Integrative bioinformatics and graph-based methods for predicting adverse effects of developmental drugs

Adverse drug effects are complex phenomena that involve the interplay between drug molecules and their protein targets at various levels of biological organisation, from molecular to organismal. Many factors are known to contribute toward the safety profile of a drug, including the chemical properties of the drug molecule itself, the biological properties of drug targets and other proteins that are involved in pharmacodynamics and pharmacokinetics aspects of drug action, and the characteristics of the intended patient population. A multitude of scattered publicly available resources exist that cover these important aspects of drug activity. These include manually curated biological databases, high-throughput experimental results from gene expression and human genetics resources as well as drug labels and registered clinical trial records. This thesis proposes an integrated analysis of these disparate sources of information to help bridge the gap between the molecular and the clinical aspects of drug action. For example, to address the commonly held assumption that narrowly expressed proteins make safer drug targets, an integrative data-driven analysis was conducted to systematically investigate the relationship between the tissue expression profile of drug targets and the organs affected by clinically observed adverse drug reactions. Similarly, human genetics data were used extensively throughout the thesis to compare adverse symptoms induced by drug molecules with the phenotypes associated with the genes encoding their target proteins. One of the main outcomes of this thesis was the generation of a large knowledge graph, which incorporates diverse molecular and phenotypic data in a structured network format. To leverage the integrated information, two graph-based machine learning methods were developed to predict a wide range of adverse drug effects caused by approved and developmental therapies

In silico methods for the prediction of drug-induced cardiotoxicity

Unexpected adverse reactions, especially unsafe cardiac effects, are a major concern of pharmaceutical companies that can prompt them to both discontinue drugs currently in development and withdraw drugs already on the market. Therefore, the safety assessment is a key stage of both the drug development process and the current regulatory framework of clinical trials. Given the importance of unforeseen acute electrophysiological effects in precipitating potentially lethal arrhythmias, the current preclinical testing stages of drug development are largely focused on their detection. However, a substantial number of drugs also affect cardiac function on many other levels, including contractility, mitochondria function and cell signalling. A number of in vitro, in vivo and in silico approaches capable of detecting different types of possible cardiovascular side effects have been proposed recently. Among those, human-based computational methods hold a great potential to increase the productivity of drug discovery pipelines, drive a more rational drug design and replace costly animal experiments that have limited translational ability for humans. Therefore, the goal of this thesis is to propose a computational approach to predict drug-induced cardiotoxicity. A multi-label machine learning classification approach is used to simultaneously predict multiple forms of clinical cardiac side effects and take into account relationships between those forms of toxicity. In the last part of this thesis, the effects of trafficking impairment, as one of the cardiotoxicity mechanisms, are then investigated using simulations of action potential models

Network-driven strategies to integrate and exploit biomedical data

[eng] In the quest for understanding complex biological systems, the scientific community has been delving into protein, chemical and disease biology, populating biomedical databases with a wealth of data and knowledge. Currently, the field of biomedicine has entered a Big Data era, in which computational-driven research can largely benefit from existing knowledge to better understand and characterize biological and chemical entities. And yet, the heterogeneity and complexity of biomedical data trigger the need for a proper integration and representation of this knowledge, so that it can be effectively and efficiently exploited. In this thesis, we aim at developing new strategies to leverage the current biomedical knowledge, so that meaningful information can be extracted and fused into downstream applications. To this goal, we have capitalized on network analysis algorithms to integrate and exploit biomedical data in a wide variety of scenarios, providing a better understanding of pharmacoomics experiments while helping accelerate the drug discovery process. More specifically, we have (i) devised an approach to identify functional gene sets associated with drug response mechanisms of action, (ii) created a resource of biomedical descriptors able to anticipate cellular drug response and identify new drug repurposing opportunities, (iii) designed a tool to annotate biomedical support for a given set of experimental observations, and (iv) reviewed different chemical and biological descriptors relevant for drug discovery, illustrating how they can be used to provide solutions to current challenges in biomedicine.[cat] En la cerca d’una millor comprensió dels sistemes biològics complexos, la comunitat científica ha estat aprofundint en la biologia de les proteïnes, fàrmacs i malalties, poblant les bases de dades biomèdiques amb un gran volum de dades i coneixement. En l’actualitat, el camp de la biomedicina es troba en una era de “dades massives” (Big Data), on la investigació duta a terme per ordinadors se’n pot beneficiar per entendre i caracteritzar millor les entitats químiques i biològiques. No obstant, la heterogeneïtat i complexitat de les dades biomèdiques requereix que aquestes s’integrin i es representin d’una manera idònia, permetent així explotar aquesta informació d’una manera efectiva i eficient. L’objectiu d’aquesta tesis doctoral és desenvolupar noves estratègies que permetin explotar el coneixement biomèdic actual i així extreure informació rellevant per aplicacions biomèdiques futures. Per aquesta finalitat, em fet servir algoritmes de xarxes per tal d’integrar i explotar el coneixement biomèdic en diferents tasques, proporcionant un millor enteniment dels experiments farmacoòmics per tal d’ajudar accelerar el procés de descobriment de nous fàrmacs. Com a resultat, en aquesta tesi hem (i) dissenyat una estratègia per identificar grups funcionals de gens associats a la resposta de línies cel·lulars als fàrmacs, (ii) creat una col·lecció de descriptors biomèdics capaços, entre altres coses, d’anticipar com les cèl·lules responen als fàrmacs o trobar nous usos per fàrmacs existents, (iii) desenvolupat una eina per descobrir quins contextos biològics corresponen a una associació biològica observada experimentalment i, finalment, (iv) hem explorat diferents descriptors químics i biològics rellevants pel procés de descobriment de nous fàrmacs, mostrant com aquests poden ser utilitzats per trobar solucions a reptes actuals dins el camp de la biomedicina

