Factors Affecting the Binding of Diltiazem to Rainbow Trout Plasma: Implications for the Risk Assessment of Pharmaceuticals in Aquatic Systems
Bibliographic record
Abstract
Abstract The accumulation of organic toxicants in fish plasma, and how they partition between the bound and unbound fraction once absorbed, are important metrics in models that seek to predict the risk of such contaminants in aquatic settings. Rapid equilibrium dialysis of diltiazem, an ionizable weak base and important human pharmaceutical contaminant of freshwaters, was conducted with rainbow trout (Oncorhynchus mykiss) plasma. The effect of fed state, fish sex, fish strain/size, and dialysis buffer pH on the binding of radiolabeled diltiazem (9 ng ml−1) was assessed. In fed fish, 24.6%–29.5% of diltiazem was free, unbound to plasma proteins. Although starvation of fish resulted in a decrease in plasma protein, the bound fraction of diltiazem remained relatively constant. Consequently, the protein-bound concentration of diltiazem increased with length of starvation. In general, rainbow trout strain was a significant factor affecting plasma binding, although the two strains tested also differed markedly in size. Dialysis buffer pH significantly influenced plasma binding, with a higher unbound diltiazem fraction at pH 6.8 than pH 8.0. These data indicate that empirical measures of plasma binding in fish are important for accurate risk assessment and that the physiological status of a fish is likely to impact its sensitivity to toxicants such as diltiazem. Environ Toxicol Chem 2022;41:3125–3133. © 2022 SETAC
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".