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Banerjee A, Roy K. Read-across-based intelligent learning: development of a global q-RASAR model for the efficient quantitative predictions of skin sensitization potential of diverse organic chemicals. ENVIRONMENTAL SCIENCE. PROCESSES & IMPACTS 2023; 25:1626-1644. [PMID: 37682520 DOI: 10.1039/d3em00322a] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/09/2023]
Abstract
Environmental chemicals and contaminants cause a wide array of harmful implications to terrestrial and aquatic life which ranges from skin sensitization to acute oral toxicity. The current study aims to assess the quantitative skin sensitization potential of a large set of industrial and environmental chemicals acting through different mechanisms using the novel quantitative Read-Across Structure-Activity Relationship (q-RASAR) approach. Based on the identified important set of structural and physicochemical features, Read-Across-based hyperparameters were optimized using the training set compounds followed by the calculation of similarity and error-based RASAR descriptors. Data fusion, further feature selection, and removal of prediction confidence outliers were performed to generate a partial least squares (PLS) q-RASAR model, followed by the application of various Machine Learning (ML) tools to check the quality of predictions. The PLS model was found to be the best among different models. A simple user-friendly Java-based software tool was developed based on the PLS model, which efficiently predicts the toxicity value(s) of query compound(s) along with their status of Applicability Domain (AD) in terms of leverage values. This model has been developed using structurally diverse compounds and is expected to predict efficiently and quantitatively the skin sensitization potential of environmental chemicals to estimate their occupational and health hazards.
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Affiliation(s)
- Arkaprava Banerjee
- 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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Zhu T, Yu Y, Tao T. A comprehensive evaluation of liposome/water partition coefficient prediction models based on the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) method: Challenges from different descriptor dimension reduction methods and machine learning algorithms. JOURNAL OF HAZARDOUS MATERIALS 2023; 443:130181. [PMID: 36257111 DOI: 10.1016/j.jhazmat.2022.130181] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/31/2022] [Revised: 10/09/2022] [Accepted: 10/10/2022] [Indexed: 06/16/2023]
Abstract
The liposome/water partition coefficient (Klip/w) is a key parameter to evaluate the bioaccumulation potential of pollutants. Considering that it is difficult to determine the Klip/w values of all pollutants through experiments, researchers gradually developed models to predict it. However, there is currently no research on how to comprehensively evaluate prediction models and recommend a compelling optimal modeling method. To remedy the defect of single parameters in a traditional model comparison, the TOPSIS evaluation method, based on entropy weight, was first proposed. We use this method to comprehensively evaluate models from multiple angles in this study. Thirty QSPR models, including 3 descriptor dimension reduction methods and 10 algorithms (belonging to 4 tribes), were used to predict Klip/w and verify the effectiveness of the comprehensive assessment method. The results showed that RF (descriptor dimension reduction method), symbolism (tribes) and RF (algorithm) exhibited significant advantages in establishing the Klip/w value prediction model. At present, the application of TOPSIS in environmental model evaluations is almost absent. We hope that the proposed TOPSIS evaluation method can be applied to more chemical datasets and provide a more systematic and comprehensive basis for the application of the QSPR model in environmental studies and other fields.
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Affiliation(s)
- Tengyi Zhu
- School of Environmental Science and Engineering, Yangzhou University, Yangzhou 225127, Jiangsu, China.
| | - Yan Yu
- School of Environmental Science and Engineering, Yangzhou University, Yangzhou 225127, Jiangsu, China
| | - Tianyun Tao
- College of Agriculture, Yangzhou University, Yangzhou 225009, Jiangsu, China
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3
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Chou WC, Lin Z. Machine learning and artificial intelligence in physiologically based pharmacokinetic modeling. Toxicol Sci 2023; 191:1-14. [PMID: 36156156 PMCID: PMC9887681 DOI: 10.1093/toxsci/kfac101] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/03/2023] Open
Abstract
Physiologically based pharmacokinetic (PBPK) models are useful tools in drug development and risk assessment of environmental chemicals. PBPK model development requires the collection of species-specific physiological, and chemical-specific absorption, distribution, metabolism, and excretion (ADME) parameters, which can be a time-consuming and expensive process. This raises a need to create computational models capable of predicting input parameter values for PBPK models, especially for new compounds. In this review, we summarize an emerging paradigm for integrating PBPK modeling with machine learning (ML) or artificial intelligence (AI)-based computational methods. This paradigm includes 3 steps (1) obtain time-concentration PK data and/or ADME parameters from publicly available databases, (2) develop ML/AI-based approaches to predict ADME parameters, and (3) incorporate the ML/AI models into PBPK models to predict PK summary statistics (eg, area under the curve and maximum plasma concentration). We also discuss a neural network architecture "neural ordinary differential equation (Neural-ODE)" that is capable of providing better predictive capabilities than other ML methods when used to directly predict time-series PK profiles. In order to support applications of ML/AI methods for PBPK model development, several challenges should be addressed (1) as more data become available, it is important to expand the training set by including the structural diversity of compounds to improve the prediction accuracy of ML/AI models; (2) due to the black box nature of many ML models, lack of sufficient interpretability is a limitation; (3) Neural-ODE has great potential to be used to generate time-series PK profiles for new compounds with limited ADME information, but its application remains to be explored. Despite existing challenges, ML/AI approaches will continue to facilitate the efficient development of robust PBPK models for a large number of chemicals.
