1
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Li Z, Xiong J. A dynamic inventory database for assessing age-, gender-, and route-specific chronic internal exposure to chemicals in support of human exposome research. JOURNAL OF ENVIRONMENTAL MANAGEMENT 2023; 339:117867. [PMID: 37027904 DOI: 10.1016/j.jenvman.2023.117867] [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: 01/16/2023] [Revised: 03/31/2023] [Accepted: 04/02/2023] [Indexed: 05/03/2023]
Abstract
In this study, we proposed a dynamic inventory database to evaluate chronic internal exposure to chemicals at a population level, which enables users to perform modeling exercises specific to a particular chemical, route of exposure, age group, and gender. The database was built based on the steady-state solution of physiologically based kinetic (PBK) models. The biotransfer factors [BTF, the steady-state ratio between the chemical concentration in human tissues and the average daily dose (ADD) of the chemical] of 931 organic chemicals in major organs and tissues were simulated for a total of 14 population age groups for males and females. The results indicated that infants and children had the highest simulated BTFs of chemicals, and middle-aged adults had the lowest simulated BTFs. The route-specific analysis of the simulated BTFs indicated that the biotransformation half-life and octanol-water partition coefficient of chemicals had a profound impact on the BTFs. Organ- and chemical-specific results indicated that the biotransfer potential of chemicals in human bodies was primarily determined by bio-thermodynamic variables (e.g., lipid contents). In conclusion, the proposed inventory database can be conveniently used to access chronic internal exposure doses of chemicals by multiplying the route-specific ADD values for different population groups. In future studies, we recommend incorporating human biotransformation data, partition coefficients of ionizable chemicals, age-specific vulnerable indicators (e.g., the degree of maturation of immune systems), physiological variations within the same age group (e.g., intensity of daily physical activities), growth rates (i.e., the dilution effect on chemical biotransfer), and all possible target organs of carcinogenicity (e.g., bladder) into the proposed dynamic inventory database to help promote human exposome research.
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Affiliation(s)
- Zijian Li
- School of Public Health (Shenzhen), Sun Yat-sen University, Shenzhen, Guangdong, 518107, China.
| | - Jie Xiong
- School of Public Health (Shenzhen), Sun Yat-sen University, Shenzhen, Guangdong, 518107, China
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2
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Lemée P, Fessard V, Habauzit D. Prioritization of mycotoxins based on mutagenicity and carcinogenicity evaluation using combined in silico QSAR methods. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2023; 323:121284. [PMID: 36804886 DOI: 10.1016/j.envpol.2023.121284] [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: 09/26/2022] [Revised: 02/01/2023] [Accepted: 02/12/2023] [Indexed: 06/18/2023]
Abstract
Mycotoxins and their metabolites are a family of compounds that contains a great diversity of both structure and biological properties. Information on their toxicity is spread within several databases and in scientific literature. Due to the number of molecules and their structure diversity, the cost and time required for hazard evaluation of each compound is unrealistic. In that purpose, new approach methodologies (NAMs) can be applied to evaluate such large set of molecules. Among them, quantitative structure-activity relationship (QSAR) in silico models could be useful to predict the mutagenic and carcinogenic properties of mycotoxins. First, a complete list of 904 mycotoxins and metabolites was built. Then, some known mycotoxins were used to determine the best QSAR tools for mutagenicity and carcinogenicity predictions. The best tool was further applied to the whole list of 904 mycotoxins. At the end, 95 mycotoxins were identified as both mutagen and carcinogen and should be prioritized for further evaluation.
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Affiliation(s)
- Pierre Lemée
- ANSES (French Agency for Food, Environmental and Occupational Health & Safety), Toxicology of Contaminants Unit, Fougères, France
| | - Valérie Fessard
- ANSES (French Agency for Food, Environmental and Occupational Health & Safety), Toxicology of Contaminants Unit, Fougères, France
| | - Denis Habauzit
- ANSES (French Agency for Food, Environmental and Occupational Health & Safety), Toxicology of Contaminants Unit, Fougères, France.
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3
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Fairman K, Choi MK, Gonnabathula P, Lumen A, Worth A, Paini A, Li M. An Overview of Physiologically-Based Pharmacokinetic Models for Forensic Science. TOXICS 2023; 11:126. [PMID: 36851001 PMCID: PMC9964742 DOI: 10.3390/toxics11020126] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/24/2022] [Revised: 12/16/2022] [Accepted: 01/25/2023] [Indexed: 06/18/2023]
Abstract
A physiologically-based pharmacokinetic (PBPK) model represents the structural components of the body with physiologically relevant compartments connected via blood flow rates described by mathematical equations to determine drug disposition. PBPK models are used in the pharmaceutical sector for drug development, precision medicine, and the chemical industry to predict safe levels of exposure during the registration of chemical substances. However, one area of application where PBPK models have been scarcely used is forensic science. In this review, we give an overview of PBPK models successfully developed for several illicit drugs and environmental chemicals that could be applied for forensic interpretation, highlighting the gaps, uncertainties, and limitations.
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Affiliation(s)
- Kiara Fairman
- Division of Biochemical Toxicology, National Center for Toxicological Research, United States Food and Drug Administration, Jefferson, AR 72079, USA
| | - Me-Kyoung Choi
- Division of Biochemical Toxicology, National Center for Toxicological Research, United States Food and Drug Administration, Jefferson, AR 72079, USA
| | - Pavani Gonnabathula
- Division of Biochemical Toxicology, National Center for Toxicological Research, United States Food and Drug Administration, Jefferson, AR 72079, USA
| | - Annie Lumen
- Division of Biochemical Toxicology, National Center for Toxicological Research, United States Food and Drug Administration, Jefferson, AR 72079, USA
| | - Andrew Worth
- European Commission, Joint Research Centre (JRC), 21027 Ispra, Italy
| | | | - Miao Li
- Division of Biochemical Toxicology, National Center for Toxicological Research, United States Food and Drug Administration, Jefferson, AR 72079, USA
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4
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McNair D. Artificial Intelligence and Machine Learning for Lead-to-Candidate Decision-Making and Beyond. Annu Rev Pharmacol Toxicol 2023; 63:77-97. [PMID: 35679624 DOI: 10.1146/annurev-pharmtox-051921-023255] [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] [Indexed: 01/25/2023]
Abstract
The use of artificial intelligence (AI) and machine learning (ML) in pharmaceutical research and development has to date focused on research: target identification; docking-, fragment-, and motif-based generation of compound libraries; modeling of synthesis feasibility; rank-ordering likely hits according to structural and chemometric similarity to compounds having known activity and affinity to the target(s); optimizing a smaller library for synthesis and high-throughput screening; and combining evidence from screening to support hit-to-lead decisions. Applying AI/ML methods to lead optimization and lead-to-candidate (L2C) decision-making has shown slower progress, especially regarding predicting absorption, distribution, metabolism, excretion, and toxicology properties. The present review surveys reasons why this is so, reports progress that has occurred in recent years, and summarizes some of the issues that remain. Effective AI/ML tools to derisk L2C and later phases of development are important to accelerate the pharmaceutical development process, ameliorate escalating development costs, and achieve greater success rates.
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Affiliation(s)
- Douglas McNair
- Global Health, Integrated Development, Bill & Melinda Gates Foundation, Seattle, Washington, USA;
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5
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Luconi M, Sogorb MA, Markert UR, Benfenati E, May T, Wolbank S, Roncaglioni A, Schmidt A, Straccia M, Tait S. Human-Based New Approach Methodologies in Developmental Toxicity Testing: A Step Ahead from the State of the Art with a Feto-Placental Organ-on-Chip Platform. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:15828. [PMID: 36497907 PMCID: PMC9737555 DOI: 10.3390/ijerph192315828] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/24/2022] [Revised: 11/20/2022] [Accepted: 11/25/2022] [Indexed: 06/17/2023]
Abstract
Developmental toxicity testing urgently requires the implementation of human-relevant new approach methodologies (NAMs) that better recapitulate the peculiar nature of human physiology during pregnancy, especially the placenta and the maternal/fetal interface, which represent a key stage for human lifelong health. Fit-for-purpose NAMs for the placental-fetal interface are desirable to improve the biological knowledge of environmental exposure at the molecular level and to reduce the high cost, time and ethical impact of animal studies. This article reviews the state of the art on the available in vitro (placental, fetal and amniotic cell-based systems) and in silico NAMs of human relevance for developmental toxicity testing purposes; in addition, we considered available Adverse Outcome Pathways related to developmental toxicity. The OECD TG 414 for the identification and assessment of deleterious effects of prenatal exposure to chemicals on developing organisms will be discussed to delineate the regulatory context and to better debate what is missing and needed in the context of the Developmental Origins of Health and Disease hypothesis to significantly improve this sector. Starting from this analysis, the development of a novel human feto-placental organ-on-chip platform will be introduced as an innovative future alternative tool for developmental toxicity testing, considering possible implementation and validation strategies to overcome the limitation of the current animal studies and NAMs available in regulatory toxicology and in the biomedical field.
