Nucleic Acid Aptamers Increase the Anticancer Efficiency and Reduce the Toxicity of Cisplatin-Arabinogalactan Conjugates <i>In Vivo</i>
Bibliographic record
Abstract
Cisplatin is an effective drug for treating various cancer types. However, it is highly toxic for both healthy and tumor cells. Therefore, there is a need to reduce its therapeutic dose and increase targeted bioavailability. One of the ways to achieve this could be the coating of cisplatin with polysaccharides and specific carriers for targeted delivery. Nucleic acid aptamers could be used as carriers for the specific delivery of medicine to cancer cells. Cisplatin-arabinogalactan-aptamer (Cis-AG-Ap) conjugate was synthesized based on Cis-dichlorodiammineplatinum, Siberian larch arabinogalactan, and aptamer AS-42 specific to heat-shock proteins (HSP) 71 kDa (Hspa8) and HSP 90-beta (Hsp90ab1). The antitumor effect was estimated using ascites and metastatic Ehrlich tumor models. Cis-AG-Ap toxicity was assessed by blood biochemistry on healthy mice. Here, we demonstrated enhanced anticancer activity of Cis-AG-Ap and its specific accumulation in tumor foci. It was shown that targeted delivery allowed a 15-fold reduction in the therapeutic dose of cisplatin and its toxicity. Cis-AG-Ap sufficiently suppressed the growth of Ehrlich's ascites carcinoma, the mass and extent of tumor metastasis in vivo . Arabinogalactan and the aptamers promoted cisplatin efficiency by enhancing its bioavailability. The described strategy could be very promising for targeted anticancer therapy.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".