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• W3040953618 abstract "Vol. 128, No. 7 ResearchOpen AccessA Probabilistic Approach to Evaluate the Risk of Decreased Total Triiodothyronine Hormone Levels following Chronic Exposure to PFOS and PFHxS via Contaminated Drinking Wateris corrected byErratum: “A Probabilistic Approach to Evaluate the Risk of Decreased Total Triiodothyronine Hormone Levels following Chronic Exposure to PFOS and PFHxS via Contaminated Drinking Water” Antero Vieira Silva, Joakim Ringblom, Christian Lindh, Kristin Scott, Kristina Jakobsson, and Mattias Öberg Antero Vieira Silva Address correspondence to Antero V. Silva, Unit of Integrative Toxicology, Institute for Environmental Medicine (IMM), Karolinska Institutet, Nobels väg, 13, 171 65 Stockholm, Sweden, Telephone: +46 8 524 885 41. Email: E-mail Address: [email protected] Unit of Integrative Toxicology, Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden Search for more papers by this author , Joakim Ringblom Unit of Integrative Toxicology, Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden Search for more papers by this author , Christian Lindh Department of Laboratory Medicine, Division of Occupational and Environmental Medicine, Lund University, Lund, Sweden Search for more papers by this author , Kristin Scott Department of Laboratory Medicine, Division of Occupational and Environmental Medicine, Lund University, Lund, Sweden Search for more papers by this author , Kristina Jakobsson Department of Laboratory Medicine, Division of Occupational and Environmental Medicine, Lund University, Lund, Sweden Department of Public Health and Community Medicine, Division of Occupational and Environmental Medicine, University of Gothenburg, Gothenburg, Sweden Search for more papers by this author , and Mattias Öberg Unit of Integrative Toxicology, Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden Search for more papers by this author Published:8 July 2020CID: 076001https://doi.org/10.1289/EHP6654Cited by:1AboutSectionsPDF Supplemental Materials ToolsDownload CitationsTrack Citations ShareShare onFacebookTwitterLinked InRedditEmail AbstractBackground:Extensive exposure to per- and polyfluoroalkyl substances (PFAS) have been observed in many countries. Current deterministic frameworks for risk assessment lack the ability to predict the likelihood of effects and to assess uncertainty. When exposure exceeds tolerable intake levels, these shortcomings hamper risk management and communication.Objective:The integrated probabilistic risk assessment (IPRA) combines dose-response and exposure data to estimate the likelihood of adverse effects. We evaluated the usefulness of the IPRA for risk characterization related to decreased levels of total triiodothyronine (T3) in humans following a real case of high exposure to PFAS via drinking water.Methods:PFAS exposure was defined as serum levels from residents of a contaminated area in Ronneby, Sweden. Median levels were 270 ng/mL [perfluorooctane sulfonic acid (PFOS)] and 229 ng/mL [perfluorohexane sulfonic acid (PFHxS)] for individuals who resided in Ronneby 1 y before the exposure termination. This data was integrated with data from a subchronic toxicity study in monkeys exposed daily to PFOS. Benchmark dose modeling was employed to describe separate dose–effect relationship for males and females, and extrapolation factor distributions were used to estimate the corresponding human benchmark dose. The critical effect level was defined as a 10% decrease in total T3.Results:The median probability of critical exposure, following a combined exposure to PFOS and PFHxS, was estimated to be [2.1% (90% CI: 0.4%–13.1%)]. Gender-based analysis showed that this risk was almost entirely distributed among women, namely [3.9% (90% CI: 0.8%–21.6%)].Discussion:The IPRA was compared with the traditional deterministic Margin of Exposure (MoE) approach. We conclude that probabilistic risk characterization represents an important step forward in the ability to adequately analyze group-specific health risks. Moreover, quantifying the sources of uncertainty is desirable, as it improves the awareness among stakeholders and will guide future efforts to improve accuracy. https://doi.org/10.1289/EHP6654IntroductionPer- and polyfluoroalkyl