WeChat Mini Program
Old Version Features

Addressing the Osimertinib Resistance Mutation EGFR-L858R/C797S with Reversible Aminopyrimidines.

ACS medicinal chemistry letters(2023)

Department of Chemistry and Chemical Biology

Cited 0|Views12
Abstract
Drug resistance mutations emerging during the treatment of non-small cell lung cancer (NSCLC) with epidermal growth factor receptor (EGFR) inhibitors represent a major challenge in personalized cancer treatment and require constant development of new inhibitors. For the covalent irreversible EGFR inhibitor osimertinib, the predominant resistance mechanism is the acquired C797S mutation, which abolishes the covalent anchor point and thus results in a dramatic loss in potency. In this study, we present next-generation reversible EGFR inhibitors with the potential to overcome this EGFR-C797S resistance mutation. For this, we combined the reversible methylindole-aminopyrimidine scaffold known from osimertinib with the affinity driving isopropyl ester of mobocertinib. By occupying the hydrophobic back pocket, we were able to generate reversible inhibitors with subnanomolar activity against EGFR-L858R/C797S and EGFR-L858R/T790M/C797S with cellular activity on EGFR-L858R/C797S dependent Ba/F3 cells. Additionally, we were able to resolve cocrystal structures of these reversible aminopyrimidines, which will guide further inhibitor design toward C797S-mutated EGFR.
More
Translated text
Key words
Cancer,Non-small cell lung cancer,Kinase inhibitor,Structure-based drug design,Epidermal growth factor receptor
求助PDF
上传PDF
Bibtex
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
  • Pretraining has recently greatly promoted the development of natural language processing (NLP)
  • We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
  • We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
  • The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
  • Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example
Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
Summary is being generated by the instructions you defined