Assessing the arsenic-contaminated rice (Oryza sativa) associated children skin lesions

Title: Assessing the arsenic-contaminated rice (Oryza sativa) associated children skin lesions

Authors: Chung-Min Liao, Tzu-Ling Lin, Nan-Hung Hsieh, Wei-Yu Chen

PII: S0304-3894(09)01803-2

DOI: doi:10.1016/j.jhazmat.2009.11.019

Reference: HAZMAT 10887

To appear in: Journal of Hazardous Materials

Received date: 7-6-2009

Revised date: 19-9-2009

Accepted date: 3-11-2009

Please cite this article as: C.-M. Liao, T.-L. Lin, N.-H. Hsieh, W.-Y. Chen, Assessing the arsenic-contaminated rice (Oryza sativa) associated children skin lesions, Journal

of Hazardous Materials (2008), doi:10.1016/j.jhazmat.2009.11.019

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Accepted Manuscript

1

Assessing the arsenic-contaminated rice (Oryza sativa)

associated children skin lesions

Chung-Min Liao*, Tzu-Ling Lin, Nan-Hung Hsieh, Wei-Yu Chen

Department of Bioenvironmental Systems Engineering, National Taiwan University, Taipei, Taiwan 10617 Republic of China

*Corresponding author. E-mail address: [email protected]

Abstract

The purpose of this study was to assess the potential risk of children skin lesions

from arsenic-contaminated rice (Oryza sativa) consumption in West Bengal (India).

Published age- and gender-specific skin lesions data in West Bengal were reanalyzed

and incorporated into a Weibull dose-response model to predict children skin lesion

prevalence. Monomethylarsonous acid (MMA(III)) levels in urine was used as a

biomarker that could be predicted from a human physiologically based

pharmacokinetic (PBPK) model. This study integrated arsenic contents in irrigation

water, bioaccumulation factors of paddy soil, cooking methods, and arsenic

bioavailability of cooked rice in gastrointestinal tract into a probabilistic risk model.

Results indicated that children aged between 13 – 18 years might pose a relative

higher potential risk of skin lesions to arsenic-contaminated cooked rice (odds ratios

(ORs) = 1.18 (95% CI 1.12 – 2.15)) than those of 1 – 6 years children (ORs = 0.98

(0.85 – 1.40)). This study revealed the need to consider the relationships between

cooking method and arsenic in cooked rice when assessing the risk associated with

children skin lesions from rice consumption. This study suggested that

arsenic-associated skin lesions risk from arsenic-contaminated rice consumption

would be reduced significantly by adopting traditional rice cooking method (wash

until clean; rice: water = 1:6; discard excess water) as followed in West Bengal (India)

and using water containing lower arsenic (e.g., < 10 g L-1) for cooking.

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1. Introduction

Recently, one of the most pressing issues in facing with dietary arsenic exposure

in Bangladesh and West Bengal (India) is human health risk potential from

arsenic-contaminated rice consumption [1-5]. The magnitude of groundwater

contaminated with naturally occurring arsenic of alluvial aquifers is particularly

severe in Bangladesh and West Bengal (India) where arsenic contaminated

groundwater is not only used for drinking water but is also widely used for irrigation

of crops [6,7]. Particularly, nearly 30 – 50% of the areas of Bangladesh and West

Bengal (India) are irrigated with arsenic-contaminated groundwater to grow dry

season rice crops [8-10].

Arsenic-contaminated groundwater for crops irrigation had resulted in elevated

arsenic concentration in agricultural soils in Bangladesh, West Bengal (India), and

elsewhere [8-15]. Paddy rice (Oryza sativa) is the main agricultural crop grown in the

arsenic-affected areas of Bangladesh and West Bengal (India) [11]. In the long term

this may lead to the accumulation of arsenic in paddy soils and potentially have

adverse effects on rice yield and quality, creating a potential risk for future food

production and human health effects.

The mechanisms of arsenic transfer from arsenic-contaminated irrigation water

to paddy soil and transfer from arsenic-contaminated paddy soil to rice are largely

unknown. However, a parsimonious bioaccumulation model may provide a surrogate

method to estimate the kinetic constants for the biotransformation processes. The

toxic effect of arsenic in any foodstuff is highly dependent on its chemical speciation.

Inorganic arsenic compounds are generally thought to be more toxic than organic

forms. Meharg et al. [8] indicated that rice grain grown in the arsenic-affected soils

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[10] indicated that nearly 81% of recovered arsenic was found to be inorganic in

Bangladeshi and Indian rice based on the pot experiments.

Laparra et al. [16] indicated that arsenate bioavailability in cooked rice was

estimated to be 63 – 99% from a gastrointestinal digestion simulation study. Juhasz et

al. [17] indicated that arsenic bioavailability in rice is highly dependent on arsenic

speciation varied with rice cultivar, arsenic in irrigation water, and arsenic speciation

in cooking water. Juhasz et al. [17] suggested that in assessing arsenic dietary

exposure from cooked rice, arsenic speciation and bioavailability are crucial

parameters that are needed to be considered. Pal et al. [4] and Sengupta et al. [18]

revealed that arsenic content in cooked rice was strong dependent on the cooking

methods, indicating that the total arsenic in cooked rice is less than that of in raw rice

at rice washing water arsenic concentration of 10 g L-1, whereas an average 35 –

40% increased in cooked rice at 50 g L-1 of washing water. After cooking, inorganic

arsenic contents increase significantly, suggesting that the cooking method together

with rice washing water should be considered in arsenic-associated health risk

assessment [5,16,18-22].

Some evidence suggests that arsenic-induced skin lesions are early biomarkers

of other outcomes such as nonmelanoma skin cancer and cancer of the internal organs

[23]. Chronic arsenic exposure and skin lesions (keratosis and hyperpigmentation) are

inextricably linked [21,24-27]. There is, however, no effective therapy for skin lesions

nowadays [28]. Recently, health effects for arsenic exposure in young children have

become a regulatory focus [29,30]. Data used to assess the impact of arsenic exposure

on the children arsenic-associated skin lesions are limited but indicate consistently

that they have been posed the potential risks [21,23]. Arsenic methylation of urinary

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relationship between arsenic methylation capacity and children skin lesions together

with its effect on manifestation of skin cancer, arsenic-induced skin lesions as a model

system was selected to assess children health effects in arseniasis-endemic areas.

