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1.  The eTOX Data-Sharing Project to Advance in Silico Drug-Induced Toxicity Prediction 
The high-quality in vivo preclinical safety data produced by the pharmaceutical industry during drug development, which follows numerous strict guidelines, are mostly not available in the public domain. These safety data are sometimes published as a condensed summary for the few compounds that reach the market, but the majority of studies are never made public and are often difficult to access in an automated way, even sometimes within the owning company itself. It is evident from many academic and industrial examples, that useful data mining and model development requires large and representative data sets and careful curation of the collected data. In 2010, under the auspices of the Innovative Medicines Initiative, the eTOX project started with the objective of extracting and sharing preclinical study data from paper or pdf archives of toxicology departments of the 13 participating pharmaceutical companies and using such data for establishing a detailed, well-curated database, which could then serve as source for read-across approaches (early assessment of the potential toxicity of a drug candidate by comparison of similar structure and/or effects) and training of predictive models. The paper describes the efforts undertaken to allow effective data sharing intellectual property (IP) protection and set up of adequate controlled vocabularies) and to establish the database (currently with over 4000 studies contributed by the pharma companies corresponding to more than 1400 compounds). In addition, the status of predictive models building and some specific features of the eTOX predictive system (eTOXsys) are presented as decision support knowledge-based tools for drug development process at an early stage.
PMCID: PMC4264217  PMID: 25405742
in silico toxicity; in vitro toxicity; in vivo toxicity; data sharing; data integration; ontologies; decision support system; predictive models; read-across; QSAR
2.  A Dynamic View of Molecular Switch Behavior at Serotonin Receptors: Implications for Functional Selectivity 
PLoS ONE  2014;9(10):e109312.
Functional selectivity is a property of G protein-coupled receptors that allows them to preferentially couple to particular signaling partners upon binding of biased agonists. Publication of the X-ray crystal structure of serotonergic 5-HT1B and 5-HT2B receptors in complex with ergotamine, a drug capable of activating G protein coupling and β-arrestin signaling at the 5-HT1B receptor but clearly favoring β-arrestin over G protein coupling at the 5-HT2B subtype, has recently provided structural insight into this phenomenon. In particular, these structures highlight the importance of specific residues, also called micro-switches, for differential receptor activation. In our work, we apply classical molecular dynamics simulations and enhanced sampling approaches to analyze the behavior of these micro-switches and their impact on the stabilization of particular receptor conformational states. Our analysis shows that differences in the conformational freedom of helix 6 between both receptors could explain their different G protein-coupling capacity. In particular, as compared to the 5-HT1B receptor, helix 6 movement in the 5-HT2B receptor can be constrained by two different mechanisms. On the one hand, an anchoring effect of ergotamine, which shows an increased capacity to interact with the extracellular part of helices 5 and 6 and stabilize them, hinders activation of a hydrophobic connector region at the center of the receptor. On the other hand, this connector region in an inactive conformation is further stabilized by unconserved contacts extending to the intracellular part of the 5-HT2B receptor, which hamper opening of the G protein binding site. This work highlights the importance of considering receptor capacity to adopt different conformational states from a dynamic perspective in order to underpin the structural basis of functional selectivity.
PMCID: PMC4196896  PMID: 25313636
3.  Network medicine analysis of COPD multimorbidities 
Respiratory Research  2014;15(1):111.
Patients with chronic obstructive pulmonary disease (COPD) often suffer concomitant disorders that worsen significantly their health status and vital prognosis. The pathogenic mechanisms underlying COPD multimorbidities are not completely understood, thus the exploration of potential molecular and biological linkages between COPD and their associated diseases is of great interest.
We developed a novel, unbiased, integrative network medicine approach for the analysis of the diseasome, interactome, the biological pathways and tobacco smoke exposome, which has been applied to the study of 16 prevalent COPD multimorbidities identified by clinical experts.
