Open Source Drug Discovery: Highly Potent Antimalarial Compounds Derived from the Tres Cantos Arylpyrroles

The development of new antimalarial compounds remains a pivotal part of the strategy for malaria elimination. Recent large-scale phenotypic screens have provided a wealth of potential starting points for hit-to-lead campaigns. One such public set is explored, employing an open source research mechanism in which all data and ideas were shared in real time, anyone was able to participate, and patents were not sought. One chemical subseries was found to exhibit oral activity but contained a labile ester that could not be replaced without loss of activity, and the original hit exhibited remarkable sensitivity to minor structural change. A second subseries displayed high potency, including activity within gametocyte and liver stage assays, but at the cost of low solubility. As an open source research project, unexplored avenues are clearly identified and may be explored further by the community; new findings may be cumulatively added to the present work

PREDICTIVE CHEMINFORMATICS ANALYSIS OF DIVERSE CHEMOGENOMICS DATA SOURCES: APPLICATIONS TO DRUG DISCOVERY, ASSAY INTERFERENCE, AND TEXT MINING

In this dissertation, we describe the cheminformatics analysis of diverse chemogenomics data sources as well as the application of these data to several drug discovery efforts. In Chapter 1, we describe the discovery and characterization of novel Ebola virus inhibitors through QSAR-based virtual screening. In Chapter 2, we report the discovery and analysis of a series of potent and selective doublecortin-like kinase 1 (DCLK1) inhibitors using QSAR modeling, virtual screening, Matched Molecular Pair Analysis (MMPA), and molecular docking. In Chapter 3, we performed a large-scale analysis of publicly available data in PubChem to probe the reliability and applicability of Pan-Assay INterference compoundS (PAINS) alerts, a popular computational drug screening tool. In Chapter 4, we explore the PubMed database as a novel source of biomedical data and describe the development of Chemotext, a publicly available web server capable of text-mining the published literature.Doctor of Philosoph

