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Deciphering quinazoline derivatives' interactions with EGFR: a computational quest for advanced cancer therapy through 3D-QSAR, virtual screening, and MD simulations
The epidermal growth factor receptor (EGFR) presents a crucial target for combatting cancer mortality. This study employs a suite of computational techniques, including 3D-QSAR, ligand-based virtual screening, molecular docking, fingerprinting analysis, ADME, and DFT-based analyses (MESP, HOMO, LUMO...
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Published in: | Frontiers in pharmacology 2024-10, Vol.15, p.1399372 |
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creator | Anwar, Sirajudheen Alanazi, Jowaher Ahemad, Nafees Raza, Shafaq Chohan, Tahir Ali Saleem, Hammad |
description | The epidermal growth factor receptor (EGFR) presents a crucial target for combatting cancer mortality.
This study employs a suite of computational techniques, including 3D-QSAR, ligand-based virtual screening, molecular docking, fingerprinting analysis, ADME, and DFT-based analyses (MESP, HOMO, LUMO), supplemented by molecular dynamics simulations and MMGB/PBSA free energy calculations, to explore the binding dynamics of quinazoline derivatives with EGFR. With strong q2 and r2 values from CoMFA and CoMSIA models, our 3D- QSAR models reliably predict EGFR inhibitors' efficacy.
Utilizing a potent model compound as a reference, an E-pharmacophore model was developed to sift through the eMolecules database, identifying 19 virtual screening hits based on ShapeTanimoto, ColourTanimoto, and TanimotoCombo scores. These hits, assessed via 3D- QSAR, showed pIC
predictions consistent with experimental data. Our analyses elucidate key features essential for EGFR inhibition, reinforced by ADME studies that reveal favorable pharmacokinetic profiles for most compounds. Among the primary phytochemicals examined, potential EGFR inhibitors were identified. Detailed MD simulation analyses on three select ligands-1Q1, 2Q17, and VS1-demonstrated their stability and consistent interaction over 200 ns, with MM/GBSA values corroborating their docking scores and highlighting 1Q1 and VS1's superior EGFR1 affinity. These results position VS1 as an especially promising lead in EGFR1 inhibitor development, contributing valuable insights towards crafting novel, effective EGFR1 inhibitors. |
doi_str_mv | 10.3389/fphar.2024.1399372 |
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This study employs a suite of computational techniques, including 3D-QSAR, ligand-based virtual screening, molecular docking, fingerprinting analysis, ADME, and DFT-based analyses (MESP, HOMO, LUMO), supplemented by molecular dynamics simulations and MMGB/PBSA free energy calculations, to explore the binding dynamics of quinazoline derivatives with EGFR. With strong q2 and r2 values from CoMFA and CoMSIA models, our 3D- QSAR models reliably predict EGFR inhibitors' efficacy.
Utilizing a potent model compound as a reference, an E-pharmacophore model was developed to sift through the eMolecules database, identifying 19 virtual screening hits based on ShapeTanimoto, ColourTanimoto, and TanimotoCombo scores. These hits, assessed via 3D- QSAR, showed pIC
predictions consistent with experimental data. Our analyses elucidate key features essential for EGFR inhibition, reinforced by ADME studies that reveal favorable pharmacokinetic profiles for most compounds. Among the primary phytochemicals examined, potential EGFR inhibitors were identified. Detailed MD simulation analyses on three select ligands-1Q1, 2Q17, and VS1-demonstrated their stability and consistent interaction over 200 ns, with MM/GBSA values corroborating their docking scores and highlighting 1Q1 and VS1's superior EGFR1 affinity. These results position VS1 as an especially promising lead in EGFR1 inhibitor development, contributing valuable insights towards crafting novel, effective EGFR1 inhibitors.</description><identifier>ISSN: 1663-9812</identifier><identifier>EISSN: 1663-9812</identifier><identifier>DOI: 10.3389/fphar.2024.1399372</identifier><identifier>PMID: 39512829</identifier><language>eng</language><publisher>Switzerland: Frontiers Media S.A</publisher><subject>3D-QSAR ; anti-cancer ; EGFR ; in-silico ; Pharmacology ; simulations ; virtual screening</subject><ispartof>Frontiers in pharmacology, 2024-10, Vol.15, p.1399372</ispartof><rights>Copyright © 2024 Anwar, Alanazi, Ahemad, Raza, Chohan and Saleem.</rights><rights>Copyright © 2024 Anwar, Alanazi, Ahemad, Raza, Chohan and Saleem. 2024 Anwar, Alanazi, Ahemad, Raza, Chohan and Saleem</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c306t-e9b844e91d80724016420758cd16c5fe1f8b034f194984e370424445553fd6063</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC11540632/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC11540632/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,727,780,784,885,27924,27925,53791,53793</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/39512829$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Anwar, Sirajudheen</creatorcontrib><creatorcontrib>Alanazi, Jowaher</creatorcontrib><creatorcontrib>Ahemad, Nafees</creatorcontrib><creatorcontrib>Raza, Shafaq</creatorcontrib><creatorcontrib>Chohan, Tahir Ali</creatorcontrib><creatorcontrib>Saleem, Hammad</creatorcontrib><title>Deciphering quinazoline derivatives' interactions with EGFR: a computational quest for advanced cancer therapy through 3D-QSAR, virtual screening, and MD simulations</title><title>Frontiers in pharmacology</title><addtitle>Front Pharmacol</addtitle><description>The epidermal growth factor receptor (EGFR) presents a crucial target for combatting cancer mortality.
