Bibliographic record
Abstract
Neuroendocrine Prostate Cancer (NEPC) is an aggressive subtype of prostate cancer, affecting approximately 2% of all prostate cancer patients.While NEPC is a rare disease, it has no known treatment and often leads to death within a year of diagnosis.As result of low incidence rates, these rare conditions typically do not get the same pharmaceutical interest, as discovery research rarely recovers the cost for development and clinical trials.Drug repurposing is a viable option for rare conditions and the use of high-throughput screens can lead to drug repurposing discovery.Chemotherapy has moved to personalize therapy, focusing on molecular alterations for specific tumors.NEPC cell lines (specifically, LnCAP and 22rv1) have been shown to overexpress n-MYC (MYCN), which is a proto-oncogene not typically expressed in adult tissue.This transcription factor is typically present in developing tissue that results in neural tissue.The LnCAP cell lines (LnCAP-P and LnCAP-n-MYC) have been shown to be a in vitro NEPC model, and along with Benign Prostate Hyperplasia tissue (BPH-1) as a control, these cell lines were screened using the Targetmol Drug library, which contained 1813 small molecules at concentrations of 100 nM, 500 nM and 1000 nM in 384-well optic plates using high-throughput microscope and RFP fluorescence in cell lines.Fludarabine and Fludarabine phosphate were identified as potential n-MYC selective hits and validation of Fludarabine phosphate using Alamar Blue viability reagent in 96-well plates were done using BPH-1, LnCAP, and 22rv1.Result confirmed Fludarabine phosphate selectively inhibited cell viability in n-MYC overexpressed cells in both LnCAP and 22rv1.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".