Chimeric design, synthesis, and biological assays of a new nonpeptide insulin-mimetic vanadium compound to inhibit protein tyrosine phosphatase 1B

Prior to its total synthesis, a new vanadium coordination compound, called TSAG0101, was computationally designed to inhibit the enzyme protein tyrosine phosphatase 1B (PTP1B). The PTP1B acts as a negative regulator of insulin signaling by blocking the active site where phosphate hydrolysis of the insulin receptor takes place. TSAG001, [VVO2(OH)(picolinamide)], was characterized by infrared (IR) and nuclear magnetic resonance (NMR) spectroscopy; IR: ν/cm−1 3,570 (NH), 1,627 (C=O, coordinated), 1,417 (C−N), 970/842 (O=V=O), 727 δ. (pyridine ring); 13C NMR: 5 bands between 122 and 151 ppm and carbonyl C shifted to 180 ppm; and 1H NMR: 4 broad bands from 7.6 to 8.2 ppm and NH2 shifted to 8.8 ppm. In aqueous solution, in presence or absence of sodium citrate as a biologically relevant and ubiquitous chelator, TSAG0101 undergoes neither ligand exchange nor reduction of its central vanadium atom during 24 hours. TSAG0101 shows blood glucose lowering effects in rats but it produced no alteration of basal- or glucose-induced insulin secretion on β cells during in vitro tests, all of which excludes a direct mechanism evidencing the extrapancreatic nature of its activity. The lethal dose (LD50) of TSAG0101 was determined in Wistar mice yielding a value of 412 mg/kg. This value is one of the highest among vanadium compounds and classifies it as a mild toxicity agent when compared with literature data. Due to its nonsubstituted, small-sized scaffold design, its remarkable complex stability, and low toxicity; TSAG0101 should be considered as an innovative insulin-mimetic principle with promising properties and, therefore, could become a new lead compound for potential nonpeptide PTP1B inhibitors in antidiabetic drug research. In view of the present work, the inhibitory concentration (IC50) and extended solution stability will be tested

Prediction of Neurological Enzyme Targets for Known and New Compounds with a Model using Galvez's Topological Indices

The 18th International Electronic Conference on Synthetic Organic Chemistry session Computational ChemistryAlzheimer's Disease (AD), Parkinson, and other neurodegenerative diseases are a major health problem nowadays. In this sense, the discovery of new drugs for neurodiseases treatment is a goal of the major importance. Public databases, like ChEMBL, contain a large amount of data about multiplexing assays of inhibitors of a group of enzymes with special relevance in central nervous system. Mono Amino Oxidases (MAOs), Acetyl Cholinesterase (AChE), Glycogen Synthase Kinase-3 (GSK-3), AChE (AChE), and 5α-reductases (5αRs). This data conform an important information source for the application of multi-target computational models. However, almost all the computational models known focus in only one target. In this work, we developed mt-QSAR for inhibitors of 8 different enzymes promising in the treatment of different neurodiseases. In so doing, we combined by the first time the software DRAGON with Moving Average parameters with this objective. The best DRAGON model found predict with very high accuracy, specificity, and sensitivity >90% a very large data set >10000 cases in training and validation series. We also report experimental results about the assay of several 7

Identification by virtual screening of protein tyrosine phosphatase 1B and matrix metalloproteinase 13 inhibitors for the treatment of obesity and obesity-associated disorders

