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Aakash A, Kulsoom R, Khan S, Siddiqui MS, Nabi D. Novel Models for Accurate Estimation of Air-Blood Partitioning: Applications to Individual Compounds and Complex Mixtures of Neutral Organic Compounds. J Chem Inf Model 2023; 63:7056-7066. [PMID: 37956246 PMCID: PMC10685450 DOI: 10.1021/acs.jcim.3c01288] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2023] [Revised: 10/23/2023] [Accepted: 10/25/2023] [Indexed: 11/15/2023]
Abstract
The air-blood partition coefficient (Kab) is extensively employed in human health risk assessment for chemical exposure. However, current Kab estimation approaches either require an extensive number of parameters or lack precision. In this study, we present two novel and parsimonious models to accurately estimate Kab values for individual neutral organic compounds, as well as their complex mixtures. The first model, termed the GC×GC model, was developed based on the retention times of nonpolar chemical analytes on comprehensive two-dimensional gas chromatography (GC×GC). This model is unique in its ability to estimate the Kab values for complex mixtures of nonpolar organic chemicals. The GC×GC model successfully accounted for the Kab variance (R2 = 0.97) and demonstrated strong prediction power (RMSE = 0.31 log unit) for an independent set of nonpolar chemical analytes. Overall, the GC×GC model can be used to estimate Kab values for complex mixtures of neutral organic compounds. The second model, termed the partition model (PM), is based on two types of partition coefficients: octanol to water (Kow) and air to water (Kaw). The PM was able to effectively account for the variability in Kab data (n = 344), yielding an R2 value of 0.93 and root-mean-square error (RMSE) of 0.34 log unit. The predictive power and explanatory performance of the PM were found to be comparable to those of the parameter-intensive Abraham solvation models (ASMs). Additionally, the PM can be integrated into the software EPI Suite, which is widely used in chemical risk assessment for initial screening. The PM provides quick and reliable estimation of Kab compared to ASMs, while the GC×GC model is uniquely suited for estimating Kab values for complex mixtures of neutral organic compounds. In summary, our study introduces two novel and parsimonious models for the accurate estimation of Kab values for both individual compounds and complex mixtures.
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Affiliation(s)
- Ahmad Aakash
- Institute
of Environmental Science and Engineering (IESE), School of Civil and
Environmental Engineering (SCEE), National
University of Sciences and Technology (NUST), H-12, 48000 Islamabad, Pakistan
| | - Ramsha Kulsoom
- Institute
of Environmental Science and Engineering (IESE), School of Civil and
Environmental Engineering (SCEE), National
University of Sciences and Technology (NUST), H-12, 48000 Islamabad, Pakistan
| | - Saba Khan
- Institute
of Environmental Science and Engineering (IESE), School of Civil and
Environmental Engineering (SCEE), National
University of Sciences and Technology (NUST), H-12, 48000 Islamabad, Pakistan
| | - Musab Saeed Siddiqui
- Institute
of Environmental Science and Engineering (IESE), School of Civil and
Environmental Engineering (SCEE), National
University of Sciences and Technology (NUST), H-12, 48000 Islamabad, Pakistan
| | - Deedar Nabi
- Institute
of Environmental Science and Engineering (IESE), School of Civil and
Environmental Engineering (SCEE), National
University of Sciences and Technology (NUST), H-12, 48000 Islamabad, Pakistan
- GEOMAR
Helmholtz Center for Ocean Research, Wischhofstrasse 1-3, 24148 Kiel, Germany
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Chen M, Du R, Zhang T, Li C, Bao W, Xin F, Hou S, Yang Q, Chen L, Wang Q, Zhu A. The Application of a Physiologically Based Toxicokinetic Model in Health Risk Assessment. TOXICS 2023; 11:874. [PMID: 37888724 PMCID: PMC10611306 DOI: 10.3390/toxics11100874] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/13/2023] [Revised: 10/17/2023] [Accepted: 10/19/2023] [Indexed: 10/28/2023]
Abstract
Toxicokinetics plays a crucial role in the health risk assessments of xenobiotics. Classical compartmental models are limited in their ability to determine chemical concentrations in specific organs or tissues, particularly target organs or tissues, and their limited interspecific and exposure route extrapolation hinders satisfactory health risk assessment. In contrast, physiologically based toxicokinetic (PBTK) models quantitatively describe the absorption, distribution, metabolism, and excretion of chemicals across various exposure routes and doses in organisms, establishing correlations with toxic effects. Consequently, PBTK models serve as potent tools for extrapolation and provide a theoretical foundation for health risk assessment and management. This review outlines the construction and application of PBTK models in health risk assessment while analyzing their limitations and future perspectives.
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Affiliation(s)
- Mengting Chen
- Key Laboratory of Ministry of Education for Gastrointestinal Cancer, School of Basic Medical Sciences, Fujian Medical University, Fuzhou 350108, China
| | - Ruihu Du
- Department of Toxicology, School of Public Health, Peking University, Beijing 100191, China
| | - Tao Zhang
- Department of Toxicology, School of Public Health, Peking University, Beijing 100191, China
| | - Chutao Li
- Key Laboratory of Ministry of Education for Gastrointestinal Cancer, School of Basic Medical Sciences, Fujian Medical University, Fuzhou 350108, China
| | - Wenqiang Bao
- Key Laboratory of Ministry of Education for Gastrointestinal Cancer, School of Basic Medical Sciences, Fujian Medical University, Fuzhou 350108, China
| | - Fan Xin
- Key Laboratory of Ministry of Education for Gastrointestinal Cancer, School of Basic Medical Sciences, Fujian Medical University, Fuzhou 350108, China
| | - Shaozhang Hou
- Department of Pathology, School of Basic Medical Sciences, Ningxia Medical University, Yinchuan 750004, China
| | - Qiaomei Yang
- Department of Gynecology, Fujian Maternity and Child Health Hospital (Fujian Obstetrics and Gynecology Hospital), Fuzhou 350001, China
| | - Li Chen
- Department of Gynecology, Fujian Maternity and Child Health Hospital (Fujian Obstetrics and Gynecology Hospital), Fuzhou 350001, China
| | - Qi Wang
- Department of Toxicology, School of Public Health, Peking University, Beijing 100191, China
- Key Laboratory of State Administration of Traditional Chinese Medicine for Compatibility Toxicology, Beijing 100191, China
- Beijing Key Laboratory of Toxicological Research and Risk Assessment for Food Safety, Beijing 100191, China
| | - An Zhu
- Key Laboratory of Ministry of Education for Gastrointestinal Cancer, School of Basic Medical Sciences, Fujian Medical University, Fuzhou 350108, China
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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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Watanabe-Matsumoto S, Yoshida K, Meiseki Y, Ishida S, Hirose A, Yamada T. A physiologically based kinetic modeling of ethyl tert-butyl ether in humans–An illustrative application of quantitative structure-property relationship and Monte Carlo simulation. J Toxicol Sci 2022; 47:77-87. [DOI: 10.2131/jts.47.77] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
Affiliation(s)
- Saori Watanabe-Matsumoto
- Division of Risk Assessment, Center for Biological Safety Research, National Institute of Health Sciences
| | - Kikuo Yoshida
- Division of Risk Assessment, Center for Biological Safety Research, National Institute of Health Sciences
| | - Yuriko Meiseki
- Division of Risk Assessment, Center for Biological Safety Research, National Institute of Health Sciences
| | - Seiichi Ishida
- Division of Pharmacology, Center for Biological Safety Research, National Institute of Health Sciences
| | - Akihiko Hirose
- Division of Risk Assessment, Center for Biological Safety Research, National Institute of Health Sciences
| | - Takashi Yamada
- Division of Risk Assessment, Center for Biological Safety Research, National Institute of Health Sciences
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Linakis MW, Sayre RR, Pearce RG, Sfeir MA, Sipes NS, Pangburn HA, Gearhart JM, Wambaugh JF. Development and evaluation of a high throughput inhalation model for organic chemicals. JOURNAL OF EXPOSURE SCIENCE & ENVIRONMENTAL EPIDEMIOLOGY 2020; 30:866-877. [PMID: 32546826 PMCID: PMC7483974 DOI: 10.1038/s41370-020-0238-y] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/18/2019] [Revised: 04/27/2020] [Accepted: 06/03/2020] [Indexed: 05/12/2023]
Abstract
Currently it is difficult to prospectively estimate human toxicokinetics (particularly for novel chemicals) in a high-throughput manner. The R software package httk has been developed, in part, to address this deficiency, and the aim of this investigation was to develop a generalized inhalation model for httk. The structure of the inhalation model was developed from two previously published physiologically based models from Jongeneelen and Berge (Ann Occup Hyg 55:841-864, 2011) and Clewell et al. (Toxicol Sci 63:160-172, 2001), while calculated physicochemical data was obtained from EPA's CompTox Chemicals Dashboard. In total, 142 exposure scenarios across 41 volatile organic chemicals were modeled and compared to published data. The slope of the regression line of best fit between log-transformed simulated and observed blood and exhaled breath concentrations was 0.46 with an r2 = 0.45 and a root mean square error (RMSE) of direct comparison between the log-transformed simulated and observed values of 1.11. Approximately 5.1% (n = 108) of the data points analyzed were >2 orders of magnitude different than expected. The volatile organic chemicals examined in this investigation represent small, generally lipophilic molecules. Ultimately this paper details a generalized inhalation component that integrates with the httk physiologically based toxicokinetic model to provide high-throughput estimates of inhalation chemical exposures.