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Affiliation(s)
- Michaela Luconi
- Department of Experimental and Clinical Biomedical Sciences “Mario Serio”, University of Florence, Viale Pieraccini 6, 50139 Florence, Italy
- I.N.B.B. (Istituto Nazionale Biostrutture e Biosistemi), Viale Medaglie d’Oro 305, 00136 Rome, Italy
| | - Miguel A. Sogorb
- Instituto de Bioingeniería, Universidad Miguel Hernández de Elche, Avenida de la Universidad s/n, 03202 Elche, Spain
| | - Udo R. Markert
- Placenta Lab, Department of Obstetrics, University Hospital Jena, Am Klinikum 1, 07747 Jena, Germany
| | - Emilio Benfenati
- Department of Environmental Health Sciences, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via Mario Negri 2, 20156 Milan, Italy
| | - Tobias May
- InSCREENeX GmbH, Inhoffenstr. 7, 38124 Braunschweig, Germany
| | - Susanne Wolbank
- Ludwig Boltzmann Institut for Traumatology, The Research Center in Cooperation with AUVA, Austrian Cluster for Tissue Regeneration, Donaueschingenstrasse 13, 1200 Vienna, Austria
| | - Alessandra Roncaglioni
- Department of Environmental Health Sciences, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via Mario Negri 2, 20156 Milan, Italy
| | - Astrid Schmidt
- Placenta Lab, Department of Obstetrics, University Hospital Jena, Am Klinikum 1, 07747 Jena, Germany
| | - Marco Straccia
- FRESCI by Science&Strategy SL, C/Roure Monjo 33, Vacarisses, 08233 Barcelona, Spain
| | - Sabrina Tait
- Centre for Gender-Specific Medicine, Istituto Superiore di Sanità, 00161 Rome, Italy
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Kendrick JS, Webber C. One small step in time, one giant leap for DMPK kind - A CRO perspective of the evolving core discipline of drug development. Xenobiotica 2022; 52:797-810. [PMID: 36097976 DOI: 10.1080/00498254.2022.2124389] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/14/2022]
Abstract
As the Space Race or Formula 1 drives innovation, efficiency and progress in home technology and home car markets, so Drug Metabolism and Pharmacokinetics (DMPK) drives scientific innovation and value for drug development companies. Stand still and fall behind as the saying goes, and these analogies are true as much in the design and conduct of DMPK studies as they are in technology and manufacturing sectors.This short review showcases the impact that DMPK has had on drug development and how it has changed in the last 10 years, illustrating the value added scientific benefit, cost and time saving, that innovative DMPK program design and study conduct have. Examples and case studies spanning novel in vitro alternatives such as organ-on-a-chip (OOAC) developments; use of in vivo microsampling across small and large animal species; to how challenging historical paradigms in Absorption, Distribution, Metabolism and Excretion (ADME) studies; and embracing new technologies to address regulatory concerns, are presented.The continual pace of change has kept DMPK at the core of pharmaceutical, crop and chemical evaluation, and this is set to continue as regulators use this discipline to inform decision making. With new modalities and new scientific questions, DMPK will continue to evolve, with the likes of new in vitro, in vivo and in silico models becoming central to candidate selection and progression.
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7
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Najjar A, Punt A, Wambaugh J, Paini A, Ellison C, Fragki S, Bianchi E, Zhang F, Westerhout J, Mueller D, Li H, Shi Q, Gant TW, Botham P, Bars R, Piersma A, van Ravenzwaay B, Kramer NI. Towards best use and regulatory acceptance of generic physiologically based kinetic (PBK) models for in vitro-to-in vivo extrapolation (IVIVE) in chemical risk assessment. Arch Toxicol 2022; 96:3407-3419. [PMID: 36063173 PMCID: PMC9584981 DOI: 10.1007/s00204-022-03356-5] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2022] [Accepted: 08/03/2022] [Indexed: 11/28/2022]
Abstract
With an increasing need to incorporate new approach methodologies (NAMs) in chemical risk assessment and the concomitant need to phase out animal testing, the interpretation of in vitro assay readouts for quantitative hazard characterisation becomes more important. Physiologically based kinetic (PBK) models, which simulate the fate of chemicals in tissues of the body, play an essential role in extrapolating in vitro effect concentrations to in vivo bioequivalent exposures. As PBK-based testing approaches evolve, it will become essential to standardise PBK modelling approaches towards a consensus approach that can be used in quantitative in vitro-to-in vivo extrapolation (QIVIVE) studies for regulatory chemical risk assessment based on in vitro assays. Based on results of an ECETOC expert workshop, steps are recommended that can improve regulatory adoption: (1) define context and implementation, taking into consideration model complexity for building fit-for-purpose PBK models, (2) harmonise physiological input parameters and their distribution and define criteria for quality chemical-specific parameters, especially in the absence of in vivo data, (3) apply Good Modelling Practices (GMP) to achieve transparency and design a stepwise approach for PBK model development for risk assessors, (4) evaluate model predictions using alternatives to in vivo PK data including read-across approaches, (5) use case studies to facilitate discussions between modellers and regulators of chemical risk assessment. Proof-of-concepts of generic PBK modelling approaches are published in the scientific literature at an increasing rate. Working on the previously proposed steps is, therefore, needed to gain confidence in PBK modelling approaches for regulatory use.
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Affiliation(s)
| | - Ans Punt
- Wageningen Food Safety Research, Wageningen, The Netherlands
| | - John Wambaugh
- Center for Computational Toxicology and Exposure, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, NC USA
| | | | | | - Styliani Fragki
- National Institute for Public Health and the Environment, Bilthoven, The Netherlands
| | | | | | - Joost Westerhout
- The Netherlands Organisation for Applied Scientific Research TNO, Utrecht, The Netherlands
| | - Dennis Mueller
- Research and Development, Crop Science, Bayer AG, Monheim, Germany
| | - Hequn Li
- Unilever Safety and Environmental Assurance Centre, Colworth Science Park, Sharnbrook, Bedfordshire UK
| | - Quan Shi
- Shell Global Solutions International B.V, The Hague, The Netherlands
| | - Timothy W. Gant
- School of Public Health, Faculty of Medicine, Imperial College London, London, UK
| | - Phil Botham
- Syngenta, Jealott’s Hill, Bracknell, Berkshire UK
| | - Rémi Bars
- Crop Science Division, Bayer S.A.S., Sophia Antipolis, France
| | - Aldert Piersma
- National Institute for Public Health and the Environment, Bilthoven, The Netherlands
| | | | - Nynke I. Kramer
- Toxicology Division, Wageningen University, PO Box 8000, 6700 EA Wageningen, The Netherlands
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8
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Petit P. Toxicological and Exposure Database Inventory: A review. Int J Hyg Environ Health 2022; 246:114055. [DOI: 10.1016/j.ijheh.2022.114055] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2022] [Revised: 10/14/2022] [Accepted: 10/14/2022] [Indexed: 11/06/2022]
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9
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Ezuruike U, Zhang M, Pansari A, De Sousa Mendes M, Pan X, Neuhoff S, Gardner I. Guide to development of compound files for PBPK modeling in the Simcyp population-based simulator. CPT Pharmacometrics Syst Pharmacol 2022; 11:805-821. [PMID: 35344639 PMCID: PMC9286711 DOI: 10.1002/psp4.12791] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2021] [Revised: 02/08/2022] [Accepted: 03/18/2022] [Indexed: 01/19/2023] Open
Abstract
The Simcyp Simulator is a software platform for population physiologically‐based pharmacokinetic (PBPK) modeling and simulation. It links in vitro data to in vivo absorption, distribution, metabolism, excretion and pharmacokinetic/pharmacodynamic outcomes to explore clinical scenarios and support drug development decisions, including regulatory submissions and drug labels. This tutorial describes the different input parameters required, as well as the considerations needed when developing a PBPK model within the Simulator, for a small molecule intended for oral administration. A case study showing the development and application of a PBPK model for ondansetron is herein used to aid the understanding of different PBPK model development concepts.
