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Bishop PL, Mansouri K, Eckel WP, Lowit MB, Allen D, Blankinship A, Lowit AB, Harwood DE, Johnson T, Kleinstreuer NC. Evaluation of in silico model predictions for mammalian acute oral toxicity and regulatory application in pesticide hazard and risk assessment. Regul Toxicol Pharmacol 2024; 149:105614. [PMID: 38574841 DOI: 10.1016/j.yrtph.2024.105614] [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: 09/29/2023] [Revised: 01/29/2024] [Accepted: 03/27/2024] [Indexed: 04/06/2024]
Abstract
The United States Environmental Protection Agency (USEPA) uses the lethal dose 50% (LD50) value from in vivo rat acute oral toxicity studies for pesticide product label precautionary statements and environmental risk assessment (RA). The Collaborative Acute Toxicity Modeling Suite (CATMoS) is a quantitative structure-activity relationship (QSAR)-based in silico approach to predict rat acute oral toxicity that has the potential to reduce animal use when registering a new pesticide technical grade active ingredient (TGAI). This analysis compared LD50 values predicted by CATMoS to empirical values from in vivo studies for the TGAIs of 177 conventional pesticides. The accuracy and reliability of the model predictions were assessed relative to the empirical data in terms of USEPA acute oral toxicity categories and discrete LD50 values for each chemical. CATMoS was most reliable at placing pesticide TGAIs in acute toxicity categories III (>500-5000 mg/kg) and IV (>5000 mg/kg), with 88% categorical concordance for 165 chemicals with empirical in vivo LD50 values ≥ 500 mg/kg. When considering an LD50 for RA, CATMoS predictions of 2000 mg/kg and higher were found to agree with empirical values from limit tests (i.e., single, high-dose tests) or definitive results over 2000 mg/kg with few exceptions.
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Affiliation(s)
- Patricia L Bishop
- Animal Research Issues, The Humane Society of the United States, Washington, DC, USA.
| | - Kamel Mansouri
- National Toxicology Program Interagency Center for the Evaluation of Alternative Toxicological Methods, National Institute of Environmental Health Sciences, Research Triangle Park, NC, USA
| | - William P Eckel
- US Environmental Protection Agency, Office of Pesticide Programs, Washington, DC, USA
| | - Michael B Lowit
- US Environmental Protection Agency, Office of Pesticide Programs, Washington, DC, USA
| | - David Allen
- Predictive Toxicology and Information Sciences Group, Discovery and Safety Assessment Division, Inotiv, Morrisville, NC, USA
| | - Amy Blankinship
- US Environmental Protection Agency, Office of Pesticide Programs, Washington, DC, USA
| | - Anna B Lowit
- US Environmental Protection Agency, Office of Pollution Prevention and Toxics, Washington, DC, USA
| | - D Ethan Harwood
- US Environmental Protection Agency, Office of Pesticide Programs, Washington, DC, USA
| | - Tamara Johnson
- US Environmental Protection Agency, Office of Pesticide Programs, Washington, DC, USA
| | - Nicole C Kleinstreuer
- National Toxicology Program Interagency Center for the Evaluation of Alternative Toxicological Methods, National Institute of Environmental Health Sciences, Research Triangle Park, NC, USA
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2
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Moudgal C, Anger LT, Muster W, Nguyen R, Melnikov F, Siramshetty VB, Graham J. The application of acute oral toxicity computational models in dangerous goods classification. Toxicol Ind Health 2023; 39:687-699. [PMID: 37860984 DOI: 10.1177/07482337231209091] [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: 10/21/2023]
Abstract
Acute oral toxicity (AOT) data inform the acute toxicity potential of a compound and guides occupational safety and transportation practices. AOT data enable the categorization of a chemical into the appropriate AOT Globally Harmonized System (GHS) category based on the severity of the hazard. AOT data are also utilized to identify compounds that are Dangerous Goods (DGs) and subsequent transportation guidance for shipping of these hazardous materials. Proper identification of DGs is challenging for novel compounds that lack data. It is not feasible to err on the side of caution for all compounds lacking AOT data and to designate them as DGs, as shipping a compound as a DG has cost, resource, and time implications. With the wealth of available historical AOT data, AOT testing approaches are evolving, and in silico AOT models are emerging as tools that can be utilized with confidence to assess the acute toxicity potential of de novo molecules. Such approaches align with the 3R principles, offering a reduction or even replacement of traditional in vivo testing methods and can also be leveraged for product stewardship purposes. Utilizing proprietary historical in vivo AOT data for 210 pharmaceutical compounds (PCs), we evaluated the performance of two established in silico AOT programs: the Leadscope AOT Model Suite and the Collaborative Acute Toxicity Modeling Suite. These models accurately identified 94% and 97% compounds that were not DGs (GHS categories 4, 5, and not classified (NC)) suggesting that the models are fit-for-purpose in identifying PCs with low acute oral toxicity potential (LD50 >300 mg/kg). Utilization of these models to identify compounds that are not DGs can enable them to be de-prioritized for in vivo testing. This manuscript provides a detailed evaluation and assessment of the two models and recommends the most suitable applications of such models.