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Affiliation(s)
- Matthew W Linakis
- United States Air Force, 711th Human Performance Wing, Airman Readiness Optimization, Wright-Patterson AFB, Wright-Patterson AFB, OH, 45433, USA
- UES, Inc., Dayton, OH, 45432, USA
| | - Risa R Sayre
- Oak Ridge Institute for Science and Education, Oak Ridge, TN, 37831, USA
- National Center for Computational Toxicology, United States Environmental Protection Agency, Research Triangle Park, NC, 27711, USA
| | - Robert G Pearce
- Oak Ridge Institute for Science and Education, Oak Ridge, TN, 37831, USA
- National Center for Computational Toxicology, United States Environmental Protection Agency, Research Triangle Park, NC, 27711, USA
| | - Mark A Sfeir
- Oak Ridge Institute for Science and Education, Oak Ridge, TN, 37831, USA
- National Center for Computational Toxicology, United States Environmental Protection Agency, Research Triangle Park, NC, 27711, USA
| | - Nisha S Sipes
- National Institute of Environmental Health Sciences, Research Triangle Park, NC, 27711, USA
| | - Heather A Pangburn
- United States Air Force, 711th Human Performance Wing, Molecular Bioeffects, Wright-Patterson AFB, Wright-Patterson AFB, OH, 45433, USA
| | - Jeffery M Gearhart
- United States Air Force, 711th Human Performance Wing, Airman Readiness Optimization, Wright-Patterson AFB, Wright-Patterson AFB, OH, 45433, USA
- Henry M. Jackson Foundation for the Advancement of Military Medicine, Wright-Patterson AFB, Wright-Patterson AFB, OH, 45433, USA
| | - John F Wambaugh
- National Center for Computational Toxicology, United States Environmental Protection Agency, Research Triangle Park, NC, 27711, USA.
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Sarigiannis DΑ, Karakitsios SP, Handakas E, Gotti A. Development of a generic lifelong physiologically based biokinetic model for exposome studies. ENVIRONMENTAL RESEARCH 2020; 185:109307. [PMID: 32229354 DOI: 10.1016/j.envres.2020.109307] [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: 06/12/2019] [Revised: 01/30/2020] [Accepted: 02/24/2020] [Indexed: 06/10/2023]
Abstract
The current study within the frame of the HEALS project aims at the development of a lifelong physiologically based biokinetic (PBBK) model for exposome studies. The aim was to deliver a comprehensive modelling framework for addressing a large chemical space. Towards this aim, the delivered model can easily adapt parameters from existing ad-hoc models or complete the missing compound specific parameters using advanced quantitative structure activity relationship (QSAR). All major human organs are included, as well as arterial, venous, and portal blood compartments. Xenobiotics and their metabolites are linked through the metabolizing tissues. This is mainly the liver, but also other sites of metabolism might be considered (intestine, brain, skin, placenta) based on the presence or not of the enzymes involved in the metabolism of the compound of interest. Each tissue is described by three mass balance equations for (a) red blood cells, (b) plasma and interstitial tissue and (c) cells respectively. The anthropometric parameters of the models are time dependent, so as to provide a lifetime internal dose assessment, as well as to describe the continuously changing physiology of the mother and the developing fetus. An additional component of flexibility is that the biokinetic processes that relate to metabolism are related with either Michaelis-Menten kinetics, as well as intrinsic clearance kinetics. The capability of the model is demonstrated in the assessment of internal exposure and the prediction of expected biomonitored levels in urine for three major compounds within the HEALS project, namely bisphenol A (BPA), Bis(2-ethylhexyl) phthalate (DEHP) and cadmium (Cd). The results indicated that the predicted urinary levels fit very well with the ones from human biomonitoring (HBM) studies; internal exposure to plasticizers is very low (in the range of ng/L), while internal exposure to Cd is in the range of μg/L.
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Affiliation(s)
- Dimosthenis Α Sarigiannis
- Aristotle University of Thessaloniki, Department of Chemical Engineering, Environmental Engineering Laboratory, University Campus, Thessaloniki, 54124, Greece; HERACLES Research Center on the Exposome and Health, Center for Interdisciplinary Research and Innovation, Balkan Center, Bldg. B, 10th km Thessaloniki-Thermi Road, 57001, Greece; School for Advanced Study (IUSS), Science, Technology and Society Department, Environmental Health Engineering, Piazza Della Vittoria 15, Pavia, 27100, Italy.
