Abstract
Background: The Computer-Aided Drug Discovery (CADD) approach was used to develop a few Epidermal Growth Factor Receptor (EGFR) kinase inhibitors. EGFR kinase expression is highly associated with genomic instability, higher proliferation, lower hormone receptor levels, and HER2 over-expression. It is more common in breast cancer. Thus, EGFR Kinase is one of the main targets in discovering new cancer medicine.
Objective: To computationally validate some amides substituted β-amino enones as EGFR inhibitors and to carry out associated in vitro anticancer agents.
Methods: We used tools such as molecular docking, MD simulations, DFT calculations, and ADMET predictions in silico to establish a preliminary SAR. In vitro, we used BT474 (ER+HER2+) and MCF-7 (ER-HER2) cell lines along with normal breast cell epithelial cells (MFC-10a) for anticancer studies and EGFR kinase inhibition assay studies. As the Reactive Oxygen Species (ROS) plays the main role in cancer development, we also analyzed the antioxidant potentials of these compounds.
Results: Among the family of eleven amides substituted (Z)-β-amino enones (5a-k), compounds 5b, 5c, 5g, and 5h showed valuable in silico and in vitro bio-activity. Remarkably, the in-silico results almost coincided with in vitro study results.
Conclusion: We recommend compounds 5b, 5c, 5g, and 5h for pre-clinical and clinical evaluation to establish them as future cancer therapeutics.
Keywords: ADMET, Anticancer, Breast cancer, CADD, EGFR Kinase, Molecular Docking
Call for Papers in Thematic Issues
Artificial Intelligence in Biomedical Research: Enhancing Data Analysis for Drug Discovery and Development
This thematic issue highlights the transformative impact of Artificial Intelligence (AI), with a particular focus on Machine Learning (ML) and Deep Learning (DL) techniques, in advancing biomedical research. As a result of these cutting-edge AI methodologies, drug discovery and development are revolutionizing data analysis. Rapid advances in artificial intelligence technologies ...read more
Computer-Aided Drug Discoveries for Emerging Diseases
Computer-aided drug design is a rapidly growing research field that continues to gain momentum, attracting increasing interest from the scientific community. This trend is largely driven by the growing utilization of machine learning and artificial intelligence in drug design and discovery. Artificial Intelligence has proven efficacy across various applications, including ...read more
Deep Learning Approaches in Bioinformatics for Computer-Aided Drug Development Targeting Brain Tumors
The integration of deep learning and bioinformatics is revolutionizing the field of computer-aided drug development, particularly in the fight against brain tumors one of the most aggressive and lethal types of cancer. Brain tumors present significant challenges due to their heterogeneity and complexity, which require novel approaches for early diagnosis ...read more
Related Journals
Anti-Cancer Agents in Medicinal Chemistry
Current Analytical Chemistry
Current Bioactive Compounds
Combinatorial Chemistry & High Throughput Screening
Current Medicinal Chemistry
Central Nervous System Agents in Medicinal Chemistry
Letters in Drug Design & Discovery
Current Drug Discovery Technologies
The Natural Products Journal
Current Catalysis
Related Books
Therapeutic Insights into Herbal Medicine through the Use of Phytomolecules
Biochar - Solid Carbon for Sustainable Agriculture
DFT-Based Studies On Atomic Clusters
Basics of Organic Chemistry: A Textbook for Undergraduate Students
Science of Spices and Culinary Herbs - Latest Laboratory, Pre-clinical, and Clinical Studies
Hydrotalcite-based Materials: Synthesis, Characterization and Application
The 2-Dimensional World of Graphene
Advances in Dye Degradation
The Chemistry inside Spices & Herbs: Research and Development
The Chemistry inside Spices & Herbs: Research and Development
7