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Affiliation(s)
| | - Lennart T Anger
- Safety Assessment, Genentech Inc, South San Francisco, CA, USA
| | | | - Ruthi Nguyen
- EHS, Genentech Inc, South San Francisco, CA, USA
| | - Fjodor Melnikov
- Safety Assessment, Genentech Inc, South San Francisco, CA, USA
| | | | - Jessica Graham
- Safety Assessment, Genentech Inc, South San Francisco, CA, USA
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3
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Mamada H, Takahashi M, Ogino M, Nomura Y, Uesawa Y. Predictive Models Based on Molecular Images and Molecular Descriptors for Drug Screening. ACS OMEGA 2023; 8:37186-37195. [PMID: 37841172 PMCID: PMC10568689 DOI: 10.1021/acsomega.3c04073] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/06/2023] [Accepted: 08/30/2023] [Indexed: 10/17/2023]
Abstract
Various toxicity and pharmacokinetic evaluations as screening experiments are needed at the drug discovery stage. Currently, to reduce the use of animal experiments and developmental expenses, the development of high-performance predictive models based on quantitative structure-activity relationship analysis is desired. From these evaluation targets, we selected 50% lethal dose (LD50), blood-brain barrier penetration (BBBP), and the clearance (CL) pathway for this investigation and constructed predictive models for each target using 636-11,886 compounds. First, we constructed predictive models using the DeepSnap-deep learning (DL) method and images of compounds as features. The calculated area under the curve (AUC) and balanced accuracy (BAC) were, respectively, 0.887 and 0.818 for LD50, 0.893 and 0.824 for BBBP, and 0.883 and 0.763 for the CL pathway. Next, molecular descriptors (MDs) of compounds were calculated using Molecular Operating Environment, alvaDesc, and ADMET Predictor to construct predictive models using the MD-based method. Using these MDs, we constructed predictive models using DataRobot. The calculated AUC and BAC were, respectively, 0.931 and 0.805 for LD50, 0.919 and 0.849 for BBBP, and 0.900 and 0.807 for the CL pathway. In this investigation, we constructed predictive models combining the DeepSnap-DL and MD-based methods. In ensemble models using the mean predictive probability of the DeepSnap-DL and MD-based methods, the calculated AUC and BAC were, respectively, 0.942 and 0.842 for LD50, 0.936 and 0.853 for BBBP, and 0.908 and 0.832 for the CL pathway, with improved predictive performance observed for all variables compared with either single method alone. Moreover, in consensus models that adopted only compounds for which the results of the two methods agreed, the calculated BAC for LD50, BBBP, and the CL pathway were 0.916, 0.918, and 0.847, respectively, indicating higher predictive performance than the ensemble models for all three variables. The predictive models combining the DeepSnap-DL and MD-based methods displayed high predictive performance for LD50, BBBP, and the CL pathway. Therefore, the application of this approach to prediction targets in various drug discovery screenings is expected to accelerate drug discovery.
