Automatic filtering and substantiation of drug safety signals

Identifying Drug-Drug Interactions in Spontaneous Reports Utilizing Signal Detection and Biological Plausibility Aspects

Translational approaches can benefit post-marketing drug safety surveillance through the growing availability of systems pharmacology data. Here, we propose a novel Bayesian framework for identifying drug-drug interaction (DDI) signals and differentiating between individual drug and drug combination signals. This framework is coupled with a systems pharmacology approach for automated biological plausibility assessment. Integrating statistical and biological evidence, our method achieves a 16.5% improvement (AUC: from 0.620 to 0.722) with drug-target-adverse event associations, 16.0% (AUC: from 0.580 to 0.673) with drug enzyme, and 15.0% (AUC: from 0.568 to 0.653) with drug transporter information. Applying this approach to detect potential DDI signals of QT prolongation and rhabdomyolysis within the FDA Adverse Event Reporting System (FAERS), we emphasize the significance of systems pharmacology in enhancing statistical signal detection in pharmacovigilance. Our study showcases the promise of data-driven biological plausibility assessment in the context of challenging post-marketing DDI surveillance.

Network, Transcriptomic and Genomic Features Differentiate Genes Relevant for Drug Response

Understanding the mechanisms underlying drug therapeutic action and toxicity is crucial for the prevention and management of drug adverse reactions, and paves the way for a more efficient and rational drug design. The characterization of drug targets, drug metabolism proteins, and proteins associated to side effects according to their expression patterns, their tolerance to genomic variation and their role in cellular networks, is a necessary step in this direction. In this contribution, we hypothesize that different classes of proteins involved in the therapeutic effect of drugs and in their adverse effects have distinctive transcriptomics, genomics and network features. We explored the properties of these proteins within global and organ-specific interactomes, using multi-scale network features, evaluated their gene expression profiles in different organs and tissues, and assessed their tolerance to loss-of-function variants leveraging data from 60K subjects. We found that drug targets that mediate side effects are more central in cellular networks, more intolerant to loss-of-function variation, and show a wider breadth of tissue expression than targets not mediating side effects. In contrast, drug metabolizing enzymes and transporters are less central in the interactome, more tolerant to deleterious variants, and are more constrained in their tissue expression pattern. Our findings highlight distinctive features of proteins related to drug action, which could be applied to prioritize drugs with fewer probabilities of causing side effects.

Severe pneumonia caused by antipsihotic drugs: What does not suit, the patient or the drug?

Introduction: Antipsychotic drugs are generally categorized as typical antipsychotics (sometimes referred to as first-generation or conventional antipsychotics, or neuroleptics) and atypical antipsychotics; both are approved for the treatment of acute and chronic psychoses (i.e, schizophrenia), mania, agitation, and other psychiatric disorders. In 2005 the US Food and Drug Administration issued a warning about the increased risk of all-cause mortality associated with atypical antipsychotic use in elderly patients with dementia. Community acquired pneumonia (CAP) was one of the most frequently reported causes of death. The same warning was extended to typical antipsychotics in 2008 with extension to people with or without dementia. Case report: We present a 65-year-old Caucasian woman who was admitted to hospital due to massive pneumonia. She was suffered forschisophrenia 15-years and at moment of admission she was in remission. She had continuously high fever up to 40 degrees. All collected cultures (blood, sputum, urine, smear of aspirating catheter) were negative. She was treated with various antibiotics without improvement. After changing antipsychotic drugs, she showed slow improvement until total recovery after 3 months. Discussion and conclusion: Antipsychotic-associated CAP seems to be a clinically relevant issue in frail elderly patients, as consistently documented in several epidemiologic investigations. No clear evidence exists for an increased risk of pneumonia in younger patients treated with antipsychotics. In elderly populations, the increase in risk is dose-dependent, and is more pronounced in the early phases of treatment. Future studies should better define the mechanism underlying antipsychotic-induced pneumonia and identify subgroups of antipsychotic users at higher risk of developing pneumonia.

