Although three generations of Epidermal growth factor receptor (EGFR) - TK inhibitors have been approved for the treatment of Non-small-cell lung cancers (NSCLC), their clinical application is still largely hindered by acquired drug resistance mediated new EGFR mutations and side effects. The Proteolysis targeting chimera (PROTAC) technology has the potential to overcome acquired resistance from mutant EGFR through a novel mechanism of action. In this study, we developed the candidate degrader IV-3 by structural modifications of the lead compound 13, which exhibited limited antiproliferative activity against HCC-827 cells. Compared to compound 13, IV-3 exhibited remarkable anti-proliferative activity against HCC-827 cells, NCI-H1975 cells, and NCI-H1975-TM cells (IC50 = 0.009 µM, 0.49 µM and 3.24 µM, respectively), as well as significantly inducing degradation of EGFR protein in these cell lines (DC50 = 17.93 nM, 0.25 µM and 0.63 µM, respectively). Further investigations confirmed that IV-3 exhibited superior anti-tumor activity in all xenograft tumor models through the degradation of mutant EGFR protein. Moreover, IV-3 showed no inhibitory activity against A431 and A549 cells expressing wild-type EGFR, thereby eliminating potential toxic side effects emerging from wild-type EGFR inhibition. Overall, our study provides promising insights into EGFR-PROTACs as a potential therapeutic strategy against EGFR-acquired mutation.
Antineoplastic Agents , Cell Proliferation , ErbB Receptors , Mutation , Proteolysis , ErbB Receptors/metabolism , ErbB Receptors/antagonists & inhibitors , ErbB Receptors/genetics , Humans , Antineoplastic Agents/pharmacology , Antineoplastic Agents/chemistry , Antineoplastic Agents/chemical synthesis , Cell Proliferation/drug effects , Proteolysis/drug effects , Animals , Structure-Activity Relationship , Drug Discovery , Mice , Protein Kinase Inhibitors/pharmacology , Protein Kinase Inhibitors/chemistry , Protein Kinase Inhibitors/chemical synthesis , Drug Screening Assays, Antitumor , Molecular Structure , Cell Line, Tumor , Dose-Response Relationship, Drug , Lung Neoplasms/drug therapy , Lung Neoplasms/pathology , Lung Neoplasms/metabolism , Mice, Nude , Proteolysis Targeting Chimera
This paper proposes an optimal structured deep convolutional neural network (DCNN) based on the marine predator algorithm (MPA) to construct a novel automatic diagnosis platform that may help radiologists identify COVID-19 and non-COVID-19 patients based on CT scan categorization and analysis. The goal is met with the help of three modifications based on the regular MPA. First, a novel encoding scheme based on Internet Protocol (IP) addresses is proposed, followed by introducing an Enfeebled layer to build a variable-length DCNN. Finally, the learning process divides big datasets into smaller chunks that are randomly evaluated. The proposed model is compared to the COVID-CT and SARS-CoV-2 datasets to undertake a complete evaluation. Following that, the performance of the developed model (DCNN-IPMPA) is compared to that of a typical DCNN and seven variable-length models using five well-known comparison metrics, as well as the receiver operating characteristic and precision-recall curves. The results show that the DCNN-IPMPA outperforms other benchmarks, with a final accuracy of 97.21% on the SARS-CoV-2 dataset and 97.94% on the COVID-CT dataset. Also, timing analysis indicates that the DCNN processing time is the best among all benchmarks as expected; however, DCNN-IPMPA represents a competitive result compared to the standard DCNN.