Our analyses indicate that all COPD multimorbidities studied here are related at the molecular and biological level, sharing genes, proteins and biological pathways. By inspecting the connections of COPD with their associated diseases in more detail, we identified known biological pathways involved in COPD, such as inflammation, endothelial dysfunction or apoptosis, serving as a proof of concept of the methodology. More interestingly, we found previously overlooked biological pathways that might contribute to explain COPD multimorbidities, such as hemostasis in COPD multimorbidities other than cardiovascular disorders, and cell cycle pathway in the association of COPD with depression. Moreover, we also observed similarities between COPD multimorbidities at the pathway level, suggesting common biological mechanisms for different COPD multimorbidities. Finally, chemicals contained in the tobacco smoke target an average of 69% of the identified proteins participating in COPD multimorbidities.
The network medicine approach presented here allowed the identification of plausible molecular links between COPD and comorbid diseases, and showed that many of them are targets of the tobacco exposome, proposing new areas of research for understanding the molecular underpinning of COPD multimorbidities.
Electronic supplementary material
The online version of this article (doi:10.1186/s12931-014-0111-4) contains supplementary material, which is available to authorized users.
PMCID: PMC4177421  PMID: 25248857
Diseasome; Systems biology; Network medicine; Comorbidity; Multimorbidity; COPD; Tobacco chemicals
4.  Gathering and Exploring Scientific Knowledge in Pharmacovigilance 
PLoS ONE  2013;8(12):e83016.
Pharmacovigilance plays a key role in the healthcare domain through the assessment, monitoring and discovery of interactions amongst drugs and their effects in the human organism. However, technological advances in this field have been slowing down over the last decade due to miscellaneous legal, ethical and methodological constraints. Pharmaceutical companies started to realize that collaborative and integrative approaches boost current drug research and development processes. Hence, new strategies are required to connect researchers, datasets, biomedical knowledge and analysis algorithms, allowing them to fully exploit the true value behind state-of-the-art pharmacovigilance efforts. This manuscript introduces a new platform directed towards pharmacovigilance knowledge providers. This system, based on a service-oriented architecture, adopts a plugin-based approach to solve fundamental pharmacovigilance software challenges. With the wealth of collected clinical and pharmaceutical data, it is now possible to connect knowledge providers’ analysis and exploration algorithms with real data. As a result, new strategies allow a faster identification of high-risk interactions between marketed drugs and adverse events, and enable the automated uncovering of scientific evidence behind them. With this architecture, the pharmacovigilance field has a new platform to coordinate large-scale drug evaluation efforts in a unique ecosystem, publicly available at
PMCID: PMC3859628  PMID: 24349421
6.  Drug-Induced Acute Myocardial Infarction: Identifying ‘Prime Suspects’ from Electronic Healthcare Records-Based Surveillance System 
PLoS ONE  2013;8(8):e72148.
Drug-related adverse events remain an important cause of morbidity and mortality and impose huge burden on healthcare costs. Routinely collected electronic healthcare data give a good snapshot of how drugs are being used in ‘real-world’ settings.
To describe a strategy that identifies potentially drug-induced acute myocardial infarction (AMI) from a large international healthcare data network.
Post-marketing safety surveillance was conducted in seven population-based healthcare databases in three countries (Denmark, Italy, and the Netherlands) using anonymised demographic, clinical, and prescription/dispensing data representing 21,171,291 individuals with 154,474,063 person-years of follow-up in the period 1996–2010. Primary care physicians’ medical records and administrative claims containing reimbursements for filled prescriptions, laboratory tests, and hospitalisations were evaluated using a three-tier triage system of detection, filtering, and substantiation that generated a list of drugs potentially associated with AMI. Outcome of interest was statistically significant increased risk of AMI during drug exposure that has not been previously described in current literature and is biologically plausible.
Overall, 163 drugs were identified to be associated with increased risk of AMI during preliminary screening. Of these, 124 drugs were eliminated after adjustment for possible bias and confounding. With subsequent application of criteria for novelty and biological plausibility, association with AMI remained for nine drugs (‘prime suspects’): azithromycin; erythromycin; roxithromycin; metoclopramide; cisapride; domperidone; betamethasone; fluconazole; and megestrol acetate.
Although global health status, co-morbidities, and time-invariant factors were adjusted for, residual confounding cannot be ruled out.