Network-based technologies for early drug discovery

Although the traditional drug discovery approach has led to the development of many successful drugs, the attrition rates remain high. Recent advances in systems-oriented approaches (systems-biology and/or pharmacology) and 'omics technologies has led to a plethora of new computational tools that promise to enable a more-informed and successful implementation of the reductionist, one drug for one target for one disease, approach. These tools, based on biomolecular pathways and interaction networks, offer a systematic approach to unravel the mechanism(s) of a disease and link them to the chemical space and network footprint of a drug. Drug discovery can draw upon this holistic approach to identify the most-promising targets and compounds during the early phases of development.

Biological substantiation of antipsychotic-associated pneumonia: Systematic literature review and computational analyses

Introduction

Antipsychotic (AP) safety has been widely investigated. However, mechanisms underlying AP-associated pneumonia are not well-defined.

Aim

The aim of this study was to investigate the known mechanisms of AP-associated pneumonia through a systematic literature review, confirm these mechanisms using an independent data source on drug targets and attempt to identify novel AP drug targets potentially linked to pneumonia.

Methods

A search was conducted in Medline and Web of Science to identify studies exploring the association between pneumonia and antipsychotic use, from which information on hypothesized mechanism of action was extracted. All studies had to be in English and had to concern AP use as an intervention in persons of any age and for any indication, provided that the outcome was pneumonia. Information on the study design, population, exposure, outcome, risk estimate and mechanism of action was tabulated. Public repositories of pharmacology and drug safety data were used to identify the receptor binding profile and AP safety events. Cytoscape was then used to map biological pathways that could link AP targets and off-targets to pneumonia.

Results

The literature search yielded 200 articles; 41 were included in the review. Thirty studies reported a hypothesized mechanism of action, most commonly activation/inhibition of cholinergic, histaminergic and dopaminergic receptors. In vitro pharmacology data confirmed receptor affinities identified in the literature review. Two targets, thromboxane A2 receptor (TBXA2R) and platelet activating factor receptor (PTAFR) were found to be novel AP target receptors potentially associated with pneumonia. Biological pathways constructed using Cytoscape identified plausible biological links potentially leading to pneumonia downstream of TBXA2R and PTAFR.

Conclusion

Innovative approaches for biological substantiation of drug-adverse event associations may strengthen evidence on drug safety profiles and help to tailor pharmacological therapies to patient risk factors.

Feasibility of Prioritizing Drug-Drug-Event Associations Found in Electronic Health Records

Background and Objective

Several studies have demonstrated the ability to detect adverse events potentially related to multiple drug exposure via data mining. However, the number of putative associations produced by such computational approaches is typically large, making experimental validation difficult. We theorized that those potential associations for which there is evidence from multiple complementary sources are more likely to be true, and explored this idea using a published database of drug–drug-adverse event associations derived from electronic health records (EHRs).

Methods

We prioritized drug–drug-event associations derived from EHRs using four sources of information: (1) public databases, (2) sources of spontaneous reports, (3) literature, and (4) non-EHR drug–drug interaction (DDI) prediction methods. After pre-filtering the associations by removing those found in public databases, we devised a ranking for associations based on the support from the remaining sources, and evaluated the results of this rank-based prioritization.

Results

We collected information for 5983 putative EHR-derived drug–drug-event associations involving 345 drugs and ten adverse events from four data sources and four prediction methods. Only seven drug–drug-event associations (<0.5 %) had support from the majority of evidence sources, and about one third (1777) had support from at least one of the evidence sources.

Conclusions

Our proof-of-concept method for scoring putative drug–drug-event associations from EHRs offers a systematic and reproducible way of prioritizing associations for further study. Our findings also quantify the agreement (or lack thereof) among complementary sources of evidence for drug–drug-event associations and highlight the challenges of developing a robust approach for prioritizing signals of these associations.

Electronic supplementary material

The online version of this article (doi:10.1007/s40264-015-0352-2) contains supplementary material, which is available to authorized users.

