Self-Associating Poly(ethylene oxide)-<i>block</i>-poly(α-carboxyl-ε-caprolactone) Drug Conjugates for the Delivery of STAT3 Inhibitor JSI-124: Potential Application in Cancer Immunotherapy
Bibliographic record
Abstract
Constitutive activation of signal transducer and activator of transcription 3 (STAT3) in tumor cells and tumor associated dendritic cells (DCs) plays a major role in the progression of cancer. JSI-124 (cucurbitacin I) is a potent inhibitor of STAT3; however, its poor solubility and nonspecificity limit its effectiveness in cancer immunotherapy. In order to achieve a nanocarrier for solubilization and passive targeting of JSI-124 to tumor cells and tumor associated DCs, the drug was chemically conjugated to pendent COOH groups of self-associating poly(ethylene oxide)- block -poly(α-carboxylate-ε-caprolactone) (PEO- b -PCCL). Developed PEO- b -P(CL-JSI-124) conjugates self-assembled to polymeric micelles of 40 nm size range with negligible drug release under physiological mimicking conditions. The conjugation of JSI-124 to PEO- b -PCCL was confirmed by 1 H NMR, thin layer chromatography (TLC), and HPLC with a conjugation of 8.9% w/w of the polymer. As expected, JSI-124 nanoconjugates showed lower potency in p-STAT3 inhibition and direct anticancer activity in B16-F10 melanoma cells. Interestingly, JSI-124 nanoconjugates were more powerful than free drug in reducing the level of p-STAT3 in tumor exposed bone marrow derived dendritic cells (BMDCs). The JSI-124 nanoconjugates were also significantly more active than free drug in reversing the immunosuppressive effect of B16-F10 tumor and led to significantly better phenotypical and functional stimulation of tumor exposed immature BMDCs in the presence of immune adjuvants like LPS and CpG. Our findings points to great promise for PEO- b -P(CL-JSI-124) micelles for modulation of immunosuppressive microenvironment in melanoma tumors, implicating application of this strategy in cancer immunotherapy.
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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".