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Colombo E, Luca Viganò E, Raitano G, Lombardo A, Manganaro A, Sommovigo A, Benfenati E. The VERA software: Implementation of the acute fish toxicity endpoint and its application to pharmaceutical compounds. Chemosphere 2024; 358:142232. [PMID: 38714244 DOI: 10.1016/j.chemosphere.2024.142232] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/25/2024] [Revised: 04/10/2024] [Accepted: 05/02/2024] [Indexed: 05/09/2024]
Abstract
The Virtual Extensive Read-Across software (VERA) is a new tool for read-across using a global similarity score, molecular groups, and structural alerts to find clusters of similar substances; these clusters are then used to identify suitable similar substances and make an assessment for the target substance. A beta version of VERA GUI is free and available at vegahub.eu; the source code of the VERA algorithm is available on GitHub. In the past we described its use to assess carcinogenicity, a classification endpoint. The aim here is to extend the automated read-across approach to assess continuous endpoints as well. We addressed acute fish toxicity. VERA evaluation on the acute fish toxicity endpoint was done on a dataset containing general substances (pesticides, industrial products, biocides, etc.), obtaining an overall R2 of 0.68. We employed the VERA algorithm also on active pharmaceutical ingredients (APIs). We included a portion of the APIs in the training dataset to predict APIs, successfully achieving an overall R2 of 0.63. VERA evaluates the assessment's reliability, and we reached an R2 of 0.78 and Root Mean Square Error (RMSE) of 0.44 for predictions with high reliability.
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Affiliation(s)
- Erika Colombo
- Laboratory of Environmental Toxicology and Chemistry, Department of Environmental Health Sciences, Istituto di Ricerche Farmacologiche Mario Negri IRCSS, Milano, Italy
| | - Edoardo Luca Viganò
- Laboratory of Environmental Toxicology and Chemistry, Department of Environmental Health Sciences, Istituto di Ricerche Farmacologiche Mario Negri IRCSS, Milano, Italy
| | - Giuseppa Raitano
- Laboratory of Environmental Toxicology and Chemistry, Department of Environmental Health Sciences, Istituto di Ricerche Farmacologiche Mario Negri IRCSS, Milano, Italy
| | - Anna Lombardo
- Laboratory of Environmental Toxicology and Chemistry, Department of Environmental Health Sciences, Istituto di Ricerche Farmacologiche Mario Negri IRCSS, Milano, Italy
| | - Alberto Manganaro
- Laboratory of Environmental Toxicology and Chemistry, Department of Environmental Health Sciences, Istituto di Ricerche Farmacologiche Mario Negri IRCSS, Milano, Italy
| | | | - Emilio Benfenati
- Laboratory of Environmental Toxicology and Chemistry, Department of Environmental Health Sciences, Istituto di Ricerche Farmacologiche Mario Negri IRCSS, Milano, Italy.
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2
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Sündermann J, Bitsch A, Kellner R, Doll T. Is read-across for chemicals comparable to medical device equivalence and where to use it for conformity assessment? Regul Toxicol Pharmacol 2024; 149:105622. [PMID: 38588771 DOI: 10.1016/j.yrtph.2024.105622] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2024] [Revised: 03/07/2024] [Accepted: 04/05/2024] [Indexed: 04/10/2024]
Abstract
Novel medical devices must conform to medical device regulation (MDR) for European market entry. Likewise, chemicals must comply with the Registration, Evaluation, Authorization and Restriction of Chemicals (REACh) regulation. Both pose regulatory challenges for manufacturers, but concordantly provide an approach for transferring data from an already registered device or compound to the one undergoing accreditation. This is called equivalence for medical devices and read-across for chemicals. Although read-across is not explicitly prohibited in the process of medical device accreditation, it is usually not performed due to a lack of guidance and acceptance criteria from the authorities. Nonetheless, a scientifically justified read-across of material-based endpoints, as well as toxicological assessment of chemical aspects, such as extractables and leachables, can prevent failure of MDR device equivalence if data is lacking. Further, read-across, if applied correctly can facilitate the standard MDR conformity assessment. The need for read-across within medical device registration should let authorities to reconsider device accreditation and the formulation of respective guidance documents. Acceptance criteria like in the European Chemicals Agency (ECHA) read-across assessment framework (RAAF) are needed. This can reduce the impact of the MDR and help with keeping high European innovation device rate, beneficial for medical device patients.
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Affiliation(s)
- Jan Sündermann
- Fraunhofer Institute for Toxicology and Experimental Medicine ITEM, Nikolai-Fuchs-Str. 1, 30625, Hannover, Germany.
| | - Annette Bitsch
- Fraunhofer Institute for Toxicology and Experimental Medicine ITEM, Nikolai-Fuchs-Str. 1, 30625, Hannover, Germany
| | - Rupert Kellner
- Fraunhofer Institute for Toxicology and Experimental Medicine ITEM, Nikolai-Fuchs-Str. 1, 30625, Hannover, Germany
| | - Theodor Doll
- Department of Otolaryngology and Cluster of Excellence "Hearing4all", Hannover Medical School, Carl-Neuberg-Straße 1, 30625, Hannover, Germany
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3
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Li F, Wang P, Fan T, Zhang N, Zhao L, Zhong R, Sun G. Prioritization of the ecotoxicological hazard of PAHs towards aquatic species spanning three trophic levels using 2D-QSTR, read-across and machine learning-driven modelling approaches. J Hazard Mater 2024; 465:133410. [PMID: 38185092 DOI: 10.1016/j.jhazmat.2023.133410] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/19/2023] [Revised: 12/24/2023] [Accepted: 12/29/2023] [Indexed: 01/09/2024]
Abstract
Polycyclic aromatic hydrocarbons (PAHs) represent a common group of environmental pollutants that endanger various aquatic organisms via various pathways. To better prioritize the ecotoxicological hazard of PAHs to aquatic environment, we used 2D descriptors-based quantitative structure-toxicity relationship (QSTR) to assess the toxicity of PAHs toward six aquatic model organisms spanning three trophic levels. According to strict OECD guideline, six easily interpretable, transferable and reproducible 2D-QSTR models were constructed with high robustness and reliability. A mechanistic interpretation unveiled the key structural factors primarily responsible for controlling the aquatic ecotoxicity of PAHs. Furthermore, quantitative read-across and different machine learning approaches were employed to validate and optimize the modelling approach. Importantly, the optimum QSTR models were further applied for predicting the ecotoxicity of hundreds of untested/unknown PAHs gathered from Pesticide Properties Database (PPDB). Especially, we provided a priority list in terms of the toxicity of unknown PAHs to six aquatic species, along with the corresponding mechanistic interpretation. In summary, the models can serve as valuable tools for aquatic risk assessment and prioritization of untested or completely new PAHs chemicals, providing essential guidance for formulating regulatory policies.
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Affiliation(s)
- Feifan Li
- Beijing Key Laboratory of Environmental and Viral Oncology, Faculty of Environment and Life, Beijing University of Technology, Beijing 100124, China; Solid Waste and Chemicals Management Center, Ministry of Ecology and Environment, Beijing 100029, China
| | - Peng Wang
- Department of Neurosurgery, the First Medical Center of Chinese PLA General Hospital, Beijing 100853, China
| | - Tengjiao Fan
- Beijing Key Laboratory of Environmental and Viral Oncology, Faculty of Environment and Life, Beijing University of Technology, Beijing 100124, China; Department of Medical Technology, Beijing Pharmaceutical University of Staff and Workers, Beijing 100079, China
| | - Na Zhang
- Beijing Key Laboratory of Environmental and Viral Oncology, Faculty of Environment and Life, Beijing University of Technology, Beijing 100124, China
| | - Lijiao Zhao
- Beijing Key Laboratory of Environmental and Viral Oncology, Faculty of Environment and Life, Beijing University of Technology, Beijing 100124, China
| | - Rugang Zhong
- Beijing Key Laboratory of Environmental and Viral Oncology, Faculty of Environment and Life, Beijing University of Technology, Beijing 100124, China
| | - Guohui Sun
- Beijing Key Laboratory of Environmental and Viral Oncology, Faculty of Environment and Life, Beijing University of Technology, Beijing 100124, China.
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4
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Kamp H, Kocabas NA, Faulhammer F, Synhaeve N, Rushton E, Flick B, Giri V, Sperber S, Higgins LG, Penman MG, van Ravenzwaay B, Rooseboom M. Utility of in vivo metabolomics to support read-across for UVCB substances under REACH. Arch Toxicol 2024; 98:755-768. [PMID: 38265474 PMCID: PMC10861390 DOI: 10.1007/s00204-023-03638-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2023] [Accepted: 11/13/2023] [Indexed: 01/25/2024]
Abstract
Structure-based grouping of chemicals for targeted testing and read-across is an efficient way to reduce resources and animal usage. For substances of unknown or variable composition, complex reaction products, or biological materials (UVCBs), structure-based grouping is virtually impossible. Biology-based approaches such as metabolomics could provide a solution. Here, 15 steam-cracked distillates, registered in the EU through the Lower Olefins Aromatics Reach Consortium (LOA), as well as six of the major substance constituents, were tested in a 14-day rat oral gavage study, in line with the fundamental elements of the OECD 407 guideline, in combination with plasma metabolomics. Beyond signs of clinical toxicity, reduced body weight (gain), and food consumption, pathological investigations demonstrated the liver, thyroid, kidneys (males only), and hematological system to be the target organs. These targets were confirmed by metabolome pattern recognition, with no additional targets being identified. While classical toxicological parameters did not allow for a clear distinction between the substances, univariate and multivariate statistical analysis of the respective metabolomes allowed for the identification of several subclusters of biologically most similar substances. These groups were partly associated with the dominant (> 50%) constituents of these UVCBs, i.e., indene and dicyclopentadiene. Despite minor differences in clustering results based on the two statistical analyses, a proposal can be made for the grouping of these UVCBs. Both analyses correctly clustered the chemically most similar compounds, increasing the confidence that this biological approach may provide a solution for the grouping of UVCBs.
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Affiliation(s)
- H Kamp
- BASF Metabolome Solutions GmbH, Berlin, Germany
| | | | | | | | - E Rushton
- LyondellBasell, Rotterdam, The Netherlands
| | - B Flick
- BASF SE, Ludwigshafen, Germany
- NUVISAN ICB GmbH, Toxicology, 13353, Berlin, Germany
| | - V Giri
- BASF SE, Ludwigshafen, Germany
| | | | - L G Higgins
- LOA C/O Penman Consulting Ltd, Brussels, Belgium
| | - M G Penman
- LOA C/O Penman Consulting Ltd, Brussels, Belgium
| | | | - M Rooseboom
- Shell Global Solution International B.V, The Hague, The Netherlands
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5
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Kumar A, Kumar V, Ojha PK, Roy K. Chronic aquatic toxicity assessment of diverse chemicals on Daphnia magna using QSAR and chemical read-across. Regul Toxicol Pharmacol 2024; 148:105572. [PMID: 38325631 DOI: 10.1016/j.yrtph.2024.105572] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2023] [Revised: 01/06/2024] [Accepted: 01/26/2024] [Indexed: 02/09/2024]
Abstract
We have modeled here chronic Daphnia toxicity taking pNOEC (negative logarithm of no observed effect concentration in mM) and pEC50 (negative logarithm of half-maximal effective concentration in mM) as endpoints using QSAR and chemical read-across approaches. The QSAR models were developed by strictly obeying the OECD guidelines and were found to be reliable, predictive, accurate, and robust. From the selected features in the developed models, we have found that an increase in lipophilicity and saturation, the presence of electrophilic or electronegative or heavy atoms, the presence of sulphur, amine, and their related functionality, an increase in mean atomic polarizability, and higher number of (thio-) carbamates (aromatic) groups are responsible for chronic toxicity. Therefore, this information might be useful for the development of environmentally friendly and safer chemicals and data-gap filling as well as reducing the use of identified toxic chemicals which have chronic toxic effects on aquatic ecosystems. Approved classes of drugs from DrugBank databases and diverse groups of chemicals from the Chemical and Product Categories (CPDat) database were also assessed through the developed models.
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Affiliation(s)
- Ankur Kumar
- Drug Discovery and Development (DDD) Laboratory, Department of Pharmaceutical Technology, Jadavpur University, Kolkata, 700032, India
| | - Vinay Kumar
- Drug Theoretics and Cheminformatics (DTC) Laboratory, Department of Pharmaceutical Technology, Jadavpur University, Kolkata, 700032, India
| | - Probir Kumar Ojha
- Drug Discovery and Development (DDD) Laboratory, Department of Pharmaceutical Technology, Jadavpur University, Kolkata, 700032, India
| | - Kunal Roy
- Drug Theoretics and Cheminformatics (DTC) Laboratory, Department of Pharmaceutical Technology, Jadavpur University, Kolkata, 700032, India.
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6
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Asai T, Umeshita K, Sakurai M, Sakane S. Development of an in silico evaluation system that quantitatively predicts skin sensitization using OECD Guideline No. 497 ITSv2 defined approach for skin sensitization classification. Food Chem Toxicol 2024; 185:114444. [PMID: 38253282 DOI: 10.1016/j.fct.2024.114444] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2023] [Revised: 12/06/2023] [Accepted: 01/08/2024] [Indexed: 01/24/2024]
Abstract
The Integrated Testing Strategy version 2 (ITSv2) Defined Approach, which is a reliable skin sensitization hazard and multi-step risk assessment method, does not support quantitative risk assessment such as local lymph node assay EC3 values. In this study, we developed a high-performance in silico evaluation system that quantitatively predicts the EC3 values of chemical substances by combining the ITSv2 Defined Approach for hazard identification (ITSv2 HI) with machine learning models. This system uses in chemico/in vitro test data, molecular descriptors, and distance information based on read-across concepts as explanatory variables. The system achieves an R2 value of 0.617 on external-validation data. Substances misclassified in ITSv2 HI are considered to have properties that do not match the correspondence between tests expressing the adverse outcome pathway assumed in the ITSv2 Defined Approach and skin sensitization. Therefore, ITSv2 HI is assumed to be correct within the applicability domains of this system. When using only substances within the applicability domains to reconstruct CatBoost models, the R2 value reached 0.824 on the external-validation data, representing an improvement in system performance. The results demonstrate the utility of explanatory variables that reflect the read-across concept and the advantages of integrating multiple prediction methods.
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Affiliation(s)
- Takaho Asai
- Safety & Analysis, R&D Support, Sunstar Inc., 3-1 Asahi-machi, Takatsuki, Osaka, 569-1195, Japan.
| | - Kazuhiko Umeshita
- Safety & Analysis, R&D Support, Sunstar Inc., 3-1 Asahi-machi, Takatsuki, Osaka, 569-1195, Japan
| | - Michiko Sakurai
- Safety & Analysis, R&D Support, Sunstar Inc., 3-1 Asahi-machi, Takatsuki, Osaka, 569-1195, Japan
| | - Shinji Sakane
- Safety & Analysis, R&D Support, Sunstar Inc., 3-1 Asahi-machi, Takatsuki, Osaka, 569-1195, Japan
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7
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Fine J, Allain L, Schlingemann J, Ponting DJ, Thomas R, Johnson GE. Nitrosamine acceptable intakes should consider variation in molecular weight: The implication of stoichiometric DNA damage. Regul Toxicol Pharmacol 2023; 145:105505. [PMID: 37805106 DOI: 10.1016/j.yrtph.2023.105505] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2023] [Revised: 09/15/2023] [Accepted: 10/02/2023] [Indexed: 10/09/2023]
Abstract
N-nitrosamines (NAs) are a class of compounds of which many, especially of the small dialkyl type, are indirect acting DNA alkylating mutagens. Their presence in pharmaceuticals is subject to very strict acceptable daily intake (AI) limits, which are traditionally expressed on a mass basis. Here we demonstrate that AIs that are not experimentally derived for a specific compound, but via statistical extrapolation or read across to a suitable analog, should be expressed on a molar scale or corrected for the target substance's molecular weight. This would account for the mechanistic aspect that each nitroso group can, at maximum, account for a single DNA mutation and the number of molecules per mass unit is proportional to the molecular weight (MW). In this regard we have re-calculated the EMA 18 ng/day regulatory default AI for unknown nitrosamines on a molar scale and propose a revised default AI of 163 pmol/day. In addition, we provide MW-corrected AIs for those nitrosamine drug substance related impurities (NDSRIs) for which EMA has pre-assigned AIs by read-across. Regulatory acceptance of this fundamental scientific tenet would allow one to derive nitrosamine limits for NDSRIs that both meet the health-protection goals and are technically feasible.