A strategy to identify potentially drug-induced AMI from electronic healthcare data has been proposed that takes into account not only statistical association, but also public health relevance, novelty, and biological plausibility. Although this strategy needs to be further evaluated using other healthcare data sources, the list of ‘prime suspects’ makes a good starting point for further clinical, laboratory, and epidemiologic investigation.
PMCID: PMC3756064  PMID: 24015213
8.  A Chemocentric Approach to the Identification of Cancer Targets 
PLoS ONE  2012;7(4):e35582.
A novel chemocentric approach to identifying cancer-relevant targets is introduced. Starting with a large chemical collection, the strategy uses the list of small molecule hits arising from a differential cytotoxicity screening on tumor HCT116 and normal MRC-5 cell lines to identify proteins associated with cancer emerging from a differential virtual target profiling of the most selective compounds detected in both cell lines. It is shown that this smart combination of differential in vitro and in silico screenings (DIVISS) is capable of detecting a list of proteins that are already well accepted cancer drug targets, while complementing it with additional proteins that, targeted selectively or in combination with others, could lead to synergistic benefits for cancer therapeutics. The complete list of 115 proteins identified as being hit uniquely by compounds showing selective antiproliferative effects for tumor cell lines is provided.
PMCID: PMC3338416  PMID: 22558171
9.  Automatic Filtering and Substantiation of Drug Safety Signals 
PLoS Computational Biology  2012;8(4):e1002457.
Drug safety issues pose serious health threats to the population and constitute a major cause of mortality worldwide. Due to the prominent implications to both public health and the pharmaceutical industry, it is of great importance to unravel the molecular mechanisms by which an adverse drug reaction can be potentially elicited. These mechanisms can be investigated by placing the pharmaco-epidemiologically detected adverse drug reaction in an information-rich context and by exploiting all currently available biomedical knowledge to substantiate it. We present a computational framework for the biological annotation of potential adverse drug reactions. First, the proposed framework investigates previous evidences on the drug-event association in the context of biomedical literature (signal filtering). Then, it seeks to provide a biological explanation (signal substantiation) by exploring mechanistic connections that might explain why a drug produces a specific adverse reaction. The mechanistic connections include the activity of the drug, related compounds and drug metabolites on protein targets, the association of protein targets to clinical events, and the annotation of proteins (both protein targets and proteins associated with clinical events) to biological pathways. Hence, the workflows for signal filtering and substantiation integrate modules for literature and database mining, in silico drug-target profiling, and analyses based on gene-disease networks and biological pathways. Application examples of these workflows carried out on selected cases of drug safety signals are discussed. The methodology and workflows presented offer a novel approach to explore the molecular mechanisms underlying adverse drug reactions.
Author Summary
Adverse drug reactions (ADRs) constitute a major cause of morbidity and mortality worldwide. Due to the relevance of ADRs for both public health and pharmaceutical industry, it is important to develop efficient ways to monitor ADRs in the population. In addition, it is also essential to comprehend why a drug produces an adverse effect. To unravel the molecular mechanisms of ADRs, it is necessary to consider the ADR in the context of current biomedical knowledge that might explain it. Nowadays there are plenty of information sources that can be exploited in order to accomplish this goal. Nevertheless, the fragmentation of information and, more importantly, the diverse knowledge domains that need to be traversed, pose challenges to the task of exploring the molecular mechanisms of ADRs. We present a novel computational framework to aid in the collection and exploration of evidences that support the causal inference of ADRs detected by mining clinical records. This framework was implemented as publicly available tools integrating state-of-the-art bioinformatics methods for the analysis of drugs, targets, biological processes and clinical events. The availability of such tools for in silico experiments will facilitate research on the mechanisms that underlie ADR, contributing to the development of safer drugs.