Physiologically based pharmacokinetic (PBPK) models are potentially powerful

tools in quantitative risk assessments for target tissue dose estimates. These models

can be useful for human health risk assessments because the PBPK modeling permits

the calculation of target tissue doses through integration of information on the

external dose, the physiological structure of the human, and biochemical properties of

metals. The most human PBPK models for arsenic have a number of similarities

[32-35]. The simplest PBPK model for arsenic came from Yu [34]. Yu [34] extended

the simplest PBPK model to fit the human child including arsenite (As(III)), arsenate

(As(V)), monomethly arsenic (MMA), and dimethylarsinic acid (DMA), and

considering both reductive metabolism and methylation. Yu [34] noted that reduction

of As(V) to As(III) is a second-order process, dependent on the concentration of both

As(V) and glutathione (GSH), suggesting the potential use of a GSH

synthesis/depletion submodel linked to the primary kinetic model through the process

of arsenic reduction. Yu [35] further refined the model to fit the human adult,

indicating that the input parameters that most significantly affected the output of the

model were the maximum methylation reaction rate, the level of GSH for

determination of the reaction rate of As(V) to As(III), and the urinary excretion

constants.

The purpose of this study was to provide a probabilistic risk model for

predicting and assessing the arsenic-associated children skin lesions risk from rice O.

sativa consumption in West Bengal (India). A human PBPK model was linked with a Weibull dose-response model to formulate a probabilistic risk model. The likelihood

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of risk was predicted based on the proposed PBPK-Weibull framework followed the

published gender/age-specific epidemiological data on arsenic exposure, skin lesions

prevalence, and at-risk population from studies conducted in West Bengal (India).

Therefore, a risk-based predictive model for arsenic exposure associated children skin

lesions from arsenic-contaminated rice consumption was presented. It hoped that this

paper can demonstrate the concepts that can be applied generally to the risk

assessment in the face of arsenic-associated human health effects in children.

2. Materials and methods

2.1. Quantitative arsenic epidemiological data

Epidemiological data on the arsenic-associated children skin lesions are limited

and scarcely. Yet, a remarkable dataset (Appendix A) covers arsenic epidemiology of

gander-specific and age-adjusted prevalence of arsenic-induced skin lesions of

keratosis and hyperpigmentation in West Bengal (India) [24] gave us the opportunity

to test all theoretical considerations of arsenic exposure effects and quantify its

strength. A major strength of their study is that it is the first large population-based

study with individual exposure data, providing critical information to characterize the

exposure-response relationships. The dataset was reanalyzed from the cross-sectional

survey conducted between April 1995 and March 1996 to reconstruct quantitatively

the pooled arsenic epidemiological data of gender- age-, and skin lesion-specific

cumulative prevalence ratios. A total of 7818 individuals were participated in the

drinking water study. Water-arsenic levels were obtained from 7683 of the participants

(4093 females and 3590 males).

Guha Mazumder and co-workers [24] used a standardized questionnaire to collect

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medical symptoms, and height and weight. A detailed description of the recruitment

procedure for cross-sectional survey and skin lesions cases ascertainment of keratosis

and hyperpigmentation has been reported previously [24]. Guha Mazumder et al. [24]

indicated that the age-adjusted prevalence of keratosis was associated strongly to

water arsenic levels, rising from zero in the lowest exposure level (< 50 g L-1) to

8.3 10-2 for female based on drinking water arsenic level > 800 g L-1, and increasing

from 0.2 10-2 in lowest exposure category to 10.7 10-2 for male in the highest

exposure level (> 800 g L-1). Their finding indicated that the steepest

exposure-response relationships were found for males. This is due in part to the fact

that males have greater water consumption. Their study demonstrated that men had

roughly 2 – 3 times the prevalence of both keratosis and hypergigmentation compared

to women based on the calculation by dose per body weight. Those with poor

nutritional status had an age-adjusted prevalence keratosis that was 1.6 times greater

than those considered to be adequately nourished. This suggested that malnutrition

may play a role in increasing susceptibility.

Furthermore, the larger number of study participants, 1-year follow-up with more

skin lesions cases, and a wider range of arsenic exposure levels (< 50 – > 800 g L-1)

together with gender specific age groups (< 9 – > 60 years) gives us a unique

opportunity to investigate the dose-response relationships between ingested arsenic

exposure and skin lesions risks.

2.2. Weibull dose-response function and bioaccumulation of rice

A the Weibull probability density function was used to account for the

age-specific prevalence ratio for human long-term exposure to low doses of arsenic,

) ) ( exp( ) ( )) ( , ( 2 1 2 2 k k t C t k C C t g , (1)

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where g(t, (C))represents the skin lesion-specific prevalence ratio for human exposed to arsenic concentration C ( g L-1) at age t (yr), (C) is the

concentration-dependent shape parameter, and k0, k1, k2, and k3 are the skin

lesion-specific best-fitted parameters. The cumulative prevalence ratio for human

exposed to arsenic concentration C at age t can then be obtained by integral of Eq. (1)

as, ) ) ( exp( 1 ) ) ( exp( 1 )) ( , ( ) , ( 2 1 2 3 0 0 k k k t t k C k t C dt C t g C t P . (3)

A simple bioaccumulation model was used to describe the arsenic accumulate in

paddy soil from irrigation water and then from paddy soil bioconcentrate in paddy

rice, W W R W S R W S w S R W S R C K K C K C C C C C C , (4)

where CR is the arsenic concentration in rice (μg g-1), CW is the dissolved arsenic

concentration in irrigation water (μg mL-1

), CS is the arsenic concentration in paddy

soil (μg g-1

), KS-W (mL g-1), KR-S (g g-1), and KR-W (mL g-1) are the bioaccumulation

constants for soil-water, rice-soil, and rice-water interfaces, respectively.

2.3. Human PBPK model for arsenic

A prototypical human PBPK model for arsenic included compartments for the

lung, liver kidney, muscle, fat tissue, skin, and GI tract (Fig. 1A) [32-35]. In this study,

the speciation of arsenic considered included As(III), As(V), dimethylarsinous acid

(MMA(III)), monomethylarsonic acid (MMA(V)), dimethylarsinous acid (DMA(III)),

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MMA(III) and MMA(V) and between DMA(III) and DMA(V) were incorporated into

the kidney and liver compartments (Fig. 1B). The uptake of bioavailable arsenic was

considered in the GI tract compartment characterizing by Caco-2 cells [16] (Fig. 1A).

In Caco-2 cells, average arsenic retention, transport, and total uptake (retention +

transport) from cooked rice can be estimated to be 2.3% (95% CI 0.36 – 8.43 %),

6.70% (2.67 – 15.45%), and 9.33% (3.31 – 22.87%), respectively, based on Laparra et

al. [16].