From Knowledgebases to Toxicity Prediction and Promiscuity Assessment

Polypharmacology marked a paradigm shift in drug discovery from the traditional ‘one drug, one target’ approach to a multi-target perspective, indicating that highly effective drugs favorably modulate multiple biological targets. This ability of drugs to show activity towards many targets is referred to as promiscuity, an essential phenomenon that may as well lead to undesired side-effects. While activity at therapeutic targets provides desired biological response, toxicity often results from non-specific modulation of off-targets. Safety, efficacy and pharmacokinetics have been the primary concerns behind the failure of a majority of candidate drugs. Computer-based (in silico) models that can predict the pharmacological and toxicological profiles complement the ongoing efforts to lower the high attrition rates. High-confidence bioactivity data is a prerequisite for the development of robust in silico models. Additionally, data quality has been a key concern when integrating data from publicly-accessible bioactivity databases. A majority of the bioactivity data originates from high- throughput screening campaigns and medicinal chemistry literature. However, large numbers of screening hits are considered false-positives due to a number of reasons. In stark contrast, many compounds do not demonstrate biological activity despite being tested in hundreds of assays. This thesis work employs cheminformatics approaches to contribute to the aforementioned diverse, yet highly related, aspects that are crucial in rationalizing and expediting drug discovery. Knowledgebase resources of approved and withdrawn drugs were established and enriched with information integrated from multiple databases. These resources are not only useful in small molecule discovery and optimization, but also in the elucidation of mechanisms of action and off- target effects. In silico models were developed to predict the effects of small molecules on nuclear receptor and stress response pathways and human Ether-à-go-go-Related Gene encoded potassium channel. Chemical similarity and machine-learning based methods were evaluated while highlighting the challenges involved in the development of robust models using public domain bioactivity data. Furthermore, the true promiscuity of the potentially frequent hitter compounds was identified and their mechanisms of action were explored at the molecular level by investigating target-ligand complexes. Finally, the chemical and biological spaces of the extensively tested, yet inactive, compounds were investigated to reconfirm their potential to be promising candidates.Die Polypharmakologie beschreibt einen Paradigmenwechsel von "einem Wirkstoff - ein Zielmolekül" zu "einem Wirkstoff - viele Zielmoleküle" und zeigt zugleich auf, dass hochwirksame Medikamente nur durch die Interaktion mit mehreren Zielmolekülen Ihre komplette Wirkung entfalten können. Hierbei ist die biologische Aktivität eines Medikamentes direkt mit deren Nebenwirkungen assoziiert, was durch die Interaktion mit therapeutischen bzw. Off-Targets erklärt werden kann (Promiskuität). Ein Ungleichgewicht dieser Wechselwirkungen resultiert oftmals in mangelnder Wirksamkeit, Toxizität oder einer ungünstigen Pharmakokinetik, anhand dessen man das Scheitern mehrerer potentieller Wirkstoffe in ihrer präklinischen und klinischen Entwicklungsphase aufzeigen kann. Die frühzeitige Vorhersage des pharmakologischen und toxikologischen Profils durch computergestützte Modelle (in-silico) anhand der chemischen Struktur kann helfen den Prozess der Medikamentenentwicklung zu verbessern. Eine Voraussetzung für die erfolgreiche Vorhersage stellen zuverlässige Bioaktivitätsdaten dar. Allerdings ist die Datenqualität oftmals ein zentrales Problem bei der Datenintegration. Die Ursache hierfür ist die Verwendung von verschiedenen Bioassays und „Readouts“, deren Daten zum Großteil aus primären und bestätigenden Bioassays gewonnen werden. Während ein Großteil der Treffer aus primären Assays als falsch-positiv eingestuft werden, zeigen einige Substanzen keine biologische Aktivität, obwohl sie in beiden Assay- Typen ausgiebig getestet wurden (“extensively assayed compounds”). In diese Arbeit wurden verschiedene chemoinformatische Methoden entwickelt und angewandt, um die zuvor genannten Probleme zu thematisieren sowie Lösungsansätze aufzuzeigen und im Endeffekt die Arzneimittelforschung zu beschleunigen. Hierfür wurden nicht redundante, Hand-validierte Wissensdatenbanken für zugelassene und zurückgezogene Medikamente erstellt und mit weiterführenden Informationen angereichert, um die Entdeckung und Optimierung kleiner organischer Moleküle voran zu treiben. Ein entscheidendes Tool ist hierbei die Aufklärung derer Wirkmechanismen sowie Off-Target-Interaktionen. Für die weiterführende Charakterisierung von Nebenwirkungen, wurde ein Hauptaugenmerk auf Nuklearrezeptoren, Pathways in welchen Stressrezeptoren involviert sind sowie den hERG-Kanal gelegt und mit in-silico Modellen simuliert. Die Erstellung dieser Modelle wurden Mithilfe eines integrativen Ansatzes aus “state-of-the-art” Algorithmen wie Ähnlichkeitsvergleiche und “Machine- Learning” umgesetzt. Um ein hohes Maß an Vorhersagequalität zu gewährleisten, wurde bei der Evaluierung der Datensätze explizit auf die Datenqualität und deren chemische Vielfalt geachtet. Weiterführend wurden die in-silico-Modelle dahingehend erweitert, das Substrukturfilter genauer betrachtet wurden, um richtige Wirkmechanismen von unspezifischen Bindungsverhalten (falsch- positive Substanzen) zu unterscheiden. Abschließend wurden der chemische und biologische Raum ausgiebig getesteter, jedoch inaktiver, kleiner organischer Moleküle (“extensively assayed compounds”) untersucht und mit aktuell zugelassenen Medikamenten verglichen, um ihr Potenzial als vielversprechende Kandidaten zu bestätigen

Computational Approaches: Drug Discovery and Design in Medicinal Chemistry and Bioinformatics

This book is a collection of original research articles in the field of computer-aided drug design. It reports the use of current and validated computational approaches applied to drug discovery as well as the development of new computational tools to identify new and more potent drugs

Translational Research for Zoonotic Parasites: New Findings toward Improved Diagnostics, Therapy and Prevention

In this book is reported novel information on diagnosis, treatment, and control of parasites that are naturally transmitted from animal reservoirs to humans. Subjects: Public Health and Healthcare: Prevention; Medicine and Pharmacology: Therapy

