Computational Methods for the Analysis and Prediction of EGFR-Mutated Lung Cancer Drug Resistance: Recent Advances in Drug Design, Challenges and Future Prospects
<p dir="ltr">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 a mutation in the epidermal g...
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2023
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| _version_ | 1864513507175694336 |
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| author | Rizwan Qureshi (15279193) |
| author2 | Bin Zou (539731) Tanvir Alam (638619) Jia Wu (169990) Victor H. F. Lee (10738845) Hong Yan (27984) |
| author2_role | author author author author author |
| author_facet | Rizwan Qureshi (15279193) Bin Zou (539731) Tanvir Alam (638619) Jia Wu (169990) Victor H. F. Lee (10738845) Hong Yan (27984) |
| author_role | author |
| dc.creator.none.fl_str_mv | Rizwan Qureshi (15279193) Bin Zou (539731) Tanvir Alam (638619) Jia Wu (169990) Victor H. F. Lee (10738845) Hong Yan (27984) |
| dc.date.none.fl_str_mv | 2023-01-10T09:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1109/tcbb.2022.3141697 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Computational_Methods_for_the_Analysis_and_Prediction_of_EGFR-Mutated_Lung_Cancer_Drug_Resistance_Recent_Advances_in_Drug_Design_Challenges_and_Future_Prospects/26827777 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Biomedical and clinical sciences Clinical sciences Oncology and carcinogenesis Pharmacology and pharmaceutical sciences Non-small cell lung cancer (NSCLC) epidermal growth factor receptor (EGFR) molecular modeling computational methods molecular dynamics (MD) simulation AlphaFold2 deep learning Drugs Immune system Proteins Lung cancer Computational modeling Inhibitors Biological system modeling |
| dc.title.none.fl_str_mv | Computational Methods for the Analysis and Prediction of EGFR-Mutated Lung Cancer Drug Resistance: Recent Advances in Drug Design, Challenges and Future Prospects |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">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 a mutation in the epidermal growth factor receptor (EGFR) can lead to lung cancer. EGFR Tyrosine Kinase Inhibitors (TKIs) are developed to target the kinase domain of EGFR. These TKIs produce promising results at the 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. The important EGFR mutants and the different generations of EGFR–TKIs, with the survival and response rates are discussed. Next, we evaluate the role of important EGFR parameters in drug resistance mechanism, including structural dynamics, hydrogen bonds, stability, dimerization, binding free energies, and signaling pathways. Personalized drug resistance prediction models, drug response curve, drug synergy, and other data-driven methods are also discussed. Recent advancements in deep learning; such as AlphaFold2, deep generative models, big data analytics, and the applications of statistics and permutation are also highlighted. 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.</p><h2>Other Information</h2><p dir="ltr">Published in: IEEE/ACM Transactions on Computational Biology and Bioinformatics<br>License: <a href="https://creativecommons.org/licenses/by/4.0" target="_blank">https://creativecommons.org/licenses/by/4.0</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1109/tcbb.2022.3141697" target="_blank">https://dx.doi.org/10.1109/tcbb.2022.3141697</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_7e09837a2ee36797f695f34d6fa8391c |
| identifier_str_mv | 10.1109/tcbb.2022.3141697 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/26827777 |
