Abstract 1377: Volume scanning, a rational approach to covalent PI3Kα inhibitors
Bibliographic record
Abstract
Abstract Inhibitors of the phosphatidylinositol 3-kinase (PI3K) - protein kinase B (PKB/Akt) - mechanistic target of rapamycin (mTOR) axis are considered valuable assets in cancer therapy. A considerable effort has been dedicated to the development of drugs targeting class I PI3Ks, which are evaluated in preclinical and clinical studies.[1-5] Here we present a strategy to convert a phase II clinical candidate, a pan-PI3K inhibitor (PQR309, bimiralisib)[5,6], into a highly selective, covalent PI3Kα inhibitor with the aim to minimize off-target and on-target metabolic side effects of PI3K inhibitor cancer therapy. We exploited a rational approach to increase target selectivity by covalently targeting PI3Kα at the non-conserved nucleophilic Cys862. A combination of warhead activity design, proximity screening and an optimized orientation allowed a tight control of reversible inhibitor binding in combination with an isoform-specific covalent reaction. To avoid off-target reactions, all warheads' reactivities were determined and optimize for selectivity and of Cys862 modification. An extensive Structure Activity Relationship (SAR) study was performed and a wide range of linear and restricted rotation linkers were introduced. A comprehensive understanding of the kinetics of irreversible inhibition acquired by kinetic TR-FRET assays and subsequent determination of kchem, kinact and calculated Ki allowed the establishment of a SAR, for compound selection with minimal off-target reactivity and high PI3Kα selectivity. X-ray crystallography and MS-based proteomics validated the covalent modification of Cys862. Our pilot compounds exceed specificity and potency over an experimental dimethyl-substituted enone, CNX-1351.[7] Moreover, our compounds display increased stability in rat liver microsomal assays and outperform the rapidly metabolized CNX-1351. Our strategy to investigate and tune warheads' reactivity represents a major step forward in the rational design of covalent chemical tools, overcoming the serendipity in the discovery of irreversible compounds. Moreover, we provide highly selective chemical tools to dissect PI3K isoform signaling in physiology and disease. A clarification of the role of the different PI3K isoforms in insulin signaling allows to address the challenges in isoform selectivity and to develop PI3K inhibitors showing ideal isoform specificity. [1] Rageot D et. al. J Med Chem. 2019, 62 (13), 6241-6261. [2] Borsari C et. al. ACS Med Chem Lett. 2019, 10 (10), 1473-1479. [3] Marone R et al. Biochim Biophys Acta 2008, 1784 (1), 159-185. [4] Wymann MP and Schneiter R Nat Rev Mol Cell Biol. 2008, 9 (2), 162-176. [5] Beaufils F et. al. J Med Chem. 2017, 60 (17), 7524-7538. [6] Wicki A et. al. Eur J Cancer. 2018, 96, 6-16. [7] Nacht M et. al. J Med Chem. 2013, 56 (3), 712-721. Citation Format: Chiara Borsari, Erhan Keles, Jacob McPhail, Alexander Schäfer, Rohitha Sriramaratnam, Martina De Pascale, Matthias Gstaiger, John Burke, Matthias P. Wymann. Volume scanning, a rational approach to covalent PI3Kα inhibitors [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 1377.
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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) | 0.001 | 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 | 0.001 | 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".