Colloidal Drug Formulations Can Explain “Bell-Shaped” Concentration–Response Curves
Bibliographic record
Abstract
Drug efficacy does not always increase sigmoidally with concentration, which has puzzled the community for decades. Unlike standard sigmoidal curves, bell-shaped concentration-response curves suggest more complex biological effects, such as multiple-binding sites or multiple targets. Here, we investigate a physical property-based mechanism for bell-shaped curves. Beginning with the observation that some drugs form colloidal aggregates at relevant concentrations, we determined concentration-response curves for three aggregating anticancer drugs, formulated both as colloids and as free monomer. Colloidal formulations exhibited bell-shaped curves, losing activity at higher concentrations, while monomeric formulations gave typical sigmoidal curves, sustaining a plateau of maximum activity. Inverting the question, we next asked if molecules with bell-shaped curves, reported in the literature, form colloidal aggregates at relevant concentrations. We selected 12 molecules reported to have bell-shaped concentration-response curves and found that five of these formed colloids. To understand the mechanism behind the loss of activity at concentrations where colloids are present, we investigated the diffusion of colloid-forming dye Evans blue into cells. We found that colloidal species are excluded from cells, which may explain the mechanism behind toxicological screens that use Evans blue, Trypan blue, and related dyes.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".