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Affiliation(s)
| | | | | | - David J Ponting
- Lhasa Limited, Granary Wharf House, 2 Canal Wharf, Leeds, UK
| | - Robert Thomas
- Lhasa Limited, Granary Wharf House, 2 Canal Wharf, Leeds, UK
| | - George E Johnson
- Institute of Life Science, Swansea University Medical School, Swansea, UK
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8
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Masuda-Herrera M, Rosen HT, Burild A, Broschard T, Bell T, Graham J, Griffin T, Hillegass J, Leavitt P, Huta B, Parris P, Bruen U, Cruz M, Bercu J. Harmonisation of read-across methodology for drug substance extractables and leachables (E&Ls). Regul Toxicol Pharmacol 2023; 145:105494. [PMID: 37748702 DOI: 10.1016/j.yrtph.2023.105494] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2023] [Revised: 08/25/2023] [Accepted: 09/11/2023] [Indexed: 09/27/2023]
Abstract
Health-based exposure limits (HBELs) are derived for leachables from polymeric components that interact with the drug substance which exceed a safety concern threshold (SCT). However, given the nature of leachables, there is not always chemical-specific toxicology data. Read-across methodology specific to extractables and leachables (E&Ls) was developed based on survey data collected from 11 pharmaceutical companies and methodology used in other industries. One additional challenge for E&L read-across is most toxicology data is from the oral route of administration, whereas the parenteral route is very common for the leachable HBEL derivation. A conservative framework was developed to estimate oral bioavailability and the corresponding oral to parenteral extrapolation factor using physical chemical data. When this conservative framework was tested against 73 compounds with oral bioavailability data, it was found that the predicted bioavailability based on physico-chemical properties was conservatively greater than or equal to the experimental bioavailability 79% of the time. In conclusion, an E&L read-across methodology has been developed to provide a consistent, health protective framework for deriving HBELs when toxicology data is limited.
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Affiliation(s)
- Melisa Masuda-Herrera
- Gilead Sciences, Inc., Nonclinical Safety and Pathobiology (NSP), Foster City, CA, 94404, USA.
| | - Hannah T Rosen
- University of California Berkeley, Nutritional Sciences and Toxicology, Berkeley, CA, 94720, USA
| | - Anders Burild
- Novo Nordisk A/S, Safety Sciences, Imaging and Data Management, Novo Nordisk Park, 2760, Måløv, Denmark
| | - Thomas Broschard
- Merck Healthcare KGaA, Frankfurter Str. 250, 64293, Darmstadt, Germany
| | - Tyler Bell
- Takeda Pharmaceutical Co., Rochester, NY, USA
| | - Jessica Graham
- Genentech, Inc., 1 DNA Way, South San Francisco, CA, 94080, USA
| | - Troy Griffin
- Teva Branded Pharmaceutical Products R&D, Inc., West Chester, PA, 19380, USA
| | - Jedd Hillegass
- Bristol Myers Squibb, 1 Squibb Drive, New Brunswick, NJ, 08903, USA
| | - Penny Leavitt
- Bristol Myers Squibb, 1 Squibb Drive, New Brunswick, NJ, 08903, USA
| | - Brian Huta
- Pfizer Worldwide Research and Development, Sandwich, UK
| | | | - Uma Bruen
- Organon, LLC., Jersey City, NJ, 07302, USA
| | - Maureen Cruz
- Faegre Drinker Biddle & Reath LLP, Washington, DC, USA
| | - Joel Bercu
- Gilead Sciences, Inc., Nonclinical Safety and Pathobiology (NSP), Foster City, CA, 94404, USA
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9
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Enoch SJ, Hasarova Z, Cronin MTD, Bridgwood K, Rao S, Kluxen FM, Frericks M. Metabolism-based category formation for the prioritisation of genotoxicity hazard assessment for plant protection product residues (part 3): Strobilurins. Regul Toxicol Pharmacol 2023; 144:105484. [PMID: 37633329 DOI: 10.1016/j.yrtph.2023.105484] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2023] [Revised: 08/15/2023] [Accepted: 08/23/2023] [Indexed: 08/28/2023]
Abstract
In dietary risk assessment of plant protection products, residues of active ingredients and their metabolites need to be evaluated for their genotoxic potential. The European Food Safety Authority recommend a tiered approach focussing assessment and testing on classes of similar chemicals. To characterise similarity, in terms of metabolism, a metabolic similarity profiling scheme has been developed from an analysis of 46 chemicals of strobilurin fungicides and their metabolites for which either Ames, chromosomal aberration or micronucleus test results are publicly available. This profiling scheme consists of a set of ten sub-structures, each linked to a key metabolic transformation present in the strobilurin metabolic space. This metabolic similarity profiling scheme was combined with covalent chemistry profiling and physico-chemistry properties to develop chemical categories suitable for chemical prioritisation via read-across. The method is a robust and reproducible approach to such read-across predictions, with the potential to reduce unnecessary testing. The key challenge in the approach was identified as being the need for metabolism data and individual groups of plant protection products as the basis for the development of such profiling schemes.
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Affiliation(s)
- S J Enoch
- School of Pharmacy and Biomolecular Sciences, Liverpool John Moores University, Liverpool, England, UK.
| | - Z Hasarova
- School of Pharmacy and Biomolecular Sciences, Liverpool John Moores University, Liverpool, England, UK
| | - M T D Cronin
- School of Pharmacy and Biomolecular Sciences, Liverpool John Moores University, Liverpool, England, UK
| | | | - S Rao
- Gowan Company, Yuma, AZ, USA
| | - F M Kluxen
- ADAMA Deutschland GmbH, Cologne, Germany
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10
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Gautier F, Assaf Vandecasteele H, Tourneix F, van Vliet E, Alépée N, Bury D. Skin sensitisation prediction using read-across, an illustrative next generation risk assessment (NGRA) case study for vanillin. Regul Toxicol Pharmacol 2023; 143:105458. [PMID: 37453556 DOI: 10.1016/j.yrtph.2023.105458] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2023] [Revised: 07/11/2023] [Accepted: 07/13/2023] [Indexed: 07/18/2023]
Abstract
Skin sensitisation is a key adverse human health effect to be addressed in the safety assessment of cosmetic ingredients. Regulatory demands and scientific progress have led to the development of a Next Generation Risk Assessment (NGRA) framework, relying on the use of New Approach Methodologies (NAM) Defined Approaches (DA) and read-across instead of generating animal data. This case study illustrates the application of read-across for the prediction of the skin sensitisation potential of vanillin at the hypothetical use concentration of 0.5% in a shower gel and face cream. A three-step process was applied to select the most suitable analogues based on their protein reactivity, structural characteristics, physicochemical properties, skin metabolism profile and availability of skin sensitisation data. The applied read-across approach predicted a weak skin sensitiser potential for vanillin corresponding with a Local Lymph Node Assay EC3 value of 10%. Based on this EC3 value a point of departure of 2500 μg/cm2 was derived, resulting in an acceptable exposure level (AEL) of 25 μg/cm2. Because the consumer exposure levels (CEL) for the face cream (13.5 μg/cm2) and shower gel (0.05 μg/cm2) scenarios were lower than the AEL, the NGRA concluded both uses as safe.
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Affiliation(s)
| | | | - Fleur Tourneix
- L'Oréal, Research & Innovation, Aulnay-Sous-Bois, France
| | - Erwin van Vliet
- Innovitox Consulting & Services, Regentenland 35, 3994TZ, Houten, the Netherlands
| | | | - Dagmar Bury
- L'Oréal, Research & Innovation, Clichy, France.
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11
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Araya S, Pfister T, Blum K, Clemann N, Faltermann S, Wiesner L, Hawkins W, van de Gevel I, Versyck K. Controlling cleaning agent residues in pharmaceutical manufacturing: A harmonized scientific strategy. Regul Toxicol Pharmacol 2023:105430. [PMID: 37308050 DOI: 10.1016/j.yrtph.2023.105430] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2023] [Revised: 05/23/2023] [Accepted: 06/02/2023] [Indexed: 06/14/2023]
Abstract
This paper proposes a scientifically justified and harmonized strategy to control cleaning agent ingredients' (CAIs) residues in pharmaceutical manufacturing. Firstly, we demonstrate that worst-case cleaning validation calculations on CAI residuals with representative GMP standard cleaning limits (SCLs) are enough to control CAI residues of low concern to safe levels. Secondly, a new harmonized strategy for the toxicological assessment of CAI residuals is presented and validated. The results establish a framework applicable to cleaning agent mixtures based on hazard and exposure considerations. This framework is primarily based on the hierarchy of a single CAI's critical effect, where the lowest resulting limit may become the driver of the cleaning validation process. The six critical effect groups are: (1) CAIs of low concern based on safe exposure reasoning; (2) CAIs of low concern based on the mode of action reasoning; (3) CAIs with local concentration-dependent critical effects; (4) CAIs with dose-dependent systemic critical effects for which a route-specific PDE should be calculated; (5) poorly characterized CAIs with unknown critical effect for which a default value of 100 μg/day is proposed; (6) poorly characterized CAIs which should be avoided because of potential mutagenicity and/or potency.
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Affiliation(s)
| | - T Pfister
- F. Hoffmann-La Roche AG, Switzerland
| | - K Blum
- GlaxoSmithKline GmbH & Co. KG, Germany
| | - N Clemann
- F. Hoffmann-La Roche AG, Switzerland
| | | | | | - W Hawkins
- SafeBridge Europe Ltd., United Kingdom
| | - I van de Gevel
- Janssen Pharmaceutical Companies of Johnson & Johnson, Belgium
| | - K Versyck
- Janssen Pharmaceutical Companies of Johnson & Johnson, Belgium
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12
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De P, Roy K. Computational modeling of PET imaging agents for vesicular acetylcholine transporter (VAChT) protein binding affinity: application of 2D-QSAR modeling and molecular docking techniques. In Silico Pharmacol 2023; 11:9. [PMID: 37035236 PMCID: PMC10073372 DOI: 10.1007/s40203-023-00146-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2022] [Accepted: 03/31/2023] [Indexed: 04/07/2023] Open
Abstract
The neurotransmitter acetylcholine (ACh) plays a ubiquitous role in cognitive functions including learning and memory with widespread innervation in the cortex, subcortical structures, and the cerebellum. Cholinergic receptors, transporters, or enzymes associated with many neurodegenerative diseases, including Alzheimer's disease (AD) and Parkinson's disease (PD), are potential imaging targets. In the present study, we have developed 2D-quantitative structure-activity relationship (2D-QSAR) models for 19 positron emission tomography (PET) imaging agents targeted against presynaptic vesicular acetylcholine transporter (VAChT). VAChT assists in the transport of ACh into the presynaptic storage vesicles, and it becomes one of the main targets for the diagnosis of various neurodegenerative diseases. In our work, we aimed to understand the important structural features of the PET imaging agents required for their binding with VAChT. This was done by feature selection using a Genetic Algorithm followed by the Best Subset Selection method and developing a Partial Least Squares- based 2D-QSAR model using the best feature combination. The developed QSAR model showed significant statistical performance and reliability. Using the features selected in the 2D-QSAR analysis, we have also performed similarity-based chemical read-across predictions and obtained encouraging external validation statistics. Further, we have also performed molecular docking analysis to understand the molecular interactions occurring between the PET imaging agents and the VAChT receptor. The molecular docking results were correlated with the QSAR features for a better understanding of the molecular interactions. This research serves to fulfill the experimental data gap, highlighting the applicability of computational methods in the PET imaging agents' binding affinity prediction. Graphical abstract Supplementary Information The online version contains supplementary material available at 10.1007/s40203-023-00146-4.
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Affiliation(s)
- Priyanka De
- Drug Theoretics and Cheminformatics Laboratory, Department of Pharmaceutical Technology, Jadavpur University, Kolkata, 700032 India
| | - Kunal Roy
- Drug Theoretics and Cheminformatics Laboratory, Department of Pharmaceutical Technology, Jadavpur University, Kolkata, 700032 India
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Nath A, Ojha PK, Roy K. Computational modeling of aquatic toxicity of polychlorinated naphthalenes (PCNs) employing 2D-QSAR and chemical read-across. Aquat Toxicol 2023; 257:106429. [PMID: 36842883 DOI: 10.1016/j.aquatox.2023.106429] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/17/2022] [Revised: 01/06/2023] [Accepted: 02/11/2023] [Indexed: 06/18/2023]
Abstract
Polychlorinated naphthalenes (PCNs) are produced from a variety of industrial sources, and they reach the aquatic ecosystems by the dry-wet deposition from the atmosphere and also by the drainage from the land surfaces. Then the PCNs can be transmitted through the food chain to humans and show toxic effects on different aquatic animals as well as humans. Considering this scenario, it is an obligatory task to explore the toxicity data of PCNs more deeply for the species of an aquatic ecosystem (green algae-Daphnia magna-fish), and to extrapolate those data for humans. But the toxicity data for different aquatic species are quite limited. The laboratory experimentations are complicated and ethically troublesome to fill toxicity data gaps; therefore, different in silico methods (e.g., QSAR, quantitative read-across predictions) are emerging as crucial ways to fill the data gaps and hazard assessments. In the present study, we developed individual toxicity models as well as interspecies models from the 75 PCN toxicity data against three aquatic species (green algae-Daphnia magna-fish) by employing easily interpretable 2D descriptors; these models were validated rigorously employing different globally accepted internal and external validation metrics. Then we interpreted the modelled descriptors mechanistically with the endpoint values for better understanding. And finally, we endeavored to improve the prediction quality in terms of external validation metrics by employing a novel quantitative read-across approach by pooling the descriptors from the developed individual QSAR models.
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Affiliation(s)
- Aniket Nath
- Drug Theoretics and Cheminformatics Laboratory, Department of Pharmaceutical Technology, Jadavpur University, Kolkata-700032, India
| | - Probir Kumar Ojha
- Drug Theoretics and Cheminformatics Laboratory, Department of Pharmaceutical Technology, Jadavpur University, Kolkata-700032, India
| | - Kunal Roy
- Drug Theoretics and Cheminformatics Laboratory, Department of Pharmaceutical Technology, Jadavpur University, Kolkata-700032, India.
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Paul R, Chatterjee M, Roy K. First report on soil ecotoxicity prediction against Folsomia candida using intelligent consensus predictions and chemical read-across. Environ Sci Pollut Res Int 2022; 29:88302-88317. [PMID: 35829883 DOI: 10.1007/s11356-022-21937-w] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/12/2022] [Accepted: 07/05/2022] [Indexed: 06/15/2023]
Abstract
Soil invertebrates serve as an outstanding biological indicator of the terrestrial ecosystem and overall soil quality, considering their high sensitivity when compared to other indicators of soil quality. In this study, the available soil ecotoxicity data (pEC50) against the soil invertebrate Folsomia candida (C. name: Springtail) (n = 45) were collated from the database of ECOTOX (cfpub.epa.gov/ecotox) and subjected to QSAR analysis using 2D descriptors. Four partial least squares (PLS) models were built based on the features selected through genertic algorithm followed by the best subset selection. These four models were then used as inputs for Intelligent Consensus Predictor version 1.2 (PLS version) to get the final consensus predictions, using the best selection of predictions (compound-wise) from four "qualified" individual models. Both internal and external validations metrics of the consensus predictions are well- balanced and within the acceptable range as per the OECD criteria. The consensus model was found to be better than the previous developed models for this endpoint. Predictions were also made using the Chemical Read-across approach, which showed even better external validation metric values than the consensus predictions. From the selected features in the QSAR models, it has been found out that molecular weight and presence of a di-thiophosphate group, electron donor groups, and polyhalogen substitutions have a significant impact on the soil ecotoxicity. The soil ecotoxicological risk assessment of organic chemicals can therefore be prioritized by these features. The models developed from diverse structural organic compounds can be applied to any new query compound for data gap filling.
