PAT5A: A Partial Agonist of Peroxisome Proliferator-Activated Receptor ␥ Is a Potent Antidiabetic Thiazolidinedione Yet Weakly Adipogenic

ABSTRACT -(2S)-pyrrolidine-2-methoxyl]phenylmethylene[thiazolidine-2,4-dione, malic acid salt]], a chemically distinct unsaturated thiazolidinedione, activates peroxisome proliferator-activated receptor ␥ (PPAR␥) submaximally in vitro with the binding affinity ϳ10 times less than that of rosiglitazone, a highly potent thiazolidinedione. PAT5A reduces plasma glucose level and improves insulin sensitivity in insulin resistant db/db mice, similar to that of rosiglitazone, while exerting a relatively weak adipogenic effect. In contrast to rosiglitazone, PAT5A inhibits cholesterol and fatty acid biosynthesis suggesting that PAT5A possesses a unique receptor-independent non-PPAR related property. PAT5A induces qualitatively similar but quantitatively different protease digestion patterns and interacts with PPAR␥ differently than rosiglitazone. PAT5A shows differential cofactor recruitment and gene activation than that of rosiglitazone. Thus, the partial agonism of PAT5A to PPAR␥ together with its receptor independent effects may contribute to its antidiabetic potency similar to rosiglitazone in vivo despite reduced affinity for PPAR␥. These biological effects suggest that PAT5A is a PPAR␥ modulator that activates some (insulin sensitization), but not all (adipogenesis), PPAR␥-signaling pathways. The nuclear receptor superfamily consists of several ligand-regulated transcription factors that include the steroid and thyroid hormone receptors, vitamin D 3 receptor, retinoic acid receptors, and the peroxisome proliferator-activated receptors (PPARs), among others Recently, we have reported impressive euglycemic and hypolipidemic activities of PAT5A, a novel thiazolidinedione analog containing pyridine [5-[4-[N-(2-pyridyl)-(2S)-pyrrolidine-2-methoxyl]phenylmethylene[thiazolidine-2,4-Article, publication date, and citation information can be found a

Sobiva omaduste profiiliga ühendite tuvastamine keemiliste struktuuride andmekogudest

Keemiliste ühendite digitaalsete andmebaaside kasutuselevõtuga kaasneb vajadus leida neist arvutuslikke vahendeid kasutades sobivate omadustega molekule. Probleem on eriti huvipakkuv ravimitööstuses, kus aja- ja ressursimahukate katsete asendamine arvutustega, võimaldab märkimisväärset säästu. Kuigi tänapäevaste arvutusmeetodite piiratud võimsuse tõttu ei ole lähemas tulevikus võimalik kogu ravimidisaini protsessi algusest lõpuni arvutitesse ümber kolida, on lugu teine, kui vaadelda suuri andmekogusid. Arvutusmeetod, mis töötab teadaoleva statistilise vea piires, visates välja mõne sobiva ühendi ja lugedes mõni ekslikult aktiivseks, tihendab lõppkokkuvõttes andmekomplekti tuntaval määral huvitavate ühendite suhtes. Seetõttu on ravimiarenduse lihtsamate ja vähenõudlikkumade etappide puhul, nagu juhtühendite või ravimikandidaatide leidmine, edukalt võimalik rakendada arvutuslikke vahendeid. Selline tegevus on tuntud virtuaalsõelumisena ning käesolevasse töösse on sellest avarast ja kiiresti arenevast valdkonnast valitud mõningad suunad, ning uuritud nende võimekust ja tulemuslikkust erinevate projektide raames. Töö tulemusena on valminud arvutusmudelid teatud tüüpi ühendite HIV proteaasi vastase aktiivsuse ja tsütotoksilisuse hindamiseks; koostatud uus sõelumismeetod; leitud potentsiaalsed ligandid HIV proteaasile ja pöördtranskriptaasile; ning kokku pandud farmakokineetiliste filtritega eeltöödeldud andmekomplekt – mugav lähtepositsioon edasisteks töödeks.With the implementation of digital chemical compound libraries, creates the need for finding compounds from them that fit the desired profile. The problem is of particular interest in drug design, where replacing the resource-intensive experiments with computational methods, would result in significant savings in time and cost. Although due to the limitations of current computational methods, it is not possible in foreseeable future to transfer all of the drug development process into computers, it is a different story with large molecular databases. An in silico method, working within a known error margin, is still capable of significantly concentrating the data set in terms of attractive compounds. That allows the use of computational methods in less stringent steps of drug development, such as finding lead compounds or drug candidates. This approach is known as virtual screening, and today it is a vast and prospective research area comprising of several paradigms and numerous individual methods. The present thesis takes a closer look on some of them, and evaluates their performance in the course of several projects. The results of the thesis include computational models to estimate the HIV protease inhibition activity and cytotoxicity of certain type of compounds; a few prospective ligands for HIV protease and reverse transcriptase; pre-filtered dataset of compounds – convenient starting point for subsequent projects; and finally a new virtual screening method was developed

