MOLECULAR STRUCTURE-BASED PREDICTION OF HUMAN ABDOMINAL SKIN PERMEABILITY COEFFICIENTS FOR SEVERAL ORGANIC COMPOUNDS
Bibliographic record
Abstract
Mechanistically based predictions of skin permeability coefficients (Kp) derived solely on the basis of molecular structure information of organic compounds have not been reported previously. The objective of the present study was to predict the human abdominal Kp of structurally unrelated organic compounds using a mechanistic equation that takes into account compound-specific and species-specific determinants of Kp and relates the compound-specific determinants directly to molecular structure information. The method consisted of obtaining predictions based on a modified form of the conventional equation used to describe Kp (= P x D/L, where P, D, and L represent the stratum corneum:water partition coefficient, diffusion coefficient, and path length of diffusion). The value of P was predicted from the n-octanol:water partition coefficient (Po:w) computed from molecular structure information as well as the lipid, protein, and water contents in stratum corneum. The value of D was predicted according to the conventional Einstein-Stoke equation using the molar volume (Vmolar) of compounds computed from molecular structure information as well as data on viscosity of the stratum corneum components. Finally, the value of L was estimated in accordance with the characteristics of the diffusion pathways in the stratum corneum (i.e., transcellular and intercellular pathways). The average ratio of predicted Kp to the corresponding mean experimental values obtained from the literature was 1.25 (SD = 0.68, r = .95) for 47 structurally unrelated organic compounds (volatile organics and drugs), which were characterized by the presence of less than 2 hydrogen bonding groups and Vmolar < 231 cm3/mol. The present study is the first initiative that permits prediction of the human Kp of organic compounds by using molecular structure information as the sole chemical-specific input in a mechanistic equation.
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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 | 0.002 |
| 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 | 0.000 | 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".