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Affiliation(s)
| | - Mian Zhang
- Simcyp Division, Certara UK Limited, Sheffield, UK
| | | | | | - Xian Pan
- Simcyp Division, Certara UK Limited, Sheffield, UK
| | | | - Iain Gardner
- Simcyp Division, Certara UK Limited, Sheffield, UK
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10
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Gonçalves BM, Graceli JB, da Rocha PB, Tilli HP, Vieira EM, de Sibio MT, Peghinelli VV, Deprá IC, Mathias LS, Olímpio RMC, Belik VC, Nogueira CR. Placental model as an important tool to study maternal-fetal interface. Reprod Toxicol 2022; 112:7-13. [PMID: 35714933 DOI: 10.1016/j.reprotox.2022.06.005] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2022] [Revised: 05/29/2022] [Accepted: 06/09/2022] [Indexed: 10/18/2022]
Abstract
The placenta is a temporary organ that plays critical roles at the maternal-fetal interface. Normal development and function of the placenta is dependent on hormonal signaling pathways that make the placenta a target of endocrine disrupting chemical (EDC) action. Studies showing association between prenatal exposure, hormone disruption, and reproductive damage indicate that EDCs are developmentally toxic and can impact future generations. In this context, new placental models (trophoblast-derived cell lines, organotypic or 3D cell models, and physiologically based kinetic models) have been developed in order to create new approach methodology (NAM) to assess and even prevent such disastrous toxic harm in future generations. With the widespread discouragement of conducting animal studies, it has become irrefutable to develop in vitro models that can serve as a substitute for in vivo models. The goal of this review is to discuss the newest in vitro models to understand the maternal-fetal interface and predict placental development, physiology, and dysfunction generated by failures in molecular hormone control mechanisms, which, consequently, may change epigenetic programming to increase susceptibility to metabolic and other disorders in the offspring. We summarize the latest placental models for developmental toxicology studies, focusing mainly on three-dimensional (3D) culture models.
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Affiliation(s)
- Bianca M Gonçalves
- Department of Clinical Medicine, Medical School, São Paulo State University (UNESP), Botucatu, SP, Brazil.
| | - Jones B Graceli
- Department of Morphology, Health Sciences Center, Federal University of Espirito Santo (UFES), Vitória, ES, Brazil
| | - Paula B da Rocha
- Department of Clinical Medicine, Medical School, São Paulo State University (UNESP), Botucatu, SP, Brazil
| | - Helena P Tilli
- Department of Clinical Medicine, Medical School, São Paulo State University (UNESP), Botucatu, SP, Brazil
| | - Ester M Vieira
- Department of Clinical Medicine, Medical School, São Paulo State University (UNESP), Botucatu, SP, Brazil
| | - Maria T de Sibio
- Department of Clinical Medicine, Medical School, São Paulo State University (UNESP), Botucatu, SP, Brazil
| | - Vinícius V Peghinelli
- Department of Clinical Medicine, Medical School, São Paulo State University (UNESP), Botucatu, SP, Brazil
| | - Igor C Deprá
- Department of Clinical Medicine, Medical School, São Paulo State University (UNESP), Botucatu, SP, Brazil
| | - Lucas S Mathias
- Department of Clinical Medicine, Medical School, São Paulo State University (UNESP), Botucatu, SP, Brazil
| | - Regiane M C Olímpio
- Department of Clinical Medicine, Medical School, São Paulo State University (UNESP), Botucatu, SP, Brazil
| | - Virgínia C Belik
- Department of Clinical Medicine, Medical School, São Paulo State University (UNESP), Botucatu, SP, Brazil
| | - Célia R Nogueira
- Department of Clinical Medicine, Medical School, São Paulo State University (UNESP), Botucatu, SP, Brazil.
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11
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Chang X, Tan YM, Allen DG, Bell S, Brown PC, Browning L, Ceger P, Gearhart J, Hakkinen PJ, Kabadi SV, Kleinstreuer NC, Lumen A, Matheson J, Paini A, Pangburn HA, Petersen EJ, Reinke EN, Ribeiro AJS, Sipes N, Sweeney LM, Wambaugh JF, Wange R, Wetmore BA, Mumtaz M. IVIVE: Facilitating the Use of In Vitro Toxicity Data in Risk Assessment and Decision Making. TOXICS 2022; 10:232. [PMID: 35622645 PMCID: PMC9143724 DOI: 10.3390/toxics10050232] [Citation(s) in RCA: 25] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/25/2022] [Accepted: 04/24/2022] [Indexed: 02/04/2023]
Abstract
During the past few decades, the science of toxicology has been undergoing a transformation from observational to predictive science. New approach methodologies (NAMs), including in vitro assays, in silico models, read-across, and in vitro to in vivo extrapolation (IVIVE), are being developed to reduce, refine, or replace whole animal testing, encouraging the judicious use of time and resources. Some of these methods have advanced past the exploratory research stage and are beginning to gain acceptance for the risk assessment of chemicals. A review of the recent literature reveals a burst of IVIVE publications over the past decade. In this review, we propose operational definitions for IVIVE, present literature examples for several common toxicity endpoints, and highlight their implications in decision-making processes across various federal agencies, as well as international organizations, including those in the European Union (EU). The current challenges and future needs are also summarized for IVIVE. In addition to refining and reducing the number of animals in traditional toxicity testing protocols and being used for prioritizing chemical testing, the goal to use IVIVE to facilitate the replacement of animal models can be achieved through their continued evolution and development, including a strategic plan to qualify IVIVE methods for regulatory acceptance.
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Affiliation(s)
- Xiaoqing Chang
- Inotiv-RTP, 601 Keystone Park Drive, Suite 200, Morrisville, NC 27560, USA; (X.C.); (D.G.A.); (S.B.); (L.B.); (P.C.)
| | - Yu-Mei Tan
- U.S. Environmental Protection Agency, Office of Pesticide Programs, 109 T.W. Alexander Drive, Durham, NC 27709, USA;
| | - David G. Allen
- Inotiv-RTP, 601 Keystone Park Drive, Suite 200, Morrisville, NC 27560, USA; (X.C.); (D.G.A.); (S.B.); (L.B.); (P.C.)
| | - Shannon Bell
- Inotiv-RTP, 601 Keystone Park Drive, Suite 200, Morrisville, NC 27560, USA; (X.C.); (D.G.A.); (S.B.); (L.B.); (P.C.)
| | - Paul C. Brown
- U.S. Food and Drug Administration, Center for Drug Evaluation and Research, 10903 New Hampshire Avenue, Silver Spring, MD 20903, USA; (P.C.B.); (A.J.S.R.); (R.W.)
| | - Lauren Browning
- Inotiv-RTP, 601 Keystone Park Drive, Suite 200, Morrisville, NC 27560, USA; (X.C.); (D.G.A.); (S.B.); (L.B.); (P.C.)
| | - Patricia Ceger
- Inotiv-RTP, 601 Keystone Park Drive, Suite 200, Morrisville, NC 27560, USA; (X.C.); (D.G.A.); (S.B.); (L.B.); (P.C.)
| | - Jeffery Gearhart
- The Henry M. Jackson Foundation, Air Force Research Laboratory, 711 Human Performance Wing, Wright-Patterson Air Force Base, OH 45433, USA;
| | - Pertti J. Hakkinen
- National Library of Medicine, National Center for Biotechnology Information, 8600 Rockville Pike, Bethesda, MD 20894, USA;
| | - Shruti V. Kabadi
- U.S. Food and Drug Administration, Center for Food Safety and Applied Nutrition, Office of Food Additive Safety, 5001 Campus Drive, HFS-275, College Park, MD 20740, USA;
| | - Nicole C. Kleinstreuer
- National Institute of Environmental Health Sciences, National Toxicology Program Interagency Center for the Evaluation of Alternative Toxicological Methods, P.O. Box 12233, Research Triangle Park, NC 27709, USA;
| | - Annie Lumen
- U.S. Food and Drug Administration, National Center for Toxicological Research, 3900 NCTR Road, Jefferson, AR 72079, USA;
| | - Joanna Matheson
- U.S. Consumer Product Safety Commission, Division of Toxicology and Risk Assessment, 5 Research Place, Rockville, MD 20850, USA;
| | - Alicia Paini
- European Commission, Joint Research Centre (JRC), 21027 Ispra, Italy;
| | - Heather A. Pangburn
- Air Force Research Laboratory, 711 Human Performance Wing, 2729 R Street, Area B, Building 837, Wright-Patterson Air Force Base, OH 45433, USA;
| | - Elijah J. Petersen
- U.S. Department of Commerce, National Institute of Standards and Technology, 100 Bureau Drive, Gaithersburg, MD 20899, USA;
| | - Emily N. Reinke
- U.S. Army Public Health Center, 8252 Blackhawk Rd., Aberdeen Proving Ground, MD 21010, USA;
| | - Alexandre J. S. Ribeiro
- U.S. Food and Drug Administration, Center for Drug Evaluation and Research, 10903 New Hampshire Avenue, Silver Spring, MD 20903, USA; (P.C.B.); (A.J.S.R.); (R.W.)
| | - Nisha Sipes
- U.S. Environmental Protection Agency, Center for Computational Toxicology and Exposure, 109 TW Alexander Dr., Research Triangle Park, NC 27711, USA; (N.S.); (J.F.W.); (B.A.W.)
| | - Lisa M. Sweeney
- UES, Inc., 4401 Dayton-Xenia Road, Beavercreek, OH 45432, Assigned to Air Force Research Laboratory, 711 Human Performance Wing, Wright-Patterson Air Force Base, OH 45433, USA;
| | - John F. Wambaugh
- U.S. Environmental Protection Agency, Center for Computational Toxicology and Exposure, 109 TW Alexander Dr., Research Triangle Park, NC 27711, USA; (N.S.); (J.F.W.); (B.A.W.)
