulatory agencies and across the industry. Examples include the ICH M7 pharmaceutical impurities guideline and the published in silico toxicology protocols. To support an efficient, transparent, consistent and fully documented implementation of these protocols, a new and novel interactive software solution is described to perform such an integrated hazard assessment based on public and proprietary information.
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Affiliation(s)
| | | | - Dave Bower
- Instem, 1393 Dublin Road, Columbus, Ohio 43215, USA
| | | | - Scott Miller
- Instem, 1393 Dublin Road, Columbus, Ohio 43215, USA
| | - Manuela Pavan
- Innovatune, Via Giulio Zanon 130/D, 35129 Padova, Italy
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18
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A Novel UHPLC–MS/MS Method Development and Validation for Identification and Quantification of Genotoxic Impurity Bis (2-Chloroethyl) Amine in Aripiprazole Drug Substance. Chromatographia 2022. [DOI: 10.1007/s10337-021-04123-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
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19
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Moorthy MK, Ali SM, Reddy GVS. Development and validation of LC‐QTOF‐MS/MS method for identification and determination of low levels of a genotoxic impurity, 4,6‐dichloro‐5‐nitro‐2‐(propylthio) pyrimidine in ticagrelor API. Biomed Chromatogr 2022; 36:e5336. [DOI: 10.1002/bmc.5336] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2021] [Revised: 12/29/2021] [Accepted: 01/03/2022] [Indexed: 11/08/2022]
Affiliation(s)
- Manchuri Krishna Moorthy
- Department of Chemistry Jawaharlal Nehru Technological University Anantapur Ananthapuramu Andhra Pradesh India
| | - Shaik Mahammad Ali
- Department of Chemistry Jawaharlal Nehru Technological University Anantapur Ananthapuramu Andhra Pradesh India
| | - Gopireddy Venkata Subba Reddy
- Department of Chemistry Jawaharlal Nehru Technological University Anantapur College of Engineering Kadapa Andhra Pradesh India
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20
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Kovarich S, Cappelli CI. Use of In Silico Methods for Regulatory Toxicological Assessment of Pharmaceutical Impurities. Methods Mol Biol 2022; 2425:537-560. [PMID: 35188646 DOI: 10.1007/978-1-0716-1960-5_21] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
The use of novel non-testing methodologies to support the toxicological assessment of drug impurities is having a growing impact in the regulatory framework for pharmaceutical development and marketed products. For DNA reactive (mutagenic) impurities specific recommendations for the use of in silico structure-based approaches (namely (Q)SAR methodologies) are provided in the ICH M7 guideline. In 2018 a draft reflection paper has been published by EMA addressing open issues in the qualification approach of non-genotoxic impurities (NGI) according to the ICH Q3A/Q3B guidelines, and proposing the use of alternative testing strategies, including TTC, (Q)SAR, read-across, and in vitro approaches, to gather impurity-specific safety information.In the present chapter we describe a workflow to perform the safety assessment of drug impurities based on non-testing in silico methodologies. The proposed approach consists of a stepwise decision scheme including three key phases: PHASE 1: assessment of bacterial mutagenicity and consequent classification of impurities according to ICH M7; PHASE 2: risk characterization of mutagenic impurities (Classes 1, 2 or 3); PHASE 3: qualification of non-mutagenic impurities (Classes 4 or 5). The proposed decision scheme offers the possibility to acquire impurity-specific data, also if testing is not feasible, and to decide on further in vitro testing, besides meeting 3R's principle.
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21
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Benigni R. In silico assessment of genotoxicity. Combinations of sensitive structural alerts minimize false negative predictions for all genotoxicity endpoints and can single out chemicals for which experimentation can be avoided. Regul Toxicol Pharmacol 2021; 126:105042. [PMID: 34506881 DOI: 10.1016/j.yrtph.2021.105042] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2021] [Revised: 08/27/2021] [Accepted: 09/03/2021] [Indexed: 11/15/2022]
Abstract
Genotoxicity assessment of chemicals has a crucial role in most regulations. Due to labor, time, cost, and animal welfare issues, attention is being given to (Q)SAR methods. A strategic application of alternative methods is to first use a sequence of conservative (very sensitive) (Q)SARs and/or in vitro models to arrive at the conclusion that no further testing is necessary for negatives, and to use mechanistically based, Weight-Of-Evidence approach to evaluate the chemicals showing positive results. The ICH M7 guideline to detect DNA-reactive impurities in drugs follows these lines (recommending solely (Q)SAR in step 1). However, ICH M7 focuses only on Ames test. Here a large database of more than 6000 chemicals positive in at least one endpoint (in vitro gene mutations or chromosomal aberrations, in vivo micronucleus, aneugenicity) were analyzed with structural alerts implemented in the OECD QSAR Toolbox, resulting in maximum 3% false negatives. These promising results indicate that it may be possible to extend the approach to the whole range of genotoxicity endpoints required by regulations. Since structural alerts may generate false positives, cautious follow-up of positives is recommended (with e.g., statistically based QSARs, read across of similar chemicals, expert judgement, and experimentation when necessary).
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22
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Assessing the impact of expert knowledge on ICH M7 (Q)SAR predictions. Is expert review still needed? Regul Toxicol Pharmacol 2021; 125:105006. [PMID: 34273441 DOI: 10.1016/j.yrtph.2021.105006] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2021] [Revised: 07/08/2021] [Accepted: 07/10/2021] [Indexed: 11/21/2022]
Abstract
The ICH M7 (R1) guideline recommends the use of complementary (Q)SAR models to assess the mutagenic potential of drug impurities as a state-of-the-art, high-throughput alternative to empirical testing. Additionally, it includes a provision for the application of expert knowledge to increase prediction confidence and resolve conflicting calls. Expert knowledge, which considers structural analogs and mechanisms of activity, has been valuable when models return an indeterminate (equivocal) result or no prediction (out-of-domain). A retrospective analysis of 1002 impurities evaluated in drug regulatory applications between April 2017 and March 2019 assessed the impact of expert review on (Q)SAR predictions. Expert knowledge overturned the default predictions for 26% of the impurities and resolved 91% of equivocal predictions and 75% of out-of-domain calls. Of the 261 overturned default predictions, 15% were upgraded to equivocal or positive and 79% were downgraded to equivocal or negative. Chemical classes with the most overturns were primary aromatic amines (46%), aldehydes (45%), Michael-reactive acceptors (37%), and non-primary alkyl halides (33%). Additionally, low confidence predictions were the most often overturned. Collectively, the results suggest that expert knowledge continues to play an important role in an ICH M7 (Q)SAR prediction workflow and triaging predictions based on chemical class and probability can improve (Q)SAR review efficiency.