The biotransformation mechanism of arsenic in the body consists of an

oxidation/reduction and two methylation reactions in that MMA and DMA also

subject to oxidation/reduction [36] (Fig. 1B). Gong et al. [36] indicated that MMA(III)

in urine at 25 C was oxidized completely to MMA(V) within 14 d (i.e., K4 = 5.95

10-3 h-1), whereas the conversion of DMA(III) to DMA(V) was completely in 17 h

(i.e., K6 = 5.88 10-2 h-1) (Fig. 1B). No information was available for reduction of

MMA and DMA. It assumed reasonably that reduction rates of K3 and K5 can be

estimated from the proportionality of K1/K2. The oxidation/reduction of inorganic

arsenic takes place in the plasma and in the kidney and liver, whereas the methylation

of As(III) takes place mainly in the liver and kidney followed by Michaelis-Menten

kinetics [34,35]. Mann et al. [32,33] suggested that the reduction of As(V) to As(III)

can be modeled as a first-order oxidation/reduction reaction. It assumed kidney and

urine having the same levels of arsenic species.

The age-specific distribution of secondary methylation ratio (DMA/MMA) was

used to adjust the age-dependent arsenic methylation rate constants based on a study

focused on the excretion of arsenic species in children urine from an arsenic exposed

area in Bangladesh [37]. A fitted normal distribution of

2

1.32 2.56 exp( 0.5(( 0.54) /1.0) )

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1C). The dynamic behavior of PBPK and metabolic processes in the PBPK model can

be described by a set of first-order differential equations (Appendix B). The

physiological parameters, age-adjusted metabolic constants, tissue/blood partition

coefficients, and biochemical parameters are listed in Tables C1, C2, and C3,

respectively (Appendix C).

2.4. Arsenic distributions in irrigation water, paddy soil, and rice

Data sources were derived from published relevant literature where available.

The recently published data were analyzed to obtain the arsenic distributions in

irrigation water, paddy soil, and paddy rice in West Bengal (India) based on Eq. (4).

Arsenic concentration profiles in arsenic-contaminated irrigation water were

estimated from Rhaman et al. [7]. Rhaman et al. [7] carried out an in-depth research to

determine arsenic contamination in groundwater in an arsenic-affected village of

Rajapur in Murshidabad district, West Bengal (India), where the agricultural system

was mostly groundwater dependent. The result indicated that 91% and 63% of

hand-pump tube-wells contained arsenic concentrations of > 10 and > 50 g L-1,

respectively. The distributions of arsenic in paddy soil in West Bengal (India) were

based on Norra et al. [9]. Norra et al. [9] carried out a study in an intensively

cultivated agricultural area of the Bengal delta Plain in West Bengal (India) to

determine the arsenic contamination degrees in paddy soil. Norra et al. [9] indicated

that arsenic concentration in the uppermost paddy soil was found to be 38 g g-1.

On the other hand, the published data regarding arsenic levels in raw rice were

adopted from Roychowdhury et al. [38]. Roychowdhury et al. [38] carried out an

investigation for determining the arsenic levels in food composites collected from the

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The result indicated that the highest mean arsenic levels in rice ranged from 226.18 –

245.39 ng g-1. To determine the percentage of arsenic retained in cooked rice varied

by different cooking methods, three major cooking methods (designed as cooking

methods A, B, and C) used in West Bengal (India) were adopted to estimate arsenic

levels in cooked rice [18]. Cooking method A is a traditional method used in West

Bengal by which raw rice is washed 5 – 6 times with a ratio of boiled water: weight of

rice = 5 – 6:1. In method B, raw rice is washed as method A, yet the boiled water:

weight of rice = 1.5 – 2:1. On the other hand, in method C, the raw rice is unwashed

with boiled water: weight of rice = 1.5 – 2:1.

2.5. Risk estimation

Odds ratio (OR) was estimated to assess relative magnitude of the effect of

arsenic exposure on likelihood of prevalence of children skin lesions at a particular

setting. OR can be calculated as: OR =Pexp(CU MMA(III),t)/Pcon(CU MMA(III),t)where

) ,

( U MMA(III)

exp C t

P is the exposed prevalence ratio as a function of urine MMA(III) level and age t,P_con(C_{U MMA(III)},t)is the control prevalence ratio in that prevalence ratio can be predicted by Weibull model and urine MMA(III) level can be predicted from

PBPK model.

To assess risk contribution from cooked rice to total arsenic intake, a

parsimonious model [3, 8], 100 (As in cooked rice 0.575 kg-1/(As in cooked rice

0.575 kg-1 + As in drinking water 2.0 L)), was adopted to estimate the dietary

arsenic exposure including contaminated drinking water calculated from the proposed

PBPK model. Here rice consumption rate of 0.575 kg d-1 and drinking water

consumption of 2.0 L d-1 for 13-18 yr children were used to calculate the percentage

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The TableCurve 3D (Version 4, AISN Software Inc., Mapleton, OR, USA) was

used to perform model fitting to pooled published arsenic epidemiological data to

reflect the reasonable trend of dose-response relationships. The Berkeley Madonna:

Modeling and Analysis of Dynamic Systems (Version 8.3.9,

http://www.berkeleymadonna.com) was used to perform the PBPK simulations. To

explicitly quantify the uncertainty/variability of data, a Monte Carlo simulation was

performed with 10000 iterations (stability condition) to obtain the 95% confidence

interval (CI). The Monte Carlo simulation was implemented by using the Crystal Ball

software (Version 2000.2, Decisioneering Inc., Denver, CO, USA). The 2 and

Kolmogorov-Smirnov (K-S) statistics were used to optimize the goodness-of-fit of the

distribution.

3. Results

3.1. Fitting Weibull model to arsenic epidemiological data

Table 1 shows the gender-specific best-fitted parameters k0, k1, k2, and k3 in

Weibull dose-response model for hyperpigmentation and keratosis obtained by fitting

Eq. (3) to gender- and skin lesion-specific cumulative prevalence ratios in West

Bengal (India) (Tables A1 and A2). The results indicate that male skin lesions have

the highest r2 values (0.94 – 0.96) than female skin lesions (r2 = 0.91) (Table 1).

Specifically, arsenic exposure has notably influence than age (k1 = 0.61 – 0.70, k2 =

0.12 – 0.18) for all gender skin lesions, indicating that arsenic exposure is attributable

mainly to skin lesion prevalence for residents in West Bengal (India) (Table 1). A

similar trend was revealed for arsenic-specific cumulative prevalence ratios for

gender- and age-specific skin lesions in the ages of the 6th, 12th, and 18th yr (Fig. 2A,

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increasing of arsenic exposure concentration and age.