PMCID: PMC3320573  PMID: 22496632
10.  Inroads to Predict in Vivo Toxicology—An Introduction to the eTOX Project 
There is a widespread awareness that the wealth of preclinical toxicity data that the pharmaceutical industry has generated in recent decades is not exploited as efficiently as it could be. Enhanced data availability for compound comparison (“read-across”), or for data mining to build predictive tools, should lead to a more efficient drug development process and contribute to the reduction of animal use (3Rs principle). In order to achieve these goals, a consortium approach, grouping numbers of relevant partners, is required. The eTOX (“electronic toxicity”) consortium represents such a project and is a public-private partnership within the framework of the European Innovative Medicines Initiative (IMI). The project aims at the development of in silico prediction systems for organ and in vivo toxicity. The backbone of the project will be a database consisting of preclinical toxicity data for drug compounds or candidates extracted from previously unpublished, legacy reports from thirteen European and European operation-based pharmaceutical companies. The database will be enhanced by incorporation of publically available, high quality toxicology data. Seven academic institutes and five small-to-medium size enterprises (SMEs) contribute with their expertise in data gathering, database curation, data mining, chemoinformatics and predictive systems development. The outcome of the project will be a predictive system contributing to early potential hazard identification and risk assessment during the drug development process. The concept and strategy of the eTOX project is described here, together with current achievements and future deliverables.
PMCID: PMC3317745  PMID: 22489185
predictive toxicology; in silico toxicity; in vitro toxicity; in vivo toxicity; Knowledge Management; Expert Systems; Decision Support System; Data Integration; Manual Curation; ontology; histopathology; computational models; QSAR; data sharing
11.  Gene-Disease Network Analysis Reveals Functional Modules in Mendelian, Complex and Environmental Diseases 
PLoS ONE  2011;6(6):e20284.
Scientists have been trying to understand the molecular mechanisms of diseases to design preventive and therapeutic strategies for a long time. For some diseases, it has become evident that it is not enough to obtain a catalogue of the disease-related genes but to uncover how disruptions of molecular networks in the cell give rise to disease phenotypes. Moreover, with the unprecedented wealth of information available, even obtaining such catalogue is extremely difficult.
Principal Findings
We developed a comprehensive gene-disease association database by integrating associations from several sources that cover different biomedical aspects of diseases. In particular, we focus on the current knowledge of human genetic diseases including mendelian, complex and environmental diseases. To assess the concept of modularity of human diseases, we performed a systematic study of the emergent properties of human gene-disease networks by means of network topology and functional annotation analysis. The results indicate a highly shared genetic origin of human diseases and show that for most diseases, including mendelian, complex and environmental diseases, functional modules exist. Moreover, a core set of biological pathways is found to be associated with most human diseases. We obtained similar results when studying clusters of diseases, suggesting that related diseases might arise due to dysfunction of common biological processes in the cell.
For the first time, we include mendelian, complex and environmental diseases in an integrated gene-disease association database and show that the concept of modularity applies for all of them. We furthermore provide a functional analysis of disease-related modules providing important new biological insights, which might not be discovered when considering each of the gene-disease association repositories independently. Hence, we present a suitable framework for the study of how genetic and environmental factors, such as drugs, contribute to diseases.
The gene-disease networks used in this study and part of the analysis are available at
PMCID: PMC3114846  PMID: 21695124
12.  Induced Effects of Sodium Ions on Dopaminergic G-Protein Coupled Receptors 
PLoS Computational Biology  2010;6(8):e1000884.
G-protein coupled receptors, the largest family of proteins in the human genome, are involved in many complex signal transduction pathways, typically activated by orthosteric ligand binding and subject to allosteric modulation. Dopaminergic receptors, belonging to the class A family of G-protein coupled receptors, are known to be modulated by sodium ions from an allosteric binding site, although the details of sodium effects on the receptor have not yet been described. In an effort to understand these effects, we performed microsecond scale all-atom molecular dynamics simulations on the dopaminergic D2 receptor, finding that sodium ions enter the receptor from the extracellular side and bind at a deep allosteric site (Asp2.50). Remarkably, the presence of a sodium ion at this allosteric site induces a conformational change of the rotamer toggle switch Trp6.48 which locks in a conformation identical to the one found in the partially inactive state of the crystallized human β2 adrenergic receptor. This study provides detailed quantitative information about binding of sodium ions in the D2 receptor and reports a possibly important sodium-induced conformational change for modulation of D2 receptor function.