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23
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Tcheremenskaia O, Benigni R. Toward regulatory acceptance and improving the prediction confidence of in silico approaches: a case study of genotoxicity. Expert Opin Drug Metab Toxicol 2021; 17:987-1005. [PMID: 34078212 DOI: 10.1080/17425255.2021.1938540] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
Abstract
Introduction: Genotoxicity is an imperative component of the human health safety assessment of chemicals. Its secure forecast is of the utmost importance for all health prevention strategies and regulations.Areas covered: We surveyed several types of alternative, animal-free approaches ((quantitative) structure-activity relationship (Q)SAR, read-across, Adverse Outcome Pathway, Integrated Approaches to Testing and Assessment) for genotoxicity prediction within the needs of regulatory frameworks, putting special emphasis on data quality and uncertainties issues.Expert opinion: (Q)SAR models and read-across approaches for in vitro bacterial mutagenicity have sufficient reliability for use in prioritization processes, and as support in regulatory decisions in combination with other types of evidence. (Q)SARs and read-across methodologies for other genotoxicity endpoints need further improvements and should be applied with caution. It appears that there is still large room for improvement of genotoxicity prediction methods. Availability of well-curated high-quality databases, covering a broader chemical space, is one of the most important needs. Integration of in silico predictions with expert knowledge, weight-of-evidence-based assessment, and mechanistic understanding of genotoxicity pathways are other key points to be addressed for the generation of more accurate and trustable results.
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Affiliation(s)
- Olga Tcheremenskaia
- Environmental and Health Department, Istituto Superiore Di Sanità (ISS), Rome, Italy, Rome, Italy
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24
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Selective formation of a phenathridine derivative by photodegradation of azilsartan. Bioorg Med Chem Lett 2021; 41:128011. [PMID: 33811993 DOI: 10.1016/j.bmcl.2021.128011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2021] [Revised: 03/22/2021] [Accepted: 03/28/2021] [Indexed: 11/17/2022]
Abstract
Photodegradation of azilsartan produces a phenanthridine derivative, with its molecular structure determined by 1H and 13C NMR spectroscopy. This structure is confirmed by single-crystal X-ray diffraction and alternative synthesis. The phenanthridine ring formation is explained through the ring closure of an imidoylnitrene intermediate produced by decarboxylation of the 5-oxo-1,2,4-oxadiazole ring (oxadiazolone).
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25
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Pradeep P, Judson R, DeMarini DM, Keshava N, Martin TM, Dean J, Gibbons CF, Simha A, Warren SH, Gwinn MR, Patlewicz G. Evaluation of Existing QSAR Models and Structural Alerts and Development of New Ensemble Models for Genotoxicity Using a Newly Compiled Experimental Dataset. ACTA ACUST UNITED AC 2021; 18. [PMID: 34504984 DOI: 10.1016/j.comtox.2021.100167] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
Abstract
Regulatory agencies world-wide face the challenge of performing risk-based prioritization of thousands of substances in commerce. In this study, a major effort was undertaken to compile a large genotoxicity dataset (54,805 records for 9299 substances) from several public sources (e.g., TOXNET, COSMOS, eChemPortal). The names and outcomes of the different assays were harmonized, and assays were annotated by type: gene mutation in Salmonella bacteria (Ames assay) and chromosome mutation (clastogenicity) in vitro or in vivo (chromosome aberration, micronucleus, and mouse lymphoma Tk +/- assays). This dataset was then evaluated to assess genotoxic potential using a categorization scheme, whereby a substance was considered genotoxic if it was positive in at least one Ames or clastogen study. The categorization dataset comprised 8442 chemicals, of which 2728 chemicals were genotoxic, 5585 were not and 129 were inconclusive. QSAR models (TEST and VEGA) and the OECD Toolbox structural alerts/profilers (e.g., OASIS DNA alerts for Ames and chromosomal aberrations) were used to make in silico predictions of genotoxicity potential. The performance of the individual QSAR tools and structural alerts resulted in balanced accuracies of 57-73%. A Naïve Bayes consensus model was developed using combinations of QSAR models and structural alert predictions. The 'best' consensus model selected had a balanced accuracy of 81.2%, a sensitivity of 87.24% and a specificity of 75.20%. This in silico scheme offers promise as a first step in ranking thousands of substances as part of a prioritization approach for genotoxicity.