| | - Spyros P Karakitsios
- Aristotle University of Thessaloniki, Department of Chemical Engineering, Environmental Engineering Laboratory, University Campus, Thessaloniki, 54124, Greece; HERACLES Research Center on the Exposome and Health, Center for Interdisciplinary Research and Innovation, Balkan Center, Bldg. B, 10th km Thessaloniki-Thermi Road, 57001, Greece
| | - Evangelos Handakas
- Aristotle University of Thessaloniki, Department of Chemical Engineering, Environmental Engineering Laboratory, University Campus, Thessaloniki, 54124, Greece
| | - Alberto Gotti
- EUCENTRE, Via Adolfo Ferrata, 1, Pavia, 27100, Italy
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7
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Mando H, Hassan A, Gharaghani S. Novel and Predictive QSAR Model for Steroidal and Nonsteroidal 5α- Reductase Type II Inhibitors. Curr Drug Discov Technol 2020; 18:317-332. [PMID: 32208118 DOI: 10.2174/1570163817666200324170457] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2019] [Revised: 01/16/2020] [Accepted: 01/16/2020] [Indexed: 11/22/2022]
Abstract
AIMS AND OBJECTIVE In this study, a novel quantitative structure activity relationship (QSAR) model has been developed for inhibitors of human 5-alpha reductase type II, which are used to treat benign prostate hypertrophy (BPH). METHODS The dataset consisted of 113 compounds-mainly nonsteroidal-with known inhibitory concentration. Then 3D structures of compounds were optimized and molecular structure descriptors were calculated. The stepwise multiple linear regression was used to select descriptors encoding the inhibitory activity of the compounds. Multiple linear regression (MLR) was used to build up the linear QSAR model. RESULTS The results obtained revealed that the descriptors which best describe the activity were atom type electropological state, carbon type, radial distribution function (RDF), barysz matrix and molecular linear free energy relation. The suggested model could achieve satisfied square correlation coefficient of R2 = 0.72, higher than of many previous studies, indicating its superiority. Rigid validation criteria were met using external data with Q2 ˃ 0.5 and R2 = 0.75, reflecting the predictive power of the model. CONCLUSION The QSAR model was applied for screening botanical components of herbal preparations used to treat BPH, and could predict the activity of some, among others, making reasonable attribution to the proposed effect of these preparations. Gamma tocopherol was found to be an active inhibitor, in consistence with many previous studies, anticipating the power of this model in the prediction of new candidate molecules and suggesting further investigations.
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Affiliation(s)
- Huda Mando
- Department of Pharmaceutical Chemistry and Quality Control of Medicaments, Faculty of Pharmacy, Damascus University, Damascus, Syrian Arab Republic
| | - Ahmad Hassan
- Department of Pharmaceutical Chemistry and Quality Control of Medicaments, Faculty of Pharmacy, Damascus University, Damascus, Syrian Arab Republic
| | - Sajjad Gharaghani
- Laboratory of Bioinformatics and Drug Design, Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran
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Volarath P, Zang Y, Kabadi SV. Application of Computational Methods for the Safety Assessment of Food Ingredients. ACTA ACUST UNITED AC 2019. [DOI: 10.1007/978-3-030-16443-0_12] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/17/2023]
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9
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Desalegn A, Bopp S, Asturiol D, Lamon L, Worth A, Paini A. Role of Physiologically Based Kinetic modelling in addressing environmental chemical mixtures - A review. ACTA ACUST UNITED AC 2019; 10:158-168. [PMID: 31218267 PMCID: PMC6559215 DOI: 10.1016/j.comtox.2018.09.001] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2017] [Revised: 06/24/2018] [Accepted: 09/26/2018] [Indexed: 11/21/2022]
Abstract
The availability and applicability of Physiologically Based Kinetic (PBK) models for mixtures is reviewed. PBK models can support risk assessment of mixtures by incorporating the toxicokinetic processes. Quantitative structure-activity relationship (QSAR) models can be used to fill data gaps in PBK modelling. PBK models for mixtures can be improved by including various types of interactions.
The role of Physiologically Based Kinetic (PBK) modelling in assessing mixture toxicology has been growing for the last three decades. It has been widely used to investigate and address interactions in mixtures. This review describes the current state-of-the-art of PBK models for chemical mixtures and to evaluate the applications of PBK modelling for mixtures with emphasis on their role in chemical risk assessment. A total of 35 mixture PBK models were included after searching web resources (Scopus, PubMed, Web of Science, and Google Scholar), screening for duplicates, and excluding articles based on eligibility criteria. Binary mixtures and volatile organic compounds accounted for two-thirds of the chemical mixtures identified. The most common exposure route and modelled system were found to be inhalation and rats respectively. Twenty two (22) models were for binary mixtures, 5 for ternary mixtures, 3 for quaternary mixtures, and 5 for complex mixtures. Both bottom-up and top-down PBK modelling approaches are described. Whereas bottom-up approaches are based on a series of binary interactions, top-down approaches are based on the lumping of mixture components. Competitive inhibition is the most common type of interaction among the various types of mixtures, and usually becomes a concern at concentrations higher than environmental exposure levels. It leads to reduced biotransformation that either means a decrease in the amount of toxic metabolite formation or an increase in toxic parent chemical accumulation. The consequence is either lower or higher toxicity compared to that estimated for the mixture based on the additivity principle. Therefore, PBK modelling can play a central role in predicting interactions in chemical mixture risk assessment.
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Affiliation(s)
| | | | | | | | | | - Alicia Paini
- Corresponding author at: European Commission, Joint Research Centre, Via E. Fermi 2749, 21027 Ispra, VA, Italy.
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Sarigiannis DA, Karakitsios S, Dominguez-Romero E, Papadaki K, Brochot C, Kumar V, Schuhmacher M, Sy M, Mielke H, Greiner M, Mengelers M, Scheringer M. Physiology-based toxicokinetic modelling in the frame of the European Human Biomonitoring Initiative. ENVIRONMENTAL RESEARCH 2019; 172:216-230. [PMID: 30818231 DOI: 10.1016/j.envres.2019.01.045] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/27/2018] [Revised: 01/25/2019] [Accepted: 01/28/2019] [Indexed: 06/09/2023]
Abstract
Given the opportunities provided by internal dosimetry modelling in the interpretation of human biomonitoring (HBM) data, the assessment of the links between exposure to chemicals and observed HBM data can be effectively supported by PBTK modelling. This paper gives a comprehensive review of available human PBTK models for compounds selected as a priority by the European Human Biomonitoring Initiative (HBM4EU). We highlight their advantages and deficiencies and suggest steps for advanced internal dose modelling. The review of the available PBTK models highlighted the conceptual differences between older models compared to the ones developed recently, reflecting commensurate differences in research questions. Due to the lack of coordinated strategies for deriving useful biomonitoring data for toxicokinetic properties, significant problems in model parameterisation still remain; these are further increased by the lack of human toxicokinetic data due to ethics issues. Finally, questions arise as well as to the extent they are really representative of interindividual variability. QSARs for toxicokinetic properties is a complementary approach for PBTK model parameterisation, especially for data poor chemicals. This approach could be expanded to model chemico-biological interactions such as intestinal absorption and renal clearance; this could serve the development of more complex generic PBTK models that could be applied to newly derived chemicals. Another gap identified is the framework for mixture interaction terms among compounds that could eventually interact in metabolism. From the review it was concluded that efforts should be shifted toward the development of generic multi-compartmental and multi-route models, supported by targeted biomonitoring coupled with parameterisation by both QSAR approach and experimental (in-vivo and in-vitro) data for newly developed and data poor compounds.
