A Physiological Pharmacokinetic Model Based on Tissue Lipid Content for Simulating Inhalation Pharmacokinetics of Highly Lipophilic Volatile Organic Chemicals
Bibliographic record
Abstract
The highly lipophilic volatile organic chemicals (HLVOCs) are distributed almost uniquely in the neutral lipid fraction of tissues and blood. As suggested by their high n-octanol:water partition coefficient (>1000), their solubility in water fraction of tissues and blood is negligible. Hypothetically, then, the kinetics of HLVOCs can be simulated solely with the consideration of their solubility and distribution in neutral lipid-equivalent (NLE) fractions of the tissues and blood. The objectives of the present study were therefore (i) to develop a physiological pharmacokinetic model based on NLE content of tissues and blood, and (ii) to apply this model framework for simulating the inhalation pharmacokinetics of HLVOCs (i.e., d-limonene, alpha-pinene, and 1,2,4-trimethylbenzene) in humans. The PBPK model developed in this study consisted of tissue compartments that represented only their NLE content. All biological parameters, except alveolar ventilation rate, were expressed on the basis of their NLE content. Tissue:blood partition coefficients were not used since the solubility of HLVOCs in tissue neutral lipids and blood neutral lipids is considered to be the same. The NLE-based physiological pharmacokinetic model was then used to simulate the uptake and disposition kinetics of alpha-pinene, d-limonene, and 1,2,4-trimethylbenzene in humans. The NLE-based model developed in this study represents a novel tool for simulating the lipid concentrations and pharmacokinetics of HLVOCs without the use of tissue:blood partition coefficients.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 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".