substances (PFAS) is a class of chemicals used in a multitude of applications (Buck et al. 2011; Lau 2015). Although they have useful technical properties, some of these chemicals have been found worldwide in human, animal, and environmental samples (Banzhaf et al. 2017; Jian et al. 2017). Worldwide, the presence of high levels of these chemicals in drinking water, groundwater, and ecosystems has been associated with industrial production sites or the usage of aqueous film-forming firefighting foams (Banzhaf et al. 2017; Filipovic et al. 2015; Thalheimer et al. 2017). In a situation where exposure is clearly above the level of concern, for example, by exceeding tolerable intake levels, there is a need to characterize the risk in terms of probability for adverse health effects. Probabilistic risk assessment has been proposed to provide decision makers and other stakeholders with information about the magnitude of health risks and, at the same time, present uncertainties in a transparent and precise manner (WHO/ILO/UNEP 2014). This approach has so far not been used to evaluate exposure scenarios with PFAS, and it remains to be elucidated how probabilistic risk estimates differ from the traditional deterministic approaches that calculate a discrete Margin of Exposure (MoE) for the exposed population.The main aim of the present study was to perform an integrated probabilistic risk assessment (IPRA) analysis and to discuss its usefulness as a potential tool for risk assessment in comparison with the traditionally used deterministic MoE approach. The IPRA method, first described by van der Voet and Slob (2007), combines dose–response data with exposure data in a probabilistic manner in order to generate a distribution of individual margins of exposure (IMoE) and to estimate a probability of critical exposure (PoCE), that is, the likelihood of having an exposure that exceeds a predefined effect level. In addition, the IPRA approach takes data variation into account, which can be divided by variability and uncertainty sources (e.g., intraspecies variability and duration extrapolation uncertainty) and quantifies their contribution to the final estimate (van der Voet and Slob 2007). This uncertainty estimation is a major advantage over traditional deterministic risk assessment strategies such as the MoE approach, which results in a single estimate where the impact of uncertainty remains unknown (van der Voet and Slob 2007). The IPRA approach expresses the estimated risk though a confidence interval (CI) that describes the percentile of the population at risk of having an effect due to the chemical exposure (van der Voet and Slob 2007). Previously, the approach has been used for both human and environmental risk assessment purposes for agents as diverse as nanoparticles (Jacobs et al. 2016), cadmium, mycotoxins, pesticides, and acrylamide (Bokkers et al. 2009). The IPRA approach is most often used in cases of general population exposure and, to the best of our knowledge, it has not been applied to cases where the population has had an extensive exposure to soil and water contaminants.In order to investigate the usefulness of the probabilistic approach for risk assessment purposes, we performed an analysis focusing on PFAS in drinking water, an emerging issue in many industrialized countries. PFAS represent a large class of chemicals known to be bioaccumulative, persistent and toxic (Jian et al. 2017). Among other effects, PFAS have been described as hepato- and immunotoxic and as developmental toxicants (ATSDR 2018; Wang et al. 2017). A growing body of literature points toward the understanding that thyroid hormone levels can be affected following exposure to perfluorooctane sulfonic acid (PFOS) and perfluorohexane sulfonic acid (PFHxS). Disruption of the thyroid hormone system has been reported in both epidemiological (Dallaire et al. 2009; Knox et al. 2011; Wen et al. 2013) and animal studies (Chang et al. 2008; Curran et al. 2008; Luebker et al. 2005; Martin et al. 2007; Seacat et al. 2002; Thibodeaux et al. 2003). However, not all epidemiological studies have found a negative correlation with PFOS levels in serum (Olsen et al. 2003), nor all animal studies (Chang et al. 2017). Nevertheless, PFOS has been identified as a thyroid hormone disruptor (ATSDR 2018; Coperchini et al. 2017).The mechanistic aspects of PFOS-induced effects on the total triiodothyronine (T3) hormone are numerous and complex. In epidemiological studies, effects on the thyroid hormone system might be difficult to observe due to a large interindividual variation. Moreover, the hypothalamus–pituitary–thyroid (HPT) axis is regulated by a tight feedback mechanism. Thus, xenobiotics leading to changes in circulating T3 and thyroxine (T4) levels can be considered disruptive to the HPT homeostasis, but that is not necessarily the cause of disease given that this can be compensated for by an increase in thyroid-stimulating hormone (TSH) secretion. It is plausible that PFOS exerts its thyroid-disrupting effects by displacing circulating T3 and T4 hormones from their binding proteins in the bloodstream, namely albumin, thyroxine-binding globulin, and transthyretin (Chen and Guo 2009; Hebert and MacManus-Spencer 2010; Ren et al. 2015, 2016).For the IPRA analysis performed in the present study, we used serum levels of PFAS measured in habitants of the Swedish municipality of Ronneby. In December 2013, high levels of PFAS were detected in the outgoing water of the Brantafors waterworks, which provides drinking water to approximately one-third of Ronneby’s 28,000 inhabitants (Li et al. 2018; Livsmedelsverket 2013a). The Brantafors waterworks are located about 2km from the firefighting training location of Ronneby’s airfield where PFAS-containing firefighting foams were estimated to have been used since the mid-1980s (Li et al. 2018). However, the exact composition of the firefighting foams that were used, the average annual volume usage, and the frequency of the firefighting training sessions in the Ronneby airfield are unknown. The two main contaminants identified in drinking water delivered from Brantafors were PFOS and PFHxS (Li et al. 2018; Livsmedelsverket 2013a). Based on data from this population, the average half-lives of PFOS and PFHxS were estimated to be 3.4 y and 5.3 y, respectively (Li et al. 2018).Given the persistency and potential toxic effects of PFOS and PFHxS, both compounds are currently objects of concern to various international agencies (ECHA 2017; U.S. EPA 2016). PFOS has also been listed under Annex B (Restriction) of the Stockholm Convention on Persistent Organic Pollutants (UNEP 2017a). Chemicals proposed for listing under the convention and currently under review include PFHxS and perfluorooctanoic acid (PFOA) (UNEP 2017b).Dose–response data were obtained from an animal study where cynomolgus monkeys were exposed daily to PFOS (Seacat et al. 2002). Among other effects, the exposure caused a significant decrease of total T3 and free T3 and increased levels of TSH (Seacat et al. 2002) (see Figure S1). In 2008, the European Food Safety Authority (EFSA) selected the paper by Seacat et al. (2002) as a critical study to establish a tolerable daily intake (TDI) value for PFOS (EFSA 2008).Traditionally, risk is characterized by estimating the MoE, calculated by dividing an experimentally derived point-of-departure (POD), such as a no-observed-adverse-effect-level (NOAEL) or benchmark dose lower bound (BMDL), and an exposure metric from a typical or worst-case scenario. If the exposure is 100 times lower than the POD, there is generally no concern for health effects. However, this deterministic MoE approach does not express the contribution of uncertainty or variability to the estimate, nor does it inform how many individuals are likely to be affected if the exposure exceeds the reference value. For PFAS, there is currently no consensus regarding the threshold for a safe level of exposure and multiple guideline values, for both PFOS and PFHxS, have been established by different agencies (Table 1).Table 1 A noncomprehensive compilation of guideline values for chronic exposure to PFOS and PFHxS expressed in terms of body weights.Table 1 has seven columns, namely, authority, year, PFOS limit value, PFHxS limit value, effect, reference, and MoE.AuthorityYearPFOS limit valuePFHxS limit valueEffectReferenceMoEATSDR2018—MRL=20 ng PFHxS/kg BW per dayCombined UF of 300Rat, thyroid follicular cell damageATSDR 20180.59Swedish National Food Agency (Livsmedelsverket)2013—TDI=5,000 ng PFHxS/kg BW per dayCombined UF of 