L'obesitat és un dels principals problemes de salut pública del segle XXI. La gran expansió econòmica de les últimes dècades en els països desenvolupats ha contribuit a l’increment del consum d’aliments poc saludables i a l’ús excessiu de tecnologies d’estalvi d’energia. Aquests canvis han generat estils de vida poc saludables i el consegüent augment de la prevalença d'obesitat. Així doncs, l'obesitat sorgeix com una resposta natural a un entorn antinatural. Amb l'augment continu de la població obesa en cada generació, la prevalença de trastorns associats a l'obesitat com la diabetis tipus II i l'artrosi també augmenta, i la perspectiva de desenvolupar una teràpia mèdica específica per a cada pacient va guanyant interès. En aquest sentit, les dianes proteïna tirosina fosfatasa 1B (PTP1B) i la metaloproteasa de la matriu 13 (MMP-13) estan implicades tant en l’obesitat com, respectivament, la diabetis mellitus de tipus II i l’artrosi. La present tesi doctoral es centra en el desenvolupament d'estratègies de cribratge virtual per tal d’identificar compostos que modulin l'activitat d'aquestes dues dianes i puguin influir positivament en l'obesitat i els trastorns associats a l'obesitat.La obesidad es uno de los principales problemas de salud pública del siglo XXI. La gran expansión económica de las últimas décadas en los países desarrollados a contribuido al incremento del consumo de alimentos poco saludables y al uso excesivo de tecnologías de ahorro de energía. Estos cambios han generado estilos de vida poco saludables y el consiguiente aumento de la prevalencia de obesidad. Así pues, la obesidad surge como una respuesta natural a un entorno antinatural. Con el aumento continuo de la población obesa en cada generación, la prevalencia de trastornos asociados a la obesidad como la diabetes tipo II y la artrosis también aumenta, y la perspectiva de desarrollar una terapia médica específica para cada paciente va ganando interés. En este sentido, las dianas proteína tirosina fosfatasa 1B (PTP1B) y la metaloproteasa de la matriz 13 (MMP-13) están implicadas tanto en la obesidad como, respectivamente, la diabetes mellitus de tipo II y la artrosis. La presente tesis doctoral se centra en el desarrollo de estrategias de cribado virtual para identificar compuestos que modulen la actividad de estas dos dianas y puedan influir positivamente en la obesidad y los trastornos asociados a la obesidad.Obesity is one of the major public health problems in the 21st century. The great economic expansion of the last decades in developed countries has contributed to the increased consumption of unhealthy foods and the excessive usage of energy-saving technologies. These have in turn led to the development of unhealthy lifestyles and the consequent increase of obesity prevalence. Thus, obesity has emerged as a natural response to an unnatural environment. With the continuous increase in obese population in each generation, the prevalence of obesity-associated disorders such as type II diabetes and osteoarthritis is also on the rise, and the prospect of developing a medical therapy specific for each patient earns increasing interest. In this regard, the targets protein tyrosine phosphatase 1B (PTP1B) and matrix metalloproteinase 13 (MMP-13) are involved in both obesity and, respectively, type II diabetes mellitus and osteoarthritis. The present doctoral thesis focuses on developing virtual screening strategies to identify compounds that modulate the activity of these two targets which may have a positive influence on both obesity and its associated disorders

Development and application of fast fuzzy pharmacophore-based virtual screening methods for scaffold hopping