DisGeNET: a comprehensive platform integrating information on human disease-associated genes and variants

The information about the genetic basis of human diseases lies at the heart of precision medicine and drug discovery. However, to realize its full potential to support these goals, several problems, such as fragmentation, heterogeneity, availability and different conceptualization of the data must be overcome. To provide the community with a resource free of these hurdles, we have developed DisGeNET (http://www.disgenet.org), one of the largest available collections of genes and variants involved in human diseases. DisGeNET integrates data from expert curated repositories, GWAS catalogues, animal models and the scientific literature. DisGeNET data are homogeneously annotated with controlled vocabularies and community-driven ontologies. Additionally, several original metrics are provided to assist the prioritization of genotype-phenotype relationships. The information is accessible through a web interface, a Cytoscape App, an RDF SPARQL endpoint, scripts in several programming languages and an R package. DisGeNET is a versatile platform that can be used for different research purposes including the investigation of the molecular underpinnings of specific human diseases and their comorbidities, the analysis of the properties of disease genes, the generation of hypothesis on drug therapeutic action and drug adverse effects, the validation of computationally predicted disease genes and the evaluation of text-mining methods performance.

Useful Interplay Between Spontaneous ADR Reports and Electronic Healthcare Records in Signal Detection

Background and Objective

Spontaneous reporting systems (SRSs) remain the cornerstone of post-marketing drug safety surveillance despite their well-known limitations. Judicious use of other available data sources is essential to enable better detection, strengthening and validation of signals. In this study, we investigated the potential of electronic healthcare records (EHRs) to be used alongside an SRS as an independent system, with the aim of improving signal detection.

Methods

A signal detection strategy, focused on a limited set of adverse events deemed important in pharmacovigilance, was performed retrospectively in two data sources—(1) the Exploring and Understanding Adverse Drug Reactions (EU-ADR) database network and (2) the EudraVigilance database—using data between 2000 and 2010. Five events were considered for analysis: (1) acute myocardial infarction (AMI); (2) bullous eruption; (3) hip fracture; (4) acute pancreatitis; and (5) upper gastrointestinal bleeding (UGIB). Potential signals identified in each system were verified using the current published literature. The complementarity of the two systems to detect signals was expressed as the percentage of the unilaterally identified signals out of the total number of confirmed signals. As a proxy for the associated costs, the number of signals that needed to be reviewed to detect one true signal (number needed to detect [NND]) was calculated. The relationship between the background frequency of the events and the capability of each system to detect signals was also investigated.

Results

The contribution of each system to signal detection appeared to be correlated with the background incidence of the events, being directly proportional to the incidence in EU-ADR and inversely proportional in EudraVigilance. EudraVigilance was particularly valuable in identifying bullous eruption and acute pancreatitis (71 and 42 % of signals were correctly identified from the total pool of known associations, respectively), while EU-ADR was most useful in identifying hip fractures (60 %). Both systems contributed reasonably well to identification of signals related to UGIB (45 % in EudraVigilance, 40 % in EU-ADR) but only fairly for signals related to AMI (25 % in EU-ADR, 20 % in EudraVigilance). The costs associated with detection of signals were variable across events; however, it was often more costly to detect safety signals in EU-ADR than in EudraVigilance (median NNDs: 7 versus 5).

Conclusion

An EHR-based system may have additional value for signal detection, alongside already established systems, especially in the presence of adverse events with a high background incidence. While the SRS appeared to be more cost effective overall, for some events the costs associated with signal detection in the EHR might be justifiable.

Electronic supplementary material

The online version of this article (doi:10.1007/s40264-015-0341-5) contains supplementary material, which is available to authorized users.

Data Extraction and Management in Networks of Observational Health Care Databases for Scientific Research: A Comparison of EU-ADR, OMOP, Mini-Sentinel and MATRICE Strategies

Introduction:

We see increased use of existing observational data in order to achieve fast and transparent production of empirical evidence in health care research. Multiple databases are often used to increase power, to assess rare exposures or outcomes, or to study diverse populations. For privacy and sociological reasons, original data on individual subjects can’t be shared, requiring a distributed network approach where data processing is performed prior to data sharing.

