ArticleJournal of exposure science & environmental epidemiology2022
Simulating toxicokinetic variability to identify susceptible and highly exposed populations.
Article in Journal of exposure science & environmental epidemiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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.
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.
Who cites it
8 citing papers in PubMed.
- The environmental neuroactive chemicals list of prioritized substances for human biomonitoring and neurotoxicity testing: A database and high-throughput toxicokinetics approach.Environmental research · 2025Article
- Reducing uncertainty in dose-response assessments by incorporating Bayesian benchmark dose modeling and in vitro data on population variability.Risk analysis : an official publication of the Society for Risk Analysis · 2025Article
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- Invited Perspective: Uneven Progress Addressing Population Variability in Human Health Risk Assessment.Environmental health perspectives · 2024Article
- Cutting-edge computational chemical exposure research at the U.S. Environmental Protection Agency.Environment international · 2023Review
- Exposure forecasting - ExpoCast - for data-poor chemicals in commerce and the environment.Journal of exposure science & environmental epidemiology · 2022Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
backgroundToxicokinetic (TK) data needed for chemical risk assessment are not available for most chemicals. To support a greater number of chemicals, the U.S. Environmental Protection Agency (EPA) created the open-source R package "httk" (High Throughput ToxicoKinetics). The "httk" package provides functions and data tables for simulation and statistical analysis of chemical TK, including a population variability simulator that uses biometrics data from the National Health and Nutrition Examination Survey (NHANES).
objectiveHere we modernize the "HTTK-Pop" population variability simulator based on the currently available data and literature. We provide explanations of the algorithms used by "httk" for variability simulation and uncertainty propagation.
methodsWe updated and revised the population variability simulator in the "httk" package with the most recent NHANES biometrics (up to the 2017-18 NHANES cohort). Model equations describing glomerular filtration rate (GFR) were revised to more accurately represent physiology and population variability. The model output from the updated "httk" package was compared with the current version.
resultsThe revised population variability simulator in the "httk" package now provides refined, more relevant, and better justified estimations. SIGNIFICANCE: Fulfilling the U.S. EPA's mission to provide open-source data and models for evaluations and applications by the broader scientific community, and continuously improving the accuracy of the "httk" package based on the currently available data and literature.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.