New arylated benzo[h]quinolines induce anti-cancer activity by oxidative stress-mediated DNA damage

© 2016 The Author(s).The anti-cancer activity of the benzo[h]quinolines was evaluated on cultured human skin cancer (G361), lung cancer (H460), breast cancer (MCF7) and colon cancer (HCT116) cell lines. The inhibitory effect of these compounds on the cell growth was determined by the MTT assay. The compounds 3e, 3f, 3h and 3j showed potential cytotoxicity against these human cancer cell lines. Effect of active compounds on DNA oxidation and expression of apoptosis related gene was studied. We also developed a quantitative method to measure the activity of cyclin-dependent kinases-2 (CDK2) by western blotting in the presence of active compound. In addition, molecular docking revealed that benzo[h]quinolines can correctly dock into the hydrophobic pocket of the targets receptor protein aromatase and CDK2, while their bioavailability/drug-likeness was predicted to be acceptable but requires future optimization. These findings reveal that benzo[h]quinolines act as anti-cancer agents by inducing oxidative stress-mediated DNA damage

Protein kinases: Structure modeling, inhibition, and protein-protein interactions

Human protein kinases belong to a large and diverse enzyme family that contains more than 500 members. Deregulation of protein kinases is associated with many disorders, and this is why protein kinases are attractive targets for drug discovery. Due to the high conservation of the ATP binding pocket among this family, designing specific and/or selective inhibitors against certain member(s) is challenging. Several studies have been conducted on protein kinases to validate them as suitable drug targets. Although there are numerous target-validated protein kinases, the efforts to develop small molecule inhibitors have so far led to only a limited number of therapeutic agents and drug candidates. In our studies, we tried to understand the basic structural features of protein kinases using available computational tools. There are wide structural variations between different states of the same protein kinase that affect the enzyme specificity and inhibition. Many protein kinases do not yet have an available X-ray crystal structure and have not yet been validated to be drug targets. For these reasons, we developed a new homology modeling approach to facilitate modeling non-crystallized protein kinases and protein kinase states. Our homology modeling approach was able to model proteins having long amino acid sequences and multiple protein domains with reliable model quality and a manageable amount of computational time. Then, we checked the applicability of different docking algorithms (the routinely used computational methodology in virtual screening) in protein kinase studies. After performing the basic study of kinase structure modeling, we focused our research on cyclin dependent kinase 2 (CDK2) and glycogen synthase kinase-3β (GSK-3β). We prepared a non-redundant database from 303 available CDK2 PDB structures. We removed all structural anomalies and proceeded to use the CDK2 database in studying CDK2 structure in its different states, upon ATP, ligand and cyclin binding. We clustered the database based on our findings, and the CDK2 clusters were used to generate protein ligand interaction fingerprints (PLIF). We generated a PLIF-based pharmacophore model which is highly selective for CDK2 ligands. A virtual screening workflow was developed making use of the PLIF-based pharmacophore model, ligand fitting into the CDK2 active site and selective CDK2 shape scoring. We studied the structural basis for selective inhibition of CDK2 and GSK-3β. We compared the amino acid sequence, the 3D features, the binding pockets, contact maps, structural geometry, and Sphoxel maps. From this study we found 1) the ligand structural features that are required for the selective inhibition of CDK2 and GSK-3β, and 2) the amino acid residues which are essential for ligand binding and selective inhibition. We used the findings of this study to design a virtual screening workflow to search for selective inhibitors for CDK2 and GSK-3β. Because protein–protein interactions are essential in the function of protein kinases, and in particular of CDK2, we used protein–protein docking knowledge and binding energy calculations to examine CDK2 and cyclin binding. We applied this study to the voltage dependent calcium channel 1 (VDAC1) binding to Bax. We were able to provide important data relevant to future experimental researchers such as on the possibility of Bax to cross biological membranes and the most relevant amino acid residues in VDAC1 that interact with Bax

