Development, Testing, Parameterization, and Calibration of a Human Physiologically Based Pharmacokinetic Model for the Plasticizer, Hexamoll® Diisononyl-Cyclohexane-1, 2-Dicarboxylate Using In Silico, In Vitro, and Human Biomonitoring Data

Metabolomics in human SGBS cells as new approach method for studying adipogenic effects: Analysis of the effects of DINCH and MINCH on central carbon metabolism

Growing evidence suggests that exposure to certain metabolism-disrupting chemicals (MDCs), such as the phthalate plasticizer DEHP, might promote obesity in humans, contributing to the spread of this global health problem. Due to the restriction on the use of phthalates, there has been a shift to safer declared substitutes, including the plasticizer diisononyl-cyclohexane-1,2-dicarboxylate (DINCH). Notwithstanding, recent studies suggest that the primary metabolite monoisononyl-cyclohexane-1,2-dicarboxylic acid ester (MINCH), induces differentiation of human adipocytes and affects enzyme levels of key metabolic pathways. Given the lack of methods for assessing metabolism-disrupting effects of chemicals on adipose tissue, we used metabolomics to analyze human SGSB cells exposed to DINCH or MINCH. Concentration analysis of DINCH and MINCH revealed that uptake of MINCH in preadipocytes was associated with increased lipid accumulation during adipogenesis. Although we also observed intracellular uptake for DINCH, the solubility of DINCH in cell culture medium was limited, hampering the analysis of possible effects in the μM concentration range. Metabolomics revealed that MINCH induces lipid accumulation similar to peroxisome proliferator-activated receptor gamma (PPARG)-agonist rosiglitazone through upregulation of the pyruvate cycle, which was recently identified as a key driver of de novo lipogenesis. Analysis of the metabolome in the presence of the PPARG-inhibitor GW9662 indicated that the effect of MINCH on metabolism was mediated at least partly by a PPARG-independent mechanism. However, all effects of MINCH were only observed at high concentrations of 10 μM, which are three orders of magnitudes higher than the current concentrations of plasticizers in human serum. Overall, the assessment of the effects of DINCH and MINCH on SGBS cells by metabolomics revealed no adipogenic potential at physiologically relevant concentrations. This finding aligns with previous in vivo studies and supports the potential of our method as a New Approach Method (NAM) for the assessment of adipogenic effects of environmental chemicals.

Occurrence and prioritization of non-volatile substances in recycled PET flakes produced in China

Recycled PET (rPET) is gaining popularity for use in the production of new food contact materials (FCMs) under the context of circular economy. However, the limited information on contaminants in rPET from China and concerns about their potential risk are major obstacles to their use in FCM in China. Fifty-five non-volatile compounds were tentatively identified in 126 batches of hot-washed rPET flakes aimed for food packaging applications in China. Although the 55 substances are not necessarily migratable and may not end up in the contacting media, their presence indicates a need for proper management and control across the value chain. For this reason, the 55 substances prioritized on the basis of level of concerns and in-silico genotoxicity profiler. Among them, dimethoxyethyl phthalate, dibutyl phthalate, bis(2-ethylhexyl) phthalate were classified as level V substances, and Michler's ketone and 4-nitrophenol were both categorized as level V substances and had the genotoxic structure alert, while 2,4,5-trimethylaniline was specified with genotoxic structure alert. The above substances have high priority and may pose a potential risk to human health, therefore special attention should be paid to their migration from rPET. Aside from providing valuable information on non-volatile contaminants present in hot-washed rPET flakes coming from China, this article proposed a prioritization workflow that can be of great help to identify priority substances deserving special attention across the value chain.