Case Descriptions and Variation Among Sites:

We created a conceptual framework distinguishing three steps in local data processing: (1) data reorganization into a data structure common across the network; (2) derivation of study variables not present in original data; and (3) application of study design to transform longitudinal data into aggregated data sets for statistical analysis. We applied this framework to four case studies to identify similarities and differences in the United States and Europe: Exploring and Understanding Adverse Drug Reactions by Integrative Mining of Clinical Records and Biomedical Knowledge (EU-ADR), Observational Medical Outcomes Partnership (OMOP), the Food and Drug Administration’s (FDA’s) Mini-Sentinel, and the Italian network—the Integration of Content Management Information on the Territory of Patients with Complex Diseases or with Chronic Conditions (MATRICE).

Findings:

National networks (OMOP, Mini-Sentinel, MATRICE) all adopted shared procedures for local data reorganization. The multinational EU-ADR network needed locally defined procedures to reorganize its heterogeneous data into a common structure. Derivation of new data elements was centrally defined in all networks but the procedure was not shared in EU-ADR. Application of study design was a common and shared procedure in all the case studies. Computer procedures were embodied in different programming languages, including SAS, R, SQL, Java, and C++.

Conclusion:

Using our conceptual framework we found several areas that would benefit from research to identify optimal standards for production of empirical knowledge from existing databases.an opportunity to advance evidence-based care management. In addition, formalized CM outcomes assessment methodologies will enable us to compare CM effectiveness across health delivery settings.

An inhibitor of HIV-1 protease modulates constitutive eIF2α dephosphorylation to trigger a specific integrated stress response

Inhibitors of the HIV aspartyl protease [HIV protease inhibitors (HIV-PIs)] are the cornerstone of treatment for HIV. Beyond their well-defined antiretroviral activity, these drugs have additional effects that modulate cell viability and homeostasis. However, little is known about the virus-independent pathways engaged by these molecules. Here we show that the HIV-PI Nelfinavir decreases translation rates and promotes a transcriptional program characteristic of the integrated stress response (ISR). Mice treated with Nelfinavir display hallmarks of this stress response in the liver, including α subunit of translation initiation factor 2 (eIF2α) phosphorylation, activating transcription factor-4 (ATF4) induction, and increased expression of known downstream targets. Mechanistically, Nelfinavir-mediated ISR bypassed direct activation of the eIF2α stress kinases and instead relied on the inhibition of the constitutive eIF2α dephosphorylation and down-regulation of the phophatase cofactor CReP (Constitutive Repressor of eIF2α Phosphorylation; also known as PPP1R15B). These findings demonstrate that the modulation of eIF2α-specific phosphatase cofactor activity can be a rheostat of cellular homeostasis that initiates a functional ISR and suggest that the HIV-PIs could be repositioned as therapeutics in human diseases to modulate translation rates and stress responses.

Large-Scale Predictive Drug Safety: From Structural Alerts to Biological Mechanisms

The recent explosion of data linking drugs, proteins, and pathways with safety events has promoted the development of integrative systems approaches to large-scale predictive drug safety. The added value of such approaches is that, beyond the traditional identification of potentially labile chemical fragments for selected toxicity end points, they have the potential to provide mechanistic insights for a much larger and diverse set of safety events in a statistically sound nonsupervised manner, based on the similarity to drug classes, the interaction with secondary targets, and the interference with biological pathways. The combined identification of chemical and biological hazards enhances our ability to assess the safety risk of bioactive small molecules with higher confidence than that using structural alerts only. We are still a very long way from reliably predicting drug safety, but advances toward gaining a better understanding of the mechanisms leading to adverse outcomes represent a step forward in this direction.

