Clinical Advances in Triple Negative Breast Cancer Treatment: Focus on Poly (L-lactide-coglycolide) Nanoparticles
Bibliographic record
Abstract
Triple negative breast cancer (TNBC) is the most aggressive type of breast cancer and is associated with high probability of metastasis and poor prognosis. Chemotherapeutics and surgery remain the most common options for TNBC patients; however, chemotherapeutic resistance and relapse of tumors limit the progression free survival and patient life span. This review provides an overview of recent chemotherapeutics that are in clinical trial, and the combination of drugs that are being investigated to overcome the drug resistance and to improve patient survival in different molecular subtypes of TNBCs. Nanotherapeutics have emerged as a promising platform for TNBC treatment and aim to improve the selectivity and solubility of drugs, reduce systemic side effects, and overcome multi-drug resistance. The study explores the role of nanoparticles for TNBC treatment and summarizes the types of nanoparticles that are in clinical trials. Poly(L-lactide-co-glycolide) (PLGA) is the most studied polymeric carrier for drug delivery and for TNBC treatment in research and in clinics. This review is about providing recent advancements in PLGA nanotherapeutic formulations and their application to help treat TNBC. Some background on current chemotherapies and pathway inhibitors is provided so that the readers are aware of what is currently considered for TNBC. Some of the pathway inhibitors may also be of importance for nanotherapeutics development. SIGNIFICANCE STATEMENT: This minireview summarizes the progress on chemotherapeutics and nanoparticle delivery for treatment of TNBC and specifically highlights the lead compounds that are in clinical trials.
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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 | 0.001 | 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) | 0.003 | 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".