Design, synthesis and evaluation of the Brigatinib analogues as potent inhibitors against tertiary EGFR mutants (EGFRdel19/T790M/C797S and EGFRL858R/T790M/C797S)

Design, preparation and biological evaluation of new Rociletinib-inspired analogs as irreversible EGFR inhibitors to treat non-small-cell-lung cancer

Epidermal growth factor receptor (EGFR) kinase has been implicated in the uncontrolled cell growth associated with non-small cell lung cancer (NSCLC). This has prompted the development of 3 generations of EGFR inhibitors over the last 2 decades due to the rapid development of drug resistance issues caused by clinical mutations, including T790M, L858R and the double mutant T790M & L858R. In this work we report the design, preparation and biological assessment of new irreversible 2,4-diaminopyrimidine-based inhibitors of EGFR kinase. Twenty new compounds have been prepared and evaluated which incorporate a range of electrophilic moieties. These include acrylamide, 2-chloroacetamide and (2E)-3-phenylprop-2-enamide, to allow reaction with residue Cys797. In addition, more polar groups have been incorporated to provide a better balance of physical properties than clinical candidate Rociletinib. Inhibitory activities against EGFR wildtype (WT) and EGFR T790M & L858R have been evaluated along with cytotoxicity against EGFR-overexpressing (A549, A431) and normal cell lines (HepG2). Selectivity against JAK3 kinase as well as physicochemical properties determination (logD7.4 and phosphate buffer solubility) have been used to profile the compounds. We have identified 20, 21 and 23 as potent mutant EGFR inhibitors (≤20 nM), with comparable or better selectivity over WT EGFR, and lower activity at JAK3, than Osimertinib or Rociletinib. Compounds 21 displayed the best combination of EGFR mutant activity, JAK3 selectivity, cellular activity and physicochemical properties. Finally, kinetic studies on 21 were performed, confirming a covalent mechanism of action at EGFR.

Chemical Scaffolds for the Clinical Development of Mutant-Selective and Reversible Fourth-Generation EGFR-TKIs in NSCLC

In nonsmall cell lung cancer (NSCLC), as well as in other tumors, the targeted therapy is mainly represented by tyrosine kinase inhibitors (TKIs), small molecules able to target oncogenic driver alterations affecting the gene encoding the epidermal growth factor receptor (EGFR). Up to now, several different TKIs have been developed. However, cancer cells showed an incredible adaptive tumor response to the inhibition of the sequentially mutated EGFR (EGFRM+), triggering the need to explore novel pharmacochemical strategies. This Review summarizes the recent efforts in the development of new reversible next-generation EGFR TKIs to fight the resistance against T790M and C797S mutations. Specifically, after giving an overview of the role of the EGFR's signaling pathways in cancer progression, we are going to discuss the most relevant approved drugs and drug candidates in terms of chemical structure, binding modalities, and their potency and selectivity against the mutated EGFR over the wild-type form. This could provide important guidelines and rationale for the discovery and iterative development of new drugs.

Machine Learning-Based Virtual Screening and Identification of the Fourth-Generation EGFR Inhibitors

Epidermal growth factor receptor (EGFR) plays a pivotal regulatory role in treating patients with advanced nonsmall cell lung cancer (NSCLC). Following the emergence of the EGFR tertiary CIS C797S mutation, all types of inhibitors lose their inhibitory activity, necessitating the urgent development of new inhibitors. Computer systems employ machine learning methods to process substantial volumes of data and construct models that enable more accurate predictions of the outcomes of new inputs. The purpose of this article is to uncover innovative fourth-generation epidermal growth factor receptor tyrosine kinase inhibitors (EGFR-TKIs) with the aid of machine learning techniques. The paper's data set was high-dimensional and sparse, encompassing both structured and unstructured descriptors. To address this considerable challenge, we introduced a fusion framework to select critical molecule descriptors by integrating the full quadratic effect model and the Lasso model. Based on structural descriptors obtained from the full quadratic effect model, we conceived and synthesized a variety of small-molecule inhibitors. These inhibitors demonstrated potent inhibitory effects on the two mutated kinases L858R/T790M/C797S and Del19/T790M/C797S. Moreover, we applied our model to virtual screening, successfully identifying four hit compounds. We have evaluated these hit ADME characteristics and look forward to conducting activity evaluations on them in the future to discover a new generation of EGFR-TKI.