Apixaban is a novel oral anticoagulant that prevents clotting by directly inhibiting Factor Xa in the coagulation cascade. Due to its different pharmacokinetics, previous standards for testing anticoagulant concentrations are ineffective at measuring apixaban. In this study, Hyphen Biomed Biophen Direct Xa Inhibitor and Biophen Heparin chromogenic kits from Aniara Diagnostica were used along with a NanoDrop™ One/OneC Microvolume UV-Vis Spectrophotometer to see if either of these kits provide acceptable precision and accuracy for the quantification of apixaban in plasma samples, as well as if there is a significant difference in these two kits at varying concentrations of apixaban. Apixaban is a novel oral anticoagulant that prevents clotting by directly inhibiting Factor Xa in the coagulation cascade. Due to its different pharmacokinetics, previous standards for testing anticoagulant concentrations are ineffective at measuring apixaban. In this study, Hyphen Biomed Biophen Direct Xa Inhibitor and Biophen Heparin chromogenic kits from Aniara Diagnostica were used along withused witha NanoDrop™ One/OneC Microvolume UV-Vis Spectrophotometer to see if either of these kits provide acceptable precision and accuracy for the quantification of apixaban in plasma samples,as well as and to evaluate if there is a significant difference in these two kits at varying concentrations of apixaban.

The small Molecule Drug Discovery field has been heavily dependent on suppressor discovery by structural binding prediction. Despite all successes in targeting different types of molecular targets in cells, many are considered undruggable. Over 85% of proteins and over 99% of RNAs are still considered hard to drug in cancer. The main challenge in suppressing their activity would be their unknown or super complicated structures. In addition, many of them present a dynamic 3D structure with no pocket. Since computational structural-based drug discovery has been developed for proteins with rigid structures and obvious pockets, it has not been successful in discovering small molecule candidate drugs against dynamic or unknown structures. In addition, structurally binding to some targets like RNAs does not guarantee their activity inhibition. Therefore, there has been a need for a computational approach to discover drugs against hard-to-drug targets with no structural information involved. I introduce a new small molecule drug discovery approach using Artificial Intelligence (AI), specifically Deep Learning (DL). This method does not require any input data from the sequence or the 3D structure of the target. Rather than targeting biomolecules' structure, AI models learn the biology of suppressing the target's activity called functional-based modeling. In three different projects, we prove the efficiency of AI-based functional-based drug discovery compared to traditional computational drug discovery. First, the collection of one of the biggest molecular datasets for molecular machine learning. MolData is one of the biggest categorized molecular datasets ever published for AI drug discovery. Second, I introduce RiboStrike, an AI-based model capable of discovering candidate drugs against micro RNAs. RiboStrike is the state-of-the-art model capable of discovering small molecule candidate drugs against RNA regardless of their size, structure, and coding functionality. We successfully discovered three candidate drugs against the functionality of miR21. Third, I introduce AMPdeep, an AI-based approach to discovering the hydrolysis of Anti-microbial Peptides (AMPs).

Organic cation transporters (OCTs) play a pivotal role in the absorption, tissue distribution, and excretion of a diverse array of substances, and currently the nature of the biochemical interactions between substrate and OCTs are unknown. Therefore, identifying which amino acid residues are critical for OCT-substrate interactions is of central importance to understanding and predicting interactions between drugs and OCTs. A three-dimensional (3-D) homology model of human OCT3 was generated using the crystal structure of a high affinity phosphate transporter from Piriformospora indica (PiPT) as template, and putative binding pocket for the prototypical hOCT3 ligand 1-methyl-4-phenylpyridinium (MPP+) was identified through docking studies. Five residues, Phe36, Val40, Trp358, Glu451 and Asp478, were identified as potentially mediating hOCT3-MPP+ interactions, and confirmed through in vitro studies. Additionally, 3-D homology modeling of the functional hOCT3 mutant Val40Leu, and all non-functional hOCT3 mutants, indicated changes in binding pocket architecture consistent with weakening of ligand-transporter interactions. Docking of structurally divergent hOCT3 substrates indicated binding interactions in the same general region as that identified for MPP+, albeit with mostly unique residues. Interspecies differences were explored by generating 3-D homology models for rat and murine Oct3. Results from docking studies using compounds exhibiting vastly different binding affinities (Km or IC50) towards the OCT3/Oct3 orthologs were consistent with varying strength in ligand-transporter binding pocket interactions. Finally, a series of novel compounds exhibiting anti-depressant-like activity was screened for OCT interaction in vitro, and demonstrated significant inhibitory effects on OCTs for many of the compounds.