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Affiliation(s)
- Dimosthenis A Sarigiannis
- Aristotle University of Thessaloniki, Department of Chemical Engineering, Environmental Engineering Laboratory, University Campus, Thessaloniki 54124, Greece; HERACLES Research Center on the Exposome and Health, Center for Interdisciplinary Research and Innovation, Balkan Center, Bldg. B, 10th km Thessaloniki-Thermi Road, 57001, Greece.
| | - Spyros Karakitsios
- Aristotle University of Thessaloniki, Department of Chemical Engineering, Environmental Engineering Laboratory, University Campus, Thessaloniki 54124, Greece; HERACLES Research Center on the Exposome and Health, Center for Interdisciplinary Research and Innovation, Balkan Center, Bldg. B, 10th km Thessaloniki-Thermi Road, 57001, Greece
| | | | - Krystalia Papadaki
- Aristotle University of Thessaloniki, Department of Chemical Engineering, Environmental Engineering Laboratory, University Campus, Thessaloniki 54124, Greece
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Zhang Y, Zhang N, Niu Z. Health risk assessment of trihalomethanes mixtures from daily water-related activities via multi-pathway exposure based on PBPK model. ECOTOXICOLOGY AND ENVIRONMENTAL SAFETY 2018; 163:427-435. [PMID: 30075445 DOI: 10.1016/j.ecoenv.2018.07.073] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/11/2018] [Revised: 07/03/2018] [Accepted: 07/19/2018] [Indexed: 06/08/2023]
Abstract
In this study, the concentrations of trihalomethanes (THMs) in tap water and direct drinking water were analyzed, and based on the human behavior patterns and building parameters, the concentrations of THMs in indoor air were simulated with the water-air concentration conversion model. In addition, concentrations of THMs in human tissues were predicted based on physiologically based pharmacokinetic (PBPK) model, and the health risk of THMs for participants were estimated. Furthermore, the carcinogenic risk of mixtures according to the method proposed by USEPA and PBPK model based method were calculated and compared. The concentrations of chloroform, bromodichloromethane, dibromochloromethane and bromoform in tap water were 11.28-16.21, 4.83-6.28, 0.81-1.32 and 0.08-0.21 μg/L, and those in direct drinking water were 3.29-6.88, 0.35-0.47, 0.03-0.08 and 0.04-0.08 μg/L, respectively. The results of water-air concentration conversion model demonstrated that pollutants in air had a strong correlation with water-related activities. Multi-pathway PBPK model showed that THMs concentrations in liver, kidney and richly perfused tissue were higher than those in other tissues. The results of risk assessment showed that the mean risk levels of mixtures were 1.69 × 10-4 and 1.72 × 10-4 calculated by the USEPA recommended method and PBPK based method, which seriously exceeded the acceptable level. TCM and BDCM were the major risk factors, and inhalation was the main exposure route of THMs.
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Affiliation(s)
- Ying Zhang
- MOE Key Laboratory of Pollution Processes and Environmental Criteria, College of Environmental Science and Engineering, Nankai University, Tianjin 300350, China
| | - Ning Zhang
- MOE Key Laboratory of Pollution Processes and Environmental Criteria, College of Environmental Science and Engineering, Nankai University, Tianjin 300350, China; Hunan Architectural Design Institute Limited Company, Hunan 410012, China
| | - Zhiguang Niu
- School of Environmental Science and Engineering, Tianjin University, Tianjin 300072, China.
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Vlot AHC, de Witte WEA, Danhof M, van der Graaf PH, van Westen GJP, de Lange ECM. Target and Tissue Selectivity Prediction by Integrated Mechanistic Pharmacokinetic-Target Binding and Quantitative Structure Activity Modeling. AAPS JOURNAL 2017; 20:11. [PMID: 29204742 DOI: 10.1208/s12248-017-0172-7] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/16/2017] [Accepted: 11/20/2017] [Indexed: 12/31/2022]
Abstract
Selectivity is an important attribute of effective and safe drugs, and prediction of in vivo target and tissue selectivity would likely improve drug development success rates. However, a lack of understanding of the underlying (pharmacological) mechanisms and availability of directly applicable predictive methods complicates the prediction of selectivity. We explore the value of combining physiologically based pharmacokinetic (PBPK) modeling with quantitative structure-activity relationship (QSAR) modeling to predict the influence of the target dissociation constant (K D) and the target dissociation rate constant on target and tissue selectivity. The K D values of CB1 ligands in the ChEMBL database are predicted by QSAR random forest (RF) modeling for the CB1 receptor and known off-targets (TRPV1, mGlu5, 5-HT1a). Of these CB1 ligands, rimonabant, CP-55940, and Δ8-tetrahydrocanabinol, one of the active ingredients of cannabis, were selected for simulations of target occupancy for CB1, TRPV1, mGlu5, and 5-HT1a in three brain regions, to illustrate the principles of the combined PBPK-QSAR modeling. Our combined PBPK and target binding modeling demonstrated that the optimal values of the K D and k off for target and tissue selectivity were dependent on target concentration and tissue distribution kinetics. Interestingly, if the target concentration is high and the perfusion of the target site is low, the optimal K D value is often not the lowest K D value, suggesting that optimization towards high drug-target affinity can decrease the benefit-risk ratio. The presented integrative structure-pharmacokinetic-pharmacodynamic modeling provides an improved understanding of tissue and target selectivity.
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Affiliation(s)
- Anna H C Vlot
- Division of Pharmacology, Leiden Academic Centre for Drug Research, Leiden University, Einsteinweg 55, 2333, CC, Leiden, The Netherlands
| | - Wilhelmus E A de Witte
- Division of Pharmacology, Leiden Academic Centre for Drug Research, Leiden University, Einsteinweg 55, 2333, CC, Leiden, The Netherlands
| | - Meindert Danhof
- Division of Pharmacology, Leiden Academic Centre for Drug Research, Leiden University, Einsteinweg 55, 2333, CC, Leiden, The Netherlands
| | - Piet H van der Graaf
- Division of Pharmacology, Leiden Academic Centre for Drug Research, Leiden University, Einsteinweg 55, 2333, CC, Leiden, The Netherlands.,Certara Quantitative Systems Pharmacology, Canterbury Innovation Centre, Canterbury, CT2 7FG, UK
| | - Gerard J P van Westen
- Division of Medicinal Chemistry, Leiden Academic Centre for Drug Research, Leiden University, Einsteinweg 55, 2333, CC, Leiden, The Netherlands
| | - Elizabeth C M de Lange
- Division of Pharmacology, Leiden Academic Centre for Drug Research, Leiden University, Einsteinweg 55, 2333, CC, Leiden, The Netherlands.
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Cosnier F, Nunge H, Bonfanti É, Grossmann S, Lambert-Xollin AM, Muller S, Sébillaud S, Thomas A, Gaté L, Campo P. Toluene and methylethylketone: effect of combined exposure on their metabolism in rat. Xenobiotica 2017; 48:684-694. [PMID: 28783416 DOI: 10.1080/00498254.2017.1362604] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Abstract
1. Multiple exposures are ubiquitous in industrial environments. In this article, we highlight the risks faced by workers and complete the data available on the metabolic impact of a common mixture: toluene (TOL) and methylethylketone (MEK). 2. Rats were exposed by inhalation under controlled conditions either to each solvent individually, or to mixtures of the two. How the interaction between the two solvents affected their fate in the blood and brain, their main relevant urinary metabolites (o-cresol, benzylmercapturic acid for TOL and 2,3-butanediols for MEK) and their hepatic metabolism were investigated. 3. Although the cytochrome P450 concentration was unchanged, and the activities of CYP1A2 and CYP2E1 isoforms were not additively or synergistically induced by co-exposure, TOL metabolism was inhibited by the presence of MEK (and vice versa). Depending on the relative proportions of each compound in the mixture, this sometimes resulted in a large increase in blood and brain concentrations. Apart from extreme cases (unbalanced mixtures), the amount of o-cresol and benzylmercapturic acid (and to a lesser extent 2,3-butanediols) excreted were proportional to the blood solvent concentrations. 4. In a co-exposure context, ortho-cresol and benzylmercapturic acid can be used as urinary biomarkers in biomonitoring for employees to relatively accurately assess TOL exposure.
