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      Development, Testing, Parameterisation and Calibration of a Human PBPK Model for the Plasticiser, Di-(2-propylheptyl) Phthalate (DPHP) Using in Silico, in vitro and Human Biomonitoring Data

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          Abstract

          A physiologically based pharmacokinetic model for Di-(2-propylheptyl) phthalate (DPHP) was developed to interpret the biokinetics in humans after single oral doses. The model was parameterized with in vitro and in silico derived parameters and uncertainty and sensitivity analysis was used during the model development process to assess structure, biological plausibility and behaviour prior to simulation and analysis of human biological monitoring data. To provide possible explanations for some of the counter-intuitive behaviour of the biological monitoring data the model included a simple lymphatic uptake process for DPHP and enterohepatic recirculation (EHR) for DPHP and the mono ester metabolite mono-(2-propylheptyl) phthalate (MPHP). The model was used to simultaneously simulate the concentration-time profiles of blood DPHP, MPHP and the urinary excretion of two metabolites, mono-(2-propyl-6-hydroxyheptyl) phthalate (OH-MPHP) and mono-(2-propyl-6-carboxyhexyl) phthalate (cx-MPHP). The availability of blood and urine measurements permitted a more robust qualitative and quantitative investigation of the importance of EHR and lymphatic uptake. Satisfactory prediction of blood DPHP and urinary metabolites was obtained whereas blood MPHP was less satisfactory. However, the delayed peak of DPHP concentration relative to MPHP in blood and second order metabolites in urine could be explained as a result of three processes: 1) DPHP entering the systemic circulation from the lymph, 2) rapid and very high protein binding and 3) the efficiency of the liver in removing DPHP absorbed via the hepatic route. The use of sensitivity analysis is considered important in the evaluation of uncertainty around in vitro and in silico derived parameters. By quantifying their impact on model output sufficient confidence in the use of a model should be afforded. This approach could expand the use of PBPK models since parameterization with in silico techniques allows for rapid model development. This in turn could assist in reducing the use of animals in toxicological evaluations by enhancing the utility of “read across” techniques.

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          Most cited references63

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          Physiological Parameter Values for Physiologically Based Pharmacokinetic Models

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            Assessing exposure to phthalates - the human biomonitoring approach.

            Some phthalates are developmental and reproductive toxicants in animals. Exposure to phthalates is considered to be potentially harmful to human health as well. Based on a comprehensive literature research, we present an overview of the sources of human phthalate exposure and results of exposure assessments with special focus on human biomonitoring data. Among the general population, there is widespread exposure to a number of phthalates. Foodstuff is the major source of phthalate exposure, particularly for the long-chain phthalates such as di(2-ethylhexyl) phthalate. For short-chain phthalates such as di-n-butyl-phthalate, additional pathways are of relevance. In general, children are exposed to higher phthalate doses than adults. Especially, high exposures can occur through some medications or medical devices. By comparing exposure data with existing limit values, one can also assess the risks associated with exposure to phthalates. Within the general population, some individuals exceed tolerable daily intake values for one or more phthalates. In high exposure groups, (intensive medical care, medications) tolerable daily intake transgressions can be substantial. Recent findings from animal studies suggest that a cumulative risk assessment for phthalates is warranted, and a cumulative exposure assessment to phthalates via human biomonitoring is a major step into this direction. Copyright © 2011 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.
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              Calculation of molecular lipophilicity: State-of-the-art and comparison of log P methods on more than 96,000 compounds.

              We first review the state-of-the-art in development of log P prediction approaches falling in two major categories: substructure-based and property-based methods. Then, we compare the predictive power of representative methods for one public (N = 266) and two in house datasets from Nycomed (N = 882) and Pfizer (N = 95809). A total of 30 and 18 methods were tested for public and industrial datasets, respectively. Accuracy of models declined with the number of nonhydrogen atoms. The Arithmetic Average Model (AAM), which predicts the same value (the arithmetic mean) for all compounds, was used as a baseline model for comparison. Methods with Root Mean Squared Error (RMSE) greater than RMSE produced by the AAM were considered as unacceptable. The majority of analyzed methods produced reasonable results for the public dataset but only seven methods were successful on the both in house datasets. We proposed a simple equation based on the number of carbon atoms, NC, and the number of hetero atoms, NHET: log P = 1.46(+/-0.02) + 0.11(+/-0.001) NC-0.11(+/-0.001) NHET. This equation outperformed a large number of programs benchmarked in this study. Factors influencing the accuracy of log P predictions were elucidated and discussed. (c) 2008 Wiley-Liss, Inc. and the American Pharmacists Association
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                Author and article information

                Contributors
                Journal
                Front Pharmacol
                Front Pharmacol
                Front. Pharmacol.
                Frontiers in Pharmacology
                Frontiers Media S.A.
                1663-9812
                02 September 2021
                2021
                : 12
                : 692442
                Affiliations
                [ 1 ]Health and Safety Executive, Buxton, United Kingdom
                [ 2 ]National Center for Toxicological Research, US Food and Drug Administration, Jefferson, AR, United States
                Author notes

                Edited by: Eleonore Fröhlich, Medical University of Graz, Austria

                Reviewed by: Malarvannan Govindan, University of Antwerp, Belgium

                Raju Prasad Sharma, Leiden Academic Centre for Drug Research, Netherlands

                *Correspondence: Kevin McNally, kevin.mcnally@ 123456hse.gov.uk ; George Loizou, George.Loizou@ 123456hse.gov.uk

                This article was submitted to Predictive Toxicology, a section of the journal Frontiers in Pharmacology

                Article
                692442
                10.3389/fphar.2021.692442
                8443793
                34539393
                4d095464-2a32-43b0-ab72-61de5abe323e
                Copyright © 2021 McNally, Sams, Hogg, Lumen and Loizou.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 08 April 2021
                : 13 August 2021
                Funding
                Funded by: European Chemical Industry Council 10.13039/100009420
                Categories
                Pharmacology
                Original Research

                Pharmacology & Pharmaceutical medicine
                plasticiser,dphp,pbpk,in silico,in vitro,biomonitoring,bayesian,markov chain monte carlo

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