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Published on: April 6, 2016
Supervised Screening of EGFR Inhibitors Validated through Computational Structural Biology Approaches
Aamir Mehmood1, Daixi Li2, Jiayi Li1
1State Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic & Developmental Sciences and School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200030, P. R. China.
Abstract:
One of the prominent challenges in breast cancer (BC) treatment is human epidermal growth factor receptor (EGFR) overexpression, which facilitates tumor proliferation and presents a viable target for anticancer therapies. This study integrates multiomics data to pinpoint promising therapeutic compounds and employs a machine learning (ML)-based similarity search to identify effective alternatives. We used BC cell line data from the Cancer Cell Line Encyclopedia (CCLE) and Genomics of Drug Sensitivity in Cancer (GDSC) databases and single-cell RNA sequencing (scRNA-seq) information that established afatinib as an efficacious candidate demonstrating superior IC50 values. Next, ML models, including support vector machine (SVM), artificial neural networks (ANN), and random forest (RF), were trained on ChEMBL data to classify compounds with similar activity to the reference drug as active or inactive. The promising candidates underwent computational structural biology assessments for their molecular interactions and conformational dynamics. Our findings indicate that compounds ChEMBL233324, ChEMBL233325, ChEMBL234580, and ChEMBL372692 exhibit potent repressive action against EGFR, underscoring their potential as active antibreast cancer agents.
Insights
This study identifies novel compounds targeting EGFR overexpression in breast cancer (BC) using multiomics and machine learning. These potential new drugs show promise for more effective BC treatments.
Area of Science:
- Oncology
- Pharmacology
- Computational Biology
Background:
- Human epidermal growth factor receptor (EGFR) overexpression is a key driver of breast cancer (BC) proliferation.
- Targeting EGFR presents a significant therapeutic opportunity for breast cancer treatment.
Purpose of the Study:
- To identify novel therapeutic compounds for breast cancer by targeting EGFR.
- To leverage multiomics data and machine learning for drug discovery.
Main Methods:
- Integrated multiomics data from CCLE, GDSC, and scRNA-seq to identify EGFR as a target and afatinib as a reference drug.
- Employed machine learning models (SVM, ANN, RF) trained on ChEMBL data for drug-like compound screening.
- Conducted computational structural biology assessments to evaluate molecular interactions and dynamics of candidate compounds.
Main Results:
- Afatinib demonstrated superior IC50 values against BC cell lines.
- Machine learning models successfully classified compounds with similar activity to afatinib.
- Four compounds (ChEMBL233324, ChEMBL233325, ChEMBL234580, ChEMBL372692) showed potent EGFR repression.
Conclusions:
- Identified four novel compounds with significant potential as breast cancer therapeutics.
- These compounds exhibit potent repressive action against EGFR, offering new avenues for BC treatment.
- The study highlights the efficacy of integrating multiomics and machine learning in drug discovery for breast cancer.
