In silico models in drug development: where we are

The use and utility of computational models in drug development has significantly grown in the last decades, fostered by the availability of high throughput datasets and new data analysis strategies. These in silico approaches are demonstrating their ability to generate reliable predictions as well as new knowledge on the mode of action of drugs and the mechanisms underlying their side effects, altogether helping to reduce the costs of drug development. The aim of this review is to provide a panorama of developments in the field in the last two years.We acknowledge support from ISCIII-FEDER (CPII16/00026), the EU H2020 Programme 2014-2020 under grant agreements no. 681002 (EU-ToxRisk) and no. 676559 (ELIXIR-EXCELERATE), and the IMI2-JU under grants agreements no. 116030 (TransQST) and no. 777365 (eTRANSAFE), resources of which are composed of financial contribution from the EU-H2020 and EFPIA companies in kind contribution. The Research Programme on Biomedical Informatics (GRIB) is a member of the Spanish National Bioinformatics Institute (INB), funded by ISCIII and FEDER. The DCEXS is a ‘Unidad de Excelencia María de Maeztu’, funded by the MINECO (ref: MDM-2014-0370)

Pathway databases and tools for their exploitation: benefits, current limitations and challenges

In past years, comprehensive representations of cell signalling pathways have been developed by manual curation from literature, which requires huge effort and would benefit from information stored in databases and from automatic retrieval and integration methods. Once a reconstruction of the network of interactions is achieved, analysis of its structural features and its dynamic behaviour can take place. Mathematical modelling techniques are used to simulate the complex behaviour of cell signalling networks, which ultimately sheds light on the mechanisms leading to complex diseases or helps in the identification of drug targets. A variety of databases containing information on cell signalling pathways have been developed in conjunction with methodologies to access and analyse the data. In principle, the scenario is prepared to make the most of this information for the analysis of the dynamics of signalling pathways. However, are the knowledge repositories of signalling pathways ready to realize the systems biology promise? In this article we aim to initiate this discussion and to provide some insights on this issue.This work was generated in the framework of the @neurIST and the/nEU-ADR projects co-financed by the European Commission through/nthe contracts no. IST-027703 and ICT-215847, respectively

Benchmarking post-GWAS analysis tools in major depression: Challenges and implications

Our knowledge of complex disorders has increased in the last years thanks to the identification of genetic variants (GVs) significantly associated with disease phenotypes by genome-wide association studies (GWAS). However, we do not understand yet how these GVs functionally impact disease pathogenesis or their underlying biological mechanisms. Among the multiple post-GWAS methods available, fine-mapping and colocalization approaches are commonly used to identify causal GVs, meaning those with a biological effect on the trait, and their functional effects. Despite the variety of post-GWAS tools available, there is no guideline for method eligibility or validity, even though these methods work under different assumptions when accounting for linkage disequilibrium and integrating molecular annotation data. Moreover, there is no benchmarking of the available tools. In this context, we have applied two different fine-mapping and colocalization methods to the same GWAS on major depression (MD) and expression quantitative trait loci (eQTL) datasets. Our goal is to perform a systematic comparison of the results obtained by the different tools. To that end, we have evaluated their results at different levels: fine-mapped and colocalizing GVs, their target genes and tissue specificity according to gene expression information, as well as the biological processes in which they are involved. Our findings highlight the importance of fine-mapping as a key step for subsequent analysis. Notably, the colocalizing variants, altered genes and targeted tissues differed between methods, even regarding their biological implications. This contribution illustrates an important issue in post-GWAS analysis with relevant consequences on the use of GWAS results for elucidation of disease pathobiology, drug target prioritization and biomarker discovery.IMI2-JU resources which are composed of financial contributions from the European Union’s Horizon 2020 Research and Innovation Programme and EFPIA (GA: 116030 TransQST and GA: 777365 eTRANSAFE), and the EU H2020 Programme 2014–2020 (GA: 676559 Elixir-Excelerate); Project 001-P-001647—Valorisation of EGA for Industry and Society funded by the European Regional Development Fund (ERDF) and Generalitat de Catalunya; Agència de Gestió d’Ajuts Universitaris i de Recerca Generalitat de Catalunya (2017SGR00519), and the Institute of Health Carlos III (project IMPaCT-Data, exp. IMP/00019), co-funded by the European Union, European Regional Development Fund (ERDF, “A way to make Europe”). The Research Programme on Biomedical Informatics (GRIB) is a member of the Spanish National Bioinformatics Institute (INB), funded by ISCIII and ERDF (PRB2-ISCIII (PT13/0001/0023, of the PE I + D + i 2013–2016)). The MELIS is a ‘Unidad de Excelencia María de Maeztu’, funded by the MINECO (MDM-2014-0370). JP-G was supported by Instituto de Salud Carlos III-Fondo Social Europeo (FI18/00034). This statement is a requirement from our funding agencies and therefore has to be included in the Funding section