Author Summary
G-protein coupled receptors represent more than 50% of the current drug targets, hence playing a crucial role in drug discovery today. A deeper understanding of G-protein coupled receptor functioning and modulation will help in the development of new drugs that are able to interact with such systems in a more subtle way than simple agonists or antagonists. In the present work, we studied the energetics of sodium ions, which have been described to act as an allosteric regulator within the D2 receptor using long-time molecular dynamics simulations, in order to gain insight into the molecular mechanism by which they exert this effect. In our simulations, we observed how sodium ions are able to induce a conformational change of the Trp6.48, a molecular rotamer switch which is implicated in the activation mechanism of G-protein coupled receptors. This observation, never reported before, has interesting implications for the design of drugs able to interact in a proper way with D2 receptor in particular and GPCR in general.
PMCID: PMC2920834  PMID: 20711351
13.  From SNPs to pathways: integration of functional effect of sequence variations on models of cell signalling pathways 
BMC Bioinformatics  2009;10(Suppl 8):S6.
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.
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.
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.
PMCID: PMC2745588  PMID: 19758470
14.  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.
PMCID: PMC2724977  PMID: 19638971
biological pathways; cell signalling; network models; pathway databases; systems biology
15.  OSIRISv1.2: A named entity recognition system for sequence variants of genes in biomedical literature 
BMC Bioinformatics  2008;9:84.
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.
Our group has previously reported the development of OSIRIS, a system aimed at the retrieval of literature about allelic variants of genes . Here we describe the development of a new version of OSIRIS (OSIRISv1.2, ) 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.
OSIRISv1.2 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.
PMCID: PMC2277400  PMID: 18251998
16.  INFOBIOMED: European Network of Excellence on Biomedical Informatics to Support Individualised Healthcare 
INFOBIOMED is an European Network of Excellence (NoE) funded by the Information Society Directorate-General of the European Commission (EC). A consortium of European organizations from ten different countries is involved within the network. Four pilots, all related to linking clinical and genomic information, are being carried out. From an informatics perspective, various challenges, related to data integration and mining, are included.
PMCID: PMC1560781  PMID: 16779328
17.  Role of UEV-1, an Inactive Variant of the E2 UbiquitinConjugating Enzymes, in In Vitro Differentiation and Cell Cycle Behavior of HT-29-M6 Intestinal Mucosecretory Cells 
Molecular and Cellular Biology  1998;18(1):576-589.
By means of differential RNA display, we have isolated a cDNA corresponding to transcripts that are down-regulated upon differentiation of the goblet cell-like HT-29-M6 human colon carcinoma cell line. These transcripts encode proteins originally identified as CROC-1 on the basis of their capacity to activate transcription of c-fos. We show that these proteins are similar in sequence, and in predicted secondary and tertiary structure, to the ubiquitin-conjugating enzymes, also known as E2. Despite the similarities, these proteins lack a critical cysteine residue essential for the catalytic activity of E2 enzymes and, in vitro, they do not conjugate or transfer ubiquitin to protein substrates. These proteins constitute a distinct subfamily within the E2 protein family and are highly conserved in phylogeny from yeasts to mammals. Therefore, we have designated them UEV (ubiquitin-conjugating E2 enzyme variant) proteins, defined as proteins similar in sequence and structure to the E2 ubiquitin-conjugating enzymes but lacking their enzymatic activity (HW/GDB-approved gene symbol, UBE2V). At least two human genes code for UEV proteins, and one of them, located on chromosome 20q13.2, is expressed as at least four isoforms, generated by alternative splicing. All human cell types analyzed expressed at least one of these isoforms. Constitutive expression of exogenous human UEV in HT-29-M6 cells inhibited their capacity to differentiate upon confluence and caused both the entry of a larger proportion of cells in the division cycle and an accumulation in G2-M. This was accompanied with a profound inhibition of the mitotic kinase, cdk1. These results suggest that UEV proteins are involved in the control of differentiation and could exert their effects by altering cell cycle distribution.
PMCID: PMC121525  PMID: 9418904

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