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Affiliation(s)
- Wei-Chun Chou
- Department of Environmental and Global Health, College of Public Health and Health Professions, University of Florida, Gainesville, FL 32610, USA
- Center for Environmental and Human Toxicology, University of Florida, Gainesville, FL 32608, USA
| | - Zhoumeng Lin
- Department of Environmental and Global Health, College of Public Health and Health Professions, University of Florida, Gainesville, FL 32610, USA
- Center for Environmental and Human Toxicology, University of Florida, Gainesville, FL 32608, USA
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4
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Chang X, Tan YM, Allen DG, Bell S, Brown PC, Browning L, Ceger P, Gearhart J, Hakkinen PJ, Kabadi SV, Kleinstreuer NC, Lumen A, Matheson J, Paini A, Pangburn HA, Petersen EJ, Reinke EN, Ribeiro AJS, Sipes N, Sweeney LM, Wambaugh JF, Wange R, Wetmore BA, Mumtaz M. IVIVE: Facilitating the Use of In Vitro Toxicity Data in Risk Assessment and Decision Making. TOXICS 2022; 10:232. [PMID: 35622645 PMCID: PMC9143724 DOI: 10.3390/toxics10050232] [Citation(s) in RCA: 35] [Impact Index Per Article: 17.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/25/2022] [Accepted: 04/24/2022] [Indexed: 02/04/2023]
Abstract
During the past few decades, the science of toxicology has been undergoing a transformation from observational to predictive science. New approach methodologies (NAMs), including in vitro assays, in silico models, read-across, and in vitro to in vivo extrapolation (IVIVE), are being developed to reduce, refine, or replace whole animal testing, encouraging the judicious use of time and resources. Some of these methods have advanced past the exploratory research stage and are beginning to gain acceptance for the risk assessment of chemicals. A review of the recent literature reveals a burst of IVIVE publications over the past decade. In this review, we propose operational definitions for IVIVE, present literature examples for several common toxicity endpoints, and highlight their implications in decision-making processes across various federal agencies, as well as international organizations, including those in the European Union (EU). The current challenges and future needs are also summarized for IVIVE. In addition to refining and reducing the number of animals in traditional toxicity testing protocols and being used for prioritizing chemical testing, the goal to use IVIVE to facilitate the replacement of animal models can be achieved through their continued evolution and development, including a strategic plan to qualify IVIVE methods for regulatory acceptance.
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Affiliation(s)
- Xiaoqing Chang
- Inotiv-RTP, 601 Keystone Park Drive, Suite 200, Morrisville, NC 27560, USA; (X.C.); (D.G.A.); (S.B.); (L.B.); (P.C.)
| | - Yu-Mei Tan
- U.S. Environmental Protection Agency, Office of Pesticide Programs, 109 T.W. Alexander Drive, Durham, NC 27709, USA;
| | - David G. Allen
- Inotiv-RTP, 601 Keystone Park Drive, Suite 200, Morrisville, NC 27560, USA; (X.C.); (D.G.A.); (S.B.); (L.B.); (P.C.)
| | - Shannon Bell
- Inotiv-RTP, 601 Keystone Park Drive, Suite 200, Morrisville, NC 27560, USA; (X.C.); (D.G.A.); (S.B.); (L.B.); (P.C.)
| | - Paul C. Brown
- U.S. Food and Drug Administration, Center for Drug Evaluation and Research, 10903 New Hampshire Avenue, Silver Spring, MD 20903, USA; (P.C.B.); (A.J.S.R.); (R.W.)
