Design Novel Dual Agonists for Treating Type-2 Diabetes by Targeting Peroxisome Proliferator-Activated Receptors with Core Hopping Approach

Owing to their unique functions in regulating glucose, lipid and cholesterol metabolism, PPARs (peroxisome proliferator-activated receptors) have drawn special attention for developing drugs to treat type-2 diabetes. By combining the lipid benefit of PPAR-alpha agonists (such as fibrates) with the glycemic advantages of the PPAR-gamma agonists (such as thiazolidinediones), the dual PPAR agonists approach can both improve the metabolic effects and minimize the side effects caused by either agent alone, and hence has become a promising strategy for designing effective drugs against type-2 diabetes. In this study, by means of the powerful “core hopping” and “glide docking” techniques, a novel class of PPAR dual agonists was discovered based on the compound GW409544, a well-known dual agonist for both PPAR-alpha and PPAR-gamma modified from the farglitazar structure. It was observed by molecular dynamics simulations that these novel agonists not only possessed the same function as GW409544 did in activating PPAR-alpha and PPAR-gamma, but also had more favorable conformation for binding to the two receptors. It was further validated by the outcomes of their ADME (absorption, distribution, metabolism, and excretion) predictions that the new agonists hold high potential to become drug candidates. Or at the very least, the findings reported here may stimulate new strategy or provide useful insights for discovering more effective dual agonists for treating type-2 diabetes. Since the “core hopping” technique allows for rapidly screening novel cores to help overcome unwanted properties by generating new lead compounds with improved core properties, it has not escaped our notice that the current strategy along with the corresponding computational procedures can also be utilized to find novel and more effective drugs for treating other illnesses

Recent Advances in Fragment-Based QSAR and Multi-Dimensional QSAR Methods

This paper provides an overview of recently developed two dimensional (2D) fragment-based QSAR methods as well as other multi-dimensional approaches. In particular, we present recent fragment-based QSAR methods such as fragment-similarity-based QSAR (FS-QSAR), fragment-based QSAR (FB-QSAR), Hologram QSAR (HQSAR), and top priority fragment QSAR in addition to 3D- and nD-QSAR methods such as comparative molecular field analysis (CoMFA), comparative molecular similarity analysis (CoMSIA), Topomer CoMFA, self-organizing molecular field analysis (SOMFA), comparative molecular moment analysis (COMMA), autocorrelation of molecular surfaces properties (AMSP), weighted holistic invariant molecular (WHIM) descriptor-based QSAR (WHIM), grid-independent descriptors (GRIND)-based QSAR, 4D-QSAR, 5D-QSAR and 6D-QSAR methods

3D QSAR Pharmacophore Modeling, in Silico Screening, and Density Functional Theory (DFT) Approaches for Identification of Human Chymase Inhibitors

Human chymase is a very important target for the treatment of cardiovascular diseases. Using a series of theoretical methods like pharmacophore modeling, database screening, molecular docking and Density Functional Theory (DFT) calculations, an investigation for identification of novel chymase inhibitors, and to specify the key factors crucial for the binding and interaction between chymase and inhibitors is performed. A highly correlating (r = 0.942) pharmacophore model (Hypo1) with two hydrogen bond acceptors, and three hydrophobic aromatic features is generated. After successfully validating “Hypo1”, it is further applied in database screening. Hit compounds are subjected to various drug-like filtrations and molecular docking studies. Finally, three structurally diverse compounds with high GOLD fitness scores and interactions with key active site amino acids are identified as potent chymase hits. Moreover, DFT study is performed which confirms very clear trends between electronic properties and inhibitory activity (IC50) data thus successfully validating “Hypo1” by DFT method. Therefore, this research exertion can be helpful in the development of new potent hits for chymase. In addition, the combinational use of docking, orbital energies and molecular electrostatic potential analysis is also demonstrated as a good endeavor to gain an insight into the interaction between chymase and inhibitors

Chemoinformatische Entwicklung von Naturstofffragmenten, sowie deren strukturbiologische und biochemische Evaluierung zur fragmentbasierten Inhibitorentwicklung

