The QSARome of the receptorome: Quantitative structure-activity relationship modeling of multiple ligand sets acting at multiple receptors

Recent advances in High Throughput Screening (HTS) led to the rapid growth of chemical libraries of small molecules, which calls for improved computational tools and predictive models for Virtual Screening (VS). Thus this dissertation focuses on both the development and application of predictive Quantitative Structure-Activity Relationship (QSAR) models and aims to discover novel therapeutic agents for certain diseases. First, this dissertation adopts the combinatorial QSAR framework created by our lab, including the first application of the Distance Weighted Discrimination (DWD) method that resulted in a set of robust QSAR models for the 5-HT7 receptor. VS using these models, followed by the experimental test of identified compounds, led to the finding of five known drugs as potent 5-HT7 binders. Eventually, droperidol (Ki = 3.5 nM) and perospirone (Ki = 8.6 nM) proved to be strong 5-HT7 antagonists. Second, we intended to enhance VS hit rate. To that end, we developed a cost/benefit ratio as an evaluation performance metric for QSAR models. This metric was applied in the Decision Tree machine learning method in two ways: (1) as a benchmarking criterion to compare the prediction performances of different classifiers and (2) as a target function to build QSAR classification trees. This metric may be more suitable for imbalanced HTS data that include few active but many inactive compounds. Finally, a novel QSAR strategy was developed in response to the polygenic nature of most psychotic disorders, related mainly to G-Protein-Coupled Receptors (GPCRs), one class of molecular targets of greatest interest to the pharmaceutical industry. We curated binding data for thousands of GPCR ligands, and developed predictive QSAR models to assess the GPCR binding profiles of untested compounds that could be used to identify potential drug candidates. This comprehensive study yielded a compendium of validated QSAR predictors (the GPCR QSARome), providing effective in silico tools to search for novel antipsychotic drugs. The advances in results and procedures achieved in these studies will be integrated into the current computational strategies for rational drug design and discovery boosted by our lab, so that predictive QSAR modeling will become a reliable support tool for drug discovery programs

In silico strategies to study polypharmacology of G-protein-coupled receptors

The development of drugs that simultaneously target multiple receptors in a rational way (i.e., 'magic shotguns') is regarded as a promising approach for drug discovery to treat complex, multi-factorial and multi-pathogenic diseases. My major goal is to develop and employ different computational approaches towards the rational design of drugs with selective polypharmacology towards guanine nucleotide-binding protein (G-protein)-coupled receptors (GPCRs) to treat central nervous system diseases. Our methodologies rely on the advances in chemocentric informatics and chemogenomics to generate experimentally testable hypotheses that are derived by fusing independent lines of evidence. We posit that such hypothesis fusion approach allows us to improve the overall success rates of in silico lead identification efforts. We have developed an integrated computational approach that combines Quantitative Structure-Activity Relationships (QSAR) modeling, model-based virtual screening (VS), gene expression analysis and mining of the biological literature for drug discovery. The dissertation research described herein is focused on: (1) The development of robust data-driven Quantitative Structure-Activity Relationship (QSAR) models of single target GPCR datasets that will amount to the compendium of GPCR predictors: the GPCR QSARome; (2) The development of robust data-driven QSAR models for families of GPCRs and other trans-membrane molecular targets (i.e., sigma receptors) and the application of models as virtual screening tools for the quick prioritization of compounds for biological testing across receptor families; (3) The development of novel integrative chemocentric informatics approaches to predict receptor-mediated clinical effects of chemicals. Results indicated that our computational efforts to establish a compendium of computational predictors and devise an integrative chemocentric informatics approach to study polypharmacology in silico will eventually lead to useful and reliable tools aimed at identifying and enriching chemical libraries with compounds that have the desired activities for more than one molecular target of interest