3.2. Arsenic distributions in water, soil, rice, and cooked rice

The distributions of arsenic in paddy rice, paddy soil, and irrigation water can be

best described by the lognormal model (Fig. 3A). Median arsenic level in paddy rice

was estimated to be 0.24 g g-1 dry wt (95% CI 0.12 – 0.48) with a geometric

standard deviation (gsd) of 1.41, whereas the estimates of arsenic in paddy soil and

irrigation water were 15.66 g g-1 (95% CI 5.42 – 43.71) (gsd = 1.70) and 0.06 g

mL-1 (95% CI 0.01 – 0.58) (gsd = 2.93), respectively (Fig. 3A). The factors describing

the accumulation of arsenic concentration from irrigation water to paddy water (KS-W)

and from paddy soil to rice (KR-S) were estimated to be 248 mL g-1 (95%CI 24.54 –

2943.15) (gsd = 3.33) and 15.13 10-3 g g-1 (95%CI 4.51 10-3 – 53.73 10-3) (gsd =

1.88), respectively (Fig. 3B). Given that KS-W and KR-S, bioaccumulation factor

between irrigation water and paddy rice KR-W can be estimated to be 3.84 mL g-1

(95%CI 0.38 – 36.53) (gsd = 3.13) (Fig. 3B).

A baseline relationship between arsenic in cooking water and arsenic in cooked

rice can be determined by fitting a linear model (y = 35.14 + 1.44x, r2 = 0.99) to the

published data (Fig. 4A). Given the best-fitted model in Fig. 4A and cooking

method-specific estimated percentage of arsenic retained in cooked rice in Fig. 4B,

cooking method-specific cooked rice arsenic contents can then be calculated. Fig. 4

C – E shows the profiles describing the relations of cooking method-specific arsenic

contents in cooked rice varied with arsenic in cooking water.

3.3. Risk estimates

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100% were used to represent the fraction of absorbed arsenic from cooked rice in GI

tract. The results show that male average ORs (0.84 – 1.49 for = 100% and 0.91 –

1.28 for = 22.87%) were greater than those of female (0.83 – 1.26 for = 100% and

0.87 – 1.14 for = 22.87%) among three children age groups (Table 2). ORs of

hyperpigmentation (0.83 – 1.49 for = 100% and 0.87 – 1.28 for = 22.87%) were

greater than that of keratosis (0.97 – 1.12 for = 100% and 0.97 – 1.05 for =

22.87%) and increased with increasing of ages in West Bengal (India) (Table 2).

Further analysis also revealed that cooking Method C gave higher ORs (1.01 – 1.49)

than those of Methods A and B (ORs = 0.84 – 1.24 for Method A and ORs = 1.01 –

1.43 for Method B). The result indicates that among the three cooking methods,

Method A had the lowest risk estimate.

Overall predicted OR distributions of children skin lesions gave the mean

estimates of 0.98 (95% CI 0.85 – 1.40), 1.09 (1.03 – 1.92) and 1.18 (1.12 – 2.51) for

three age groups of 1 – 6, 7 – 12, and 13 – 18 yr, respectively (Fig. 5). The findings

indicated that children aged between 7 – 18 years may pose a relative higher potential

risk (overall ORs = 1.09 – 1.18) of skin lesions exposed to chronic arsenic from

arsenic-contaminated cooked rice consumption compared to 1 – 6 yr-old children (OR

= 0.98). The result also showed that arsenic intake from cooked rice accounts for

20.6% and 34.1% of arsenic consumption if cooked rice contained 0.1 and 0.2 g g-1

of arsenic, respectively, with 100 g L-1 arsenic in water at a 575 g d-1 rice

consumption rate and a 2 L d-1 of drinking water intake for 13 – 18 year of age (Fig.

6).

In the present case in West Bengal (India), with a nearly 1 g g-1 arsenic (upper

limit of 95% CI) in cooked rice based on median 0.23 g g-1 arsenic in paddy rice

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estimates 81.2% contribution by cooked rice to dietary arsenic exposure if the 13 – 18

yr children were drinking 2 L of 60 g L-1 arsenic-contaminated water (median

arsenic in groundwater). At lower concentrations of arsenic in groundwater (e.g., 10

g L-1), cooked rice contained arsenic becomes the dominant source of dietary arsenic

exposure (Fig. 6). These findings suggested that consumption of arsenic-contaminated

cooked rice is a major source of arsenic exposure in West Bengal (India).

4. Discussion

The use of PBPK models in risk assessment has grown substantially in the last

decade and should only increase in the future [39]. These types of approaches are

necessary to allow more reliable low-dose predictions since they can take into account

not only low-dose exposure regimens but also the effects of species differences and

nonlinear kinetics for biotransformation. Although building these models may be

time-consuming and has to be done for each chemical independently, the knowledge

generated is essential to perform risk and safety assessment on low-dose metal

exposure regimens with higher levels of confidence.

The Weibull dose-response model based on the published arsenic

epidemiological data should provide better estimates of skin lesions prevalence for

areas where arsenic concentrations are relative high (e.g., Bangladesh and Taiwan)

and for areas where incidence/prevalence rates must be extrapolates to low arsenic

concentrations (e.g., USA) [10]. It anticipated that this Weibull model-based arsenic

epidemiology and human PBPK approach, which accounts to arsenic-associated

children skin lesions risk estimates, might provide the basis of a future

population-based risk management strategy.

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dose response profile selection that are dependent on the use of arsenic

epidemiological data to characterize particular aspects of risk analysis. The main

potential application envisaged for Weibull-PBPK approach is with respect to human

health, and there is clearly a need for further development and to investigate how well

the approach can be transferred from West Bengal (India) to Bangladesh or Taiwan

populations to account for plausible greater chronic arsenic exposure and

environmental variations.

Williams et al. [12] suggested that the maximum tolerable daily intake (MTDI)

of arsenic by rice consumption must not exceed 2 g kg-1 body mass recommended by

World Health Organization (WHO). From a conservative point of view, if a 25 kg 10

yr of age child in West Bengal (India) consumes 0.4 kg d-1 of cooked rice [40] with

the present estimated average cooked rice of 500 g kg-1 arsenic; cooked rice will

contribute 8 g kg-1 body weight per day. When added to the water consumption (2 L

d-1 60 g L-1 / 25 kg = 4.8 g kg-1 d-1), total consumption is nearly 6.5 times the

WHO’s arsenic MTDI. The present estimated arsenic daily intake from water and cooked rice (8 + 4.8 = 12.8 g kg-1 d-1) is consistent with the estimated provisional

tolerable daily intake value of 12.9 g kg-1 d-1 for high arsenic-affected families in

West Bengal, India by Uchino et al. [40].

The proposed PBPK modeling and Weibull model-based epidemiological

framework provides a template for integrating the irrigation water arsenic data,

bioaccumulation of paddy soil and rice, epidemiological data, and risk modeling to

estimate the children arsenic-associated skin lesions risk from rice consumption. The

results revealed that arsenic-rich groundwater in tube-wells in West Bengal would

lead to high accumulation of arsenic in paddy soils and potential arsenic transfer into

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pronounced arsenic-affected areas. Although the present findings pointed out that

consumption of arsenic-contaminated cooked rice in West Bengal (India) are unlikely

to pose substantial children skin lesions risk (overall mean ORs = 1.09 – 1.18). Yet,

the consequences for arsenic consumption from arsenic-contaminated cooked rice are

considerable to the regions where arsenic levels in rice increased from cultivation on

arsenic contaminated paddy soils [8,10,14,41].