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Affiliation(s)
- Rahul Paul
- Drug Theoretics and Cheminformatics Laboratory, Department of Pharmaceutical Technology, Jadavpur University, Kolkata, 700032, India
| | - Mainak Chatterjee
- Drug Theoretics and Cheminformatics Laboratory, Department of Pharmaceutical Technology, Jadavpur University, Kolkata, 700032, India
| | - Kunal Roy
- Drug Theoretics and Cheminformatics Laboratory, Department of Pharmaceutical Technology, Jadavpur University, Kolkata, 700032, India.
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15
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Banerjee A, De P, Kumar V, Kar S, Roy K. Quick and efficient quantitative predictions of androgen receptor binding affinity for screening Endocrine Disruptor Chemicals using 2D-QSAR and Chemical Read-Across. Chemosphere 2022; 309:136579. [PMID: 36174732 DOI: 10.1016/j.chemosphere.2022.136579] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/07/2022] [Revised: 09/18/2022] [Accepted: 09/20/2022] [Indexed: 06/16/2023]
Abstract
Endocrine Disruptor Chemicals are synthetic or natural molecules in the environment that promote adverse modifications of endogenous hormone regulation in humans and/or in animals. In the present research, we have applied two-dimensional quantitative structure-activity relationship (2D-QSAR) modeling to analyze the structural features of these chemicals responsible for binding to the androgen receptors (logRBA) in rats. We have collected the receptor binding data from the EDKB database (https://www.fda.gov/science-research/endocrine-disruptor-knowledge-base/accessing-edkb-database) and then employed the DTC-QSAR tool, available from https://dtclab.webs.com/software-tools, for dataset division, feature selection, and model development. The final partial least squares model was evaluated using various stringent validation criteria. From the model, we interpreted that hydrophobicity, steroidal nucleus, bulkiness and a hydrogen bond donor at an appropriate position contribute to the receptor binding affinity, while presence of electron rich features like aromaticity and polar groups decrease the receptor binding affinity. Additionally we have also performed chemical Read-Across predictions using Read-Across-v3.1 available from https://sites.google.com/jadavpuruniversity.in/dtc-lab-software/home, and the results for the external validation metrics were found to be better than the QSAR-derived predictions. The best quality of external predictions emerged from the q-RASAR approach which combines both read-across and QSAR. To explore the essential features responsible for the receptor binding, pharmacophore mapping, molecular docking along with molecular dynamics simulation were also performed, and the results are in accordance with the QSAR/q-RASAR findings.
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Affiliation(s)
- Arkaprava Banerjee
- Drug Theoretics and Cheminformatics Laboratory, Department of Pharmaceutical Technology, Jadavpur University, Kolkata, 700032, India
| | - Priyanka De
- Drug Theoretics and Cheminformatics Laboratory, Department of Pharmaceutical Technology, Jadavpur University, Kolkata, 700032, India
| | - Vinay Kumar
- Drug Theoretics and Cheminformatics Laboratory, Department of Pharmaceutical Technology, Jadavpur University, Kolkata, 700032, India
| | - Supratik Kar
- Interdisciplinary Center for Nanotoxicity, Department of Chemistry, Physics and Atmospheric Sciences, Jackson State University, Jackson, MS 39217, United States
| | - Kunal Roy
- Drug Theoretics and Cheminformatics Laboratory, Department of Pharmaceutical Technology, Jadavpur University, Kolkata, 700032, India.
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16
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Chatterjee M, Roy K. Chemical similarity and machine learning-based approaches for the prediction of aquatic toxicity of binary and multicomponent pharmaceutical and pesticide mixtures against Aliivibrio fischeri. Chemosphere 2022; 308:136463. [PMID: 36122748 DOI: 10.1016/j.chemosphere.2022.136463] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/23/2022] [Revised: 09/10/2022] [Accepted: 09/12/2022] [Indexed: 06/15/2023]
Abstract
Different classes of chemicals are present in the environment as mixtures. Among them, pharmaceuticals and pesticides are of major concern due to their improper use and disposal, and subsequent additive and non-additive effects. To assess the environmental risk posed by the mixtures of pharmaceuticals and pesticides, a quantitative structure-activity relationship (QSAR) model has been developed in this study using the pEC50 values of 198 binary and multi-component mixtures against the marine bacterium Aliivibrio fischeri. The developed partial least squares (PLS) model has been rigorously validated and proved to be a robust and extremely predictive one. To address the chances of overestimation of validation metrics, three cross-validation tests (mixtures out, compounds out, and everything out) have been applied, and the results were satisfactory. The use of simple 2-dimensional descriptors makes the prediction much quick, and also makes the model easily interpretable. A machine learning-based chemical read-across prediction has also been performed to justify the effectiveness of selected structural features in this study. In a nutshell, this study proves QSAR and chemical read-across as effective alternative approaches for the toxicity prediction of pharmaceutical and pesticide mixtures and also approves the use of mixture descriptors for modelling mixtures successfully.
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Affiliation(s)
- Mainak Chatterjee
- Drug Theoretics and Cheminformatics Laboratory, Department of Pharmaceutical Technology, Jadavpur University, Kolkata, 700032, India
| | - Kunal Roy
- Drug Theoretics and Cheminformatics Laboratory, Department of Pharmaceutical Technology, Jadavpur University, Kolkata, 700032, India.
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Lizarraga LE, Suter GW, Lambert JC, Patlewicz G, Zhao JQ, Dean JL, Kaiser P. Advancing the science of a read-across framework for evaluation of data-poor chemicals incorporating systematic and new approach methods. Regul Toxicol Pharmacol 2022; 137:105293. [PMID: 36414101 DOI: 10.1016/j.yrtph.2022.105293] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2022] [Revised: 10/18/2022] [Accepted: 11/09/2022] [Indexed: 11/21/2022]
Abstract
The assessment of human health hazards posed by chemicals traditionally relies on toxicity studies in experimental animals. However, most chemicals currently in commerce do not meet the minimum data requirements for hazard identification and dose-response analysis in human health risk assessment. Previously, we introduced a read-across framework designed to address data gaps for screening-level assessment of chemicals with insufficient in vivo toxicity information (Wang et al., 2012). It relies on inference by analogy from suitably tested source analogues to a target chemical, based on structural, toxicokinetic, and toxicodynamic similarity. This approach has been used for dose-response assessment of data-poor chemicals relevant to the U.S. EPA's Superfund program. We present herein, case studies of the application of this framework, highlighting specific examples of the use of biological similarity for chemical grouping and quantitative read-across. Based on practical knowledge and technological advances in the fields of read-across and predictive toxicology, we propose a revised framework. It includes important considerations for problem formulation, systematic review, target chemical analysis, analogue identification, analogue evaluation, and incorporation of new approach methods. This work emphasizes the integration of systematic methods and alternative toxicity testing data and tools in chemical risk assessment to inform regulatory decision-making.
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Affiliation(s)
- Lucina E Lizarraga
- Center for Public Health and Environmental Assessment, U.S. Environmental Protection Agency, 26 W. Martin L. King Drive, Cincinnati, OH, 45268, USA.
| | - Glenn W Suter
- Office of Research and Development, Emeritus, U.S. Environmental Protection Agency, 26 W. Martin L. King Drive, Cincinnati, OH, 45268, USA
| | - Jason C Lambert
- Center for Computational Toxicology & Exposure (CCTE), U.S. Environmental Protection Agency, Research Triangle Park, NC, 27709, USA
| | - Grace Patlewicz
- Center for Computational Toxicology & Exposure (CCTE), U.S. Environmental Protection Agency, Research Triangle Park, NC, 27709, USA
| | - Jay Q Zhao
- Center for Public Health and Environmental Assessment, U.S. Environmental Protection Agency, 26 W. Martin L. King Drive, Cincinnati, OH, 45268, USA
| | - Jeffry L Dean
- Center for Public Health and Environmental Assessment, U.S. Environmental Protection Agency, 26 W. Martin L. King Drive, Cincinnati, OH, 45268, USA
| | - Phillip Kaiser
- Center for Public Health and Environmental Assessment, U.S. Environmental Protection Agency, 26 W. Martin L. King Drive, Cincinnati, OH, 45268, USA
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18
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Lee SH, Kim J, Kim J, Park J, Park S, Kim KB, Lee BM, Kwon S. Current trends in read-across applications for chemical risk assessments and chemical registrations in the Republic of Korea. J Toxicol Environ Health B Crit Rev 2022; 25:393-404. [PMID: 36250612 DOI: 10.1080/10937404.2022.2133033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
Read-across, an alternative approach for hazard assessment, has been widely adopted when in vivo data are unavailable for chemicals of interest. Read-across is enabled via in silico tools such as quantitative structure activity relationship (QSAR) modeling. In this study, the current status of structure activity relationship (SAR)-based read-across applications in the Republic of Korea (ROK) was examined considering both chemical risk assessments and chemical registrations from different sectors, including regulatory agencies, industry, and academia. From the regulatory perspective, the Ministry of Environment (MOE) established the Act on Registration and Evaluation of Chemicals (AREC) in 2019 to enable registrants to submit alternative data such as information from read-across instead of in vivo data to support hazard assessment and determine chemical-specific risks. Further, the Ministry of Food and Drug Safety (MFDS) began to consider read-across approaches for establishing acceptable intake (AI) limits of impurities occurring during pharmaceutical manufacturing processes under the ICH M7 guideline. Although read-across has its advantages, this approach also has limitations including (1) lack of standardized criteria for regulatory acceptance, (2) inconsistencies in the robustness of scientific evidence, and (3) deficiencies in the objective reliability of read-across data. The application and acceptance rate of read-across may vary among regulatory agencies. Therefore, sufficient data need to be prepared to verify the hypothesis that structural similarities might lead to similarities in properties of substances (between source and target chemicals) prior to adopting a read-across approach. In some cases, additional tests may be required during the registration process to clarify long-term effects on human health or the environment for certain substances that are data deficient. To improve the quality of read-across data for regulatory acceptance, cooperative efforts from regulatory agencies, academia, and industry are needed to minimize limitations of read-across applications.
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Affiliation(s)
- Sang Hee Lee
- Chemicals Registration & Evaluation Team, Risk Assessment Research Division, National Institute of Environmental Research, Ministry of Environment, Inchon, Republic of Korea
| | - Jongwoon Kim
- Chemical Safety Research Center, Korea Research Institute of Chemical Technology, Daejeon, Republic of Korea
| | - Jinyong Kim
- Environment, Safety and Health DepartmentChemical Products and Biocides Safety Center, Korea Environmental Industry and Technology Institute (KEITI), Inchon, Republic of Korea
| | - Jaehyun Park
- Pharmaceutical Standardization Division, Drug Evaluation Department, National Institute of Food and Drug Safety Evaluation, Ministry of Food and Drug Safety, Osong Health Technology Administration Complex, Cheongju, Chungcheongbuk-do, Republic of Korea
| | | | - Kyu-Bong Kim
- College of Pharmacy, Dankook University, Chungnam 31116, Republic of Korea
| | - Byung-Mu Lee
- Division of Toxicology, College of Pharmacy, Sungkyunkwan University, Seobu-ro 2066, Suwon, Republic of Korea
| | - Seok Kwon
- Global Product Stewardship, Research & Development, Singapore Innovation Center, Procter & Gamble (P&G) International Operationsr, Singapore
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Patlewicz G, Richard AM, Williams AJ, Judson RS, Thomas RS. Towards reproducible structure-based chemical categories for PFAS to inform and evaluate toxicity and toxicokinetic testing. Comput Toxicol 2022; 24:10.1016/j.comtox.2022.100250. [PMID: 36969381 PMCID: PMC10031514 DOI: 10.1016/j.comtox.2022.100250] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Per- and Polyfluoroalkyl substances (PFAS) are a class of synthetic chemicals that are in widespread use and present concerns for persistence, bioaccumulation and toxicity. Whilst a handful of PFAS have been characterised for their hazard profiles, the vast majority of PFAS have not been studied. The US Environmental Protection Agency (EPA) undertook a research project to screen ~150 PFAS through an array of different in vitro high throughput toxicity and toxicokinetic tests in order to inform chemical category and read-across approaches. A previous publication described the rationale behind the selection of an initial set of 75 PFAS, whereas herein, we describe how various category approaches were applied and extended to inform the selection of a second set of 75 PFAS from our library of approximately 430 commercially procured PFAS. In particular, we focus on the challenges in grouping PFAS for prospective analysis and how we have sought to develop and apply objective structure-based categories to profile the testing library and other PFAS inventories. We additionally illustrate how these categories can be enriched with other information to facilitate read-across inferences once experimental data become available. The availability of flexible, objective, reproducible and chemically intuitive categories to explore PFAS constitutes an important step forward in prioritising PFAS for further testing and assessment.
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Affiliation(s)
- Grace Patlewicz
- Center for Computational Toxicology and Exposure (CCTE), Office of Research and Development, US Environmental Protection Agency, 109 TW Alexander Dr, Research Triangle Park, NC 27711, USA
| | - Ann M. Richard
- Center for Computational Toxicology and Exposure (CCTE), Office of Research and Development, US Environmental Protection Agency, 109 TW Alexander Dr, Research Triangle Park, NC 27711, USA
| | - Antony J. Williams
- Center for Computational Toxicology and Exposure (CCTE), Office of Research and Development, US Environmental Protection Agency, 109 TW Alexander Dr, Research Triangle Park, NC 27711, USA
| | - Richard S. Judson
- Center for Computational Toxicology and Exposure (CCTE), Office of Research and Development, US Environmental Protection Agency, 109 TW Alexander Dr, Research Triangle Park, NC 27711, USA
| | - Russell S. Thomas
- Center for Computational Toxicology and Exposure (CCTE), Office of Research and Development, US Environmental Protection Agency, 109 TW Alexander Dr, Research Triangle Park, NC 27711, USA
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Sweeney LM. Case study on the impact of the source of metabolism parameters in next generation physiologically based pharmacokinetic models: Implications for occupational exposures to trimethylbenzenes. Regul Toxicol Pharmacol 2022; 134:105238. [PMID: 35931234 DOI: 10.1016/j.yrtph.2022.105238] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2022] [Revised: 07/14/2022] [Accepted: 07/19/2022] [Indexed: 10/16/2022]
Abstract
Physiologically based pharmacokinetic (PBPK) models are a means of making important linkages between exposure assessment and in vitro toxicity. A key constraint on rapid application of PBPK models in risk assessment is traditional reliance on substance-specific in vivo toxicokinetic data to evaluate model quality. Bounding conditions, in silico, in vitro, and chemical read-across approaches have been proposed as alternative sources for metabolic clearance estimates. A case study to test consistency of predictive ability across these approaches was conducted using trimethylbenzenes (TMB) as prototype chemicals. Substantial concordance was found among TMB isomers with respect to accuracy (or inaccuracy) of approaches to estimating metabolism; for example, the bounding conditions never reproduced the human in vivo toxicokinetic data within two-fold. Using only approaches that gave acceptable prediction of in vivo toxicokinetics for the source compound (1,2,4-TMB) substantially narrowed the range of plausible internal doses for a given external dose for occupational, emergency response, and environmental/community health risk assessment scenarios for TMB isomers. Thus, risk assessments developed using the target compound models with a constrained subset of metabolism estimates (determined for source chemical models) can be used with greater confidence that internal dosimetry will be estimated with accuracy sufficient for the purpose at hand.
