An assessment of the interindividual variability of internal dosimetry during multi-route exposure to drinking water contaminants

Cardiopulmonary parameters and organ blood flows for workers expressed in terms of VO2 for use in physiologically based toxicokinetic modeling

Minute ventilation rates (VE), alveolar ventilation rates (VA), cardiac outputs (Q), liver blood flow (LBF) and kidneys blood flows (KBF) for physiologically based toxicokinetic modeling and occupational health risk assessment in active workers have apparently not been determined. Minute energy expenditure rates (E) and oxygen consumption rates (VO₂) in workers during exertions and their aggregate daytime activities are obtained by using open-circuit wearable devices for indirect calorimetry measurements and the doubly labeled water method respectively. Hundreds of E (in kcal/min) and VO₂ (in L of O₂/min) were previously reported for workers. The oxygen uptake factors of 0.2059 ± 0.0019 and 0.2057 ± 0.0018 L of O₂/kcal during postprandial and fasting phases respectively enabled conversion of E into VO₂. Equations determined in this study based upon more than 25 000 published measurements enable the calculation of 15 parameters in the same worker only by using the VO₂ reflecting workload. These parameters, notably VE, VA, VE/VO₂ VA/Q, Q, LBF and KBF were found to be interrelated. Altering one of these changes the order of magnitude of the others. Q, LBF and KBF decrease when supine adults at rest switch to an upright position. This effect of gravity diminished when VO₂ increased. The fall in LBF and KBF during exertion might enhance muscle blood flow as reported previously. Taken together these equations and data may improve the accuracy of physiologically based toxicokinetic modeling as well as occupational health assessment studies in active workers exposed to xenobiotics.List of main abbreviations: AVOD: arterioveinous oxygen content difference.BMI: body mass index (in kg/m²).BSA: body surface area (in m²).BTPS: body temperature and saturated with water vapor.Bw: body weight (in kg).E: minute energy expenditure rate (in kcal/min).F_GE: organ blood flow factor for the gravitational effect on blood circulation.H: oxygen uptake factor, volume of oxygen (at STPD) consumed to produce 1 kcal of energy expended.KBF: kidneys blood flow (in ml/min).LBF: liver blood flow (in ml/min).P_BF: liver or kidneys blood flows expressed in terms of percentages (in %) of Q_{sup C} values: namely P_BF = (LBF or KBF/Q_{sup C}) x 100.Q: cardiac output (in L/min or ml/min).Q_{sup C:} cardiac output for the cohort of males or females in supination (in ml/min).STPD: standard temperature and pressure, dry air.sup: values measured when adults are in the supine position.up: values measured when adults are in the upright position.VDphys: physiological dead space at BTPS (in L).VT: tidal volume at BTPS (in L).VA: alveolar ventilation rate at BTPS (in L/min).VA/Q: ventilation-perfusion ratio (unitless).VE: minute ventilation rate at BTPS (in L/min).VO₂: oxygen consumption rate (i.e. the oxygen uptake) at STPD (in L/min).VQ: ventilatory equivalent for VO₂ (VE at BTPS /VO₂ at STPD).

Reverse dosimetry modeling of toluene exposure concentrations based on biomonitoring levels from the Canadian health measures survey

Biomonitoring might provide useful estimates of population exposure to environmental chemicals. However, data uncertainties stemming from interindividual variability are common in large population biomonitoring surveys. Physiologically based pharmacokinetic (PBPK) models might be used to account for age- and gender-related variability in internal dose. The objective of this study was to reconstruct air concentrations consistent with blood toluene measures reported in the third Canadian Health Measures Survey using reverse dosimetry PBPK modeling techniques. Population distributions of model's physiological parameters were described based upon age, weight, and size for four subpopulations (12-19, 20-39, 40-59, and 60-79 years old). Monte Carlo simulations applied to PBPK modeling allowed converting the distributions of venous blood measures of toluene obtained from CHMS into related air levels. Based upon blood levels observed at the 50^th, 90^th and 95^th percentiles, corresponding air toluene concentrations were estimated for teenagers aged 12-19 years as being, respectively, 0.009, 0.04 and 0.06 ppm. Similarly, values were computed for adults aged 20-39 years (0.007, 0.036, and 0.06 ppm), 40-59 years (0.007, 0.036 and 0.06 ppm) and 60-79 years (0.006, 0.022 and 0.04 ppm). These estimations are well below Health Canada's maximum recommended chronic air guidelines for toluene. In conclusion, PBPK modeling and reverse dosimetry may be combined to help interpret biomonitoring data for chemical exposure in large population surveys and estimate the associated toxicological health risk.

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

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.