Relating Essential Proteins to Drug Side-Effects Using Canonical Component Analysis: A Structure-Based Approach

The molecular mechanism of many drug side-effects is unknown and difficult to predict. Previous methods for explaining side-effects have focused on known drug targets and their pathways. However, low affinity binding to proteins that are not usually considered drug targets may also drive side-effects. In order to assess these alternative targets, we used the 3D structures of 563 essential human proteins systematically to predict binding to 216 drugs. We first benchmarked our affinity predictions with available experimental data. We then combined singular value decomposition and canonical component analysis (SVD-CCA) to predict side-effects based on these novel target profiles. Our method predicts side-effects with good accuracy (average AUC: 0.82 for side effects present in <50% of drug labels). We also noted that side-effect frequency is the most important feature for prediction and can confound efforts at elucidating mechanism; our method allows us to remove the contribution of frequency and isolate novel biological signals. In particular, our analysis produces 2768 triplet associations between 50 essential proteins, 99 drugs, and 77 side-effects. Although experimental validation is difficult because many of our essential proteins do not have validated assays, we nevertheless attempted to validate a subset of these associations using experimental assay data. Our focus on essential proteins allows us to find potential associations that would likely be missed if we used recognized drug targets. Our associations provide novel insights about the molecular mechanisms of drug side-effects and highlight the need for expanded experimental efforts to investigate drug binding to proteins more broadly.

Targets of drugs are generally, and targets of drugs having side effects are specifically good spreaders of human interactome perturbations

Network-based methods are playing an increasingly important role in drug design. Our main question in this paper was whether the efficiency of drug target proteins to spread perturbations in the human interactome is larger if the binding drugs have side effects, as compared to those which have no reported side effects. Our results showed that in general, drug targets were better spreaders of perturbations than non-target proteins, and in particular, targets of drugs with side effects were also better spreaders of perturbations than targets of drugs having no reported side effects in human protein-protein interaction networks. Colorectal cancer-related proteins were good spreaders and had a high centrality, while type 2 diabetes-related proteins showed an average spreading efficiency and had an average centrality in the human interactome. Moreover, the interactome-distance between drug targets and disease-related proteins was higher in diabetes than in colorectal cancer. Our results may help a better understanding of the network position and dynamics of drug targets and disease-related proteins, and may contribute to develop additional, network-based tests to increase the potential safety of drug candidates.

A chemo-centric view of human health and disease

Efforts to compile the phenotypic effects of drugs and environmental chemicals offer the opportunity to adopt a chemo-centric view of human health that does not require detailed mechanistic information. Here, we consider thousands of chemicals and analyze the relationship of their structures with adverse and therapeutic responses. Our study includes molecules related to the etiology of 934 health threatening conditions and used to treat 835 diseases. We first identify chemical moieties that could be independently associated with each phenotypic effect. Using these fragments, we build accurate predictors for approximately 400 clinical phenotypes, finding many privileged and liable structures. Finally, we connect two diseases if they relate to similar chemical structures. The resulting networks of human conditions are able to predict disease comorbidities, as well as identifying potential drug side effects and opportunities for drug repositioning, and show a remarkable coincidence with clinical observations.

Identifying plausible adverse drug reactions using knowledge extracted from the literature

Pharmacovigilance involves continually monitoring drug safety after drugs are put to market. To aid this process; algorithms for the identification of strongly correlated drug/adverse drug reaction (ADR) pairs from data sources such as adverse event reporting systems or Electronic Health Records have been developed. These methods are generally statistical in nature, and do not draw upon the large volumes of knowledge embedded in the biomedical literature. In this paper, we investigate the ability of scalable Literature Based Discovery (LBD) methods to identify side effects of pharmaceutical agents. The advantage of LBD methods is that they can provide evidence from the literature to support the plausibility of a drug/ADR association, thereby assisting human review to validate the signal, which is an essential component of pharmacovigilance. To do so, we draw upon vast repositories of knowledge that has been extracted from the biomedical literature by two Natural Language Processing tools, MetaMap and SemRep. We evaluate two LBD methods that scale comfortably to the volume of knowledge available in these repositories. Specifically, we evaluate Reflective Random Indexing (RRI), a model based on concept-level co-occurrence, and Predication-based Semantic Indexing (PSI), a model that encodes the nature of the relationship between concepts to support reasoning analogically about drug-effect relationships. An evaluation set was constructed from the Side Effect Resource 2 (SIDER2), which contains known drug/ADR relations, and models were evaluated for their ability to "rediscover" these relations. In this paper, we demonstrate that both RRI and PSI can recover known drug-adverse event associations. However, PSI performed better overall, and has the additional advantage of being able to recover the literature underlying the reasoning pathways it used to make its predictions.