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Affiliation(s)
- Lisa M Sweeney
- UES, Inc, 4401 Dayton Xenia Road, Dayton, OH, 45432, USA(contractor assigned to the U.S. Air Force Research Laboratory 711th Human Performance Wing, Wright Patterson AFB, OH USA).
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Enoch SJ, Hasarova Z, Cronin MTD, Frericks M. Sub-structure-based category formation for the prioritisation of genotoxicity hazard assessment for pesticide residues (part 2): Triazoles. Regul Toxicol Pharmacol 2022;:105237. [PMID: 35917984 DOI: 10.1016/j.yrtph.2022.105237] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2022] [Revised: 07/07/2022] [Accepted: 07/19/2022] [Indexed: 11/23/2022]
Abstract
In dietary risk assessment, residues of pesticidal ingredients or their metabolites need to be evaluated for their genotoxic potential. The European Food Safety Authority recommend a tiered approach focussing assessment and testing on classes of similar chemicals. To characterise similarity and to identify structural alerts associated with genotoxic concern, a set of chemical sub-structures was derived for an example dataset of 66 triazole agrochemicals for which either Ames, chromosomal aberration or micronucleus test results are publicly available. This analysis resulted in a set of ten structural alerts that define the chemical space, in terms of the common parent and metabolic scaffolds, associated with the triazole chemical class. An analysis of the available profiling schemes for DNA and protein reactivity shows the importance of investigating the predictivity of such schemes within a well-defined area of structural space. Structural space alerts, covalent chemistry profiling and physico-chemistry properties were combined to develop chemical categories suitable for chemical prioritisation. The method is a robust and reproducible approach to such read-across predictions, with the potential to reduce unnecessary testing. The key challenge in the approach was identified as being the need for pesticide-class specific metabolism data as the basis for structural space alert development.
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Murphy F, Jacobsen NR, Di Ianni E, Johnston H, Braakhuis H, Peijnenburg W, Oomen A, Fernandes T, Stone V. Grouping MWCNTs based on their similar potential to cause pulmonary hazard after inhalation: a case-study. Part Fibre Toxicol 2022; 19:50. [PMID: 35854357 PMCID: PMC9297605 DOI: 10.1186/s12989-022-00487-6] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2022] [Accepted: 06/23/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND The EU-project GRACIOUS developed an Integrated Approach to Testing and Assessment (IATA) to support grouping high aspect ratio nanomaterials (HARNs) presenting a similar inhalation hazard. Application of grouping reduces the need to assess toxicity on a case-by-case basis and supports read-across of hazard data from substances that have the data required for risk assessment (source) to those that lack such data (target). The HARN IATA, based on the fibre paradigm for pathogenic fibres, facilitates structured data gathering to propose groups of similar HARN and to support read-across by prompting users to address relevant questions regarding HARN morphology, biopersistence and inflammatory potential. The IATA is structured in tiers, allowing grouping decisions to be made using simple in vitro or in silico methods in Tier1 progressing to in vivo approaches at the highest Tier3. Here we present a case-study testing the applicability of GRACIOUS IATA to form an evidence-based group of multiwalled carbon nanotubes (MWCNT) posing a similar predicted fibre-hazard, to support read-across and reduce the burden of toxicity testing. RESULTS The case-study uses data on 15 different MWCNT, obtained from the published literature. By following the IATA, a group of 2 MWCNT was identified (NRCWE006 and NM-401) based on a high degree of similarity. A pairwise similarity assessment was subsequently conducted between the grouped MWCNT to evaluate the potential to conduct read-across and fill data gaps required for regulatory hazard assessment. The similarity assessment, based on expert judgement of Tier 1 assay results, predicts both MWCNT are likely to cause a similar acute in vivo hazard. This result supports the possibility for read-across of sub-chronic and chronic hazard endpoint data for lung fibrosis and carcinogenicity between the 2 grouped MWCNT. The implications of accepting the similarity assessment based on expert judgement of the MWCNT group are considered to stimulate future discussion on the level of similarity between group members considered sufficient to allow regulatory acceptance of a read-across argument. CONCLUSION This proof-of-concept case-study demonstrates how a grouping hypothesis and IATA may be used to support a nuanced and evidence-based grouping of 'similar' MWCNT and the subsequent interpolation of data between group members to streamline the hazard assessment process.
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Affiliation(s)
- Fiona Murphy
- NanoSafety Group, Heriot-Watt University, Edinburgh, UK.
| | | | - Emilio Di Ianni
- National Research Centre for the Working Environment (NFA), Copenhagen, Denmark
| | | | - Hedwig Braakhuis
- National Institute for Public Health and the Environment (RIVM), Bilthoven, The Netherlands
| | - Willie Peijnenburg
- National Institute for Public Health and the Environment (RIVM), Bilthoven, The Netherlands.,Institute of Environmental Sciences, Leiden University, Leiden, The Netherlands
| | - Agnes Oomen
- National Institute for Public Health and the Environment (RIVM), Bilthoven, The Netherlands
| | | | - Vicki Stone
- NanoSafety Group, Heriot-Watt University, Edinburgh, UK
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Banerjee A, Roy K. First report of q-RASAR modeling toward an approach of easy interpretability and efficient transferability. Mol Divers 2022. [PMID: 35767129 DOI: 10.1007/s11030-022-10478-6] [Citation(s) in RCA: 33] [Impact Index Per Article: 16.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2022] [Accepted: 06/03/2022] [Indexed: 12/21/2022]
Abstract
Quantitative structure-activity relationship (QSAR) and read-across techniques have recently been merged into a new emerging field of read-across structure-activity relationship (RASAR) that uses the chemical similarity concepts of read-across (an unsupervised step) and finally develops a supervised learning model (like QSAR). The RASAR method has so far been used only in case of graded predictions or classification modeling. In this work, we attempt, for the first time, to apply RASAR for quantitative predictions (q-RASAR) using a case study of androgen receptor binding affinity data. We have computed a number of error-based and similarity-based measures such as weighted standard deviation of the predicted values, coefficient of variation of the computed predictions, average similarity level of close training compounds for each query molecule, standard deviation and coefficient of variation of similarity levels, maximum similarity levels to positive and negative close training compounds, a concordance measure indicating similarity to positive, negative or both classes of close training compounds, etc. We have clubbed these additional measures along with the selected chemical descriptors from the previously developed QSAR model and redeveloped new partial least squares models from the training set, and predicted the endpoint using the query data set. Interestingly, these new models outperform the internal and external validation quality of the original QSAR model. In this study, we have also introduced a new similarity-based concordance measure (Banerjee-Roy coefficient) that can significantly contribute to the model quality. A q-RASAR model also has the advantage over read-across predictions in providing easy interpretation and indicating quantitative contributions of important chemical features. The strategy described here should be applicable to other biological/toxicological/property data modeling for enhanced quality of predictions, easy interpretability, and efficient transferability.
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24
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Nakagawa S, Hayashi A, Nukada Y, Yamane M. Comparison of toxicological effects and exposure levels between triclosan and its structurally similar chemicals using in vitro tests for read-across case study. Regul Toxicol Pharmacol 2022; 132:105181. [PMID: 35526779 DOI: 10.1016/j.yrtph.2022.105181] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2021] [Revised: 03/02/2022] [Accepted: 04/25/2022] [Indexed: 11/27/2022]
Abstract
Read-across based on structural and biological similarities is expected to be a promising alternative method for assessing systemic toxicity. A concrete strategy for quantitative chemical risk assessment would be to stack read-across case studies and extract key considerations from them. Thus, we developed a read-across case study by comparing the toxicological effects based on adverse outcome pathways and exposure levels of different structurally similar chemicals for a target organ. In this study, we selected the hepatotoxicity of triclosan and its structurally similar chemicals including diclosan and 1-chloro-3-(4-chlorophenoxy)benzene. The results of in vitro toxicogenomics showed that disorders of cholesterol synthesis were commonly detected with both triclosan and diclosan. The decrease in hepatocellular cholesterol levels was similar in the cells treated with triclosan and diclosan. Furthermore, the exposure levels of triclosan and diclosan for the liver were similar. Collectively, these results suggest that triclosan and diclosan show similar toxicological effects and severity of hepatotoxicity. Considering the existing repeated dose toxicity data, our prediction results are reasonable regarding the toxicological effect and its severity. Thus, the present study demonstrated the usability of comparing toxicological effects and exposure levels using read-across for quantitative chemical risk assessment.
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Affiliation(s)
- Shota Nakagawa
- Kao Corporation, Safety Science Research, 2606, Akabane, Ichikai-Machi, Haga-Gun Tochigi, 321-3497, Japan.
| | - Akane Hayashi
- Kao Corporation, Safety Science Research, 2606, Akabane, Ichikai-Machi, Haga-Gun Tochigi, 321-3497, Japan
| | - Yuko Nukada
- Kao Corporation, Safety Science Research, 2606, Akabane, Ichikai-Machi, Haga-Gun Tochigi, 321-3497, Japan
| | - Masayuki Yamane
- Kao Corporation, Safety Science Research, 2606, Akabane, Ichikai-Machi, Haga-Gun Tochigi, 321-3497, Japan
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25
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Crofton KM, Bassan A, Behl M, Chushak YG, Fritsche E, Gearhart JM, Marty MS, Mumtaz M, Pavan M, Ruiz P, Sachana M, Selvam R, Shafer TJ, Stavitskaya L, Szabo DT, Szabo ST, Tice RR, Wilson D, Woolley D, Myatt GJ. Current status and future directions for a neurotoxicity hazard assessment framework that integrates in silico approaches. Comput Toxicol 2022; 22:100223. [PMID: 35844258 PMCID: PMC9281386 DOI: 10.1016/j.comtox.2022.100223] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/27/2023]
Abstract
Neurotoxicology is the study of adverse effects on the structure or function of the developing or mature adult nervous system following exposure to chemical, biological, or physical agents. The development of more informative alternative methods to assess developmental (DNT) and adult (NT) neurotoxicity induced by xenobiotics is critically needed. The use of such alternative methods including in silico approaches that predict DNT or NT from chemical structure (e.g., statistical-based and expert rule-based systems) is ideally based on a comprehensive understanding of the relevant biological mechanisms. This paper discusses known mechanisms alongside the current state of the art in DNT/NT testing. In silico approaches available today that support the assessment of neurotoxicity based on knowledge of chemical structure are reviewed, and a conceptual framework for the integration of in silico methods with experimental information is presented. Establishing this framework is essential for the development of protocols, namely standardized approaches, to ensure that assessments of NT and DNT based on chemical structures are generated in a transparent, consistent, and defendable manner.
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Affiliation(s)
| | - Arianna Bassan
- Innovatune srl, Via Giulio Zanon 130/D, 35129 Padova,
Italy
| | - Mamta Behl
- Division of the National Toxicology Program, National
Institutes of Environmental Health Sciences, Durham, NC 27709, USA
| | - Yaroslav G. Chushak
- Henry M Jackson Foundation for the Advancement of Military
Medicine, Wright-Patterson AFB, OH 45433, USA
| | - Ellen Fritsche
- IUF – Leibniz Research Institute for Environmental
Medicine & Medical Faculty Heinrich-Heine-University, Düsseldorf,
Germany
| | - Jeffery M. Gearhart
- Henry M Jackson Foundation for the Advancement of Military
Medicine, Wright-Patterson AFB, OH 45433, USA
| | | | - Moiz Mumtaz
- Agency for Toxic Substances and Disease Registry, US
Department of Health and Human Services, Atlanta, GA, USA
| | - Manuela Pavan
- Innovatune srl, Via Giulio Zanon 130/D, 35129 Padova,
Italy
| | - Patricia Ruiz
- Agency for Toxic Substances and Disease Registry, US
Department of Health and Human Services, Atlanta, GA, USA
| | - Magdalini Sachana
- Environment Health and Safety Division, Environment
Directorate, Organisation for Economic Co-Operation and Development (OECD), 75775
Paris Cedex 16, France
| | - Rajamani Selvam
- Office of Clinical Pharmacology, Office of Translational
Sciences, Center for Drug Evaluation and Research (CDER), U.S. Food and Drug
Administration (FDA), Silver Spring, MD 20993, USA
| | - Timothy J. Shafer
- Biomolecular and Computational Toxicology Division, Center
for Computational Toxicology and Exposure, US EPA, Research Triangle Park, NC,
USA
| | - Lidiya Stavitskaya
- Office of Clinical Pharmacology, Office of Translational
Sciences, Center for Drug Evaluation and Research (CDER), U.S. Food and Drug
Administration (FDA), Silver Spring, MD 20993, USA
| | | | | | | | - Dan Wilson
- The Dow Chemical Company, Midland, MI 48667, USA
| | | | - Glenn J. Myatt
- Instem, Columbus, OH 43215, USA
- Corresponding author.
(G.J. Myatt)
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26
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Hamilton CM, Winter MJ, Margiotta-Casaluci L, Owen SF, Tyler CR. Are synthetic glucocorticoids in the aquatic environment a risk to fish? Environ Int 2022; 162:107163. [PMID: 35240385 DOI: 10.1016/j.envint.2022.107163] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/19/2021] [Revised: 02/21/2022] [Accepted: 02/22/2022] [Indexed: 05/27/2023]
Abstract
The glucocorticosteroid, or glucocorticoid (GC), system is largely conserved across vertebrates and plays a central role in numerous vital physiological processes including bone development, immunomodulation, and modification of glucose metabolism and the induction of stress-related behaviours. As a result of their wide-ranging actions, synthetic GCs are widely prescribed for numerous human and veterinary therapeutic purposes and consequently have been detected extensively within the aquatic environment. Synthetic GCs designed for humans are pharmacologically active in non-mammalian vertebrates, including fish, however they are generally detected in surface waters at low (ng/L) concentrations. In this review, we assess the potential environmental risk of synthetic GCs to fish by comparing available experimental data and effect levels in fish with those in mammals. We found the majority of compounds were predicted to have insignificant risk to fish, however some compounds were predicted to be of moderate and high risk to fish, although the dataset of compounds used for this analysis was small. Given the common mode of action and high level of inter-species target conservation exhibited amongst the GCs, we also give due consideration to the potential for mixture effects, which may be particularly significant when considering the potential for environmental impact from this class of pharmaceuticals. Finally, we also provide recommendations for further research to more fully understand the potential environmental impact of this relatively understudied group of commonly prescribed human and veterinary drugs.
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Affiliation(s)
- Charles M Hamilton
- Biosciences, University of Exeter, Geoffrey Pope Building, Stocker Road, Exeter, Devon EX4 4QD, UK
| | - Matthew J Winter
- Biosciences, University of Exeter, Geoffrey Pope Building, Stocker Road, Exeter, Devon EX4 4QD, UK
| | - Luigi Margiotta-Casaluci
- Department of Analytical, Environmental & Forensic Sciences, School of Cancer & Pharmaceutical Sciences, King's College London, London SE1 9NH, UK
| | - Stewart F Owen
- AstraZeneca, Global Environment, Macclesfield, Cheshire SK10 2NA, UK
| | - Charles R Tyler
- Biosciences, University of Exeter, Geoffrey Pope Building, Stocker Road, Exeter, Devon EX4 4QD, UK.
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27
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Lee BM, Lee SH, Yamada T, Park S, Wang Y, Kim KB, Kwon S. Read-across approaches: current applications and regulatory acceptance in Korea, Japan, and China. J Toxicol Environ Health A 2022; 85:184-197. [PMID: 34670481 DOI: 10.1080/15287394.2021.1992323] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
The aim of this paper was to investigate the current status of read-across approaches in the Republic of Korea, Japan, and China in terms of applications and regulatory acceptance. In the Republic of Korea, over the last 6 years, approximately 8% of safety data records used for chemical registrations were based upon read-across, and a guideline published on the use of read-across results in 2017. In Japan, read-across is generally accepted for screening hazard classification of toxicological endpoints according to the Chemical Substances Control Law (CSCL). In China, read-across data, along with data from other animal alternatives are accepted as a data source for chemical registrations, but could be only considered when testing is not technically feasible. At present, read-across is not widely used for chemical registrations and regulatory acceptance of read-across may differ among countries in Asia. With consideration of the advantages and limitations of read-across, it is expected that read-across may soon gradually be employed in Asian countries. Thus, regulatory agencies need to prepare for this progression.