EXPLORATION OF THE SRX-PRX AXIS AS A SMALL-MOLECULE TARGET

Lung cancer is a leading cause of cancer-related mortality irrespective of gender. The Sulfiredoxin (Srx) and Peroxiredoxin (Prx) are a group of thiol-based antioxidant proteins that plays an essential role in non-small cell lung cancer. Understanding the molecular characteristics of the Srx-Prx interaction may help design the strategies for future development of therapeutic tools. Based on existing literature and preliminary data from our lab, we hypothesized that the Srx plays a critical role in lung carcinogenesis and targeting the Srx-Prx axis or Srx alone may facilitate future development of targeted therapeutics for prevention and treatment of lung cancer. First, we demonstrated the oncogenic role of Srx in urethane-induced lung carcinogenesis in genetically modified FVB mice. The Srx-null mice showed resistance to urethane-induced lung cancer. Second, we demonstrated the Srx and Prx sites important for Srx-Prx interaction. The orientation of this arm is demonstrated to cause some steric hindrance for the Srx-Prx interaction as it substantially reduces the rate of association between Srx and Prx. Finally, we carried out virtual screening to identify molecules that can successfully target Srx-Prx interaction. Multiple in-silico filters were used to minimize the number of chemicals to be tested. We identified ISO1 as an inhibitor of the Srx-Prx interaction. KD value for Srx-ISO1 interaction is calculated to be 42 nM. Together, these data helps to identify an inhibitor (ISO1) of the Srx-Prx interaction that can be further pursued to be developed as a chemotherapeutic tool

Lead optimization for new antimalarials and Successful lead identification for metalloproteinases: A Fragment-based approach Using Virtual Screening

Lead optimization for new antimalarials and Successful lead identification for metalloproteinases: A Fragment-based approach Using Virtual Screening Computer-aided drug design is an essential part of the modern medicinal chemistry, and has led to the acceleration of many projects. The herein described thesis presents examples for its application in the field of lead optimization and lead identification for three metalloproteins. DOXP-reductoisomerase (DXR) is a key enzyme of the mevalonate independent isoprenoid biosynthesis. Structure-activity relationships for 43 DXR inhibitors are established, derived from protein-based docking, ligand-based 3D QSAR and a combination of both approaches as realized by AFMoC. As part of an effort to optimize the properties of the established inhibitor Fosmidomycin, analogues have been synthesized and tested to gain further insights into the primary determinants of structural affinity. Unfortunately, these structures still leave the active Fosmidomycin conformation and detailed reaction mechanism undetermined. This fact, together with the small inhibitor data set provides a major challenge for presently available docking programs and 3D QSAR tools. Using the recently developed protein tailored scoring protocol AFMoC precise prediction of binding affinities for related ligands as well as the capability to estimate the affinities of structurally distinct inhibitors has been achieved. Farnesyltransferase is a zinc-metallo enzyme that catalyzes the posttranslational modification of numerous proteins involved in intracellular signal transduction. The development of farnesyltransferase inhibitors is directed towards the so-called non-thiol inhibitors because of adverse drug effects connected to free thiols. A first step on the way to non-thiol farnesyltransferase inhibitors was the development of an CAAX-benzophenone peptidomimetic based on a pharmacophore model. On its basis bisubstrate analogues were developed as one class of non-thiol farnesyltransferase inhibitors. In further studies two aryl binding and two distinct specificity sites were postulated. Flexible docking of model compounds was applied to investigate the sub-pockets and design highly active non-thiol farnesyltransferase inhibitor. In addition to affinity, special attention was paid towards in vivo activity and species specificity. The second part of this thesis describes a possible strategy for computer-aided lead discovery. Assembling a complex ligand from simple fragments has recently been introduced as an alternative to traditional HTS. While frequently applied experimentally, only a few examples are known for computational fragment-based approaches. Mostly, computational tools are applied to compile the libraries and to finally assess the assembled ligands. Using the metalloproteinase thermolysin (TLN) as a model target, a computational fragment-based screening protocol has been established. Starting with a data set of commercially available chemical compounds, a fragment library has been compiled considering (1) fragment likeness and (2) similarity to known drugs. The library is screened for target specificity, resulting in 112 fragments to target the zinc binding area and 75 fragments targeting the hydrophobic specificity pocket of the enzyme. After analyzing the performance of multiple docking programs and scoring functions forand the most 14 candidates are selected for further analysis. Soaking experiments were performed for reference fragment to derive a general applicable crystallization protocol for TLN and subsequently for new protein-fragment complex structures. 3-Methylsaspirin could be determined to bind to TLN. Additional studies addressed a retrospective performance analysis of the applied scoring functions and modification on the screening hit. Curios about the differences of aspirin and 3-methylaspirin, 3-chloroaspirin has been synthesized and affinities could be determined to be 2.42 mM; 1.73 mM und 522 μM respectively. The results of the thesis show, that computer aided drug design approaches could successfully support projects in lead optimization and lead identification. fragments in general, the fragments derived from the screening are docke

