Ratiometric dosing of anticancer drug combinations: Controlling drug ratios after systemic administration regulates therapeutic activity in tumor-bearing mice
Bibliographic record
Abstract
Anticancer drug combinations can act synergistically or antagonistically against tumor cells in vitro depending on the ratios of the individual agents comprising the combination. The importance of drug ratios in vivo, however, has heretofore not been investigated, and combination chemotherapy treatment regimens continue to be developed based on the maximum tolerated dose of the individual agents. We systematically examined three different drug combinations representing a range of anticancer drug classes with distinct molecular mechanisms (irinotecan/floxuridine, cytarabine/daunorubicin, and cisplatin/daunorubicin) for drug ratio-dependent synergy. In each case, synergistic interactions were observed in vitro at certain drug/drug molar ratio ranges (1:1, 5:1, and 10:1, respectively), whereas other ratios were additive or antagonistic. We were able to maintain fixed drug ratios in plasma of mice for 24 hours after i.v. injection for all three combinations by controlling and overcoming the inherent dissimilar pharmacokinetics of individual drugs through encapsulation in liposomal carrier systems. The liposomes not only maintained drug ratios in the plasma after injection, but also delivered the formulated drug ratio directly to tumor tissue. In vivo maintenance of drug ratios shown to be synergistic in vitro provided increased efficacy in preclinical tumor models, whereas attenuated antitumor activity was observed when antagonistic drug ratios were maintained. Fixing synergistic drug ratios in pharmaceutical carriers provides an avenue by which anticancer drug combinations can be optimized prospectively for maximum therapeutic activity during preclinical development and differs from current practice in which dosing regimens are developed empirically in late-stage clinical trials based on tolerability.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".