Computational Methods for the Analysis and Prediction of EGFR-mutated Lung Cancer Drug Resistance : Recent Advances in Drug Design, Challenges and Future Prospects

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

14 Scopus Citations
View graph of relations

Author(s)

  • Tanvir Alam
  • Jia Wu
  • Victor H. F. Lee

Related Research Unit(s)

Detail(s)

Original languageEnglish
Pages (from-to)238-255
Journal / PublicationIEEE/ACM Transactions on Computational Biology and Bioinformatics
Volume20
Issue number1
Online published10 Jan 2022
Publication statusPublished - Jan 2023

Abstract

Lung cancer is a major cause of cancer deaths worldwide, and has a very low survival rate. Non-small cell lung cancer (NSCLC) is the largest subset of lung cancers, which accounts for about 85% of all cases. It has been well established that mutation in epidermal growth factor receptor (EGFR) can lead to lung cancer. EGFR Tyrosine Kinase Inhibitors are developed to target the kinase domain of EGFR. These TKIs produce promising results at initial stage of therapy, but the efficacy becomes limited due to the development of drug resistance. In this paper, we provide a comprehensive overview of computational methods, for understanding drug resistance mechanisms. Next, we evaluate the role of important EGFR parameters in drug resistance mechanism, including structural dynamics, stability, dimerization, binding free energies, and signaling pathways. Personalized drug resistance prediction models, drug response curves, drug synergy, and other data-driven methods are also discussed. We explore limitations in the current methodologies and discuss strategies to overcome them. We believe this review will serve as a reference for researchers; to apply computational techniques for precision medicine, analyzing structures of protein-drug complexes, drug discovery, and understanding the drug response and resistance mechanisms in lung cancer patients. © 2022 IEEE.

Research Area(s)

  • AlphaFold2, Biological system modeling, Computational methods, Computational modeling, Deep Learning, Drugs, Epidermal growth factor receptor (EGFR), Immune system, Inhibitors, Lung cancer, Molecular dynamics (MD) simulation, Molecular modeling, Non-small cell lung cancer (NSCLC), Proteins

Citation Format(s)

Computational Methods for the Analysis and Prediction of EGFR-mutated Lung Cancer Drug Resistance: Recent Advances in Drug Design, Challenges and Future Prospects. / Qureshi, Rizwan; Zou, Bin; Alam, Tanvir et al.
In: IEEE/ACM Transactions on Computational Biology and Bioinformatics, Vol. 20, No. 1, 01.2023, p. 238-255.

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review