| | - Ronald Wange
- U.S. Food and Drug Administration, Center for Drug Evaluation and Research, 10903 New Hampshire Avenue, Silver Spring, MD 20903, USA; (P.C.B.); (A.J.S.R.); (R.W.)
| | - Barbara A. Wetmore
- U.S. Environmental Protection Agency, Center for Computational Toxicology and Exposure, 109 TW Alexander Dr., Research Triangle Park, NC 27711, USA; (N.S.); (J.F.W.); (B.A.W.)
| | - Moiz Mumtaz
- Agency for Toxic Substances and Disease Registry, Office of the Associate Director for Science, 1600 Clifton Road, S102-2, Atlanta, GA 30333, USA
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12
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Lautz LS, Stoopen G, Ginting AJ, Hoogenboom RLAP, Punt A. Fipronil and fipronil sulfone in chicken: From in vitro experiments to in vivo PBK model predictions. Food Chem Toxicol 2022; 165:113086. [PMID: 35500697 DOI: 10.1016/j.fct.2022.113086] [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: 02/16/2022] [Revised: 04/14/2022] [Accepted: 04/26/2022] [Indexed: 11/18/2022]
Abstract
In 2017 a large-scale fipronil contamination in eggs occurred in several European countries. Fipronil and its metabolites have the potential to be transferred into the eggs of laying hens, thereby entering the human food chain. Here, first the metabolism of fipronil was measured in vitro using chicken liver S9. The results show that fipronil is mainly metabolised into fipronil sulfone and the clearance obtained in vitro was extrapolated to in vivo liver clearance. In a second step a physiologically based kinetic model was developed with a focus on fipronil and its major sulfone metabolite and the model outcome was compared to available in vivo data in eggs from the literature. The experimentally obtained clearance was used as model input to evaluate whether such an in vitro-based model can be used in an early phase of a contamination incident to predict the time-concentration curves. Overall, all model predictions were within a 10-fold difference and the estimated elimination half-life for fipronil equivalents was 14 days. In vitro experiments are definitely recommended compared to in vivo studies, since they provide a fast first insight into the behaviour of a chemical in an organism.
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Affiliation(s)
- L S Lautz
- Wageningen Food Safety Research, Akkermaalsbos 2, 6708, WB Wageningen, the Netherlands.
| | - G Stoopen
- Wageningen Food Safety Research, Akkermaalsbos 2, 6708, WB Wageningen, the Netherlands
| | - A J Ginting
- Wageningen Food Safety Research, Akkermaalsbos 2, 6708, WB Wageningen, the Netherlands
| | - R L A P Hoogenboom
- Wageningen Food Safety Research, Akkermaalsbos 2, 6708, WB Wageningen, the Netherlands
| | - A Punt
- Wageningen Food Safety Research, Akkermaalsbos 2, 6708, WB Wageningen, the Netherlands
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13
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Bahrami F, Rossi RM, Defraeye T. Predicting transdermal fentanyl delivery using physics-based simulations for tailored therapy based on the age. Drug Deliv 2022; 29:950-969. [PMID: 35319323 PMCID: PMC8956318 DOI: 10.1080/10717544.2022.2050846] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022] Open
Abstract
Transdermal fentanyl patches are an effective alternative to the sustained release of oral morphine for chronic pain management. Due to the narrow therapeutic range of fentanyl, the concentration of fentanyl in the blood needs to be carefully monitored. Only then can effective pain relief be achieved while avoiding adverse effects such as respiratory depression. This study developed a physics-based digital twin of a patient by implementing drug uptake, pharmacokinetics, and pharmacodynamics models. The twin was employed to predict the in-silico effect of conventional fentanyl transdermal in a 20–80-year-old virtual patient. The results show that, with increasing age, the maximum transdermal fentanyl flux and maximum concentration of fentanyl in the blood decreased by 11.4% and 7.0%, respectively. However, the results also show that as the patient's age increases, the pain relief increases by 45.2%. Furthermore, the digital twin was used to propose a tailored therapy based on the patient's age. This predesigned therapy customized the duration of applying the commercialized fentanyl patches. According to this therapy, a 20-year-old patient needs to change the patch 2.1 times more frequently than conventional therapy, which leads to 30% more pain relief and 315% more time without pain. In addition, the digital twin was updated by the patient's pain intensity feedback. Such therapy increased the patient's breathing rate while providing effective pain relief, so a safer treatment. We quantified the added value of a patient's physics-based digital twin and sketched the future roadmap for implementing such twin-assisted treatment into the clinics.
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Affiliation(s)
- Flora Bahrami
- Empa, Swiss Federal Laboratories for Materials Science and Technology, Laboratory for Biomimetic Membranes and Textiles, St. Gallen, Switzerland.,ARTORG Center for Biomedical Engineering Research, University of Bern, Bern, Switzerland
| | - René Michel Rossi
- Empa, Swiss Federal Laboratories for Materials Science and Technology, Laboratory for Biomimetic Membranes and Textiles, St. Gallen, Switzerland
| | - Thijs Defraeye
- Empa, Swiss Federal Laboratories for Materials Science and Technology, Laboratory for Biomimetic Membranes and Textiles, St. Gallen, Switzerland
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14
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Paini A, Campia I, Cronin MTD, Asturiol D, Ceriani L, Exner TE, Gao W, Gomes C, Kruisselbrink J, Martens M, Meek MEB, Pamies D, Pletz J, Scholz S, Schüttler A, Spînu N, Villeneuve DL, Wittwehr C, Worth A, Luijten M. Towards a qAOP framework for predictive toxicology - Linking data to decisions. COMPUTATIONAL TOXICOLOGY (AMSTERDAM, NETHERLANDS) 2022; 21:100195. [PMID: 35211660 PMCID: PMC8850654 DOI: 10.1016/j.comtox.2021.100195] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/27/2021] [Revised: 07/23/2021] [Accepted: 10/09/2021] [Indexed: 12/22/2022]
Abstract
Chemical toxicity assessment depends on the quantification of kinetics and dynamics. Quantitative AOPs (qAOPs) are toxicodynamic models based on Adverse Outcome Pathways. Existing e-resources could form the basis of an e-infrastructure for qAOP modelling. Best practices for qAOP development, assessment and application are needed. Three qAOP case studies are presented to illustrate a modelling workflow.
The adverse outcome pathway (AOP) is a conceptual construct that facilitates organisation and interpretation of mechanistic data representing multiple biological levels and deriving from a range of methodological approaches including in silico, in vitro and in vivo assays. AOPs are playing an increasingly important role in the chemical safety assessment paradigm and quantification of AOPs is an important step towards a more reliable prediction of chemically induced adverse effects. Modelling methodologies require the identification, extraction and use of reliable data and information to support the inclusion of quantitative considerations in AOP development. An extensive and growing range of digital resources are available to support the modelling of quantitative AOPs, providing a wide range of information, but also requiring guidance for their practical application. A framework for qAOP development is proposed based on feedback from a group of experts and three qAOP case studies. The proposed framework provides a harmonised approach for both regulators and scientists working in this area.
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Affiliation(s)
- Alicia Paini
- European Commission, Joint Research Centre (JRC), Ispra, Italy
| | - Ivana Campia
- European Commission, Joint Research Centre (JRC), Ispra, Italy
| | | | - David Asturiol
- European Commission, Joint Research Centre (JRC), Ispra, Italy
| | | | - Thomas E Exner
- Edelweiss Connect GmbH, Technology Park Basel, Basel, Switzerland
| | - Wang Gao
- Institut National de l'Environnement Industriel et des Risques (INERIS), Verneuil-en-Halatte, France
| | | | | | | | | | - David Pamies
- Department of Physiology, Lausanne and Swiss Centre for Applied Human Toxicology (SCAHT), University of Lausanne, Lausanne, Switzerland
| | - Julia Pletz
- Liverpool John Moores University, Liverpool, United Kingdom
| | - Stefan Scholz
- Helmholtz Centre for Environmental Research GmbH - UFZ, Leipzig, Germany
| | - Andreas Schüttler
- Helmholtz Centre for Environmental Research GmbH - UFZ, Leipzig, Germany
| | - Nicoleta Spînu
- Liverpool John Moores University, Liverpool, United Kingdom
| | - Daniel L Villeneuve
- US Environmental Protection Agency, Great Lakes Toxicology and Ecology Division, Duluth, MN, USA
| | | | - Andrew Worth
- European Commission, Joint Research Centre (JRC), Ispra, Italy
| | - Mirjam Luijten
- National Institute for Public Health and the Environment (RIVM), Bilthoven, The Netherlands
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15
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Bartels M, van Osdol W, Le Merdy M, Chappelle A, Kuhl A, West R. In silico predictions of absorption of MDI substances after dermal or inhalation exposures to support a category based read-across assessment. Regul Toxicol Pharmacol 2022; 129:105117. [PMID: 35017021 DOI: 10.1016/j.yrtph.2022.105117] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2021] [Revised: 12/28/2021] [Accepted: 01/06/2022] [Indexed: 01/08/2023]
Abstract
Methylenediphenyl diisocyanate (MDI) substances used polyurethane production can range from their simplest monomeric forms (e.g., 4,4'-MDI) to mixtures of the monomers with various homologues, homopolymer, and prepolymer derivatives. The relative dermal or inhalation absorption of 39 constituents of these substances in human were predicted using the GastroPlus® program. Predicted dermal uptake and absorption of the three MDI monomers from an acetone vehicle was 84-86% and 1.4-1.5%, respectively, with lower uptake and absorption predicted for the higher MW analogs. Lower absorption was predicted from exposures in a more lipophilic vehicle (1-octanol). Modeled inhalation exposures afforded the highest pulmonary absorption for the MDI monomers (38-54%), with 3-27% for the MW range of 381-751, and <0.1% for the remaining, higher MW derivatives. Predicted oral absorption, representing mucociliary transport, ranged from 5 to 10% for the MDI monomers, 10-25% for constituents of MW 381-751, and ≤3% for constituents with MW > 900. These in silico evaluations should be useful in category-based, worst-case, Read-Across assessments for MDI monomers and modified MDI substances for potential systemic effects. Predictions of appreciable mucociliary transport may also be useful to address data gaps in oral toxicity testing for this category of compounds.