Die Fragmentbasierte Wirkstoffentwicklung (FBDD) hat sich als wertvolle Methode zur Detektion neuer Moleküle mit inhibitorischen Eigenschaften etabliert. Allerdings bestehen die derzeit üblicherweise verwendeten Fragment-Bibliotheken aus sp2-reichen Molekülen und es werden daher verstärkt Wege gesucht den erforschbaren chemischen Strukturraum zu erweitern.1-4 Biologisch validierte Naturstoffe sind reich an Stereozentren und decken einen Bereich des chemischen Strukturraums ab, der von im Allgemeinen genutzten synthetischen Molekülen nicht abgebildet wird.5 Daher wurde im Rahmen dieser Arbeit erfolgreich ein Algorithmus entwickelt, der in der Lage ist Naturstoffe zu Naturstofffragmeten zu reduzieren und dabei die wichtigen funktionellen Gruppen, sowie Anknüpf- bzw. Modifikationspunkte an ihrer natürlichen Position zu erhalten. Es wurde ein Datensatz mit über 180,000 annotierten Naturstoffen analysiert und eine repräsentative Bibliothek aus 2,000 von Naturstoffen abgeleiteten Fragmenten zusammengestellt, die die Eigenschaften aller generierten ~110,000 Naturstofffragmente und der ursprünglichen Naturstoffe widerspiegeln und reich sind an sp3-konfigurierten Stereozentren. Diese Strukturen unterscheiden sich erwartungsgemäß vom bisher untersuchbaren chemischen Strukturraum. Dennoch sind für ungefähr die Hälfte der virtuellen Bibliothek repräsentative Fragmente kommerziell verfügbar. Mit Hilfe dieser Naturstofffragmente konnten, auch bei etablierten Zielproteinen, bisher unbekannte inhibitorische Fragmente gefunden werden. Unter anderem wurde das Konzept durch die Identifizierung von neuen Inhibitoren der aktiven Form und neuartigen Stabilisatoren der inaktiven Form von p38a MAP Kinase validiert. Zudem konnten insgesamt 52 Fragmente als neuartige Phosphataseinhibitoren gefunden werden.Fragment-based drug discovery (FBDD) has proven to be a valuable method to find new inhibitory small molecules, but it mostly employs sp2-rich compounds and the community is intensively looking for new fragment collections to expand the explorable chemical space.1-4 Biologically validated natural products (NPs) are rich in stereogenic centers and populate areas of chemical space not occupied by average synthetic molecules.5 Therefore, a new algorithm was developed that successively degenerates natural products to natural product-derived fragments keeping their attachment points at natural positions. In this work a large set (> 180,000) of natural product structures was analyzed to arrive at ca. 2,000 natural product derived fragments which are structurally highly diverse, resemble the properties of NP scaffolds and NPs themselves and are rich in sp3-configured centers. The structures of these cluster centers differ from previously explored fragment libraries, but for nearly half of the clusters representative members are commercially available. We validate their usefulness for the discovery of novel ligand and inhibitor types by identification of novel fragments stabilizing the inactive conformations of p38a MAP kinase and inhibitors of several phosphatases

QSAR METHODS DEVELOPMENT, VIRTUAL AND EXPERIMENTAL SCREENING FOR CANNABINOID LIGAND DISCOVERY

G protein coupled receptors (GPCRs) are the largest receptor family in mammalian genomes and are known to regulate wide variety of signals such as ions, hormones and neurotransmitters. It has been estimated that GPCRs represent more than 30% of current drug targets and have attracted many pharmaceutical industries as well as academic groups for potential drug discovery. Cannabinoid (CB) receptors, members of GPCR superfamily, are also involved in the activation of multiple intracellular signal transductions and their endogenous ligands or cannabinoids have attracted pharmacological research because of their potential therapeutic effects. In particular, the cannabinoid subtype-2 (CB2) receptor is known to be involved in immune system signal transductions and its ligands have the potential to be developed as drugs to treat many immune system disorders without potential psychotic side-effects. Therefore, this work was focused on discovering novel CB2 ligands by developing novel quantitative structure-activity relationship (QSAR) methods and performing virtual and experimental screenings. Three novel QSAR methods were developed to predict biological activities and binding affinities of ligands. In the first method, a traditional fragment-based approach was improved by introducing a fragment similarity concept that enhanced the prediction accuracy remarkably. In the second method, pharmacophoric and morphological descriptors were incorporated to derive a novel QSAR regression model with good prediction accuracy. In the third method, a novel fingerprint-based artificial neural network QSAR model was developed to overcome the similar scaffold requirement of many fragment-based and other 3D-QSAR methods. These methods provide a foundation for virtual screening and hit ranking of chemical ligands from large chemical space. In addition, several novel CB2 selective ligands within nM binding affinities were discovered. These ligands were proven to be inverse agonists as validated by functional assays and could be useful probes to study CB2 signaling as well as potential drug candidates for autoimmune disesases

Understanding Designer Mental Models to Support Computer Directed Analogical Design

Analysis of alternative concepts has a significant impact on design project outcomes, and yet many design teams fail to consider a significantly broad range of conceptual solutions. Within the realm of conceptual design exists a technique called design by analogy (DBA) -- the practice of reapplying old solutions to new problems. DBA mitigates the effort required to generate a large field of candidate concepts by leveraging existing knowledge from a wide variety of domains, making it an attractive approach toward improving design outcomes. Unfortunately, DBA is challenging in the absence of expert knowledge. Designers need computational support in order to effectively identify a large number of high-quality analogical connections across a wide variety of domains. With this challenge in mind, the goal of this dissertation is to improve the body of knowledge regarding computational support for design by analogy. More specifically, this body of work includes five manuscripts. Manuscript 0 presents a review of several function-related design abstractions, including their impacts on education and industry. Manuscript 1 studies analogy retrieval in a novel design context and catalogs the types of abstract similarity (including function) commonly used to form analogies. Manuscript 2 examines a scalable approach to capturing analogy-relevant design knowledge to support large-scale analogy searching. Manuscripts 3 and 4 examine and modify a technique from de novo drug design for quickly indexing and retrieving design analogies. Manuscript 3 examines the domain independence of the technique, and manuscript 4 develops it as a large-scale design analogy search method. The body of work contributes to a greater understanding of (1) the abstractions used by designers during conceptual design, (2) the use of human computation to support conceptual design activities, and (3) large scale solution screening using a variety of mixed design abstractions. This understanding advances the creation of tools that enable designers to consider a wide range of conceptual solutions in spite of lacking domain expertise