Date of Award

2023

Document Type

Thesis

Degree Name

Master of Science in Biology

Department

Life Sciences

First Advisor

Jeanmaire Molin

Committee Chair and Members

Jeanmaire Molina, Chair

Fatma Abo

Joseph W Morin

Keywords

Bioprospecting, Natural products, Naturally-derived drugs, Pharmaceutical drugs, Pharmacology, Phylogenetics

Abstract

About 60% of approved drugs during the last 30 years are either directly or indirectly from natural products. Previous studies have shown that the phylogeny has predictive utility in drug discovery. In this study the pharmacological applications of various naturally-derived drugs were mapped on the phylogeny reconstructed from DNA sequences of the source organisms to decipher phylogenetic patterns that may facilitate drug discovery. A multi-step approach was employed starting with a literature search to compile a comprehensive list of pharmaceutical drugs derived from natural sources. A phylogeny was reconstructed from the source organisms and their pharmacological uses mapped on the phylogeny as “traits” based on the organ-system targeted, with the goal of finding clades with a predominant pharmacological application, such that a member within that clade missing such application may be hypothesized to also possess this use/“trait” due to common ancestry. Unexplored taxa belonging to six clades may be good sources of novel pharmaceuticals based on phylogenetic patterns: Kingdoms Bacteria (100%) and Fungi (67%) with many of its investigated taxa with antibiotic effects and therapeutic against infectious diseases; K. Animalia: Subphylum Vertebrata (67%)—for endocrine applications, K. Animalia: Class Ascidiacea (100%)—for oncological applications. K. Plantae: Fabids subgroup (62.5%)—nervous/ psychopharmacological uses, and K. Plantae: Solanaceae family—gastrointestinal uses (50%) as well as nervous applications (67%). These findings underscore the pharmacological utility of the phylogeny shedding light on new avenues for pharmaceutical research and innovation.

Download

Share

COinS