NAD(P)H:QUINONE OXIDOREDUCTASE (NQO1)-DIRECTED LAVENDAMYCIN ANTITUMOR AGENTS: STRUCTURE-BASED DESIGN, MOLECULAR MODELING AND STRUCTURE-ACTIVITY STUDIES

NAD(P)H:quinone oxidoreductase 1 (NQO1) is a two-electron reductase that catalyzes an NAD(P)H-dependent activation of many quinone-based antitumor agents. NQO1, expressed at high levels in many human solid tumors, can be used as a target for enzyme-directed bioreductive antitumor drug development. We hypothesized that lavendamycins, quinolinedione antitumor antibiotics, can be activated by NQO1 in cancer cells that overexpress NQO1 to exhibit selective toxicity toward those cells. The effects of functional group changes on the metabolism of lavendamycins by recombinant human NQO1 were studied using a spectrophotometric assay. These structure-activity relationship (SAR) studies determined key structural features that were required for lavendamycin substrate specificity. Cytotoxicity toward human colon adenocarcinoma NQO1-deficient (BE) and NQO1-rich (BE-NQ) cells was also determined using colorimetric and clonogenic assays. The best lavendamycin substrates for NQO1 were also the most selectively toxic to the BE-NQ cells compared to BE cells. To facilitate structure-based design of more optimal lavendamycin substrates and NQO1-directed lavendamycin antitumor agent development, we developed a 1H69 crystal structure-based in silico model of the NQO1 active site and performed lavendamycin-docking studies. The docking was performed using the FlexX module of SYBYL software. Lavendamycin analogues were designed as NQO1 substrates utilizing our SAR and docking data as structure-based design criteria. Docking and biological studies on the analogues were performed and were consistent suggesting the in silico model of the enzyme possessed practical predictive power. Our results also suggested practicality of the design criteria resulting in the discovery of good NQO1 substrates with selective toxicity toward BE-NQ cells. The mechanisms of NQO1-mediated selective cytotoxicity of good lavendamycin substrates in BE and BE-NQ cells were also investigated including induction of oxidative stress and apoptosis. Biomarkers of oxidative stress including formation of 8-hydroxy-2\u27-deoxyguanosine (8-oxo-2dG), an indicator of oxidative DNA damage, and depletion of reduced glutathione (GSH) were examined using an HPLC-based method and a colorimetric assay, respectively. Induction of apoptosis was examined using a colorimetric assay. Our results revealed that oxidative stress and subsequent apoptosis induction by a good lavendamycin substrate was NQO1 dependent and that the poor substrate for NQO1 caused neither oxidative stress nor apoptosis