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Affiliation(s)
- Byung-Mu Lee
- Division of Toxicology, College of Pharmacy, Sungkyunkwan University, Gyeonggi-Do, Korea
| | - Sang Hee Lee
- Chemicals Registration & Evaluation Team, Risk Assessment Research Division, National Institute of Environmental Research, Ministry of Environment, Incheon, Korea
| | - Takashi Yamada
- Division of Risk Assessment, Center for Biological Safety Research, National Institute of Health Sciences, Kawasaki, Japan
| | | | - Ying Wang
- Procter & Gamble (P&G) Technology (Beijing) Co., Ltd, Beijing, PR China
| | - Kyu-Bong Kim
- College of Pharmacy, Dankook University, Chungnam, Korea
| | - Seok Kwon
- Global Product Stewardship, Research & Development, Singapore Innovation Center, Procter & Gamble (P&G) International Operations, Singapore, Singapore
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28
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Caballero Alfonso AY, Mora Lagares L, Novic M, Benfenati E, Kumar A. Exploration of structural requirements for azole chemicals towards human aromatase CYP19A1 activity: Classification modeling, structure-activity relationships and read-across study. Toxicol In Vitro 2022; 81:105332. [PMID: 35176449 DOI: 10.1016/j.tiv.2022.105332] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2021] [Revised: 01/10/2022] [Accepted: 02/10/2022] [Indexed: 01/23/2023]
Abstract
Human aromatase, also called CYP19A1, plays a major role in the conversion of androgens into estrogens. Inhibition of aromatase is an important target for estrogen receptor (ER)-responsive breast cancer therapy. Use of azole compounds as aromatase inhibitors is widespread despite their low selectivity. A toxicological evaluation of commonly used azole-based drugs and agrochemicals with respect to CYP19A1is currently requested by the European Union- Registration, Evaluation, Authorization and Restriction of Chemicals (EU-REACH) regulations due to their potential as endocrine disruptors. In this connection, identification of structural alerts (SAs) is an effective strategy for the toxicological assessment and safe drug design. The present study describes the identification of SAs of azole-based chemicals as guiding experts to predict the aromatase activity. Total 21 SAs associated with aromatase activity were extracted from dataset of 326 azole-based drugs/chemicals obtained from Tox21 library. A cross-validated classification model having high accuracy (error rate 5%) was proposed which can precisely classify azole chemicals into active/inactive toward aromatase. In addition, mechanistic details and toxicological properties (agonism/antagonism) of azoles with respect to aromatase were explored by comparing active and inactive chemicals using structure-activity relationships (SAR). Lastly, few structural alerts were applied to form chemical categories for read-across applications.
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Affiliation(s)
- Ana Y Caballero Alfonso
- Laboratory of Environmental Chemistry and Toxicology, Department of Environmental Health Sciences, Istituto di RicercheFarmacologiche "Mario Negri"-IRCCS, Milano, Italy; Jozef Stefan International Postgraduate School, Jamova cesta 39, 1000 Ljubljana, Slovenia
| | - Liadys Mora Lagares
- Jozef Stefan International Postgraduate School, Jamova cesta 39, 1000 Ljubljana, Slovenia; Theory Department, Laboratory for Cheminformatics, National Institute of Chemistry, Ljubljana, Slovenia
| | - Marjana Novic
- Theory Department, Laboratory for Cheminformatics, National Institute of Chemistry, Ljubljana, Slovenia
| | - Emilio Benfenati
- Laboratory of Environmental Chemistry and Toxicology, Department of Environmental Health Sciences, Istituto di RicercheFarmacologiche "Mario Negri"-IRCCS, Milano, Italy
| | - Anil Kumar
- Department of Applied Sciences, University Institute of Engineering and Technology, Panjab University, Chandigarh 160014, India.
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29
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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] [What about the content of this article? (0)] [Affiliation(s)] [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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30
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Enoch SJ, Hasarova Z, Cronin MTD, Bridgwood K, Rao S, Kluxen FM, Frericks M. Sub-structure-based category formation for the prioritisation of genotoxicity hazard assessment for pesticide residues: Sulphonyl ureas. Regul Toxicol Pharmacol 2022; 129:105115. [PMID: 35017022 DOI: 10.1016/j.yrtph.2022.105115] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2021] [Revised: 12/19/2021] [Accepted: 01/05/2022] [Indexed: 10/19/2022]
Abstract
In dietary risk assessment, residues of pesticidal ingredients or their metabolites need to be evaluated for their genotoxic potential. The European Food Safety Authority recommend a tiered approach focussing assessment and testing on classes of similar chemicals. To characterise similarity and to identify structural alerts associated with genotoxic concern, a set of chemical sub-structures was derived for an example dataset of 74 sulphonyl urea agrochemicals for which either Ames, chromosomal aberration or micronucleus test results are publicly available. This analysis resulted in a set of seven structural alerts that define the chemical space, in terms of the common parent and metabolic scaffolds, associated with the sulphonyl urea chemical class. An analysis of the available profiling schemes for DNA and protein reactivity shows the importance of investigating the predictivity of such schemes within a well-defined area of structural space. Structural space alerts, covalent chemistry profiling and physico-chemistry properties were combined to develop chemical categories suitable for chemical prioritisation. The method is a robust and reproducible approach to such read-across predictions, with the potential to reduce unnecessary testing. The key challenge in the approach was identified as being the need for pesticide-class specific metabolism data as the basis for structural space alert development.
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Affiliation(s)
- S J Enoch
- School of Pharmacy and Biomolecular Sciences, Liverpool John Moores University, Liverpool, England, UK.
| | - Z Hasarova
- School of Pharmacy and Biomolecular Sciences, Liverpool John Moores University, Liverpool, England, UK
| | - M T D Cronin
- School of Pharmacy and Biomolecular Sciences, Liverpool John Moores University, Liverpool, England, UK
| | | | - S Rao
- Gowan Company, Yuma, AZ, USA
| | - F M Kluxen
- ADAMA Deutschland GmbH, Cologne, Germany
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31
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Loosli F, Rasmussen K, Rauscher H, Cross RK, Bossa N, Peijnenburg W, Arts J, Matzke M, Svendsen C, Spurgeon D, Clausen PA, Ruggiero E, Wohlleben W, von der Kammer F. Refinement of the selection of physicochemical properties for grouping and read-across of nanoforms. NanoImpact 2022; 25:100375. [PMID: 35559881 DOI: 10.1016/j.impact.2021.100375] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/30/2021] [Revised: 11/24/2021] [Accepted: 12/08/2021] [Indexed: 06/15/2023]
Abstract
Before placing a new nanoform (NF) on the market, its potential adverse effects must be evaluated. This may e.g. be done via hazard and risk assessment. Grouping and read-across of NFs is a possible strategy to reduce resource consumption, maximising the use of existing data for assessment of NFs. The GRACIOUS project provides a framework in which possible grouping and read-across for NFs is mainly based on an evaluation of their similarity. The impact of NFs on human health and the environment depends strongly on the concentration of the NF and its physicochemical properties, such as chemical composition, size distribution, shape, etc. Hence, knowledge of the most relevant physicochemical properties is essential information for comparing similarity. The presented work aims to refine existing proposals for sets of descriptors (descriptor array) that are needed to describe distinct NFs of a material to identify the most relevant ones for grouping and read-across. The selection criteria for refining this descriptor array are explained and demonstrated. Relevant protocols and methods are proposed for each physicochemical property. The required and achievable measurement accuracies of the refined descriptor array are reviewed, as this information is necessary for similarity assessment of NFs based on individual physicochemical properties.
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Affiliation(s)
- Frédéric Loosli
- Environmental Geosciences, Centre for Microbiology and Environmental Systems Science, University of Vienna, Wien, Austria.
| | | | - Hubert Rauscher
- European Commission, Joint Research Centre (JRC), Ispra, Italy
| | - Richard K Cross
- UK Centre for Ecology and Hydrology, Pollution, Wallingford, Oxfordshire, United Kingdom
| | - Nathan Bossa
- Leitat Technological Center, 08225 Terrassa, Barcelona, Spain
| | - Willie Peijnenburg
- National Institute of Public Health and the Environment (RIVM), Center for Safety of Substances and Products, Bilthoven, the Netherlands; Leiden University, Institute of Environmental Sciences (CML), P.O. Box 9518, 2300 RA Leiden, the Netherlands
| | - Josje Arts
- Nouryon Chemicals BV, Velperweg 76, 6824 BM Arnhem, the Netherlands
| | - Marianne Matzke
- UK Centre for Ecology and Hydrology, Pollution, Wallingford, Oxfordshire, United Kingdom
| | - Claus Svendsen
- UK Centre for Ecology and Hydrology, Pollution, Wallingford, Oxfordshire, United Kingdom
| | - David Spurgeon
- UK Centre for Ecology and Hydrology, Pollution, Wallingford, Oxfordshire, United Kingdom
| | - Per Axel Clausen
- The National Research Centre for the Working Environment (NFA), Lersø Parkallé 105, 2100 Copenhagen East, Denmark
| | - Emmanuel Ruggiero
- BASF SE, Dept. of Material Physics, Dept. of Experimental Toxicology and Ecology, 67056 Ludwigshafen, Germany
| | - Wendel Wohlleben
- BASF SE, Dept. of Material Physics, Dept. of Experimental Toxicology and Ecology, 67056 Ludwigshafen, Germany
| | - Frank von der Kammer
- Environmental Geosciences, Centre for Microbiology and Environmental Systems Science, University of Vienna, Wien, Austria
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32
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Manganelli S, Gamba A, Colombo E, Benfenati E. Using VEGAHUB Within a Weight-of-Evidence Strategy. Methods Mol Biol 2022; 2425:479-495. [PMID: 35188643 DOI: 10.1007/978-1-0716-1960-5_18] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Industrial needs and regulatory requirements have played a significant role in accelerating the use of nontesting methods including in silico tools as alternatives to animal testing. The main interest is not solely on the use of in silico tools, or in read-across, but on better toxicological safety assessment of substances, and for this purpose more advanced, integrated strategies have to be implemented. VEGAHUB wants to promote this broader view, not necessarily focused on a specific approach. Applying multiple tools and complementary approaches instead of one technique may provide more elements for a more robust evaluation, but at the same time it is important to have a conceptual scheme to integrate multiple, heterogeneous lines of evidence. We will show how the user can benefit from the diversity of tools available within the platform VEGAHUB for assessing the biological properties of chemical substances on an example of (non)mutagenicity.
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Affiliation(s)
| | - Alessio Gamba
- Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milan, Italy
| | - Erika Colombo
- Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milan, Italy
| | - Emilio Benfenati
- Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milan, Italy
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33
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Jeliazkova N, Bleeker E, Cross R, Haase A, Janer G, Peijnenburg W, Pink M, Rauscher H, Svendsen C, Tsiliki G, Zabeo A, Hristozov D, Stone V, Wohlleben W. How can we justify grouping of nanoforms for hazard assessment? Concepts and tools to quantify similarity. NanoImpact 2022; 25:100366. [PMID: 35559874 DOI: 10.1016/j.impact.2021.100366] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/09/2021] [Revised: 10/15/2021] [Accepted: 11/12/2021] [Indexed: 06/15/2023]
Abstract
The risk of each nanoform (NF) of the same substance cannot be assumed to be the same, as they may vary in their physicochemical characteristics, exposure and hazard. However, neither can we justify a need for more animal testing and resources to test every NF individually. To reduce the need to test all NFs, (regulatory) information requirements may be fulfilled by grouping approaches. For such grouping to be acceptable, it is important to demonstrate similarities in physicochemical properties, toxicokinetic behaviour, and (eco)toxicological behaviour. The GRACIOUS Framework supports the grouping of NFs, by identifying suitable grouping hypotheses that describe the key similarities between different NFs. The Framework then supports the user to gather the evidence required to test these hypotheses and to subsequently assess the similarity of the NFs within the proposed group. The evidence needed to support a hypothesis is gathered by an Integrated Approach to Testing and Assessment (IATA), designed as decision trees constructed of decision nodes. Each decision node asks the questions and provides the methods needed to obtain the most relevant information. This White paper outlines existing and novel methods to assess similarity of the data generated for each decision node, either via a pairwise analysis conducted property-by-property, or by assessing multiple decision nodes simultaneously via a multidimensional analysis. For the pairwise comparison conducted property-by-property we included in this White paper: The x-fold, Bayesian and Arsinh-OWA distance algorithms performed comparably in the scoring of similarity between NF pairs. The Euclidean distance was also useful, but only with proper data transformation. The x-fold method does not standardize data, and thus produces skewed histograms, but has the advantage that it can be implemented without programming knowhow. A range of multidimensional evaluations, using for example dendrogram clustering approaches, were also investigated. Multidimensional distance metrics were demonstrated to be difficult to use in a regulatory context, but from a scientific perspective were found to offer unexpected insights into the overall similarity of very different materials. In conclusion, for regulatory purposes, a property-by-property evaluation of the data matrix is recommended to substantiate grouping, while the multidimensional approaches are considered to be tools of discovery rather than regulatory methods.
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Affiliation(s)
| | - Eric Bleeker
- National Institute for Public Health and the Environment (RIVM), Bilthoven, the Netherlands
| | - Richard Cross
- UKRI Centre for Ecology and Hydrology, MacLean Building, Benson Lane, Wallingford OX10 8BB, UK
| | - Andrea Haase
- German Federal Institute for Risk Assessment (BfR), Department of Chemical and Product Safety, Berlin, Germany
| | - Gemma Janer
- LEITAT Technological Center, Barcelona, Spain
| | - Willie Peijnenburg
- National Institute for Public Health and the Environment (RIVM), Bilthoven, the Netherlands; Institute of Environmental Sciences (CML), Leiden University, Leiden, the Netherlands
| | - Mario Pink
- German Federal Institute for Risk Assessment (BfR), Department of Chemical and Product Safety, Berlin, Germany
| | - Hubert Rauscher
- European Commission, Joint Research Centre (JRC), Ispra, Italy
| | - Claus Svendsen
- UKRI Centre for Ecology and Hydrology, MacLean Building, Benson Lane, Wallingford OX10 8BB, UK
| | - Georgia Tsiliki
- Athena-Research and Innovation Center in Information, Communication and Knowledge Technologies, Marousi, Greece
| | | | | | - Vicki Stone
- NanoSafety Research Group, Heriot-Watt University, Riccarton, Edinburgh EH14 4AS, UK
| | - Wendel Wohlleben
- BASF SE, Dept. Material Physics and Dept Experimental Toxicology & Ecology, Ludwigshafen, Germany.
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34
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Kovarich S, Cappelli CI. Use of In Silico Methods for Regulatory Toxicological Assessment of Pharmaceutical Impurities. Methods Mol Biol 2022; 2425:537-560. [PMID: 35188646 DOI: 10.1007/978-1-0716-1960-5_21] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
The use of novel non-testing methodologies to support the toxicological assessment of drug impurities is having a growing impact in the regulatory framework for pharmaceutical development and marketed products. For DNA reactive (mutagenic) impurities specific recommendations for the use of in silico structure-based approaches (namely (Q)SAR methodologies) are provided in the ICH M7 guideline. In 2018 a draft reflection paper has been published by EMA addressing open issues in the qualification approach of non-genotoxic impurities (NGI) according to the ICH Q3A/Q3B guidelines, and proposing the use of alternative testing strategies, including TTC, (Q)SAR, read-across, and in vitro approaches, to gather impurity-specific safety information.In the present chapter we describe a workflow to perform the safety assessment of drug impurities based on non-testing in silico methodologies. The proposed approach consists of a stepwise decision scheme including three key phases: PHASE 1: assessment of bacterial mutagenicity and consequent classification of impurities according to ICH M7; PHASE 2: risk characterization of mutagenic impurities (Classes 1, 2 or 3); PHASE 3: qualification of non-mutagenic impurities (Classes 4 or 5). The proposed decision scheme offers the possibility to acquire impurity-specific data, also if testing is not feasible, and to decide on further in vitro testing, besides meeting 3R's principle.