Prediction of Selective Neuroprotective JNK3 Inhibitory Activity of Plumbagin and Its derivatives using Insilico Computational Methods

In the present work, rational approach combining e- pharmacophore modelling and structure based virtual screening was employed on the target JNK3 to identify potential JNK3 inhibitors. The pharmacophore-based approaches are well known for their strength to propose a diverse set of molecules having diverse molecular frameworks but owing to a desired biological activity for one target. The hits were further analyzed and ranked by using dock score, binding energy, ADME parameters and MD simulations. Plumbagin derivatives have shown good potential of binding with the targeted protein as indicated by ΔGbind score. They also possessed desired ADME properties. Thereafter, MD simulation studies were carried out two best docked complexes. Based on the key amino acid residues interactions, molecular dynamics simulation sindicates that the docked complex of 5-Methoxy-2-methyl-(4(trifluoromethyl) benzylamino) naphthalene1, 4-dione and shikonin with JNK3 protein (3OY1) have a good stability in the binding pocket. The significant interaction with residue MET 149 was observed in both molecular docking and molecular dynamics simulations studies. By confirm the binding affinities of the ligand and the accurate interactions, molecular dynamics simulations valid the results of molecular docking. The results showed that the best classified compounds 5-Methoxy- 2-methyl-3-(4(trifluoromethyl)benzylamino) naphthalene1, 4-dione and shikonin with highest docking score and binding affinity and stable hydrogen bond with MET149 and hydrophobic interactions with Met146, Val79, Val145, Leu144, Ala91, Ile92, Ile124, and Leu128. relative to reference compounds. The outcome reveal that this study provides evidence for the consideration of plumbagin derivatives as pontential JNK3 inhibitors. Therefore, reliable computer-aided drug design methods could play an increasingly important role in the future drug discovery process. The Insilico studies results revealed that 5-Methoxy-2-methyl-3-(4 (trifluoromethyl) benzylamino) naphthalene1,4-dione and Shikonin as a potent, selective JNK3 inhibitors. This was found out by screening of generated pharmacophore hypothesis, molecular docking and molecular dynamics study of plumbagin and its derivatives. Further, in vitro evaluation of 5-Methoxy-2-methyl-3-(4-(trifluoromethyl) benzylamino) naphthalene1,4-dione and Shikonin is the futuristic requirement in order to perceive additional activity validation

Identification of structure activity relationships in primary screening data of high-throughput screening assays

