Abstract
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.
School
School of Sciences and Engineering
Department
Chemistry Department
Degree Name
MS in Chemistry
Graduation Date
Spring 6-21-2023
Submission Date
5-21-2023
First Advisor
Dr. Hassan Mohamed El-Said Azzazy
Second Advisor
Dr. Mohamed Elsayed Elshakre
Committee Member 1
Dr. Ismail Awadallah Salama
Committee Member 2
Dr. Eslam Mohamed Dabbish
Extent
167 p.
Document Type
Master's Thesis
Institutional Review Board (IRB) Approval
Approval has been obtained for this item
Recommended Citation
APA Citation
AlKarimy, S.
(2023).Computational QSAR & Molecular Docking of Anti- inflammatory Asthma Drugs Against Human Serum Albumin: A Computational Approach Towards Drug Design & Drug Discovery [Master's Thesis, the American University in Cairo]. AUC Knowledge Fountain.
https://fount.aucegypt.edu/etds/2105
MLA Citation
AlKarimy, Sara. Computational QSAR & Molecular Docking of Anti- inflammatory Asthma Drugs Against Human Serum Albumin: A Computational Approach Towards Drug Design & Drug Discovery. 2023. American University in Cairo, Master's Thesis. AUC Knowledge Fountain.
https://fount.aucegypt.edu/etds/2105