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Suter GW, Lizarraga LE. Clearly weighing the evidence in read-across can improve assessments of data-poor chemicals. Regul Toxicol Pharmacol 2021; 129:105111. [PMID: 34973387 DOI: 10.1016/j.yrtph.2021.105111] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2021] [Revised: 12/22/2021] [Accepted: 12/27/2021] [Indexed: 02/07/2023]
Abstract
This paper provides a systematic weight-of-evidence method for read-across analyses of data-poor chemicals. The read-across technique extrapolates toxicity from analogous chemicals for which suitable test data are available to a target chemical. To determine that a candidate analogue is the 'best' and is sufficiently similar, the evidence for similarity of each candidate analogue to the target is weighed. We present a systematic weight of evidence method that provides transparency and imposes a consistent and rigorous inferential process. The method assembles relevant information concerning structure, physicochemical attributes, toxicokinetics, and toxicodynamics of the target and analogues. The information is then organized by evidence types and subtypes and weighted in terms of properties: relevance, strength, and reliability into weight levels, expressed as symbols. After evidence types are weighted, the bodies of evidence are weighted for collective properties: number, diversity, and coherence. Finally, the weights for the types and bodies of evidence are weighed for each analogue, and, if the overall weight of evidence is sufficient for one or more analogues, the analogue with the greatest weight is used to estimate the endpoint effect. We illustrate this WoE approach with a read-across analysis for screening the organochlorine contaminant, p,p'-dichlorodiphenyldichloroethane (DDD), for noncancer oral toxicity.
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Affiliation(s)
- Glenn W Suter
- Office of Research and Development, Emeritus, U.S. Environmental Protection Agency, 26 W. Martin L. King Drive, Cincinnati, OH, 45268, USA.
| | - Lucina E Lizarraga
- Center for Public Health and Environmental Assessment, U.S. Environmental Protection Agency, 26 W. Martin L. King Drive, Cincinnati, OH, 45268, USA.
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36
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Araya S, Pfister T, Gromek K, Hawkins W, Thomsen ST, Clemann N, Faltermann S, Wiesner L. PDE concept for controlling cleaning agent residues in pharmaceuticals- A critical analysis. Regul Toxicol Pharmacol 2021; 128:105095. [PMID: 34890761 DOI: 10.1016/j.yrtph.2021.105095] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2021] [Revised: 11/05/2021] [Accepted: 12/02/2021] [Indexed: 02/07/2023]
Abstract
Cleaning agents (CAs) are used in multipurpose facilities to control carryover contamination of active pharmaceutical ingredients (APIs) to scientifically justified limits. While this is often done with the PDE methodology used for API impurities, it is unclear if it is justifiable and necessary for cleaning agents, which generally represent a comparatively lower health risk. Comparing calculated oral PDE values for CA ingredients (CAIs) from four companies with PDEs of a selected number of small-molecule APIs showed that the toxicity of CAIs is several orders of magnitude lower. Furthermore, a critical review of the toxicity and everyday exposure to the general population of the main CAIs functional groups showed that the expected health risks are generally negligible. This is particularly true if the associated mode of actions cause local toxicity that is usually irrelevant at the concentration of potential residue carryover. This work points towards alternative approaches to the PDE concept to control CAIs' contamination and provides some guidance on grouping and identifying compounds with lower health risks based on exposure and mode of action reasoning. In addition, this work supports the concept that limit values should only be set for CAIs of toxicological concern.
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Affiliation(s)
| | | | | | - W Hawkins
- SafeBridge Europe Ltd., United Kingdom
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37
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Wehr MM, Sarang SS, Rooseboom M, Boogaard PJ, Karwath A, Escher SE. RespiraTox - Development of a QSAR model to predict human respiratory irritants. Regul Toxicol Pharmacol 2021; 128:105089. [PMID: 34861320 DOI: 10.1016/j.yrtph.2021.105089] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2021] [Revised: 11/17/2021] [Accepted: 11/23/2021] [Indexed: 11/25/2022]
Abstract
Respiratory irritation is an important human health endpoint in chemical risk assessment. There are two established modes of action of respiratory irritation, 1) sensory irritation mediated by the interaction with sensory neurons, potentially stimulating trigeminal nerve, and 2) direct tissue irritation. The aim of our research was to, develop a QSAR method to predict human respiratory irritants, and to potentially reduce the reliance on animal testing for the identification of respiratory irritants. Compounds are classified as irritating based on combined evidence from different types of toxicological data, including inhalation studies with acute and repeated exposure. The curated project database comprised 1997 organic substances, 1553 being classified as irritating and 444 as non-irritating. A comparison of machine learning approaches, including Logistic Regression (LR), Random Forests (RFs), and Gradient Boosted Decision Trees (GBTs), showed, the best classification was obtained by GBTs. The LR model resulted in an area under the curve (AUC) of 0.65, while the optimal performance for both RFs and GBTs gives an AUC of 0.71. In addition to the classification and the information on the applicability domain, the web-based tool provides a list of structurally similar analogues together with their experimental data to facilitate expert review for read-across purposes.
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Affiliation(s)
- Matthias M Wehr
- Fraunhofer Institute for Toxicology and Experimental Medicine - ITEM, Hannover, Germany.
| | | | | | - Peter J Boogaard
- Shell International, Shell Health, The Hague, Netherlands; Wageningen University & Research, Wageningen, Netherlands
| | | | - Sylvia E Escher
- Fraunhofer Institute for Toxicology and Experimental Medicine - ITEM, Hannover, Germany.
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38
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Varsou DD, Ellis LJA, Afantitis A, Melagraki G, Lynch I. Ecotoxicological read-across models for predicting acute toxicity of freshly dispersed versus medium-aged NMs to Daphnia magna. Chemosphere 2021; 285:131452. [PMID: 34265725 DOI: 10.1016/j.chemosphere.2021.131452] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/24/2021] [Revised: 06/29/2021] [Accepted: 07/04/2021] [Indexed: 06/13/2023]
Abstract
Nanoinformatics models to predict the toxicity/ecotoxicity of nanomaterials (NMs) are urgently needed to support commercialization of nanotechnologies and allow grouping of NMs based on their physico-chemical and/or (eco)toxicological properties, to facilitate read-across of knowledge from data-rich NMs to data-poor ones. Here we present the first ecotoxicological read-across models for predicting NMs ecotoxicity, which were developed in accordance with ECHA's recommended strategy for grouping of NMs as a means to explore in silico the effects of a panel of freshly dispersed versus environmentally aged (in various media) Ag and TiO2 NMs on the freshwater zooplankton Daphnia magna, a keystone species used in regulatory testing. The dataset used to develop the models consisted of dose-response data from 11 NMs (5 TiO2 NMs of identical cores with different coatings, and 6 Ag NMs with different capping agents/coatings) each dispersed in three different media (a high hardness medium (HH Combo) and two representative river waters containing different amounts of natural organic matter (NOM) and having different ionic strengths), generated in accordance with the OECD 202 immobilization test. The experimental hypotheses being tested were (1) that the presence of NOM in the medium would reduce the toxicity of the NMs by forming an ecological corona, and (2) that environmental ageing of NMs reduces their toxicity compared to the freshly dispersed NMs irrespective of the medium composition (salt only or NOM-containing). As per the ECHA guidance, the NMs were grouped into two categories - freshly dispersed and 2-year-aged and explored in silico to identify the most important features driving the toxicity in each group. The final predictive models have been validated according to the OECD criteria and a QSAR model report form (QMRF) report included in the supplementary information to support adoption of the models for regulatory purposes.
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Affiliation(s)
| | - Laura-Jayne A Ellis
- School of Geography, Earth and Environmental Sciences, University of Birmingham, B15 2TT, Birmingham, UK
| | | | - Georgia Melagraki
- Division of Physical Sciences and Applications, Hellenic Military Academy, Vari, Greece.
| | - Iseult Lynch
- School of Geography, Earth and Environmental Sciences, University of Birmingham, B15 2TT, Birmingham, UK.
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39
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Bassan A, Alves VM, Amberg A, Anger LT, Auerbach S, Beilke L, Bender A, Cronin MT, Cross KP, Hsieh JH, Greene N, Kemper R, Kim MT, Mumtaz M, Noeske T, Pavan M, Pletz J, Russo DP, Sabnis Y, Schaefer M, Szabo DT, Valentin JP, Wichard J, Williams D, Woolley D, Zwickl C, Myatt GJ. In silico approaches in organ toxicity hazard assessment: current status and future needs in predicting liver toxicity. Comput Toxicol 2021; 20:100187. [PMID: 35340402 PMCID: PMC8955833 DOI: 10.1016/j.comtox.2021.100187] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/15/2023]
Abstract
Hepatotoxicity is one of the most frequently observed adverse effects resulting from exposure to a xenobiotic. For example, in pharmaceutical research and development it is one of the major reasons for drug withdrawals, clinical failures, and discontinuation of drug candidates. The development of faster and cheaper methods to assess hepatotoxicity that are both more sustainable and more informative is critically needed. The biological mechanisms and processes underpinning hepatotoxicity are summarized and experimental approaches to support the prediction of hepatotoxicity are described, including toxicokinetic considerations. The paper describes the increasingly important role of in silico approaches and highlights challenges to the adoption of these methods including the lack of a commonly agreed upon protocol for performing such an assessment and the need for in silico solutions that take dose into consideration. A proposed framework for the integration of in silico and experimental information is provided along with a case study describing how computational methods have been used to successfully respond to a regulatory question concerning non-genotoxic impurities in chemically synthesized pharmaceuticals.
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Affiliation(s)
- Arianna Bassan
- Innovatune srl, Via Giulio Zanon 130/D, 35129 Padova, Italy
| | - Vinicius M. Alves
- The National Institute of Environmental Health Sciences, Division of the National Toxicology, Program, Research Triangle Park, NC 27709, USA
| | - Alexander Amberg
- Sanofi, R&D Preclinical Safety Frankfurt, Industriepark Hoechst, D-65926 Frankfurt am Main, Germany
| | | | - Scott Auerbach
- The National Institute of Environmental Health Sciences, Division of the National Toxicology, Program, Research Triangle Park, NC 27709, USA
| | - Lisa Beilke
- Toxicology Solutions Inc., San Diego, CA, USA
| | - Andreas Bender
- AI and Data Analytics, Clinical Pharmacology and Safety Sciences, R&D, AstraZeneca, Cambridge, UK
- Centre for Molecular Informatics, Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW
| | - Mark T.D. Cronin
- School of Pharmacy and Biomolecular Sciences, Liverpool John Moores University, Liverpool, L3 3AF, UK
| | | | - Jui-Hua Hsieh
- The National Institute of Environmental Health Sciences, Division of the National Toxicology, Program, Research Triangle Park, NC 27709, USA
| | - Nigel Greene
- Data Science and AI, DSM, IMED Biotech Unit, AstraZeneca, Boston, USA
| | - Raymond Kemper
- Nuvalent, One Broadway, 14th floor, Cambridge, MA, 02142, USA
| | - Marlene T. Kim
- US Food and Drug Administration, Center for Drug Evaluation and Research, Silver Spring, MD, 20993, USA
| | - Moiz Mumtaz
- Office of the Associate Director for Science (OADS), Agency for Toxic Substances and Disease, Registry, US Department of Health and Human Services, Atlanta, GA, USA
| | - Tobias Noeske
- Imaging and Data Analytics, Clinical Pharmacology & Safety Sciences, R&D, AstraZeneca, Gothenburg, Sweden
| | - Manuela Pavan
- Innovatune srl, Via Giulio Zanon 130/D, 35129 Padova, Italy
| | - Julia Pletz
- School of Pharmacy and Biomolecular Sciences, Liverpool John Moores University, Liverpool, L3 3AF, UK
| | - Daniel P. Russo
- Department of Chemistry, Rutgers University, Camden, NJ 08102, USA
- The Rutgers Center for Computational and Integrative Biology, Camden, NJ 08102, USA
| | - Yogesh Sabnis
- UCB Biopharma SRL, Chemin du Foriest – B-1420 Braine-l’Alleud, Belgium
| | - Markus Schaefer
- Sanofi, R&D Preclinical Safety Frankfurt, Industriepark Hoechst, D-65926 Frankfurt am Main, Germany
| | | | | | - Joerg Wichard
- Bayer AG, Genetic Toxicology, Müllerstr. 178, 13353 Berlin, Germany
| | - Dominic Williams
- Functional & Mechanistic Safety, Clinical Pharmacology & Safety Sciences, AstraZeneca, Darwin Building 310, Cambridge Science Park, Milton Rd, Cambridge CB4 0FZ, UK
| | - David Woolley
- ForthTox Limited, PO Box 13550, Linlithgow, EH49 7YU, UK
| | - Craig Zwickl
- Transendix LLC, 1407 Moores Manor, Indianapolis, IN 46229, USA
| | - Glenn J. Myatt
- Instem, 1393 Dublin Road, Columbus, OH 43215. USA
- Corresponding author. (G.J. Myatt)
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40
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Bassan A, Alves VM, Amberg A, Anger LT, Beilke L, Bender A, Bernal A, Cronin MT, Hsieh JH, Johnson C, Kemper R, Mumtaz M, Neilson L, Pavan M, Pointon A, Pletz J, Ruiz P, Russo DP, Sabnis Y, Sandhu R, Schaefer M, Stavitskaya L, Szabo DT, Valentin JP, Woolley D, Zwickl C, Myatt GJ. In silico approaches in organ toxicity hazard assessment: Current status and future needs for predicting heart, kidney and lung toxicities. Comput Toxicol 2021; 20:100188. [PMID: 35721273 PMCID: PMC9205464 DOI: 10.1016/j.comtox.2021.100188] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/12/2023]
Abstract
The kidneys, heart and lungs are vital organ systems evaluated as part of acute or chronic toxicity assessments. New methodologies are being developed to predict these adverse effects based on in vitro and in silico approaches. This paper reviews the current state of the art in predicting these organ toxicities. It outlines the biological basis, processes and endpoints for kidney toxicity, pulmonary toxicity, respiratory irritation and sensitization as well as functional and structural cardiac toxicities. The review also covers current experimental approaches, including off-target panels from secondary pharmacology batteries. Current in silico approaches for prediction of these effects and mechanisms are described as well as obstacles to the use of in silico methods. Ultimately, a commonly accepted protocol for performing such assessment would be a valuable resource to expand the use of such approaches across different regulatory and industrial applications. However, a number of factors impede their widespread deployment including a lack of a comprehensive mechanistic understanding, limited in vitro testing approaches and limited in vivo databases suitable for modeling, a limited understanding of how to incorporate absorption, distribution, metabolism, and excretion (ADME) considerations into the overall process, a lack of in silico models designed to predict a safe dose and an accepted framework for organizing the key characteristics of these organ toxicants.
