Development and Assessment of Conventional and Targeted Drug Combinations for Use in the Treatment of Aggressive Breast Cancers
Bibliographic record
Abstract
Combination chemotherapy has been at the forefront of cancer treatment for over 40 years. However, the rationale for selecting drug combinations and the process used to demonstrate clinical effectiveness has primarily followed trial and error methodology. Typically, the selection and assessment of combined drug therapies has been based on the effectiveness of each agent as monotherapy in treating the neoplasm and avoiding overlapping toxicities, followed by clinical trials to establish dose scheduling, toxicity, and efficacy. Unfortunately, this scheme is inefficient in terms of the time required to complete and revise these clinical trials based on the outcome to optimize the drug combination. A more rational approach for the development of combination oncology products should consider (i) in vitro assays for assessing therapeutic effects of drug combinations (antagonistic, additive or synergistic interactions) when added simultaneously; (ii) methods for measuring these interactions in vivo; (iii) the importance of understanding pharmacokinetic and biodistribution parameters when using drug combinations; (iv) the need to assess pathways known to contribute to cancer cell survival as well as metastasis; and (iv) the need to assess the fate of different cell populations (cancer and stroma) contributing to the development of cancer. Therefore, the goal of this article is to provide a road map for the preclinical development of drug combination products that will have improved therapeutic activity and a high likelihood of providing beneficial therapeutic outcomes in patients with aggressive cancers with a specific focus on patients with breast cancer.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".