Acyclovir, Ganciclovir, and Zidovudine Transfer into Rat Milk
Bibliographic record
Abstract
Treatment with antiviral agents that accumulate in breast milk may offer a novel approach to reduce the rates of vertical transmission of important viruses and the risk of clinical illness in suckling neonates. The present study evaluated the extent and mechanism of transfer of three antiviral nucleoside analogues into milk in a lactating rat model system. Acyclovir (0.26 mg/h), ganciclovir (0.13 mg/h), and zidovudine (0.5 mg/h) were each infused to steady-state concentrations in six rats 15 to 16 days postpartum. The observed ratios of the concentrations in milk to the concentrations in serum (observed milk-to-serum ratio), calculated from the ratio of the steady-state concentration in serum to the steady-state concentration in milk, determined the extent of drug transfer into milk. To identify the mechanism of transfer into milk, the observed milk-to-serum ratio was compared to a predicted milk-to-serum ratio estimated from an in vitro passive diffusion model of transfer of each drug into milk. High-pressure liquid chromatography methods determined milk and serum drug concentrations. Mean +/- standard deviation observed milk-to-serum ratios for acyclovir, ganciclovir, and zidovudine were 5.1 +/- 1.4, 1.6 +/- 0.33, and 1.0 +/- 0.29, respectively, compared with their corresponding predicted ratios of 1.1, 0.85, and 0.71. These results suggest that acyclovir accumulates in milk due to active transport mechanisms, while passive diffusion processes govern the transfer of both ganciclovir and zidovudine into milk. The presence of all three antiviral drugs in milk and the potential for active drug transfer into milk warrants further investigation of the accumulation of other antiviral drugs in milk and their therapeutic benefits in reducing the vertical transmission of viruses and clinical sequelae in the breast-feeding infant.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| 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 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".