| | - Lauren Browning
- Inotiv-RTP, 601 Keystone Park Drive, Suite 200, Morrisville, NC 27560, USA; (X.C.); (D.G.A.); (S.B.); (L.B.); (P.C.)
| | - Patricia Ceger
- Inotiv-RTP, 601 Keystone Park Drive, Suite 200, Morrisville, NC 27560, USA; (X.C.); (D.G.A.); (S.B.); (L.B.); (P.C.)
| | - Jeffery Gearhart
- The Henry M. Jackson Foundation, Air Force Research Laboratory, 711 Human Performance Wing, Wright-Patterson Air Force Base, OH 45433, USA;
| | - Pertti J. Hakkinen
- National Library of Medicine, National Center for Biotechnology Information, 8600 Rockville Pike, Bethesda, MD 20894, USA;
| | - Shruti V. Kabadi
- U.S. Food and Drug Administration, Center for Food Safety and Applied Nutrition, Office of Food Additive Safety, 5001 Campus Drive, HFS-275, College Park, MD 20740, USA;
| | - Nicole C. Kleinstreuer
- National Institute of Environmental Health Sciences, National Toxicology Program Interagency Center for the Evaluation of Alternative Toxicological Methods, P.O. Box 12233, Research Triangle Park, NC 27709, USA;
| | - Annie Lumen
- U.S. Food and Drug Administration, National Center for Toxicological Research, 3900 NCTR Road, Jefferson, AR 72079, USA;
| | - Joanna Matheson
- U.S. Consumer Product Safety Commission, Division of Toxicology and Risk Assessment, 5 Research Place, Rockville, MD 20850, USA;
| | - Alicia Paini
- European Commission, Joint Research Centre (JRC), 21027 Ispra, Italy;
| | - Heather A. Pangburn
- Air Force Research Laboratory, 711 Human Performance Wing, 2729 R Street, Area B, Building 837, Wright-Patterson Air Force Base, OH 45433, USA;
| | - Elijah J. Petersen
- U.S. Department of Commerce, National Institute of Standards and Technology, 100 Bureau Drive, Gaithersburg, MD 20899, USA;
| | - Emily N. Reinke
- U.S. Army Public Health Center, 8252 Blackhawk Rd., Aberdeen Proving Ground, MD 21010, USA;
| | - Alexandre J. S. Ribeiro
- U.S. Food and Drug Administration, Center for Drug Evaluation and Research, 10903 New Hampshire Avenue, Silver Spring, MD 20903, USA; (P.C.B.); (A.J.S.R.); (R.W.)
| | - Nisha Sipes
- U.S. Environmental Protection Agency, Center for Computational Toxicology and Exposure, 109 TW Alexander Dr., Research Triangle Park, NC 27711, USA; (N.S.); (J.F.W.); (B.A.W.)
| | - Lisa M. Sweeney
- UES, Inc., 4401 Dayton-Xenia Road, Beavercreek, OH 45432, Assigned to Air Force Research Laboratory, 711 Human Performance Wing, Wright-Patterson Air Force Base, OH 45433, USA;
| | - John F. Wambaugh
- U.S. Environmental Protection Agency, Center for Computational Toxicology and Exposure, 109 TW Alexander Dr., Research Triangle Park, NC 27711, USA; (N.S.); (J.F.W.); (B.A.W.)
| | - Ronald Wange
- U.S. Food and Drug Administration, Center for Drug Evaluation and Research, 10903 New Hampshire Avenue, Silver Spring, MD 20903, USA; (P.C.B.); (A.J.S.R.); (R.W.)
| | - Barbara A. Wetmore
- U.S. Environmental Protection Agency, Center for Computational Toxicology and Exposure, 109 TW Alexander Dr., Research Triangle Park, NC 27711, USA; (N.S.); (J.F.W.); (B.A.W.)