In silico models in drug development: where we are

The use and utility of computational models in drug development has significantly grown in the last decades, fostered by the availability of high throughput datasets and new data analysis strategies. These in silico approaches are demonstrating their ability to generate reliable predictions as well as new knowledge on the mode of action of drugs and the mechanisms underlying their side effects, altogether helping to reduce the costs of drug development. The aim of this review is to provide a panorama of developments in the field in the last two years.We acknowledge support from ISCIII-FEDER (CPII16/00026), the EU H2020 Programme 2014-2020 under grant agreements no. 681002 (EU-ToxRisk) and no. 676559 (ELIXIR-EXCELERATE), and the IMI2-JU under grants agreements no. 116030 (TransQST) and no. 777365 (eTRANSAFE), resources of which are composed of financial contribution from the EU-H2020 and EFPIA companies in kind contribution. The Research Programme on Biomedical Informatics (GRIB) is a member of the Spanish National Bioinformatics Institute (INB), funded by ISCIII and FEDER. The DCEXS is a ‘Unidad de Excelencia María de Maeztu’, funded by the MINECO (ref: MDM-2014-0370)

ResMarkerDB: a database of biomarkers of response to antibody therapy in breast and colorectal cancer

The clinical efficacy of therapeutic monoclonal antibodies for breast and colorectal cancer has greatly contributed to the improvement of patients' outcomes by individualizing their treatments according to their genomic background. However, primary or acquired resistance to treatment reduces its efficacy. In this context, the identification of biomarkers predictive of drug response would support research and development of new alternative treatments. Biomarkers play a major role in the genomic revolution, supporting disease diagnosis and treatment decision-making. Currently, several molecular biomarkers of treatment response for breast and colorectal cancer have been described. However, information on these biomarkers is scattered across several resources, and needs to be identified, collected and properly integrated to be fully exploited to inform monitoring of drug response in patients. Therefore, there is a need of resources that offer biomarker data in a harmonized manner to the user to support the identification of actionable biomarkers of response to treatment in cancer. ResMarkerDB was developed as a comprehensive resource of biomarkers of drug response in colorectal and breast cancer. It integrates data of biomarkers of drug response from existing repositories, and new data extracted and curated from the literature (referred as ResCur). ResMarkerDB currently features 266 biomarkers of diverse nature. Twenty-five percent of these biomarkers are exclusive of ResMarkerDB. Furthermore, ResMarkerDB is one of the few resources offering non-coding DNA data in response to drug treatment. The database contains more than 500 biomarker-drug-tumour associations, covering more than 100 genes. ResMarkerDB provides a web interface to facilitate the exploration of the current knowledge of biomarkers of response in breast and colorectal cancer. It aims to enhance translational research efforts in identifying actionable biomarkers of drug response in cancer.Instituto de Salud Carlos III-Fondo Europeo de Desarrollo Regional [grant numbers: PIE15/00008, CP10/00524, CPII16/00026]; Instituto de Salud Carlos III-Fondo Social Europeo [FI18/00034]; and the European Commission Horizon 2020 Programme 2014–2020 under grant agreements MedBioinformatics [grant number: 634143] and Elixir-Excelerate [grant number: 676559]. The Research Programme on Biomedical Informatics is a member of the Spanish National Bioinformatics Institute, Plataforma de Recursos Biomoleculares y Bioinformáticos-Instituto de Salud Carlos III [grant number: PT13/0001/0023], of the PE I + D + i 2013–2016, funded by Instituto de Salud Carlos III and Fondo Europeo de Desarrollo Regional. The Departamento de Ciencias Experimentales y de la Salud is a Unidad de Excelencia María de Maeztu, funded by the Ministerio de Economía y Competitividad (reference number: MDM-2014-0370)

MedBioinformatics: developing integrative bioinformatics applications for personalized medicine

