Quantitative Structure−Property Relationships for Interspecies Extrapolation of the Inhalation Pharmacokinetics of Organic Chemicals
Bibliographic record
Abstract
The objectives of this study were to (i) develop quantitative structure-property relationships (QSPRs) for blood:air partition coefficients (Pb:a), tissue:air partition coefficients (Pt:a), and hepatic clearance (CLh) and (ii) conduct interspecies extrapolations of the pharmacokinetics of low molecular weight volatile organic chemicals (VOCs) by incorporating the above QSPRs within a physiologically based pharmacokinetic (PBPK) modeling framework. Pb:a and Pt:a were predicted using the following algorithm: FnlxPo:a+FwxPw:a+f(b)xFpxPp:a, where Fnl=content of neutral lipid equivalents in biological matrix, Fw=content of water equivalents in biological matrix, Fp=protein content of blood and tissues, Po:a=vegetable oil:air partition coefficient, Pw:a=water:air partition coefficient, f(b)=fraction of total protein involved in the partitioning process, and Pp:a=protein:air partition coefficient. CLh was estimated as follows: Qlx[(CLintxC(P4502E1)xVl)/(Ql+CLintxC(P4502E1)xVl)], where CLint=intrinsic clearance normalized for P450 2E1 content, Ql=blood flow to the liver, C(P4502E1)=hepatic concentration of P450 2E1 in the species of interest, and Vl=volume of liver. QSPRs relating molecular fragments of 46 VOCs and parameters required for estimating Pb:a, Pt:a, and CLh (namely, Po:a, Pw:a, Pp:a, and CLint) were established using a group contribution method (f(i)xCi, where f=frequency of occurrence of the group i in a given molecule and Ci=contribution of the group i to Po:a, Pw:a, Pp:a, or CLint). Values of group contributions were determined by multilinear regression of experimental data. The species specific parameters required for solving the above algorithms were obtained from the literature. These algorithms, once incorporated into a multispecies PBPK modeling framework, enabled extrapolation of the kinetics of chemicals across species. The inhalation pharmacokinetics of dichloromethane and toluene as well as two de novo compounds (1,2,4-trimethyl benzene and ethyl benzene) were extrapolated from rat to human, using the present modeling methodology. This study has demonstrated that it is possible to extrapolate the pharmacokinetic behavior of chemicals from rats to humans on the basis of QSPRs and species specific physiological information.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 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".