Unveiling some FDA-approved drugs as inhibitors of the store-operated Ca2+ entry pathway.

The store-operated calcium entry (SOCE) pathway is an important route for generating cytosolic Ca2+ signals that regulate a diverse array of biological processes. Abnormal SOCE seem to underlie several diseases that notably include allergy, inflammation and cancer. Therefore, any modulator of this pathway is likely to have significant impact in cell biology under both normal and abnormal conditions. In this study, we screened the FDA-approved drug library for agents that share significant similarity in 3D shape and surface electrostatics with few, hitherto best known inhibitors of SOCE. This has led to the identification of five drugs that showed dose-dependent inhibition of SOCE in cell-based assay, probably through interacting with the Orai1 protein which effectively mediates SOCE. Of these drugs, leflunomide and teriflunomide could suppress SOCE significantly at clinically-relevant doses and this provides for an additional mechanism towards the therapeutic utility of these drugs as immunosuppressants. The other three drugs namely lansoprazole, tolvaptan and roflumilast, were less potent in suppressing SOCE but were more selective and thus they may serve as novel scaffolds for future development of new, more efficacious SOCE inhibitors

Drug Repurposing in Dermatology: Molecular Biology and Omics Approach

The withdrawal of several blockbuster drugs due to severe adverse effects and the failure of several developed drugs in clinical trials raised questions about the efficacy of current approaches of drug discovery. Moreover, the limitation of resources and the long and costive process of drug discovery made a lot of pharmaceutical companies to employ drug repurposing strategies to get new insights about activities that were not considered during their initial discovery. The development of therapeutics for treatment of dermatological condition is not considered as priority although it affects the lifestyle of thousands of people around the world. Serendipity and observations have contributed significantly in this field but immerse efforts have been exerted to find systematic methods to identify new indications for drugs, especially with the unprecedented progress in molecular biology and omics. So, in this chapter, we will emphasize on different approaches used for drug repositioning and how it was applied to find new therapeutics for different dermatoses

The Polypharmacology Browser PPB2: Target Prediction Combining Nearest Neighbors with Machine Learning

Here we report PPB2 as a target prediction tool assigning targets to a query molecule based on ChEMBL data. PPB2 computes ligand similarities using molecular fingerprints encoding composition (MQN), molecular shape and pharmacophores (Xfp), and substructures (ECfp4), and features an unprecedented combination of nearest neighbor (NN) searches and Naïve Bayes (NB) machine learning, together with simple NN searches, NB and Deep Neural Network (DNN) machine learning models as further options. Although NN(ECfp4) gives the best results in terms of recall in a 10-fold cross-validation study, combining NN searches with NB machine learning provides superior precision statistics, as well as better results in a case study predicting off-targets of a recently reported TRPV6 calcium channel inhibitor, illustrating the value of this combined approach. PPB2 is available to assess possible off-targets of small molecule drug-like compounds by public access at ppb2.gdb.tools

Advances and Challenges in Computational Target Prediction

Target deconvolution is a vital initial step in preclinical drug development to determine research focus and strategy. In this respect, computational target prediction is used to identify the most probable targets of an orphan ligand or the most similar targets to a protein under investigation. Applications range from the fundamental analysis of the mode-of-action over polypharmacology or adverse effect predictions to drug repositioning. Here, we provide a review on published ligand- and target-based as well as hybrid approaches for computational target prediction, together with current limitations and future directions.Medicinal Chemistr

Advances in the Development of Shape Similarity Methods and Their Application in Drug Discovery

Molecular similarity is a key concept in drug discovery. It is based on the assumption that structurally similar molecules frequently have similar properties. Assessment of similarity between small molecules has been highly effective in the discovery and development of various drugs. Especially, two-dimensional (2D) similarity approaches have been quite popular due to their simplicity, accuracy and efficiency. Recently, the focus has been shifted toward the development of methods involving the representation and comparison of three-dimensional (3D) conformation of small molecules. Among the 3D similarity methods, evaluation of shape similarity is now gaining attention for its application not only in virtual screening but also in molecular target prediction, drug repurposing and scaffold hopping. A wide range of methods have been developed to describe molecular shape and to determine the shape similarity between small molecules. The most widely used methods include atom distance-based methods, surface-based approaches such as spherical harmonics and 3D Zernike descriptors, atom-centered Gaussian overlay based representations. Several of these methods demonstrated excellent virtual screening performance not only retrospectively but also prospectively. In addition to methods assessing the similarity between small molecules, shape similarity approaches have been developed to compare shapes of protein structures and binding pockets. Additionally, shape comparisons between atomic models and 3D density maps allowed the fitting of atomic models into cryo-electron microscopy maps. This review aims to summarize the methodological advances in shape similarity assessment highlighting advantages, disadvantages and their application in drug discovery

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