The aim of the thesis was to identify structure activity relationships (SAR) in the primary screening data of high-throughput screening (HTS) assays. The strategy was to perform a hierarchical clustering of the molecules, assign the primary screening data to the created clusters and derive models from the clusters. The models should serve to identify singletons, clusters enriched with actives, not confirmed hits and false-negatives. Two hierarchical clustering algorithms, NIPALSTREE and hierarchical k-means have been developed and adapted for this purpose, respectively. A graphical user interface (GUI) has been implemented to extract SAR from the clustering results. Retrospective and prospective applications of the clustering approach were performed. SAR models were created by combining the clustering results with different chemoinformatic methods. NIPALSTREE projects a data set onto one dimension using principle component analysis. The data set is sorted according to the scoring vector and split at the median position into two subsets. The algorithm is applied recursively onto the subsets. The hierarchical k-means recursively separates a data set into two clusters using the k-means algorithm. Both algorithms are capable of clustering large data sets with more than a million data points. They were validated and compared to each other on the basis of different structural classes. NIPALSTREE provided with the loading vectors first insights into SAR whereas the hierarchical k-means yielded superior results. A GUI was developed allowing the display of and the navigation in the clustering results. Functionalities were integrated to analyse the clusters in the dendrogram, molecules in a cluster, and physicochemical properties of a molecule. Measures were developed to identify clusters enriched with actives, to characterize singletons and to analyse selectivity and specificity. Different protease inhibitors of the COBRA database were examined using the hierarchical k-means algorithm. Supported by similarity searches and nearest neighbour analyses thrombin inhibitor singletons were quickly isolated and displayed in the dendrogram. By scaling enrichment factors to the logarithm of the dendrogram level, clusters enriched with different structural classes of factor Xa inhibitors were simultaneously identified. The observed co-clustering of other protease inhibitors provided a deeper insight into selectivity and specificity and shows the utility of the approach for constructing focussed screening libraries. Specificity was analyzed by extracting and clustering relative frequencies of the protease inhibitors from the clusters of dendrogram level 7. A unique ligand based point of view on the pocketome of the protease enzymes was obtained. To identify not confirmed hits and false-negatives in the primary screening data of HTS assays, three assays were retrospectively analysed with the hierarchical k-means algorithm. A rule catalogue was developed judging hits in terminal clusters based on the cluster size, the percent control values of the entries in a cluster, the overall hit rate, the hit rate in the cluster and the environment of a cluster in the dendrogram. It resulted in the identification of a high proportion of not confirmed hits and provided for each hit a rating in context of related non-hits. This allows prioritizing compounds for follow-up studies. Non-hits and hits were retrieved from terminal clusters containing hits. Molecules bearing false-negative scaffolds were co-extracted and enriched. To minimize the number of false-positives in the extracted lists, Bayesian regularized artificial neutral network classification models were trained with the data. Applying the models marked improvement of enrichment factors for the false-negatives was obtained. It proofs the scaffold-hopping potential of the approach. NIPALSTREE, the hierarchical k-means algorithm and self-organising maps were prospectively applied to identify novel lead candidates for dopamine D3 receptors. Compounds with novel scaffolds and low nanomolar binding affinity (65 nM, compound 42) were identified. To provide a deeper insight into the SAR of these molecules, different alternative computational methods were employed. Support vector-based regression and partial least squares were examined. Predictive models for dopamine D2 and D3 receptor binding affinity values were obtained. Important features explaining SAR were extracted from the models. The prospective application of the models to the diverse and novel virtual screening data was of limited success only. Docking studies were performed using a homology model of the dopamine D3 receptor. The visual inspection of the binding modes resulted in the hypothesis of two alternative binding pockets for the aryl moiety of dopamine D3 receptor antagonists. A pharmacophore model was created simultaneously requiring both aryl moieties. Virtual screening with the model identified a nanomolar hit (65 nM, compound 59) corroborating the hypothesis of the two binding pockets and providing a new lead structure for dopamine D3 receptors. The presented data shows that the combined approach of hierarchically clustering a data set in combination with the subsequent usage of the clusters for model generation is suited to extract SAR from screening data. The models are successful in identifying singletons, clusters enriched with actives, not confirmed hits and false-negative scaffolds.Das Ziel der Arbeit war es, Struktur-Aktivitätsbeziehungen (SAR) in primären Screeningdaten von Hochdurchsatzscreening (HTS)- Assays zu finden. Als Strategie sollten die Moleküle hierarchisch geclustert werden, die primären