Doctor of Philosophy, The Ohio State University, 2019, Pharmaceutical Sciences

The use of data-driven methods and machine learning has become increasingly pervasive in many industries, including drug discovery and design, as computing power and large amounts of data become increasingly available. In an effort to efficiently leverage this data, cheminformatics has emerged as a data-driven, interdisciplinary field that focuses on storing, accessing, and applying chemical information. Cheminformatics methods and tools facilitate the management and analysis of large annotated chemical datasets that would be difficult or impossible to do manually. A famous application of leveraging large amounts of chemical data was performed by Christopher A. Lipinski in 1997. Lipinski analyzed a large set of bioavailable synthetic drug molecules and identified trends in their molecular properties, which has since been referred to as the “Lipinski's Rule of 5”. While these rules are far from absolute, Lipinski's analysis demonstrates the utility of leveraging large amounts of chemical data to gain important insights. This thesis describes the application of cheminformatics methods to tackle two very different research problems: 1) the analysis and binding of a class of protein degraders called proteolysis targeting chimeras (PROTACs) and 2) the development of a target fishing application for the prediction of mechanism of action of natural products. PROTACs are a novel class of small molecule therapeutics that are garnering significant interest. Unlike traditional small molecule therapeutics, PROTACs simultaneously bind to both their protein target and an E3 ligase to induce degradation. The requirement to simultaneously bind two proteins necessitates a high molecular weight as PROTACs must contain two unique binding moieties that are connected by a linker. As a result, PROTAC molecules are expected to lie outside of the traditional drug-like chemical space described by Lipinski. To gain a better understanding of the physicochemical properties of PROTACs curre (open full item for complete abstract)

Committee: James Fuchs (Advisor); Xioalin Cheng (Advisor); Karl Werbovetz (Committee Member); Lara Sucheston-Campbell (Committee Member) Subjects: Chemistry; Computer Science; Molecular Chemistry; Molecules; Pharmacy Sciences

MS, University of Cincinnati, 2008, Engineering : Computer Science

Noroviruses have been recognized as the most important cause of non-bacterial epidemic acute gastroenteritis, affecting individuals of all ages. With the identification of trisaccharides' binding site(s) for norovirus (both VA387 and Norwalk virus strains), the door has turned open to find biologically active chemicals with better or equal binding affinity compared to trisaccharides. In this thesis as a first objective, trisaccharides binding site(s) on noroviruses were identified via computational docking and validated with experiments results. In addition to experimentally identified and computationally predicted binding site, a second stable binding site for Norwalk virus was also computationally predicted. Completion of the first goal paved the way for the second and most important aim of the research, which was to computationally identify lead candidates from a library of two million drug-like compounds that bind to the viral receptor pocket identified earlier, thus inhibiting the binding of host histo-blood group antigens (HBGA). Delivering on the second objective, a selection of 255 potential drug-like compounds were obtained successfully. Finally, an approach to score and cluster chemicals based on binding energy and binding similarity (specificity) to trisaccharides was developed and successfully implemented.

Committee: Yizong Cheng PhD (Committee Chair); Jarek Meller PhD (Committee Chair); Ali Minai PhD (Committee Chair) Subjects: Bioinformatics; Biomedical Research; Computer Science; Molecules; Virology

Doctor of Philosophy, The Ohio State University, 2012, Pharmacy

Discovery of novel drug candidates for a particular disease condition has traditionally been carried out by experimentally screening thousands of compounds for desired activity in anticipation of a few being successful. However, this approach is expensive because a large number of compounds need to be synthesized or purchased and tested for biological activity. The success rates of such blind screens are very poor. Recently, rational drug design methods have gained popularity due to progress in structural genomics, chemical biology, and computational chemistry. In particular, in silico methods have improved the screening efficiency in the past five years due to advancement in multi-core, multi-threaded computational hardware and software development. By utilizing rational approaches, we have developed and applied protocols capable of rapidly and inexpensively screen hundreds of thousands of compounds virtually to disease targets. A novel kinase, maternal embroyonic leucine zipper kinase (MELK), has been reported to be involved in tumor progression. We developed an ensemble virtual screening method utilizing multiple induced fit models of MELK to screen 500,000 compounds. We tested 23 compounds and identified one potent (15; Kd =0.37 µM), one moderate (9; Kd=3.2 µM), and several weak MELK inhibitors. Human protein arginine methyl transferase 5 (PRMT5) has been shown to methylate arginine residues of histone protein, subsequently silencing tumor suppressor genes. To target this aberrant epigenetic pathway, a comparative model of the PRMT5 was constructed. We computationally screened 10,000 compounds and eight small molecules were selected for biological assay. Enzyme inhibition assays show that two compounds were capable of selectively inhibiting PRMT5 activity. The compounds were also proven to inhibit cellular proliferation in several cancer cell lines. Leishmaniasis is a parasitic disease which affects millions of people all over the world. The Leishmania UDP-gluco (open full item for complete abstract)

Committee: Chenglong Li PhD (Advisor); Robert Baiocchi MD, PhD (Committee Member); Karl Werbovetz PhD (Committee Member) Subjects: Pharmaceuticals; Pharmacy Sciences