The current environment for drug discovery and disease treatment relies heavily on genomic analysis, structural biology and chemical biology techniques. With the enormous advances in genomic analysis and structural biology, the use of and desire for targeted therapies has increased. However, as more genomic data for cancer disease state pathology becomes available we must ask increasingly difficult questions and even produce new technologies, such as activity-based probes, to answer these questions. In particular, targeted kinase inhibitors for the treatment of cancer has become a mainstay for drug development for both industry and academia, but it is evident that the genomic data is not always indicative of protein expression. Additionally, protein expression alone does not completely characterize functional activity. Therefore, in order to more accurately validate drug targets and predict drug efficacy, we must not only identify possible targets but also determine their activity in vivo. The goal of this work was to develop a probe for Protein Kinase A that would act by alkylating a conserved cysteine in the substrate-binding pocket of the enzyme. We hypothesized that by targeting the substrate-binding pocket we could effectively utilize the natural substrate selectivity filters as well as take into account multiple endogenous regulatory mechanisms. We produced probes utilizing portions of the pseudosubstrate inhibitor PKI that demonstrate the ability to label the catalytic subunit of Protein Kinase A in an activity-dependent manner, thus making it an important first step in a new class of activity-based probes for the kinome.

Familial hypercholesterolemias (FH) are inherited mutations that cause elevated total cholesterol and low-density lipoprotein cholesterol levels (LDL-C) which lead to premature coronary heart diseases. Pharmacogenetics is the study of inherited genetic differences in drug metabolic pathways which can affect the patient’s response to the drug. Single Nucleotide Morphism (SNP) mutations in the LDLR, apoB, LDRAP1, and PCSK9 genes are linked to familial hypercholesterolemia. The mutations in the LDLR gene are the most common while mutations in the apoB and PCSK9 genes are the least common in hypercholesterolemia patients. My research will study how pharmacogenetics can be used to diagnose and prescribe patients with FH who have apoB and PCSK9 double gene mutations. I will genotype and sequence the PCR amplified gene segments of the patients with FH to identify any of the 6 apoB SNPs and any of the 3 PCSK9 SNPs that are known. Then, I will provide 4 different treatments: placebo, antisense therapy (mipomersen), PCSK9 inhibitor (alirocumab), and a combination of mipomersen + alirocumab, and I will measure the LDL-C levels before and after a 12-week trial. I hypothesize that individuals with both apoB and PCSK9 gene mutations with the known SNPs that cause loss of function will be more responsive when given both treatments by observing a significant decrease in LDL-C levels.

Respiratory Syncytial Virus (RSV) is a potentially life-threatening respiratory pathogen that infects approximately 64 million children and immunocompromised adults globally per year. Currently, there is a need for prophylactic and therapeutic approaches effective against primary and secondary RSV infections. This project focuses on the development of a simple, smart, and scalable anti-RSV nanotherapeutic that combines novel cellular antiviral defense mechanisms targeting the inhibition of viral fusion and replication. An ICAM-1 targeted liposomal nanocarrier will be synthesized and coated with a layer of chitosan containing the anti-fusion HR2-D peptide as an extracellular defense mechanism. Additionally, chitosan complexed to dual expressing short hairpin RNA (shRNA) recombinant plasmids will be encapsulated within the nanocarrier, and provide an intracellular defense mechanism that will interfere with the expression of the NS1 and P proteins. In combination, both defense mechanisms are expected to induce a synergistic anti-RSV effect that will surpass those of conventional therapeutics. Through this research, the NS1 and P containing plasmid (pSH-NS1-P) was cloned, and the nanotherapeutic was successfully synthesized. Based on the acquired results, pSH-NS1-P was shown to express anti-RSV effects, and it was also concluded that both inserts were producing active shRNA. Additionally, the anti-RSV efficiency of HR2-D was confirmed. Overall, this research will lead to development of a dual-mechanistic anti-viral nanotherapeutic.