In conclusion, rice can contain a relatively high amount of arsenic. Human

arsenic intake from rice consumption can be substantial because rice is particularly

efficient in assimilating arsenic from paddy soils, although the mechanism has not

been elucidated. This study revealed that the bioavailability (i.e., the fraction of

absorbed arsenic that reaches the GI tract) of inorganic arsenic from cooked rice play

an important role in risk assessment. As to our knowledge, very little research has

been done in this area. This study implicated the need to consider the relationships

between cooking method and arsenic in cooked rice when assessing the risk

associated with children skin lesions from arsenic-contaminated rice consumption.

This study also indicated that arsenic-associated skin lesions risk from

arsenic-contaminated rice consumption would be reduced substantially by adopting

traditional rice cooking method A (wash until clean; rice: water = 1:6; discard excess

water) as followed in West Bengal (India) and using water containing lower arsenic

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Accepted Manuscript

Appendix A: Study Data

Table A1

Epidemiological data of gender– and age–specific hyperpigmentation prevalence ratio varied with arsenic exposure concentrations in West Bengal (India)a

Arsenic concentration (μg L–1)

Age group <50 50–99 100–149 150–199 200–349 350–499 500–799 ≥800 Total

Male ≤9 0.0 (0)b 0.0 (0) 4.6 (3) 3.7 (1) 3.9 (3) 0.0 (0) 7.1 (2) 7.19 (2) 2.0 (12) 10–19 0.0 (0) 2.7 (2) 2.0 (1) 3.6 (2) 9.4 (9) 11.8 (6) 3.1 (2) 13.8 (4) 3.5 (26) 20–29 0.8 (3) 1.3 (1) 12.5 (7) 11.5 (6) 17.7 (14) 14.0 (6) 13.6 (8) 30.69 (9) 7.5 (54) 30–39 0.4 (1) 3.2 (2) 15.8 (6) 12.5 (5) 13.3 (10) 22.7 (10) 22.6 (12) 33.3 (6) 9.0 (52) 40–49 0.0 (0) 11.6 (5) 10.3 (3) 8.3 (2) 13.2 (7) 40.9 (9) 16.0 (4) 25.0 (3) 9.0 (53) 50–59 2.5 (3) 6.9 (2) 5.9 (1) 6.7 (1) 28.6 (10) 15.8 (3) 39.1 (9) 45.5 (5) 12.6 (34) ≥60 0.0 (0) 2.9 (1) 45.0 (9) 9.5 (2) 18.5 (5) 6.3 (1) 33.3 (5) 0.0 (0) 8.7 (23) All age 0.5 (7) 3.4 (13) 11.0 (30) 8.1 (19) 13.2 (58) 15.5 (38) 12.5 (40) 22.5 (29) 6.5 (254) Age–adjusted 0.4 3.2 11.0 7.8 13.1 15.7 13.8 22.7 6.4 Female ≤9 0.0 (0) 0.0 (0) 1.9 (1) 0.0 (0) 2.4 (2) 12.0 (6) 0.0 (0) 0.0 (0) 1.7 (9) 10–19 0.0 (0) 0.0 (0) 1.7 (1) 5.6 (3) 7.7 (9) 1.8 (1) 3.1 (2) 11.5 (3) 2.2 (19) 20–29 0.0 (0) 0.0 (0) 1.0 (1) 4.0 (3) 4.4 (6) 11.1 (7) 6.0 (5) 8.3 (2) 2.1 (24) 30–39 0.0 (0) 1.3 (1) 12.5 (6) 6.5 (3) 8.9 (7) 12.5 (5) 0.0 (0) 6.7 (1) 3.5 (23) 40–49 1.4 (2) 0.0 (0) 13.0 (3) 37.0 (1) 16.7 (6) 14.3 (3) 17.9 (5) 20.0 (2) 6.2 (22) 50–59 1.9 (3) 2.6 (1) 13.0 (3) 11.1 (2) 0.0 (0) 5.6 (1) 16.7 (5) 27.3 (3) 5.6 (12) ≥60 0.0 (0) 6.9 (2) 11.1 (1) 11.8 (2) 7.4 (2) 15.0 (3) 0.0 (0) 33.3 (2) 5.6 (12) All age 0.3 (5) 1.0 (4) 5.1 (16) 5.4 (14) 6.3 (32) 9.7 (26) 5.1 (17) 11.0 (13) 3.1 (127) age–adjusted 0.3 0.8 5.7 5.1 6.5 9.5 5.3 11.5 31 a

Adopted from Guha mazumder et al. [24].

b

Accepted Manuscript

Table A2

Epidemiological data of gender– and age–specific keratosis ratio varied with arsenic exposure concentrations in West Bengal (India)a

Arsenic concentration (μg L–1)

Age group <50 50–99 100–149 150–199 200–349 350–499 500–799 ≥800 Total

Male ≤9 0.0 (0)b 0.0 (0) 0.0 (0) 3.7 (1) 1.3 (1) 2.0 (1) 0.0 (0) 0.0 (0) 0.5 (3) 10–19 0.3 (1) 0.0 (0) 0.0 (0) 1.8 (1) 5.2 (5) 3.9 (2) 3.1 (2) 6.9 (2) 1.7 (13) 20–29 0.0 (0) 0.0 (0) 1.8 (1) 3.8 (2) 5.1 (4) 7.0 (3) 10.2 (6) 20.0 (5) 2.8 (21) 30–39 0.4 (1) 3.7 (2) 2.6 (1) 7.5 (3) 6.7 (5) 15.9 (7) 18.9 (10) 22.2 (4) 5.7 (33) 40–49 0.0 (0) 4.7 (2) 0.0 (0) 8.3 (2) 5.7 (3) 27.3 (6) 12.0 (3) 8.3 (1) 4.6 (17) 50–59 0.8 (1) 6.9 (2) 59 (1) 6.7 (1) 8.6 (3) 15.8 (3) 13.0 (3) 9.1 (1) 5.6 (15) ≥60 0.8 (1) 0.0 (0) 5.0 (1) 4.8 (1) 3.7 (1) 0.0 (0) 13.3 (2) 0.0 (0) 2.3 (6) All age 0.3 (4) 1.6 (6) 1.5 (4) 4.7 (11) 5.0 (22) 8.9 (22) 8.1 (26) 10.1 (13) 3.0 (108) Age–adjusted 0.2 1.5 1.6 4.7 4.9 9.0 8.9 10.7 3.0 Female ≤9 0.0 (0) 0.0 (0) 0.0 (0) 0.0 (0) 0.0 (0) 2.0 (1) 0.0 (0) 0.0 (0) 0.2 (1) 10–19 0.0 (0) 0.0 (0) 0.0 (0) 1.9 (1) 2.6 (3) 0.0 (0) 3.1 (2) 11.5 (3) 1.0 (9) 20–29 0.0 (0) 0.0 (0) 1.0 (1) 1.4 (1) 1.5 (2) 3.2 (2) 0.0 (0) 4.2 (1) 0.6 (7) 30–39 0.0 (0) 2.5 (2) 0.0 (0) 2.2 (1) 2.5 (2) 2.5 (1) 4.6 (1) 0.0 (0) 1.2 (8) 40–49 0.0 (0) 0.0 (0) 0.0 (0) 0.0 (0) 5.6 (2) 9.5 (2) 10.7 (3) 10.0 (1) 2.3 (8) 50–59 0.0 (0) 0.0 (0) 4.4 (1) 11.1 (2) 0.0 (0) 0.0 (0) 10.0 (3) 27.3 (3) 2.8 (8) ≥60 0.0 (0) 0.0 (0) 11.1 (1) 5.9 (1) 3.7 (1) 5.0 (1) 0.0 (0) 33.3 (2) 2.8 (9) All age 0.0 (0) 0.5 (2) 1.0 (3) 2.3 (6) 2.0 (10) 2.6 (7) 3.0 (10) 8.5 (10) 1.2 (6) age–adjusted 0.0 0.4 1.2 2.3 2.0 2.7 3.1 8.3 1.2 a

