Targeting Mantle Cell Lymphoma with Anti-SYK Nanoparticles
Bibliographic record
Abstract
The pentapeptide mimic 1,4-bis(9-O-dihydroquinidinyl)phthalazine / hydroquinidine 1,4-phathalazinediyl diether ("compound 61") (C-61) is the first reported inhibitor targeting the P-site of SYK. Here we report a nanotechnology platform to target C-61 to mantle cell lymphoma (MCL) cells. Liposomal nanoparticles (NP) loaded with C-61 were prepared using the standard thin film evaporation method. The entrapment of C-61 was obtained using the pH gradient procedure with lactobionic acid (LBA) being used as a low pH buffer inside the NP. Formulation F6A was selected as a lead candidate for further biological testing. The average diameter, zeta potential and C-61 content of the F6A NP was 40 nm, 0.1 mV, and 12.6 mg/ml, respectively. F6A induces apoptosis in SYK+ but not SYK- leukemia/lymphoma cells. We also evaluated the cytotoxic activity of F6A in the context of an in vitro artificial bone marrow assay platform based on a 3D scaffold with inverted colloidal crystal geometry mimicking the structural topology of actual bone marrow matrix. The ability of C-61 to induce apoptosis in ALL-1 cells was not adversely affected by the scaffolds. F6A, but not the drug-free NP formulation F6B, caused apoptosis of MCL cell lines MAVER-1 and MINO within 24h. Further development of rationally designed SYK inhibitors and their nanoscale formulations may provide the foundation for therapeutic innovation against a broad spectrum of lymphoid malignancies, including MCL.
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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) | 0.000 | 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".