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Affiliation(s)
| | | | | | - Anne Chappelle
- International Isocyanate Institute, Mountain Lakes, NJ, USA
| | - Adam Kuhl
- Huntsman LLC, The Woodlands, Texas, USA
| | - Robert West
- International Isocyanate Institute, Mountain Lakes, NJ, USA
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16
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Alexander-White C, Bury D, Cronin M, Dent M, Hack E, Hewitt NJ, Kenna G, Naciff J, Ouedraogo G, Schepky A, Mahony C, Europe C. A 10-step framework for use of read-across (RAX) in next generation risk assessment (NGRA) for cosmetics safety assessment. Regul Toxicol Pharmacol 2022; 129:105094. [PMID: 34990780 DOI: 10.1016/j.yrtph.2021.105094] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2020] [Revised: 07/12/2021] [Accepted: 12/02/2021] [Indexed: 02/07/2023]
Abstract
This paper presents a 10-step read-across (RAX) framework for use in cases where a threshold of toxicological concern (TTC) approach to cosmetics safety assessment is not possible. RAX builds on established approaches that have existed for more than two decades using chemical properties and in silico toxicology predictions, by further substantiating hypotheses on toxicological similarity of substances, and integrating new approach methodologies (NAM) in the biological and kinetic domains. NAM include new types of data on biological observations from, for example, in vitro assays, toxicogenomics, metabolomics, receptor binding screens and uses physiologically-based kinetic (PBK) modelling to inform about systemic exposure. NAM data can help to substantiate a mode/mechanism of action (MoA), and if similar chemicals can be shown to work by a similar MoA, a next generation risk assessment (NGRA) may be performed with acceptable confidence for a data-poor target substance with no or inadequate safety data, based on RAX approaches using data-rich analogue(s), and taking account of potency or kinetic/dynamic differences.
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Affiliation(s)
- Camilla Alexander-White
- MKTox & Co Ltd, 36 Fairford Crescent, Downhead Park, Milton Keynes, Buckinghamshire, MK15 9AQ, UK.
| | - Dagmar Bury
- L'Oreal Research & Innovation, 9 Rue Pierre Dreyfus, 92110, Clichy, France
| | - Mark Cronin
- School of Pharmacy and Biomolecular Sciences, Liverpool John Moores University, Byrom Street, Liverpool L3 AF, UK
| | - Matthew Dent
- Unilever, Safety & Environmental Assurance Centre, Colworth House, Sharnbrook, Bedfordshire, MK44 1ET, UK
| | - Eric Hack
- ScitoVation, Research Triangle Park, Durham, NC, USA
| | - Nicola J Hewitt
- Cosmetics Europe, 40 Avenue Hermann-Debroux, 1160, Brussels, Belgium
| | - Gerry Kenna
- Cosmetics Europe, 40 Avenue Hermann-Debroux, 1160, Brussels, Belgium
| | - Jorge Naciff
- The Procter & Gamble Company, Cincinnati, OH, 45040, USA
| | - Gladys Ouedraogo
- L'Oreal Research & Innovation, 1 Avenue Eugène Schueller, Aulnay sous bois, France
| | | | | | - Cosmetics Europe
- Cosmetics Europe, 40 Avenue Hermann-Debroux, 1160, Brussels, Belgium.
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17
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Watanabe-Matsumoto S, Yoshida K, Meiseki Y, Ishida S, Hirose A, Yamada T. A physiologically based kinetic modeling of ethyl tert-butyl ether in humans–An illustrative application of quantitative structure-property relationship and Monte Carlo simulation. J Toxicol Sci 2022; 47:77-87. [DOI: 10.2131/jts.47.77] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
Affiliation(s)
- Saori Watanabe-Matsumoto
- Division of Risk Assessment, Center for Biological Safety Research, National Institute of Health Sciences
| | - Kikuo Yoshida
- Division of Risk Assessment, Center for Biological Safety Research, National Institute of Health Sciences
| | - Yuriko Meiseki
- Division of Risk Assessment, Center for Biological Safety Research, National Institute of Health Sciences
| | - Seiichi Ishida
- Division of Pharmacology, Center for Biological Safety Research, National Institute of Health Sciences
| | - Akihiko Hirose
- Division of Risk Assessment, Center for Biological Safety Research, National Institute of Health Sciences
| | - Takashi Yamada
- Division of Risk Assessment, Center for Biological Safety Research, National Institute of Health Sciences
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18
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Thompson CV, Firman JW, Goldsmith MR, Grulke CM, Tan YM, Paini A, Penson PE, Sayre RR, Webb S, Madden JC. A Systematic Review of Published Physiologically-based Kinetic Models and an Assessment of their Chemical Space Coverage. Altern Lab Anim 2021; 49:197-208. [PMID: 34836462 DOI: 10.1177/02611929211060264] [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] [Indexed: 11/15/2022]
Abstract
Across multiple sectors, including food, cosmetics and pharmaceutical industries, there is a need to predict the potential effects of xenobiotics. These effects are determined by the intrinsic ability of the substance, or its derivatives, to interact with the biological system, and its concentration-time profile at the target site. Physiologically-based kinetic (PBK) models can predict organ-level concentration-time profiles, however, the models are time and resource intensive to generate de novo. Read-across is an approach used to reduce or replace animal testing, wherein information from a data-rich chemical is used to make predictions for a data-poor chemical. The recent increase in published PBK models presents the opportunity to use a read-across approach for PBK modelling, that is, to use PBK model information from one chemical to inform the development or evaluation of a PBK model for a similar chemical. Essential to this process, is identifying the chemicals for which a PBK model already exists. Herein, the results of a systematic review of existing PBK models, compliant with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) format, are presented. Model information, including species, sex, life-stage, route of administration, software platform used and the availability of model equations, was captured for 7541 PBK models. Chemical information (identifiers and physico-chemical properties) has also been recorded for 1150 unique chemicals associated with these models. This PBK model data set has been made readily accessible, as a Microsoft Excel® spreadsheet, providing a valuable resource for those developing, using or evaluating PBK models in industry, academia and the regulatory sectors.