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Affiliation(s)
- Hideaki Mamada
- Drug
Metabolism and Pharmacokinetics Research Laboratories, Central Pharmaceutical
Research Institute, Japan Tobacco Inc., 1-1 Murasaki-cho, Takatsuki, Osaka 569-1125, Japan
| | - Mari Takahashi
- Drug
Metabolism and Pharmacokinetics Research Laboratories, Central Pharmaceutical
Research Institute, Japan Tobacco Inc., 1-1 Murasaki-cho, Takatsuki, Osaka 569-1125, Japan
| | - Mizuki Ogino
- Drug
Metabolism and Pharmacokinetics Research Laboratories, Central Pharmaceutical
Research Institute, Japan Tobacco Inc., 1-1 Murasaki-cho, Takatsuki, Osaka 569-1125, Japan
| | - Yukihiro Nomura
- Drug
Metabolism and Pharmacokinetics Research Laboratories, Central Pharmaceutical
Research Institute, Japan Tobacco Inc., 1-1 Murasaki-cho, Takatsuki, Osaka 569-1125, Japan
| | - Yoshihiro Uesawa
- Department
of Medical Molecular Informatics, Meiji
Pharmaceutical University, 2-522-1 Noshio, Kiyose, Tokyo 204-858, Japan
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4
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Lane TR, Harris J, Urbina F, Ekins S. Comparing LD 50/LC 50 Machine Learning Models for Multiple Species. ACS CHEMICAL HEALTH & SAFETY 2023. [DOI: 10.1021/acs.chas.2c00088] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/25/2023]
Affiliation(s)
- Thomas R. Lane
- Collaborations Pharmaceuticals, Inc., 840 Main Campus Drive, Lab 3510, Raleigh, North Carolina 27606, United States
| | - Joshua Harris
- Collaborations Pharmaceuticals, Inc., 840 Main Campus Drive, Lab 3510, Raleigh, North Carolina 27606, United States
| | - Fabio Urbina
- Collaborations Pharmaceuticals, Inc., 840 Main Campus Drive, Lab 3510, Raleigh, North Carolina 27606, United States
| | - Sean Ekins
- Collaborations Pharmaceuticals, Inc., 840 Main Campus Drive, Lab 3510, Raleigh, North Carolina 27606, United States
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5
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Ramos-Souza C, Bandoni DH, Bragotto APA, De Rosso VV. Risk assessment of azo dyes as food additives: Revision and discussion of data gaps toward their improvement. Compr Rev Food Sci Food Saf 2023; 22:380-407. [PMID: 36374221 DOI: 10.1111/1541-4337.13072] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2022] [Revised: 10/09/2022] [Accepted: 10/15/2022] [Indexed: 11/16/2022]
Abstract
The food industry uses dyes mainly to overcome color loss during the processing and storage of products, with the azo dyes currently being the most employed. Studies on the safety of using these dyes in foods started in the 1950s and have indicated the potential for concern. This review discusses the risk assessment of food intake containing artificial azo dyes. There are case reports and, subsequently, double-blind placebo-controlled trials in some individuals who may experience adverse effects from the intake of azo dyes, but it is unclear whether these adverse effects are restricted to specific populations or more generalized. In view of this, different toxicological endpoints are evaluated to verify toxic effects in in vitro and in vivo models and to establish the no observed adverse effect level. Exposure estimation studies have shown that human exposure to azo dyes via oral intake is mainly below the acceptable daily intake established by advisory bodies. However, most countries do not have studies that estimate the oral intake of azo dyes. In this case, local food diversity and racial-ethnic specificities are not considered when stating the exposure estimate is below the acceptable daily intake for the human population and thus may not represent actual intake. Concerning the scenario established above, this review discusses the most critical gaps to be overcome to contribute to the direction of future studies and the development of more effective public policies concerning the safety of the intake of artificial azo dyes.