Adopted from Guha mazumder et al. [24].

b

Accepted Manuscript

Appendix B: Equations used for the proposed human arsenic PBPK model Lung As3+ As5+ MMA5+ MMA3+ ) ( 5 5 5 5 MMA Lung MMA Lung MMA a Lung MMA Lung P C C Q dt dA ) ( 3 3 3 3 MMA Lung MMA Lung MMA a Lung MMA Lung P C C Q dt dA DMA5+ DMA3+ ) ( 5 5 5 5 DMA Lung DMA Lung DMA a Lung DMA Lung P C C Q dt dA ) ( 3 3 3 3 DMA Lung DMA Lung DMA a Lung DMA Lung P C C Q dt dA Kidney (urine) As3+ As5+ MMA5+ MMA3+ 3 5 3 5 5 5 5 3 5 3 5 3 max, 3 4 , ( ) As MMA As MMA MMA Kid Kid

MMA MMA MMA

Kid Kid

Kid a _{MMA As MMA As} Kid Kid

Kid m Kid Kid

V C dA C Q C K C K C dt P K C 5 5 MMA Kid

day Urine _MMA

Kid C W K P 3 5 3 3 3 3 5 3 3 3 5 3 max, 3 4 , ( )

MMA DMA MMA

MMA MMA

Kid Kid

MMA MMA MMA

Kid Kid

Kid a _MMA Kid Kid _{MMA DMA MMA}

Kid m Kid Kid

V C dA C Q C K C K C dt P K C 3 3 MMA Kid

day Urine _MMA

Kid C W K P 3 3 5 3 3 1 2 3+ Lung ( ) (K K ) P As Lung As Lung As As

Lung a Lung Lung Lung

dA C Q C C C V dt 5 5 5 5 5 1 2 5+ Lung ( ) (K K ) P As As Lung As Lung As As

Lung a Lung Lung Lung

dA C Q C C C V dt 3 5+ 3 3 3 3 5 3 3+ 3+ 5+ 3 MMA max,Kid 1 2 As As MMA Kid m,Kid V ( ) (K K ) P K As As As As Kid As As As Kid Kid

Kid a Kid Kid Kid _As

Kid C dA C Q C C C V dt C 3+ 5+ 3 3 3+ 5+ 3 3 As DMA max,Kid urine As DMA m,Kid Kid V K K P As As Kid Kid day As As Kid C C W C 5 5 5 5 5 3 5+ 1 2 urine 5+ As As Kid Kid ( ) (K K ) K P P As As As As As As

Kid Kid Kid

Kid a Kid Kid Kid day

dA C C

Q C C C V W

Accepted Manuscript

MMA DMA DMA

DMA DMA

Kid Kid

DMA DMA DMA

Kid Kid

Kid a _{DMA MMA DMA DMA} Kid Kid

Kid m Kid Kid

V C dA C Q C K C K C dt P K C 3 3 3 3 5 3 3 5 6 3 ( )

DMA DMA DMA

DMA DMA DMA

Kid Kid Kid

Kid a _DMA Kid Kid day Urine _DMA

Kid Kid dA C C Q C K C K C W K dt P P Skin As3+ As5+ MMA5+ MMA3+ 5 5 5 5 5 )

( _MMA day Skin SkinMMA

Skin MMA Skin MMA a Skin MMA Skin C K W P C C Q dt dMMA 3 3 3 3 3 )

( _{day Skin Skin}MMA

MMA Skin MMA Skin MMA a Skin MMA Skin _{W K C} P C C Q dt dMMA DMA5+ DMA3+ 5 5 5 5 5 )

( _DMA day Skin SkinDMA

Skin DMA Skin DMA a Skin DMA Skin C K W P C C Q dt dMMA 3 3 3 3 3 )

( _DMA day Skin SkinDMA

Skin DMA Skin DMA a Skin DMA Skin C K W P C C Q dt dMMA G.I. tract As3+ As5+ MMA5+ MMA3+ 5 5 5 5 5 5 5 5 5 ( ) ( )

MMA MMA MMA

MMA MMA

GI GI GI Liver

GI a _MMA GI _{MMA MMA} day GI GI

GI GI Liver dMMA C C C Q C Q W K C dt P P P 3 3 3 3 3 3 3 3 ) ( ) ( 3 MMA GI GI day MMA Liver MMA Liver MMA GI MMA GI GI MMA GI MMA GI MMA a GI GI C K W P C P C Q P C C Q dt dMMA DMA5+ DMA3+ 5 5 5 5 5 5 5 5 ) ( ) ( 5 DMA GI GI day DMA Liver DMA Liver DMA GI DMA GI GI DMA GI DMA GI DMA a GI GI C K W P C P C Q P C C Q dt dDMA 3 3 3 3 3 3 3 3 ) ( ) ( 3 DMA GI GI day DMA Liver DMA Liver DMA GI DMA GI GI DMA GI DMA GI DMA a GI GI C K W P C P C Q P C C Q dt dDMA Liver As3+ 3 3 3 5 3 3 3+ 1 2 Skin As Skin ( ) (K K ) K P As As As As As As Skin Skin

Skin a Skin Skin Skin day Skin

dA C Q C C C V W C dt 5 5 5 5 3 5 5+ 1 2 Skin As Skin ( ) (K K ) K P As As As As As As Skin Skin