| | - Moiz Mumtaz
- Agency for Toxic Substances and Disease Registry, Office of the Associate Director for Science, 1600 Clifton Road, S102-2, Atlanta, GA 30333, USA
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Zhu T, Gu Y, Cheng H, Chen M. Versatile modelling of polyoxymethylene-water partition coefficients for hydrophobic organic contaminants using linear and nonlinear approaches. THE SCIENCE OF THE TOTAL ENVIRONMENT 2020; 728:138881. [PMID: 32361362 DOI: 10.1016/j.scitotenv.2020.138881] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/25/2020] [Revised: 04/19/2020] [Accepted: 04/20/2020] [Indexed: 06/11/2023]
Abstract
Environmental fate or transport of hydrophobic organic contaminants (HOCs) depends on the partitioning properties of compounds within various environmental phases. Due to the wide application of polyoxymethylene (POM) in the passive sampling technique, several in silico models were developed to predict POM-water partition coefficients (KPOM-w) in accordance with the guidelines of the Organization for Economic Cooperation and Development (OECD). It is an attempt to combine conventional linear method (multiple linear regression, MLR) and popular nonlinear algorithm (artificial neural network, ANN) for estimating partition coefficients of HOCs. All models were performed on a dataset of 210 chemicals from 13 different classes. The polyparameter linear free energy relationship (pp-LFER) model included 5 molecular descriptors, namely, E, S, A, B and V, and predicted log KPOM-w with R2adj of 0.825. The values of statistical parameters including R2adj, Q2ext, RMSEtra and RMSEext for quantitative structure-property relationship (QSPR)-MLR and QSPR-ANN models with four descriptors (ALOGP, MeanDD, E1m and Mor24s) were: (0.928, 0.877, 0.498 and 0.649) and (0.943, 0.905, 0.443 and 0.571), with high similarity for both models, which confirmed the robustness, significance, and remarkable prediction accuracy of the QSPR models. Moreover, the mechanism interpretation revealed that the molecular volume and hydrophobicity had a major impact on distribution procedure of HOCs. The models developed herein, with the broad applicability domain (AD), provide suitable tools to fill the experimental data gap for untested chemicals and help researchers better understand the mechanistic basis of adsorption behavior of POM.
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Affiliation(s)
- Tengyi Zhu
- School of Environmental Science and Engineering, Yangzhou University, Yangzhou 225127, Jiangsu, China.
| | - Yuanyuan Gu
- School of Environmental Science and Engineering, Yangzhou University, Yangzhou 225127, Jiangsu, China
| | - Haomiao Cheng
- School of Environmental Science and Engineering, Yangzhou University, Yangzhou 225127, Jiangsu, China
| | - Ming Chen
- School of Civil Engineering, Southeast University, Nanjing 210096, China; Department of Engineering Science, University of Oxford, Oxford OX1 3PJ, UK
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Douziech M, Benítez-López A, Ernstoff A, Askham C, Hendriks AJ, King H, Huijbregts MAJ. A regression-based model to predict chemical migration from packaging to food. JOURNAL OF EXPOSURE SCIENCE & ENVIRONMENTAL EPIDEMIOLOGY 2020; 30:469-477. [PMID: 31641273 DOI: 10.1038/s41370-019-0185-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/15/2019] [Revised: 07/22/2019] [Accepted: 08/28/2019] [Indexed: 06/10/2023]
Abstract
Packaging materials can be a source of chemical contaminants in food. Process-based migration models (PMM) predict the chemical fraction transferred from packaging materials to food (FC) for application in prioritisation tools for human exposure. These models, however, have a relatively limited applicability domain and their predictive performance is typically low. To overcome these limitations, we developed a linear mixed-effects model (LMM) to statistically relate measured FC to properties of chemicals, food, packaging, and experimental conditions. We found a negative relationship between the molecular weight (MW) and FC, and a positive relationship with the fat content of the food depending on the octanol-water partitioning coefficient of the migrant. We also showed that large chemicals (MW > 400 g/mol) have a higher migration potential in packaging with low crystallinity compared with high crystallinity. The predictive performance of the LMM for chemicals not included in the database in contact with untested food items but known packaging material was higher (Coefficient of Efficiency (CoE) = 0.21) compared with a recently developed PMM (CoE = -5.24). We conclude that our empirical model is useful to predict chemical migration from packaging to food and prioritise chemicals in the absence of measurements.
