Using in vitro data to derive acceptable exposure levels for environmental chemicals: A case study on p,p’-DDE obesogenicity
Bibliographic record
Abstract
BACKGROUND: Current acceptable exposure levels are largely based on animal models, which are costly, time-consuming, and may poorly predict adverse outcomes in humans. Alternative testing methods are needed to adequately tackle the large number of environmental chemicals. OBJECTIVE: To evaluate a method integrating human in vitro data and computational modeling to calculate acceptable exposure levels through a case study on early-life p,p'-dichlorodiphenyldichloroethylene (p,p'-DDE) and developmental obesogenicity. METHODS: We reviewed in vitro studies of p,p'-DDE and obesogenicity-related endpoints to select points of departure (PODs). Nominal PODs were converted into lipid-based cellular concentrations using a dynamic mass-balance model. Cellular concentrations were converted into tolerable daily intakes and biomonitoring equivalents in pregnant individuals using a toxicokinetic model and uncertainty factors. We compared estimated biomonitoring equivalents to maternal and cord plasma levels measured in epidemiological studies reporting associations between early-life p,p'-DDE exposure and child adiposity. RESULTS: We estimated PODs for phenotypic (181,897 ng/g lipids) and transcriptomic (14,405 ng/g lipids) endpoints. Application of the toxicokinetic model and uncertainty factors led to tolerable daily intakes of 2.50-8.65 ng/kg/day (phenotypic) and 0.198-0.685 ng/kg/day (transcriptomic). Corresponding biomonitoring equivalents were 54.4-188 ng/g lipids (phenotypic) and 4.31-14.9 ng/g lipids (transcriptomic). Mean/median concentrations measured in epidemiological studies of p,p'-DDE exposure and child adiposity were mostly within or above the range of concentrations produced using the phenotypic in vitro POD. CONCLUSION: This study adds to a growing body of literature on the potential of in vitro data combined with computational modeling for chemical risk assessment, while also identifying challenges to regulatory adoption.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".