Pharmacogenetic modeling of human cytochrome P450 2D6; On the force of variation in inducing toxicity

Understanding the way in which drugs are metabolized by CYP2D6 and hence the underlying mechanisms that define potential toxicity is crucial to avoid adverse reactions. The high occurrence of CYP2D6 polymorphs enhances the complexity of the toxicity assessment of a drug candidate and should be tackled from early drug discovery phase on. The research described in this PhD thesis has been performed to provide novel fundamental insights regarding the metabolic activity of CYP2D6 wild-type and several polymorphs using various state-of-the-art in silico techniques. The results of the CYP2D6-focused studies enhance our knowledge regarding the enzyme particularities, and can be used to accelerate the development of CYP2D6 modeling tools with more accurate and reliable predictions

The LeFE Algorithm: Embracing the Complexity of Gene Expression in the Interpretation of Microarray Data

The LeFE algorithm has been developed to address the complex, non-linear regulation of gene expression. Interpretation of microarray data remains a challenge, and most methods fail to consider the complex, nonlinear regulation of gene expression. To address that limitation, we introduce Learner of Functional Enrichment (LeFE), a statistical/machine learning algorithm based on Random Forest, and demonstrate it on several diverse datasets: smoker/never smoker, breast cancer classification, and cancer drug sensitivity. We also compare it with previously published algorithms, including Gene Set Enrichment Analysis. LeFE regularly identifies statistically significant functional themes consistent with known biology.National Cancer Institute's Center for Cancer Researc

Deep Artificial Neural Networks and Neuromorphic Chips for Big Data Analysis: Pharmaceutical and Bioinformatics Applications

[Abstract] Over the past decade, Deep Artificial Neural Networks (DNNs) have become the state-of-the-art algorithms in Machine Learning (ML), speech recognition, computer vision, natural language processing and many other tasks. This was made possible by the advancement in Big Data, Deep Learning (DL) and drastically increased chip processing abilities, especially general-purpose graphical processing units (GPGPUs). All this has created a growing interest in making the most of the potential offered by DNNs in almost every field. An overview of the main architectures of DNNs, and their usefulness in Pharmacology and Bioinformatics are presented in this work. The featured applications are: drug design, virtual screening (VS), Quantitative Structure–Activity Relationship (QSAR) research, protein structure prediction and genomics (and other omics) data mining. The future need of neuromorphic hardware for DNNs is also discussed, and the two most advanced chips are reviewed: IBM TrueNorth and SpiNNaker. In addition, this review points out the importance of considering not only neurons, as DNNs and neuromorphic chips should also include glial cells, given the proven importance of astrocytes, a type of glial cell which contributes to information processing in the brain. The Deep Artificial Neuron–Astrocyte Networks (DANAN) could overcome the difficulties in architecture design, learning process and scalability of the current ML methods.Galicia. Consellería de Cultura, Educación e Ordenación Universitaria; GRC2014/049Galicia. Consellería de Cultura, Educación e Ordenación Universitaria; R2014/039Instituto de Salud Carlos III; PI13/0028

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

Current state-of-the-art of the research conducted in mapping protein cavities – binding sites of bioactive compounds, peptides or other proteins

Ο σκοπός της διπλωματικής εργασίας είναι η διερεύνηση και αποτύπωση των ερευνητικών μελετών που αφορούν στον χαρακτηρισμό μιας πρωτεϊνικής κοιλότητας – κέντρου πρόσδεσης βιοδραστικών ενώσεων, πεπτιδίων ή άλλων πρωτεϊνών. Στην παρούσα εργασία χρησιμοποιήθηκε η μέθοδος της βιβλιογραφικής επισκόπησης. Παρουσιάζονται τα κυριότερα ευρήματα προηγούμενων ερευνών που σχετίζονται με τη διαδικασία σχεδιασμού φαρμάκων και τον εντοπισμό φαρμακοφόρων με βάση ένα σύνολο προσδετών. Στη συνέχεια συγκρίνονται διαδικασίες επεξεργασίας και ανάλυσης της πρωτεϊνικής κοιλότητας προγενέστερων ερευνών με τη προσέγγιση που προτάθηκε από τους Παπαθανασίου και Φωτόπουλου το 2015. Αναδεικνύονται βασικά πλεονεκτήματα της προσέγγισης αυτής, όπως η εφαρμογή του αλγορίθμου πολυδιάστατη k-means ομαδοποίηση (multidimensional k-means clustering). Η εύρεση βιβλιογραφίας βασίστηκε σε αναζήτηση επιστημονικών άρθρων σε ξενόγλωσσα επιστημονικά περιοδικά, σε κεφάλαια βιβλίων και σε διάφορα άρθρα σε ηλεκτρονικούς ιστότοπους σχετικά με τον σχεδιασμό φαρμάκων και τις κοιλότητες που απαντώνται στις πρωτεΐνες. Στην παρούσα εργασία παρουσιάζονται εν συντομία εργαλεία που εντοπίστηκαν χρησιμοποιώντας λέξεις κλειδιά όπως για παράδειγμα δυναμική πρωτεϊνικής κοιλότητας, καταλυτικό κέντρο ενός ενζύμου, πρόσδεση, πρωτεϊνική θήκη κλπ. Στη συνέχεια συγκροτήθηκε κατάλογος με τα εργαλεία βιοπληροφορικής ανάλυσης που βρέθηκαν και ακολούθησε εκτενής αναφορά επιλεκτικά σε κάποια από αυτά. Κριτήριο επιλογής αυτών των εργαλείων αποτέλεσε η ημερομηνία δημοσίευσής τους, οι αλγόριθμοι και η μεθοδολογία που χρησιμοποιούν. Τα εργαλεία αυτά κατηγοριοποιήθηκαν με βάση τις λέξεις κλειδιά που χρησιμοποιήθηκαν για την εξόρυξη των δεδομένων από την βιβλιογραφία. Τέλος πραγματοποιήθηκε συγκριτική μελέτη αυτών αναδεικνύοντας τα πλεονεκτήματα και εστιάζοντας στην περαιτέρω αξιοποίησή τους.The aim of this thesis was to report on the current state-of-the-art of the research conducted concerning mapping of protein cavities with a potential function role as binding sites of bioactive compounds, peptides or other proteins. A literature review was performed with emphasis on the relevant tools developed during the last decade. In addition, the main research findings regarding drug design and druggable targets based on binding sites are presented. Processes performed in protein cavity detection and analysis, of previous research articles, are compared with the approach described by Anaxagoras Fotopoulos and Athanasios Papathanasiou (2015). The results showed that a competitive advantage of their approach is the multidimensional k-means algorithm for clustering. For the bibliographic review the scientific knowledgebase has been used, which includes international articles and journals, book chapters, as well as online articles regarding drug design and protein cavity. Search keywords such as protein cavity dynamics, catalytic sites of enzymes, protein pocket etc. were used to identify bioinformatics tools with text mining. A catalogue of the most recently developed tools is presented followed by a brief description of selected tools. The selection criteria imposed for preparing the catalogue and the detailed description included the publication date, as well as the algorithms and the methods they use. The tools were then classified according to the search keywords. The findings of this research are discussed, and the algorithms and methods they use are compared, highlighting the advantages of protein cavity detection