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Affiliation(s)
- Mélanie Douziech
- Department of Environmental Science, Institute for Water and Wetland Research, Radboud University Nijmegen, P.O. Box 9010, 6500 GL, Nijmegen, The Netherlands.
| | - Ana Benítez-López
- Estación Biológica de Doñana, Integrative Ecology, Avd. Americo Vespucio s/n, 41001, Sevilla, Spain
| | - Alexi Ernstoff
- Quantis, EPFL Innovation Park-Bâtiment D, 1015, Lausanne, Switzerland
| | | | - A Jan Hendriks
- Department of Environmental Science, Institute for Water and Wetland Research, Radboud University Nijmegen, P.O. Box 9010, 6500 GL, Nijmegen, The Netherlands
| | - Henry King
- Safety & Environmental Assurance Centre, Unilever, Colworth Science Park, Bedfordshire, MK441LQ, UK
| | - Mark A J Huijbregts
- Department of Environmental Science, Institute for Water and Wetland Research, Radboud University Nijmegen, P.O. Box 9010, 6500 GL, Nijmegen, The Netherlands
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7
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Gholivand K, Mohammadpanah F, Pooyan M, Valmoozi AAE, Sharifi M, Mani-Varnosfaderani A, Hosseini Z. Synthesis, crystal structure, insecticidal activities, molecular docking and QSAR studies of some new phospho guanidines and phospho pyrazines as cholinesterase inhibitors. PESTICIDE BIOCHEMISTRY AND PHYSIOLOGY 2019; 157:122-137. [PMID: 31153459 DOI: 10.1016/j.pestbp.2019.03.010] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/02/2019] [Revised: 03/10/2019] [Accepted: 03/14/2019] [Indexed: 06/09/2023]
Abstract
Novel phospho guanidine and phospho pyrazine derivatives were synthesized and characterized by 31P, 13C, 1HNMR and IR spectroscopy to obtain novel and human-safe insecticides. Compound 35 [(C4H4N2NH)2P(O)(C6H6)] was investigated by X-ray crystallography. The inhibitory effects of synthesized compounds were evaluated on human and insect acetylcholinesterase (AChE) using in vitro Ellman method. A few of these compounds, which had low human toxicity, were selected for assessing the killing effects (in vivo) on the elm leaf beetle (X.luteola). The in vitro and in vivo results indicated that compounds bearing both phosphoryl groups and aromatic systems were found to possess a good selectivity for the inhibition of insect AChE over human AChE; up to 550-fold selectivity was achieved for compound 19. Docking studies were performed to explain reasons for the selective behavior of AChE inhibitors. Additionally, the quantitative structure-activity relationship (QSAR) and density functional theory (DFT) results of AChEs demonstrated that the size, shape, dipole moment, and ability to form hydrogen bond played the main role in both models. In addition, the aromatic π - π interactions and charge of the amide nitrogen had a major effect on insecticidal activity of the compounds. The present research can be helpful to gain a better understanding of the interactions between the insect AChE and its inhibitors and introduces compounds which are capable of becoming human-safe insecticides.
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Affiliation(s)
| | | | - Mahsa Pooyan
- Department of Chemistry, Tarbiat Modares University, Tehran, Iran
| | | | - Mahboobeh Sharifi
- Department of plant protection, Agricultural and Natural Resources Research Center, ARREO, Gorgan, Iran
| | | | - Zahra Hosseini
- Department of Chemistry, Tarbiat Modares University, Tehran, Iran
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Khan PM, Roy K, Benfenati E. Chemometric modeling of Daphnia magna toxicity of agrochemicals. CHEMOSPHERE 2019; 224:470-479. [PMID: 30831498 DOI: 10.1016/j.chemosphere.2019.02.147] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/30/2018] [Revised: 02/21/2019] [Accepted: 02/22/2019] [Indexed: 06/09/2023]
Abstract
Over the past few years, the ecotoxicological hazard potential of agrochemicals has received much attention in the industries and regulatory agencies. In the current work, we have developed quantitative structure-activity relationship (QSAR) models for Daphnia magna toxicities of different classes of agrochemicals (fungicides, herbicides, insecticides and microbiocides) individually as well as for the combined set with the application of Organization for Economic Co-operation and Development (OECD) recommended guidelines. The models for the individual data sets as well as for the combined set were generated employing only simple and interpretable two-dimensional descriptors, and subsequently strictly validated using test set compounds. The validated individual models were used to generate consensus models, with the objective to improve the prediction quality and reduced prediction errors. All the individual models of different classes of agrochemicals as well as the global set of agrochemicals showed encouraging statistical quality and prediction ability. The general observations from the derived models suggest that the toxicity increases with lipophilicity and decreases with polarity. The generated models of different classes of agrochemicals and also for the combined set should be applicable for data gap filling for new or untested agrochemical compounds falling within the applicability domain of the developed models.