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Affiliation(s)
- Arianna Bassan
- Innovatune srl, Via Giulio Zanon 130/D, 35129 Padova, Italy
| | - Vinicius M. Alves
- The National Institute of Environmental Health Sciences, Division of the National Toxicology Program, Research Triangle Park, NC 27709, United States
| | - Alexander Amberg
- Sanofi, R&D Preclinical Safety Frankfurt, Industriepark Hoechst, D-65926 Frankfurt am Main, Germany
| | - Lennart T. Anger
- Genentech, Inc., 1 DNA Way, South San Francisco, CA 94080, United States
| | - Lisa Beilke
- Toxicology Solutions Inc., San Diego, CA, United States
| | - Andreas Bender
- AI and Data Analytics, Clinical Pharmacology and Safety Sciences, R&D, AstraZeneca, Cambridge, UK
- Centre for Molecular Informatics, Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, United States
| | | | - Mark T.D. Cronin
- School of Pharmacy and Biomolecular Sciences, Liverpool John Moores University, Liverpool, L3 3AF, UK
| | - Jui-Hua Hsieh
- The National Institute of Environmental Health Sciences, Division of the National Toxicology Program, Research Triangle Park, NC 27709, United States
| | | | - Raymond Kemper
- Nuvalent, One Broadway, 14th floor, Cambridge, MA 02142, United States
| | - Moiz Mumtaz
- Agency for Toxic Substances and Disease Registry, US Department of Health and Human Services, Atlanta, GA, United States
| | - Louise Neilson
- Broughton Nicotine Services, Oak Tree House, West Craven Drive, Earby, Lancashire BB18 6JZ UK
| | - Manuela Pavan
- Innovatune srl, Via Giulio Zanon 130/D, 35129 Padova, Italy
| | - Amy Pointon
- Functional and Mechanistic Safety, Clinical Pharmacology and Safety Sciences, R&D, AstraZeneca, Cambridge, UK
| | - Julia Pletz
- School of Pharmacy and Biomolecular Sciences, Liverpool John Moores University, Liverpool, L3 3AF, UK
| | - Patricia Ruiz
- Agency for Toxic Substances and Disease Registry, US Department of Health and Human Services, Atlanta, GA, United States
| | - Daniel P. Russo
- The Rutgers Center for Computational and Integrative Biology, Camden, NJ 08102, United States
- Department of Chemistry, Rutgers University, Camden, NJ 08102, United States
| | - Yogesh Sabnis
- UCB Biopharma SRL, Chemin du Foriest, B-1420 Braine-l’Alleud, Belgium
| | - Reena Sandhu
- SafeDose Ltd., 20 Dundas Street West, Suite 921, Toronto, Ontario M5G2H1, Canada
| | - Markus Schaefer
- Sanofi, R&D Preclinical Safety Frankfurt, Industriepark Hoechst, D-65926 Frankfurt am Main, Germany
| | - Lidiya Stavitskaya
- US Food and Drug Administration, Center for Drug Evaluation and Research, Silver Spring, MD 20993, USA
| | | | | | - David Woolley
- ForthTox Limited, PO Box 13550, Linlithgow, EH49 7YU, UK
| | - Craig Zwickl
- Transendix LLC, 1407 Moores Manor, Indianapolis, IN 46229, United States
| | - Glenn J. Myatt
- Instem, 1393 Dublin Road, Columbus, OH 43215, United States
- Corresponding author: (G.J. Myatt)
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41
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Tice RR, Bassan A, Amberg A, Anger LT, Beal MA, Bellion P, Benigni R, Birmingham J, Brigo A, Bringezu F, Ceriani L, Crooks I, Cross K, Elespuru R, Faulkner DM, Fortin MC, Fowler P, Frericks M, Gerets HHJ, Jahnke GD, Jones DR, Kruhlak NL, Lo Piparo E, Lopez-Belmonte J, Luniwal A, Luu A, Madia F, Manganelli S, Manickam B, Mestres J, Mihalchik-Burhans AL, Neilson L, Pandiri A, Pavan M, Rider CV, Rooney JP, Trejo-Martin A, Watanabe-Sailor KH, White AT, Woolley D, Myatt GJ. In Silico Approaches In Carcinogenicity Hazard Assessment: Current Status and Future Needs. Comput Toxicol 2021; 20. [PMID: 35368437 DOI: 10.1016/j.comtox.2021.100191] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
Historically, identifying carcinogens has relied primarily on tumor studies in rodents, which require enormous resources in both money and time. In silico models have been developed for predicting rodent carcinogens but have not yet found general regulatory acceptance, in part due to the lack of a generally accepted protocol for performing such an assessment as well as limitations in predictive performance and scope. There remains a need for additional, improved in silico carcinogenicity models, especially ones that are more human-relevant, for use in research and regulatory decision-making. As part of an international effort to develop in silico toxicological protocols, a consortium of toxicologists, computational scientists, and regulatory scientists across several industries and governmental agencies evaluated the extent to which in silico models exist for each of the recently defined 10 key characteristics (KCs) of carcinogens. This position paper summarizes the current status of in silico tools for the assessment of each KC and identifies the data gaps that need to be addressed before a comprehensive in silico carcinogenicity protocol can be developed for regulatory use.
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Affiliation(s)
- Raymond R Tice
- RTice Consulting, Hillsborough, North Carolina, 27278, USA
| | | | - Alexander Amberg
- Sanofi Preclinical Safety, Industriepark Höchst, 65926 Frankfurt, Germany
| | - Lennart T Anger
- Genentech, Inc., South San Francisco, California, 94080, USA
| | - Marc A Beal
- Healthy Environments and Consumer Safety Branch, Health Canada, Government of Canada, Ottawa, Ontario, Canada K1A 0K9
| | | | | | - Jeffrey Birmingham
- GlaxoSmithKline, David Jack Centre for R&D, Ware, Hertfordshire, SG12 0DP, United Kingdom
| | - Alessandro Brigo
- Roche Pharmaceutical Research & Early Development, Pharmaceutical Sciences, Roche Innovation, Center Basel, F. Hoffmann-La Roche Ltd, CH-4070, Basel, Switzerland
| | | | - Lidia Ceriani
- Humane Society International, 1000 Brussels, Belgium
| | - Ian Crooks
- British American Tobacco (Investments) Ltd, GR&D Centre, Southampton, SO15 8TL, United Kingdom
| | | | - Rosalie Elespuru
- Food and Drug Administration, Center for Devices and Radiological Health, Silver Spring, Maryland, 20993, USA
| | - David M Faulkner
- Chemical Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, California, USA
| | - Marie C Fortin
- Department of Pharmacology and Toxicology, Ernest Mario School of Pharmacy, Rutgers University, Piscataway, New Jersey, 08855, USA
| | - Paul Fowler
- FSTox Consulting (Genetic Toxicology), Northamptonshire, United Kingdom
| | | | | | - Gloria D Jahnke
- Division of the National Toxicology Program, National Institute of Environmental Health Sciences, Research Triangle Park, North Carolina, 27709, USA
| | | | - Naomi L Kruhlak
- Food and Drug Administration, Center for Drug Evaluation and Research, Silver Spring, Maryland, 20993, USA
| | - Elena Lo Piparo
- Chemical Food Safety Group, Nestlé Research, CH-1000 Lausanne 26, Switzerland
| | - Juan Lopez-Belmonte
- Cuts Ice Ltd Chemical Food Safety Group, Nestlé Research, CH-1000 Lausanne 26, Switzerland
| | - Amarjit Luniwal
- North American Science Associates (NAMSA) Inc., Minneapolis, Minnesota, 55426, USA
| | - Alice Luu
- Healthy Environments and Consumer Safety Branch, Health Canada, Government of Canada, Ottawa, Ontario, Canada K1A 0K9
| | - Federica Madia
- European Commission, Joint Research Centre (JRC), Ispra, Italy
| | - Serena Manganelli
- Chemical Food Safety Group, Nestlé Research, CH-1000 Lausanne 26, Switzerland
| | | | - Jordi Mestres
- IMIM Institut Hospital Del Mar d'Investigacions Mèdiques and Universitat Pompeu Fabra, Doctor Aiguader 88, Parc de Recerca Biomèdica, 08003 Barcelona, Spain; and Chemotargets SL, Baldiri Reixac 4, Parc Científic de Barcelona, 08028, Barcelona, Spain
| | | | - Louise Neilson
- Broughton Nicotine Services, Oak Tree House, Earby, Lancashire, BB18 6JZ United Kingdom
| | - Arun Pandiri
- Division of the National Toxicology Program, National Institute of Environmental Health Sciences, Research Triangle Park, North Carolina, 27709, USA
| | | | - Cynthia V Rider
- Division of the National Toxicology Program, National Institute of Environmental Health Sciences, Research Triangle Park, North Carolina, 27709, USA
| | - John P Rooney
- Integrated Laboratory Systems, LLC., Morrisville, North Carolina, 27560, USA
| | | | - Karen H Watanabe-Sailor
- School of Mathematical and Natural Sciences, Arizona State University, West Campus, Glendale, Arizona, 85306, USA
| | - Angela T White
- GlaxoSmithKline, David Jack Centre for R&D, Ware, Hertfordshire, SG12 0DP, United Kingdom
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42
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Escher SE, Aguayo-Orozco A, Benfenati E, Bitsch A, Braunbeck T, Brotzmann K, Bois F, van der Burg B, Castel J, Exner T, Gadaleta D, Gardner I, Goldmann D, Hatley O, Golbamaki N, Graepel R, Jennings P, Limonciel A, Long A, Maclennan R, Mombelli E, Norinder U, Jain S, Capinha LS, Taboureau OT, Tolosa L, Vrijenhoek NG, van Vugt-Lussenburg BMA, Walker P, van de Water B, Wehr M, White A, Zdrazil B, Fisher C. A read-across case study on chronic toxicity of branched carboxylic acids (1): Integration of mechanistic evidence from new approach methodologies (NAMs) to explore a common mode of action. Toxicol In Vitro 2021; 79:105269. [PMID: 34757180 DOI: 10.1016/j.tiv.2021.105269] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2021] [Revised: 10/17/2021] [Accepted: 10/27/2021] [Indexed: 02/04/2023]
Abstract
This read-across case study characterises thirteen, structurally similar carboxylic acids demonstrating the application of in vitro and in silico human-based new approach methods, to determine biological similarity. Based on data from in vivo animal studies, the read-across hypothesis is that all analogues are steatotic and so should be considered hazardous. Transcriptomic analysis to determine differentially expressed genes (DEGs) in hepatocytes served as first tier testing to confirm a common mode-of-action and identify differences in the potency of the analogues. An adverse outcome pathway (AOP) network for hepatic steatosis, informed the design of an in vitro testing battery, targeting AOP relevant MIEs and KEs, and Dempster-Shafer decision theory was used to systematically quantify uncertainty and to define the minimal testing scope. The case study shows that the read-across hypothesis is the critical core to designing a robust, NAM-based testing strategy. By summarising the current mechanistic understanding, an AOP enables the selection of NAMs covering MIEs, early KEs, and late KEs. Experimental coverage of the AOP in this way is vital since MIEs and early KEs alone are not confirmatory of progression to the AO. This strategy exemplifies the workflow previously published by the EUTOXRISK project driving a paradigm shift towards NAM-based NGRA.
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Affiliation(s)
- Sylvia E Escher
- Fraunhofer Institute for Toxicology and Experimental Medicine, Chemical Safety and Toxicology, Germany.
| | | | - Emilio Benfenati
- Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milano, Italy
| | - Annette Bitsch
- Fraunhofer Institute for Toxicology and Experimental Medicine, Chemical Safety and Toxicology, Germany
| | - Thomas Braunbeck
- Aquatic Ecology and Toxicology Group, Center for Organismal Studies, University of Heidelberg, Heidelberg, Germany
| | - Katharina Brotzmann
- Aquatic Ecology and Toxicology Group, Center for Organismal Studies, University of Heidelberg, Heidelberg, Germany
| | - Frederic Bois
- Certara UK Ltd, Simcyp Division, Sheffield, United Kingdom
| | | | - Jose Castel
- Instituto de Investigación Sanitaria La Fe, Valencia, Spain
| | | | - Domenico Gadaleta
- Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milano, Italy
| | - Iain Gardner
- Certara UK Ltd, Simcyp Division, Sheffield, United Kingdom
| | - Daria Goldmann
- University of Vienna, Department of Pharmaceutical Sciences, Division of Pharmaceutical Chemistry, Vienna, Austria
| | - Oliver Hatley
- Certara UK Ltd, Simcyp Division, Sheffield, United Kingdom
| | | | - Rabea Graepel
- Leiden Academic Centre for Drug Research (LACDR), Leiden University, Leiden, the Netherlands
| | - Paul Jennings
- Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | | | | | | | | | | | - Sankalp Jain
- University of Vienna, Department of Pharmaceutical Sciences, Division of Pharmaceutical Chemistry, Vienna, Austria
| | | | | | - Laia Tolosa
- Instituto de Investigación Sanitaria La Fe, Valencia, Spain
| | - Nanette G Vrijenhoek
- Leiden Academic Centre for Drug Research (LACDR), Leiden University, Leiden, the Netherlands
| | | | | | - Bob van de Water
- Leiden Academic Centre for Drug Research (LACDR), Leiden University, Leiden, the Netherlands
| | - Matthias Wehr
- Fraunhofer Institute for Toxicology and Experimental Medicine, Chemical Safety and Toxicology, Germany
| | - Andrew White
- Unilever Safety and Environmental Assurance Centre, Sharnbrook, Bedfordshire, United Kingdom
| | - Barbara Zdrazil
- University of Vienna, Department of Pharmaceutical Sciences, Division of Pharmaceutical Chemistry, Vienna, Austria
| | - Ciarán Fisher
- Certara UK Ltd, Simcyp Division, Sheffield, United Kingdom
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43
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Fortaner S, Mendoza-De Gyves E, Cole T, Lostia AM. Determination of in vitro metabolic hepatic clearance of valproic acid (VPA) and five analogues by UPLC-MS-QTOF, applicable in alternatives to animal testing. J Chromatogr B Analyt Technol Biomed Life Sci 2021; 1181:122893. [PMID: 34419711 DOI: 10.1016/j.jchromb.2021.122893] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2021] [Revised: 07/30/2021] [Accepted: 08/01/2021] [Indexed: 11/28/2022]
Abstract
Laboratory measurements of intrinsic clearance support the development of TK models, with potential relevance to weight of evidence toxicity assessments of xenobiotics, including read-across, the concept of predictive estimation by data extrapolation between chemicals of similar structure (analogues). In this work a procedure with analytical method for determination of in vitro hepatic metabolic clearance, relevant to biotransformation toxicokinetic (TK) modelling, is presented. Cryopreserved primary human hepatocytes represent a suitable cells, due to their biological characteristics, for providing an in vitro model for simulating in vivo metabolic clearance. The experimental part considered an adequate sequential time-frame for collecting samples and controls for all chemicals tested, including centrifugation and aliquoting of the corresponding fractions until the instrumental session. For the first time, in vitro hepatocyte intrinsic clearance was measured for six analogue test chemicals: valproic acid, 2-ethyl caproic acid, octanoic acid, valeric acid, 2-methyl butyric acid and 2-trans pentenoic acid, during incubated cell culture exposure up to 2 h or 3.5 h. The time dependence of any metabolism was determined from analysis of the supernatant at intervals using a new developed analytical method for UPLC coupled with QTOF mass spectrometer. The chemicals could then be ranked by their relative intrinsic clearance. The analyses were reproducible, with coherence of the calculated in vitro intrinsic clearance between experiments.
