Date of Award
1-1-2024
Document Type
Dissertation
Degree Name
Ph.D. in Pharmaceutical Sciences
First Advisor
Ikhlas A. Khan
Second Advisor
Cole Stevens
Third Advisor
Samir Ross
Relational Format
dissertation/thesis
Abstract
Since the identification of useful bioactive natural products can often involve tedious dereplication measures to decipher complex mixtures, much emphasis is placed on methods that can effectively characterize natural products. Several in silico methods, such as quantitative structure-activity relationships (QSARs), molecular docking, and quantum calculations, provide researchers with valuable tools to overcome the challenge of complex natural product mixtures. To exemplify the abilities of computational methodologies to further the field of pharmacognosy, this dissertation elaborates on several research projects involving computational approaches in pharmacognosy, including: (1) global assessment of pregnane X receptor (PXR) agonism via multiple QSAR models, (2) differentiation between the interactions of parent and metabolite compounds of lavender essential oil (EO) with selected neurological protein targets through molecular docking protocols, and (3) establishment of the absolute configuration (AC) of the retrochalcone, licochalcone L.
Chapter 1 provides a concise introduction to confounding problems in pharmacognosy and culminates in evidence for computational methods that promise to solve the aforementioned difficulties in the natural products field.
Chapter 2 discusses an exhaustive approach in QSAR modeling to evaluate the potentials of more than 500 structurally diverse compounds to agonize PXR. Multiple QSAR models of PXR agonism were constructed to assess the abilities of either traditional or more computationally intensive methods to correlate the published activities of the PXR agonists to more than 100 clusters of chemical scaffolds. Intensive data curation and results analysis revealed that the machine-learning-based 3D-QSAR model generated the model with the best fit and with satisfactory predictivity. 3D-QSAR modeling also provided useful data to create a visual representation of the space within PXR occupied by the binding of all 500 agonists. A summary of the QSAR results indicated not only that the prediction of new agonists for PXR was improved over previous QSAR studies but also that specific functional groups within the agonists could be identified that might confer increased or decreased binding affinity.
Chapter 3 deals with the elucidation of the roles of parent compounds and metabolites thereof found in lavender EO in the modulation of neurological receptors involved in mood and mental health. Computational techniques involved in this work included predictive modeling of phase I human metabolism and molecular docking protocols of parent and metabolite lavender EO compounds to selected GPCRs. Validation of the computational results was warranted; therefore, synthetic routes to yield selected metabolites of lavender EO parent compounds were carried out. Complete functional and binding assay results for GPCR activity screening are forthcoming, but preliminary analysis indicates some promise of lavender EO compounds to modulate protein targets that participate in anxiolysis, sleep prolongation, or sedation.
Chapter 4 provides evidence in support of a combined experimental and in silico approach for the assignment of the AC of complex natural products. By relating the experimental optical rotation and ECD data of an under-described retrochalcone to the calculated optical rotation and ECD data, the AC of licochalcone L, a minor constituent of Glycyrrhiza inflata, could be appropriately assigned as the (E, 2″S)-isomer. This also provided an opportunity to explore the plausible biosynthesis of such a molecule by G. inflata by reviewing the known biosynthetic pathways of chalcones.
Chapter 5 concludes the dissertation by presenting a summary of the contents. Following the summary, the conclusions of each research project are succinctly stated.
Recommended Citation
Neal, William Michael, "Combined in Silico and Experimental Approaches of Pharmacognosy to Resolve Complex Natural Product Issues" (2024). Electronic Theses and Dissertations. 2851.
https://egrove.olemiss.edu/etd/2851