The goal of this thesis was the development, evaluation and application of novel virtual screening approaches for the rational compilation of high quality pharmacological screening libraries. The criteria for a high quality were a high probability of the selected molecules to be active compared to randomly selected molecules and diversity in the retrieved chemotypes of the selected molecules to be prepared for the attrition of single lead structures. For the latter criterion the virtual screening approach had to perform “scaffold hopping”. The first molecular descriptor that was explicitly reported for that purpose was the topological pharmacophore CATS descriptor, representing a correlation vector (CV) of all pharmacophore points in a molecule. The representation is alignment-free and thus renders fast screening of large databases feasible. In a first series of experiments the CATS descriptor was conceptually extended to the three-dimensional pharmacophore-pair CATS3D descriptor and the molecular surface based SURFCATS descriptor. The scaling of the CATS3D descriptor, the combination of CATS3D with different similarity metrics and the dependence of the CATS3D descriptor on the threedimensional conformations of the molecules in the virtual screening database were evaluated in retrospective screening experiments. The “scaffold hopping” capabilities of CATS3D and SURFCATS were compared to CATS and the substructure fingerprint MACCS keys. Prospective virtual screening with CATS3D similarity searching was applied for the TAR RNA and the metabotropic glutamate receptor 5 (mGlur5). A combination of supervised and unsupervised neural networks trained on CATS3D descriptors was applied prospectively to compile a focused but still diverse library of mGluR5 modulators. In a second series of experiments the SQUID fuzzy pharmacophore model method was developed, that was aimed to provide a more general query for virtual screening than the CATS family descriptors. A prospective application of the fuzzy pharmacophore models was performed for TAR RNA ligands. In a last experiment a structure-/ligand-based pharmacophore model was developed for taspase1 based on a homology model of the enzyme. This model was applied prospectively for the screening for the first inhibitors of taspase1. The effect of different similarity metrics (Euc: Euclidean distance, Manh: Manhattan distance and Tani: Tanimoto similarity) and different scaling methods (unscaled, scaling1: scaling by the number of atoms, and scaling2: scaling by the added incidences of potential pharmacophore points of atom pairs) on CATS3D similarity searching was evaluated in retrospective virtual screening experiments. 12 target classes of the COBRA database of annotated ligands from recent scientific literature were used for that purpose. Scaling2, a new development for the CATS3D descriptor, was shown to perform best on average in combination with all three similarity metrics (enrichment factor ef (1%): Manh = 11.8 ± 4.3, Euc = 11.9 ± 4.6, Tani = 12.8 ± 5.1). The Tanimoto coefficient was found to perform best with the new scaling method. Using the other scaling methods the Manhattan distance performed best (ef (1%): unscaled: Manh = 9.6 ± 4.0, Euc = 8.1 ± 3.5, Tani = 8.3 ± 3.8; scaling1: Manh = 10.3 ± 4.1, Euc = 8.8 ± 3.6, Tani = 9.1 ± 3.8). Since CATS3D is independent of an alignment, the dependence of a “receptor relevant” conformation might also be weaker compared to other methods like docking. Using such methods might be a possibility to overcome problems like protein flexibility or the computational expensive calculation of many conformers. To test this hypothesis, co-crystal structures of 11 target classes served as queries for virtual screening of the COBRA database. Different numbers of conformations were calculated for the COBRA database. Using only a single conformation already resulted in a significant enrichment of isofunctional molecules on average (ef (1%) = 6.0 ± 6.5). This observation was also made for ligand classes with many rotatable bonds (e.g. HIV-protease: 19.3 ± 6.2 rotatable bonds in COBRA, ef (1%) = 12.2 ± 11.8). On average only an improvement from using the maximum number of conformations (on average 37 conformations / molecule) to using single conformations of 1.1 fold was found. It was found that using more conformations actives and inactives equally became more similar to the reference compounds according to the CATS3D representations. Applying the same parameters as before to calculate conformations for the crystal structure ligands resulted in an average Cartesian RMSD of the single conformations to the crystal structure conformations of 1.7 ± 0.7 Å. For the maximum number of conformations, the RMSD decreased to 1.0 ± 0.5 Å (1.8 fold improvement on average). To assess the virtual screening performance and the scaffold hopping potential of CATS3D and SURFACATS, these descriptors were compared to CATS and the MACCS keys, a fingerprint based on exact chemical substructures. Retrospective screening of ten classes of the COBRA database was performed. According to the average enrichment factors the MACCS keys performed best (ef (1%): MACCS = 17.4 ± 6.4, CATS = 14.6 ± 5.4, CATS3D = 13.9 ± 4.9, SURFCATS = 12.2 ± 5.5). The classes, where MACCS performed best, consisted of a lower average fraction of different scaffolds relative to the number of molecules (0.44 ± 0.13), than the classes, where CATS performed best (0.65 ± 0.13). CATS3D was the best performing method for only a single target class with an intermediate fraction of scaffolds (0.55). SURFCATS was not found to perform best for a single class. These results indicate that CATS and the CATS3D descriptors might be better suited to find novel scaffolds than the MACCS keys. All methods were also shown to complement each other by retrieving scaffolds that were not found by the other methods. A prospective evaluation of CATS3D similarity searching was done for metabotropic glutamate receptor 5 (mGluR5) allosteric modulators. Seven known antagonists of mGluR5 with sub-micromolar IC50 were used as reference ligands for virtual screening of the 20,000 most drug-like compounds – as predicted by an artificial neural network approach – of the Asinex vendor database (194,563 compounds). Eight of 29 virtual screening hits were found with a Ki below 50 µM in a binding assay. Most of the ligands were only moderately specific for mGluR5 (maximum of > 4.2 fold selectivity) relative to mGluR1, the most similar receptor to mGluR5. One ligand exhibited even a better Ki for mGluR1 than for mGluR5 (mGluR5: Ki > 100 µM, mGluR1: Ki = 14 µM). All hits had different scaffolds than the reference molecules. It was demonstrated that the compiled library contained molecules that were different from the reference structures – as estimated by MACCS substructure fingerprints – but were still considered isofunctional by both CATS and CATS3D pharmacophore approaches. Artificial neural networks (ANN) provide an alternative to similarity searching in virtual screening, with the advantage that they incorporate knowledge from a learning procedure. A combination of artificial neural networks for the compilation of a focused but still structurally diverse screening