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Affiliation(s)
- Frédéric Cosnier
- a Department of Toxicology and Biometrology, Institut National de Recherche et de Sécurité (INRS) , Vandœuvre-lès-Nancy , France
| | - Hervé Nunge
- a Department of Toxicology and Biometrology, Institut National de Recherche et de Sécurité (INRS) , Vandœuvre-lès-Nancy , France
| | - Élodie Bonfanti
- a Department of Toxicology and Biometrology, Institut National de Recherche et de Sécurité (INRS) , Vandœuvre-lès-Nancy , France
| | - Stéphane Grossmann
- a Department of Toxicology and Biometrology, Institut National de Recherche et de Sécurité (INRS) , Vandœuvre-lès-Nancy , France
| | - Anne-Marie Lambert-Xollin
- a Department of Toxicology and Biometrology, Institut National de Recherche et de Sécurité (INRS) , Vandœuvre-lès-Nancy , France
| | - Samuel Muller
- a Department of Toxicology and Biometrology, Institut National de Recherche et de Sécurité (INRS) , Vandœuvre-lès-Nancy , France
| | - Sylvie Sébillaud
- a Department of Toxicology and Biometrology, Institut National de Recherche et de Sécurité (INRS) , Vandœuvre-lès-Nancy , France
| | - Aurélie Thomas
- a Department of Toxicology and Biometrology, Institut National de Recherche et de Sécurité (INRS) , Vandœuvre-lès-Nancy , France
| | - Laurent Gaté
- a Department of Toxicology and Biometrology, Institut National de Recherche et de Sécurité (INRS) , Vandœuvre-lès-Nancy , France
| | - Pierre Campo
- a Department of Toxicology and Biometrology, Institut National de Recherche et de Sécurité (INRS) , Vandœuvre-lès-Nancy , France
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14
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Development of QSARs for parameterizing Physiology Based ToxicoKinetic models. Food Chem Toxicol 2017; 106:114-124. [DOI: 10.1016/j.fct.2017.05.029] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2017] [Revised: 04/13/2017] [Accepted: 05/14/2017] [Indexed: 11/23/2022]
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15
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Lin S, Mortimer M, Chen R, Kakinen A, Riviere JE, Davis TP, Ding F, Ke PC. NanoEHS beyond Toxicity - Focusing on Biocorona. ENVIRONMENTAL SCIENCE. NANO 2017; 7:1433-1454. [PMID: 29123668 PMCID: PMC5673284 DOI: 10.1039/c6en00579a] [Citation(s) in RCA: 31] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/03/2023]
Abstract
The first phase of environmental health and safety of nanomaterials (nanoEHS) studies has been mainly focused on evidence-based investigations that probe the impact of nanoparticles, nanomaterials and nano-enabled products on biological and ecological systems. The integration of multiple disciplines, including colloidal science, nanomaterial science, chemistry, toxicology/immunology and environmental science, is necessary to understand the implications of nanotechnology for both human health and the environment. While strides have been made in connecting the physicochemical properties of nanomaterials with their hazard potential in tiered models, fundamental understanding of nano-biomolecular interactions and their implications for nanoEHS is largely absent from the literature. Research on nano-biomolecular interactions within the context of natural systems not only provides important clues for deciphering nanotoxicity and nanoparticle-induced pathology, but also presents vast new opportunities for screening beneficial material properties and designing greener products from bottom up. This review highlights new opportunities concerning nano-biomolecular interactions beyond the scope of toxicity.
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Affiliation(s)
- Sijie Lin
- College of Environmental Science and Engineering, State Key Laboratory of Pollution Control and Resource Reuse, Tongji University, 1239 Siping Road, Shanghai 200092, China
| | - Monika Mortimer
- Bren School of Environmental Science and Management, Earth Research Institute and University of California Center for the Environmental Implications of Nanotechnology (UC CEIN), University of California, Santa Barbara, California 93106, United States
| | - Ran Chen
- Nanotechnology Innovation Center of Kansas State, Kansas State University, Manhattan, Kansas 66506, United States
| | - Aleksandr Kakinen
- ARC Center of Excellence in Convergent Bio-Nano Science and Technology, Monash Institute of Pharmaceutical Sciences, Monash University, 381 Royal Parade, Parkville, VIC 3052, Australia
| | - Jim E. Riviere
- Nanotechnology Innovation Center of Kansas State, Kansas State University, Manhattan, Kansas 66506, United States
| | - Thomas P. Davis
- ARC Center of Excellence in Convergent Bio-Nano Science and Technology, Monash Institute of Pharmaceutical Sciences, Monash University, 381 Royal Parade, Parkville, VIC 3052, Australia
- Department of Chemistry, University of Warwick, Gibbet Hill, Coventry, CV4 7AL, United Kingdom
| | - Feng Ding
- Department of Physics and Astronomy, Clemson University, Clemson, SC 29634, United States
| | - Pu Chun Ke
- ARC Center of Excellence in Convergent Bio-Nano Science and Technology, Monash Institute of Pharmaceutical Sciences, Monash University, 381 Royal Parade, Parkville, VIC 3052, Australia
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Manrai AK, Cui Y, Bushel PR, Hall M, Karakitsios S, Mattingly CJ, Ritchie M, Schmitt C, Sarigiannis DA, Thomas DC, Wishart D, Balshaw DM, Patel CJ. Informatics and Data Analytics to Support Exposome-Based Discovery for Public Health. Annu Rev Public Health 2017; 38:279-294. [PMID: 28068484 PMCID: PMC5774331 DOI: 10.1146/annurev-publhealth-082516-012737] [Citation(s) in RCA: 66] [Impact Index Per Article: 9.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
The complexity of the human exposome-the totality of environmental exposures encountered from birth to death-motivates systematic, high-throughput approaches to discover new environmental determinants of disease. In this review, we describe the state of science in analyzing the human exposome and provide recommendations for the public health community to consider in dealing with analytic challenges of exposome-based biomedical research. We describe extant and novel analytic methods needed to associate the exposome with critical health outcomes and contextualize the data-centered challenges by drawing parallels to other research endeavors such as human genomics research. We discuss efforts for training scientists who can bridge public health, genomics, and biomedicine in informatics and statistics. If an exposome data ecosystem is brought to fruition, it will likely play a role as central as genomic science has had in molding the current and new generations of biomedical researchers, computational scientists, and public health research programs.