Prediction and visualization of the carcinogenic potential of chemicals with short-term omics assays

Drug candidates that induce or promote cancer formation must be identified and eliminated during the preclinical phase of drug development to minimize the risk of adverse, carcinogenic effects in patients. Genotoxic carcinogens can be identified with short-term assays. In contrast, the lifetime rodent cancer bioassay that is used to identify nongenotoxic carcinogenic substances, requires a high number of test animals and takes up to five years for completion. In addition, the lifetime rodent cancer bioassay does not provide sufficient data to evaluate the human risk if carcinogenic effects are observed in rodents. This can result in discontinuation of the development of the drug candidate or a black label warning on the drug packaging. The application of high-throughput omics methods such as transcriptomics or proteomics in toxicological studies is a promising approach for the development of short-term alternatives to the lifetime rodent cancer bioassay. However, these omics methods are difficult to use for life sciences researchers and few specialized visualization tools exist for toxicogenomics data. Furthermore, most existing studies used only a single omics platform to determine the molecular effects of carcinogens. This thesis introduces new approaches that integrate multiple omics platforms for the identification of nongenotoxic carcinogens and presents analysis and visualization tools that were specifically developed for toxicogenomics data. We performed a series of experiments to demonstrate that our multi-omics approach improves the prediction performance compared to single-omics approaches. To facilitate the access to our analysis and visualization tools, we implemented two web platforms, the ZBIT Bioinformatics Toolbox and MARCARviz. These web platforms enable toxicologists to gain new insights into the mechanisms of nongenotoxic tumor promotion. Furthermore, we demonstrated that our multi-omics approach can provide the basis of new short-term alternatives to the lifetime rodent cancer bioassay.Arzneimittelkandidaten die die Entstehung und das Wachstum von Tumoren begünstigen, müssen in der präklinischen Phase der Medikamentenentwicklung identifiziert und aus der weiteren Entwicklung ausgeschlossen werden, um das Risiko von gefährlichen, tumorfördernden Nebenwirkungen für Patienten zu minimieren. Während genotoxische Substanzen mit Schnelltests identifiziert werden können, dauert das aktuelle Standardprüfverfahren zur Erkennung von nicht-genotoxischen, karzinogenen Substanzen bis zu fünf Jahre und benötigt eine große Anzahl an Versuchstieren. Außerdem können aus dem Ergebnis keine Hinweise auf den Mechanismus gezogen werden wenn bei der Prüfung Tumore gefunden werden, was zur Einstellung der Entwicklung des Arzneimittelkandidaten oder zu einer Black-Box-Warnung auf der Verpackung führen kann. Die Anwendung von modernen Hochdurchsatz-Technologien in toxikologische Studien, Toxikogenomik genannt, ist ein vielversprechender Ansatz zur Entwicklung von Prüfverfahren, die weniger Zeit und Versuchstiere benötigen. Allerdings sind die Methoden aus der Toxikogenomik für Toxikologen oft schwierig anzuwenden. Außerdem berücksichtigten die meisten existierenden Studien nur Daten einer einzelnen omics-Technologie und es existieren nur wenige spezialisierte Visualisierungswerkzeuge für toxikogenomische Daten. Diese Arbeit stellt neue Analyse- und Visualisierungswerkzeuge vor, die spezifisch für toxikogenomische Studien entwickelt wurden, sowie integrative Ansätze, die es ermöglichen Daten von mehreren omics-Plattformen zu berücksichtigen, um die Identifikation von nicht-genotoxischen Karzinogenen zu verbessern. Wir beschreiben eine Reihe von Experimenten mit einem neuen Toxikogenomikdatensatz, um zu demonstrieren, dass unsere integrativen Ansätze die Vorhersage der Karzinogenität von Substanzen verbessern. Die Weiterentwicklung der von uns beschriebenen integrativen Verfahren bietet möglicherweise Alternativen zu dem aktuell verwendeten, zeitaufwändigen Verfahren zur Feststellung der Karzinogenität. Außerdem beschreiben wir neue Webplattformen zur Analyse und Visualisierung von Expressionsdaten aus der Toxikogenomik, die wir entwickelt haben, um Toxikologen den Zugang zu bioinformatischen Werkzeugen zu vereinfachen. Mit diesen neuen Webplattformen können Toxikologen neue Erkenntnisse über die Wirkmechanismen der nicht-genotoxischen Krebsentstehung gewinnen