library was employed prospectively for mGluR5. Ensembles of neural networks were trained on CATS3D representations of the training data for the prediction of “mGluR5-likeness” and for “mGluR5/mGluR1 selectivity”, the most similar receptor to mGluR5, yielding Matthews cc between 0.88 and 0.92 as well as 0.88 and 0.91 respectively. The best 8,403 hits (the focused library: the intersection of the best hits from both prediction tasks) from virtually ranking the Enamine vendor database (ca. 1,000,000 molecules), were further analyzed by two self-organizing maps (SOMs), trained on CATS3D descriptors and on MACCS substructure fingerprints. A diverse and representative subset of the hits was obtained by selecting the most similar molecules to each SOM neuron. Binding studies of the selected compounds (16 molecules from each map) gave that three of the molecules from the CATS3D SOM and two of the molecules from the MACCS SOM showed mGluR5 binding. The best hit with a Ki of 21 µM was found in the CATS3D SOM. The selectivity of the compounds for mGluR5 over mGluR1 was low. Since the binding pockets in the two receptors are similar the general CATS3D representation might not have been appropriate for the prediction of selectivity. In both SOMs new active molecules were found in neurons that did not contain molecules from the training set, i. e. the approach was able to enter new areas of chemical space with respect to mGluR5. The combination of supervised and unsupervised neural networks and CATS3D seemed to be suited for the retrieval of dissimilar molecules with the same class of biological activity, rather than for the optimization of molecules with respect to activity or selectivity. A new virtual screening approach was developed with the SQUID (Sophisticated Quantification of Interaction Distributions) fuzzy pharmacophore method. In SQUID pairs of Gaussian probability densities are used for the construction of a CV descriptor. The Gaussians represent clusters of atoms comprising the same pharmacophoric feature within an alignment of several active reference molecules. The fuzzy representation of the molecules should enhance the performance in scaffold hopping. Pharmacophore models with different degrees of fuzziness (resolution) can be defined which might be an appropriate means to compensate for ligand and receptor flexibility. For virtual screening the 3D distribution of Gaussian densities is transformed into a two-point correlation vector representation which describes the probability density for the presence of atom-pairs, comprising defined pharmacophoric features. The fuzzy pharmacophore CV was used to rank CATS3D representations of molecules. The approach was validated by retrospective screening for cyclooxygenase 2 (COX-2) and thrombin ligands. A variety of models with different degrees of fuzziness were calculated and tested for both classes of molecules. Best performance was obtained with pharmacophore models reflecting an intermediate degree of fuzziness. Appropriately weighted fuzzy pharmacophore models performed better in retrospective screening than CATS3D similarity searching using single query molecules, for both COX-2 and thrombin (ef (1%): COX-2: SQUID = 39.2., best CATS3D result = 26.6; Thrombin: SQUID = 18.0, best CATS3D result = 16.7). The new pharmacophore method was shown to complement MOE pharmacophore models. SQUID fuzzy pharmacophore and CATS3D virtual screening were applied prospectively to retrieve novel scaffolds of RNA binding molecules, inhibiting the Tat-TAR interaction. A pharmacophore model was built up from one ligand (acetylpromazine, IC50 = 500 µM) and a fragment of another known ligand (CGP40336A), which was assumed to bind with a comparable binding mode as acetylpromazine. The fragment was flexible aligned to the TAR bound NMR conformation of acetylpromazine. Using an optimized SQUID pharmacophore model the 20,000 most druglike molecules from the SPECS database (229,658 compounds) were screened for Tat-TAR ligands. Both reference inhibitors were also applied for CATS3D similarity searching. A set of 19 molecules from the SQUID and CATS3D results was selected for experimental testing. In a fluorescence resonance energy transfer (FRET) assay the best SQUID hit showed an IC50 value of 46 µM, which represents an approximately tenfold improvement over the reference acetylpromazine. The best hit from CATS3D similarity searching showed an IC50 comparable to acetylpromazine (IC50 = 500 µM). Both hits contained different molecular scaffolds than the reference molecules. Structure-based pharmacophores provide an alternative to ligand-based approaches, with the advantage that no ligands have to be known in advance and no topological bias is introduced. The latter is e.g. favorable for hopping from peptide-like substrates to drug-like molecules. A homology model of the threonine aspartase taspase1 was calculated based on the crystal structures of a homologous isoaspartyl peptidase. Docking studies of the substrate with GOLD identified a binding mode where the cleaved bond was situated directly above the reactive N-terminal threonine. The predicted enzyme-substrate complex was used to derive a pharmacophore model for virtual screening for novel taspase1 inhibitors. 85 molecules were identified from virtual screening with the pharmacophore model as potential taspase1- inhibitors, however biochemical data was not available before the end of this thesis. In summary this thesis demonstrated the successful development, improvement and application of pharmacophore-based virtual screening methods for the compilation of molecule-libraries for early phase drug development. The highest potential of such methods seemed to be in scaffold hopping, the non-trivial task of finding different molecules with the same biological activity.Ziel dieser Arbeit war die Entwicklung, Untersuchung und Anwendung von neuen virtuellen Screening-Verfahren für den rationalen Entwurf hoch-qualitativer Molekül-Datenbanken für das pharmakologische Screening. Anforderung für eine hohe Qualität waren eine hohe a priori Wahrscheinlichkeit für das Vorhandensein aktiver Moleküle im Vergleich zu zufällig zusammengestellten Bibliotheken, sowie das Vorhandensein einer Vielfalt unterschiedlicher Grundstrukturen unter den selektierten Molekülen, um gegen den Ausfall einzelner Leitstrukturen in der weiteren Entwicklung abgesichert zu sein. Notwendig für die letztere Eigenschaft ist die Fähigkeit eines Verfahrens zum „Grundgerüst-Springen“. Der erste Molekül-Deskriptor, der explizit für das „Grundgerüst-Springen“ eingesetzt wurde war der CATS Deskriptor – ein topologischer Korrelations-Vektor („correlation vector“, CV) über alle Pharmakophor-Punkte eines Moleküls. Der Vergleich von Molekülen über den CATS Deskriptor geschieht ohne eine Überlagerung der Moleküle, was den effizienten Einsatz solcher Verfahren für sehr große Molekül-Datenbanken ermöglicht. In einer ersten Serie von Versuchen wurde der CATS Deskriptor erweitert zu dem dreidimensionalen CATS3D Deskriptor und dem auf der Molekül-Oberfläche basierten SURFCATS Deskriptor. In retrospektiven Studien wurde für diese Deskriptoren der Einfluss verschiedener Skalierungs-Methoden, die Kombination mit unterschiedlichen