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Affiliation(s)
- Prachi Pradeep
- Oak Ridge Institute for Science and Education, Oak Ridge, Tennessee, USA
- Center for Computational Toxicology and Exposure, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina, USA
| | - Richard Judson
- Center for Computational Toxicology and Exposure, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina, USA
| | - David M DeMarini
- Center for Computational Toxicology and Exposure, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina, USA
| | - Nagalakshmi Keshava
- Center for Computational Toxicology and Exposure, U.S. Environmental Protection Agency, Cincinnati, Ohio, USA
| | - Todd M Martin
- Center for Computational Toxicology and Exposure, U.S. Environmental Protection Agency, Cincinnati, Ohio, USA
| | - Jeffry Dean
- Center for Public Health and Environmental Assessment, U.S. Environmental Protection Agency, Cincinnati, Ohio, USA
| | - Catherine F Gibbons
- Center for Public Health and Environmental Assessment, U.S. Environmental Protection Agency, Washington, District of Columbia, USA
| | - Anita Simha
- ORAU, contractor to U.S. Environmental Protection Agency through the National Student Services Contract, Research Triangle Park, North Carolina, USA
| | - Sarah H Warren
- Center for Computational Toxicology and Exposure, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina, USA
| | - Maureen R Gwinn
- Center for Computational Toxicology and Exposure, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina, USA
| | - Grace Patlewicz
- Center for Computational Toxicology and Exposure, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina, USA
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26
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Guo X, Stolee JA, Fillon YA, Zou L. Trace-Level Determination of Acrylonitrile Generated in the Manufacturing Process of Oligonucleotides by Static Headspace Gas Chromatography with an Electron Impact(+) Mass Detector. Org Process Res Dev 2021. [DOI: 10.1021/acs.oprd.0c00527] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/23/2023]
Affiliation(s)
- Xun Guo
- Analytical Development, Biogen Inc., Cambridge, Massachusetts 02142, United States
| | - Jessica A. Stolee
- Analytical Development, Biogen Inc., Cambridge, Massachusetts 02142, United States
| | - Yannick A. Fillon
- Antisense Oligonucleotide Development, Biogen Inc., Cambridge, Massachusetts 02142, United States
| | - Lanfang Zou
- Analytical Development, Biogen Inc., Cambridge, Massachusetts 02142, United States
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27
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Bercu J, Masuda-Herrera MJ, Trejo-Martin A, Hasselgren C, Lord J, Graham J, Schmitz M, Milchak L, Owens C, Lal SH, Robinson RM, Whalley S, Bellion P, Vuorinen A, Gromek K, Hawkins WA, van de Gevel I, Vriens K, Kemper R, Naven R, Ferrer P, Myatt GJ. A cross-industry collaboration to assess if acute oral toxicity (Q)SAR models are fit-for-purpose for GHS classification and labelling. Regul Toxicol Pharmacol 2020; 120:104843. [PMID: 33340644 DOI: 10.1016/j.yrtph.2020.104843] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2020] [Revised: 11/19/2020] [Accepted: 12/14/2020] [Indexed: 11/25/2022]
Abstract
This study assesses whether currently available acute oral toxicity (AOT) in silico models, provided by the widely employed Leadscope software, are fit-for-purpose for categorization and labelling of chemicals. As part of this study, a large data set of proprietary and marketed compounds from multiple companies (pharmaceutical, plant protection products, and other chemical industries) was assembled to assess the models' performance. The absolute percentage of correct or more conservative predictions, based on a comparison of experimental and predicted GHS categories, was approximately 95%, after excluding a small percentage of inconclusive (indeterminate or out of domain) predictions. Since the frequency distribution across the experimental categories is skewed towards low toxicity chemicals, a balanced assessment was also performed. Across all compounds which could be assigned to a well-defined experimental category, the average percentage of correct or more conservative predictions was around 80%. These results indicate the potential for reliable and broad application of these models across different industrial sectors. This manuscript describes the evaluation of these models, highlights the importance of an expert review, and provides guidance on the use of AOT models to fulfill testing requirements, GHS classification/labelling, and transportation needs.
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Affiliation(s)
- Joel Bercu
- Gilead Sciences, 333 Lakeside Drive, Foster City, CA, USA
| | | | | | | | - Jean Lord
- Ultragenyx, 60 Leveroni Court, Novato, CA, 94949, USA
| | - Jessica Graham
- Bristol Myers Squibb, 1 Squibb Dr, New Brunswick, NJ, 08903, USA
| | | | | | - Colin Owens
- 3M Company, 3M Center, St. Paul, MN, 55144-1000, USA
| | - Surya Hari Lal
- Syngenta Crop Protection, Product Safety Department, Jealott's Hill International Research Centre, Bracknell, Berkshire, RG42 6EY, UK(1)
| | - Richard Marchese Robinson
- Syngenta Crop Protection, Product Safety Department, Jealott's Hill International Research Centre, Bracknell, Berkshire, RG42 6EY, UK(1)
| | - Sarah Whalley
- Syngenta Crop Protection, Product Safety Department, Jealott's Hill International Research Centre, Bracknell, Berkshire, RG42 6EY, UK(1)
| | | | | | - Kamila Gromek
- Galapagos SASU, 102 Avenue Gaston Roussel, 93230, Romainville, France
| | - William A Hawkins
- GlaxoSmithKline, Park Road, Ware, Hertfordshire, SG12 0DP, United Kingdom
| | - Iris van de Gevel
- Janssen Pharmaceutical Companies of Johnson & Johnson, 2340, Beerse, Belgium
| | - Kathleen Vriens
- Janssen Pharmaceutical Companies of Johnson & Johnson, 2340, Beerse, Belgium
| | - Raymond Kemper
- Vertex Pharmaceuticals Inc., Discovery and Investigative Toxicology, 50 Northern Ave, Boston, MA, USA
| | - Russell Naven
- Vertex Pharmaceuticals Inc., Discovery and Investigative Toxicology, 50 Northern Ave, Boston, MA, USA
| | - Pierre Ferrer
- Department of Veterinary Physiology and Pharmacology, Interdisciplinary Faculty of Toxicology Program, Texas A&M University, 4466 TAMU, College Station, TX, 77843-4466, USA
| | - Glenn J Myatt
- Leadscope (an Instem company), 1393 Dublin Rd, Columbus, OH, 43215, USA.
