Drug Ratio-Dependent Antagonism: A New Category of Multidrug Resistance and Strategies for Its Circumvention
Bibliographic record
Abstract
A newly identified form of multidrug resistance (MDR) in tumor cells is presented, pertaining to the commonly encountered resistance of cancer cells to anticancer drug combinations at discrete drug:drug ratios. In vitro studies have revealed that whether anticancer drug combinations interact synergistically or antagonistically can depend on the ratio of the combined agents. Failure to control drug ratios in vivo due to uncoordinated pharmacokinetics could therefore lead to drug resistance if tumor cells are exposed to antagonistic drug ratios. Consequently, the most efficacious drug combination may not occur at the typically employed maximum tolerated doses of the combined drugs if this leads to antagonistic ratios in vivo after administration and resistance to therapeutic effects of the drug combination. Our approach to systematically screen a wide range of drug ratios and concentrations and encapsulate the drug combination in a liposomal delivery vehicle at identified synergistic ratios represents a means to mitigate this drug ratio-dependent MDR mechanism. The in vivo efficacy of the improved agents (CombiPlex formulations) is demonstrated and contrasted with the decreased efficacy when drug combinations are exposed to tumor cells in vivo at antagonistic ratios.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".