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Affiliation(s)
- Caroline Ramos-Souza
- Nutrition and Food Service Research Center, Federal University of São Paulo (UNIFESP), Santos, São Paulo, Brazil
| | - Daniel Henrique Bandoni
- Nutrition and Food Service Research Center, Federal University of São Paulo (UNIFESP), Santos, São Paulo, Brazil
| | | | - Veridiana Vera De Rosso
- Nutrition and Food Service Research Center, Federal University of São Paulo (UNIFESP), Santos, São Paulo, Brazil
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6
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Zwickl CM, Graham J, Jolly R, Bassan A, Ahlberg E, Amberg A, Anger LT, Barton-Maclaren T, Beilke L, Bellion P, Brigo A, Cronin MT, Custer L, Devlin A, Burleigh-Flayers H, Fish T, Glover K, Glowienke S, Gromek K, Jones D, Karmaus A, Kemper R, Piparo EL, Madia F, Martin M, Masuda-Herrera M, McAtee B, Mestre J, Milchak L, Moudgal C, Mumtaz M, Muster W, Neilson L, Patlewicz G, Paulino A, Roncaglioni A, Ruiz P, Suarez D, Szabo DT, Valentin JP, Vardakou I, Woolley D, Myatt G. Principles and Procedures for Assessment of Acute Toxicity Incorporating In Silico Methods. COMPUTATIONAL TOXICOLOGY (AMSTERDAM, NETHERLANDS) 2022; 24:100237. [PMID: 36818760 PMCID: PMC9934006 DOI: 10.1016/j.comtox.2022.100237] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Acute toxicity in silico models are being used to support an increasing number of application areas including (1) product research and development, (2) product approval and registration as well as (3) the transport, storage and handling of chemicals. The adoption of such models is being hindered, in part, because of a lack of guidance describing how to perform and document an in silico analysis. To address this issue, a framework for an acute toxicity hazard assessment is proposed. This framework combines results from different sources including in silico methods and in vitro or in vivo experiments. In silico methods that can assist the prediction of in vivo outcomes (i.e., LD50) are analyzed concluding that predictions obtained using in silico approaches are now well-suited for reliably supporting assessment of LD50-based acute toxicity for the purpose of GHS classification. A general overview is provided of the endpoints from in vitro studies commonly evaluated for predicting acute toxicity (e.g., cytotoxicity/cytolethality as well as assays targeting specific mechanisms). The increased understanding of pathways and key triggering mechanisms underlying toxicity and the increased availability of in vitro data allow for a shift away from assessments solely based on endpoints such as LD50, to mechanism-based endpoints that can be accurately assessed in vitro or by using in silico prediction models. This paper also highlights the importance of an expert review of all available information using weight-of-evidence considerations and illustrates, using a series of diverse practical use cases, how in silico approaches support the assessment of acute toxicity.