Gathering and exploring scientific knowledge in pharmacovigilance

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 http://bioinformatics.ua.pt/euadr/.

Analysis of chemical and biological features yields mechanistic insights into drug side effects

Side effects (SEs) are the unintended consequence of therapeutic treatments, but they can also be seen as valuable readouts of drug effects, resulting from the perturbation of biological systems by chemical compounds. Unfortunately, biology and chemistry are often considered separately, leading to incomplete models unable to provide a unified view of SEs. Here, we investigate the molecular bases of over 1,600 SEs by navigating both chemical and biological spaces. We identified characteristic molecular traits for 1,162 SEs, 38% of which can be explained using solely biological arguments, and only 6% are exclusively associated with the chemistry of the compounds, implying that the drug action is somewhat unspecific. Overall, we provide mechanistic insights for most SEs and emphasize the need to blend biology and chemistry to surpass intricate phenomena not captured in the molecular biology view.

Mining clinical text for signals of adverse drug-drug interactions

BACKGROUND AND OBJECTIVE

Electronic health records (EHRs) are increasingly being used to complement the FDA Adverse Event Reporting System (FAERS) and to enable active pharmacovigilance. Over 30% of all adverse drug reactions are caused by drug-drug interactions (DDIs) and result in significant morbidity every year, making their early identification vital. We present an approach for identifying DDI signals directly from the textual portion of EHRs.

METHODS

We recognize mentions of drug and event concepts from over 50 million clinical notes from two sites to create a timeline of concept mentions for each patient. We then use adjusted disproportionality ratios to identify significant drug-drug-event associations among 1165 drugs and 14 adverse events. To validate our results, we evaluate our performance on a gold standard of 1698 DDIs curated from existing knowledge bases, as well as with signaling DDI associations directly from FAERS using established methods.

RESULTS

Our method achieves good performance, as measured by our gold standard (area under the receiver operator characteristic (ROC) curve >80%), on two independent EHR datasets and the performance is comparable to that of signaling DDIs from FAERS. We demonstrate the utility of our method for early detection of DDIs and for identifying alternatives for risky drug combinations. Finally, we publish a first of its kind database of population event rates among patients on drug combinations based on an EHR corpus.

CONCLUSIONS

It is feasible to identify DDI signals and estimate the rate of adverse events among patients on drug combinations, directly from clinical text; this could have utility in prioritizing drug interaction surveillance as well as in clinical decision support.

Drug-induced acute myocardial infarction: identifying 'prime suspects' from electronic healthcare records-based surveillance system

BACKGROUND

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.

OBJECTIVE

To describe a strategy that identifies potentially drug-induced acute myocardial infarction (AMI) from a large international healthcare data network.

METHODS

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.

RESULTS

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.

LIMITATIONS

Although global health status, co-morbidities, and time-invariant factors were adjusted for, residual confounding cannot be ruled out.

CONCLUSION

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.

Postmarketing safety surveillance : where does signal detection using electronic healthcare records fit into the big picture?

The safety profile of a drug evolves over its lifetime on the market; there are bound to be changes in the circumstances of a drug's clinical use which may give rise to previously unobserved adverse effects, hence necessitating surveillance postmarketing. Postmarketing surveillance has traditionally been carried out by systematic manual review of spontaneous reports of adverse drug reactions. Vast improvements in computing capabilities have provided opportunities to automate signal detection, and several worldwide initiatives are exploring new approaches to facilitate earlier detection, primarily through mining of routinely-collected data from electronic healthcare records (EHR). This paper provides an overview of ongoing initiatives exploring data from EHR for signal detection vis-à-vis established spontaneous reporting systems (SRS). We describe the role SRS has played in regulatory decision making with respect to safety issues, and evaluate the potential added value of EHR-based signal detection systems to the current practice of drug surveillance. Safety signal detection is both an iterative and dynamic process. It is in the best interest of public health to integrate and understand evidence from all possibly relevant information sources on drug safety. Proper evaluation and communication of potential signals identified remains an imperative and should accompany any signal detection activity.