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Affiliation(s)
- Pathan Mohsin Khan
- Department of Pharmacoinformatics, National Institute of Pharmaceutical Educational and Research (NIPER), Chunilal Bhawan, 168, Manikata Main Road, 700054, Kolkata, India
| | - Kunal Roy
- Drug Theoretics and Cheminformatics Laboratory, Department of Pharmaceutical Technology, Jadavpur University, 188 Raja S C Mullick Road, 700032, Kolkata, India; Laboratory of Environmental Chemistry and Toxicology, Department of Environmental Health Sciences, Istituto Di Ricerche Farmacologiche Mario Negri IRCCS, Via La Masa, 19, 20156, Milano, Italy.
| | - Emilio Benfenati
- Laboratory of Environmental Chemistry and Toxicology, Department of Environmental Health Sciences, Istituto Di Ricerche Farmacologiche Mario Negri IRCCS, Via La Masa, 19, 20156, Milano, Italy
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Paini A, Leonard J, Joossens E, Bessems J, Desalegn A, Dorne J, Gosling J, Heringa M, Klaric M, Kliment T, Kramer N, Loizou G, Louisse J, Lumen A, Madden J, Patterson E, Proença S, Punt A, Setzer R, Suciu N, Troutman J, Yoon M, Worth A, Tan Y. Next generation physiologically based kinetic (NG-PBK) models in support of regulatory decision making. COMPUTATIONAL TOXICOLOGY (AMSTERDAM, NETHERLANDS) 2019; 9:61-72. [PMID: 31008414 PMCID: PMC6472623 DOI: 10.1016/j.comtox.2018.11.002] [Citation(s) in RCA: 65] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/29/2018] [Revised: 11/02/2018] [Accepted: 11/08/2018] [Indexed: 02/06/2023]
Abstract
The fields of toxicology and chemical risk assessment seek to reduce, and eventually replace, the use of animals for the prediction of toxicity in humans. In this context, physiologically based kinetic (PBK) modelling based on in vitro and in silico kinetic data has the potential to a play significant role in reducing animal testing, by providing a methodology capable of incorporating in vitro human data to facilitate the development of in vitro to in vivo extrapolation of hazard information. In the present article, we discuss the challenges in: 1) applying PBK modelling to support regulatory decision making under the toxicology and risk-assessment paradigm shift towards animal replacement; 2) constructing PBK models without in vivo animal kinetic data, while relying solely on in vitro or in silico methods for model parameterization; and 3) assessing the validity and credibility of PBK models built largely using non-animal data. The strengths, uncertainties, and limitations of PBK models developed using in vitro or in silico data are discussed in an effort to establish a higher degree of confidence in the application of such models in a regulatory context. The article summarises the outcome of an expert workshop hosted by the European Commission Joint Research Centre (EC-JRC) - European Union Reference Laboratory for Alternatives to Animal Testing (EURL ECVAM), on "Physiologically-Based Kinetic modelling in risk assessment - reaching a whole new level in regulatory decision-making" held in Ispra, Italy, in November 2016, along with results from an international survey conducted in 2017 and recently reported activities occurring within the PBK modelling field. The discussions presented herein highlight the potential applications of next generation (NG)-PBK modelling, based on new data streams.