Refinement and calibration of a human PBPK model for the plasticiser, Di-(2-propylheptyl) phthalate (DPHP) using in silico, in vitro and human biomonitoring data

An existing physiologically based pharmacokinetic model for Di-(2-propylheptyl) phthalate (DPHP) was refined to improve the simulations of the venous blood concentrations of the primary monoester metabolite, mono-(2-propylheptyl) phthalate (MPHP). This was considered a significant deficiency that should be addressed because the primary metabolite of other high molecular weight phthalates has been associated with toxicity. The various processes that influence the concentration of DPHP and MPHP in blood were re-evaluated and modified. A few simplifications of the existing model were made, including the removal of enterohepatic recirculation (EHR) of MPHP. However, the primary development was describing the partial binding of MPHP to plasma proteins following uptake of DPHP and metabolism in the gut affording better simulation of the trends observed in the biological monitoring data. Secondly, the relationship between blood concentrations and the urinary excretion of secondary metabolites was explored further because the availability of two data streams provides a better understanding of the kinetics than reliance on just one. Most human studies are conducted with few volunteers and generally with the absence of blood metabolite measurements which would likely imply an incomplete understanding of the kinetics. This has important implications for the "read across" approach proposed as part of the development of New Approach Methods for the replacement of animals in chemical safety assessments. This is where the endpoint of a target chemical is predicted by using data for the same endpoint from another more "data rich" source chemical. Validation of a model parameterized entirely with in vitro and in silico derived parameters and calibrated against several data streams would constitute a data rich source chemical and afford more confidence for future evaluations of other similar chemicals using the read-across approach.

Development, testing, parameterisation, and calibration of a human PBK model for the plasticiser, di (2-ethylhexyl) adipate (DEHA) using in silico, in vitro and human biomonitoring data

Introduction: A physiologically based biokinetic model for di (2-ethylhexyl) adipate (DEHA) based on a refined model for di-(2-propylheptyl) phthalate (DPHP) was developed to interpret the metabolism and biokinetics of DEHA following a single oral dosage of 50 mg to two male and two female volunteers. Methods: The model was parameterized using in vitro and in silico methods such as, measured intrinsic hepatic clearance scaled from in vitro to in vivo and algorithmically predicted parameters such as plasma unbound fraction and tissue:blood partition coefficients (PCs). Calibration of the DEHA model was achieved using concentrations of specific downstream metabolites of DEHA excreted in urine. The total fractions of ingested DEHA eliminated as specific metabolites were estimated and were sufficient for interpreting the human biomonitoring data. Results: The specific metabolites of DEHA, mono-2-ethyl-5-hydroxyhexyl adipate (5OH-MEHA), mono-2-ethyl-5-oxohexyl adipate (5oxo-MEHA), mono-5-carboxy-2-ethylpentyl adipate (5cx-MEPA) only accounted for ∼0.45% of the ingested DEHA. Importantly, the measurements of adipic acid, a non-specific metabolite of DEHA, proved to be important in model calibration. Discussion: The very prominent trends in the urinary excretion of the metabolites, 5cx-MEPA and 5OH-MEHA allowed the important absorption mechanisms of DEHA to be modelled. The model should be useful for the study of exposure to DEHA of the general human population.

Development, testing, parameterisation, and calibration of a human PBPK model for the plasticiser, di-(2-ethylhexyl) terephthalate (DEHTP) using in silico, in vitro and human biomonitoring data

A physiologically based pharmacokinetic model for di-(2-ethylhexyl) terephthalate (DEHTP) based on a refined model for di-(2-propylheptyl) phthalate (DPHP) was developed to interpret the metabolism and biokinetics of DEHTP following a single oral dose of 50 mg to three male volunteers. In vitro and in silico methods were used to generate parameters for the model. For example, measured intrinsic hepatic clearance scaled from in vitro to in vivo and plasma unbound fraction and tissue:blood partition coefficients (PCs) were predicted algorithmically. Whereas the development and calibration of the DPHP model was based upon two data streams, blood concentrations of parent chemical and first metabolite and the urinary excretion of metabolites, the model for DEHTP was calibrated against a single data stream, the urinary excretion of metabolites. Despite the model form and structure being identical significant quantitative differences in lymphatic uptake between the models were observed. In contrast to DPHP the fraction of ingested DEHTP entering lymphatic circulation was much greater and of a similar magnitude to that entering the liver with evidence for the dual uptake mechanisms discernible in the urinary excretion data. Further, the absolute amounts absorbed by the study participants, were much higher for DEHTP relative to DPHP. The in silico algorithm for predicting protein binding performed poorly with an error of more than two orders of magnitude. The extent of plasma protein binding has important implications for the persistence of parent chemical in venous blood-inferences on the behaviour of this class of highly lipophilic chemicals, based on calculations of chemical properties, should be made with extreme caution. Attempting read across for this class of highly lipophilic chemicals should be undertaken with caution since basic adjustments to PCs and metabolism parameters would be insufficient, even when the structure of the model itself is appropriate. Therefore, validation of a model parameterized entirely with in vitro and in silico derived parameters would need to be calibrated against several human biomonitoring data streams to constitute a data rich source chemical to afford confidence for future evaluations of other similar chemicals using the read-across approach.