Understanding virtual solvent through large-scale ligand discovery

Predicting new ligands and their binding poses for a protein target relies on an understanding of the physical forces that exist between the water-submerged protein and ligand. The relative favorability of these molecular and atomic interactions between the protein and ligand compared with their interactions with water determine the binding affinity, which in turn can be converted into a binding free energy. Protein-ligand binding energetics are, with varying levels of success, encoded into scoring functions, which at their best, can only partially emulate the true binding affinity of a protein-ligand binding event. In the context of virtually screening millions or hundreds of millions of drug-like ligands, molecular docking algorithms take advantage of scoring functions to rank the binding energies of these molecules relative to one another to help prioritize the most promising ligands.The focus of this dissertation is the balance between scoring function energy terms with an emphasis on water energetics, specifically the desolvation of the protein upon ligand binding. It is thought that our limited understanding of water is largely responsible for our limitations in discovering and designing drugs. This is due to the large number of roles that water can play, as well as its significant, and even dominant, contribution to protein-ligand binding energetics, which in the realm of molecular docking, is typically under-modeled or completely neglected. First, I focus on the incorporation of receptor desolvation into the standard DOCK3.7 scoring function to more accurately model protein-ligand binding interactions by including further contributions of water. This is the original implementation of Grid Inhomogeneous Solvation Theory applied to the model cavity, cytochrome c peroxidate, and spearheaded by Trent Balius and Marcus Fischer. Second, I discuss an extension of GIST in DOCK3.7, a new implementation that relies on pre-computed Gaussian-weighted GIST receptor desolvation enthalpies. This results in negligible slowdown of the standard DOCK3.7 scoring function, similar performance to the original implementation of GIST, and the identification of new ligands for the drug-like model system, AmpC β-lactamase. The work on receptor desolvation contained within these two chapters inspires the name of this thesis, and were started in my rotation and have continued until the end. Third, I focus on the use of property-matched and property-unmatched decoys for use in retrospective enrichment calculations prior to running a large-scale molecular docking virtual screen. Decoy molecules share the same physical properties as ligands that bind a protein but are topologically dissimilar to ensure that they do not actually bind the protein. What we found was that charge mismatching between ligands and decoys could bias one’s docking setup towards artifactually strong performance. Chapter 3 focuses on how we both decreased and increased the property space of decoys relative to ligands to safeguard against these docking setup biases. Fourth, I employ this knowledge of protein-ligand binding affinities to identify novel selective melatonin receptor ligands that are active in in vivo circadian rhythm assays. Finally, I discuss my current project on the CB1 cannabinoid receptor in the context of analgesia, followed by future directions

Modellierung von Metalloenzymen: 3D-QSAR-Untersuchungen an Carboanhydrase-Isoenzymen und virtuelles Screening nach Peptiddeformylase-Inhibitoren

