Evariste

We are pleased to be presenting two posters at the 2024 edition of the AACR Annual Meeting, held in San Diego from April 5-10. The abstracts for these can be found below:

Session PO.CH01.02 - Lead Identification and Optimization

Presenter/Authors: A. Brennan, J. Lanz, D. S. Miller, O. Vipond, N. Harrison, J. Aaronson, A. Hercot; Evariste Ltd, London, United Kingdom

‍Abstract 1813: Discovery, biomarker validation, and pre-clinical profiling of a novel PKMYT1 inhibitor.

Protein kinase membrane-associated tyrosine/threonine 1 (PKMYT1) is a negative regulator of CDK1 activity and has been identified as a putative synthetic lethal target in cancers which upregulate cyclin E (CCNE1). We used high-throughput virtual screening to identify inhibitors of PKMYT1 with high lipophilic ligand efficiency (LLE), which were rapidly optimized to potent leads with exceptional selectivity over a key off target, WEE1. Machine learning-driven optimization rapidly reduced mouse hepatocyte clearance to acceptable levels, delivering a compound with best-in-class potency, selectivity, and pharmacokinetic profiles in relevant animal models. Tool compounds from this series were used to validate a differentiated biomarker for sensitivity to PKMYT1 inhibition in cellular models, which could significantly expand the potential patient population. In vivo proof-of-concept studies are ongoing in a variety of cell line- and patient-derived xenograft models which represent the novel biomarker previously identified, to further validate our therapeutic strategy.

Session PO.ET02.05 - Cancer Treatment: New Technologies

Presenter/Authors: D. S. Miller, O. Vipond, A. Brennan, A. Hercot; Evariste Ltd, London, United Kingdom

Abstract 5813: AI-driven identification and validation of novel synthetic lethal gene pairs through deep mining of cancer dependency data.

The targeting of synthetic lethal (SL) gene pairs is emerging as a new paradigm in precision oncology, with the potential to deliver treatments with higher efficacy and fewer side-effects. However, existing approaches have focused on a few well-validated pairs, and there is a clear need to expand the toolbox of potential SL targets. We have performed deep mining of cancer dependency data using a suite of machine learning tools and AI algorithms underpinned by robust statistical analysis to identify the next generation of SL gene pairs.To ensure robust, high confidence validation of multiple SL pairs simultaneously, we have used three cell lines and orthogonal assay formats. This has led to the confirmation of new druggable SL genes suitable for clinical development. We have identified the cancer subtypes with the strongest evidence of SL vulnerabilities and quantified patient populations who would most benefit from novel therapeutic agents. For one gene pair, we have generated novel best-in-class inhibitors of the target of interest, with exceptional selectivity over the SL partner, and used these chemical tools to validate the SL relationship in 2D and 3D models.

We are looking forward to meeting fellow researchers and anybody interested in our platform and pipeline. If we don't get the chance to meet at the conference, please feel free to reach out to CSO Alfie Brennan and Principal Biologist Danny Miller with any questions!

DDR Conference Poster