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28
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Snodin DJ. A Primer for Pharmaceutical Process Development Chemists and Analysts in Relation to Impurities Perceived to Be Mutagenic or “Genotoxic”. Org Process Res Dev 2020. [DOI: 10.1021/acs.oprd.0c00343] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- David J. Snodin
- Xiphora Biopharma Consulting, 9 Richmond Apartments, Redland Court Road, Bristol BS6 7BG, U.K
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Management of pharmaceutical ICH M7 (Q)SAR predictions - The impact of model updates. Regul Toxicol Pharmacol 2020; 118:104807. [PMID: 33058939 PMCID: PMC7734868 DOI: 10.1016/j.yrtph.2020.104807] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2020] [Revised: 09/29/2020] [Accepted: 10/09/2020] [Indexed: 12/15/2022]
Abstract
Pharmaceutical applicants conduct (Q)SAR assessments on identified and theoretical impurities to predict their mutagenic potential. Two complementary models—one rule-based and one statistical-based—are used, followed by expert review. (Q)SAR models are continuously updated to improve predictions, with new versions typically released on a yearly basis. Numerous releases of (Q)SAR models will occur during the typical 6–7 years of drug development until new drug registration. Therefore, it is important to understand the impact of model updates on impurity mutagenicity predictions over time. Compounds representative of pharmaceutical impurities were analyzed with three rule- and three statistical-based models covering a 4–8 year period, with the individual time frame being dependent on when the individual models were initially made available. The largest changes in the combined outcome of two complementary models were from positive or equivocal to negative and from negative to equivocal. Importantly, the cumulative change of negative to positive predictions was small in all models (<5%) and was further reduced when complementary models were combined in a consensus fashion. We conclude that model updates of the type evaluated in this manuscript would not necessarily require re-running a (Q)SAR prediction unless there is a specific need. However, original (Q)SAR predictions should be evaluated when finalizing the commercial route of synthesis for marketing authorization.
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Foster RS, Fowkes A, Cayley A, Thresher A, Werner ALD, Barber CG, Kocks G, Tennant RE, Williams RV, Kane S, Stalford SA. The importance of expert review to clarify ambiguous situations for (Q)SAR predictions under ICH M7. Genes Environ 2020; 42:27. [PMID: 32983286 PMCID: PMC7510098 DOI: 10.1186/s41021-020-00166-y] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2020] [Accepted: 09/07/2020] [Indexed: 12/21/2022] Open
Abstract
The use of in silico predictions for the assessment of bacterial mutagenicity under the International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use (ICH) M7 guideline is recommended when two complementary (quantitative) structure-activity relationship (Q)SAR models are used. Using two systems may increase the sensitivity and accuracy of predictions but also increases the need to review predictions, particularly in situations where results disagree. During the 4th ICH M7/QSAR Workshop held during the Joint Meeting of the 6th Asian Congress on Environmental Mutagens (ACEM) and the 48th Annual Meeting of the Japanese Environmental Mutagen Society (JEMS) 2019, speakers demonstrated their approaches to expert review using 20 compounds provided ahead of the workshop that were expected to yield ambiguous (Q)SAR results. Dr. Chris Barber presented a selection of the reviews carried out using Derek Nexus and Sarah Nexus provided by Lhasa Limited. On review of these compounds, common situations were recognised and are discussed in this paper along with standardised arguments that may be used for such scenarios in future.
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Affiliation(s)
- Robert S Foster
- Lhasa Limited, Granary Wharf House, 2 Canal Wharf, Leeds, LS11 5PS UK
| | - Adrian Fowkes
- Lhasa Limited, Granary Wharf House, 2 Canal Wharf, Leeds, LS11 5PS UK
| | - Alex Cayley
- Lhasa Limited, Granary Wharf House, 2 Canal Wharf, Leeds, LS11 5PS UK
| | - Andrew Thresher
- Lhasa Limited, Granary Wharf House, 2 Canal Wharf, Leeds, LS11 5PS UK
| | | | - Chris G Barber
- Lhasa Limited, Granary Wharf House, 2 Canal Wharf, Leeds, LS11 5PS UK
| | - Grace Kocks
- Lhasa Limited, Granary Wharf House, 2 Canal Wharf, Leeds, LS11 5PS UK
| | - Rachael E Tennant
- Lhasa Limited, Granary Wharf House, 2 Canal Wharf, Leeds, LS11 5PS UK
| | | | - Steven Kane
- Lhasa Limited, Granary Wharf House, 2 Canal Wharf, Leeds, LS11 5PS UK
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Benigni R, Bassan A, Pavan M. In silico models for genotoxicity and drug regulation. Expert Opin Drug Metab Toxicol 2020; 16:651-662. [DOI: 10.1080/17425255.2020.1785428] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
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32
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Bolt HM, Hengstler JG. The rapid development of computational toxicology. Arch Toxicol 2020; 94:1371-1372. [PMID: 32382955 PMCID: PMC7261728 DOI: 10.1007/s00204-020-02768-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2020] [Accepted: 04/28/2020] [Indexed: 11/15/2022]
Affiliation(s)
- Hermann M Bolt
- Department of Toxicology, Leibniz Research Centre for Working Environment and Human Factors at TU Dortmund (IfADo), Ardeystr. 67, 44139, Dortmund, Germany.
| | - Jan G Hengstler
- Department of Toxicology, Leibniz Research Centre for Working Environment and Human Factors at TU Dortmund (IfADo), Ardeystr. 67, 44139, Dortmund, Germany
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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: 51] [Impact Index Per Article: 12.8] [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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34
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Schoeny R, Cross KP, DeMarini DM, Elespuru R, Hakura A, Levy DD, Williams RV, Zeiger E, Escobar PA, Howe JR, Kato M, Lott J, Moore MM, Simon S, Stankowski LF, Sugiyama KI, van der Leede BJM. Revisiting the bacterial mutagenicity assays: Report by a workgroup of the International Workshops on Genotoxicity Testing (IWGT). MUTATION RESEARCH-GENETIC TOXICOLOGY AND ENVIRONMENTAL MUTAGENESIS 2020; 849:503137. [PMID: 32087853 DOI: 10.1016/j.mrgentox.2020.503137] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/17/2019] [Accepted: 01/09/2020] [Indexed: 11/26/2022]
Abstract
The International Workshop on Genotoxicity Testing (IWGT) meets every four years to obtain consensus on unresolved issues associated with genotoxicity testing. At the 2017 IWGT meeting in Tokyo, four sub-groups addressed issues associated with the Organization for Economic Cooperation and Development (OECD) Test Guideline TG471, which describes the use of bacterial reverse-mutation tests. The strains sub-group analyzed test data from >10,000 chemicals, tested additional chemicals, and concluded that some strains listed in TG471 are unnecessary because they detected fewer mutagens than other strains that the guideline describes as equivalent. Thus, they concluded that a smaller panel of strains would suffice to detect most mutagens. The laboratory proficiency sub-group recommended (a) establishing strain cell banks, (b) developing bacterial growth protocols that optimize assay sensitivity, and (c) testing "proficiency compounds" to gain assay experience and establish historical positive and control databases. The sub-group on criteria for assay evaluation recommended that laboratories (a) track positive and negative control data; (b) develop acceptability criteria for positive and negative controls; (c) optimize dose-spacing and the number of analyzable doses when there is evidence of toxicity; (d) use a combination of three criteria to evaluate results: a dose-related increase in revertants, a clear increase in revertants in at least one dose relative to the concurrent negative control, and at least one dose that produced an increase in revertants above control limits established by the laboratory from historical negative controls; and (e) establish experimental designs to resolve unclear results. The in silico sub-group summarized in silico utility as a tool in genotoxicity assessment but made no specific recommendations for TG471. Thus, the workgroup identified issues that could be addressed if TG471 is revised. The companion papers (a) provide evidence-based approaches, (b) recommend priorities, and (c) give examples of clearly defined terms to support revision of TG471.