A review of common non-ortho-phthalate plasticizers for use in food contact materials

Several non-ortho phthalate plasticizers, including ATBC, DEHA, DINCH, DOTP, and ESBO, are currently used in flexible PVC applications for food packaging and processing. The aim of this review is to summarize the available toxicity, migration, and human biomonitoring data. Available assessments from US CPSC, EFSA, other governmental and non-governmental organizations, and published toxicology studies were used to show that these plasticizers are generally well-studied and demonstrate low toxicity with a focus on potential carcinogenicity, reproductive, developmental, and endocrine related adverse effects as well as biodegradation, aquatic toxicity, and bioaccumulation. Seven other plasticizers, 2EHESBO, ASE, CMCHA, DBT, DEHCH, PETV, and TOTM, have at least some recent but limited food contact clearances; assessments from CPSC, EFSA, and robust summaries in the REACH dossiers were reviewed for these products. Data gaps were found for some of these products; however, there were no concerns raised by the existing data, and they for now have limited use in food contact applications. Migration of ASE, COMGHA, DINCH, DOTP, DEHCH, and TOTM in simulants for aqueous and low alcohol foods ranged from <0.02 to 0.165 mg/kg, which showed they are below established migration limits and well-suited for these applications. Human biomonitoring data are available for DINCH, DOTP, DEHA, DINA, and TOTM, and are essential for determining exposure from all uses.

An integrated exposure and pharmacokinetic modeling framework for assessing population-scale risks of phthalates and their substitutes

To effectively incorporate in vitro-in silico-based methods into the regulation of consumer product safety, a quantitative connection between product phthalate concentrations and in vitro bioactivity data must be established for the general population. We developed, evaluated, and demonstrated a modeling framework that integrates exposure and pharmacokinetic models to convert product phthalate concentrations into population-scale risks for phthalates and their substitutes. A probabilistic exposure model was developed to generate the distribution of multi-route exposures based on product phthalate concentrations, chemical properties, and human activities. Pharmacokinetic models were developed to simulate population toxicokinetics using Bayesian analysis via the Markov chain Monte Carlo method. Both exposure and pharmacokinetic models demonstrated good predictive capability when compared with worldwide studies. The distributions of exposures and pharmacokinetics were integrated to predict the population distributions of internal dosimetry. The predicted distributions showed reasonable agreement with the U.S. biomonitoring surveys of urinary metabolites. The "source-to-outcome" local sensitivity analysis revealed that food contact materials had the greatest impact on body burden for di(2-ethylhexyl) adipate (DEHA), di-2-ethylhexyl phthalate (DEHP), di(isononyl) cyclohexane-1,2-dicarboxylate (DINCH), and di(2-propylheptyl) phthalate (DPHP), whereas the body burden of diethyl phthalate (DEP) was most sensitive to the concentration in personal care products. The upper bounds of predicted plasma concentrations showed no overlap with ToxCast in vitro bioactivity values. Compared with the in vitro-to-in vivo extrapolation (IVIVE) approach, the integrated modeling framework has significant advantages in mapping product phthalate concentrations to multi-route risks, and thus is of great significance for regulatory use with a relatively low input requirement. Further integration with new approach methodologies will facilitate these in vitro-in silico-based risk assessments for a broad range of products containing an equally broad range of chemicals.

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

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.