This study employs a suite of computational techniques, including 3D-QSAR, ligand-based virtual screening, molecular docking, fingerprinting analysis, ADME, and DFT-based analyses (MESP, HOMO, LUMO), supplemented by molecular dynamics simulations and MMGB/PBSA free energy calculations, to explore the binding dynamics of quinazoline derivatives with EGFR. With strong q2 and r2 values from CoMFA and CoMSIA models, our 3D- QSAR models reliably predict EGFR inhibitors' efficacy.
Utilizing a potent model compound as a reference, an E-pharmacophore model was developed to sift through the eMolecules database, identifying 19 virtual screening hits based on ShapeTanimoto, ColourTanimoto, and TanimotoCombo scores. These hits, assessed via 3D- QSAR, showed pIC
predictions consistent with experimental data. Our analyses elucidate key features essential for EGFR inhibition, reinforced by ADME studies that reveal favorable pharmacokinetic profiles for most compounds. Among the primary phytochemicals examined, potential EGFR inhibitors were identified. Detailed MD simulation analyses on three select ligands-1Q1, 2Q17, and VS1-demonstrated their stability and consistent interaction over 200 ns, with MM/GBSA values corroborating their docking scores and highlighting 1Q1 and VS1's superior EGFR1 affinity. These results position VS1 as an especially promising lead in EGFR1 inhibitor development, contributing valuable insights towards crafting novel, effective EGFR1 inhibitors.</description><subject>3D-QSAR</subject><subject>anti-cancer</subject><subject>EGFR</subject><subject>in-silico</subject><subject>Pharmacology</subject><subject>simulations</subject><subject>virtual screening</subject><issn>1663-9812</issn><issn>1663-9812</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><sourceid>DOA</sourceid><recordid>eNpVkktvEzEQx1eIilZtvwAH5BscmuDn7poLqpq2VCpCFDhbE6-duNrYW9sbVL4P3xPnQdX6MtY8fjNj_6vqLcFTxlr50Q5LiFOKKZ8SJiVr6KvqiNQ1m8iW0NfP7ofVaUr3uJxNXs3fVIdMCkJbKo-qvzOj3bA00fkFehidhz-hd96grrjWkN3apPfI-Wwi6OyCT-i3y0t0eX119wkB0mE1jBk2EegLwKSMbIgIujV4bTqkNyaiXFrA8FhsDONiidhs8v3H-d0ZWruYx1KadDTGlynOEPgOfZ2h5FZjvyWnk-rAQp_M6d4eV7-uLn9efJncfru-uTi_nWiG6zwxct5ybiTpWtxQjknNKW5EqztSa2ENse0cM26J5LLlhjWYU865EILZrsY1O65udtwuwL0aoltBfFQBnNo6QlwoiNnp3iht57hriAAtWi4aIm0H2AJmoA1viC6szzvWMM5XptPG5wj9C-jLiHdLtQhrRYjgZRZaCB_2hBi2L6tWLmnT9-BNGJNi5Q8ZFWWXkkp3qTqGlKKxT30IVhu9qK1e1EYvaq-XUvTu-YRPJf_Vwf4Bnvy_QQ</recordid><startdate>20241024</startdate><enddate>20241024</enddate><creator>Anwar, Sirajudheen</creator><creator>Alanazi, Jowaher</creator><creator>Ahemad, Nafees</creator><creator>Raza, Shafaq</creator><creator>Chohan, Tahir Ali</creator><creator>Saleem, Hammad</creator><general>Frontiers Media S.A</general><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7X8</scope><scope>5PM</scope><scope>DOA</scope></search><sort><creationdate>20241024</creationdate><title>Deciphering quinazoline derivatives' interactions with EGFR: a computational quest for advanced cancer therapy through 3D-QSAR, virtual screening, and MD simulations</title><author>Anwar, Sirajudheen ; Alanazi, Jowaher ; Ahemad, Nafees ; Raza, Shafaq ; Chohan, Tahir Ali ; Saleem, Hammad</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c306t-e9b844e91d80724016420758cd16c5fe1f8b034f194984e370424445553fd6063</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>3D-QSAR</topic><topic>anti-cancer</topic><topic>EGFR</topic><topic>in-silico</topic><topic>Pharmacology</topic><topic>simulations</topic><topic>virtual