Bachelor of Science (BS), Ohio University, 2022, Neuroscience

Lithium, a therapeutic first introduced in 1952, is still considered the gold standard for treatment of bipolar disorder. Although many new medications have been discovered over the years, lithium is the only therapeutic used exclusively for bipolar disorder-- indicating a separate mechanism of action from antipsychotic and antidepressive treatments (Kaidanovich-Beilin et al., 2011). Genetic and pharmacological studies have shown that lithium's therapeutic efficacy is at least partially due to inhibition of glycogen synthase kinase-3 beta, or GSK3b (Kaidanovich-Beilin et al., 2011). Development of small molecule therapeutics to target GSK3b with better drug like qualities such as higher affinity and selectivity can lead to advancements in our understanding and treatment of bipolar disorder. This study investigates the structure activity relationship of small molecule GSK3b inhibitors through ab initio calculations, protein kinase sequence alignment, and computational docking studies. We found that although the ATP binding pocket is highly conserved, a proline residue unique to GSK3 may contribute to selectivity of small molecule inhibitors. In addition, small molecule inhibitors may show increased selectivity for GSK3b through preferential SH-π interactions with cysteine residue 199. Finally, docking studies suggest that GSK3b may be too dynamic or ligand-specific for predictive docking. Overall, this work has improved understanding of how small molecule inhibitors may utilize the GSK3b ATP binding pocket and their potential to treat bipolar disorder.

Committee: Jennifer Hines (Advisor) Subjects: Neurosciences

Doctor of Philosophy, The Ohio State University, 2017, Pharmaceutical Sciences

IL-6 is a pleiotropic cytokine that participates in various cellular processes such as acute-phase response, immune response, and hematopoiesis. It is also involved in cell proliferation, survival, and apoptosis. Excessive IL-6 signaling leads to chronic inflammation and promotes malignancies. Blockade of IL-6 is therefore a potential strategy for developing cancer therapeutics. Most current successful anti-IL-6 signaling drugs are antibodies with very few small molecule drugs reported. More importantly, a structure-based rationale for IL-6/gp130 protein-protein interaction (PPI) inhibitor design was absent. Small molecule inhibitor design targeting PPI interfaces is very challenging. Lack of success against shallow PPI interfaces, as present in IL-6/gp130, suggests that more effective approaches are needed to tackle the problem. Computational modeling techniques are especially valuable in PPI inhibitor design. In this dissertation, varied computational methods were used to facilitate the rational design of small molecule inhibitors against the shallow IL-6/gp130 interface. We started from a natural product, Madindoline A (MDL-A), which was reported as a highly selective IL-6 inhibitor by binding to gp130 extracellular domains albeit with relatively weak binding affinity (288 µM) and limited inhibitory efficacy in cancer cellular assays. Through dynamics simulations and extensive free energy analyses, we identified two hot spots at the IL-6 site III/gp130 D1 domain interface and characterized the MDL-A binding mode on the D1 domain of gp130 (gp130-D1). Based on these findings, we optimized the MDL-A scaffold and designed several generations of inhibitors with three different strategies through structure-based approaches and ensemble molecular dockings. As compared to MDL-A, the latest generation of inhibitors (LLM-4x series) have 15-fold improved affinities, which were determined by surface plasmon resonance (SPR). Furthermore, we correlated our computed energy m (open full item for complete abstract)

Committee: Chenglong Li (Advisor); Werner Tjarks (Committee Chair); Karl Werbovetz (Committee Member) Subjects: Pharmacy Sciences

Doctor of Philosophy, The Ohio State University, 2005, Biophysics

Transporter proteins and receptors play a pivotal role in drug absorption, distribution and excretion. However, very few of the transporters have been crystallized and not all pharmaceutically significant receptors have been studied extensively. Nonetheless, currently available functional as well as structural data provide an attractive scaffold for generating combined models that merge ligand-based structure-activity relationship and protein-based homology structures. The resultant models offer features that extend the predictive function of previous single models. This dissertation is aimed at presenting alternative approaches for studying transporter and receptor structure by applying in silico technologies with the following specific aims: (1) to develop thoroughly validated, highly predictive Quantitative Structure Activity Relationship (QSAR) models and pharmacophore models for pharmaceutically important transporters and receptors; (2) to generate comparative three-dimensional models for essential drug targets; and (3) to identify novel inhibitors towards significant drug targets through database screening using pharmacophore models generated in aim 1. Chapter 1 presents an overview of in silico approaches for studying drug targets. A summary of the significance of transporters and receptors in human health is provided along with a comprehensive review of recent successful in silico applications. Also included is a detailed description of the methods used in later studies. Chapter 2 – 9 describe the QSAR and pharmacophore studies as well as pharmacophore-based database screening results for transporters involved in: drug absorption, i.e., nucleoside transporter and peptide transporters; drug elimination, i.e., organic cation transporter, organic anion transporting polypeptides and drug efflux, i.e., P-glycoprotein, and for pharmaceutically important receptors, i.e., androgen receptor, bile acid receptor. The significance of each drug target is first presented, (open full item for complete abstract)

Committee: James Dalton (Advisor) Subjects: Biophysics, General