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Affiliation(s)
- A. Paini
- European Commission Joint Research Centre, Ispra, Italy
| | - J.A. Leonard
- Oak Ridge Institute for Science and Education, 100 ORAU Way, Oak Ridge, TN 37830, USA
| | - E. Joossens
- European Commission Joint Research Centre, Ispra, Italy
| | - J.G.M. Bessems
- European Commission Joint Research Centre, Ispra, Italy
- Flemish Institute for Technological Research (VITO), Mol, Belgium
| | - A. Desalegn
- European Commission Joint Research Centre, Ispra, Italy
| | - J.L. Dorne
- European Food Safety Authority, 1a, Via Carlo Magno, 1A, 43126 Parma PR, Italy
| | - J.P. Gosling
- School of Mathematics, University of Leeds, Leeds, UK
| | - M.B. Heringa
- RIVM - The National Institute for Public Health and the Environment, Bilthoven, The Netherlands
| | | | - T. Kliment
- European Commission Joint Research Centre, Ispra, Italy
| | - N.I. Kramer
- Institute for Risk Assessment Sciences, Utrecht University, P.O. Box 80177, 3508TD Utrecht, The Netherlands
| | - G. Loizou
- Health and Safety Executive, Buxton, UK
| | - J. Louisse
- Division of Toxicology, Wageningen University, Tuinlaan 5, 6703 HE Wageningen, The Netherlands
- RIKILT Wageningen University and Research, Akkermaalsbos 2, 6708 WB Wageningen, The Netherlands
| | - A. Lumen
- Division of Biochemical Toxicology, National Center for Toxicological Research, US Food and Drug Administration, Jefferson, AR 72079, USA
| | - J.C. Madden
- School of Pharmacy and Bimolecular Sciences, Liverpool John Moores University, Byrom Street, Liverpool L3 3AF, UK
| | - E.A. Patterson
- School of Engineering, University of Liverpool, Liverpool L69 3GH, UK
| | - S. Proença
- European Commission Joint Research Centre, Ispra, Italy
- Institute for Risk Assessment Sciences, Utrecht University, P.O. Box 80177, 3508TD Utrecht, The Netherlands
| | - A. Punt
- RIKILT Wageningen University and Research, Akkermaalsbos 2, 6708 WB Wageningen, The Netherlands
| | - R.W. Setzer
- U.S. Environmental Protection Agency, National Exposure Research Laboratory, 109 TW Alexander Drive, Research Triangle Park, NC 27709, USA
| | - N. Suciu
- DiSTAS, Università Cattolica del Sacro Cuore, Piacenza, Italy
| | - J. Troutman
- Central Product Safety, The Procter & Gamble Company, Cincinnati, OH, USA
| | - M. Yoon
- ScitoVation, 6 Davis Drive, PO Box 110566, Research Triangle Park, NC 27709, USA
- ToxStrategies, Research Triangle Park Office, 1249 Kildaire Farm Road 134, Cary, NC 27511, USA
| | - A. Worth
- European Commission Joint Research Centre, Ispra, Italy
| | - Y.M. Tan
- School of Engineering, University of Liverpool, Liverpool L69 3GH, UK
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Madden J, Webb S, Enoch S, Colley H, Murdoch C, Shipley R, Sharma P, Yang C, Cronin M. In silico prediction of skin metabolism and its implication in toxicity assessment. ACTA ACUST UNITED AC 2017. [DOI: 10.1016/j.comtox.2017.07.001] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
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Dixit VA, Lal LA, Agrawal SR. Recent advances in the prediction of non‐
CYP450
‐mediated drug metabolism. WILEY INTERDISCIPLINARY REVIEWS-COMPUTATIONAL MOLECULAR SCIENCE 2017. [DOI: 10.1002/wcms.1323] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Affiliation(s)
- Vaibhav A. Dixit
- Department of Pharmaceutical Chemistry, School of Pharmacy & Technology Management (SPTM)Shri Vile Parle Kelavani Mandal's (SVKM's), Narsee Monjee Institute of Management Studies (NMIMS)ShirpurIndia
| | - L. Arun Lal
- Department of Pharmaceutical Chemistry, School of Pharmacy & Technology Management (SPTM)Shri Vile Parle Kelavani Mandal's (SVKM's), Narsee Monjee Institute of Management Studies (NMIMS)ShirpurIndia
| | - Simran R. Agrawal
- Department of Pharmaceutical Chemistry, School of Pharmacy & Technology Management (SPTM)Shri Vile Parle Kelavani Mandal's (SVKM's), Narsee Monjee Institute of Management Studies (NMIMS)ShirpurIndia
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12
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Papa E, Sangion A, Arnot JA, Gramatica P. Development of human biotransformation QSARs and application for PBT assessment refinement. Food Chem Toxicol 2017; 112:535-543. [PMID: 28412404 DOI: 10.1016/j.fct.2017.04.016] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2016] [Revised: 04/03/2017] [Accepted: 04/10/2017] [Indexed: 12/14/2022]
Abstract
Toxicokinetics heavily influence chemical toxicity as the result of Absorption, Distribution, Metabolism (Biotransformation) and Elimination (ADME) processes. Biotransformation (metabolism) reactions can lead to detoxification or, in some cases, bioactivation of parent compounds to more toxic chemicals. Moreover, biotransformation has been recognized as a key process determining chemical half-life in an organism and is thus a key determinant for bioaccumulation assessment for many chemicals. This study addresses the development of QSAR models for the prediction of in vivo whole body human biotransformation (metabolism) half-lives measured or empirically-derived for over 1000 chemicals, mainly represented by pharmaceuticals. Models presented in this study meet regulatory standards for fitting, validation and applicability domain. These QSARs were used, in combination with literature models for the prediction of biotransformation half-lives in fish, to refine the screening of the potential PBT behaviour of over 1300 Pharmaceuticals and Personal Care Products (PPCPs). The refinement of the PBT screening allowed, among others, for the identification of PPCPs, which were predicted as PBTs on the basis of their chemical structure, but may be easily biotransformed. These compounds are of lower concern in comparison to potential PBTs characterized by large predicted biotransformation half-lives.