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Affiliation(s)
- Rita Schoeny
- Rita Schoeny, LLC, Washington, DC 20002, United States.
| | - Kevin P Cross
- Leadscope, Inc., 1393 Dublin Road, Columbus, OH 43215, United States
| | - David M DeMarini
- U.S. Environmental Protection Agency, Research Triangle Park, NC 27711, United States
| | - Rosalie Elespuru
- U.S. Food and Drug Administration, Center for Devices and Radiological Health, Silver Spring, MD 20993, United States
| | - Atsushi Hakura
- Tsukuba Drug Safety, Eisai Co., Ltd., Tsukuba, Ibaraki, 300-2635, Japan
| | - Dan D Levy
- U.S. Food and Drug Administration, Center for Food Safety and Applied Nutrition, College Park, MD 20740 United States
| | | | - Errol Zeiger
- Errol Zeiger Consulting, 800 Indian Springs Road, Chapel Hill, NC 27514, United States
| | | | | | - Masayuki Kato
- CMIC Pharma Science Co., Ltd., Hokuto, Yamanashi, Japan
| | - Jasmin Lott
- Boehringer Ingelheim Pharma GmbH & Co., KG, Birkendorfer Strasse 65, 88397 Biberach an der Riss, Germany
| | - Martha M Moore
- Ramboll US Corporation Little Rock, AR 72223, United States
| | - Stephanie Simon
- Merck KGaA, Frankfurter Straβe 250, Darmstadt, 64293, Germany
| | - Leon F Stankowski
- Charles River Laboratories - Skokie, LLC, 8025 Lamon Ave., Skokie, IL 60077, United States
| | - Kei-Ichi Sugiyama
- Division of Genetics and Mutagenesis, National Institute of Health Sciences, Kawasaki, Kanagawa, 210-9501, Japan
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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] [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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36
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Townsend PA, Grayson MN. Density Functional Theory Transition-State Modeling for the Prediction of Ames Mutagenicity in 1,4 Michael Acceptors. J Chem Inf Model 2019; 59:5099-5103. [DOI: 10.1021/acs.jcim.9b00966] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
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37
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Raitano G, Roncaglioni A, Manganaro A, Honma M, Sousselier L, Do QT, Paya E, Benfenati E. Integrating in silico models for the prediction of mutagenicity (Ames test) of botanical ingredients of cosmetics. ACTA ACUST UNITED AC 2019. [DOI: 10.1016/j.comtox.2019.100108] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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38
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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: 41] [Impact Index Per Article: 8.2] [Reference Citation Analysis] [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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Sinha M, Dhawan A, Parthasarathi R. In Silico Approaches in Predictive Genetic Toxicology. Methods Mol Biol 2019; 2031:351-373. [PMID: 31473971 DOI: 10.1007/978-1-4939-9646-9_20] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/05/2023]
Abstract
Genetic toxicology testing is a weight-of-evidence approach to identify and characterize chemical substances that can cause genetic modifications in somatic and/or germ cells. Prediction of genetic toxicology using computational tools is gaining more attention and preferred by regulatory authorities as an alternate safety assessment for in vivo or in vitro approaches. Due to the cost and time associated with experimental genetic toxicity tests, it is essential to develop more robust in silico methods to predict chemical genetic toxicity. A number of in silico genotoxicity predictive tools/models are developed based on the experimental data gathered over the years. These in silico tools are divided into statistical quantitative structure-activity relationships (QSAR)-based approaches and expert-based systems. This chapter covers the state of the art in silico toxicology approaches and standardized protocols, essential for conducting genetic toxicity predictions of chemicals. This chapter also highlights various parameters for the validation of the prediction results obtained from QSAR models.
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Affiliation(s)
- Meetali Sinha
- Computational Toxicology Facility, Academy of Scientific and Innovative Research (AcSIR), CSIR-Indian Institute of Toxicology Research, Lucknow, Uttar Pradesh, India
| | - Alok Dhawan
- Nanomaterials Toxicology Group, CSIR-Indian Institute of Toxicology Research, Lucknow, Uttar Pradesh, India
| | - Ramakrishnan Parthasarathi
- Computational Toxicology Facility, Academy of Scientific and Innovative Research (AcSIR), CSIR-Indian Institute of Toxicology Research, Lucknow, Uttar Pradesh, India.