Characterization of the human kinetic adjustment factor for the health risk assessment of environmental contaminants

A default uncertainty factor of 3.16 (√10) is applied to account for interindividual variability in toxicokinetics when performing non-cancer risk assessments. Using relevant human data for specific chemicals, as WHO/IPCS suggests, it is possible to evaluate, and replace when appropriate, this default factor by quantifying chemical-specific adjustment factors for interindividual variability in toxicokinetics (also referred to as the human kinetic adjustment factor, HKAF). The HKAF has been determined based on the distributions of pharmacokinetic parameters (e.g., half-life, area under the curve, maximum blood concentration) in relevant populations. This article focuses on the current state of knowledge of the use of physiologically based algorithms and models in characterizing the HKAF for environmental contaminants. The recent modeling efforts on the computation of HKAF as a function of the characteristics of the population, chemical and its mode of action (dose metrics), as well as exposure scenario of relevance to the assessment are reviewed here. The results of these studies, taken together, suggest the HKAF varies as a function of the sensitive subpopulation and dose metrics of interest, exposure conditions considered (route, duration, and intensity), metabolic pathways involved and theoretical model underlying its computation. The HKAF seldom exceeded the default value of 3.16, except in very young children (i.e., <≈ 3 months) and when the parent compound is the toxic moiety. Overall, from a public health perspective, the current state of knowledge generally suggest that the default uncertainty factor is sufficient to account for human variability in non-cancer risk assessments of environmental contaminants.

Accounting for the impact of short-term variations in the levels of trihalomethane in drinking water on exposure assessment for epidemiological purposes. Part II: biological aspects

The variability of trihalomethane (THM) levels in drinking water raises the question of whether or not short-term variations (within-day) should be accounted for when assessing exposure to contaminants suspected of being carcinogenic and reprotoxic agents. The purpose of this study was to determine the magnitude of the impact on predicted biological levels of THMs (internal doses) exerted by within-day variations of THMs in drinking water. A database extracted from a campaign in the Québec City distribution system served to produce 81, 79 and 64 concentration profiles for the three most abundant THMs, namely chloroform (TCM), dichlorobromomethane (DCBM) and chlorodibromomethane (CDBM), respectively. Using a physiologically based toxicokinetic modeling approach, we simulated exposures (1.5 l water per day and a 10-min shower) based on each of these profiles and predicted, for 2000 individuals (Monte-Carlo simulations), maximum blood concentrations (Cmax), areas under the time versus blood concentrations curve (24 h-AUCcv) and total absorbed doses (ADs). Three different hypotheses were tested: [A] assuming a constant THM concentration in water (e.g., mean value of a day); [B] accounting for within-day variations in THM levels; and [C] a worst-case scenario assuming within-day variations and showering while THM levels were maximal. For each exposure profile, exposure indicator and individual, we calculated the ratios of values obtained according to each hypothesis (e.g., CmaxB/CmaxA and CmaxC/CmaxA) and the values corresponding to the 5th and 95th percentiles of these ratios. The closer these percentiles are to the value of 1, the smaller the error associated with assuming constant THM concentrations rather than their actual variability. Results showed that the minimal gap between these percentiles was TCM-AD(B)/TCM-AD(A) (5th=0.91; 95th=1.09), whereas the maximal gap was CDBM-Cmax(C)/CDBM-Cmax(A) (5th=0.50; 95th=3.40). Overall, TCM and ADs were the less affected (TCM<DCBM<CDBM and AD<AUCcv<Cmax) when accounting for within-day variations in water levels.

Comparison of trihalomethanes in tap water and blood: a case study in the United States

BACKGROUND

Epidemiological studies have used various measures to characterize trihalomethane (THM) exposures, but the relationship of these indicators to exposure biomarkers remains unclear.

OBJECTIVES

We examined temporal and spatial variability in baseline blood THM concentrations and assessed the relationship between these concentrations and several exposure indicators (tap water concentration, water-use activities, multiroute exposure metrics).

METHODS

We measured water-use activity and THM concentrations in blood and residential tap water from 150 postpartum women from three U.S. locations.

RESULTS

Blood ΣTHM [sum of chloroform (TCM), bromodichloromethane (BDCM), dibromo-chloromethane (DBCM), and bromoform (TBM)] concentrations varied by site and season. As expected based on variable tap water concentrations and toxicokinetic properties, the proportion of brominated species (BDCM, DBCM, and TBM) in blood varied by site (site 1, 24%; site 2, 29%; site 3, 57%) but varied less markedly than in tap water (site 1, 35%; site 2, 75%; site 3, 68%). The blood-water ΣTHM Spearman rank correlation coefficient was 0.36, with correlations higher for individual brominated species (BDCM, 0.62; DBCM, 0.53; TBM, 0.54) than for TCM (0.37). Noningestion water activities contributed more to the total exposure metric than did ingestion, but tap water THM concentrations were more predictive of blood THM levels than were metrics that incorporated water use.

CONCLUSIONS

Spatial and temporal variability in THM concentrations was greater in water than in blood. We found consistent blood-water correlations across season and site for BDCM and DBCM, and multivariate regression results suggest that water THM concentrations may be an adequate surro-gate for baseline blood levels.