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Affiliation(s)
- Courtney V Thompson
- School of Pharmacy and Biomolecular Sciences, 4589Liverpool John Moores University, Liverpool, UK
| | - James W Firman
- School of Pharmacy and Biomolecular Sciences, 4589Liverpool John Moores University, Liverpool, UK
| | - Michael R Goldsmith
- Office of Research and Development, Center for Computational Toxicology and Exposure, Chemical Characterization and Exposure Division, 427887US Environmental Protection Agency, Research Triangle Park, NC, USA
| | - Christopher M Grulke
- Office of Research and Development, Center for Computational Toxicology and Exposure, Chemical Characterization and Exposure Division, 427887US Environmental Protection Agency, Research Triangle Park, NC, USA
| | - Yu-Mei Tan
- Office of Pesticide Programs, Health Effects Division, 138030US Environmental Protection Agency, Research Triangle Park, NC, USA
| | - Alicia Paini
- 99013European Commission Joint Research Centre (JRC), Ispra, Italy
| | - Peter E Penson
- School of Pharmacy and Biomolecular Sciences, 4589Liverpool John Moores University, Liverpool, UK
| | - Risa R Sayre
- Office of Research and Development, Center for Computational Toxicology and Exposure, Chemical Characterization and Exposure Division, 427887US Environmental Protection Agency, Research Triangle Park, NC, USA
| | - Steven Webb
- Syngenta, Product Safety, Early Stage Research, 101825Jealott's Hill International Research Centre, Bracknell, UK
| | - Judith C Madden
- School of Pharmacy and Biomolecular Sciences, 4589Liverpool John Moores University, Liverpool, UK
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19
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Lowe K, Dawson J, Phillips K, Minucci J, Wambaugh JF, Qian H, Ramanarayanan T, Egeghy P, Ingle B, Brunner R, Mendez E, Embry M, Tan YM. Incorporating human exposure information in a weight of evidence approach to inform design of repeated dose animal studies. Regul Toxicol Pharmacol 2021; 127:105073. [PMID: 34743952 DOI: 10.1016/j.yrtph.2021.105073] [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: 08/16/2021] [Revised: 10/23/2021] [Accepted: 10/27/2021] [Indexed: 10/20/2022]
Abstract
Human health risks from chronic exposures to environmental chemicals are typically estimated from potential human exposure estimates and dose-response data obtained from repeated-dose animal toxicity studies. Various criteria are available for selecting the top (highest) dose used in these animal studies. For example, toxicokinetic (TK) and toxicological data provided by shorter-term or dose range finding studies can be evaluated in a weight of evidence approach to provide insight into the dose range that would provide dose-response data that are relevant to human exposures. However, there are concerns that a top dose resulting from the consideration of TK data may be too low compared to other criteria, such as the limit dose or the maximum tolerated dose. In this paper, we address several concerns related to human exposures by discussing 1) the resources and methods available to predict human exposure levels and the associated uncertainty and variability, and 2) the margin between predicted human exposure levels and the dose levels used in repeated-dose animal studies. A series of case studies, ranging from data-rich to data-poor chemicals, are presented to demonstrate that expected human exposures to environmental chemicals are typically orders of magnitude lower than no-observed-adverse-effect levels/lowest-observed-adverse-effect levels (NOAELs/LOAELs) when available (used as conservative surrogates for top doses). The results of these case studies support that a top dose based, in part, on TK data is typically orders of magnitude higher than expected human exposure levels.
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Affiliation(s)
- Kelly Lowe
- U.S. Environmental Protection Agency, Office of Pesticide Programs, Washington, DC, USA
| | - Jeffrey Dawson
- U.S. Environmental Protection Agency, Office of Chemical Safety and Pollution Prevention, Washington, DC, USA
| | - Katherine Phillips
- U.S. Environmental Protection Agency, Office of Research & Development, Durham, NC, USA
| | - Jeffrey Minucci
- U.S. Environmental Protection Agency, Office of Research & Development, Durham, NC, USA
| | - John F Wambaugh
- U.S. Environmental Protection Agency, Office of Research & Development, Durham, NC, USA
| | - Hua Qian
- ExxonMobil Biomedical Sciences, Inc., Annandale, NJ, USA
| | | | - Peter Egeghy
- U.S. Environmental Protection Agency, Office of Research & Development, Durham, NC, USA
| | - Brandall Ingle
- U.S. Environmental Protection Agency, Office of Pesticide Program, Durham, NC, USA
| | - Rachel Brunner
- U.S. Environmental Protection Agency, Office of Pesticide Program, Durham, NC, USA
| | - Elizabeth Mendez
- U.S. Environmental Protection Agency, Office of Pesticide Programs, Washington, DC, USA
| | - Michelle Embry
- Health and Environmental Sciences Institute, Washington, DC, USA.
| | - Yu-Mei Tan
- U.S. Environmental Protection Agency, Office of Pesticide Program, Durham, NC, USA
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20
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Armitage JM, Hughes L, Sangion A, Arnot JA. Development and intercomparison of single and multicompartment physiologically-based toxicokinetic models: Implications for model selection and tiered modeling frameworks. ENVIRONMENT INTERNATIONAL 2021; 154:106557. [PMID: 33892222 DOI: 10.1016/j.envint.2021.106557] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/08/2020] [Revised: 02/05/2021] [Accepted: 04/02/2021] [Indexed: 05/21/2023]
Abstract
This study describes the development and intercomparison of generic physiologically-based toxicokinetic (PBTK) models for humans comprised of internally consistent one-compartment (1Co-) and multi-compartment (MCo-) implementations (G-PBTK). The G-PBTK models were parameterized for an adult male (70 kg) using common physiological parameters and in vitro biotransformation rate estimates and subsequently evaluated using independent concentration versus time data (n = 6) and total elimination half-lives (n = 15) for diverse organic chemicals. The model performance is acceptable considering the inherent uncertainty in the biotransformation rate data and the absence of model calibration. The G-PBTK model was then applied using hypothetical neutral organics, acidic ionizable organics and basic ionizable organics (IOCs) to identify combinations of partitioning properties and biotransformation rates leading to substantial discrepancies between 1Co- and MCo-PBTK calculations for whole body concentrations and half-lives. The 1Co- and MCo-PBTK model calculations for key toxicokinetic parameters are broadly consistent unless biotransformation is rapid (e.g., half-life less than five days). When half-lives are relatively short, discrepancies are greatest for the neutral organics and least for the acidic IOCs which follows from the estimated volumes of distribution (e.g., VDSS = 9.6-15.4 L/kg vs 0.3-1.6 L/kg for the neutral and acidic compounds respectively) and the related approach to internal chemical equilibrium. The model intercomparisons demonstrate that 1Co-PBTK models can be applied with confidence to many exposure scenarios, particularly those focused on chronic or repeat exposures and for prioritization and screening-level decision contexts. However, MCo-PBTK models may be necessary in certain contexts, particularly for intermittent, short-term and highly variable exposures. A key recommendation to guide model selection and the development of tiered PBTK modeling frameworks that emerges from this study is the need to harmonize models with respect to parameterization and process descriptions to the greatest extent possible when proceeding from the application of simpler to more complex modeling tools as part of chemical assessment activities.
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Affiliation(s)
- James M Armitage
- AES Armitage Environmental Sciences, Inc., Ottawa, Ontario K1L 8C3, Canada; Department of Physical and Environmental Sciences, University of Toronto Scarborough, Toronto, Ontario M1C 1A4, Canada.
| | - Lauren Hughes
- ARC Arnot Research and Consulting, Toronto, Ontario M4M 1W4, Canada
| | - Alessandro Sangion
- Department of Physical and Environmental Sciences, University of Toronto Scarborough, Toronto, Ontario M1C 1A4, Canada; ARC Arnot Research and Consulting, Toronto, Ontario M4M 1W4, Canada
| | - Jon A Arnot
- Department of Physical and Environmental Sciences, University of Toronto Scarborough, Toronto, Ontario M1C 1A4, Canada; ARC Arnot Research and Consulting, Toronto, Ontario M4M 1W4, Canada; Department of Pharmacology and Toxicology, University of Toronto, Toronto, Ontario M5S 1A8, Canada
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21
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Breen M, Ring CL, Kreutz A, Goldsmith MR, Wambaugh JF. High-throughput PBTK models for in vitro to in vivo extrapolation. Expert Opin Drug Metab Toxicol 2021; 17:903-921. [PMID: 34056988 DOI: 10.1080/17425255.2021.1935867] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
INTRODUCTION Toxicity data are unavailable for many thousands of chemicals in commerce and the environment. Therefore, risk assessors need to rapidly screen these chemicals for potential risk to public health. High-throughput screening (HTS) for in vitro bioactivity, when used with high-throughput toxicokinetic (HTTK) data and models, allows characterization of these thousands of chemicals. AREAS COVERED This review covers generic physiologically based toxicokinetic (PBTK) models and high-throughput PBTK modeling for in vitro-in vivo extrapolation (IVIVE) of HTS data. We focus on 'httk', a public, open-source set of computational modeling tools and in vitro toxicokinetic (TK) data. EXPERT OPINION HTTK benefits chemical risk assessors with its ability to support rapid chemical screening/prioritization, perform IVIVE, and provide provisional TK modeling for large numbers of chemicals using only limited chemical-specific data. Although generic TK model design can increase prediction uncertainty, these models provide offsetting benefits by increasing model implementation accuracy. Also, public distribution of the models and data enhances reproducibility. For the httk package, the modular and open-source design can enable the tool to be used and continuously improved by a broad user community in support of the critical need for high-throughput chemical prioritization and rapid dose estimation to facilitate rapid hazard assessments.
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Affiliation(s)
- Miyuki Breen
- Center for Computational Toxicology and Exposure, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, NC, USA
| | - Caroline L Ring
- Center for Computational Toxicology and Exposure, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, NC, USA
| | - Anna Kreutz
- Oak Ridge Institute for Science and Education (ORISE) fellow at the Center for Computational Toxicology and Exposure, Office of Research and Development, Research Triangle Park, NC, USA
| | - Michael-Rock Goldsmith
- Center for Computational Toxicology and Exposure, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, NC, USA
| | - John F Wambaugh
- Center for Computational Toxicology and Exposure, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, NC, USA
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22
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Assessment of the predictive capacity of a physiologically based kinetic model using a read-across approach. ACTA ACUST UNITED AC 2021; 18:100159. [PMID: 34027243 PMCID: PMC8130669 DOI: 10.1016/j.comtox.2021.100159] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2020] [Revised: 02/02/2021] [Accepted: 02/03/2021] [Indexed: 11/26/2022]
Abstract
Potential regulatory application of PBK modelling information to assist read-across. Presents workflow to read across PBK model information from data-rich to data-poor chemicals. Describes appropriate analogue selection based on a set of specific criteria. Uses estragole and safrole as source chemicals for a target chemical - methyleugenol. Example of PBK model validation where in vivo kinetic data are lacking.