Ähnlichkeits- Metriken und die Auswirkung verschiedener dreidimensionaler Konformationen untersucht. Weiter wurden das Potential der entwickelten Deskriptoren CATS3D und SURFCATS im „Grundgerüst-Springen“ mit CATS und dem Substruktur-Fingerprint MACCS keys verglichen. Prospektive Anwendungen der CATS3D Ähnlichkeitssuche wurden für die TARRNA und den metabotropen Glutamat Rezeptor 5 (mGluR5) durchgeführt. Eine Kombination von überwachten und unüberwachten neuronalen Netzen wurde prospektiv für die Zusammenstellung einer fokussierten aber dennoch diversen Bibliothek von mGluR5 Modulatoren eingesetzt. In einer zweiten Reihe von Versuchen wurde der SQUID Fuzzy Pharmakophor Ansatz entwickelt, mit dem Ziel zu einer noch generelleren Molekül- Beschreibung als mit den Deskriptoren aus der CATS Familie zu gelangen. Eine prospektive Anwendung der „Fuzzy Pharmakophor“ Methode wurde für die TAR-RNA durchgeführt. In einem letzten Versuch wurde für Taspase1 ein Struktur-/Liganden-basiertes Pharmakophor- Modell auf der Grundlage eines Homologie-Modells des Enzyms entwickelt. Dieses wurde für das prospektive Screening nach Taspase1-Inhibitoren eingesetzt. Der Einfluss verschiedener Ähnlichkeits-Metriken (Euk: Euklidische Distanz, Manh: Manhattan Distanz, Tani: Tanimoto Ähnlichkeit) und verschiedener Skalierungs-Methoden (Ohne-Skalierung, Skalierung1: Skalierung aller Werte nach der Anzahl Atome, Skalierung2: Skalierung der Werte eines Paares von Pharmakophor-Punkten entsprechend der Summe aller Pharmakophor-Punkte mit denselben Pharmakophor-Typen) auf die Ähnlichkeits-Suche mit CATS3D wurde in retrospektiven virtuellen Screening Experimenten untersucht. Für diesen Zweck wurden 12 verschiedene Klassen von Rezeptoren und Enzymen aus der COBRA Datenbank von annotierten Liganden aus der jüngeren wissenschaftlichen Literatur eingesetzt. Skalierung2, eine neue Entwicklung für CATS3D, zeigte im Durchschnitt die beste Performanz in Kombination mit allen drei Ähnlichkeits-Metriken (Anreicherungs-Faktor ef (1%): Manh = 11,8 ± 4,3; Euk = 11,9 ± 4,6; Tani = 12,8 ± 5,1). Die Kombination von Skalierung2 mit dem Tanimoto Ähnlichkeits-Koeffizienten lieferte die besten Ergebnisse. In Kombination mit den anderen Skalierungen brachte die Manhattan Distanz die besten Ergebnisse (ef (1%): Ohne-Skalierung: Manh = 9,6 ± 4,0; Euk = 8,1 ± 3,5; Tani = 8,3 ± 3,8; Skalierung1: Manh = 10,3 ± 4,1; Euk = 8,8 ± 3,6; Tani = 9,1 ± 3,8). Da die CATS3D Ähnlichkeits-Suche unabhängig von der Überlagerung einzelner Moleküle ist, könnte ebenfalls eine gewisse Unabhängigkeit von der vorhandenen 3D Konformation bestehen. Eine solche Unabhängigkeit wäre interessant um die zeitaufwendige Berechnung multipler Konformationen zu umgehen. Um diese Hypothese zu untersuchen wurden Co-Kristalle von Liganden aus 11 Klassen von Rezeptoren und Enzymen ausgewählt, um als Anfrage-Strukturen im virtuellen Screening in der COBRA Datenbank zu dienen. Verschiedene Versionen der COBRA Datenbank mit unterschiedlicher Anzahl Konformationen wurden berechnet. Bereits mit einer einzigen Konformation pro Molekül konnte im Mittel eine deutliche Anreicherung an aktiven Molekülen beobachte werden (ef (1%) = 6,0 ± 6,5). Diese Beobachtung beinhaltete auch Klassen von Molekülen mit vielen rotierbaren Bindungen. (z.B. HIV-Protease: 19,3 ± 6,2 rotierbare Bindungen in COBRA, ef (1%) = 12,2 ± 11,8). Im Mittel konnten dazu bei Verwendung der maximalen Anzahl Konformationen (durchschnittlich 37 Konformationen / Molekül) nur eine Verbesserung von 1.1 festgestellt werden. Nach der CATS3D Ähnlichkeit wurden die inaktiven Moleküle im gleichen Maß ähnlicher zu den Referenzen als die aktiven Moleküle. Zum Vergleich konnte durch Verwendung multipler statt einzelner Konformationen eine 1,8-fache Verbesserung des RMSD zu den Konformationen aus den Kristall-Struktur Konformationen erreicht werden (einzelne Konformationen: 1,7 ± 0,7 Å; max. Konformationen: 1,0 ± 0,5 Å). Um die Leistungsfähigkeit von CATS3D und SURFCATS im virtuellen Screening und im Grundgerüst-Springen zu beurteilen, wurden diese Deskriptoren mit CATS und den MACCS keys, einem Fingerprint basierend auf exakten chemischen Substrukturen, verglichen. Für die retrospektive Analyse wurden 10 Klassen von Rezeptoren und Enzymen aus der COBRA Datenbank ausgewählt. Nach den mittleren Anreicherungs-Faktoren ergaben sich für MACCS die besten Resultate (ef (1%): MACCS = 17,4 ± 6,4; CATS = 14,6 ± 5,4; CATS3D = 13,9 ± 4,9; SURFCATS = 12,2 ± 5,5). Es zeigte sich, dass die Klassen, in denen MACCS die besten Ergebnisse erzielen konnte, einen geringen gemittelten Anteil von verschiedenen Grundgerüsten aufwiesen im Verhältnis zu der Anzahl an Molekülen (0,44 ± 0,13) als die Klassen, in denen CATS am besten war (0,65 ± 0,13). CATS3D war nur in einer Klasse mit einem mittleren Anteil von Grundgerüsten (0,55) die beste Methode. SURFCATS war für keine Klasse besser als alle anderen Methoden. Diese Ergebnisse deuten darauf hin, dass Methoden wie CATS und CATS3D besser geeignet sind, um neue Grundgerüste zu finden. Es konnte weiter gezeigt werden, dass sich die Methoden einander ergänzen, dass also mit jeder Methode Grundgerüste gefunden werden konnten, die mit keiner der anderen Methoden gefunden werden konnten. Eine prospektive Anwendung wurde für CATS3D in der Suche nach neuen allosterischen Modulatoren des metabotropen Glutamat Rezeptors 5 (mGluR5) durchgeführt. Sieben bekannte allosterische mGluR5 Antagonisten mit sub-mikromolaren IC50 Werten wurde als Referenzen eingesetzt. Das virtuelle Screening wurde auf den 20.000 von einem künstlichen neuronalen Netz als am wirkstoff-artigsten vorhergesagten Molekülen der Asinex Datenbank (194.563 Moleküle) durchgeführt. Acht der 29 gefundenen Hits aus dem virtuellen Screening zeigten Ki Werte unter 50 µM in einem Bindungs-Assay. Die Mehrheit der Liganden zeigte nur eine geringe Selektivität (Maximum > 4,2-fach) gegenüber mGluR1, dem ähnlichsten Rezeptor zu mGluR5. Einer der Liganden zeigte einen besseren Ki für mGluR1 als für mGluR5 (mGluR5: Ki > 100 µM, mGluR1: Ki = 14 µM). Alle gefundenen Moleküle zeigten verschiedene Grundgerüste als die Referenz Moleküle. Es konnte gezeigt werden, dass die zusammengestellte Bibliothek von den MACCS keys als unterschiedlich zu den Referenz Strukturen betrachtet wurden, von CATS und CATS3D aber noch als isofunktional betracht wurden. Künstliche neuronal Netze („artificial neural net“, ANN) bieten eine Alternative zur Ähnlichkeits-Suche im virtuellen Screening mit dem Vorteil, dass in einer Serie von Liganden enthaltenes implizites Wissen über eine Lernprozedur in ein Modell integrierte werden kann. Eine Kombination von ANNs für die Zusammenstellung einer fokussierten aber dennoch diversen Molekül-Bibliothek wurde prospektiv für die Suche nach mGluR5 Antagonisten eingesetzt. Gruppen von ANNs wurden auf den Basis von CATS3D Repräsentationen für die Vorhersage von „mGluR5-artigkeit“ und „mGluR5/mGluR1 Selektivität“ trainiert. Dabei ergaben sich Matthews cc zwischen 0,88 und 0,92 sowie zwischen 0,88 und 0,91. Die besten 8.403 Hits (die Schnittmenge der besten Hits aus beiden Vorhersagen) aus einem virtuellen Screening der Enamine Datenbank (ca. 1.000.000 Moleküle) ergab die fokussierte Bibliothek. Diese wurde weiter mit Selbstor