200Rat, liver effectsLivsmedelsverket 2013b147ATSDR2018MRL=2 ng PFOS/kg BW per day Combined UF of 300—Rat, delayed eye opening and decreased pup weightATSDR 20180.018EFSA2020aTWI=8 ng/kg BW per week for the sum of four PFAS (PFOS, PFHxS, PFNA and PFOA) ∼1.1 ng/kg BW per dayEpidemiological studies, lower antibody titersUnpublished worka0.008EFSA2018TWI=13 ng PFOS/kg BW per week∼1.86 ng PFOS/kg BW per dayNo UFs used—Epidemiological studies, increase of serum cholesterolEFSA 20180.016EFSA2008TDI=150 ng PFOS/kg BW per dayCombined UF of 200—Monkeys, lipids and thyroid metabolismEFSA 20081.3U.S. EPA2016RfD=20 ng PFOS/kg BW per dayCombined UF of 30—Rat, decreased neonatal body weightU.S. EPA 20160.18Note: —, no data; ATSDR, Agency for Toxic Substances and Disease Registry; BW, body weight; EFSA, European Food Safety Authority; EPA, Environmental Protection Agency; MoE, Margin of Exposure; MRL, minimal risk level; PFHxS, perfluorohexane sulfonic acid; PFNA, perfluorononanoic acid; PFOA, perfluorooctanoic acid; PFOS, perfluorooctane sulfonic acid; Rfd, reference dose; TWI, tolerable weekly intake; UF, uncertainty factor.aThis TWI has not been adopted at the time of publication of this article.In the present study, we used the case of the PFAS-contaminated drinking water in Ronneby and the potential effects on thyroid hormone levels to evaluate the usefulness of the IPRA method in comparison with the commonly used deterministic approach MoE.MethodsThe IPRA analysis was performed in accordance with the approach described by van der Voet and Slob (2007). This full probabilistic analysis used Monte Carlo simulations, which repeatedly sampled randomly from the input data, to emulate possible exposures and dose–effect scenarios (van der Voet and Slob 2007; WHO/ILO/UNEP 2014). The MoE can be defined as the ratio of a NOAEL or BMDL-value and a point estimate of the exposure (WHO/ILO/UNEP 2014). Analogously, it has been suggested that the IMoE in an integrated probabilistic risk assessment can be defined as follows (van der Voet and Slob 2007): IMoE=IBMDIEXP [1] where IEXP is the individual exposure distribution, and IBMD is the probabilistically derived individual benchmark dose distribution. In the present analysis, each IMoE distribution described a scenario of possible combinations that departed from the original measurements in the exposed population. This process was repeated 10,000 times so that the IEXP, IBMD, and IMoE distributions would reflect as many scenarios as possible. The analysis was performed in R (version 3.4.2; R Development Core Team). An overview of the IPRA process is given in Figure 1.Figure 1. A schematic overview of the integrated probabilistic risk assessment approach, describing the simulations performed for residents that lived in Ronneby, Sweden, for at least 1 y (n=1,845). The same process was repeated for residents living for 10 y (n=1,176) and 29 y (n=506). The IEXP distribution size was always 1,000 greater than the size of the respective population from which the draws were originally made. The IBMD distribution size matched the number of IEXPs, keeping the gender proportions. Note: BMD; benchmark dose; EF, extrapolation factor; GSDH, GSD for interindividual human variability; IBMD, probabilistically derived individual benchmark dose distribution; IEXP, individual exposure distribution; IMoE, individual margins of exposure; PoCE, probability of critical exposure; Prob, probability; TD, toxicodynamics.Exposure AssessmentThe serum concentrations were determined at the Division of Occupational and Environmental Medicine, Department of Laboratory Medicine, at Lund University, Lund, Sweden. The quantification was performed using liquid chromatography and tandem mass spectrometry after extraction by protein precipitation (Li et al. 2018). The PFAS analyses are part of an interlaboratory control progran coordinated by the University of Erlangen-Nuremberg, Germany). For the present study, we used results from serum samples from Ronneby’s municipality residents, obtained between June 2014 and December 2015, from 1,845 individuals living in households provided with PFAS-contaminated drinking water. Given the high PFAS concentrations found in the drinking water, this exposure route was assumed to outweigh all others when contributing to