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40
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Tcheremenskaia O, Battistelli CL, Giuliani A, Benigni R, Bossa C. In silico approaches for prediction of genotoxic and carcinogenic potential of cosmetic ingredients. ACTA ACUST UNITED AC 2019. [DOI: 10.1016/j.comtox.2019.03.005] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/17/2023]
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41
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Nagulakonda NNM, Ananthula RS, Krishnamurthy T, Rao MRP, Rao GN. Quantification and In Silico Toxicity Assessment of Tazarotene and its Impurities for a Quality and Safe Drug Product Development. J Chromatogr Sci 2019; 57:625-635. [PMID: 31037297 DOI: 10.1093/chromsci/bmz037] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2018] [Revised: 02/26/2019] [Accepted: 04/05/2019] [Indexed: 11/13/2022]
Abstract
Tazarotene is internationally accepted common name for ethyl 6-[(4,4-dimethylthiochroman-6-yl)ethynyl]nicotinate. It is a synthetic retinoid used for the topical treatment of mild to moderate plaque psoriasis, acne vulgaris and photo aging. To ensure the quality of drug product and drug substance, a LC-MS compatible UHPLC method was developed for quantification of drug and its related substances. Stationary phase with fused core particle technology is used for the separation of impurities. Limit of quantification and limit of detection of the method are 0.1 and 0.03%, respectively. Precision of the method for Tazarotene and all its related substances is less than 2.2% RSD. The correlation coefficient is >0.999. Accuracy of method is ranged from 95.3% to 107.0%. Application of this method in stability analysis has been demonstrated by analyzing stressed samples. Experimental design is used for the verification of robustness of the method. To ensure the safety, an in silico toxicity of the drug and its related substances were determined using TOPKAT and DEREK toxicity predictions Both UHPLC and in silico methods were validated as per the ICH Q2 and ICH M7 guidelines, which will enable a rapid product development of Tazarotene topical formulations while ensuring the safety and quality of product.
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Affiliation(s)
- Nvvss Narayana Murty Nagulakonda
- Department of Analytical chemistry, Dr. Reddy's Laboratories Ltd., Hyderabad, Telangana, India.,Department of Inorganic & Analytical Chemistry, Andhra University, Visakhapatnam, India
| | - Ravi Shekar Ananthula
- Department of Analytical chemistry, Dr. Reddy's Laboratories Ltd., Hyderabad, Telangana, India
| | - T Krishnamurthy
- Department of Analytical chemistry, Dr. Reddy's Laboratories Ltd., Hyderabad, Telangana, India
| | - Muguda Ravi Prasada Rao
- Department of Analytical chemistry, Dr. Reddy's Laboratories Ltd., Hyderabad, Telangana, India
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42
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Cronin MT, Madden JC, Yang C, Worth AP. Unlocking the potential of in silico chemical safety assessment - A report on a cross-sector symposium on current opportunities and future challenges. COMPUTATIONAL TOXICOLOGY (AMSTERDAM, NETHERLANDS) 2019; 10:38-43. [PMID: 31218266 PMCID: PMC6559213 DOI: 10.1016/j.comtox.2018.12.006] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/15/2018] [Accepted: 12/17/2018] [Indexed: 12/21/2022]
Abstract
In silico chemical safety assessment can support the evaluation of hazard and risk following potential exposure to a substance. A symposium identified a number of opportunities and challenges to implement in silico methods, such as quantitative structure-activity relationships (QSARs) and read-across, to assess the potential harm of a substance in a variety of exposure scenarios, e.g. pharmaceuticals, personal care products, and industrial chemicals. To initiate the process of in silico safety assessment, clear and unambiguous problem formulation is required to provide the context for these methods. These approaches must be built on data of defined quality, while acknowledging the possibility of novel data resources tapping into on-going progress with data sharing. Models need to be developed that cover appropriate toxicity and kinetic endpoints, and that are documented appropriately with defined uncertainties. The application and implementation of in silico models in chemical safety requires a flexible technological framework that enables the integration of multiple strands of data and evidence. The findings of the symposium allowed for the identification of priorities to progress in silico chemical safety assessment towards the animal-free assessment of chemicals.
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Affiliation(s)
- Mark T.D. Cronin
- Liverpool John Moores University, School of Pharmacy and Biomolecular Sciences, Byrom Street, Liverpool L3 3AF, United Kingdom
| | - Judith C. Madden
- Liverpool John Moores University, School of Pharmacy and Biomolecular Sciences, Byrom Street, Liverpool L3 3AF, United Kingdom
| | - Chihae Yang
- Molecular Networks GmbH, Neumeyerstraße 28, 90411 Nürnberg, Germany
| | - Andrew P. Worth
- European Commission, Joint Research Centre (JRC), Ispra, Italy
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43
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Zeiger E. The test that changed the world: The Ames test and the regulation of chemicals. MUTATION RESEARCH-GENETIC TOXICOLOGY AND ENVIRONMENTAL MUTAGENESIS 2019; 841:43-48. [DOI: 10.1016/j.mrgentox.2019.05.007] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/01/2019] [Revised: 05/07/2019] [Accepted: 05/14/2019] [Indexed: 01/12/2023]
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44
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Chakravarti SK, Saiakhov RD. Computing similarity between structural environments of mutagenicity alerts. Mutagenesis 2019; 34:55-65. [PMID: 30346583 DOI: 10.1093/mutage/gey032] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2018] [Revised: 08/16/2018] [Accepted: 09/29/2018] [Indexed: 11/12/2022] Open
Abstract
This article describes a method to generate molecular fingerprints from structural environments of mutagenicity alerts and calculate similarity between them. This approach was used to improve classification accuracy of alerts and for searching structurally similar analogues of an alerting chemical. It builds fingerprints using molecular fragments from the vicinity of the alerts and automatically accounts for the activating and deactivating/mitigating features of alerts needed for accurate predictions. This study also demonstrates the usefulness of transfer learning in which a distributed representation of chemical fragments was first trained on millions of unlabelled chemicals and then used for generating fingerprints and similarity search on smaller data sets labelled with Ames test outcomes. The distributed fingerprints gave better prediction performance and increased coverage compared to traditional binary fingerprints. The methodology was applied to four common mutagenic functionalities-primary aromatic amine, aromatic nitro, epoxide and alkyl chloride. Effects of various hyperparameters on prediction accuracy and test coverage for the k-nearest neighbours prediction method are also described, e.g. similarity thresholds, number of neighbours and size of the alert environment.