Screeningdaten den gebildeten Clustern zugeordnet und Modelle aus den Clustern abgeleitet werden. Die Modelle sollten das Auffinden von Singletons, mit Hits angereicherter Cluster, nicht bestätigter Hits und falsch Negativer ermöglichen. Zu diesem Zweck wurden zwei hierarchische Clusteralgorithmen, NIPALSTREE und hierarchischer k-means, entwickelt bzw. angepasst. Eine graphische Benutzeroberfläche (GUI) wurde implementiert, um SAR aus den Ergebnissen der Clusterung abzuleiten. Retrospektive und prospektive Anwendungen wurden mit den Clusteransätzen verfolgt. SAR Modelle wurden durch Verwendung der Ergebnisse der Clusterung mit verschiedenen chemoinformatischen Verfahren erstellt. NIPALSTREE projiziert mit Hilfe der Hauptkomponentenanalyse einen Datensatz auf eine Dimension. Der Datensatz wird anhand des Scoringvektors sortiert und, basierend auf dem Median, in zwei Teilmengen aufgetrennt. Der Algorithmus wird rekursiv auf die neu gebildeten Mengen angewandt. Der hierarchische k-means Algorithmus trennt, basierend auf dem k-means Algorithmus, einen Datensatz rekursiv in zwei Cluster auf. Beide Algorithmen sind in der Lage, große Datenmengen mit mehr als einer Million Datenpunkte zu clustern. Sie wurden anhand verschiedener Strukturklassen validiert und miteinander verglichen. NIPALSTREE erbrachte mit dem Loadingvektor erste Einblicke in die SAR, wohingegen der hierarchische k-means zu besseren Ergebnissen führte. Eine GUI wurde entwickelt, die es erlaubt, die Clusterergebnisse darzustellen und darin zu navigieren. Funktionalitäten wurden bereitgestellt, um die Cluster im Dendrogramm, die Moleküle eines Clusters und die physikochemischen Eigenschaften eines Moleküls zu analysieren. Verfahren wurden entwickelt, um mit Hits angereicherte Cluster zu finden, Singletons zu charakterisieren und Selektivität und Spezifität zu analysieren. Verschiedene Proteaseinhibitoren aus der COBRA-Datenbank wurden mit dem hierarchischen k-means Algorithmus näher betrachtet. Mit Hilfe von Ähnlichkeitssuchen und nächsten Nachbaranalysen wurden Thrombininhibitorsingletons im Dendrogram in kürzester Zeit isoliert und dargestellt. Cluster, die mit verschiedenen Strukturklassen von Faktor-Xa-Inhibitoren angereichert waren, wurden, durch Skalierung des Anreicherungsfaktors auf den Logarithmus der Dendrogrammebene, gleichzeitig im Dendrogramm identifiziert. Eine Clusterung der Faktor-Xa-Inhibitoren mit anderen Proteaseinhibitoren wurde beobachtet. Sie erbrachte einen vertieften Einblick in Selektivität und Spezifität und zeigt die Anwendbarkeit des Ansatzes zur Erstellung fokussierter Screeningbibliotheken. Durch Extrahierung und Clusterung der relativen Anteile der Proteaseinhibitoren aus den Clustern von Dendrogrammebene sieben wurde die Spezifität der Proteaseinhibitoren analysiert. Eine spezifische, Liganden basierte Betrachtung des Pocketoms der Proteaseenzyme wurde erhalten. Um nicht bestätigte Hits und falsch Negative in den primären Screening Daten von HTS Assays zu finden, wurden drei Assays in Retrospektive mit dem hierarchischen k-means analysiert. Ein Regelwerk wurde entwickelt, welches Hits anhand der Clustergröße, des Prozent-Kontrollwertes der Einträge eines Clusters, der Gesamthitrate, der Hitrate in einem Cluster und der Umgebung des Clusters im Dendrogramm bewertet. Das Regelwerk führte zum Auffindung eines großen Anteils nicht bestätigter Hits. Zudem wurde für jeden Hit eine Bewertung im Kontext verwandter Nichthits erhalten. Dies erlaubt ein Priorisieren von Molekülen für Folgeuntersuchungen. Nichthits und Hits wurden aus Endcluster, die Hits enthielten, extrahiert. Moleküle mit falsch negativen Molekülgrundgerüsten wurden koextrahiert und angereichert. Um falsch Positive in den extrahierten Listen zu minimieren, wurden Bayesische regularisierte neuronale Klassifizierungsnetze mit den Daten trainiert. Die Anwendung der Modelle ergab eine deutliche Verbesserung der Anreicherungsfaktoren der falsch Negativen. Es zeigt, dass die Methode in der Lage ist, einen Molekülgrundgerüstwechsel durchzuführen. NIPALSTREE, der hierarchische k-means und selbst organisierende Karten wurden prospektiv angewandt, um neue Leitstrukturkandidaten für Dopamin-D3-Rezeptoren zu finden. Moleküle mit neuen Molekülgrundgerüsten und Bindungsaffinitäten im niedrigen nanomolaren Bereich wurden gefunden (65 nM für Molekül 42). Um einen tieferen Einblick in die SAR dieser Moleküle zu erhalten, wurden verschiede Computerverfahren verwendet. Supportvektorregression und PLS („partial least squares“) wurden untersucht. Es war möglich, voraussagende Modelle für Dopamin-D2 und D3 Bindungsaffinitäten zu erstellen. Die SAR erklärende Moleküleigenschaften konnten aus den Modellen extrahiert werden. Die prospektive Anwendung der Modelle auf die diversen und neuen virtuellen Screeningdaten war nur von begrenztem Erfolg. Dockingstudien wurden mit einem Homologiemodell des Dopamin-D3-Rezeptors durchgeführt. Die visuelle Begutachtung der Bindemoden führte zur Hypothese zweier alternativer Bindetaschen für den Aryl-Rest von Dopamin-D3-Rezeptorantagonisten. Ein Pharmakophormodell wurde erstellt, welches beide Aryl-Reste gleichzeitig benötigt. Ein virtuelles Screening mit dem Modell identifizierte einen nanomolaren Hit (65 nM für Molekül 59), welcher die Hypothese unterstützt und eine neue Leitstruktur für Dopamin-D3-Rezeptoren darstellt. Die vorgestellten Daten zeigen, dass der kombinierte Ansatz aus hierarchischer Clusterung und anschließender Verwendung der Cluster zur Modellerstellung, SAR in HTS-Daten findet. Die Modelle sind geeignet zum Auffinden von Singletons, mit Hits angereichter Cluster, nicht bestätigter Hits und falsch negativer Molekülgrundgerüste