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Affiliation(s)
| | - Jessica Graham
- Genentech, Inc., 1 DNA Way, South San Francisco, CA 94080, USA
| | - Robert Jolly
- Eli Lilly and Company, Indianapolis, IN 46285, USA
| | - Arianna Bassan
- Innovatune srl, Via Giulio Zanon 130/D, 35129 Padova, Italy
| | - Ernst Ahlberg
- Universal Prediction AB, Gothenburg, Sweden
- Department of Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden
| | - Alexander Amberg
- Sanofi, R&D Preclinical Safety Frankfurt, Industriepark Hoechst, D-65926 Frankfurt am Main, Germany
| | | | - Tara Barton-Maclaren
- Healthy Environments and Consumer Safety Branch, Health Canada / Government of Canada
| | - Lisa Beilke
- Toxicology Solutions, Inc., 10531 4S Commons Dr. #594, San Diego, CA 92127, USA
| | - Phillip Bellion
- Boehringer Ingelheim Animal Health, Binger Str. 128, 55216 Ingelheim am Rhein, Germany
| | - Alessandro Brigo
- Roche Pharmaceutical Research & Early Development, Roche Innovation Center Basel, Grenzacherstrasse 124, 4070, Basel, Switzerland
| | | | | | - Amy Devlin
- FDA Center for Drug Evaluation and Research, Silver Spring, MD 20993, USA
| | | | - Trevor Fish
- Nelson Laboratories, Salt Lake City, Utah, USA
| | | | | | | | - David Jones
- MHRA, 10 South Colonnade, Canary Wharf, London E14 4PU
| | - Agnes Karmaus
- Integrated Laboratory Systems, LLC, Morrisville, NC, USA
| | | | - Elena Lo Piparo
- Chemical Food Safety Group, Nestlé Research, Lausanne, Switzerland
| | - Federica Madia
- European Commission, Joint Research Centre (JRC), Ispra, Italy
| | | | | | | | - Jordi Mestre
- IMIM Institut Hospital Del Mar d’Investigacions Mèdiques and Universitat Pompeu Fabra, Doctor Aiguader 88, Parc de Recerca Biomèdica, 08003 Barcelona, Spain
- Chemotargets SL, Baldiri Reixac 4, Parc Científic de Barcelona, 08028 Barcelona, Spain
| | | | | | - Moiz Mumtaz
- Office of the Associate Director for Science, Agency for Toxic Substances and Disease Registry, Centers for Disease Control and Prevention, Atlanta, GA 30333, USA
| | - Wolfgang Muster
- Roche Pharmaceutical Research & Early Development, Roche Innovation Center Basel, Grenzacherstrasse 124, 4070, Basel, Switzerland
| | | | - Grace Patlewicz
- Centre for Computational Toxicology and Exposure (CCTE), US Environmental Protection Agency, Research Triangle Park, NC, USA
| | | | - Alessandra Roncaglioni
- Laboratory of Environmental Chemistry and Toxicology, Department of Environmental Health Sciences, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milan, Italy
| | - Patricia Ruiz
- Centers for Disease Control and Prevention (CDC), Atlanta, GA 30341, USA
| | - Diana Suarez
- FSTox Consulting LTD, 2 Brooks Road Raunds Wellingborough NN9 6NS
| | | | - Jean-Pierre Valentin
- UCB-Biopharma SRL, Development Science, Avenue de l’industrie, Braine l’Alleud, Wallonia, Belgium
| | - Ioanna Vardakou
- British American Tobacco (Investments) Ltd., R&D Centre, Southampton, Hampshire SO15 8TL, UK
| | | | - Glenn Myatt
- Instem, 1393 Dublin Rd, Columbus, OH 43215, USA
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7
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Jesus JBDE, Conceição RADA, Machado TR, Barbosa MLC, Domingos TFS, Cabral LM, Rodrigues CR, Abrahim-Vieira B, Souza AMTDE. Toxicological assessment of SGLT2 inhibitors metabolites using in silico approach. AN ACAD BRAS CIENC 2022; 94:e20211287. [PMID: 36197362 DOI: 10.1590/0001-3765202220211287] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2021] [Accepted: 02/01/2022] [Indexed: 11/22/2022] Open
Abstract
Sodium-glucose cotransporter 2 inhibitors (SGLT2i) are the latest class of drugs approved to treat type 2 DM (T2DM). Although adverse effects are often caused by a metabolite rather than the drug itself, only the safety assessment of disproportionate drug metabolites is usually performed, which is of particular concern for drugs of chronic use, such as SGLT2i. Bearing this in mind, in silico tools are efficient strategies to reveal the risk assessment of metabolites, being endorsed by many regulatory agencies. Thereby, the goal of this study was to apply in silico methods to provide the metabolites toxicity assessment of the SGLT2i. Toxicological assessment from SGLT2i metabolites retrieved from the literature was estimated using the structure and/or statistical-based alert implemented in DataWarrior and ADMET predictorTM softwares. The drugs and their metabolites displayed no mutagenic, tumorigenic or cardiotoxic risks. Still, M1-2 and M3-1 were recognized as potential hepatotoxic compounds and M1-2, M1-3, M3-1, M3-2, M3-3 and M4-3, were estimated to have very toxic LD50 values in rats. All SGLT2i and the metabolites M3-4, M4-1 and M4-2, were predicted to have reproductive toxicity. These results support the awareness that metabolites may be potential mediators of drug-induced toxicities of the therapeutic agents.