In der modernen Arzneistoffentwicklung unterscheidet man die Phasen der Leitstrukturfindung und der Leitstrukturoptimierung. Die vorliegende Dissertationsschrift beinhaltet Beiträge zu beiden Bereichen. Der erste Teil der Arbeit befasst sich mit der Entwicklung und Evaluierung von Computermodellen zur Vorhersage von Affinität und Selektivität und entstammt daher dem Bereich der Leitstrukturoptimierung. Selektivitätsaspekte spielen eine wichtige Rolle, da sie das Risiko von Nebenwirkungen maßgeblich beeinflussen. Zur Modellierung und Vorhersage von Affinitäts- und Selektivitätsparametern wurden QSAR-Methoden angewendet. Das Modellsystem stellten Carboanhydrasen (CAs) dar; diese zinkhaltigen Hydrolasen katalysieren die reversible Hydratisierung von Kohlendioxid zu Bicarbonat und einem Proton. Sie sind daher in eine Vielzahl (patho)physiologischer Prozesse involviert und stellen interessante therapeutische Targets dar. Die zahlreichen Isoenzyme der CAs besitzen im Bereich der Bindetasche hohe Ähnlichkeiten in Bezug auf physikochemische Eigenschaften, so dass die Entwicklung selektiver Inhibitoren kein triviales Problem darstellt. Im Mittelpunkt der Untersuchungen standen insbesondere 3D-QSAR-Verfahren. Es wurden statistisch hochsignifikante und robuste Modelle abgeleitet, um Affinität und Selektivität von Sulfonamidinhibitoren bezüglich der Isoenzyme CA I, II und IV vorherzusagen. Es zeigte sich, dass die geringen Unterschiede im strukturbasierten Alignment unter Verwendung der drei Bindetaschen nur geringen Einfluss auf die statistischen Parameter besitzen und dass bessere Ergebnisse erzielt werden, wenn für alle Isoenzyme das auf CA II basierende Alignment benutzt wird anstelle des Alignments in der jeweiligen Bindetasche. Ursache hierfür ist wahrscheinlich die Vielzahl an Kristallstrukturen, die für CA II existieren und damit das Alignment verlässlicher machen. Die erhaltenen Isokonturkarten erlaubten eine Interpretation der Modelle im Hinblick auf die Bedeutung physikochemischer Eigenschaften für die Affinität/Selektivität. Der Vergleich zu qualitativen proteinbasierten Isokonturkarten unterstreicht den komplementären Charakter beider Methoden: Während die ligandbasierten QSAR-Verfahren implizit teilweise die Struktur der Bindetasche widerspiegeln, aber auch von den Eigenheiten des Trainingsdatensatzes abhängen, vermögen die proteinbasierten Analysen auch Informationen über Bereiche der Bindetasche zu geben, die keine Interaktionen mit Liganden des Trainingsdatensatzes ausbilden. Ein weiteres Ziel bestand darin, QSAR-Methoden für das Screening größerer Datenbanken zu verwenden. Dies erlaubt die Identifizierung besonders interessanter (d.h. affiner/selektiver) Kandidaten zur Synthese im Sinne einer Leitstrukturoptimierung. Für 3D-QSAR-Methoden musste zunächst ein Protokoll zur Automatisierung des Alignments entwickelt und validiert werden. Es zeigte sich hierbei, dass ein ligandbasiertes Alignment vergleichbare Ergebnisse zu manuellen stukturbasierten Alignmentmethoden erzielt. Die 3D-Modelle erwiesen sich als überlegen im Vergleich zu fragmentbasierten 2D-Methoden oder insbesondere zu den eigenschaftsbasierten 1D-Methoden. Als praktisches Anwendungsbeispiel der entwickelten Modelle wurde eine mehrere tausend Einträge umfassende virtuelle Ligandbibliothek aufgebaut und mit den leistungsfähigsten Modellen bewertet. Der zweite Teil der Arbeit beinhaltet ein virtuelles Screening nach neuartigen Inhibitoren der Peptiddeformylasen (PDFs) und gehört somit in den Bereich der Leitstrukturfindung. PDFs sind (meist) eisenhaltige Enzyme, die die Deformylierung von in Mitochondrien, Plastiden oder Bakterien synthetisierten Proteinen katalysieren. Ausgehend von Kristallstrukturen potenter PDF-Inhibitoren wurden 3D-Pharmakophormodelle entwickelt und validiert. Diese waren in der Lage, strukturell diverse, aus der Literatur bekannte Inhibitoren zu identifizieren (hinreichende Sensitivität) und gleichzeitig die zu durchsuchenden Datenbanken stark zu reduzieren (hinreichende Spezifität). Die Pharmakophormodelle wurden zum Screening von Datenbanken kommerziell erhältlicher Moleküle mit wirkstoffartigen Eigenschaften benutzt. Durch Docking und Scoring wurden schließlich aus etwa zwei Millionen Verbindungen elf Substanzen identifiziert und erworben, die einer biologischen Testung unterzogen werden sollen. Erste vorliegende Messergebnisse zeigen, dass mindestens zwei der Substanzen mit einem IC50-Wert von 60 nM bzw. 190 nM potente Inhibitoren der PDF1B aus E. coli sind. Dies belegt die Güte der Modelle und des angewendeten Screening-Protokolls. Inhibitoren der PDF könnten Anwendung als Herbizide, Antibiotika und Antimalaria-Therapeutika finden

