Molecular Structure-Based Prediction of the Steady-State Blood Concentrations of Inhaled Organics in Rats
Bibliographic record
Abstract
Chronic exposure to volatile organic chemicals (VOCs) in the environment leads to steady-state conditions. The establishment of quantitative relationships between steady-state blood concentrations and molecular structures of VOCs can be potentially useful. The objective of this study was therefore to investigate the relationship between the steady-state arterial blood concentration (Ca(ss)) in the rat and the molecular structures of 19 VOCs belonging to multiple chemical families (alkanes, haloalkanes, haloalkenes, and aromatics). The overall approach consisted of developing quantitative relationships between molecular fragments (CH(3), CH(2), CH, C, C horizontal lineC, H, Cl, benzene ring, and H in benzene ring structure) in alkanes, haloalkanes, haloethylenes, and aromatic hydrocarbons, as well as their Ca(ss) (associated with 1 mu mu mol/L inhalation exposure) according to an additive fragment model. This modeling approach implies that each fragment in the structure of a chemical has an additive and constant contribution to its Ca(ss). A multilinear regression was performed using a commercially available statistical software package, and the results obtained were essentially the contributions associated with each of the nine structural fragments toward Ca(ss) in the rat continuously exposed to 1 mu mu mol/L VOC in the air. The resulting model estimated adequately the Ca(ss) of VOCs initially used in the calibration (estimated/experimental ratio: 1.04 +/- 0.30, mean +/- standard deviation [SD]). This molecular structure vs. Ca(ss) relationship was then evaluated using an external dataset on Ca(ss) for three aliphatic hydrocarbons (octane, 2-methyl octane, and 1-nonene; 100 ppm exposures). The ratio of predicted to experimental Ca(ss) for these chemicals ranged from 0.6 to 1.2. The results of this study suggest that steady-state blood concentrations of inhaled VOCs can be predicted using structure-activity type models.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 source (direct Gemma or distilled Codex), 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".