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Affiliation(s)
- Jéssica B DE Jesus
- Universidade Federal do Rio de Janeiro, Faculdade de Farmácia, Departamento de Fármacos e Medicamentos, Av. Carlos Chagas Filho, 373, CCS, Bloco Lss, Cidade Universitária, 21941-902 Rio de Janeiro, RJ, Brazil
| | - Raissa A DA Conceição
- Universidade Federal do Rio de Janeiro, Faculdade de Farmácia, Departamento de Fármacos e Medicamentos, Av. Carlos Chagas Filho, 373, CCS, Bloco Lss, Cidade Universitária, 21941-902 Rio de Janeiro, RJ, Brazil
| | - Thayná R Machado
- Universidade Federal do Rio de Janeiro, Faculdade de Farmácia, Departamento de Fármacos e Medicamentos, Av. Carlos Chagas Filho, 373, CCS, Bloco Lss, Cidade Universitária, 21941-902 Rio de Janeiro, RJ, Brazil
| | - Maria L C Barbosa
- Universidade Federal do Rio de Janeiro, Faculdade de Farmácia, Departamento de Fármacos e Medicamentos, Av. Carlos Chagas Filho, 373, CCS, Bloco Lss, Cidade Universitária, 21941-902 Rio de Janeiro, RJ, Brazil
| | - Thaisa F S Domingos
- BIODATA Computing Services & Consulting, Rua Aloísio Teixeira, 278, Parque Tecnológico, Cidade Universitária, 21941-850 Rio de Janeiro, RJ, Brazil
| | - Lucio M Cabral
- Universidade Federal do Rio de Janeiro, Faculdade de Farmácia, Departamento de Fármacos e Medicamentos, Av. Carlos Chagas Filho, 373, CCS, Bloco Lss, Cidade Universitária, 21941-902 Rio de Janeiro, RJ, Brazil
| | - Carlos R Rodrigues
- Universidade Federal do Rio de Janeiro, Faculdade de Farmácia, Departamento de Fármacos e Medicamentos, Av. Carlos Chagas Filho, 373, CCS, Bloco Lss, Cidade Universitária, 21941-902 Rio de Janeiro, RJ, Brazil
| | - Bárbara Abrahim-Vieira
- Universidade Federal do Rio de Janeiro, Faculdade de Farmácia, Departamento de Fármacos e Medicamentos, Av. Carlos Chagas Filho, 373, CCS, Bloco Lss, Cidade Universitária, 21941-902 Rio de Janeiro, RJ, Brazil
| | - Alessandra M T DE Souza
- Universidade Federal do Rio de Janeiro, Faculdade de Farmácia, Departamento de Fármacos e Medicamentos, Av. Carlos Chagas Filho, 373, CCS, Bloco Lss, Cidade Universitária, 21941-902 Rio de Janeiro, RJ, Brazil
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8
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Juliana Martins F, Savacini Sagrillo F, Josiane Vinturelle Medeiros R, Gonçalves de Souza A, Rodrigues Pinto Costa A, Silva Novais J, Alves Miceli L, R Campos V, Marie Sá Figueiredo A, Claudia Cunha A, Lidmar von Ranke N, Lamim Bello M, de A Abrahim-Vieira B, M T De Souza A, A Ratcliffe N, da Costa Santos Boechat F, Cecília Bastos Vieira de Souza M, Rangel Rodrigues C, Carla Castro H. Evaluation of biological activities of quinone-4-oxoquinoline derivatives against pathogens of clinical importance. Curr Top Med Chem 2022; 22:973-991. [PMID: 35524665 DOI: 10.2174/1568026622666220504124710] [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: 12/16/2021] [Revised: 03/02/2022] [Accepted: 03/17/2022] [Indexed: 11/22/2022]
Abstract
BACKGROUND Microbial resistance has become a worldwide public health problem, and may lead to morbidity and mortality in affected patients. OBJECTIVE Therefore, this work aimed to evaluate the antibacterial activity of quinone-4-oxoquinoline derivatives. METHOD These derivatives were evaluated against Gram-positive and Gram-negative bacteria by their antibacterial activity, anti-biofilm, and hemolytic activities and by in silico assays. RESULTS The quinone-4-oxoquinoline derivatives presented broad-spectrum antibacterial activities, and in some cases were more active than commercially available reference drugs. These compounds also inhibited bacterial adhesion and the assays revealed seven non-hemolytic derivatives. The derivatives seem to cause damage to the bacterial cell membrane and those containing the carboxyl group at the C-3 position of the 4-quinolonic nucleus were more active than those containing a carboxyethyl group. CONCLUSION The isoquinoline-5,8-dione nucleus also favored antimicrobial activity. The study showed that the target of the derivatives must be a non-conventional hydrophobic allosteric binding pocket on the DNA gyrase enzyme.