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45
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Morita T, Shigeta Y, Kawamura T, Fujita Y, Honda H, Honma M. In silico prediction of chromosome damage: comparison of three (Q)SAR models. Mutagenesis 2019; 34:91-100. [PMID: 30085209 DOI: 10.1093/mutage/gey017] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2018] [Accepted: 07/23/2018] [Indexed: 11/12/2022] Open
Abstract
Two major endpoints for genotoxicity tests are gene mutation and chromosome damage (CD), which includes clastogenicity and aneugenicity detected by chromosomal aberration (CA) test or micronucleus (MN) test. Many in silico prediction systems for bacterial mutagenicity (i.e. Ames test results) have been developed and marketed. They show good performance for prediction of Ames mutagenicity. On the other hand, it seems that in silico prediction of CD does not progress as much as Ames prediction. Reasons for this include different mechanisms and detection methods, many false positives and conflicting test results. However, some (quantitative) structure-activity relationship ((Q)SAR) models (e.g. Derek Nexus [Derek], ADMEWorks [AWorks] and CASE Ultra [MCase]) can predict CA test results. Therefore, performances of the three (Q)SAR models were compared using the expanded Carcinogenicity Genotoxicity eXperience (CGX) dataset for understanding current situations and future development. The constructed dataset contained 440 chemicals (325 carcinogens and 115 non-carcinogens). Sensitivity, specificity, accuracy or applicability of each model were 56.0, 86.9, 68.6 or 89.1% in Derek, 67.7, 61.5, 65.2 or 99.3% in AWorks, and 91.0, 64.9, 80.5 or 97.7% in MCase, respectively. The performances (sensitivity and accuracy) of MCase were higher than those of Derek or AWorks. Analysis of predictivity of (Q)SAR models of certain chemical classes revealed no remarkable differences among the models. The tendency of positive prediction by (Q)SAR models was observed in alkylating agents, aromatic amines or amides, aromatic nitro compounds, epoxides, halides and N-nitro or N-nitroso compounds. In an additional investigation, high sensitivity but low specificity was noted in in vivo MN prediction by MCase. Refinement of test data to be used for in silico system (e.g. consideration of cytotoxicity or re-evaluation of conflicting test results) will be needed to improve performance of CD prediction.
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Affiliation(s)
- Takeshi Morita
- Division of Risk Assessment, National Institute of Health Sciences, Tonomachi, Kawasaki, Kanagawa, Japan
| | - Yoshiyuki Shigeta
- Division of Risk Assessment, National Institute of Health Sciences, Tonomachi, Kawasaki, Kanagawa, Japan
| | - Tomoko Kawamura
- Division of Risk Assessment, National Institute of Health Sciences, Tonomachi, Kawasaki, Kanagawa, Japan
| | - Yurika Fujita
- R&D Safety Science Research, Kao Corporation, Akabane, Ichikai-Machi, Haga-Gun, Tochigi, Japan
| | - Hiroshi Honda
- R&D Safety Science Research, Kao Corporation, Akabane, Ichikai-Machi, Haga-Gun, Tochigi, Japan
| | - Masamitsu Honma
- Division of Genetics and Mutagenesis, National Institute of Health Sciences, Tonomachi, Kawasaki, Kanagawa, Japan
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Fukuchi J, Kitazawa A, Hirabayashi K, Honma M. A practice of expert review by read-across using QSAR Toolbox. Mutagenesis 2019; 34:49-54. [PMID: 30690463 DOI: 10.1093/mutage/gey046] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/16/2023] Open
Abstract
The International Council for Harmonisation of Technical Requirement for Pharmaceuticals for Human Use (ICH) M7 guideline on 'Assessment and Control of DNA Reactive (Mutagenic) Impurities in Pharmaceuticals to Limit Potential Carcinogenic Risk' provides the application of two types of quantitative structure-activity relationship (QSAR) systems (rule- and statistics-based) as an alternative to the Ames test for evaluating the mutagenicity of impurities in pharmaceuticals. M7 guideline also states that the expert reviews can be applied when the outcomes of the two QSAR analyses show any conflicting or inconclusive prediction. However, the guideline does not provide any information of how to conduct expert reviews. Therefore, a conservative approach was chosen in this study, which is based on the intention to capture any mutagenic chemical substances. The 36 chemical substances, which are the model chemical substances in which positive mutagenicity was not observed according to the two types of QSAR analyses (i.e. the results are either conflicting or both negative), were selected from the list of chemical substances with strong mutagenicity known as the reported chemicals under the Industrial Safety and Health Act in Japan. The QSAR Toolbox was used in this study to rationally determine the positive mutagenicity of the 36 model chemical substances by applying a read-across method, a technique to evaluate the endpoint of the model chemical substances using the endpoint information of chemicals that are structurally similar to the model chemical substances. Resulting from the expert review by the read-across method, the 23 model chemical substances (63.8%) were rationally concluded as positive. In addition, 9 out of 11 model chemical substances that were assessed as negative for mutagenicity by both of the QSAR systems had positive analogues, supporting their mutagenicity. These results suggested that the read-across is a useful method, when conducting a conservative approach intended to capture any mutagenic chemical substances.
