Evidence map›Paper›PMID 30024383›Full record

ArticleEnvironmental health perspectives2018

Evaluation of a Physiologically Based Pharmacokinetic (PBPK) Model for Inorganic Arsenic Exposure Using Data from Two Diverse Human Populations.

Hisham A El-Masri, Tao Hong, Cara Henning, William Mendez, Edward E Hudgens, David J Thomas, Janice S Lee

Erratum issuedOpen access · diamondAbstract read
In one paragraph

Article in Environmental health perspectives, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
1.2field-weighted citation impact, top 23% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed, 13 citations in OpenAlex.

  1. Article
  2. A mass-balance model to assess arsenic exposure from multiple wells in Bangladesh.Journal of exposure science & environmental epidemiology · 2022
    Article
  3. Article
  4. Review
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

7 authors at 3 institutions in 1 country.

Hisham A El-MasriNational Health and Environmental Effects Research Laboratory, Office of Research and Development (ORD), U.S. Environmental Protection Agency (EPA), Durham, North Carolina, USA.
Tao HongICF International, Inc., Durham, North Carolina, USA.
Cara HenningICF International, Inc., Durham, North Carolina, USA.
William MendezICF International Inc., Fairfax, Virginia, USA.
Edward E HudgensNational Health and Environmental Effects Research Laboratory, Office of Research and Development (ORD), U.S. Environmental Protection Agency (EPA), Durham, North Carolina, USA.
David J ThomasNational Health and Environmental Effects Research Laboratory, Office of Research and Development (ORD), U.S. Environmental Protection Agency (EPA), Durham, North Carolina, USA.
Janice S LeeNational Center for Environmental Assessment, ORD, EPA, Durham, North Carolina, USA.
Environmental Protection Agency · USICF International (United States) · USNational Center for Environmental Assessment (EPA) · US

Funding

Training CoreP42ES010349 · NIEHS · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI NAVAS-ACIEN, ANA · 2000 to 2020
$53.6M
Enhancement and Maintenance of the HEALS cohortR24ES028532 · NIEHS · UNIVERSITY OF CHICAGO · PI AHSAN, HABIBUL · 2017 to 2022
$1.7M
NIEHS NIH HHS P42 ES010349NIEHS NIH HHS R24 ES028532
6 · The paper itself

Abstract

backgroundMultiple epidemiological studies exist for some of the well-studied health endpoints associated with inorganic arsenic (iAs) exposure; however, results are usually expressed in terms of different exposure/dose metrics. Physiologically based pharmacokinetic (PBPK) models may be used to obtain a common exposure metric for application in dose-response meta-analysis.

objectiveA previously published PBPK model for inorganic arsenic (iAs) was evaluated using data sets for arsenic-exposed populations from Bangladesh and the United States.

methodsThe first data set was provided by the Health Effects of Arsenic Longitudinal Study cohort in Bangladesh. The second data set was provided by a study conducted in Churchill County, Nevada, USA. The PBPK model consisted of submodels describing the absorption, distribution, metabolism and excretion (ADME) of iAs and its metabolites monomethylarsenic (MMA) and dimethylarsenic (DMA) acids. The model was used to estimate total arsenic levels in urine in response to oral ingestion of iAs. To compare predictions of the PBPK model against observations, urinary arsenic concentration and creatinine-adjusted urinary arsenic concentration were simulated. As part of the evaluation, both water and dietary intakes of arsenic were estimated and used to generate the associated urine concentrations of the chemical in exposed populations.

resultsWhen arsenic intake from water alone was considered, the results of the PBPK model underpredicted urinary arsenic concentrations for individuals with low levels of arsenic in drinking water and slightly overpredicted urinary arsenic concentrations in individuals with higher levels of arsenic in drinking water. When population-specific estimates of dietary intakes of iAs were included in exposures, the predictive value of the PBPK model was markedly improved, particularly at lower levels of arsenic intake.

conclusionsEvaluations of this PBPK model illustrate its adequacy and usefulness for oral exposure reconstructions in human health risk assessment, particularly in individuals who are exposed to relatively low levels of arsenic in water or food. https://doi.org/10.1289/EHP3096.

Indexed as

AdultAgedArsenicArsenicalsBangladeshDrinking WaterEnvironmental ExposureFemaleFood ContaminationHumansLongitudinal StudiesMaleMiddle AgedModels, TheoreticalNevadaRisk AssessmentArsenicArsenicalsDrinking WaterWater Pollutants, Chemical

Identifiers

PMID30024383
PMCPMC6108830
OpenAlexW2891806979

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.