Evidence map›Paper›PMID 39148436›Full record

ArticleRisk analysis : an official publication of the Society for Risk Analysis2025

Reducing uncertainty in dose-response assessments by incorporating Bayesian benchmark dose modeling and in vitro data on population variability.

En-Hsuan Lu, Lucie C Ford, Ivan Rusyn, Weihsueh A Chiu

Abstract read
In one paragraph

Article in Risk analysis : an official publication of the Society for Risk Analysis, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
–field-weighted citation impact
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

6 citing papers in PubMed.

  1. Extrapolation steps in quantitativeCurrent research in toxicology · 2026
    Review
  2. Article
  3. Article
  4. Article
  5. Incorporating new approach methods (NAMs) data in dose-response assessments: The future is now!Journal of toxicology and environmental health. Part B, Critical reviews · 2025
    Review
  6. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

En-Hsuan LuInterdisciplinary Faculty of Toxicology and Department of Veterinary Physiology and Pharmacology, Texas A&M University, College Station, Texas, USA.
Lucie C FordInterdisciplinary Faculty of Toxicology and Department of Veterinary Physiology and Pharmacology, Texas A&M University, College Station, Texas, USA.
Ivan RusynInterdisciplinary Faculty of Toxicology and Department of Veterinary Physiology and Pharmacology, Texas A&M University, College Station, Texas, USA.
Weihsueh A ChiuInterdisciplinary Faculty of Toxicology and Department of Veterinary Physiology and Pharmacology, Texas A&M University, College Station, Texas, USA.

Funding

Single cell, multi-parametric high throughput platform to classify endocrine disruptor potential of mixturesP42ES027704 · NIEHS · TEXAS A&M UNIVERSITY · PI Efstratios Pistikopoulos · 2017 to 2026
$21.2M
Texas A&M Center for Environmental Health Research (TiCER)P30ES029067 · NIEHS · TEXAS A&M UNIVERSITY · PI Sakhila Banu · 2019 to 2026
$13.0M
Regulatory Science in Environmental Health and ToxicologyT32ES026568 · NIEHS · TEXAS A&M UNIVERSITY · PI Weihsueh A Chiu, Natalie M Johnson · 2016 to 2026
$3.8M
NIEHS NIH HHS P30 ES029067NIEHS NIH HHS P42 ES027704NIEHS NIH HHS T32 ES026568U.S. Environmental Protection Agency STAR RD83580201U.S. Environmental Protection Agency STAR RD84045001
6 · The paper itself

Abstract

There are two primary sources of uncertainty in the interpretability of toxicity values, like the reference dose (RfD): estimates of the point of departure (POD) and the absence of chemical-specific human variability data. We hypothesize two solutions-employing Bayesian benchmark dose (BBMD) modeling to refine POD determination and combining high-throughput toxicokinetic modeling with population-based toxicodynamic in vitro data to characterize chemical-specific variability. These hypotheses were tested by deriving refined probabilistic estimates for human doses corresponding to a specific effect size (M) in the Ith population percentile (HD

Indexed as

Dose-Response Relationship, DrugBayes TheoremHumansRisk AssessmentUncertaintyBayesian benchmark dosebiomonitoring equivalentnew approach methods (NAMs)probabilistic risk assessment

Identifiers

PMID39148436
PMCPMC11787958

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

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.