Use of Anticancer Platinum Compounds in Combination Therapies and Challenges in Drug Delivery
Bibliographic record
Abstract
As one of the leading and most important metal-based drugs, platinum-based pharmaceuticals are widely used in the treatment of solid malignancies. Despite significant side effects and acquired drug resistance have limited their clinical applications, platinum has shown strong inhibitory effects for a wide assortment of tumors. Drug delivery systems using emerging technologies such as liposomes, dendrimers, polymers, nanotubes and other nanocompositions, all show promise for the safe delivery of platinum-based compounds. Due to the specificity of nano-formulations; unwanted side-effects and drug resistance can be largely averted. In addition, combinational therapy has been shown to be an effective way to improve the efficacy of platinum based anti-tumor drugs. This review first introduces drug delivery systems used for platinum and combinational therapeutic delivery. Then we highlight some of the recent advances in the field of drug delivery for combinational therapy; specifically progress in leveraging the cytotoxic nature of platinum-based drugs, the combinational effect of other drugs with platinum, while evaluating the drug targeting, side effect reducing and sitespecific nature of nanotechnology-based delivery platforms.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 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".