Computational modeling of novel quinazoline derivatives as potent epidermal growth factor receptor inhibitors
QSAR modelling on Thirty (34) novel quinazoline derivatives (EGFRWT inhibitors) as non-small cell lung cancer (NSCLC) agents was performed to develop a model with good predictive power that can predict the activities of newly designed compounds that have not been synthesised. The EGFRWT inhibitors w...
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Elsevier
2020-02-01
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| Series: | Heliyon |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2405844020301341 |
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| author | Muhammad Tukur Ibrahim Adamu Uzairu Sani Uba Gideon Adamu Shallangwa |
| author_facet | Muhammad Tukur Ibrahim Adamu Uzairu Sani Uba Gideon Adamu Shallangwa |
| author_sort | Muhammad Tukur Ibrahim |
| collection | DOAJ |
| description | QSAR modelling on Thirty (34) novel quinazoline derivatives (EGFRWT inhibitors) as non-small cell lung cancer (NSCLC) agents was performed to develop a model with good predictive power that can predict the activities of newly designed compounds that have not been synthesised. The EGFRWT inhibitors were optimized at B3LYP/6-31G* level of theory using Density Functional Theory (DFT) method. Multi-Linear Regression using Genetic Function Approximation (GFA) method was adopted in building the models. The best one among the models built was selected and reported because it was found to have passed the minimum requirement for the assessment of QSAR models with the following assessment parameters: R2 of 0.965901, R2adj of 0.893733, Qcv2 of 0.940744, R2test of 0.818991 and LOF of 0.076739. The high predicted power, reliability, robustness of the reported model was verified further by subjecting it to other assessments such VIF, Y-scrambling test and applicability domain. Molecular docking was also employed to elucidate the binding mode of some selected EGFRWT inhibitors against EGFR receptor (4ZAU) and found that molecule 17 have the highest binding affinity of -9.5 kcal/mol. It was observed that the ligand interacted with the receptor via hydrogen bond, hydrophobic bond, halogen bond, electrostatic bond and others which might me the reason why it has the highest binding affinity. Also, the ADME properties of these selected molecules were predicted and only one molecule (34) was found not orally bioavailable because it violated more than the permissible limit set by Lipinski's rule of five filters. This findings proposed a guidance for designing new potents EGFRWT inhibitors against their target enzyme. |
| first_indexed | 2024-12-20T12:30:25Z |
| format | Article |
| id | doaj.art-5fa932c3b4634698b146b12fbb8f7cf3 |
| institution | Directory Open Access Journal |
| issn | 2405-8440 |
| language | English |
| last_indexed | 2024-12-20T12:30:25Z |
| publishDate | 2020-02-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Heliyon |
| spelling | doaj.art-5fa932c3b4634698b146b12fbb8f7cf32022-12-21T19:40:45ZengElsevierHeliyon2405-84402020-02-0162e03289Computational modeling of novel quinazoline derivatives as potent epidermal growth factor receptor inhibitorsMuhammad Tukur Ibrahim0Adamu Uzairu1Sani Uba2Gideon Adamu Shallangwa3Corresponding author.; Department of Chemistry, Faculty of Physical Science, Ahmadu Bello University, P.M.B 1045, Zaria, Kaduna State, NigeriaDepartment of Chemistry, Faculty of Physical Science, Ahmadu Bello University, P.M.B 1045, Zaria, Kaduna State, NigeriaDepartment of Chemistry, Faculty of Physical Science, Ahmadu Bello University, P.M.B 1045, Zaria, Kaduna State, NigeriaDepartment of Chemistry, Faculty of Physical Science, Ahmadu Bello University, P.M.B 1045, Zaria, Kaduna State, NigeriaQSAR modelling on Thirty (34) novel quinazoline derivatives (EGFRWT inhibitors) as non-small cell lung cancer (NSCLC) agents was performed to develop a model with good predictive power that can predict the activities of newly designed compounds that have not been synthesised. The EGFRWT inhibitors were optimized at B3LYP/6-31G* level of theory using Density Functional Theory (DFT) method. Multi-Linear Regression using Genetic Function Approximation (GFA) method was adopted in building the models. The best one among the models built was selected and reported because it was found to have passed the minimum requirement for the assessment of QSAR models with the following assessment parameters: R2 of 0.965901, R2adj of 0.893733, Qcv2 of 0.940744, R2test of 0.818991 and LOF of 0.076739. The high predicted power, reliability, robustness of the reported model was verified further by subjecting it to other assessments such VIF, Y-scrambling test and applicability domain. Molecular docking was also employed to elucidate the binding mode of some selected EGFRWT inhibitors against EGFR receptor (4ZAU) and found that molecule 17 have the highest binding affinity of -9.5 kcal/mol. It was observed that the ligand interacted with the receptor via hydrogen bond, hydrophobic bond, halogen bond, electrostatic bond and others which might me the reason why it has the highest binding affinity. Also, the ADME properties of these selected molecules were predicted and only one molecule (34) was found not orally bioavailable because it violated more than the permissible limit set by Lipinski's rule of five filters. This findings proposed a guidance for designing new potents EGFRWT inhibitors against their target enzyme.http://www.sciencedirect.com/science/article/pii/S2405844020301341Physical chemistryTheoretical chemistryQSARModelingNSCLCEGFRWT |
| spellingShingle | Muhammad Tukur Ibrahim Adamu Uzairu Sani Uba Gideon Adamu Shallangwa Computational modeling of novel quinazoline derivatives as potent epidermal growth factor receptor inhibitors Heliyon Physical chemistry Theoretical chemistry QSAR Modeling NSCLC EGFRWT |
| title | Computational modeling of novel quinazoline derivatives as potent epidermal growth factor receptor inhibitors |
| title_full | Computational modeling of novel quinazoline derivatives as potent epidermal growth factor receptor inhibitors |
| title_fullStr | Computational modeling of novel quinazoline derivatives as potent epidermal growth factor receptor inhibitors |
| title_full_unstemmed | Computational modeling of novel quinazoline derivatives as potent epidermal growth factor receptor inhibitors |
| title_short | Computational modeling of novel quinazoline derivatives as potent epidermal growth factor receptor inhibitors |
| title_sort | computational modeling of novel quinazoline derivatives as potent epidermal growth factor receptor inhibitors |
| topic | Physical chemistry Theoretical chemistry QSAR Modeling NSCLC EGFRWT |
| url | http://www.sciencedirect.com/science/article/pii/S2405844020301341 |
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