Immune-Mediated Drug Induced Liver Injury: A Multidisciplinary Approach

This thesis presents an approach to expose relationships between immune mediated drug induced liver injury (IMDILI) and the three-dimensional structural features of toxic drug molecules and their metabolites. The series of analyses test the hypothesis that drugs which produce similar patterns of toxicity interact with targets within common toxicological pathways and that activation of the underlying mechanisms depends on structural similarity among toxic molecules. Spontaneous adverse drug reaction (ADR) reports were used to identify cases of IMDILI. Network map tools were used to compare the known and predicted protein interactions with each of the probe drugs to explore the interactions that are common between the drugs. The IMDILI probe set was then used to develop a pharmacophore model which became the starting point for identifying potential toxicity targets for IMDILI. Pharmacophore screening results demonstrated similarities between the probe IMDILI set of drugs and Toll-Like Receptor 7 (TLR7) agonists, suggesting TLR7 as a potential toxicity target. This thesis highlights the potential for multidisciplinary approaches in the study of complex diseases. Such approaches are particularly helpful for rare diseases where little knowledge is available, and may provide key insights into mechanisms of toxicity that cannot be gleaned from a single disciplinary study

Computational approaches to virtual screening in human central nervous system therapeutic targets

In the past several years of drug design, advanced high-throughput synthetic and analytical chemical technologies are continuously producing a large number of compounds. These large collections of chemical structures have resulted in many public and commercial molecular databases. Thus, the availability of larger data sets provided the opportunity for developing new knowledge mining or virtual screening (VS) methods. Therefore, this research work is motivated by the fact that one of the main interests in the modern drug discovery process is the development of new methods to predict compounds with large therapeutic profiles (multi-targeting activity), which is essential for the discovery of novel drug candidates against complex multifactorial diseases like central nervous system (CNS) disorders. This work aims to advance VS approaches by providing a deeper understanding of the relationship between chemical structure and pharmacological properties and design new fast and robust tools for drug designing against different targets/pathways. To accomplish the defined goals, the first challenge is dealing with big data set of diverse molecular structures to derive a correlation between structures and activity. However, an extendable and a customizable fully automated in-silico Quantitative-Structure Activity Relationship (QSAR) modeling framework was developed in the first phase of this work. QSAR models are computationally fast and powerful tool to screen huge databases of compounds to determine the biological properties of chemical molecules based on their chemical structure. The generated framework reliably implemented a full QSAR modeling pipeline from data preparation to model building and validation. The main distinctive features of the designed framework include a)efficient data curation b) prior estimation of data modelability and, c)an-optimized variable selection methodology that was able to identify the most biologically relevant features responsible for compound activity. Since the underlying principle in QSAR modeling is the assumption that the structures of molecules are mainly responsible for their pharmacological activity, the accuracy of different structural representation approaches to decode molecular structural information largely influence model predictability. However, to find the best approach in QSAR modeling, a comparative analysis of two main categories of molecular representations that included descriptor-based (vector space) and distance-based (metric space) methods was carried out. Results obtained from five QSAR data sets showed that distance-based method was superior to capture the more relevant structural elements for the accurate characterization of molecular properties in highly diverse data sets (remote chemical space regions). This finding further assisted to the development of a novel tool for molecular space visualization to increase the understanding of structure-activity relationships (SAR) in drug discovery projects by exploring the diversity of large heterogeneous chemical data. In the proposed visual approach, four nonlinear DR methods were tested to represent molecules lower dimensionality (2D projected space) on which a non-parametric 2D kernel density estimation (KDE) was applied to map the most likely activity regions (activity surfaces). The analysis of the produced probabilistic surface of molecular activities (PSMAs) from the four datasets showed that these maps have both descriptive and predictive power, thus can be used as a spatial classification model, a tool to perform VS using only structural similarity of molecules. The above QSAR modeling approach was complemented with molecular docking, an approach that predicts the best mode of drug-target interaction. Both approaches were integrated to develop a rational and re-usable polypharmacology-based VS pipeline with improved hits identification rate. For the validation of the developed pipeline, a dual-targeting drug designing model against Parkinson’s disease (PD) was derived to identify novel inhibitors for improving the motor functions of PD patients by enhancing the bioavailability of dopamine and avoiding neurotoxicity. The proposed approach can easily be extended to more complex multi-targeting disease models containing several targets and anti/offtargets to achieve increased efficacy and reduced toxicity in multifactorial diseases like CNS disorders and cancer. This thesis addresses several issues of cheminformatics methods (e.g., molecular structures representation, machine learning, and molecular similarity analysis) to improve and design new computational approaches used in chemical data mining. Moreover, an integrative drug-designing pipeline is designed to improve polypharmacology-based VS approach. This presented methodology can identify the most promising multi-targeting candidates for experimental validation of drug-targets network at the systems biology level in the drug discovery process

inSARa: Hierarchical Networks for the Analysis, Visualization and Prediction of Structure-Activity Relationships