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Affiliation(s)
- Francislene Juliana Martins
- Federal Fluminense University, Biology Institute, Postgraduate Program in Science and Biotechnology, Niterói, Rio de Janeiro, Brazil
| | - Fernanda Savacini Sagrillo
- Federal Fluminense University, Chemistry Institute, Department of Organic Chemistry, Niterói, Rio de Janeiro, Brazil
| | | | - Alan Gonçalves de Souza
- Federal Fluminense University, Chemistry Institute, Department of Organic Chemistry, Niterói, Rio de Janeiro, Brazil
| | - Amanda Rodrigues Pinto Costa
- Federal Fluminense University, Chemistry Institute, Department of Organic Chemistry, Niterói, Rio de Janeiro, Brazil
| | - Juliana Silva Novais
- Federal Fluminense University, Medical School, Postgraduate in Pathology, Niterói, Rio de Janeiro, Brazil.,Universidade Estácio de Sá (UNESA), Faculdade de Farmácia, São Gonçalo, Rio de Janeiro, Brazil
| | - Leonardo Alves Miceli
- Federal University of Rio de Janeiro, Faculdade de Farmácia, Departamento de Fármacos e Medicamentos, Rio de Janeiro, Brazil
| | - Vinícius R Campos
- Federal Fluminense University, Chemistry Institute, Department of Organic Chemistry, Niterói, Rio de Janeiro, Brazil
| | - Agnes Marie Sá Figueiredo
- Federal University of Rio de Janeiro, Microbiology Institute Professor Paulo Goes, Department of Medical Microbiology, Rio de Janeiro, Brazil
| | - Anna Claudia Cunha
- Federal Fluminense University, Chemistry Institute, Department of Organic Chemistry, Niterói, Rio de Janeiro, Brazil
| | - Natalia Lidmar von Ranke
- Federal University of Rio de Janeiro, Faculdade de Farmácia, Departamento de Fármacos e Medicamentos, Rio de Janeiro, Brazil
| | - Murilo Lamim Bello
- Federal University of Rio de Janeiro, Faculdade de Farmácia, Departamento de Fármacos e Medicamentos, Rio de Janeiro, Brazil
| | - Bárbara de A Abrahim-Vieira
- Federal University of Rio de Janeiro, Faculdade de Farmácia, Departamento de Fármacos e Medicamentos, Rio de Janeiro, Brazil
| | - Alessandra M T De Souza
- Federal University of Rio de Janeiro, Faculdade de Farmácia, Departamento de Fármacos e Medicamentos, Rio de Janeiro, Brazil
| | - Norman A Ratcliffe
- Department of Biosciences, College of Science Swansea University, SA2 8PP. UK
| | | | | | - Carlos Rangel Rodrigues
- Federal University of Rio de Janeiro, Faculdade de Farmácia, Departamento de Fármacos e Medicamentos, Rio de Janeiro, Brazil
| | - Helena Carla Castro
- Federal Fluminense University, Biology Institute, Postgraduate Program in Science and Biotechnology, Niterói, Rio de Janeiro, Brazil
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Abstract
In this chapter, we give a brief overview of the regulatory requirements for acute systemic toxicity information in the European Union, and we review structure-based computational models that are available and potentially useful in the assessment of acute systemic toxicity. Emphasis is placed on quantitative structure-activity relationship (QSAR) models implemented by means of a range of software tools. The most recently published literature models for acute systemic toxicity are also discussed, and perspectives for future developments in this field are offered.