Alzheimer’s disease is a devastating neurodegenerative disorder and the leading cause of dementia. The disease manifests via several pathologies including neuroinflammation, oxidative stress, metal ion dyshomeostasis, and cell death. To address the multifaceted nature of this disorder, the design of several diverse compounds, targeting many pathological effects, was generated. First, a series of compounds based on curcumin and diosgenin were synthesized following the bivalent design strategy. Two compounds were discovered to have neuroprotective ability, anti-oxidative function, and anti-Aß oligomerization (AßO) properties. A second set of molecules was also designed, wherein a hybrid compound strategy was utilized. Three hybrids were to shown to protect MC65 cells from Aß-induced toxicity and to have significant anti-oxidative activity. Mechanistic studies propose that protection is through disruption of interactions between AßOs and partner proteins. Furthermore, one hybrid was also shown to be able to pass the BBB. Lastly, studies of glyburide, an anti-diabetic medication, have shown an off-target anti-inflammatory effect specific for the NLRP3 inflammasome, which has been implicated in AD development. Therefore, a series of glyburide analogs were synthesized and characterized. One analog was able to successfully inhibit the NLRP3 inflammasome and reduce IL-1ß expression without affecting blood glucose. In vivo studies demonstrated an ability to prevent or ameliorate adverse inflammation-related outcomes in murine inflammatory models. Altogether, these investigations have yielded three novel series of compounds, all capable of modifying Alzheimer’s disease pathology. These results warrant future investigations into the development, optimization, and characterization of these analogs as potential treatments for Alzheimer’s disease.

Application of chemotherapeutic agents in current cancer treatment has been limited by adverse effects as poor selectivity results in systemic toxicity; most chemotherapy approaches also experience inherited or acquired drug resistance which lead to reduced treatment outcome. Research efforts have focused on the discovery of novel chemotherapies that overcome the limitations mentioned above. Ru(II) polypyridyl complexes with anti-cancer properties have been extensively studied as traditional cytotoxic agents and photodynamic therapy agents due to their photophysical and photochemical characteristics. Most research has focused on the design of Ru(II) polypyridyl complexes that have affinities to nucleic acids as inspired by the classic small molecule metal complex cisplatin. Though modifying the structures of ligands on the ruthenium metal center, the hydrophilicity, charge state and photochemical properties can be tuned, resulting to Ru(II) polypyridyl complexes that act through cellular targets other than DNA. Understanding the mechanism of action and identifying functional targets remain the challenging and complex research topic in the design and study of novel medication or candidates. With the development of semi-high throughput cytological profiling in a bacterial system, rapid investigation of the mechanism of action can be achieved to distinguish anti-cancer agents which possess different mechanisms of actions. Ru(II) polypyridyl complexes with different scaffolds have been studied and suggested to have anti-cancer properties through DNA damage response, and/or translational inhibition.