Die Kenntnis von Struktur-Aktivitäts-Beziehungen (SARs) kann die Entwicklung neuer Arzneistoffe entscheidend beschleunigen. Die fortlaufend zunehmende Menge an verfügbaren Bioaktivitätsdaten enthält potentiell diese wertvollen Schlüssel-Informationen. Die Herausforderung, die es noch zu lösen gilt, ist die Auswertung dieser Daten. Für die Bewältigung dieser Dimensionen werden heutzutage computergestützte Verfahren benötigt, die automatisiert, die wichtigsten Informationen über SARs extrahieren und möglichst anschaulich und intuitiv für den medizinischen Chemiker darstellen. Das Ziel dieser Arbeit war daher, die Entwicklung einer Methode namens inSARa (Abkürzung für „intuitive networks for Structure-Activity Relationship analysis“) zur intuitiven Analyse und Visualisierung von SARs. Die Hauptmerkmale des entwickelten Verfahrens sind hierarchische Netzwerke klar-definierter Substruktur-Beziehungen auf Basis gemeinsamer pharmakophorer Eigenschaften. Hierzu wurde das Konzept des „reduzierten Graphen“ (RG) mit dem intuitiven Konzept der „maximal gemeinsamen Substruktur“ (MCS) kombiniert, wodurch ein besonderer Synergismus für die SAR-Interpretation resultiert. Dieser ermöglicht, dass der medizinische Chemiker leicht gemeinsame bzw. bioaktivitätsbeeinflussende molekulare (pharmakophore) Merkmale in großen, auch strukturell diverseren Datensätzen, die aus Hunderten oder Tausenden von Molekülen bestehen, erfassen kann. Verschiedene Analysen (z.B. basierend auf der Bioaktivitäts-Vorhersage mittels kNN-Regression) konnten eine Komplementarität oder Überlegenheit der für inSARa verwendeten molekularen Repräsentation und Ähnlichkeitserfassung zum häufig verwendeten Ansatz der Fingerprint-basierten Ähnlichkeitsanalyse belegen. Der inSARa Hybrid Ansatz, der inSARa in verschiedenen Varianten mit Fingerprint-basierten Ähnlichkeits-Netzwerken kombiniert, zeigt zudem die Vorteile auf, die aus der Kombination beider Prinzipien resultieren können. Beim Analysieren von Datensätzen aktiver Moleküle einzelner Zielstrukturen haben sich die ohne Berücksichtigung von Bioaktivitätsinformation aufgebauten inSARa-Netzwerke als wertvoll für verschiedene essentielle Aufgaben der SAR-Analyse erwiesen. Neben gemeinsamen pharmakophoren Eigenschaften lassen sich so auf Grundlage einfacher Regeln bioisosterer Austausch, sprunghafte SARs oder „SAR Hotspots“ und sogenannte „Activity Switches“ erkennen. Die verschiedenen Typen an SAR-Information können sowohl mittels interaktiver Navigation durch die hierarchisch aufgebauten Netzwerke als auch durch automatisierte Netzwerk-Analyse (inSARaauto) identifiziert werden. Der auf inSARaauto aufbauende SARdisco Score ermöglicht zudem analog zum Fingerprint-basierten SAR-Index die globale Charakterisierung der Verteilung von SAR-(Dis-)Kontinuität in inSARa-Netzwerken. Der Vergleich der inSARa-Netzwerke verschiedener Zielstrukturen auf Basis der Schnittmenge an RG-MCSs hat außerdem gezeigt, dass die für die SAR-Interpretation entwickelten inSARa-Netzwerke auch wichtige Information im Hinblick auf Polypharmakologie enthalten. Die Ergebnisse dieser Analyse bestätigen, dass dieser RG-MCS-basierte Ansatz aufgrund seiner einfachen Interpretierbarkeit und Fokussierung auf Eigenschaften, die in die Protein-Ligand-Bindung involviert sind, das Potential für die Ergänzung verfügbarer Chemogenomik-Ansätze zur ligandbasierten Analyse von Target-Ähnlichkeiten und zur Identifizierung von Kreuzreaktivitäten aufweist. Zusammenfassend ist festzustellen, dass von dem in dieser Arbeit entwickelten inSARa-Ansatz somit durch seine vielseitige Anwendbarkeit ein wichtiger Beitrag zur Entwicklung neuer und sicherer Arzneistoffe erwartet werden kann.The analysis of Structure-Activity-Relationships (SARs) of small molecules is a fundamental task in drug discovery as this this knowledge is essential for the medicinal chemist at different stages of drug development. The increasing number of bioactivity data is a valuable source for this key information. Yet, up to now, the organization and mining of these data is one of the major challenges. To tackle this issue, computational methods aiming at the automatic extraction of SARs and their subsequent visualization are needed. Therefore, the goal of this thesis was the development of a method called inSARa (abbreviation for “intuitive networks for Structure-Activity Relationship analysis”) for the intuitive SAR analysis and visualization. The main features of the approach introduced herein are hierarchical networks of clearly-defined substructure relationships based on common pharmacophoric features. The method takes advantage of the synergy resulting from the combination of reduced graphs (RG) and the intuitive concept of the maximum common substructure (MCS). Using inSARa networks, common molecular or pharmacophoric features crucial for bioactivity modification are easily identified in data sets of different size (up to thousands of molecules) and heterogeneity. Various analyses (e.g. based on the prediction of bioactivities using kNN regression) show that the way of molecular representation and perception of similarity used in inSARa is superior to the commonly used concept of fingerprint-based similarity analysis. The inSARa Hybrid approach, which combines inSARa with fingerprint-based similarity networks in different ways, highlights the advantages resulting from the combination of both concepts. When focusing on a set of active molecules at one single target, the resulting inSARa networks are shown to be valuable for various essential tasks in SAR analysis. Based on simple rules not only common pharmacophoric patterns but also bioisosteric exchanges, activity cliffs or ‘SAR hotspots’ and ‘activity switches’ are easily identified. These different types of SAR information are either identified by interactive navigation of the hierarchical networks or automated network analysis (inSARaauto). In Analogy to the fingerprint-based SAR-Index, the SAR disco Score which is based on inSARaauto globally characterize the portion of SAR (dis)continuity in inSARa networks. Additionally, inSARa networks of a large number of different targets were pairwisely compared on the basis of the portion of common RG-MCSs. The results indicate that inSARa networks which were primarily devoloped for SAR interpretation are also valuable for gaining insights in polypharmacology. The promising results of the analysis show that the RG-MCS-based concept can complement published chemogenomic approaches for ligand-based analysis of targets similarities and the identification of cross-reactivities/off-target-relationships. The advantage of the devoloped RG-MCS approach is the easy interpretability and the the fact that molecular features involved in protein-ligand binding are represented. In summary, due to the versatility and the intuitive concept, the introduced inSARa approach is expected to support and stimulate the development of new or safer drugs