With current progress in science, there is growing interest in developing and applying Physiologically Based Kinetic (PBK) models in chemical risk assessment, as knowledge of internal exposure to chemicals is critical to understanding potential effects in vivo. In particular, a new generation of PBK models is being developed in which the model parameters are derived from in silico and in vitro methods. To increase the acceptance and use of these “Next Generation PBK models”, there is a need to demonstrate their validity. However, this is challenging in the case of data-poor chemicals that are lacking in kinetic data and for which predictive capacity cannot, therefore, be assessed. The aim of this work is to lay down the fundamental steps in using a read across framework to inform modellers and risk assessors on how to develop, or evaluate, PBK models for chemicals without in vivo kinetic data. The application of a PBK model that takes into account the absorption, distribution, metabolism and excretion characteristics of the chemical reduces the uncertainties in the biokinetics and biotransformation of the chemical of interest. A strategic flow-charting application, proposed herein, allows users to identify the minimum information to perform a read-across from a data-rich chemical to its data-poor analogue(s). The workflow analysis is illustrated by means of a real case study using the alkenylbenzene class of chemicals, showing the reliability and potential of this approach. It was demonstrated that a consistent quantitative relationship between model simulations could be achieved using models for estragole and safrole (source chemicals) when applied to methyleugenol (target chemical). When the PBK model code for the source chemicals was adapted to utilise input values relevant to the target chemical, simulation was consistent between the models. The resulting PBK model for methyleugenol was further evaluated by comparing the results to an existing, published model for methyleugenol, providing further evidence that the approach was successful. This can be considered as a “read-across” approach, enabling a valid PBK model to be derived to aid the assessment of a data poor chemical.
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Paini A, Tan YM, Sachana M, Worth A. Gaining acceptance in next generation PBK modelling approaches for regulatory assessments - An OECD international effort. COMPUTATIONAL TOXICOLOGY (AMSTERDAM, NETHERLANDS) 2021; 18:100163. [PMID: 34027244 PMCID: PMC8130668 DOI: 10.1016/j.comtox.2021.100163] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/11/2020] [Revised: 02/25/2021] [Accepted: 03/01/2021] [Indexed: 10/25/2022]
Abstract
Physiologically Based Kinetic (PBK) models are valuable tools to help define safe external levels of chemicals based on internal doses at target organs in experimental animals, humans and organisms used in environmental risk assessment. As the toxicity testing paradigm shifts to alternative testing approaches, PBK model development has started to rely (mostly or entirely) on model parameters quantified using in vitro or in silico methods. Recently, the Organisation for Economic Cooperation and Development (OECD) published a guidance document (GD) describing a scientific workflow for characterising and validating PBK models developed using in vitro and in silico data. The GD provides an assessment framework for evaluating these models, with emphasis on the major uncertainties underlying model inputs and outputs. To help end-users submit or evaluate a PBK model for regulatory purposes, the GD also includes a template for documenting the model characteristics, and a checklist for evaluating the quality of a model. This commentary highlights the principles, criteria and tools laid out in the OECD PBK model GD, with the aim of facilitating the dialogue between model developers and risk assessors.
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Affiliation(s)
- Alicia Paini
- European Commission, Joint Research Centre (JRC), Ispra, Italy
| | - Yu-Mei Tan
- U.S. Environmental Protection Agency, Office of Pesticide Programs, Research Triangle Park, NC 27709, USA
| | - Magdalini Sachana
- Environment Health and Safety Division, Organisation for Economic Cooperation and Development (OECD), Paris, France
| | - Andrew Worth
- European Commission, Joint Research Centre (JRC), Ispra, Italy
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Chaturvedi S, Garg A. An insight of techniques for the assessment of permeation flux across the skin for optimization of topical and transdermal drug delivery systems. J Drug Deliv Sci Technol 2021. [DOI: 10.1016/j.jddst.2021.102355] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
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Madden JC, Enoch SJ, Paini A, Cronin MTD. A Review of In Silico Tools as Alternatives to Animal Testing: Principles, Resources and Applications. Altern Lab Anim 2020; 48:146-172. [PMID: 33119417 DOI: 10.1177/0261192920965977] [Citation(s) in RCA: 64] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
Across the spectrum of industrial sectors, including pharmaceuticals, chemicals, personal care products, food additives and their associated regulatory agencies, there is a need to develop robust and reliable methods to reduce or replace animal testing. It is generally recognised that no single alternative method will be able to provide a one-to-one replacement for assays based on more complex toxicological endpoints. Hence, information from a combination of techniques is required. A greater understanding of the time and concentration-dependent mechanisms, underlying the interactions between chemicals and biological systems, and the sequence of events that can lead to apical effects, will help to move forward the science of reducing and replacing animal experiments. In silico modelling, in vitro assays, high-throughput screening, organ-on-a-chip technology, omics and mathematical biology, can provide complementary information to develop a complete picture of the potential response of an organism to a chemical stressor. Adverse outcome pathways (AOPs) and systems biology frameworks enable relevant information from diverse sources to be logically integrated. While individual researchers do not need to be experts across all disciplines, it is useful to have a fundamental understanding of what other areas of science have to offer, and how knowledge can be integrated with other disciplines. The purpose of this review is to provide those who are unfamiliar with predictive in silico tools, with a fundamental understanding of the underlying theory. Current applications, software, barriers to acceptance, new developments and the use of integrated approaches are all discussed, with additional resources being signposted for each of the topics.
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Affiliation(s)
- Judith C Madden
- School of Pharmacy and Biomolecular Sciences, 4589Liverpool John Moores University, Liverpool, UK
| | - Steven J Enoch
- School of Pharmacy and Biomolecular Sciences, 4589Liverpool John Moores University, Liverpool, UK
| | - Alicia Paini
- 99013European Commission, Joint Research Centre (JRC), Ispra, Italy
| | - Mark T D Cronin
- School of Pharmacy and Biomolecular Sciences, 4589Liverpool John Moores University, Liverpool, UK
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Pletz J, Blakeman S, Paini A, Parissis N, Worth A, Andersson AM, Frederiksen H, Sakhi AK, Thomsen C, Bopp SK. Physiologically based kinetic (PBK) modelling and human biomonitoring data for mixture risk assessment. ENVIRONMENT INTERNATIONAL 2020; 143:105978. [PMID: 32763630 PMCID: PMC7684529 DOI: 10.1016/j.envint.2020.105978] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/20/2019] [Revised: 07/11/2020] [Accepted: 07/12/2020] [Indexed: 06/02/2023]
Abstract
Human biomonitoring (HBM) data can provide insight into co-exposure patterns resulting from exposure to multiple chemicals from various sources and over time. Therefore, such data are particularly valuable for assessing potential risks from combined exposure to multiple chemicals. One way to interpret HBM data is establishing safe levels in blood or urine, called Biomonitoring Equivalents (BE) or HBM health based guidance values (HBM-HBGV). These can be derived by converting established external reference values, such as tolerable daily intake (TDI) values. HBM-HBGV or BE values are so far agreed only for a very limited number of chemicals. These values can be established using physiologically based kinetic (PBK) modelling, usually requiring substance specific models and the collection of many input parameters which are often not available or difficult to find in the literature. The aim of this study was to investigate the suitability and limitations of generic PBK models in deriving BE values for several compounds with a view to facilitating the use of HBM data in the assessment of chemical mixtures at a screening level. The focus was on testing the methodology with two generic models, the IndusChemFate tool and High-Throughput Toxicokinetics package, for two different classes of compounds, phenols and phthalates. HBM data on Danish children and on Norwegian mothers and children were used to evaluate the quality of the predictions and to illustrate, by means of a case study, the overall approach of applying PBK models to chemical classes with HBM data in the context of chemical mixture risk assessment. Application of PBK models provides a better understanding and interpretation of HBM data. However, the study shows that establishing safety threshold levels in urine is a difficult and complex task. The approach might be more straightforward for more persistent chemicals that are analysed as parent compounds in blood but high uncertainties have to be considered around simulated metabolite concentrations in urine. Refining the models may reduce these uncertainties and improve predictions. Based on the experience gained with this study, the performance of the models for other chemicals could be investigated, to improve the accuracy of the simulations.