Integrated olfactory receptor and microarray gene expression databases

<p>Abstract</p> <p>Background</p> <p>Gene expression patterns of olfactory receptors (ORs) are an important component of the signal encoding mechanism in the olfactory system since they determine the interactions between odorant ligands and sensory neurons. We have developed the Olfactory Receptor Microarray Database (ORMD) to house OR gene expression data. ORMD is integrated with the Olfactory Receptor Database (ORDB), which is a key repository of OR gene information. Both databases aim to aid experimental research related to olfaction.</p> <p>Description</p> <p>ORMD is a Web-accessible database that provides a secure data repository for OR microarray experiments. It contains both publicly available and private data; accessing the latter requires authenticated login. The ORMD is designed to allow users to not only deposit gene expression data but also manage their projects/experiments. For example, contributors can choose whether to make their datasets public. For each experiment, users can download the raw data files and view and export the gene expression data. For each OR gene being probed in a microarray experiment, a hyperlink to that gene in ORDB provides access to genomic and proteomic information related to the corresponding olfactory receptor. Individual ORs archived in ORDB are also linked to ORMD, allowing users access to the related microarray gene expression data.</p> <p>Conclusion</p> <p>ORMD serves as a data repository and project management system. It facilitates the study of microarray experiments of gene expression in the olfactory system. In conjunction with ORDB, ORMD integrates gene expression data with the genomic and functional data of ORs, and is thus a useful resource for both olfactory researchers and the public.</p

The metaRbolomics Toolbox in Bioconductor and beyond

Metabolomics aims to measure and characterise the complex composition of metabolites in a biological system. Metabolomics studies involve sophisticated analytical techniques such as mass spectrometry and nuclear magnetic resonance spectroscopy, and generate large amounts of high-dimensional and complex experimental data. Open source processing and analysis tools are of major interest in light of innovative, open and reproducible science. The scientific community has developed a wide range of open source software, providing freely available advanced processing and analysis approaches. The programming and statistics environment R has emerged as one of the most popular environments to process and analyse Metabolomics datasets. A major benefit of such an environment is the possibility of connecting different tools into more complex workflows. Combining reusable data processing R scripts with the experimental data thus allows for open, reproducible research. This review provides an extensive overview of existing packages in R for different steps in a typical computational metabolomics workflow, including data processing, biostatistics, metabolite annotation and identification, and biochemical network and pathway analysis. Multifunctional workflows, possible user interfaces and integration into workflow management systems are also reviewed. In total, this review summarises more than two hundred metabolomics specific packages primarily available on CRAN, Bioconductor and GitHub

Pathprinting: An integrative approach to understand the functional basis of disease

New strategies to combat complex human disease require systems approaches to biology that integrate experiments from cell lines, primary tissues and model organisms. We have developed Pathprint, a functional approach that compares gene expression profiles in a set of pathways, networks and transcriptionally regulated targets. It can be applied universally to gene expression profiles across species. Integration of large-scale profiling methods and curation of the public repository overcomes platform, species and batch effects to yield a standard measure of functional distance between experiments. We show that pathprints combine mouse and human blood developmental lineage, and can be used to identify new prognostic indicators in acute myeloid leukemia. The code and resources are available at http://​compbio.​sph.​harvard.​edu/​hidelab/​pathprin

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

Systems biology approaches to a rational drug discovery paradigm

The published manuscript is available at EurekaSelect via http://www.eurekaselect.com/openurl/content.php?genre=article&doi=10.2174/1568026615666150826114524.Prathipati P., Mizuguchi K.. Systems biology approaches to a rational drug discovery paradigm. Current Topics in Medicinal Chemistry, 16, 9, 1009. https://doi.org/10.2174/1568026615666150826114524