INTEGRATING CHEMICAL, BIOLOGICAL AND PHYLOGENETIC SPACES OF AFRICAN NATURAL PRODUCTS TO UNDERSTAND THEIR THERAPEUTIC ACTIVITY

INTEGRATING CHEMICAL, BIOLOGICAL AND PHYLOGENETIC SPACES OF AFRICAN NATURAL PRODUCTS TO UNDERSTAND THEIR THERAPEUTIC ACTIVITY Fatima Magdi Hamza Baldo This research aims to utilise ligand-based target prediction to (i) understand the mechanism of action of African natural products (ANPs), (ii) help identify patterns of phylogenetic use in African traditional medicine and (iii) elucidate the mechanism of action of phenotypically active small molecules and natural products with anti-trypanosomal activity. In Chapter 2 the objective was to utilise ligand-based target prediction to understand the mechanism of action of natural products (NPs) from African medicinal plants used against cancer. The Random Forest classifier used in this work compares the similarity of the input compounds from the natural product dataset with compound-target combinations in the training set. The more similar they are in structure, the more likely they are to modulate the same target. Natural products from plants used against cancer in Africa were predicted to modulate targets and pathways directly associated with the disease, thus understanding their mechanism of action e.g. “flap endonuclease 1” and “Mcl-1”. The “Keap1-Nrf2 Pathway” and “apoptosis modulation by HSP70”, two pathways previously linked to cancer (which are not currently targeted by marketed drugs, but have been of increasing interest in recent years) were predicted to be modulated by ANPs. In Chapter 3, we aimed to identify phylogenetic patterns in medicinal plant use and the role this plays in predicting medicinal activity. We combined chemical, predicted target and phylogenetic information of the natural products to identify patterns of use for plant families containing plant species used against cancer in African, Malay and Indian (Ayurveda) traditional medicine. Plant families that are close phylogenetically were found to produce similar natural products that act on similar targets regardless of their origin. Additionally, phylogenetic patterns were identified for African traditional plant families with medicinal species used against cancer, malaria and human African trypanosomiasis (HAT). We identified plant families that have more medicinal species than would statistically be expected by chance and rationalised this by linking their activity to their unique phyto-chemistry e.g. the napthyl-isoquinoline alkaloids, uniquely produced by Acistrocladaceae and Dioncophyllaceae, are responsible for anti-malarial and anti-trypanosome activity. In Chapter 4, information from target prediction and experimentally validated targets was combined with orthologue data to predict targets of phenotypically active small molecules and natural products screened against Trypanosoma brucei. The predicted targets were prioritised based on their essentiality for the survival of the T. brucei parasite. We predicted orthologues of targets that are essential for the survival of the trypanosome e.g. glycogen synthase kinase 3 (GSK3) and rhodesain. We also identified the biological processes predicted to be perturbed by the compounds e.g. “glycolysis”, “cell cycle”, “regulation of symbiosis, encompassing mutualism through parasitism” and “modulation of development of symbiont involved in interaction with host”. In conclusion, in silico target prediction can be used to predict protein targets of natural products to understand their molecular mechanism of action. Phylogenetic information and phytochemical information of medicinal plants can be integrated to identify plant families with more medicinal species than would be expected by chance

