Advances in the development of hybrid anticancer drugs
Bibliographic record
Abstract
INTRODUCTION: Hybrid anticancer drugs are of great therapeutic interests as they can potentially overcome most of the pharmacokinetic drawbacks encountered when using conventional anticancer drugs. In fact, the future of hybrid anticancer drugs is very bright for the discovery of highly potent and selective molecules that triggers two or more cytocidal pharmacological mechanisms of action acting in synergy to inhibit cancer tumor growth. AREAS COVERED: This review represents the most advanced and recent data in the field of hybrid anticancer agents covering mainly the past 5 years of research. It also accounts for other significant reviews already published on the topic of anticancer hybrids. The review showcases the research that is at the leading edge of hybrid anticancer drug discovery. The main areas covered by the present review are: DNA alkylating agent hybrids (e.g., platinum(II), nitrogen mustard, etc.), vitamin-D receptor, agonist-histone deacetylase inhibitors, combi-molecule therapies and other types of hybrid anticancer agents. EXPERT OPINION: The current development in the field describes strategies that have never been used before for the design of hybrid anticancer drugs. The information currently available and described in this section allows us to identify the main parameters required to design such molecules. It also provides a clear view of the future directions that must be explored for the successful development and discovery of useful hybrid anticancer drugs.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".