Combination of Selective PARP3 and PARP16 Inhibitory Analogues of Latonduine A Corrects F508del-CFTR Trafficking
Bibliographic record
Abstract
Abstract The marine natural product latonduine A (1) shows F508del-cystic fibrosis transmembrane regulator (CFTR) corrector activity in cell-based assays. Pull-down experiments, enzyme inhibition assays, and siRNA knockdown experiments suggest that the F508del-CFTR corrector activities of latonduine A and a synthetic analogue MCG315 (4) result from simultaneous inhibition of PARP3 and PARP16. A library of synthetic latonduine A analogs has been prepared in an attempt to separate the PARP3 and PARP16 inhibitory properties of latonduine A with the goal of discovering selective small-molecule PARP3 and PARP16 inhibitory cell biology tools that could confirm the proposed dual-target F508del-CFTR corrector mechanism of action. The structure activity relationship (SAR) study reported herein has resulted in the discovery of the modestly potent (IC50 3.1 μM) PARP3 selective inhibitor (±)-5-hydroxy-4-phenyl-2,3,4,5-tetrahydro-1H-benzo[c]azepin-1-one (5) that shows 96-fold greater potency for inhibition of PARP3 compared with its inhibition of PARP16 in vitro and the potent (IC50 0.362 μM) PARP16 selective inhibitor (±)-7,8-dichloro-5-hydroxy-4-(pyridin-2-yl)-2,3,4,5-tetrahydro-1H-benzo[c]azepin-1-one (6) that shows 205-fold selectivity for PARP16 compared with PARP3 in vitro. At 1 or 10 μM, neither 5 or 6 alone showed F508del-CFTR corrector activity, but when added together at 1 or 10 μM each, the combination exhibited F508del-CFTR corrector activity identical to 1 or 10 μM latonduine A (1), respectively, supporting its novel dual PARP target mechanism of action. Latonduine A (1) showed additive in vitro corrector activity in combination with the clinically approved corrector VX809, making it a potential new partner for cystic fibrosis combination drug therapies.
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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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".