the PFAS body burden (Table 2; see also Table SI) (Li et al. 2018; Livsmedelsverket 2013b). Individuals <2 years of age were excluded from the analysis due to PFAS exposure during fetal life and via breastfeeding (Mondal et al. 2014). The residence history was collected from all participants. All individuals included in the analysis lived continuously in the areas provided with PFAS-contaminated drinking water for at least 1 y before exposure was terminated in December 2013. To explore the distribution of risk in the population, we stratified the group by gender and duration of residency, that is, the group was divided into those who lived continuously in the households provided with PFAS-contaminated drinking water during the last exposure year (2013, n=1,845), the last 10 y (2004–2013, n=1,176), or the last 29 y (1985–2013, n=506) before exposure was terminated in December 2013. Population descriptors for the three groups are presented in Table 3. The 5th, 50th, and 95th percentiles of PFOS and PFHxS serum levels measured in this population are presented in Table 4. Calculations were based on exposure to PFOS only and to a combined exposure to PFOS and PFHxS, assuming an equipotent toxicity on a molar basis. PFOS and PFHxS molecular weights are 500.13 and 400.11g/mol, respectively.Table 2 Measured levels of PFAS in drinking water from the Brantafors waterworks in December 2013.Table 2, in three columns, chemical names, CASN, and concentration in nanogram per liter.Chemical nameCASNConcentration (ng/L)aPerfluorinated sulfonate acids (PFSA) Perfluorooctane sulfonic acid (PFOS)1763-23-14,000 Perfluoroheptane sulfonic acid (PFHpS)375-92-867 Perfluorohexane sulfonic acid (PFHxS)355-46-41,200 Perfluorobutane sulfonic acid (PFBS)75-22-4140Perfluorinated carboxylic acids (PFCAs) Perfluordodecanoic acid (PFDoDA)307-55-1<10 Perfluroundecanoic acid (PFUnDA)2058-94-8<10 Perfluorodecanoic acid (PFDA)335-76-2<10 Perfluorononanoic acid (PFNA)375-95-11.2 Perfluorooctanoic acid (PFOA)335-67-1130 Perfluoroheptanoic acid (PFHpA)375-85-940 Perfluorohexanoic acid (PFHxA)307-24-4340 Perfluoropentanoic acid (PFPeA)375-85-952∑PFAS5,970.2Note: CASN, Chemical Abstracts Services number; PFAS, per- and polyfluoroalkyl substances; ΣPFAS, sum of PFAS.aData from Livsmedelsverket (2013a).Table 3 Sampled Ronneby, Sweden, population description, gender, and age. Duration exposure regarding those living continuously in the areas provided with PFAS-contaminated drinking water for at least 1 y (2013), 10 y (2004–2013), or 29 y (1985–2013).Table 3, in four columns, lists categories, greater than or equal to 1 year in Ronneby (n equals 1,845), greater than or equal to 10 years in Ronneby (n equals 1,176), and greater than or equal to 29 years in Ronneby (n equals 506).Category≥1y in Ronneby (n=1,845)≥10y in Ronneby (n=1,176)≥29y in Ronneby (n=506)Women (%)54.355.755.9Median age (y)455164.5Age [y (%)] 3–1819.211.10 19–6562.164.654.3 66–9418.724.345.7Note: PFAS, per- and polyfluoroalkyl substances.Table 4 Median (5th and 95th percentiles) serum PFOS and PFHxS concentrations of the Ronneby population. Duration exposure regarding those who lived continuously in the areas provided with PFAS-contaminated drinking water for atleast 1 y (2013), 10 y (2004–2013), or 29 y (1985–2013).Table 4, in four main columns, lists PFAS serum levels in nanograms per milliliter, greater than or equal to 1 year in Ronneby (n equals 1,845), greater than or equal to 10 years in Ronneby (n equals 1,176), and greater than or equal to 29 years in Ronneby (n equals 506). The second, third, and fourth columns, each have two columns, namely, females and males.PFAS serum concentrations (ng/mL)≥1y in Ronneby (n=1,845)≥10y in Ronneby (n=1,176)≥29y in Ronneby (n=506)FemalesMalesFemalesMalesFemalesMalesPFOS270 (58, 834.5)290.2 (69.3, 830.3)367 (85.8, 909.4)379 (136.1, 900.2)473.6 (153.8, 1,007)498.4 (167.2, 999.4)PFHxS229 (41.1, 819.2)263 (52.3, 763)346.4 (63.6, 897.4)377.3 (112.3, 869.2)483.6 (124.6, 995.7)477.7 (169.7, 971.3)Note: PFAS, per- and polyfluoroalkyl substances; PFHxS, perfluorohexane sulfonic acid; PFOS, perfluorooctane sulfonic acid.In each iteration of the analysis, an IEXP distribution was created by randomly drawing, with replacement, from the group of exposed individuals in Ronneby. This IEXP