1. Journal reviews
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2.
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3. Background Perchlorate (ClO4-)
: the salts derived from perchloric acid
very reactive chemicals that are used mainly in explosives, fireworks, and rocket motors
magnesium perchlorate, potassium perchlorate, ammonium perchlorate, sodium perchlorate, and lithium perchlorate
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4. Introduction
Linked to inhibit iodide transport into thyroid follicular cells so that results reducing iodide availability to synthesis of thyroid hormones
RfD:0.7 μg/kg/day, NOAEL: 7.0 μg/kg/day
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5. Objectives
To evaluate the consistency of urinary biomarker based with intake based exposure estimates using best available information on exposures and pharmacokinetics
1) the ability of the PBPK model to predict perchlorate concentrations in single spot and cumulative urine samples using a controlled human study
2) the challenges in linking urinary biomarker concentrations to intake doses for individuals using a subset of NHANES
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6. Methods
from Greer et al., 2002
2.1 Measurement of perchlorate in urine – Controlled human study (Greer study)
Only a part of perchlorate conc. in urine samples were available! In addition, the information on time & volume of urine voids
0.02 mg/kg/d
0.1 mg/kg/d
0.5 mg/kg/d
: Main study
0.02 mg/kg/d
0.1 mg/kg/d
0.5 mg/kg/d
0.007 mg/kg/d
: Uptake study
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7. Methods
2.2 Measurement of perchlorate in urine – General population survey
NHANES
: demographics, medical history, 24h dietary recall, urine conc. tap water, sample collection time session
Target population: the susceptible population (340 of women who were at child-bearing age, non-pregnant, non-lactating)
TDS food list: data, 285 foods & beverages included
Perchlorate conc. in food: data collected between
Perchlorate conc. in food
(TDS)
Individual dietary info.
The estimated
Exposure amount +
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8. Methods 2.3 PBPK model No metabolism 100% excretion
Not used neonatal & fetal
ACSL 11.8
(from R. Clewell 2007)
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9. Methods
2.4 Analyses of urinary biomarkers for perchlorate using the PBPK model
1) Greer study
The life stage PBPK model with known data
¼ each given dose at 4 times for 14 days
1) 24hr, 2) 48hr cumulative amount, 3) spot sample conc.
2) The general population survey
MC simulation for uncertainty – regional/seasonal variability of the perchlorate in food, 3 types of proxy value
simulating 24hr continuous exposure for 6 d
The rate of perchlorate excretion in urine (μg/L) / the rate of creatinine production (g/L) vs. the observed urine conc.
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10. Concept
Fig 2. A simulated time course profile for creatinine-adjusted urinary perchlorate concentration for a NHANES subject (example)
(a) 24h dietary recall data from NHANES (b) aggregate exposure dose profile for a female subject
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11. Methods (cont.) Pair-wise correlation analysis
: total dietary intake (TDS), urinary measurement, adjusted urinary measurement, adjusted prediction.
Two subgroup in correlation analysis (for uncertainty)
1) milk drinker: market basket measurements
consideration for the contribution of dairy product
strong relationship between the measured and the predicted because of the consistency of the perchlorate conc. in milk
2) fasted and provided morning spot sample: human activity
Inconsistency of reported time session
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12. Results
Using the controlled human study
Strong correlation between the measured and the predicted in the high & medium dose group (ρ > 0.75)
No correlation in the spot urine concentration (ρ = 0.12)
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13. Results Not works well!! Good agreement!!
Using the the general population survey
Population level
Individual level
Not works well!!
Good agreement!!
Fig 4. Predicted vs. measured urinary perchlorate concentrations (creatinine adjusted)
Blue: the predicted, Red: the measured
Fig 5. CDF of the predicted and the measured creatinine-normalized urinary perchlorate concentrations
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14. Results
Good correlation bt. urinary conc. & the adjusted cr. (ρ= )
Weak correlation bt. intake & the measured (ρ= )
The correlation bt. the measured & the predicted was not improved in the milk drinker subgroup, but in the morning session.
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15. Discussion Regarding to the “Uncertainty”
Identical the estimated average intake dose (0.1 μg/kg/d)
Comparable the predicted in the population level
The lack of ρ in the individual level is resulted from
1) Reporting errors (recall bias)
2) No dietary info. from midnight to the sampling time
3) No info. of urine volume and time of voids
4) Using the average value of TDS for individual
Regarding to the “Uncertainty”
(1) Only include the fasted and provided in the morning session
(3) for ND: MC simulation, for consistency of food conc.: the subgroup “milk drinker”
(2) For the lack of data, the urinary conc. was simulated.
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16. Discussion (cont.)
For individual level exposure estimates, collecting biomarker method may be more appropriate. (than spot sample)
 To investigate this, the Greer study was used, and
based on the figure 3., 24h or the longer cumulative urine sample would be better to get a more precise result.
Since intra-individual variation of creatinine, it would be resulted in another source of “Uncertainty”.
PBPK based exposure model can predict a distribution of urinary perchlorate conc. which is similar to the measured for the general population.
Needs for the subject-specific data, inter-individual variability, and appropriate chemical specific PK, etc.
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17. QnA ?
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18. Triangular distribution
a continuous probability distribution with lower limit a, upper limit b and mode c, where a < b and a ≤ c ≤ b.
Parameter: a, b, c
Mean: (a+b+c)/3, Variance: (a2+b2+c2-ab-ac-bc)/18
Probability density distribution
Cumulative distribution function
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