| publishDate | 2023 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Computational Methods for the Analysis and Prediction of EGFR-Mutated Lung Cancer Drug Resistance: Recent Advances in Drug Design, Challenges and Future ProspectsRizwan Qureshi (15279193)Bin Zou (539731)Tanvir Alam (638619)Jia Wu (169990)Victor H. F. Lee (10738845)Hong Yan (27984)Biomedical and clinical sciencesClinical sciencesOncology and carcinogenesisPharmacology and pharmaceutical sciencesNon-small cell lung cancer (NSCLC)epidermal growth factor receptor (EGFR)molecular modelingcomputational methodsmolecular dynamics (MD) simulationAlphaFold2deep learningDrugsImmune systemProteinsLung cancerComputational modelingInhibitorsBiological system modeling<p dir="ltr">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 a mutation in the epidermal growth factor receptor (EGFR) can lead to lung cancer. EGFR Tyrosine Kinase Inhibitors (TKIs) are developed to target the kinase domain of EGFR. These TKIs produce promising results at the 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. The important EGFR mutants and the different generations of EGFR–TKIs, with the survival and response rates are discussed. Next, we evaluate the role of important EGFR parameters in drug resistance mechanism, including structural dynamics, hydrogen bonds, stability, dimerization, binding free energies, and signaling pathways. Personalized drug resistance prediction models, drug response curve, drug synergy, and other data-driven methods are also discussed. Recent advancements in deep learning; such as AlphaFold2, deep generative models, big data analytics, and the applications of statistics and permutation are also highlighted. 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.</p><h2>Other Information</h2><p dir="ltr">Published in: IEEE/ACM Transactions on Computational Biology and Bioinformatics<br>License: <a href="https://creativecommons.org/licenses/by/4.0" target="_blank">https://creativecommons.org/licenses/by/4.0</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1109/tcbb.2022.3141697" target="_blank">https://dx.doi.org/10.1109/tcbb.2022.3141697</a></p>2023-01-10T09:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1109/tcbb.2022.3141697https://figshare.com/articles/journal_contribution/Computational_Methods_for_the_Analysis_and_Prediction_of_EGFR-Mutated_Lung_Cancer_Drug_Resistance_Recent_Advances_in_Drug_Design_Challenges_and_Future_Prospects/26827777CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/268277772023-01-10T09:00:00Z |
| spellingShingle | Computational Methods for the Analysis and Prediction of EGFR-Mutated Lung Cancer Drug Resistance: Recent Advances in Drug Design, Challenges and Future Prospects Rizwan Qureshi (15279193) Biomedical and clinical sciences Clinical sciences Oncology and carcinogenesis Pharmacology and pharmaceutical sciences Non-small cell lung cancer (NSCLC) epidermal growth factor receptor (EGFR) molecular modeling computational methods molecular dynamics (MD) simulation AlphaFold2 deep learning Drugs Immune system Proteins Lung cancer Computational modeling Inhibitors Biological system modeling |
| status_str | publishedVersion |
| title | Computational Methods for the Analysis and Prediction of EGFR-Mutated Lung Cancer Drug Resistance: Recent Advances in Drug Design, Challenges and Future Prospects |
| title_full | Computational Methods for the Analysis and Prediction of EGFR-Mutated Lung Cancer Drug Resistance: Recent Advances in Drug Design, Challenges and Future Prospects |
| title_fullStr | Computational Methods for the Analysis and Prediction of EGFR-Mutated Lung Cancer Drug Resistance: Recent Advances in Drug Design, Challenges and Future Prospects |
| title_full_unstemmed | Computational Methods for the Analysis and Prediction of EGFR-Mutated Lung Cancer Drug Resistance: Recent Advances in Drug Design, Challenges and Future Prospects |
| title_short | Computational Methods for the Analysis and Prediction of EGFR-Mutated Lung Cancer Drug Resistance: Recent Advances in Drug Design, Challenges and Future Prospects |
| title_sort | Computational Methods for the Analysis and Prediction of EGFR-Mutated Lung Cancer Drug Resistance: Recent Advances in Drug Design, Challenges and Future Prospects |
| topic | Biomedical and clinical sciences Clinical sciences Oncology and carcinogenesis Pharmacology and pharmaceutical sciences Non-small cell lung cancer (NSCLC) epidermal growth factor receptor (EGFR) molecular modeling computational methods molecular dynamics (MD) simulation AlphaFold2 deep learning Drugs Immune system Proteins Lung cancer Computational modeling Inhibitors Biological system modeling |