Progress in healthcare and biomedical research involves taking advantage of the huge amount of clinical data and biological knowledge that already exists and is currently being generated. Bioinformatic methods and tools have become essential for data extraction, management, and integration. However, it is necessary to overcome two barriers: the deficit of integrative computational approaches that combine different types of data and sources, and the lack of active involvement of the potential users in the process of creating applications for the management of biomedical information

Assessment of NER solutions against the first and second CALBC Silver Standard Corpus

Background: Competitions in text mining have been used to measure the performance of automatic text processing solutions against a manually annotated gold standard corpus (GSC). The preparation of the GSC is time-consuming and costly and the final corpus consists at the most of a few thousand documents annotated with a limited set of semantic groups. To overcome these shortcomings, 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, the first version of the Silver Standard Corpus (SSC-I). The four semantic groups are chemical entities and drugs (CHED), genes and proteins (PRGE), diseases and disorders (DISO) and species (SPE). This corpus has been used for the First CALBC Challenge asking the participants to annotate the corpus with their text processing solutions. Results: All four PPs from the CALBC project and in addition, 12 challenge participants (CPs) contributed annotated data sets for an evaluation against the SSC-I. CPs could ignore the training data and deliver the annotations from their genuine annotation system, or could train a machine-learning approach on the provided pre-annotated data. In general, the performances of the annotation solutions were lower for entities from the categories CHED and PRGE in comparison to the identification of entities categorized as DISO and SPE. The best performance over all semantic groups were achieved from two annotation solutions that have been trained on the SSC-I. The data sets from participants were used to generate the harmonised Silver Standard Corpus II (SSC-II), if the participant did not make use of the annotated data set from the SSC-I for training purposes. The performances of the participants’ solutions were again measured against the SSC-II. The performances of the annotation solutions showed again better results for DISO and SPE in comparison to CHED and PRGE. Conclusions: The SSC-I delivers a large set of annotations (1,121,705) for a large number of documents (100,000 Medline abstracts). The annotations cover four different semantic groups and are sufficiently homogeneous to be reproduced with a trained classifier leading to an average F-measure of 85%. Benchmarking the annotation solutions against the SSC-II leads to better performance for the CPs’ annotation solutions in comparison to the SSC-I.This work was funded by the EU Support Action grant 231727 under the 7th EU Framework Programme within Theme “Intelligent Content and Semantics” (ICT 2007.4.2). The work performed at IMIM (Laura Furlong) was funded by the EU Support Action grant 231727 under the 7th EU Framework Programme within Theme “Intelligent Content and Semantics” (ICT 2007.4.2) and the Instituto de Salud Carlos III FEDER (CP10/00524) grant. Fabio Rinaldi and Simon Clematide are supported by the Swiss National Science Foundation (grant 105315_130558/1

ResMarkerDB: a database of biomarkers of response to antibody therapy in breast and colorectal cancer

The clinical efficacy of therapeutic monoclonal antibodies for breast and colorectal cancer has greatly contributed to the improvement of patients' outcomes by individualizing their treatments according to their genomic background. However, primary or acquired resistance to treatment reduces its efficacy. In this context, the identification of biomarkers predictive of drug response would support research and development of new alternative treatments. Biomarkers play a major role in the genomic revolution, supporting disease diagnosis and treatment decision-making. Currently, several molecular biomarkers of treatment response for breast and colorectal cancer have been described. However, information on these biomarkers is scattered across several resources, and needs to be identified, collected and properly integrated to be fully exploited to inform monitoring of drug response in patients. Therefore, there is a need of resources that offer biomarker data in a harmonized manner to the user to support the identification of actionable biomarkers of response to treatment in cancer. ResMarkerDB was developed as a comprehensive resource of biomarkers of drug response in colorectal and breast cancer. It integrates data of biomarkers of drug response from existing repositories, and new data extracted and curated from the literature (referred as ResCur). ResMarkerDB currently features 266 biomarkers of diverse nature. Twenty-five percent of these biomarkers are exclusive of ResMarkerDB. Furthermore, ResMarkerDB is one of the few resources offering non-coding DNA data in response to drug treatment. The database contains more than 500 biomarker-drug-tumour associations, covering more than 100 genes. ResMarkerDB provides a web interface to facilitate the exploration of the current knowledge of biomarkers of response in breast and colorectal cancer. It aims to enhance translational research efforts in identifying actionable biomarkers of drug response in cancer.Instituto de Salud Carlos III-Fondo Europeo de Desarrollo Regional [grant numbers: PIE15/00008, CP10/00524, CPII16/00026]; Instituto de Salud Carlos III-Fondo Social Europeo [FI18/00034]; and the European Commission Horizon 2020 Programme 2014–2020 under grant agreements MedBioinformatics [grant number: 634143] and Elixir-Excelerate [grant number: 676559]. The Research Programme on Biomedical Informatics is a member of the Spanish National Bioinformatics Institute, Plataforma de Recursos Biomoleculares y Bioinformáticos-Instituto de Salud Carlos III [grant number: PT13/0001/0023], of the PE I + D + i 2013–2016, funded by Instituto de Salud Carlos III and Fondo Europeo de Desarrollo Regional. The Departamento de Ciencias Experimentales y de la Salud is a Unidad de Excelencia María de Maeztu, funded by the Ministerio de Economía y Competitividad (reference number: MDM-2014-0370)