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Affiliation(s)
- Julia Pletz
- European Commission, Joint Research Centre (JRC), Ispra, Italy; School of Pharmacy and Biomolecular Sciences, Liverpool John Moores University, Byrom Street, Liverpool L3 3AF, UK(2)
| | - Samantha Blakeman
- European Commission, Joint Research Centre (JRC), Ispra, Italy; Oceansea Conservación del Medio Ambiente, Cádiz, Spain(2)
| | - Alicia Paini
- European Commission, Joint Research Centre (JRC), Ispra, Italy
| | | | - Andrew Worth
- European Commission, Joint Research Centre (JRC), Ispra, Italy
| | - Anna-Maria Andersson
- Department of Growth and Reproduction, Rigshospitalet, University of Copenhagen, Copenhagen 2100, Denmark
| | - Hanne Frederiksen
- Department of Growth and Reproduction, Rigshospitalet, University of Copenhagen, Copenhagen 2100, Denmark
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Nogara PA, Orian L, Rocha JBT. The Se …S/N interactions as a possible mechanism of δ-aminolevulinic acid dehydratase enzyme inhibition by organoselenium compounds: A computational study. ACTA ACUST UNITED AC 2020; 15:100127. [PMID: 32572387 PMCID: PMC7280828 DOI: 10.1016/j.comtox.2020.100127] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2020] [Revised: 05/29/2020] [Accepted: 06/03/2020] [Indexed: 01/26/2023]
Abstract
DPDS and PSA interacts with cysteine residues from AlaD active site. The Se…S interactions could be involved in the δ-AlaD inhibition. δ-AlaD from Cucumis sativus does not present cysteine residues in the active site. Se…N interactions could be involved in the organoselenium action.
Organoselenium compounds present many pharmacological properties and are promising drugs. However, toxicological effects associated with inhibition of thiol-containing enzymes, such as the δ-aminolevulinic acid dehydratase (δ-AlaD), have been described. The molecular mechanism(s) by which they inhibit thiol-containing enzymes at the atomic level, is still not well known. The use of computational methods to understand the physical–chemical properties and biological activity of chemicals is essential to the rational design of new drugs. In this work, we propose an in silico study to understand the δ-AlaD inhibition mechanism by diphenyl diselenide (DPDS) and its putative metabolite, phenylseleninic acid (PSA), using δ-AlaD enzymes from Homo sapiens (Hsδ-AlaD), Drosophila melanogaster (Dmδ-AlaD) and Cucumis sativus (Csδ-AlaD). Protein modeling homology, molecular docking, and DFT calculations are combined in this study. According to the molecular docking, DPDS and PSA might bind in the Hsδ-AlaD and Dmδ-AlaD active sites interacting with the cysteine residues by Se…S interactions. On the other hand, the DPDS does not access the active site of the Csδ-AlaD (a non-thiol protein), while the PSA interacts with the amino acids residues from the active site, such as the Lys291. These interactions might lead to the formation of a covalent bond, and consequently, to the enzyme inhibition. In fact, DFT calculations (mPW1PW91/def2TZVP) demonstrated that the selenylamide bond formation is energetically favored. The in silico data showed here are in accordance with previous experimental studies, and help us to understand the reactivity and biological activity of organoselenium compounds.
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Affiliation(s)
- Pablo Andrei Nogara
- Departamento de Bioquímica e Biologia Molecular, Universidade Federal de Santa Maria (UFSM), Santa Maria 97105-900, RS, Brazil
| | - Laura Orian
- Dipartimento di Scienze Chimiche, Università degli Studi di Padova, Via Marzolo 1, 35131 Padova, Italy
| | - João Batista Teixeira Rocha
- Departamento de Bioquímica e Biologia Molecular, Universidade Federal de Santa Maria (UFSM), Santa Maria 97105-900, RS, Brazil
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Tebby C, van der Voet H, de Sousa G, Rorije E, Kumar V, de Boer W, Kruisselbrink JW, Bois FY, Faniband M, Moretto A, Brochot C. A generic PBTK model implemented in the MCRA platform: Predictive performance and uses in risk assessment of chemicals. Food Chem Toxicol 2020; 142:111440. [PMID: 32473292 DOI: 10.1016/j.fct.2020.111440] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2020] [Revised: 05/07/2020] [Accepted: 05/15/2020] [Indexed: 12/13/2022]
Abstract
Physiologically-based toxicokinetic (PBTK) models are important tools for in vitro to in vivo or inter-species extrapolations in health risk assessment of foodborne and non-foodborne chemicals. Here we present a generic PBTK model implemented in the EuroMix toolbox, MCRA 9 and predict internal kinetics of nine chemicals (three endocrine disrupters, three liver steatosis inducers, and three developmental toxicants), in data-rich and data-poor conditions, when increasingly complex levels of parametrization are applied. At the first stage, only QSAR models were used to determine substance-specific parameters, then some parameter values were refined by estimates from substance-specific or high-throughput in vitro experiments. At the last stage, elimination or absorption parameters were calibrated based on available in vivo kinetic data. The results illustrate that parametrization plays a capital role in the output of the PBTK model, as it can change how chemicals are prioritized based on internal concentration factors. In data-poor situations, estimates can be far from observed values. In many cases of chronic exposure, the PBTK model can be summarized by an external to internal dose factor, and interspecies concentration factors can be used to perform interspecies extrapolation. We finally discuss the implementation and use of the model in the MCRA risk assessment platform.
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Affiliation(s)
- Cleo Tebby
- INERIS, Unit Models for Ecotoxicology and Toxicology (METO), Verneuil-en-Halatte, France.
| | | | | | - Emiel Rorije
- RIVM, Centre for Safety of Substances and Products, Department for Consumers and Product Safety, P.O. Box 1, 3720, BA Bilthoven, Netherlands
| | - Vikas Kumar
- URV, Universitat Rovira i Virgili, C/ Països Catalans, nº 26, 43007, Tarragona (Tarragona- Catalonia), Spain
| | - Waldo de Boer
- Wageningen University & Research, Biometris, Wageningen, Netherlands
| | | | - Frédéric Y Bois
- INERIS, Unit Models for Ecotoxicology and Toxicology (METO), Verneuil-en-Halatte, France
| | - Moosa Faniband
- Division of Occupational and Environmental Medicine, Faculty of Medicine, Lund University, Sweden
| | - Angelo Moretto
- Department of Biomedical and Clinical Sciences, Università degli Studi di Milano, Italy
| | - Céline Brochot
- INERIS, Unit Models for Ecotoxicology and Toxicology (METO), Verneuil-en-Halatte, France
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Ball N, Madden J, Paini A, Mathea M, Palmer AD, Sperber S, Hartung T, van Ravenzwaay B. Key read across framework components and biology based improvements. MUTATION RESEARCH-GENETIC TOXICOLOGY AND ENVIRONMENTAL MUTAGENESIS 2020; 853:503172. [DOI: 10.1016/j.mrgentox.2020.503172] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/20/2020] [Revised: 03/09/2020] [Accepted: 03/11/2020] [Indexed: 12/18/2022]
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Lautz L, Dorne J, Oldenkamp R, Hendriks A, Ragas A. Generic physiologically based kinetic modelling for farm animals: Part I. Data collection of physiological parameters in swine, cattle and sheep. Toxicol Lett 2020; 319:95-101. [DOI: 10.1016/j.toxlet.2019.10.021] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2019] [Revised: 10/09/2019] [Accepted: 10/22/2019] [Indexed: 11/30/2022]
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Le Feunteun S, Mackie AR, Dupont D. In silico trials of food digestion and absorption: how far are we? Curr Opin Food Sci 2020. [DOI: 10.1016/j.cofs.2020.04.006] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
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Pawar G, Madden JC, Ebbrell D, Firman JW, Cronin MTD. In Silico Toxicology Data Resources to Support Read-Across and (Q)SAR. Front Pharmacol 2019; 10:561. [PMID: 31244651 PMCID: PMC6580867 DOI: 10.3389/fphar.2019.00561] [Citation(s) in RCA: 43] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2019] [Accepted: 05/03/2019] [Indexed: 12/14/2022] Open
Abstract
A plethora of databases exist online that can assist in in silico chemical or drug safety assessment. However, a systematic review and grouping of databases, based on purpose and information content, consolidated in a single source, has been lacking. To resolve this issue, this review provides a comprehensive listing of the key in silico data resources relevant to: chemical identity and properties, drug action, toxicology (including nano-material toxicity), exposure, omics, pathways, Absorption, Distribution, Metabolism and Elimination (ADME) properties, clinical trials, pharmacovigilance, patents-related databases, biological (genes, enzymes, proteins, other macromolecules etc.) databases, protein-protein interactions (PPIs), environmental exposure related, and finally databases relating to animal alternatives in support of 3Rs policies. More than nine hundred databases were identified and reviewed against criteria relating to accessibility, data coverage, interoperability or application programming interface (API), appropriate identifiers, types of in vitro, in vivo,-clinical or other data recorded and suitability for modelling, read-across, or similarity searching. This review also specifically addresses the need for solutions for mapping and integration of databases into a common platform for better translatability of preclinical data to clinical data.
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Affiliation(s)
| | | | | | | | - Mark T. D. Cronin
- School of Pharmacy and Biomolecular Sciences, Liverpool John Moores University, Liverpool, United Kingdom
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