Rational Drug Design of Targeted and Enzyme Cleavable Vitamin E Analogs as Neoadjuvant to Chemotherapy: <i>In Vitro</i> and <i>In Vivo</i> Evaluation on Reduction of Cardiotoxicity of Doxorubicin
Bibliographic record
Abstract
Abstract Traditional drug design focus on specific target (s) expressed by cancer cells. However, cancer cells outsmart the interventions by activating survival pathways and/or downregulating cell death pathways. As the research in molecular biology of cancer grows exponentially, new methods of drug designs are needed to target multiple pathways/targets which are involved in survival of cancer cells. Vitamin E analogues including a-tocopheryl succinate (α-TOS) is a well-known anti-tumoregenic agent which is well studied both in vitro and in vivo tumor models. However, lack of targeting cancer cells and unexpected toxicity along with the poor water solubility of α-TOS compelled a rational drug design using both targeting and cleavable technologies incorporated in the new drug design. A plethora of Vitamin E derivatives (AMP-001, 002 and 003) were synthesized, characterized and studied for the improved efficacy and lowered toxicity in various cancer cells in vitro . Preliminary studies revealed AAAPT leading candidates reduced the invasive potential of brain tumor stem cells, synergized with different drugs and different treatments. AAAPT leading drug AMP-001 enhanced the therapeutic index of front-line drug Doxorubicin in triple negative breast cancer (TNBC) tumor rat model preserving the ventricular function when used as a neoadjuvant to Doxorubicin. These results may pave the way for reducing the cardiotoxicity of chemotherapy in clinical settings.
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 | 0.000 | 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".