From SNPs to pathways: integration of functional effect of sequence variations on models of cell signalling pathways

Background: Single nucleotide polymorphisms (SNPs) are the most frequent type of sequence variation between individuals, and represent a promising tool for finding genetic determinants of complex diseases and understanding the differences in drug response. In this regard, it is of particular interest to study the effect of non-synonymous SNPs in the context of biological networks such as cell signalling pathways. UniProt provides curated information about the functional and phenotypic effects of sequence variation, including SNPs, as well as on mutations of protein sequences. However, no strategy has been developed to integrate this information with biological networks, with the ultimate goal of studying the impact of the functional effect of SNPs in the structure and dynamics of biological networks. Results: First, we identified the different challenges posed by the integration of the phenotypic effect of sequence variants and mutations with biological networks. Second, we developed a strategy for the combination of data extracted from public resources, such as UniProt, NCBI dbSNP, Reactome and BioModels. We generated attribute files containing phenotypic and genotypic annotations to the nodes of biological networks, which can be imported into network visualization tools such as Cytoscape. These resources allow the mapping and visualization of mutations and natural variations of human proteins and their phenotypic effect on biological networks (e.g. signalling pathways, protein-protein interaction networks, dynamic models). Finally, an example on the use of the sequence variation data in the dynamics of a network model is presented. Conclusion: In this paper we present a general strategy for the integration of pathway and sequence variation data for visualization, analysis and modelling purposes, including the study of the functional impact of protein sequence variations on the dynamics of signalling pathways. This is of particular interest when the SNP or mutation is known to be associated to disease. We expect that this approach will help in the study of the functional impact of disease-associated SNPs on the behaviour of cell signalling pathways, which ultimately will lead to a better understanding of the mechanisms underlying complex diseases.This work was generated in the framework of the @neurIST and the EUADR projects co-financed by the European Commission through the contracts no. IST-027703 and ICT-215847, respectively

OSIRISv1.2: a named entity recognition system for sequence variants of genes in biomedical literature

Background: Single Nucleotide Polymorphisms, among other type of sequence variants, constitute key elements in genetic epidemiology and pharmacogenomics. While sequence data about genetic variation is found at databases such as dbSNP, clues about the functional and phenotypic consequences of the variations are generally found in biomedical literature. The identification of the relevant documents and the extraction of the information from them are hampered by the large size of literature databases and the lack of widely accepted standard notation for biomedical entities. Thus, automatic systems for the identification of citations of allelic variants of genes in biomedical texts are required. Results: Our group has previously reported the development of OSIRIS, a system aimed at the retrieval of literature about allelic variants of genes http://ibi.imim.es/osirisform.html. Here we describe the development of a new version of OSIRIS (OSIRISv1.2, http://ibi.imim.es/OSIRISv1.2.html webcite) which incorporates a new entity recognition module and is built on top of a local mirror of the MEDLINE collection and HgenetInfoDB: a database that collects data on human gene sequence variations. The new entity recognition module is based on a pattern-based search algorithm for the identification of variation terms in the texts and their mapping to dbSNP identifiers. The performance of OSIRISv1.2 was evaluated on a manually annotated corpus, resulting in 99% precision, 82% recall, and an F-score of 0.89. As an example, the application of the system for collecting literature citations for the allelic variants of genes related to the diseases intracranial aneurysm and breast cancer is presented. Conclusion: /nOSIRISv1.2 can be used to link literature references to dbSNP database entries with high accuracy, and therefore is suitable for collecting current knowledge on gene sequence variations and supporting the functional annotation of variation databases. The application of OSIRISv1.2 in combination with controlled vocabularies like MeSH provides a way to identify associations of biomedical interest, such as those that relate SNPs with diseases