Skin a Skin Skin Skin day Skin

dA C Q C C C V W C dt 3 3 3 3 3 5 3 3 3+ 3+ 3+ 3+ As 1 2 GI uptake As As As GI GI Liver ( ) ( ) (K K ) K K P P P As As As As As As As As GI GI GI Liver GI a GI GI GI GI day GI dA C C C Q C Q C C V W C dt 5 5 5 5 5 5 3 3 5+ 5+ 5+ 5+ As 1 2 GI uptake As As As GI GI Liver ( ) ( ) (K K ) K K P P P As As As As As As As As GI GI GI Liver GI a GI GI GI GI day GI dA C C C Q C Q C C V W C dt 3 3 3 3 3 5 3 3 3+ 3+ 3+ 1 2 Biliary As As As Liver GI Liver ( ) ( ) (K K ) W P P P As As As As As As As As

Liver Liver GI Liver

Liver a GI Liver Liver Liver Liver

dA C C C Q C Q C C V C dt 5+ 5+ 5+ 5+ 3+ MMA 3 3+ DMA max,Liver max,Liver 3+ MMA 3 3+ DMA m,Liver m,Liver V V K K III Liver Liver III Liver Liver C C C C 5 5 3 5 3 3 5 3 , m ax, DMA Kid DMA Kid Urine day As Kid DMA As Kid m As Kid DMA As Kid P C K W C K C V

Accepted Manuscript

MMA MMA MMA

MMA Liver GI Liver MMA MMA

Liver a _MMA GI _{MMA MMA} Liver Liver

Liver GI Liver C C C dMMA Q C Q K C K C dt P P P DMA5+ DMA3+ 3 5 3 3 5 3 5 5 5 5 5 5 5 , m ax, 5 ) ( ) ( As Liver DMA As Liver m As Liver DMA As Liver DMA Liver DMA Liver DMA GI DMA GI GI DMA Liver DMA Liver DMA a Liver C K C V P C P C Q P C C Q dt dDMA 3 3 3 3 5 3 3 3 3 3 5 6 ( ) ( )

DMA DMA DMA

DMA Liver GI Liver DMA DMA

Liver a _DMA GI _{DMA DMA} Liver Liver

Liver GI Liver C C C dDMA Q C Q K C K C dt P P P Muscle As3+ As5+ MMA5+ MMA3+ ) ( 5 5 5 5 MMA Muscle MMA Muscle MMA a Muscle MMA Muscle P C C Q dt dMMA ) ( 3 3 3 3 MMA Muscle MMA Muscle MMA a Muscle MMA Muscle P C C Q dt dMMA DMA5+ DMA3+ ) ( 5 5 5 5 DMA Muscle DMA Muscle DMA a Muscle DMA Muscle P C C Q dt dMMA ) ( 3 3 3 3 DMA Muscle DMA Muscle DMA a Muscle DMA Muscle P C C Q dt dMMA Fat tissue As3+ 3 3 3 5 3 3 1 2 ( ) ( ) As As As As As Fat Fat

Fat a _As Fat Fat Fat

Fat dA C Q C K C K C V dt P 5 5 5 5 5 5 3 5 5+ 5+ 5+ 1 2 Biliary As As As Liver GI Liver ( ) ( ) (K K ) W P P P As As As As As As As As

Liver Liver GI Liver

Liver a GI Liver Liver Liver Liver

dA C C C Q C Q C C V C dt 3 3 3 5 3 3+ 1 2 As Muscle ( ) (K K ) P As As As As As Muscle Muscle

Muscle a Muscle Muscle Muscle

dA C Q C C C V dt 5 5 5 5 3 5+ 1 2 As Muscle ( ) (K K ) P As As As As As Muscle Muscle

Muscle a Muscle Muscle Muscle

dA C

Q C C C V

dt

5 3 5

3 4

MMA MMA MMA

Liver Liver Biliary Liver

K C K C W C 3 3 5 3 3 5 3 , m ax, MMA Liver Biliary MMA Liver DMA MMA Liver m MMA Liver DMA MMA Liver C W C K C V 3 5 3 5 3 5 3 5 3 max, 5 6 ,

MMA DMA MMA

Liver Liver DMA DMA MMA

Liver Liver Biliary Liver

MMA DMA MMA

m Liver Liver V C K C K C W C K C 3 MMA Liver Biliary C W

Accepted Manuscript

Fat a _As Fat Fat Fat

Fat dA C Q C K C K C V dt P MMA5+ MMA3+ ) ( 5 5 5 5 MMA Fat MMA Fat MMA a Fat MMA Fat P C C Q dt dMMA ) ( 3 3 3 3 MMA Fat MMA Fat MMA a Fat MMA Fat P C C Q dt dMMA DMA5+ DMA3+ ) ( 5 5 5 5 DMA Fat DMA Fat DMA a Fat DMA Fat P C C Q dt dMMA ) ( 3 3 3 3 DMA Fat DMA Fat DMA a Fat DMA Fat P C C Q dt dMMA Blood As3+ As5+ MMA5+ MMA3+ ) ( 5 5 5 8 1 8 1 5 MMA a i i MMA i MMA i i i C Q P C Q dt dMMA ) ( 3 3 3 ₈ 1 8 1 3 MMA a i i MMA i MMA i i i C Q P C Q dt dMMA DMA5+ DMA3+ ) ( 5 5 5 8 1 8 1 5 DMA a i i DMA i DMA i i i C Q P C Q dt dDMA ) ( 3 3 3 8 1 8 1 3 DMA a i i DMA i DMA i i i C Q P C Q dt dDMA

Abbreviations and parameter symbols: Aij is the dose of arsenic species j in

organ/tissue i (μmol), Cijis the concentration of arsenic species j in organ/tissue i

(μmol L-1

), K_mj_,ikis the Michaelis-Menten constant for arsenic species j methylated to k in organ/tissue i (μmol L-1), Pijis the tissue/blood partition coefficient of arsenic

species j in tissue, Qiis the blood flow in organ/tissue i (L h -1

), Viis the volume of

organ/tissue i (L), V_max,j k_i is the maximum reaction rate for arsenic species j methylated to k in organ/tissue i (μmol h-1), WBiliary is the bile elimination amount (L), Wday is

the human daily drinking water amount (L h-1), andW_iis the percentage of the mass of organ i in body weight (%).