VIRTUAL SCREENING AND DISCOVERY OF LEAD COMPOUNDS AS POTENTIAL DNA METHYLTRANSFERASE 1 INHIBITORS AND ANTICANCER AGENTS

Epigenetic changes consist of DNA methylation, histone modification, micro RNA and genome imprinting. DNA methylation of the CpG islands is one of the main methods of epigenetic inactivation of genes and aberrant methylations at promoter regions of tumor suppressor genes can alter gene expression and play an important role in cancer development. DNA methyltransferase I (Dnmt1) is the enzyme responsible for maintaining methylation patterns during cell division and it is overexpressed in many cancers. Thus, Dnmt1 is a promising therapeutic target for development of novel anticancer agents and epigenetic modulators. We have developed two promising class of lead candidates, compounds 5-hydroxy-2-(4-hydroxyphenethyl)-3-oxo-N-pentyl-4-(4-(trifluoromethyl)phenyl)isoindoline-1-carboxamide 47, 2-(2-(1H-indol-3-yl)ethyl)-5-hydroxy-3-oxo-N-pentyl-4-(4-(trifluoromethyl)phenyl)isoindoline- 1-carboxamide 51 and 1-(4-isopropylphenyl)-2,3,4,9-tetrahydro-1H-pyrido[3,4-b]indole 96, as potential leads compounds that can be optimized for pharmaceutical applications.