Emerging Chemical Patterns for Virtual Screening and Knowledge Discovery

The adaptation and evaluation of contemporary data mining methods to chemical and biological problems is one of major areas of research in chemoinformatics. Currently, large databases containing millions of small organic compounds are publicly available, and the need for advanced methods to analyze these data increases. Most methods used in chemoinformatics, e.g. quantitative structure activity relationship (QSAR) modeling, decision trees and similarity searching, depend on the availability of large high-quality training data sets. However, in biological settings, the availability of these training sets is rather limited. This is especially true for early stages of drug discovery projects where typically only few active molecules are available. The ability of chemoinformatic methods to generalize from small training sets and accurately predict compound properties such as activity, ADME or toxicity is thus crucially important. Additionally, biological data such as results from high-throughput screening (HTS) campaigns is heavily biased towards inactive compounds. This bias presents an additional challenge for the adaptation of data mining methods and distinguishes chemoinformatics data from the standard benchmark scenarios in the data mining community. Even if a highly accurate classifier would be available, it is still necessary to evaluate the predictions experimentally. These experiments are both costly and time-consuming and the need to optimize resources has driven the development of integrated screening protocols which try to minimize experimental efforts but still reaching high hit rates of active compounds. This integration, termed “sequential screening” benefits from the complementary nature of experimental HTS and computational virtual screening (VS) methods. In this thesis, a current data mining framework based on class-specific nominal combinations of attributes (emerging patterns) is adapted to chemoinformatic problems and thoroughly evaluated. Combining emerging pattern methodology and the well-known notion of chemical descriptors, emerging chemical patterns (ECP) are defined as class- specific descriptor value range combinations. Each pattern can be thought of as a region in chemical space which is dominated by compounds from one class only. Based on chemical patterns, several experiments are presented which evaluate the performance of pattern-based knowledge mining, property prediction, compound ranking and sequential screening. ECP-based classification is implemented and evaluated on four activity classes for the prediction of compound potency levels. Compared to decision trees and a Bayesian binary QSAR method, ECP-based classification produces high accuracy in positive and negative classes even on the basis of very small training set, a result especially valuable to chemoinformatic problems. The simple nature of ECPs as class-specific descriptor value range combinations makes them easily interpretable. This is used to related ECPs to changes in the interaction network of protein-ligand complexes when the binding conformation is replaced by a computer-modeled conformation in a knowledge mining experiment. ECPs capture well-known energetic differences between binding and energy-minimized conformations and additionally present new insight into these differences on a class level analysis. Finally, the integration of ECPs and HTS is evaluated in simulated lead-optimization and sequential screening experiments. The high accuracy on very small training sets is exploited to design an iterative simulated lead optimization experiment based on experimental evaluation of randomly selected small training sets. In each iteration, all compounds predicted to be weakly active are removed and the remaining compound set is enriched with highly potent compounds. On this basis, a simulated sequential screening experiment shows that ECP-based ranking recovers 19% of available compounds while reducing the “experimental” effort to 0.2%. These findings illustrate the potential of sequential screening protocols and hopefully increase the popularity of this relatively new methodology