3 3 3 3 3 3+ 8 8 1 2 As 1 _i 1 ( ) (K K ) P As As As As As a i i i a a a a i i dA C Q Q C C C V dt 5 5 5 5 3 5+ 8 8 1 2 As 1 _i 1 ( ) (K K ) P As As As As As a i i i a a a a i i dA C Q Q C C C V dt

Accepted Manuscript

Appendix C: Relevant parameter values used in the human arsenic PBPK model

Table C1

PBPK input parameters used for four age groups

Parameters Age groups (yr)

1-6 7-12 13-18 1.Lung QLung a(L h-1) 5.17 7.65 10.84 VLung b(L) 0.257 0.507 0.888 2.Kidney QKid (L h-1) 39.3 58.1 82.4 VKid (L) 0.0669 0.132 0.231 3. Skin QSkin (L h-1) 10.3 15.3 21.7 VSkin (L) 3.45 6.80 11.9 4. G.I. tract QGI (L h-1) 41.3 61.2 86.7 VGI (L) 0.305 0.602 1.06 3 uptake K c (μmol h-1) 0.0144 0.0192 0.0244 5 uptake K (μmol h-1) 0.0216 0.0288 0.0367 5. Liver QLiver (L h-1) 10.3 15.3 21.7 VLiver (L) 0.392 0.773 1.36 WBiliary d (L d-1) 0.136 0.268 0.470 6. Muscle QMuscle (L h-1) 35.1 52.0 73.7 VMuscle (L) 6.10 12.0 21.1 7. Fat QFat (L h-1) 10.3 15.3 21.7 VFat (L) 3.19 6.30 11.0 8. Blood Vae (L) 1.20 2.37 4.16 a

Qi = Fi QT (Fi: Blood flow fraction; QT: Cardiac output rate)[42]. b Vi (L) = BW (kg)

Wi/Di (kg L-1) (Vi: Volume of organ i; BW: Body weight; Wi: Percentage of body

weight; Di: Density of organ i). c Kuptake (μmol h-1) = As in drinking water (μg L-1)

Daily drinking water (L d-1) / 74.9216 (As = 74.9216). d It assumed that WBiliary-Children

Accepted Manuscript

Table C2

Metabolic rate constants for arsenic in children

Reduction/oxidation As MMA b DMA

Reduction (h-1) K1 = 1.37 K3 = 4.47 10-3 K5 = 4.40 10-2

Oxidation (h-1) K2 = 1.83 K4 = 5.95 10-3 K6 =5.88 10-2

Methylation c

Age group 1-6 yr 7-12 yr 13-18 yr

As3+MMA5+ As3+DMA5+ MMA3+DMA5+

Liver Vmax (μmol h-1) 11.25 22.25 1.86693 10-5 (1.52 10-5-2.28 10-5)d 2.08569 10-5 (2.04 10-5-2.24 10-5) 4.84044 10-5 (4.13 10-5-5.22 10-5) Km (μmol L-1) 100 100 3.04 10-6 Kidney Vmax (μmol h-1) 7.5 10.02 1.93251 10-5 (1.61 10-5-2.34 10-5) 1.93880 10-5 (1.94 10-5-2.04 10-5) 4.01268 10-5 (3.31 10-5-4.39 10-5) Km (μmol L-1) 100 100 3.04 10-6 a

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Table C3

Partition coefficients, blood flow fraction, and tissue density used in the PBPK model

Tissue Blood flow fraction (Fi) (%)a % of body weight (Wi) (%)a Density (Di) (kg L-1)a % of total water elimination amount (%)b

Species-specific tissue/blood partition coefficient c

As(III) As(V) MMA(V) DMA(V)

Lung 2.5 1.7 1.05 12 4.15 4.15 1.8 2.075 Kidneys 19 4.4 1.05 60 4.15 4.15 1.8 2.075 Skin 5 20 1.05 20 2.5 2.5 1.25 1.25 GI tract 20 2 1.04 8 2.8 2.8 1.2 1.4 Liver 6.5 2.57 1.05 5.3 5.3 2.35 2.65 Muscle 17 40 1.04 2.6 2.6 1.8 2.8 Fat 27.5 21 0.92 0.3 0.3 0.3 0.3 Total 100 a

Adapted from Hissink et al. [44] and Yu et al. [45].

b

Adapted from Huang [46].

c

Accepted Manuscript

Figure captions

Fig. 1. Schematic of the proposed human PBPK-metabolism model for arsenic

exposure. (A) Target tissue compartments of lung, skin, fat, muscle, kidney, liver and

GI tract interconnected by blood flow in that GI tract is represented as Caco-2 cells

showing arsenic retention, transport, and total uptake. (B) Biotransformation of

arsenic showing oxidation/reduction of inorganic and organic arsenic as well as

methylation of As(III) in the kidney and liver. (C) Best-fitted model of age-specific

secondary methylation ratio (DMA/MMA).

Fig. 2. Weibull model predicted gender- and age-specific skin lesions cumulative

prevalence ratios varied with arsenic exposure concentrations for (A)

hyperpigmentation and (B) keratosis.

Fig. 3. Box and whisker plots showing (A) arsenic concentration distributions for

irrigation water, paddy soil and paddy rice and (B) bioaccumulation factor

distributions for irrigation water to paddy soil (KW-S), paddy soil to paddy rice (KS-R)

and irrigation water to paddy rice (KW-R).

Fig. 4. (A) Best-fitted model describing arsenic contents in the cooked rice varied

with different arsenic concentrations in cooking water. Error bar represents standard

deviation from mean. (B) Box and whisker plots showing the percentage of arsenic

retained in cooked rice for different cooking methods. (C, D, E) Predicted arsenic

concentration in cooked rice varied with different arsenic contents in cooking water

Accepted Manuscript

Fig. 5. Schematic representation of a descriptive model showing the interactions

among bioaccumulation factors of KW-S andKS-R, arsenic content in cooked rice with

three cooking methods, PBPK mode-predicted age group-specific urinary MMA(III)

levels, and Weibull model-predicted average odds ratios.

Fig. 6. The PBPK model predicted percentage contributions of cooked rice to daily

arsenic intake varied with arsenic contents in drinking water of 10, 100, and 1000 g

Accepted Manuscript

Table 1

Weibull model fitting parameters (mean with 95%CI) for male and female of

hyperpigmentation and keratosis

Hyperpigmentation Keratosis

Male Female Male Female

k0 5.41 10-4 (0-1.3 10-3) 2.95 10-4 (0-8.15 10-4) 1.44 10-4 (0-3.02 10-4) 8.79 10-5 (0-2.55 10-4) k1 0.62 (0.43-0.82) 0.61 (0.37-0.85) 0.70 (0.55-0.85) 0.65 (0.39-0.91) k2 0.18 (0.12-0.24) 0.17 (0.10-0.24) 0.18 (0.13-0.23) 0.12 (0.047-0.19) k3 1.00 10-4 (0-4.98 10-3) 1.00 10-4 (0-3.21 10-3) 1.00 10-4 (0-1.51 10-3) 1.00 10-4 (0-1.30 10-3) r2 0.94 0.91 0.96 0.91