screening</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Anwar, Sirajudheen</creatorcontrib><creatorcontrib>Alanazi, Jowaher</creatorcontrib><creatorcontrib>Ahemad, Nafees</creatorcontrib><creatorcontrib>Raza, Shafaq</creatorcontrib><creatorcontrib>Chohan, Tahir Ali</creatorcontrib><creatorcontrib>Saleem, Hammad</creatorcontrib><collection>PubMed</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><collection>Directory of Open Access Journals</collection><jtitle>Frontiers in pharmacology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Anwar, Sirajudheen</au><au>Alanazi, Jowaher</au><au>Ahemad, Nafees</au><au>Raza, Shafaq</au><au>Chohan, Tahir Ali</au><au>Saleem, Hammad</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Deciphering quinazoline derivatives' interactions with EGFR: a computational quest for advanced cancer therapy through 3D-QSAR, virtual screening, and MD simulations</atitle><jtitle>Frontiers in pharmacology</jtitle><addtitle>Front Pharmacol</addtitle><date>2024-10-24</date><risdate>2024</risdate><volume>15</volume><spage>1399372</spage><pages>1399372-</pages><issn>1663-9812</issn><eissn>1663-9812</eissn><abstract>The epidermal growth factor receptor (EGFR) presents a crucial target for combatting cancer mortality.
This study employs a suite of computational techniques, including 3D-QSAR, ligand-based virtual screening, molecular docking, fingerprinting analysis, ADME, and DFT-based analyses (MESP, HOMO, LUMO), supplemented by molecular dynamics simulations and MMGB/PBSA free energy calculations, to explore the binding dynamics of quinazoline derivatives with EGFR. With strong q2 and r2 values from CoMFA and CoMSIA models, our 3D- QSAR models reliably predict EGFR inhibitors' efficacy.
Utilizing a potent model compound as a reference, an E-pharmacophore model was developed to sift through the eMolecules database, identifying 19 virtual screening hits based on ShapeTanimoto, ColourTanimoto, and TanimotoCombo scores. These hits, assessed via 3D- QSAR, showed pIC
predictions consistent with experimental data. Our analyses elucidate key features essential for EGFR inhibition, reinforced by ADME studies that reveal favorable pharmacokinetic profiles for most compounds. Among the primary phytochemicals examined, potential EGFR inhibitors were identified. Detailed MD simulation analyses on three select ligands-1Q1, 2Q17, and VS1-demonstrated their stability and consistent interaction over 200 ns, with MM/GBSA values corroborating their docking scores and highlighting 1Q1 and VS1's superior EGFR1 affinity. These results position VS1 as an especially promising lead in EGFR1 inhibitor development, contributing valuable insights towards crafting novel, effective EGFR1 inhibitors.</abstract><cop>Switzerland</cop><pub>Frontiers Media S.A</pub><pmid>39512829</pmid><doi>10.3389/fphar.2024.1399372</doi><oa>free_for_read</oa></addata></record> |
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title | Deciphering quinazoline derivatives' interactions with EGFR: a computational quest for advanced cancer therapy through 3D-QSAR, virtual screening, and MD simulations |
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