Combined in silico/in vitro screening tools for identification of new insulin receptor ligands

Die Interaktion von Insulin mit dem extrazellulären Teil des Insulinrezeptors ist ein entscheidender Schritt des Insulin-Signalweges. Der Insulinrezeptor wird daraufhin autophosphoryliert und die intrazelluläre Tyrosinkinasedomäne wird aktiviert. Im Jahr 1999 publizierten Zhang et al. einen Wirkstoff der in einem Pilzextrakt gefunden wurde und den humanen Insulinrezeptor aktivieren kann, indem er direkt mit der intrazellulären Domäne der beta-Subeinheit interagiert. Diese Substanz (Demethylasterriquinone B-1, DMAQ-B1) ist in der Lage den Blutzuckerspiegel in Mausmodellen für Typ-2 Diabetes zu senken. In den letzten Jahren wurden Strukturen und Aktivitätswerte zu ca. 100 Derivaten dieser Substanz publiziert. Die meisten dieser Verbindungen enthalten eine Quinon-Substruktur, die zu toxischen Nebenwirkungen führen könnte. Da die Behandlung von Typ-2 Diabetes die Langzeittherapie mit Antidiabetes-Medikamenten beinhaltet, wäre es vorteilhaft, Insulinrezeptor aktivierende Wirkstoffe aus einer anderen Strukturklasse zu finden. Das Ziel dieser Dissertation war die Entwicklung von Computermodellen, die zur Identifizierung von neuen, Insulin- imitierenden Wirkstoffen führen können, sowie die anschließende Validierung der Modelle in biologischen (zellbasierten) Experimenten. Drei unterschiedliche ligandenbasierte Methoden, nämlich Self-organizing Maps, Fingerprint- sowie Shape-ähnlichkeit, wurden verwendet um in einer großen kommerziellen Datenbank nach potenziellen Insulinrezeptor aktivierenden Wirkstoffen zu suchen. Durch die Testung von 13 repräsentativen Verbindungen der identifizierten Substanzklassen konnten wir drei Strukturen identifizieren, die Akt, eine downstream Kinase des aktivierten Insulinrezeptors aktivierten. Eine dieser Substanzen war in der Lage die Glukoseaufnahme in Muskelzellen zu verstärken. Derivate dieser Struktur wurden untersucht, um weiterführende Informationen über Struktur-Aktivitätsbeziehungen zu erhalten. Zusätzlich wurde die Zytotoxizität der Substanzen getestet, um zu zeigen, dass die Insulin imitierende Aktivität der identifizierten Moleküle nicht mit toxischen Effekten korreliert.The binding of insulin to the extracellular part of the insulin receptor is a key step in the insulin signalling pathway. Upon binding, the receptor is autophosphorylated and the intracellular tyrosine kinase is activated. In 1999, Zhang et al. published a small molecule identified from a fungal extract, which activates the human insulin receptor by binding directly to the intracellular domain of its beta-subunit. This compound (demethylasterriquinone B-1, DMAQ-B1) was shown to lower blood glucose levels in mouse models of type 2 diabetes mellitus. During the last years, structures and activities of approximately 100 derivatives of this compound have been published. Most of these structures contained a quinone substructure, which might cause toxic side effects. Since treatment of type 2 diabetes includes long-term administration of anti-diabetic compounds, it would be beneficial to find compounds with a different type of structure which activate the insulin receptor. The aim of this dissertation was to build computational models which can be used to screen for new insulin-mimetic compounds and subsequent validation of the models by testing some of the obtained hits in relevant biological (i.e. cell-based) experiments. Three different ligand based computational methods, namely self-organizing maps, fingerprint similarity and shape similarity, have been used to screen a large vendor database for potential insulin receptor activating compounds. By testing 13 representative compounds from the identified scaffolds we found three compounds which are able to activate Akt kinase, an important downstream target of the activated insulin receptor. One of the compounds increased glucose uptake in muscle cells. Derivatives of these compounds were further investigated to gain information on structure activity relationships. Additionally, the toxicity of the compounds in cells was assessed to show that the insulin-mimetic activity of our identified molecules is not correlated with toxic effects

Development of Virtual Screening and In Silico Biomarker Identification Model for Pharmaceutical Agents

Ph.DDOCTOR OF PHILOSOPH