A Semantic Model for Federated Queries Over a Normalized Corpus

We present here a model implemented in OWL which improves information retrieval and data integration of the corpus. The model is populated with entities from CALBC and some simple queries over it are presented.
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The CALBC RDF Triple store: retrieval over large literature content

Integration of the scientific literature into a biomedical research infrastructure requires the processing of the literature, identification of the contained named entities (NEs) and concepts, and to represent the content in a standardised way.
The CALBC project partners (PPs) have produced a large-scale annotated biomedical corpus with four different semantic groups through the harmonisation of annotations from automatic text mining solutions (Silver Standard Corpus, SSC). The four semantic groups were chemical entities and drugs (CHED), genes and proteins (PRGE), diseases and disorders (DISO) and species (SPE). The content of the SSC has been fully integrated into RDF Triple Store (4,568,678 triples) and has been aligned with content from the GeneAtlas (182,840 triples), UniProtKb (12,552,239 triples for human) and the lexical resource LexEBI (BioLexicon). RDF Triple Store enables querying the scientific literature and bioinformatics resources at the same time for evidence of genetic causes, such as drug targets and disease involvement

A Semantic Model for Federated Queries Over a Normalized Corpus

We present here a model implemented in OWL which improves information retrieval and data integration of the corpus. The model is populated with entities from CALBC and some simple queries over it are presented.
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Drug repositioning and indication discovery using description logics

Drug repositioning is the discovery of new indications for approved or failed drugs. This practice is commonly done within the drug discovery process in order to adjust or expand the application line of an active molecule. Nowadays, an increasing number of computational methodologies aim at predicting repositioning opportunities in an automated fashion. Some approaches rely on the direct physical interaction between molecules and protein targets (docking) and some methods consider more abstract descriptors, such as a gene expression signature, in order to characterise the potential pharmacological action of a drug (Chapter 1). On a fundamental level, repositioning opportunities exist because drugs perturb multiple biological entities, (on and off-targets) themselves involved in multiple biological processes. Therefore, a drug can play multiple roles or exhibit various mode of actions responsible for its pharmacology. The work done for my thesis aims at characterising these various modes and mechanisms of action for approved drugs, using a mathematical framework called description logics. In this regard, I first specify how living organisms can be compared to complex black box machines and how this analogy can help to capture biomedical knowledge using description logics (Chapter 2). Secondly, the theory is implemented in the Functional Therapeutic Chemical Classification System (FTC - https://www.ebi.ac.uk/chembl/ftc/), a resource defining over 20,000 new categories representing the modes and mechanisms of action of approved drugs. The FTC also indexes over 1,000 approved drugs, which have been classified into the mode of action categories using automated reasoning. The FTC is evaluated against a gold standard, the Anatomical Therapeutic Chemical Classification System (ATC), in order to characterise its quality and content (Chapter 3). Finally, from the information available in the FTC, a series of drug repositioning hypotheses were generated and made publicly available via a web application (https://www.ebi.ac.uk/chembl/research/ftc-hypotheses). A subset of the hypotheses related to the cardiovascular hypertension as well as for Alzheimer’s disease are further discussed in more details, as an example of an application (Chapter 4). The work performed illustrates how new valuable biomedical knowledge can be automatically generated by integrating and leveraging the content of publicly available resources using description logics and automated reasoning. The newly created classification (FTC) is a first attempt to formally and systematically characterise the function or role of approved drugs using the concept of mode of action. The open hypotheses derived from the resource are available to the community to analyse and design further experiments.This work was supported by the European Molecular Biology Laboratory (EMBL)

Integration of the scientific literature into the Semantic Web: Facts from biomedical data resources

Scientific literature is the primary resource for relevant and innovative information. The integration of the literature with other data resources in the biomedical research community generates overhead that can be avoided through the used of Semantic Web Technology generating openly accessible data. Projects such as SESL and CALBC have producted significant amount of data that that are ready for exploitation.
The tutorial will teach different approaches on how to integrate the scientific literature with the content from biomedical databases, and will discuss the inferences that can be achieved. Furthermore, the tutorial will point to the resources that are ready for use and enable integration of the literature at your discretion. A good understanding of Semantic Web technology, ontologies, OWL and the existing biomedical data resources is advantageous to easily follow the tutorial

An ontology for drug-drug interactions

Proceedings of: The 6th International Workshop on Semantic Web Applications and Tools for Life Sciences (SWAT4LS 2013). Took place 2013, December 11-12, in Edinburgh, UK. The evnt Web site http://www.swat4ls.org/workshops/edinburgh2013/Drug-drug interactions form a significant risk group for adverse effects associ-ated with pharmaceutical treatment. These interactions are often reported in the literature, however, they are sparsely represented in machine-readable re-sources, such as online databases, thesauri or ontologies. These knowledge sources play a pivotal role in Natural Language Processing (NLP) systems since they provide a knowledge representation about the world or a particular do-main. While ontologies for drugs and their effects have proliferated in recent years, there is no ontology capable of describing and categorizing drug-drug in-teractions. Moreover, there is no artifact that represents all the possible mecha-nisms that can lead to a DDI. To fill this gap we propose DINTO, an ontology for drug-drug interactions and their mechanisms. In this paper we describe the classes, relationships and overall structure of DINTO. The ontology is free for use and available at https://code.google.com/p/dinto/This work was supported by the Regional Government of Madrid under the Research Network MA2VICMR [S2009/TIC-1542], by the Spanish Ministry of Education under the project MULTIMEDICA [TIN2010-20644-C03-01] and by the European Commission Seventh Framework Programme under the project TrendMiner_Enlarged (EU FP7-ICT 612336).Publicad

A novel bispecific antibody for HER2+ breast cancer: The BEAT GBR 1302

While the idea of bispecific drugs was brought up over 30 years ago, the development of formats mature enough for the clinic remained for a long time a challenge. The whole field has been hampered by major problems of manufacturability (e.g. product purity and yields) and immunogenicity. With the recent arrival of new bispecific formats, either as antibody–like molecules (containing an Fc) or scFv fragments, at least 18 bispecific molecules have entered clinical trials showing very promising results. The BEAT® format has been developed as bispecific antibodies maintaining the pharmacokinetics and the low immunogenicity of human IgG with excellent manufacturability properties. In brief, the molecule is asymmetric consisting of a Hc, a Lc and a Fc-scFv. A proprietary engineered CH3 interface mimics the natural association of the heterodimeric TCRα β chains driving heterodimerization of the Hc and Fc-scFv. CHO cell lines are generated with a volumetric productivity of several g/L and a high product purity (e.g. \u3e90% of bispecific product). Based on a built-in purification approach the BEAT molecules can be purified using a standard DSP process with yield and purity comparable to standard mAbs. The presentation will highlight a new bispecific drug targeting HER2 on tumor cellsand CD3 on cytotoxic T-cells: the GBR 1302-BEAT molecule. GBR 1302-BEAT effectively recruits cytotoxic T cells against HER2 positive breast cancer cells including the trastuzumab-resistant breast cancer cell line JIMT-1. It shows strong tumor cell lysis activity with a better in vitro potential than current HER2-targeting therapies including the ADC TDM-1. The differential killing efficacy both in vitro and in vivo of HER2 overexpressing (3+) and normal, HER2 (0) cells reveals a large therapeutic window. In addition GBR 1302 does not trigger non-specific T cell activation. The excellent manufacturing attributes and the pre-clinical efficacy and safety of GBR1302 justify further clinical development as a treatment for HER2 positive cancers