Design, Synthese und 3D-QSARneuartiger P-gp-Modulatoren

Resistenzen gegenüber verschiedensten Arzneimitteln sind ein steigendes Problem in der Therapie von Krankheiten. Das Phänomen der Multidrug-Resistenz (MDR) tritt besonders im Zusammenhang mit der Therapie von malignen Tumoren sowie bei Infektionen durch Bakterien, Pilzen, Protozoen und Viren auf. MDR ist die simultane Entwicklung einer Resistenz gegenüber chemisch unverwandten Verbindungen und kann die Konsequenz einer Überexpression von ABC- (ATP-Binding-Cassette) Transportern sein. Der wichtigste humane ABC- Transporter ist das P-Glykoprotein (P-gp). Eine häufige und daher wichtige Form der Resistenz beruht auf der Überexpression von P-gp in Tumorzellen. Dort ist P-gp für den aktiven Auswärtstransport von Zytostatika verantwortlich. P-gp-Modulatoren sind Substanzen, die das Phänomen der MDR durch Interaktion mit P-Glykoprotein wieder aufheben können. Das Design und die Synthese einer Substanzbibliothek von ca. 100 neuartigen P-gp-Modulatoren wird in der vorliegenden Arbeit behandelt. Im ersten Schritt wurde die Leitstruktur Tariquidar (XR9576) systematisch variiert, wobei alle Teilstrukturen modifiziert wurden. Besonderes Augenmerk wurde im Folgenden auf niedermolekulare P-gp-Modulatoren mit einer deutlich verringerten molaren Masse im Vergleich zur Leitstruktur gelegt. Für zukünftige Photoaffinitätsmarkierungen wurde ein photoaktivierbarer P-gp-Modulator durch die Synthese eines Tetrahydroisochinolin-ethyl-phenylamin Derivats mit Ketoprofen dargestellt. In der Substanzbibliothek fanden sich in vitro einige Vertreter mit einer ähnlich hohen Affinität zu P-gp wie die Leitstruktur Tariquidar. Die erfolgreichsten Verbindungen besitzen eine Affinität zu P-gp im mittleren nanomolaren Bereich. Eine dieser Verbindungen zeigte auch in vivo eine starke P-gp-Inhibition. Basierend auf den biologischen Daten der neuen P-gp-Modulatoren wurden 3D-QSAR-Modelle für die Hoechst-Bindungstelle des P-gps entwickelt. Dabei kamen vor allem gitterbasierte CoMFA- und CoMSIA-Methoden zur Anwendung. Die CoMFA- und CoMSIA-Modelle ergaben sehr gute Korrelationen zwischen den Vorhersagen und den Aktivitätsdaten der Verbindungen. Aufbauend auf den Erkenntnissen der Konturdiagramme der CoMSIA-Modelle wurden Strukturvorschläge für neue P-gp-Modulatoren erarbeitet

Conformational Study Of (8α,8′β)-bis(substituted Phenyl)-lignano-9,9'-lactones By Means Of Combined Computational, Database Mining, Nmr, And Chemometric Approaches

β-(3,4-Methylenedioxybenzyl)-γ-butyrolactone (MDBL) and (-)-hinokinin (HK) were obtained by partial synthesis and characterized by 1H NMR and computational methods (conformational analysis, molecular modeling, structural data mining and chemometrics). Three conformers were detected for MDBL and nine were found for HK. The energy differences are around 1 and 2 kcal mol-1 and rotation barriers are less than 3 and 5 kcal mol-1 for MDBL and HK conformers, respectively. The geometries of these conformers, obtained from semiempirical PM3 and density functional theory (DFT) B3LYP 6-3IG** calculations agree satisfactorily with 1H NMR data (vicinal proton-proton coupling constants) and structures retrieved from the Cambridge Structural Database (torsion angles). DFT combined with some variants of the Haasnoot-de Leeuuw-Altona equations gives the best predictions for the coupling constants. The molecular conformation of MDBL, of HK, and of related systems depends not only on intramolecular interactions but also on crystal packing forces and solvent-solute interactions, in particular hydrogen bonds and polar interactions. Hydration favors more stable HK conformers, which can be important for their behavior in chemical and biological systems. © 2007 American Chemical Society.1112863166333McRae, W.D., Towers, G.H.N., Biological Activities of Lignans (1984) Phytochemistry, 23, pp. 1207-1220Ayres, D.C., Loike, J.D., (1990) Chemistry and Pharmacology of Natural Products: LignansChemical, Biological and Clinical Properties, , Cambridge University Press: New YorkWard, R.S., Lignans, Neolignans, and Related Compounds (1993) Nat. Prod. Rep, 10, pp. 1-28Ward, R.S., Lignans, Neolignans, and Related Compounds (1995) Nat. Prod. Rep, 12, pp. 183-205Ward, R.S., Lignans, Neolignans, and Related Compounds (1997) Nat. 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