The effect of network biology on drug toxicology

INTRODUCTION

The high failure rate of drug candidates due to toxicity, during clinical trials, is a critical issue in drug discovery. Network biology has become a promising approach, in this regard, using the increasingly large amount of biological and chemical data available and combining it with bioinformatics. With this approach, the assessment of chemical safety can be done across multiple scales of complexity from molecular to cellular and system levels in human health. Network biology can be used at several levels of complexity.

AREAS COVERED

This review describes the strengths and limitations of network biology. The authors specifically assess this approach across different biological scales when it is applied to toxicity.

EXPERT OPINION

There has been much progress made with the amount of data that is generated by various omics technologies. With this large amount of useful data, network biology has the opportunity to contribute to a better understanding of a drug's safety profile. The authors believe that considering a drug action and protein's function in a global physiological environment may benefit our understanding of the impact some chemicals have on human health and toxicity. The next step for network biology will be to better integrate differential and quantitative data.

Pharmacovigilance using clinical notes

With increasing adoption of electronic health records (EHRs), there is an opportunity to use the free-text portion of EHRs for pharmacovigilance. We present novel methods that annotate the unstructured clinical notes and transform them into a deidentified patient-feature matrix encoded using medical terminologies. We demonstrate the use of the resulting high-throughput data for detecting drug-adverse event associations and adverse events associated with drug-drug interactions. We show that these methods flag adverse events early (in most cases before an official alert), allow filtering of spurious signals by adjusting for potential confounding, and compile prevalence information. We argue that analyzing large volumes of free-text clinical notes enables drug safety surveillance using a yet untapped data source. Such data mining can be used for hypothesis generation and for rapid analysis of suspected adverse event risk.

Systems approaches in risk assessment

Adverse drug events (ADEs) remain a universal problem in drug development, regulatory review, and clinical practice with a substantial financial burden on the global health-care system. Recent advances in molecular and "omics" technologies, along with online databases and bioinformatics, have enabled a more integrative approach to understanding drug-target (protein) interactions, both desirable and undesirable, within a biological system. This has led to the development of systems approaches to risk assessment in an attempt to complement and improve on contemporary observational and predictive strategies for assessing risk. Although still in an evolutionary phase, systems approaches have the potential to markedly advance our understanding of ADEs and ability to predict them. Systems approaches will also move personalized medicine forward by enabling better identification of individual and subgroup risk factors.

The EU-ADR Web Platform: delivering advanced pharmacovigilance tools

PURPOSE

Pharmacovigilance methods have advanced greatly during the last decades, making post-market drug assessment an essential drug evaluation component. These methods mainly rely on the use of spontaneous reporting systems and health information databases to collect expertise from huge amounts of real-world reports. The EU-ADR Web Platform was built to further facilitate accessing, monitoring and exploring these data, enabling an in-depth analysis of adverse drug reactions risks.

METHODS

The EU-ADR Web Platform exploits the wealth of data collected within a large-scale European initiative, the EU-ADR project. Millions of electronic health records, provided by national health agencies, are mined for specific drug events, which are correlated with literature, protein and pathway data, resulting in a rich drug-event dataset. Next, advanced distributed computing methods are tailored to coordinate the execution of data-mining and statistical analysis tasks. This permits obtaining a ranked drug-event list, removing spurious entries and highlighting relationships with high risk potential.

RESULTS

The EU-ADR Web Platform is an open workspace for the integrated analysis of pharmacovigilance datasets. Using this software, researchers can access a variety of tools provided by distinct partners in a single centralized environment. Besides performing standalone drug-event assessments, they can also control the pipeline for an improved batch analysis of custom datasets. Drug-event pairs can be substantiated and statistically analysed within the platform's innovative working environment.

CONCLUSIONS

A pioneering workspace that helps in explaining the biological path of adverse drug reactions was developed within the EU-ADR project consortium. This tool, targeted at the pharmacovigilance community, is available online at https://bioinformatics.ua.pt/euadr/.