Novel Strategy of Using Flaxseed Lignans as Adjuvant Therapy Against Prostate Cancer
Bibliographic record
Abstract
Increasing evidence from preclinical and clinical studies demonstrate that dietary flaxseed lignans may have a role in prevention and treatment of prostate cancer. The lignans enterolactone (ENL) and secoisolariciresinol (SECO) may contribute to the anticancer effect. Our in vitro cytotoxicity studies found that ENL and SECO reduced the IC 50 values of chemotherapeutic agents in prostate specific membrane antigen (PSMA) positive and negative prostate cancer cell lines. However, following oral consumption the flaxseed lignans exist mainly as inactive glucuronic acid conjugates with very low systemic levels of ENL and SECO. Based upon their unusual pharmacokinetic profile, we propose the use of antibody directed enzyme prodrug therapy (ADEPT) as adjuvant therapy. ADEPT is a two‐step process by which a prodrug activating enzyme is delivered specifically to the tumor site by a tumor‐specific antibody following administration of a nontoxic prodrug. Activation of the prodrug by the localized enzyme will trigger cancer cell death directly or via bystander effect. An anti‐PSMA antibody conjugated with beta‐glucuronidase (fusion protein) was successfully generated. The fusion protein binds well against PSMA positive prostate cancer cell line, LNCap, as analyzed by flow cytometry. The enzymatic activity of the fusion protein was also tested using 4‐methylumbeliferone (positive control) and enterolactone glucuronide and demonstrated ability to cleave the glucuronide. The fusion protein will be tested in prostate cancer xenograft mouse model to determine the tumor specific distribution, enzymatic activity at the tumor site, and ability to reduce tumor size and prolong survival when used in combination with chemotherapeutic agents.
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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 | 0.001 |
| 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".