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Affiliation(s)
- Junichi Fukuchi
- Division of Pharmacopoeia and Standards for Drugs, Pharmaceuticals and Medical Devices Agency, Shin-Kasumigaseki Building, Kasumigaseki, Chiyoda-ku, Tokyo, Japan
| | - Airi Kitazawa
- Division of Pharmacopoeia and Standards for Drugs, Pharmaceuticals and Medical Devices Agency, Shin-Kasumigaseki Building, Kasumigaseki, Chiyoda-ku, Tokyo, Japan.,Division of Genetics and Mutagenesis, National Institute of Health Sciences, Tonomachi, Kawasaki-ku, Kawasaki, Kanagawa, Japan
| | - Keiji Hirabayashi
- Division of Pharmacopoeia and Standards for Drugs, Pharmaceuticals and Medical Devices Agency, Shin-Kasumigaseki Building, Kasumigaseki, Chiyoda-ku, Tokyo, Japan.,Office of New Drug I, Pharmaceuticals and Medical Devices Agency, Shin-Kasumigaseki Building, Kasumigaseki, Chiyoda-ku, Tokyo, Japan
| | - Masamitsu Honma
- Division of Pharmacopoeia and Standards for Drugs, Pharmaceuticals and Medical Devices Agency, Shin-Kasumigaseki Building, Kasumigaseki, Chiyoda-ku, Tokyo, Japan.,Division of Genetics and Mutagenesis, National Institute of Health Sciences, Tonomachi, Kawasaki-ku, Kawasaki, Kanagawa, Japan
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47
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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] [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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Benigni R, Laura Battistelli C, Bossa C, Giuliani A, Fioravanzo E, Bassan A, Fuart Gatnik M, Rathman J, Yang C, Tcheremenskaia O. Evaluation of the applicability of existing (Q)SAR models for predicting the genotoxicity of pesticides and similarity analysis related with genotoxicity of pesticides for facilitating of grouping and read across. ACTA ACUST UNITED AC 2019. [DOI: 10.2903/sp.efsa.2019.en-1598] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
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49
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Amberg A, Andaya RV, Anger LT, Barber C, Beilke L, Bercu J, Bower D, Brigo A, Cammerer Z, Cross KP, Custer L, Dobo K, Gerets H, Gervais V, Glowienke S, Gomez S, Van Gompel J, Harvey J, Hasselgren C, Honma M, Johnson C, Jolly R, Kemper R, Kenyon M, Kruhlak N, Leavitt P, Miller S, Muster W, Naven R, Nicolette J, Parenty A, Powley M, Quigley DP, Reddy MV, Sasaki JC, Stavitskaya L, Teasdale A, Trejo-Martin A, Weiner S, Welch DS, White A, Wichard J, Woolley D, Myatt GJ. Principles and procedures for handling out-of-domain and indeterminate results as part of ICH M7 recommended (Q)SAR analyses. Regul Toxicol Pharmacol 2019; 102:53-64. [PMID: 30562600 PMCID: PMC7500704 DOI: 10.1016/j.yrtph.2018.12.007] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2018] [Revised: 12/10/2018] [Accepted: 12/14/2018] [Indexed: 02/08/2023]
Abstract
The International Council for Harmonization (ICH) M7 guideline describes a hazard assessment process for impurities that have the potential to be present in a drug substance or drug product. In the absence of adequate experimental bacterial mutagenicity data, (Q)SAR analysis may be used as a test to predict impurities' DNA reactive (mutagenic) potential. However, in certain situations, (Q)SAR software is unable to generate a positive or negative prediction either because of conflicting information or because the impurity is outside the applicability domain of the model. Such results present challenges in generating an overall mutagenicity prediction and highlight the importance of performing a thorough expert review. The following paper reviews pharmaceutical and regulatory experiences handling such situations. The paper also presents an analysis of proprietary data to help understand the likelihood of misclassifying a mutagenic impurity as non-mutagenic based on different combinations of (Q)SAR results. This information may be taken into consideration when supporting the (Q)SAR results with an expert review, especially when out-of-domain results are generated during a (Q)SAR evaluation.
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Affiliation(s)
- 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
| | | | - Lisa Beilke
- Toxicology Solutions Inc., San Diego, CA, USA
| | - Joel Bercu
- Gilead Sciences, 333 Lakeside Drive, Foster City, CA, USA
| | - 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
| | - Zoryanna Cammerer
- Janssen Research & Development, 1400 McKean Road, Spring House, PA, 19477, USA
| | - 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
| | - 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
| | - Stephen Gomez
- Consultant to Theravance Biopharma US, Inc., 901 Gateway Blvd, South San Francisco, CA, 94080, USA
| | - Jacky Van Gompel
- Janssen Pharmaceutical Companies of Johnson & Johnson, 2340, Beerse, Belgium
| | - James Harvey
- GlaxoSmithKline, Park Road, Ware, Hertfordshire, SG12 0DP, UK
| | | | | | | | - Robert Jolly
- Toxicology Division, Eli Lilly and Company, Indianapolis, IN, USA
| | - Raymond Kemper
- Vertex Pharmaceuticals Inc., Discovery and Investigative Toxicology, 50 Northern Ave, Boston, MA, USA
| | - Michelle Kenyon
- Pfizer Global Research & Development, 558 Eastern Point Road, Groton, CT, 06340, USA
| | - Naomi Kruhlak
- FDA Center for Drug Evaluation and Research, Silver Spring, MD, USA
| | - Penny Leavitt
- Bristol-Myers Squibb, Drug Safety Evaluation, 1 Squibb Dr, New Brunswick, NJ, 08903, 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, Switzerland
| | | | | | - Alexis Parenty
- Novartis Pharma AG, Pre-Clinical Safety, Werk Klybeck, CH-4057, Basel, Switzerland
| | - Mark Powley
- Merck Research Laboratories, West Point, PA, 19486, USA
| | | | | | | | | | | | | | - Sandy Weiner
- Janssen Research & Development, 1400 McKean Road, Spring House, PA, 19477, USA
| | | | - Angela White
- GlaxoSmithKline, Park Road, Ware, Hertfordshire, SG12 0DP, UK
| | - Joerg Wichard
- Bayer Pharma AG, Investigational Toxicology, Muellerstr. 178, D-13353, Berlin, Germany
| | - David Woolley
- ForthTox Limited, PO Box 13550, Linlithgow, EH49 7YU, UK
| | - Glenn J Myatt
- Leadscope, Inc., 1393 Dublin Rd, Columbus, OH, 43215, USA.
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Scherer N, Marcseková K, Posset T, Winter G. New studies on leachables in commercial scale protein drug filling lines using stir bar sorptive extraction coupled with TD-GC–MS and UPLC/QTOF-MS/MS analytics. Int J Pharm 2019; 555:404-419. [DOI: 10.1016/j.ijpharm.2018.11.033] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2018] [Revised: 11/11/2018] [Accepted: 11/14/2018] [Indexed: 11/28/2022]
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