Cytotoxic drugs in drinking water: A prediction and risk assessment exercise for the thames catchment in the United Kingdom
Bibliographic record
Abstract
Cytotoxic, also known as antineoplastic, drugs remain an important weapon in the fight against cancer. This study considers the water quality implications for the Thames catchment (United Kingdom) arising from the routine discharge of these drugs after use, down the drain and into the river. The review focuses on 13 different cytotoxic drugs from the alkylating agent, antimetabolite, and anthracycline antibiotic families. A geographic-information-system-based water quality model was used in the present study. The model was informed by literature values on consumption, excretion, and fate data to predict raw drinking water concentrations at the River Thames abstraction points at Farmoor, near Oxford, and Walton, in West London. To discover the highest plausible values, upper boundary values for consumption and excretion together with lower removal values for sewage treatment were used. The raw drinking water cytotoxic drug maximum concentrations at Walton (the higher of the two) representative of mean and low flow conditions were predicted to be 11 and 20 ng/L for the five combined alkylating agents, 2 and 4 ng/L for the three combined antimetabolites, and 0.05 and 0.10 ng/L the for two combined anthracycline antibiotics, respectively. If they were to escape into tap water, then the highest predicted concentrations would still be a factor of between 25 and 40 below the current recommended daily doses of concern. Although the risks may be negligible for healthy adults, more concern may be associated with special subgroup populations, such as pregnant women, their fetuses, and breast-feeding infants, due to their developmental vulnerability.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".