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

Molecular Modelling, Synthesis and Evaluation of Pyridazine Derivatives as Potent Anti-Cancer Agents

The present study was designed to synthesize and investigate the cytotoxic and therapeutic effects of Pyrazolo benzpyridazine derivatives against DMH induced colon carcinoma in Sprague Dawley(SD) Rats. Docking studies were performed with VEGFR2 in order to understand the interaction of ligand with targeted receptor. Based on docking scores and chemical availability, 6 compounds were synthesised and confirmed through spectral analytical techniques (UV, IR, NMR and MASS). DPPH and ABTS antioxidant activity were performed in synthesised compounds and they showed good antioxidant activity. They were screened against Human Colon Cancer (HT29) cell line. Out of these, Pz-5 was found to be potent with promising IC50 Value of 27.64 μM against HT29 cell line. From the acute toxicity studies conducted on Female Wistar rats, the compound PZ-5 showed to be safe upto the dose of 2000mg/kg. Further in vivo studies was carried out for PZ-5 by inducing the colon carcinoma with the chemical carcinogen DMH(20mg/kg) for 4months in SD Rats and therapeutic effect of the compound PZ-5 on various haematological and enzymatic parameters were studied by treating with two dose levels (100mg/kg,200mg/kg) for 30days. The compound PZ-5 moderately increased the in vivo antioxidant levels like Reduced Glutathione (GSH), Glutathione peroxidise (GPx), Catalase (CAT), Super oxide dismutase (SOD), and Total protein, and reduced the lipid peroxidation in a dose dependent manner. The blood cell count, serum ALP, TCA cycle enzyme succinate dehydrogenase levels were also moderately altered on treatment with the compound at a dose of 200mg/kg as compared with negative control group in a dose dependent manner. These results suggested that the compound has a significant cytotoxic activity against colon cancer