Colloidal Aggregation Affects the Efficacy of Anticancer Drugs in Cell Culture
Bibliographic record
Abstract
Many small molecules, including bioactive molecules and approved drugs, spontaneously form colloidal aggregates in aqueous solution at micromolar concentrations. Though it is widely accepted that aggregation leads to artifacts in screens for ligands of soluble proteins, the effects of colloid formation in cell-based assays have not been studied. Here, seven anticancer drugs and one diagnostic reagent were found to form colloids in both biochemical buffer and in cell culture media. In cell-based assays, the antiproliferative activities of three of the drugs were substantially reduced when in colloidal form as compared to monomeric form; a new formulation method ensured the presence of drug colloids versus drug monomers in solution. We also found that Evans Blue, a dye classically used to measure vascular permeability and to demonstrate the "enhanced permeability and retention (EPR) effect" in solid tumors, forms colloids that adsorb albumin, as opposed to older literature that suggested the reverse.
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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 | 0.000 |
| 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 | 0.000 | 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".