Computational platform for modelling, analysis,
and prediction of anti-EGFR drug resistance for lung cancer
H Yan1; GY Zhu2; M Wong3;
V Lee4
1 Department of Electrical Engineering,
City University of Hong Kong
2 Department of Chemistry, City
University of Hong Kong
3 Department of Pathology, Li Ka Shing
Faculty of Medicine, The University of Hong Kong
4 Department of Clinical Oncology, Li Ka
Shing Faculty of Medicine, The University of Hong Kong
1. Epidermal growth factor receptor (EGFR) mutation
is an important cause of drug resistance in non-small cell lung cancer
(NSCLC). We conducted computational modelling of EGFR mutants and analysis
of EGFR-drug interaction patterns.
2. Any observed EGFR mutation can be modelled mathematically, and its 3D structure can be predicted computationally. The fundamental cause of drug resistance can be found at the atomic level.
3. Different drugs can be analysed. Based on our computer model, the binding strength between an EGFR mutant and a drug can be calculated.
4. Drug resistance can be evaluated for each mutation and each drug. Thus, a comprehensive database of EGFR mutation and drug effectiveness is established and is available online. The database provides a useful reference to medical doctors.
5. Our computational framework is less expensive than wet-lab experiments. It can also be used to study drug resistance related to other diseases.
2. Any observed EGFR mutation can be modelled mathematically, and its 3D structure can be predicted computationally. The fundamental cause of drug resistance can be found at the atomic level.
3. Different drugs can be analysed. Based on our computer model, the binding strength between an EGFR mutant and a drug can be calculated.
4. Drug resistance can be evaluated for each mutation and each drug. Thus, a comprehensive database of EGFR mutation and drug effectiveness is established and is available online. The database provides a useful reference to medical doctors.
5. Our computational framework is less expensive than wet-lab experiments. It can also be used to study drug resistance related to other diseases.