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Affiliation(s)
- Arjun K Manrai
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts 02115;
| | - Yuxia Cui
- National Institute of Environmental Health Sciences, National Institutes of Health, Research Triangle Park, North Carolina 27709;
| | - Pierre R Bushel
- National Institute of Environmental Health Sciences, National Institutes of Health, Research Triangle Park, North Carolina 27709;
| | - Molly Hall
- Center for Systems Genomics, The Pennsylvania State University, College Station, Pennsylvania 16802
| | - Spyros Karakitsios
- Environmental Engineering Laboratory, Department of Chemical Engineering, Aristotle University of Thessaloniki, Thessaloniki 54124, Greece
| | - Carolyn J Mattingly
- Department of Biological Sciences, College of Sciences, North Carolina State University, Raleigh, North Carolina 27695
| | - Marylyn Ritchie
- Center for Systems Genomics, The Pennsylvania State University, College Station, Pennsylvania 16802
- Geisinger Health System, Danville, Pennsylvania 17821
| | - Charles Schmitt
- Renaissance Computing Institute, Chapel Hill, North Carolina 27517
| | - Denis A Sarigiannis
- Environmental Engineering Laboratory, Department of Chemical Engineering, Aristotle University of Thessaloniki, Thessaloniki 54124, Greece
| | - Duncan C Thomas
- Division of Biostatistics, Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, California 90089-9011
| | - David Wishart
- Departments of Biological Sciences and Computing Science, University of Alberta, Edmonton, Alberta T6G 2E8, Canada
| | - David M Balshaw
- National Institute of Environmental Health Sciences, National Institutes of Health, Research Triangle Park, North Carolina 27709;
| | - Chirag J Patel
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts 02115;
- Center for Assessment Technology and Continuous Health, Massachusetts General Hospital, Boston, Massachusetts 02114
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17
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Jónsdóttir SÓ, Reffstrup TK, Petersen A, Nielsen E. Physicologically Based Toxicokinetic Models of Tebuconazole and Application in Human Risk Assessment. Chem Res Toxicol 2016; 29:715-34. [DOI: 10.1021/acs.chemrestox.5b00341] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Affiliation(s)
- Svava Ósk Jónsdóttir
- National Food Institute, Technical University of Denmark, Mørkhøj Bygade 19, DK-2860 Søborg, Denmark
| | - Trine Klein Reffstrup
- National Food Institute, Technical University of Denmark, Mørkhøj Bygade 19, DK-2860 Søborg, Denmark
| | - Annette Petersen
- National Food Institute, Technical University of Denmark, Mørkhøj Bygade 19, DK-2860 Søborg, Denmark
| | - Elsa Nielsen
- National Food Institute, Technical University of Denmark, Mørkhøj Bygade 19, DK-2860 Søborg, Denmark
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18
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Jasper MN, Martin SA, Oshiro WM, Ford J, Bushnell PJ, El-Masri H. Application of Biologically Based Lumping To Investigate the Toxicokinetic Interactions of a Complex Gasoline Mixture. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2016; 50:3231-3238. [PMID: 26889718 DOI: 10.1021/acs.est.5b05648] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/05/2023]
Abstract
People are often exposed to complex mixtures of environmental chemicals such as gasoline, tobacco smoke, water contaminants, or food additives. We developed an approach that applies chemical lumping methods to complex mixtures, in this case gasoline, based on biologically relevant parameters used in physiologically based pharmacokinetic (PBPK) modeling. Inhalation exposures were performed with rats to evaluate the performance of our PBPK model and chemical lumping method. There were 109 chemicals identified and quantified in the vapor in the chamber. The time-course toxicokinetic profiles of 10 target chemicals were also determined from blood samples collected during and following the in vivo experiments. A general PBPK model was used to compare the experimental data to the simulated values of blood concentration for 10 target chemicals with various numbers of lumps, iteratively increasing from 0 to 99. Large reductions in simulation error were gained by incorporating enzymatic chemical interactions, in comparison to simulating the individual chemicals separately. The error was further reduced by lumping the 99 nontarget chemicals. The same biologically based lumping approach can be used to simplify any complex mixture with tens, hundreds, or thousands of constituents.
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Affiliation(s)
- Micah N Jasper
- National Health and Environmental Effects Research Laboratory, Office of Research and Development, U.S. Environmental Protection Agency , Research Triangle Park, North Carolina 27709, United States
| | - Sheppard A Martin
- National Health and Environmental Effects Research Laboratory, Office of Research and Development, U.S. Environmental Protection Agency , Research Triangle Park, North Carolina 27709, United States
| | - Wendy M Oshiro
- National Health and Environmental Effects Research Laboratory, Office of Research and Development, U.S. Environmental Protection Agency , Research Triangle Park, North Carolina 27709, United States
| | - Jermaine Ford
- National Health and Environmental Effects Research Laboratory, Office of Research and Development, U.S. Environmental Protection Agency , Research Triangle Park, North Carolina 27709, United States
| | - Philip J Bushnell
- National Health and Environmental Effects Research Laboratory, Office of Research and Development, U.S. Environmental Protection Agency , Research Triangle Park, North Carolina 27709, United States
| | - Hisham El-Masri
- National Health and Environmental Effects Research Laboratory, Office of Research and Development, U.S. Environmental Protection Agency , Research Triangle Park, North Carolina 27709, United States
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19
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Kirman CR, Aylward LL, Wetmore BA, Thomas RS, Sochaski M, Ferguson SS, Csiszar SA, Jolliet O. Quantitative Property–Property Relationship for Screening-Level Prediction of Intrinsic Clearance: A Tool for Exposure Modeling for High-Throughput Toxicity Screening Data. ACTA ACUST UNITED AC 2015. [DOI: 10.1089/aivt.2014.0008] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Affiliation(s)
| | | | - Barbara A. Wetmore
- The Hamner Institutes for Health Sciences, Research Triangle Park, North Carolina
| | - Russell S. Thomas
- The Hamner Institutes for Health Sciences, Research Triangle Park, North Carolina
| | - M. Sochaski
- The Hamner Institutes for Health Sciences, Research Triangle Park, North Carolina
| | | | - Susan A. Csiszar
- University of Michigan, School of Public Health, Ann Arbor, Michigan
| | - Olivier Jolliet
- University of Michigan, School of Public Health, Ann Arbor, Michigan
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20
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Niu Z, Zang X, Zhang Y. Using physiologically based pharmacokinetic models to estimate the health risk of mixtures of trihalomethanes from reclaimed water. JOURNAL OF HAZARDOUS MATERIALS 2015; 285:190-198. [PMID: 25497033 DOI: 10.1016/j.jhazmat.2014.11.051] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/27/2014] [Revised: 10/18/2014] [Accepted: 11/02/2014] [Indexed: 06/04/2023]
Abstract
To estimate the health risk of mixture of trihalomethanes (THMs) from reclaimed water during toilet flushing, the interaction-based Hazard Index (HI(interaction-based)) and the mixture carcinogenic risk (CRM) according to tissue dose were conducted through the integrated use of both the exposure concentrations model and the physiologically based pharmacokinetic (PBPK) model of THMs. Monte Carlo simulations were employed to implement the probabilistic risk analysis and sensitivity analysis. Nine samples were analyzed, which were collected from J Water Reclamation Plant (JWRP) in Tianjin of China. The results indicated that the mean HI(interaction-based) (=0.85) was lower than the acceptable risk level (=1). The probability that the HI(interaction-based) exceeded the acceptable risk level is 22.97%. For carcinogenic risk, the CRM ranges from 9.41×10(-7) to 3.54×10(-5), with a mean of 5.49×10(-6). Moreover, the probability of exceeding the acceptable risk level (1×10(-6)) is near 100%. And the values of HI(interaction-based) from sample no. 1, 5, and 7 exceeded 1, while the values of CRM for all samples exceeded 1×10(-6). Consequently, the reclaimed water used for flushing toilets should be paid more attention, though non-carcinogenic effect is relatively small. Furthermore, the concentrations of DBCM had greater impact on both the carcinogenic and non-carcinogenic risk based on sensitivity analysis.
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Affiliation(s)
- Zhiguang Niu
- School of Marine Science and Technology, Tianjin University, Tianjin 300072, China; School of Environmental Science and Engineering, Tianjin University, Tianjin 300072, China.
| | - Xue Zang
- School of Environmental Science and Engineering, Tianjin University, Tianjin 300072, China.
| | - Ying Zhang
- College of Environmental Science and Engineering, Nankai University, Tianjin 300071, China.