Molecular And Biochemical Analysis Of Water Stress Induced Responses In Grape

Water stress affects vine productivity, disease tolerance, and enological characteristics of grape. Florida Hybrid Bunch grape are developed through hybridization of local grape spp with Vitis vinifera. These cultivars are mostly grown in southeast region of United States. Water deficit conditions resulted due to failure of rains in the region has developed concern among Florida grape growers to increase water use efficiency of grape. The goal of this research is to identify genes and proteins differentially expressed in response to water stress and to correlate these changes with enological characteristics. Investigating transcripts and proteins will allow us to correlate them and confirm the involvement of specific genes responding to stress. Florida hybrid bunch ‘Suwannee’ grape plants were maintained under green house conditions. Water stress was induced by withholding irrigation. The leaf samples were collected from both irrigated and stressed plants at 5, 10, 15 and 20 day interval. We generated over 200 Subtractive Hybridization PCR products from control and water stressed leaf tissues. Cloning, sequencing and transcript analysis revealed that, 54 genes related to drought and defense regulated pathways out of 125 characterized transcripts. Proteins were extracted from leaf tissue with trichloroacetic acid /acetone and separated by two-dimensional polyacrylamide gel electrophoresis (2D-PAGE). The proteins were sequenced in LC/Mass Spectrophotometer. The most important differentially expressed genes include sucrose synthase, actin, isoprene synthase, ABF3, SNF1 related protein kinase, WRKY type transcription factors, AP2, ASR2, glyoxalase I and, cytochrome b which play significant role in cell permeability, transportation, photosynthesis and, maintenance in osmotic stress. We have found that ribulose bisphosphate carboxylase and phosphoribulokinase, which play major role in photosynthesis, were suppressed in response to water stress in Florida hybrid bunch. The results suggested that water stress affects expression of cDNAs associated with defense and drought regulated functions. Such profiling studies will be used to explicate specific pathways disconcerted by water deficit treatments, and in the identification of varietal differences

Locating Sweet Spots for Screening Hits and Evaluating Pan-Assay Interference Filters from the Performance Analysis of Two Lead-like Libraries

The efficiency of automated compound screening is heavily influenced by the design and the quality of the screening libraries used. We recently reported on the assembly of one diverse and one target-focused lead-like screening library. Using data from 15 enzyme-based screenings conducted using these libraries, their performance was investigated. Both libraries delivered screening hits across a range of targets, with the hits distributed across the entire chemical space represented by both libraries. On closer inspection, however, hit distribution was uneven across the chemical space, with enrichments observed in octants characterized by compounds at the higher end of the molecular weight and lipophilicity spectrum for lead-like compounds, while polar and sp³-carbon atom rich compounds were underrepresented among the screening hits. Based on these observations, we propose that screening libraries should not be evenly distributed in lead-like chemical space but be enriched in polar, aliphatic compounds. In conjunction with variable concentration screening, this could lead to more balanced hit rates across the chemical space and screening hits of higher ligand efficiency will be captured. Apart from chemical diversity, both screening libraries were shown to be clean from any pan-assay interference (PAINS) behavior. Even though some compounds were flagged to contain PAINS structural motifs, some of these motifs were demonstrated to be less problematic than previously suggested. To maximize the diversity of the chemical space sampled in a screening campaign, we therefore consider it justifiable to retain compounds containing PAINS structural motifs that were apparently clean in this analysis when assembling screening libraries

An Orally Bioavailable, Indole-3-glyoxylamide Based Series of Tubulin Polymerization Inhibitors Showing Tumor Growth Inhibition in a Mouse Xenograft Model of Head and Neck Cancer.

A number of indole-3-glyoxylamides have previously been reported as tubulin polymerization inhibitors, although none has yet been successfully developed clinically. We report here a new series of related compounds, modified according to a strategy of reducing aromatic ring count and introducing a greater degree of saturation, which retain potent tubulin polymerization activity but with a distinct SAR from previously documented libraries. A subset of active compounds from the reported series is shown to interact with tubulin at the colchicine binding site, disrupt the cellular microtubule network, and exert a cytotoxic effect against multiple cancer cell lines. Two compounds demonstrated significant tumor growth inhibition in a mouse xenograft model of head and neck cancer, a type of the disease which often proves resistant to chemotherapy, supporting further development of the current series as potential new therapeutics