distribution was 1,000 times larger than the original exposed population; for example, 1,845,000 random draws, with replacement, were performed from the sampled 1,845 individuals that lived at least 1 y in Ronneby and drank PFAS-contaminated drinking water. For each scenario, a total of 10,000 IEXP distributions were created.Estimation of the IBMDAnimal data.The benchmark dose modeling approach, introduced by Crump (1984), is a statistical method used to describe dose–response relationships. It estimates a benchmark dose (BMD), which is the dose most likely to give rise to a prespecified effect. A 90% confidence interval (CI) is derived, composed of the BMDL and BMDU, the lower and upper bound of the CI, respectively. This CI is estimated by taking into account the uncertainty in the data. BMDanimal were calculated based on data on total T3 levels reported by Seacat et al. (2002). Cynomolgus monkeys (Macaca fascicularis) were exposed once a day for 6 months to 0, 0.03, 0.15, or 0.75mg PFOS/kg body weight (BW) per day by intragastric intubation (Seacat et al. 2002). There were six animals of each sex at each dose level, except in the 0.03 mg/kgBW per day group, where there were only four animals. For the purpose of the current study, the total T3 levels measured by AniLytics (Gaithersburg, Maryland) at the end of the study (day 184) were used as the key end point (Seacat et al. 2002). The group mean serum levels of PFOS at Day 183 were used as a measure of internal exposure (Seacat et al. 2002).Dose–effect modeling.The Individual Critical Effect Doses (IBMDs), also known as individual critical effect doses (van der Voet and Slob 2007), were calculated as follows: IBMD=BMDanimalEFTD inter×EFTD intra×EFDuration [2] where BMDanimal is the serum concentration of PFOS leading to a 10% decrease in total T3 levels in cynomolgus monkeys (Seacat et al. 2002). A 10% reduction was used instead of EFSA’s 5% default critical effect size (EFSA Scientific Committee 2017) since such a small change is likely to be without biological significance and within analytical variation (Andersen et al. 2003). According to the paper by van der Voet and Slob (2007), the BMD (and not the BMDL) should be used, corresponding in this case to the best estimate of the serum concentration that leads to the defined critical effect size. The uncertainty in the data is taken into account by the distribution for the BMD, that is, the BMD uncertainty.To obtain the BMDanimal distribution, the T3 levels were fitted to the serum PFOS levels data from the study by Seacat et al. (2002) using the R package Proast (version 65.5) and following EFSA’s BMD modeling guidance (EFSA Scientific Committee 2017). In the study by Seacat et al. (2002), the PFOS serum levels and the T3 levels were reported on the group level [arithmetic mean±standard deviation (SD)]. As individual data were unavailable, it was assumed that all monkeys within a dose group had the same serum levels of PFOS, but different levels of T3 at Day 184. Data from males and females were fitted separately, using a 10% decrease in total T3 levels as the critical effect size. The model’s critical difference value, using the Akaike information criterion (AIC), was set to 2, according to EFSA’s guidance (EFSA Scientific Committee 2017). Model 3 of the exponential family had the best fit. The estimated BMDfemales was 17.6μg/L (90% CI: 2.4, 52.2μg/L) and the BMDmales was 65.4μg/L (90% CI: 29.3, 102μg/L) (Figure 2). Then, the bootstrap method (as described by Moerbeek et al. 2004) was employed to estimate 3,000,000 BMDs for males and females, respectively, constituting the BMDanimal distribution. For each iteration, an IBMD distribution was obtained by randomly drawing from the bootstrapped BMDanimal and the extrapolation factor (EF) distributions EFDuration, EFTD inter, and EFTD intra (further explained below). The number of draws in each IBMD distribution corresponded to the size of the IEXP distribution, keeping the gender proportions.Figure 2. Benchmark dose–response analysis for (A) female and (B) male monkeys, using serum PFOS concentrations at day 183 (x-axis) and total T3 levels at Day 184 (y-axis), as described in the study by Seacat et al. (2002). Doses are based on median serum concentrations measured in the dose groups. Note: a, background response, according to the fitted model; AIC, Akaike information criterion; b, potency parameter, according to the fitted model; CED, critical effect dose (also known as benchmark dose); CEDL, lower bound of the CED 90% confidence interval; CEDU, upper bound of the CED 90% confidence interval; CES, critical effect size; conv, convergence, denoted by 1 if the fit algorithm converged and 0 if not; d, steepness parameter, according to the fitted model; dtype, data type, 10 for continuous summary data (expressed in mean±SD); loglik, loglikelihood of the fitted model; PFOS, perfluorooctane sulfonic acid; T3, triiodothyronine; var, the within-group variance (related to the natural log-responses).Interspecies extrapolation.There is potentially a difference in toxicodynamic sensitivity to PFOS between the average cynomolgus monkey and the average human, and the uncertainty about this difference was accounted for by the interspecies toxicodynamics extrapolation factor (EFTD inter). This extrapolation factor was drawn from a lognormal distribution with a geometric mean (GM) of 1 and a geometric standard deviation (GSD) of 0.56. In other words, the distribution has a 50th percentile of 1 and the 95th percentile of 2. This was based on the World Health Organization, International Labour Organization, and United Nations Environment Programme (WHO/ILO/UNEP) guidance document that recommended the use of a 95th percentile of 3 if the extrapolation factor is to take both toxicokinetic and toxicodynamic differences into account after adjusting for allometric scaling (WHO/ILO/UNEP 2014). The slightly lower value of 2 instead of 3 was justified given that differences in toxicokinetics were already accounted for by using serum PFOS concentrations as a dose metric. Google ScholarIntraspecies extrapolation.In order to account for interindividual differences in sensitivity to the exposure, an extrapolation factor for the intraspecies variability in toxicodynamics was introduced (EFTD intra). It has been shown that the difference in sensitivity to a specific chemical is often approximately lognormally distributed (WHO/ILO/UNEP 2014). In accordance with data compiled by WHO’s International Programme on Chemical Safety (IPCS; WHO/ILO/UNEP 2014), we assumed a GM of 1 for EFTD intra. The GSD for interindividual variability (GSDH) of the lognormal distribution is chemical-dependent and the specific value for PFOS is unknown. We assumed that GSDH was lognormally distributed and that the 5th, 50th, and 95th percentile of the logGSDH were 0.0776, 0.221, and 0.631, respectively (WHO/ILO/UNEP 2014). A unique draw for EFTD intra based on the logGSDH drawn for was performed for each iteration (Figure 1). The EFTD intra distribution is simultaneously a source of variability and uncertainty given that toxicodynamics differences between humans can be described by a variability distribution that is, in turn, subject to uncertainty.Extrapolation between durations.The length of a chronic study should cover a considerable part of the life span of the tested species, and cynomolgus monkeys have expected life spans of 25–30 y (Choi et al. 2016). Therefore, the 6-month study performed by Seacat et al. (2002) was considered subchronic. An extrapolation factor taking into account the uncertainty in extrapolating between different durations was used (EFDuration). As suggested by WHO/ILO/UNEP (2014), EFDuration was characterized as a lognormal distribution with a GM of 2 and a GSD of 0.84, resulting in a distribution with a 50th percentile of 2 and 5th and 95th percentiles of 0.5 and 8, respectively.Individual Margin of Exposure DistributionsFor each iteration, an IMoE distribution was obtained by dividing a randomly drawn IBMD with a randomly drawn IEXP from the respective distributions (van der Voet and Slob 2007). This was done in accordance with gender, so that IBMDs derived from female monkeys were divided with an IEXP value referring to women in th" @default.
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• W3040953618 title "A Probabilistic Approach to Evaluate the Risk of Decreased Total Triiodothyronine Hormone Levels following Chronic Exposure to PFOS and PFHxS via Contaminated Drinking Water" @default.
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