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21
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Valcke M, Haddad S. Assessing human variability in kinetics for exposures to multiple environmental chemicals: a physiologically based pharmacokinetic modeling case study with dichloromethane, benzene, toluene, ethylbenzene, and m-xylene. JOURNAL OF TOXICOLOGY AND ENVIRONMENTAL HEALTH. PART A 2015; 78:409-431. [PMID: 25785556 DOI: 10.1080/15287394.2014.971477] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/04/2023]
Abstract
The objective of this study was to compare the magnitude of interindividual variability in internal dose for inhalation exposure to single versus multiple chemicals. Physiologically based pharmacokinetic models for adults (AD), neonates (NEO), toddlers (TODD), and pregnant women (PW) were used to simulate inhalation exposure to "low" (RfC-like) or "high" (AEGL-like) air concentrations of benzene (Bz) or dichloromethane (DCM), along with various levels of toluene alone or toluene with ethylbenzene and xylene. Monte Carlo simulations were performed and distributions of relevant internal dose metrics of either Bz or DCM were computed. Area under the blood concentration of parent compound versus time curve (AUC)-based variability in AD, TODD, and PW rose for Bz when concomitant "low" exposure to mixtures of increasing complexities occurred (coefficient of variation (CV) = 16-24%, vs. 12-15% for Bz alone), but remained unchanged considering DCM. Conversely, AUC-based CV in NEO fell (15 to 5% for Bz; 12 to 6% for DCM). Comparable trends were observed considering production of metabolites (AMET), except for NEO's CYP2E1-mediated metabolites of Bz, where an increased CV was observed (20 to 71%). For "high" exposure scenarios, Cmax-based variability of Bz and DCM remained unchanged in AD and PW, but decreased in NEO (CV= 11-16% to 2-6%) and TODD (CV= 12-13% to 7-9%). Conversely, AMET-based variability for both substrates rose in every subpopulation. This study analyzed for the first time the impact of multiple exposures on interindividual variability in toxicokinetics. Evidence indicates that this impact depends upon chemical concentrations and biochemical properties, as well as the subpopulation and internal dose metrics considered.
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Affiliation(s)
- Mathieu Valcke
- a Institut national de santé publique du Québec , Montréal , Quebec , Canada
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22
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PBTK modelling platforms and parameter estimation tools to enable animal-free risk assessment: recommendations from a joint EPAA--EURL ECVAM ADME workshop. Regul Toxicol Pharmacol 2013; 68:119-39. [PMID: 24287156 DOI: 10.1016/j.yrtph.2013.11.008] [Citation(s) in RCA: 90] [Impact Index Per Article: 8.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2013] [Revised: 11/07/2013] [Accepted: 11/12/2013] [Indexed: 12/25/2022]
Abstract
Information on toxicokinetics is critical for animal-free human risk assessment. Human external exposure must be translated into human tissue doses and compared with in vitro actual cell exposure associated to effects (in vitro-in vivo comparison). Data on absorption, distribution, metabolism and excretion in humans (ADME) could be generated using in vitro and QSAR tools. Physiologically-based toxicokinetic (PBTK) computer modelling could serve to integrate disparate in vitro and in silico findings. However, there are only few freely-available PBTK platforms currently available. And although some ADME parameters can be reasonably estimated in vitro or in silico, important gaps exist. Examples include unknown or limited applicability domains and lack of (high-throughput) tools to measure penetration of barriers, partitioning between blood and tissues and metabolic clearance. This paper is based on a joint EPAA--EURL ECVAM expert meeting. It provides a state-of-the-art overview of the availability of PBTK platforms as well as the in vitro and in silico methods to parameterise basic (Tier 1) PBTK models. Five high-priority issues are presented that provide the prerequisites for wider use of non-animal based PBTK modelling for animal-free chemical risk assessment.
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23
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Tong J, Xu X, Liu S, Che T, Li Y, Hu Z, Meng Y. Prediction of glass transition temperature of polyacrylate using a quantitative structure property relationship model. POLYMER SCIENCE SERIES A 2013. [DOI: 10.1134/s0965545x13080075] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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Péry ARR, Schüürmann G, Ciffroy P, Faust M, Backhaus T, Aicher L, Mombelli E, Tebby C, Cronin MTD, Tissot S, Andres S, Brignon JM, Frewer L, Georgiou S, Mattas K, Vergnaud JC, Peijnenburg W, Capri E, Marchis A, Wilks MF. Perspectives for integrating human and environmental risk assessment and synergies with socio-economic analysis. THE SCIENCE OF THE TOTAL ENVIRONMENT 2013; 456-457:307-316. [PMID: 23624004 DOI: 10.1016/j.scitotenv.2013.03.099] [Citation(s) in RCA: 29] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/07/2013] [Revised: 03/29/2013] [Accepted: 03/29/2013] [Indexed: 06/02/2023]
Abstract
For more than a decade, the integration of human and environmental risk assessment (RA) has become an attractive vision. At the same time, existing European regulations of chemical substances such as REACH (EC Regulation No. 1907/2006), the Plant Protection Products Regulation (EC regulation 1107/2009) and Biocide Regulation (EC Regulation 528/2012) continue to ask for sector-specific RAs, each of which have their individual information requirements regarding exposure and hazard data, and also use different methodologies for the ultimate risk quantification. In response to this difference between the vision for integration and the current scientific and regulatory practice, the present paper outlines five medium-term opportunities for integrating human and environmental RA, followed by detailed discussions of the associated major components and their state of the art. Current hazard assessment approaches are analyzed in terms of data availability and quality, and covering non-test tools, the integrated testing strategy (ITS) approach, the adverse outcome pathway (AOP) concept, methods for assessing uncertainty, and the issue of explicitly treating mixture toxicity. With respect to exposure, opportunities for integrating exposure assessment are discussed, taking into account the uncertainty, standardization and validation of exposure modeling as well as the availability of exposure data. A further focus is on ways to complement RA by a socio-economic assessment (SEA) in order to better inform about risk management options. In this way, the present analysis, developed as part of the EU FP7 project HEROIC, may contribute to paving the way for integrating, where useful and possible, human and environmental RA in a manner suitable for its coupling with SEA.
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Affiliation(s)
- A R R Péry
- INERIS, Parc Alata, BP2, 60550 Verneuil-en-Halatte, France.