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Affiliation(s)
- Ivanka Tsakovska
- Institute of Biophysics and Biomedical Engineering, Bulgarian Academy of Sciences, Sofia, Bulgaria.
| | - Antonia Diukendjieva
- Institute of Biophysics and Biomedical Engineering, Bulgarian Academy of Sciences, Sofia, Bulgaria
| | - Andrew P Worth
- European Commission, Joint Research Centre (JRC), Ispra, Italy
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10
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Lee SY, Lee DY, Kang JH, Jeong JW, Kim JH, Kim HW, Oh DH, Kim JM, Rhim SJ, Kim GD, Kim HS, Jang YD, Park Y, Hur SJ. Alternative experimental approaches to reduce animal use in biomedical studies. J Drug Deliv Sci Technol 2022. [DOI: 10.1016/j.jddst.2022.103131] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
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11
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Gromek K, Hawkins W, Dunn Z, Gawlik M, Ballabio D. Evaluation of the predictivity of Acute Oral Toxicity (AOT) structure-activity relationship models. Regul Toxicol Pharmacol 2021; 129:105109. [PMID: 34968630 DOI: 10.1016/j.yrtph.2021.105109] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2021] [Revised: 12/10/2021] [Accepted: 12/22/2021] [Indexed: 11/17/2022]
Abstract
Several public efforts are aimed at discovering patterns or classifiers in the high-dimensional bioactivity space that predict tissue, organ or whole animal toxicological endpoints. The current study sought to assess and compare the predictions of the Globally Harmonized System (GHS) categories and Dangerous Goods (DG) classifications based on Lethal Dose (LD50) from several available tools (ACD/Labs, Leadscope, T.E.S.T., CATMoS, CaseUltra). External validation was done using dataset of 375 substances to demonstrate their predictive capacity. All models showed very good performance for identifying non-toxic compounds, which would be useful for DG classification, developing or triaging new chemicals, prioritizing existing chemicals for more detailed and rigorous toxicity assessments, and assessing non-active pharmaceutical intermediates. This would ultimately reduce animal use and improve risk assessments. Category-to-category prediction was not optimal, mainly due to the tendency to overpredict the outcome and the general limitations of acute oral toxicity (AOT) in vivo studies. Overprediction does not specifically pose a risk to human health, it can impact transport and material packaging requirements. Performance for compounds with LD50 ≤ 300 mg/kg (approx. 5% of the dataset) was the poorest among all groups and could be potentially improved by including expert review and read-across to similar substances.
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Affiliation(s)
- Kamila Gromek
- GlaxoSmithKline, Gunnels Wood Road Stevenage Herts SG1 2NY, United Kingdom.
| | - William Hawkins
- GlaxoSmithKline, Gunnels Wood Road Stevenage Herts SG1 2NY, United Kingdom.
| | - Zoe Dunn
- GlaxoSmithKline, Gunnels Wood Road Stevenage Herts SG1 2NY, United Kingdom.
| | - Maciej Gawlik
- Department of Medicinal Chemistry, Medical University of Lublin, Poland.
| | - Davide Ballabio
- Chemometrics and QSAR Research Group, Department of Earth and Environmental Sciences, University of Milano-Bicocca, Italy.
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12
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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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