Screening of 5- and 6-Substituted Amiloride Libraries Identifies Dual-uPA/NHE1 Active and Single Target-Selective Inhibitors
Bibliographic record
Abstract
The K+-sparing diuretic amiloride shows off-target anti-cancer effects in multiple rodent models. These effects arise from the inhibition of two distinct cancer targets: the trypsin-like serine protease urokinase-type plasminogen activator (uPA), a cell-surface mediator of matrix degradation and tumor cell invasiveness, and the sodium-hydrogen exchanger isoform-1 (NHE1), a central regulator of transmembrane pH that supports carcinogenic progression. In this study, we co-screened our library of 5- and 6-substituted amilorides against these two targets, aiming to identify single-target selective and dual-targeting inhibitors for use as complementary pharmacological probes. Closely related analogs substituted at the 6-position with pyrimidines were identified as dual-targeting (pyrimidine 24 uPA IC50 = 175 nM, NHE1 IC50 = 266 nM, uPA selectivity ratio = 1.5) and uPA-selective (methoxypyrimidine 26 uPA IC50 = 86 nM, NHE1 IC50 = 12,290 nM, uPA selectivity ratio = 143) inhibitors, while high NHE1 potency and selectivity was seen with 5-morpholino (29 NHE1 IC50 = 129 nM, uPA IC50 = 10,949 nM; NHE1 selectivity ratio = 85) and 5-(1,4-oxazepine) (30 NHE1 IC50 = 85 nM, uPA IC50 = 5715 nM; NHE1 selectivity ratio = 67) analogs. Together, these amilorides comprise a new toolkit of chemotype-matched, non-cytotoxic probes for dissecting the pharmacological effects of selective uPA and NHE1 inhibition versus dual-uPA/NHE1 inhibition.
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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 | 0.000 | 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) | 0.002 | 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".