Prediction of Milk/Plasma Concentration Ratio of Drugs
Bibliographic record
Abstract
OBJECTIVE: The milk to plasma (m/p) concentration ratio of drugs is used to estimate the amount of drug offered to the suckling infant. Published literature was reviewed to identify drugs for which sufficient data exist for calculation of m/p ratio and to examine whether the existing empiric data agree with the published method of Atkinson for mathematical prediction of m/p ratios based on physiochemical characteristics. METHODS: Using a comprehensive reference text, we identified studies reporting sufficient data to calculate m/p ratio based on the AUC for milk and plasma. Subsequently, we calculated the m/p ratio with Atkinson's formula based on pKa, lipophilicity, and protein binding. We then correlated the empiric versus predicted (calculated) m/p ratios. RESULTS: Of 192 drugs of which at least some data on milk accumulation have been published, there were sufficient data to quantify m/p ratios for only 69 medications (78 studies). There was no significant correlation between the empiric m/p ratios and the predicted values using the Atkinson's model. CONCLUSIONS: Reliable data on m/p concentration ratios exist for few medications. Presently, there is no appropriate model to predict milk concentrations of drugs in humans.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| 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) | 0.002 | 0.001 |
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".