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Affiliation(s)
- Salvador Fortaner
- European Commission - Joint Research Centre, Chemical Safety and Alternative Methods Unit (F.3), Directorate F - Health, Consumers and Reference Materials, Via Enrico Fermi 2749, 21027 Ispra (VA), Italy.
| | - Emilio Mendoza-De Gyves
- European Commission - Joint Research Centre, Chemical Safety and Alternative Methods Unit (F.3), Directorate F - Health, Consumers and Reference Materials, Via Enrico Fermi 2749, 21027 Ispra (VA), Italy
| | - Thomas Cole
- European Commission - Joint Research Centre, Chemical Safety and Alternative Methods Unit (F.3), Directorate F - Health, Consumers and Reference Materials, Via Enrico Fermi 2749, 21027 Ispra (VA), Italy
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Jacques C, Jamin EL, Jouanin I, Canlet C, Tremblay-Franco M, Martin JF, Zalko D, Brunel Y, Bessou-Touya S, Debrauwer L, Ferret PJ, Duplan H. Safety assessment of cosmetics by read across applied to metabolomics data of in vitro skin and liver models. Arch Toxicol 2021; 95:3303-22. [PMID: 34459931 DOI: 10.1007/s00204-021-03136-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2021] [Accepted: 08/11/2021] [Indexed: 10/20/2022]
Abstract
As a result of the cosmetics testing ban, safety evaluations of cosmetics ingredients must now be conducted using animal-free methods. A common approach is read across, which is mainly based on structural similarities but can also be conducted using biological endpoints. Here, metabolomics was used to assess biological effects to enable a read across between a candidate cosmetic ingredient, DIV665, only studied using in vitro assays, and a structurally similar reference compound, PA102, previously investigated using traditional in vivo toxicity methods. The (1) cutaneous distribution after topical application, (2) skin metabolism, (3) liver metabolism and (4) effect on the intracellular metabolomic profiles of in vitro skin and hepatic models, SkinEthic®RHE model and HepaRG® cells were investigated. The compounds exhibited similar skin penetration and skin and liver metabolism, with small differences attributed to their physicochemical properties. The effects of both compounds on the metabolome of RHE and HepaRG® cells were similarly small, both in terms of the metabolites modulated and the magnitude of changes. The patterns of metabolome changes did not fit with any known signature relating to a mode of action known to be linked to liver toxicity e.g. modification of the Krebs cycle, urea synthesis and lipid metabolism, were more reflective of transient adaptive responses. Overall, these studies indicate that PA102 is biologically similar to DIV665, allowing read across of safety endpoints, such as in vivo sub-chronic (but not reproduction toxicity) studies, for the former to be applied to DIV665. Based on this, in the absence of animal data (which is prohibited for new chemicals), it could be concluded that DIV665 applied according to the consumer topical use scenario, is similar to PA102, and is predicted to exhibit low local skin and systemic toxicity.
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45
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Kutsarova S, Mehmed A, Cherkezova D, Stoeva S, Georgiev M, Petkov T, Chapkanov A, Schultz TW, Mekenyan OG. Automated read-across workflow for predicting acute oral toxicity: I. The decision scheme in the QSAR toolbox. Regul Toxicol Pharmacol 2021; 125:105015. [PMID: 34293429 DOI: 10.1016/j.yrtph.2021.105015] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2021] [Revised: 06/17/2021] [Accepted: 07/15/2021] [Indexed: 11/17/2022]
Abstract
A decision-scheme outlining the steps for identifying the appropriate chemical category and subsequently appropriate tested source analog(s) for data gap filling of a target chemical by read-across is described. The primary features used in the grouping of the target chemical with source analogues within a database of 10,039 discrete organic substances include reactivity mechanisms associated with protein interactions and specific-acute-oral-toxicity-related mechanisms (e.g., mitochondrial uncoupling). Additionally, the grouping of chemicals making use of the in vivo rat metabolic simulator and neutral hydrolysis. Subsequently, a series of structure-based profilers are used to narrow the group to the most similar analogues. The scheme is implemented in the OECD QSAR Toolbox, so it automatically predicts acute oral toxicity as the rat oral LD50 value in log [1/mol/kg]. It was demonstrated that due to the inherent variability in experimental data, classification distribution should be employed as more adequate in comparison to the exact classification. It was proved that the predictions falling in the adjacent GSH categories to the experimentally-stated ones are acceptable given the variation in experimental data. The model performance estimated by adjacent accuracy was found to be 0.89 and 0.54 while based on R2. The mechanistic and predictive coverages were >0.85.
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Affiliation(s)
- Stela Kutsarova
- Laboratory of Mathematical Chemistry, Prof. As. Zlatarov University, Bourgas, Bulgaria
| | - Aycel Mehmed
- Laboratory of Mathematical Chemistry, Prof. As. Zlatarov University, Bourgas, Bulgaria
| | - Daniela Cherkezova
- Laboratory of Mathematical Chemistry, Prof. As. Zlatarov University, Bourgas, Bulgaria
| | - Stoyanka Stoeva
- Laboratory of Mathematical Chemistry, Prof. As. Zlatarov University, Bourgas, Bulgaria
| | - Marin Georgiev
- Laboratory of Mathematical Chemistry, Prof. As. Zlatarov University, Bourgas, Bulgaria
| | - Todor Petkov
- Laboratory of Mathematical Chemistry, Prof. As. Zlatarov University, Bourgas, Bulgaria
| | - Atanas Chapkanov
- Laboratory of Mathematical Chemistry, Prof. As. Zlatarov University, Bourgas, Bulgaria
| | - Terry W Schultz
- The University of Tennessee, College of Veterinary Medicine, Knoxville, TN, 37996-4500, USA
| | - Ovanes G Mekenyan
- Laboratory of Mathematical Chemistry, Prof. As. Zlatarov University, Bourgas, Bulgaria.
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46
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Abstract
Risk assessment of cosmetic ingredients is a useful scientific method to characterize potential adverse effects resulting from using cosmetics. The process of risk assessment consists of four steps: hazard identification, dose-response assessment, exposure assessment, and risk characterization. Hazard identification of chemicals refers to the initial stage of risk assessment and generally utilizes animal studies to evaluate toxicity. Since 2013, however, toxicity studies of cosmetic ingredients using animals have not been permitted in the EU and alternative toxicity test methods for animal studies have momentum to be developed for cosmetic ingredients. In this paper, we briefly review the alternative test methods that are available for cosmetic ingredients including read-across, in silico, in chemico, and invitro methods. In addition, new technologies such as omics and artificial intelligence (AI) have been discussed to expand or improve the knowledge and hazard identification of cosmetic ingredients. Aggregate exposure of cosmetic ingredients is another safety issue and methods for its improvement were reviewed. There have been concerns over the safety of nano-cosmetics for a long time, but the risk of nano-cosmetics remains unclear. Therefore, current issues of cosmetic risk assessment are discussed and expert opinion will be provided for the safety of cosmetics.
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Affiliation(s)
- Kyu-Bong Kim
- College of Pharmacy, Dankook University, Cheonan, Chungnam, South Korea
| | - Seung Jun Kwack
- Department of Bio Health Science, College of Natural Science, Changwon National University, Changwon, Gyeongnam, Suwon, Gyeonggi-Do, South Korea
| | - Joo Young Lee
- College of Pharmacy, The Catholic University of Korea, Bucheon, South Korea
| | - Sam Kacew
- McLaughlin Centre for Population Health Risk Assessment, University of Ottawa, Ottawa, ON, Canada
| | - Byung-Mu Lee
- Division of Toxicology, College of Pharmacy, Sungkyunkwan University, Suwon, Gyeonggi-do, Republic of Korea
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47
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Paini A, Worth A, Kulkarni S, Ebbrell D, Madden J. 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 DOI: 10.1016/j.comtox.2021.100159] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [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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48
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Bury D, Alexander-White C, Clewell HJ, Cronin M, Desprez B, Detroyer A, Efremenko A, Firman J, Hack E, Hewitt NJ, Kenna G, Klaric M, Lester C, Mahony C, Ouedraogo G, Paini A, Schepky A. New framework for a non-animal approach adequately assures the safety of cosmetic ingredients - A case study on caffeine. Regul Toxicol Pharmacol 2021; 123:104931. [PMID: 33905778 DOI: 10.1016/j.yrtph.2021.104931] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2020] [Revised: 03/11/2021] [Accepted: 04/13/2021] [Indexed: 11/19/2022]
Abstract
This case study on the model substance caffeine demonstrates the viability of a 10-step read-across (RAX) framework in practice. New approach methodologies (NAM), including RAX and physiologically-based kinetic (PBK) modelling were used to assess the consumer safety of caffeine. Appropriate animal systemic toxicity data were used from the most relevant RAX analogue while assuming that no suitable animal toxicity data were available for caffeine. Based on structural similarities, three primary metabolites of the target chemical caffeine (theophylline, theobromine and paraxanthine) were selected as its most relevant analogues, to estimate a point of departure in order to support a next generation risk assessment (NGRA). On the basis of the pivotal mode of action (MOA) of caffeine and other methylxanthines, theophylline appeared to be the most potent and suitable analogue. A worst-case aggregate exposure assessment determined consumer exposure to caffeine from different sources, such as cosmetics and food/drinks. Using a PBK model to estimate human blood concentrations following exposure to caffeine, an acceptable Margin of Internal Exposure (MOIE) of 27-fold was derived on the basis of a RAX using theophylline animal data, which suggests that the NGRA approach for caffeine is sufficiently conservative to protect human health.
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Affiliation(s)
- Dagmar Bury
- L'Oréal, Research & Innovation, 9 Rue Pierre Dreyfus, 92110, Clichy, France.
| | - Camilla Alexander-White
- MKTox & Co Ltd, 36 Fairford Crescent, Downhead Park, Milton Keynes, Buckinghamshire, MK15 9AQ, UK
| | - Harvey J Clewell
- Ramboll Health Sciences, 3107 Armand Street, Monroe, LA, 71201, USA
| | - Mark Cronin
- School of Pharmacy and Biomolecular Sciences, Liverpool John Moores University, Byrom Street, Liverpool, L3 AF, UK
| | - Bertrand Desprez
- Cosmetics Europe, 40 Avenue Hermann-Debroux, 1160, Brussels, Belgium
| | - Ann Detroyer
- L'Oréal, Research & Innovation, 1 Avenue Eugène Schueller, Aulnay-sous-Bois, France
| | | | - James Firman
- School of Pharmacy and Biomolecular Sciences, Liverpool John Moores University, Byrom Street, Liverpool, L3 AF, UK
| | - Eric Hack
- ScitoVation, Research Triangle Park, Durham, NC, USA
| | | | - Gerry Kenna
- Cosmetics Europe, 40 Avenue Hermann-Debroux, 1160, Brussels, Belgium
| | - Martina Klaric
- Cosmetics Europe, 40 Avenue Hermann-Debroux, 1160, Brussels, Belgium
| | | | | | - Gladys Ouedraogo
- L'Oréal, Research & Innovation, 1 Avenue Eugène Schueller, Aulnay-sous-Bois, France
| | - Alicia Paini
- European Commission Joint Research Centre, Ispra, Italy
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Nakagawa S, Okamoto M, Yoshihara K, Nukada Y, Morita O. Grouping of chemicals based on the potential mechanisms of hepatotoxicity of naphthalene and structurally similar chemicals using in vitro testing for read-across and its validation. Regul Toxicol Pharmacol 2021; 121:104874. [PMID: 33493583 DOI: 10.1016/j.yrtph.2021.104874] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2020] [Revised: 12/25/2020] [Accepted: 01/19/2021] [Indexed: 11/21/2022]
Abstract
Integrated Approaches to Testing and Assessment provides a framework to improve the reliability of read-across for chemical risk assessment of systemic toxicity without animal testing. However, the availability of only a few case studies hinders the use of this concept for regulatory purposes. Thus, we compared the biological similarity of structurally similar chemicals using in vitro testing to demonstrate the validity of this concept for grouping chemicals and to extract key considerations in read-across. We analyzed the hepatotoxicity of naphthalene and three chemicals structurally similar to naphthalene (2,7-naphthalenediol, 1,5-naphthalenediol, and 1-naphthol) for which 90-day repeated dose toxicity data are available. To elucidate and compare their potential mechanisms, we conducted in vitro microarray analysis using rat primary hepatocytes and validated the results using a biomarker and metabolic activation analysis. We observed that 2,7-naphthalenediol, 1,5-naphthalenediol, and 1-naphthol had similar potential mechanisms, namely, induction of oxidative stress by their metabolic activation. Conversely, naphthalene did not show a similar toxicity effect. The existing in vivo data confirmed our grouping of chemicals based on this potential mechanism. Thus, our findings suggest that in vitro toxicogenomics and related biochemical assays are useful for comparing biological similarities and grouping chemicals based on their toxicodynamics for read-across.
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Affiliation(s)
- Shota Nakagawa
- Kao Corporation, Safety Science Research, 2606, Akabane, Ichikai-Machi, Haga-Gun, Tochigi, 321-3497, Japan.
| | - Maiko Okamoto
- Kao Corporation, Safety Science Research, 2606, Akabane, Ichikai-Machi, Haga-Gun, Tochigi, 321-3497, Japan
| | - Keita Yoshihara
- Kao Corporation, Safety Science Research, 2606, Akabane, Ichikai-Machi, Haga-Gun, Tochigi, 321-3497, Japan
| | - Yuko Nukada
- Kao Corporation, Safety Science Research, 2606, Akabane, Ichikai-Machi, Haga-Gun, Tochigi, 321-3497, Japan
| | - Osamu Morita
- Kao Corporation, Safety Science Research, 2606, Akabane, Ichikai-Machi, Haga-Gun, Tochigi, 321-3497, Japan
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50
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Cerveny D, Grabic R, Grabicová K, Randák T, Larsson DGJ, Johnson AC, Jürgens MD, Tysklind M, Lindberg RH, Fick J. Neuroactive drugs and other pharmaceuticals found in blood plasma of wild European fish. Environ Int 2021; 146:106188. [PMID: 33096467 DOI: 10.1016/j.envint.2020.106188] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/06/2020] [Revised: 09/25/2020] [Accepted: 10/02/2020] [Indexed: 06/11/2023]
Abstract
To gain a better understanding of which pharmaceuticals could pose a risk to fish, 94 pharmaceuticals representing 23 classes were analyzed in blood plasma from wild bream, chub, and roach captured at 18 sites in Germany, the Czech Republic and the UK, respectively. Based on read across from humans, we evaluated the risks of pharmacological effects occurring in the fish for each measured pharmaceutical. Twenty-three compounds were found in fish plasma, with the highest levels measured in chub from the Czech Republic. None of the German bream had detectable levels of pharmaceuticals, whereas roach from the Thames had mostly low concentrations. For two pharmaceuticals, four individual Czech fish had plasma concentrations higher than the concentrations reached in the blood of human patients taking the corresponding medication. For nine additional compounds, determined concentrations exceeded 10% of the corresponding human therapeutic plasma concentration in 12 fish. The majority of the pharmaceuticals where a clear risk for pharmacological effects was identified targets the central nervous system. These include e.g. flupentixol, haloperidol, and risperidone, all of which have the potential to affect fish behavior. In addition to identifying pharmaceuticals of environmental concern, the results emphasize the value of environmental monitoring of internal drug levels in aquatic wildlife, as well as the need for more research to establish concentration-response relationships.
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Affiliation(s)
- Daniel Cerveny
- Department of Wildlife, Fish, and Environmental Studies, Swedish University of Agricultural Sciences, Umeå, Sweden; University of South Bohemia in České Budějovice, Faculty of Fisheries and Protection of Waters, South Bohemian Research Center of Aquaculture and Biodiversity of Hydrocenoses, Zátiší 728/II, Vodňany, Czech Republic.
| | - Roman Grabic
- University of South Bohemia in České Budějovice, Faculty of Fisheries and Protection of Waters, South Bohemian Research Center of Aquaculture and Biodiversity of Hydrocenoses, Zátiší 728/II, Vodňany, Czech Republic
| | - Kateřina Grabicová
- University of South Bohemia in České Budějovice, Faculty of Fisheries and Protection of Waters, South Bohemian Research Center of Aquaculture and Biodiversity of Hydrocenoses, Zátiší 728/II, Vodňany, Czech Republic
| | - Tomáš Randák
- University of South Bohemia in České Budějovice, Faculty of Fisheries and Protection of Waters, South Bohemian Research Center of Aquaculture and Biodiversity of Hydrocenoses, Zátiší 728/II, Vodňany, Czech Republic
| | - D G Joakim Larsson
- Department of Infectious Diseases, Institute of Biomedicine, University of Gothenburg, Sweden; Centre for Antibiotic Resistance Research (CARe) at the University of Gothenburg, Sweden
| | - Andrew C Johnson
- UK Centre for Ecology and Hydrology, Wallingford OX10 8BB, United Kingdom
| | - Monika D Jürgens
- UK Centre for Ecology and Hydrology, Wallingford OX10 8BB, United Kingdom
| | - Mats Tysklind
- Department of Chemistry, Umeå University, Umeå, Sweden
| | | | - Jerker Fick
- Department of Chemistry, Umeå University, Umeå, Sweden
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