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Quantitative Property-Property Relationship for Screening-Level Prediction of Intrinsic Clearance of Volatile Organic Chemicals in Rats and Its Integration within PBPK Models to Predict Inhalation Pharmacokinetics in Humans. J Toxicol 2012; 2012:286079. [PMID: 22685458 PMCID: PMC3364689 DOI: 10.1155/2012/286079] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2011] [Revised: 01/13/2012] [Accepted: 01/13/2012] [Indexed: 01/28/2023] Open
Abstract
The objectives of this study were (i) to develop a screening-level Quantitative property-property relationship (QPPR) for intrinsic clearance (CLint) obtained from in vivo animal studies and (ii) to incorporate it with human physiology in a PBPK model for predicting the inhalation pharmacokinetics of VOCs. CLint, calculated as the ratio of the in vivo Vmax (μmol/h/kg bw rat) to the Km (μM), was obtained for 26 VOCs from the literature. The QPPR model resulting from stepwise linear regression analysis passed the validation step (R2 = 0.8; leave-one-out cross-validation Q2 = 0.75) for CLint normalized to the phospholipid (PL) affinity of the VOCs. The QPPR facilitated the calculation of CLint (L PL/h/kg bw rat) from the input data on log Pow, log blood: water PC and ionization potential. The predictions of the QPPR as lower and upper bounds of the 95% mean confidence intervals (LMCI and UMCI, resp.) were then integrated within a human PBPK model. The ratio of the maximum (using LMCI for
CLint) to minimum (using UMCI for CLint) AUC predicted by the QPPR-PBPK model was 1.36 ± 0.4 and ranged from 1.06 (1,1-dichloroethylene) to 2.8 (isoprene). Overall, the integrated QPPR-PBPK modeling method developed in this study is a pragmatic way of characterizing the impact of the lack of knowledge of CLint in predicting human pharmacokinetics of VOCs, as well as the impact of prediction uncertainty of CLint on human pharmacokinetics of VOCs.
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Bayesian Analysis of a Lipid-Based Physiologically Based Toxicokinetic Model for a Mixture of PCBs in Rats. J Toxicol 2012; 2012:895391. [PMID: 22315591 PMCID: PMC3270437 DOI: 10.1155/2012/895391] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2011] [Revised: 10/05/2011] [Accepted: 10/06/2011] [Indexed: 11/17/2022] Open
Abstract
A lipid-based physiologically based toxicokinetic (PBTK) model has been developed for a mixture of six polychlorinated biphenyls (PCBs) in rats. The aim of this study was to apply population Bayesian analysis to a lipid PBTK model, while incorporating an internal exposure-response model linking enzyme induction and metabolic rate. Lipid-based physiologically based toxicokinetic models are a subset of PBTK models that can simulate concentrations of highly lipophilic compounds in tissue lipids, without the need for partition coefficients. A hierarchical treatment of population metabolic parameters and a CYP450 induction model were incorporated into the lipid-based PBTK framework, and Markov-Chain Monte Carlo was applied to in vivo data. A mass balance of CYP1A and CYP2B in the liver was necessary to model PCB metabolism at high doses. The linked PBTK/induction model remained on a lipid basis and was capable of modeling PCB concentrations in multiple tissues for all dose levels and dose profiles.
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Mumtaz MM, Ray M, Crowell SR, Keys D, Fisher J, Ruiz P. Translational research to develop a human PBPK models tool kit-volatile organic compounds (VOCs). JOURNAL OF TOXICOLOGY AND ENVIRONMENTAL HEALTH. PART A 2012; 75:6-24. [PMID: 22047160 PMCID: PMC9041560 DOI: 10.1080/15287394.2012.625546] [Citation(s) in RCA: 20] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/26/2023]
Abstract
Toxicity and exposure evaluations remain the two of the key components of human health assessment. While improvement in exposure assessment relies on a better understanding of human behavior patterns, toxicity assessment still relies to a great extent on animal toxicity testing and human epidemiological studies. Recent advances in computer modeling of the dose-response relationship and distribution of xenobiotics in humans to important target tissues have advanced our abilities to assess toxicity. In particular, physiologically based pharmacokinetic (PBPK) models are among the tools than can enhance toxicity assessment accuracy. Many PBPK models are available to the health assessor, but most are so difficult to use that health assessors rarely use them. To encourage their use these models need to have transparent and user-friendly formats. To this end the Agency for Toxic Substances and Disease Registry (ATSDR) is using translational research to increase PBPK model accessibility, understandability, and use in the site-specific health assessment arena. The agency has initiated development of a human PBPK tool-kit for certain high priority pollutants. The tool kit comprises a series of suitable models. The models are recoded in a single computer simulation language and evaluated for use by health assessors. While not necessarily being state-of-the-art code for each chemical, the models will be sufficiently accurate to use for screening purposes. This article presents a generic, seven-compartment PBPK model for six priority volatile organic compounds (VOCs): benzene (BEN), carbon tetrachloride (CCl(4)), dichloromethane (DCM), perchloroethylene (PCE), trichloroethylene (TCE), and vinyl chloride (VC). Limited comparisons of the generic and original model predictions to published kinetic data were conducted. A goodness of fit was determined by calculating the means of the sum of the squared differences (MSSDs) for simulation vs. experimental kinetic data using the generic and original models. Using simplified solvent exposure assumptions for oral ingestion and inhalation, steady-state blood concentrations of each solvent were simulated for exposures equivalent to the ATSDR Minimal Risk Levels (MRLs). The predicted blood levels were then compared to those reported in the National Health and Nutrition Examination Survey (NHANES). With the notable exception of BEN, simulations of combined oral and inhalation MRLs using our generic VOC model yielded blood concentrations well above those reported for the 95th percentile blood concentrations for the U.S. populations, suggesting no health concerns. When the PBPK tool kit is fully developed, risk assessors will have a readily accessible tool for evaluating human exposure to a variety of environmental pollutants.
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Affiliation(s)
- M Moiz Mumtaz
- Division of Toxicology and Environmental Medicine, Agency for Toxic Substances and Disease Registry, Atlanta, Georgia 30333, USA.
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Tan YM, Clewell H, Campbell J, Andersen M. Evaluating pharmacokinetic and pharmacodynamic interactions with computational models in supporting cumulative risk assessment. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2011; 8:1613-30. [PMID: 21655141 PMCID: PMC3108131 DOI: 10.3390/ijerph8051613] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/22/2011] [Revised: 05/13/2011] [Accepted: 05/17/2011] [Indexed: 01/05/2023]
Abstract
Simultaneous or sequential exposure to multiple chemicals may cause interactions in the pharmacokinetics (PK) and/or pharmacodynamics (PD) of the individual chemicals. Such interactions can cause modification of the internal or target dose/response of one chemical in the mixture by other chemical(s), resulting in a change in the toxicity from that predicted from the summation of the effects of the single chemicals using dose additivity. In such cases, conducting quantitative cumulative risk assessment for chemicals present as a mixture is difficult. The uncertainties that arise from PK interactions can be addressed by developing physiologically based pharmacokinetic (PBPK) models to describe the disposition of chemical mixtures. Further, PK models can be developed to describe mechanisms of action and tissue responses. In this article, PBPK/PD modeling efforts conducted to investigate chemical interactions at the PK and PD levels are reviewed to demonstrate the use of this predictive modeling framework in assessing health risks associated with exposures to complex chemical mixtures.
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Affiliation(s)
- Yu-Mei Tan
- National Exposure Research Laboratory, Office of Research and Development, U.S. Environmental Protection Agency, 109 T.W. Alexander Drive, Research Triangle Park, NC 27711, USA
| | - Harvey Clewell
- Center for Human Health Assessment, The Hamner Institutes for Health Sciences, 6 Davis Drive, Research Triangle Park, NC 27709, USA; E-Mails: (H.C.); (J.C.); (M.A.)
| | - Jerry Campbell
- Center for Human Health Assessment, The Hamner Institutes for Health Sciences, 6 Davis Drive, Research Triangle Park, NC 27709, USA; E-Mails: (H.C.); (J.C.); (M.A.)
| | - Melvin Andersen
- Center for Human Health Assessment, The Hamner Institutes for Health Sciences, 6 Davis Drive, Research Triangle Park, NC 27709, USA; E-Mails: (H.C.); (J.C.); (M.A.)
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