Abstract A003: Unwinding the complexities of helicases as compelling drug targets in oncology
Bibliographic record
Abstract
Abstract In recent years, helicases have moved to the forefront of research as novel drug targets for a variety of human diseases, including cancer. As critical components of the cell cycle and DNA repair, these enzymes offer an attractive point of therapeutic intervention. Recent bioinformatic endeavours such as the Cancer DepMap have further highlighted the essentiality of these proteins in the progression of numerous cancers. Inhibitors of Werner helicase (WRN) entered Phase I evaluation in 2023, after studies highlighted its role in tumours with microsatellite instability (MSI) and impaired mismatch repair (MMR). Building on these advances, helicases now represent a significant drug target class and are the subject of intense research. However, these targets present significant complexities in their prosecution. Hit finding and robust validation of these putative active compounds remains challenging, with high attrition rates and significant sensitivity to false positives. Resultant hits require thorough biological characterisation via both biochemical and biophysical methods, alongside determination of selectivity against other closely related family members, to increase confidence in their provenance before commencing a detailed drug discovery project. To exemplify these approaches, we have profiled three commercially available WRN inhibitors from two chemical series through a selection of biochemical and biophysical assays. The three compounds exhibit excellent selectivity against 6 distinct helicase/translocase targets. In Michaelis-Menten mechanism of Inhibition studies, we defined contrasting mechanisms of inhibition for the two series and were also able to identify time dependent inhibition with one compound series. Analysing binding interactions of the compounds against protein and protein:substrate complexes, via a fluorescent thermal shift assay (FTSA) and microscale thermophoresis (MST), enabled us to further understand and interrogate the mechanism of inhibition. Herein, we describe some of our experiences, learnings and best practices when prosecuting these compelling, but challenging, targets. Citation Format: Yael Mamane, Zoe Caple, Katie Chapman, Ian Henderson, Allan Jordan, Vanessa Lyne, Cynthia Okoye, Laurent Rigoreau, Ganesh Kadamur, Stuart Thomson, Chris Tomlinson, Jana Wolf. Unwinding the complexities of helicases as compelling drug targets in oncology [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Expanding and Translating Cancer Synthetic Vulnerabilities; 2024 Jun 10-13; Montreal, Quebec, Canada. Philadelphia (PA): AACR; Mol Cancer Ther 2024;23(6 Suppl):Abstract nr A003.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".