The proteasome is a large protein complex which is responsible for the majority of protein degradation in eukaryotes. Following FDA approval of the first proteasome inhibitor bortezomib for the treatment of multiple myeloma (MM) in 2003, there has been an increasing awareness of the significant therapeutic potential of proteasome inhibitors in the treatment of cancer. As of 2017, three proteasome inhibitors are approved for the treatment of MM but in clinical trials with patients bearing solid tumors these existing proteasome inhibitors have demonstrated poor results. Notably, all three FDA-approved proteasome inhibitors rely on the combination a peptide backbone and reactive electrophilic warhead to target the proteasome, and all three primarily target the catalytic subunits conferring the proteasome’s chymotrypsin-like (CT-L) activity. It is our hypothesis that compounds with non-peptidic structures, non-covalent and reversible modes of action, and unique selectivity profiles against the proteasome’s distinct catalytic subunits could have superior pharmacodynamic and pharmacokinetic properties and may bear improved activity against solid tumors relative to existing proteasome inhibitors. In an effort to discover such compounds we have employed an approach which combines computational drug screening methods with conventional screening and classic medicinal chemistry. Our efforts began with a computational screen performed in the lab of Dr. Chang-Guo Zhan. This virtual screen narrowed a library of over 300,000 drug-like compounds down to under 300 virtual hits which were then screened for proteasome inhibitory activity in an in vitro assay. Despite screening a relatively small pool of compounds, we were able to identify 18 active compounds. The majority of these hits were non-peptide in structure and lacked any hallmarks of covalent inhibition. The further development of one compound, a tri-substituted pyrazole, provided us with a proteasome inhibitor which demonstrated cytotoxic activity in a variety of human solid cancer cell lines as well as significant anti-tumor activity in a prostate cancer mouse xenograft model. We have also evaluated the in vitro pharmacokinetic properties of our lead compound and investigated its ability to evade cross-resistance phenomena in proteasome inhibitor-resistant cell lines. We believe that our lead compound as well as our drug discovery approach itself will be of interest and use to other researchers. We hope that this research effort may aid in the further development of reversible non-peptide proteasome inhibitors and may eventually deliver new therapeutic options for patients with difficult-to-treat solid tumors.

This dissertation thesis consists of a series of chapters that are interwoven by solving interesting biological problems, employing various computational methodologies. These techniques provide meaningful physical insights to promote the scientific fields of interest. Focus of chapter 1 concerns, the importance of computational tools like docking studies in advancing structure based drug design processes. This chapter also addresses the prime concerns like scoring functions, sampling algorithms and flexible docking studies that hamper the docking successes. Information about the different kinds of flexible dockings in terms of accuracy, time limitations and success studies are presented. Later the importance of Induced fit docking studies was explained in comparison to traditional MD simulations to predict the absolute binding modes. Chapter 2 and 3 focuses on understanding, how sickle cell disease progresses through the production of sickled hemoglobin and its effects on sickle cell patients. And how, hydroxyurea, the only FDA approved treatment of sickle cell disease acts to subside sickle cell effects. It is believed the primary mechanism of action is associated with the pharmacological elevation of nitric oxide in the blood, however, the exact details of this mechanism is still unclear. HU interacts with oxy and deoxyHb resulting in slow NO production rates. However, this did not correlate with the observed increase of NO concentrations in patients undergoing HU therapy. The discrepancy can be attributed to the interaction of HU competing with other heme based enzymes such as catalase and peroxidases. In these two chapters, we investigate the atomic level details of this process using a combination of flexible-ligand / flexible-receptor virtual screening (i.e. induced fit docking, IFD) coupled with energetic analysis that decomposes interaction energies at the atomic level. Using these tools we were able to elucidate the previously unknown substrate binding modes of a series of hydroxyurea analogs to human hemoglobin, catalase and the concomitant structural changes of the enzymes. Our results are consistent with kinetic and EPR measurements of hydroxyurea-hemoglobin reactions and a full mechanism is proposed that offers new insights into possibly improving substrate binding and/or reactivity. Finally in chapter 4, we have developed a 3D bioactive structure of O6-alkylguanine-DNA alkyltransferase (AGT), a DNA repair protein using Monte Carlo conformational search process. It is known that AGT prevents DNA damage, mutations and apoptosis arising from alkylated guanines. Various Benzyl guanine analouges of O6- methylguanine were tested for activity as potential inhibitors. The nature and position of the substitutions methyl and aminomethyl profoundly affected their activity. Molecular modeling of their interactions with alkyltransferase provided a molecular explanation for these results. The square of the correlation coefficient (R2 ) obtained between E-model scores (obtained from GLIDE XP/QPLD docking calculations) vs log(ED)values via a linear regression analysis was 0.96. The models indicate that the ortho-substitution causes a steric clash interfering with binding, whereas the meta-aminomethyl substitution allows an interaction of the amino group to generate an additional hydrogen bond with the protein. Using this model for virtually screening studies resulted in identification of seven lead compounds with novel scaffolds from National Cancer Institute Diversity Set2.