Automatic discovery of drug mode of action and drug repositioning from gene expression data

2009 - 2010The identification of the molecular pathway that is targeted by a compound, combined with the dissection of the following reactions in the cellular environment, i.e. the drug mode of action, is a key challenge in biomedicine. Elucidation of drug mode of action has been attempted, in the past, with different approaches. Methods based only on transcriptional responses are those requiring the least amount of information and can be quickly applied to new compounds. On the other hand, they have met with limited success and, at the present, a general, robust and efficient gene-expression based method to study drugs in mammalian systems is still missing. We developed an efficient analysis framework to investigate the mode of action of drugs by using gene expression data only. Particularly, by using a large compendium of gene expression profiles following treatments with more than 1,000 compounds on different human cell lines, we were able to extract a synthetic consensual transcriptional response for each of the tested compounds. This was obtained by developing an original rank merging procedure. Then, we designed a novel similarity measure among the transcriptional responses to each drug, endingending up with a “drug similarity network”, where each drug is a node and edges represent significant similarities between drugs. By means of a novel hierarchical clustering algorithm, we then provided this network with a modular topology, contanining groups of highly interconnected nodes (i.e. network communities) whose exemplars form secondlevel modules (i.e. network rich-clubs), and so on. We showed that these topological modules are enriched for a given mode of action and that the hierarchy of the resulting final network reflects the different levels of similarities among the composing compound mode of actions. Most importantly, by integrating a novel drug X into this network (which can be done very quickly) the unknown mode of action can be inferred by studying the topology of the subnetwork surrounding X. Moreover, novel potential therapeutic applications can be assigned to safe and approved drugs, that are already present in the network, by studying their neighborhood (i.e. drug repositioning), hence in a very cheap, easy and fast way, without the need of additional experiments. By using this approach, we were able to correctly classify novel anti-cancer compounds; to predict and experimentally validate an unexpected similarity in the mode of action of CDK2 inhibitors and TopoIsomerase inhibitors and to predict that Fasudil, a known and FDA-approved cardiotonic agent, could be repositioned as novel enhancer of cellular autophagy. Due to the extremely safe profile of this drug and its potential ability to traverse the blood-brain barrier, this could have strong implications in the treatment of several human neurodegenerative disorders, such as Huntington and Parkinson diseases. [edited by author]IX n.s

Antioxidant and DPPH-Scavenging Activities of Compounds and Ethanolic Extract of the Leaf and Twigs of Caesalpinia bonduc L. Roxb.

Antioxidant effects of ethanolic extract of Caesalpinia bonduc and its isolated bioactive compounds were evaluated in vitro. The compounds included two new cassanediterpenes, 1α,7α-diacetoxy-5α,6β-dihydroxyl-cass-14(15)-epoxy-16,12-olide (1)and 12α-ethoxyl-1α,14β-diacetoxy-2α,5α-dihydroxyl cass-13(15)-en-16,12-olide(2); and others, bonducellin (3), 7,4’-dihydroxy-3,11-dehydrohomoisoflavanone (4), daucosterol (5), luteolin (6), quercetin-3-methyl ether (7) and kaempferol-3-O-α-L-rhamnopyranosyl-(1Ç2)-β-D-xylopyranoside (8). The antioxidant properties of the extract and compounds were assessed by the measurement of the total phenolic content, ascorbic acid content, total antioxidant capacity and 1-1-diphenyl-2-picryl hydrazyl (DPPH) and hydrogen peroxide radicals scavenging activities.Compounds 3, 6, 7 and ethanolic extract had DPPH scavenging activities with IC50 values of 186, 75, 17 and 102 μg/ml respectively when compared to vitamin C with 15 μg/ml. On the other hand, no significant results were obtained for hydrogen peroxide radical. In addition, compound 7 has the highest phenolic content of 0.81±0.01 mg/ml of gallic acid equivalent while compound 8 showed the highest total antioxidant capacity with 254.31±3.54 and 199.82±2.78 μg/ml gallic and ascorbic acid equivalent respectively. Compound 4 and ethanolic extract showed a high ascorbic acid content of 2.26±0.01 and 6.78±0.03 mg/ml respectively.The results obtained showed the antioxidant activity of the ethanolic extract of C. bonduc and deduced that this activity was mediated by its isolated bioactive compounds