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Computational QSAR & Molecular Docking of Anti- inflammatory Asthma Drugs Against Human Serum Albumin: A Computational Approach Towards Drug Design & Drug Discovery
There is a growing need for medications to fight deep-rooted and new diseases because of their socioeconomic consequences, as evidenced by the COVID-19 epidemic which slowed down the economy and affected the social life of mankind globally. This work is a step among many other steps undertaken by re...
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2023
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| _version_ | 1867613422859845632 |
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| access_status_str | Open Access |
| author | AlKarimy, Sara |
| author_browse | AlKarimy, Sara |
| author_facet | AlKarimy, Sara |
| author_sort | AlKarimy, Sara |
| collection | Thesis |
| description | There is a growing need for medications to fight deep-rooted and new diseases because of their socioeconomic consequences, as evidenced by the COVID-19 epidemic which slowed down the economy and affected the social life of mankind globally. This work is a step among many other steps undertaken by research around the world in order to achieve this objective. Asthma is one of the diseases that causes the death of thousands of lives every year. This work aims at the development of novel anti-inflammatory asthma drugs where the quantum chemical descriptors of eleven anti-inflammatory asthma drugs including Ambroxol, Amoxicillin, Budesonide, Fluticasone, Formoterol, Glycopyrrolate, Guaifenesin, Montelukast, Salbutamol, Silymarin, and Theophylline, were computed using B3LYP/6-311G++(d,p) level of theory implementing the geometry optimization. The physicochemical descriptors of these drugs were computed for evaluation against ADME and were found to satisfy bioavailability rules. The anti-inflammatory biological activity of the drugs was measured on RAW246.7 cell lines which was used with both descriptors for the establishment of the QSAR regression model. Molecular docking simulation of the binding between the eleven drugs and human serum albumin (1AO6). The drug with the highest biding energy (-6.05 kcal/mol) was Fluticasone, the drug with the highest inhibition constant was Montelukast (1.1μM) and Theophylline had the highest ligand efficiency of 29%. The model predicted a correlation between the anti-inflammatory biological activity with dipole moment, chemical hardness, hydrogen bond acceptors, number of rotatable bonds, hydration energy, electrophilicity, polar surface area, LogD7.4, and polarizability. Based on the model, substitutions (change in structure) would lead to a change in the predicted biological activity and molecular docking results. 9-substitutions were proposed on Theophylline’s position number 8 where nitroso and bromo substituted Theophylline had promising results based on the proposed model. |
| format | Thesis |
| id | oai:fount.aucegypt.edu:etds-3144 |
| institution | American University in Cairo (Egypt) |
| last_indexed | 2026-06-10T12:35:54.296Z |
| license_str | Not specified — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from AUC Knowledge Fountain — bepress |
| publishDate | 2023 |
| publishDateRange | 2023 |
| publishDateSort | 2023 |
| publisher | AUC Knowledge Fountain |
| publisherStr | AUC Knowledge Fountain |
| record_format | dspace |
| source_str | AUC Knowledge Fountain — bepress |
| spelling | oai:fount.aucegypt.edu:etds-3144 Computational QSAR & Molecular Docking of Anti- inflammatory Asthma Drugs Against Human Serum Albumin: A Computational Approach Towards Drug Design & Drug Discovery AlKarimy, Sara There is a growing need for medications to fight deep-rooted and new diseases because of their socioeconomic consequences, as evidenced by the COVID-19 epidemic which slowed down the economy and affected the social life of mankind globally. This work is a step among many other steps undertaken by research around the world in order to achieve this objective. Asthma is one of the diseases that causes the death of thousands of lives every year. This work aims at the development of novel anti-inflammatory asthma drugs where the quantum chemical descriptors of eleven anti-inflammatory asthma drugs including Ambroxol, Amoxicillin, Budesonide, Fluticasone, Formoterol, Glycopyrrolate, Guaifenesin, Montelukast, Salbutamol, Silymarin, and Theophylline, were computed using B3LYP/6-311G++(d,p) level of theory implementing the geometry optimization. The physicochemical descriptors of these drugs were computed for evaluation against ADME and were found to satisfy bioavailability rules. The anti-inflammatory biological activity of the drugs was measured on RAW246.7 cell lines which was used with both descriptors for the establishment of the QSAR regression model. Molecular docking simulation of the binding between the eleven drugs and human serum albumin (1AO6). The drug with the highest biding energy (-6.05 kcal/mol) was Fluticasone, the drug with the highest inhibition constant was Montelukast (1.1μM) and Theophylline had the highest ligand efficiency of 29%. The model predicted a correlation between the anti-inflammatory biological activity with dipole moment, chemical hardness, hydrogen bond acceptors, number of rotatable bonds, hydration energy, electrophilicity, polar surface area, LogD7.4, and polarizability. Based on the model, substitutions (change in structure) would lead to a change in the predicted biological activity and molecular docking results. 9-substitutions were proposed on Theophylline’s position number 8 where nitroso and bromo substituted Theophylline had promising results based on the proposed model. 2023-06-21T07:00:00Z thesis application/pdf https://fount.aucegypt.edu/etds/2105 https://fount.aucegypt.edu/context/etds/article/3144/viewcontent/Sara_AlKarimy__Msc_Thesis.pdf Theses and Dissertations AUC Knowledge Fountain QSAR MLR Theoretical Computational drug design drug discovery Computational Chemistry |
| spellingShingle | QSAR MLR Theoretical Computational drug design drug discovery Computational Chemistry AlKarimy, Sara Computational QSAR & Molecular Docking of Anti- inflammatory Asthma Drugs Against Human Serum Albumin: A Computational Approach Towards Drug Design & Drug Discovery |
| title | Computational QSAR & Molecular Docking of Anti- inflammatory Asthma Drugs Against Human Serum Albumin: A Computational Approach Towards Drug Design & Drug Discovery |
| title_full | Computational QSAR & Molecular Docking of Anti- inflammatory Asthma Drugs Against Human Serum Albumin: A Computational Approach Towards Drug Design & Drug Discovery |
| title_fullStr | Computational QSAR & Molecular Docking of Anti- inflammatory Asthma Drugs Against Human Serum Albumin: A Computational Approach Towards Drug Design & Drug Discovery |
| title_full_unstemmed | Computational QSAR & Molecular Docking of Anti- inflammatory Asthma Drugs Against Human Serum Albumin: A Computational Approach Towards Drug Design & Drug Discovery |
| title_short | Computational QSAR & Molecular Docking of Anti- inflammatory Asthma Drugs Against Human Serum Albumin: A Computational Approach Towards Drug Design & Drug Discovery |
| title_sort | computational qsar molecular docking of anti inflammatory asthma drugs against human serum albumin a computational approach towards drug design drug discovery |
| topic | QSAR MLR Theoretical Computational drug design drug discovery Computational Chemistry |
| url | https://fount.aucegypt.edu/etds/2105 https://fount.aucegypt.edu/context/etds/article/3144/viewcontent/Sara_AlKarimy__Msc_Thesis.pdf |
| work_keys_str_mv | AT alkarimysara computationalqsarmoleculardockingofantiinflammatoryasthmadrugsagainsthumanserumalbuminacomputationalapproachtowardsdrugdesigndrugdiscovery |