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Affiliation(s)
- Ester Papa
- QSAR Research Unit in Environmental Chemistry and Ecotoxicology, Department of Theoretical and Applied Sciences, University of Insubria, Varese Italy.
| | - Alessandro Sangion
- QSAR Research Unit in Environmental Chemistry and Ecotoxicology, Department of Theoretical and Applied Sciences, University of Insubria, Varese Italy
| | - Jon A Arnot
- ARC Arnot Research & Consulting, Toronto, ON Canada; Department of Physical and Environmental Sciences, University of Toronto Scarborough, Toronto, ON Canada; Department of Pharmacology and Toxicology, University of Toronto, Toronto, ON Canada
| | - Paola Gramatica
- QSAR Research Unit in Environmental Chemistry and Ecotoxicology, Department of Theoretical and Applied Sciences, University of Insubria, Varese Italy
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13
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Pirovano A, Brandmaier S, Huijbregts MAJ, Ragas AMJ, Veltman K, Hendriks AJ. QSARs for estimating intrinsic hepatic clearance of organic chemicals in humans. ENVIRONMENTAL TOXICOLOGY AND PHARMACOLOGY 2016; 42:190-197. [PMID: 26874337 DOI: 10.1016/j.etap.2016.01.017] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/08/2015] [Revised: 01/19/2016] [Accepted: 01/21/2016] [Indexed: 06/05/2023]
Abstract
Quantitative structure-activity relationships (QSARs) were developed to predict the in vitro clearance (CLINT) of xenobiotics metabolised in human hepatocytes (118 compounds) and microsomes (115 compounds). Clearance values were gathered from the scientific literature and multiple linear models were built and validated selecting at most 6 predictors from a pool of over 2000 potential molecular descriptors. For the hepatocytes QSAR, the explained variance (Radj(2)) was 67% and the predictive ability (Rext(2)) was 62%. For the microsomes QSAR, Radj(2) was 50% and Rext(2) 30%. For both liver assays, the most important descriptor relates to electronic properties of the compound. Functional groups of fragments were useful to identify specific compounds that have a deviating reaction rate compared to the others, such as polychlorobiphenyls (PCBs) and organic amides which were poorly metabolised by hepatocytes and microsomes, respectively. For hepatocytes, clearance was predominantly determined by electronic characteristics, while size and shape characteristics were less important and partitioning properties were absent. This may suggest that uptake across the membrane and enzyme binding are not rate-limiting steps. Particularly for hepatocytes the QSAR statistics are encouraging, allowing application of the outcomes in in vitro to in vivo extrapolation.
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Affiliation(s)
- Alessandra Pirovano
- Radboud University Nijmegen, Institute for Wetland and Water Research, Department of Environmental Science, Heyendaalseweg 135, 6525 AJ Nijmegen, The Netherlands.
| | - Stefan Brandmaier
- Helmholtz-Zentrum München - German Research Centre for Environmental Health (GmbH), Research Unit of Molecular Epidemiology, Institute of Epidemiology II, Munich, Germany
| | - Mark A J Huijbregts
- Radboud University Nijmegen, Institute for Wetland and Water Research, Department of Environmental Science, Heyendaalseweg 135, 6525 AJ Nijmegen, The Netherlands
| | - Ad M J Ragas
- Radboud University Nijmegen, Institute for Wetland and Water Research, Department of Environmental Science, Heyendaalseweg 135, 6525 AJ Nijmegen, The Netherlands; Faculty of Management, Science and Technology, Open University, Heerlen, The Netherlands
| | - Karin Veltman
- University of Michigan, School of Public Health, Department of Environmental Health Sciences, 1415 Washington Heights, Ann Arbor, MI, USA
| | - A Jan Hendriks
- Radboud University Nijmegen, Institute for Wetland and Water Research, Department of Environmental Science, Heyendaalseweg 135, 6525 AJ Nijmegen, The Netherlands
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