The tell-tale heart: population-based surveillance reveals an association of rofecoxib and celecoxib with myocardial infarction

Exploring the Hazards of Scaling Up Clinical Data Analyses: A Drug Side Effect Discovery Case Report

We assessed the scalability of pharmacological signal detection use case from a single-site CDW to a large aggregated clinical data warehouse (single-site database with 754,214 distinct patient IDs vs. multisite database with 49.8M). We aimed to explore whether a larger clinical dataset would provide clearer signals for secondary analyses such as detecting the known relationship between prednisone and weight. We found significant weight gain rate using the single-site data but not from using aggregated data (0.0104 kg/day, p<0.0001 vs. -0.050 kg/day, p<.0001). This rate was also found more consistently across 30 age and gender subgroups using the single-site data than in the aggregated data (26 vs. 18 significant weight gain findings). Contrary to our expectations, analyses of much larger aggregated clinical datasets did not yield stronger signals. Researchers must check the underlying model assumptions and account for greater heterogeneity when analyzing aggregated multisite data to ensure reliable findings.

Clinical Pharmacogenetics Implementation Consortium Guideline (CPIC) for CYP2C9 and Nonsteroidal Anti-Inflammatory Drugs

Nonsteroidal anti-inflammatory drugs (NSAIDs) are among the most commonly used analgesics due to their lack of addictive potential. However, NSAIDs have the potential to cause serious gastrointestinal, renal, and cardiovascular adverse events. CYP2C9 polymorphisms influence metabolism and clearance of several drugs in this class, thereby affecting drug exposure and potentially safety. We summarize evidence from the published literature supporting these associations and provide therapeutic recommendations for NSAIDs based on CYP2C9 genotype (updates at www.cpicpgx.org).

Leveraging Electronic Dental Record Data for Clinical Research in the National Dental PBRN Practices

OBJECTIVES

The aim of this study is to determine the feasibility of conducting clinical research using electronic dental record (EDR) data from U.S. solo and small-group general dental practices in the National Dental Practice-Based Research Network (network) and evaluate the data completeness and correctness before performing survival analyses of root canal treatment (RCT) and posterior composite restorations (PCR).

METHODS

Ninety-nine network general dentistry practices that used Dentrix or EagleSoft EDR shared de-identified data of patients who received PCR and/or RCT on permanent teeth through October 31, 2015. We evaluated the data completeness and correctness, summarized practice, and patient characteristics and summarized the two treatments by tooth type and arch location.

RESULTS

Eighty-two percent of practitioners were male, with a mean age of 49 and 22.4 years of clinical experience. The final dataset comprised 217,887 patients and 11,289,594 observations, with the observation period ranging from 0 to 37 years. Most patients (73%) were 18 to 64 years old; 56% were female. The data were nearly 100% complete. Eight percent of observations had incorrect data, such as incorrect tooth number or surface, primary teeth, supernumerary teeth, and tooth ranges, indicating multitooth procedures instead of PCR or RCT. Seventy-three percent of patients had dental insurance information; 27% lacked any insurance information. While gender was documented for all patients, race/ethnicity was missing in the dataset.

CONCLUSION

This study established the feasibility of using EDR data integrated from multiple distinct solo and small-group network practices for longitudinal studies to assess treatment outcomes. The results laid the groundwork for a learning health system that enables practitioners to learn about their patients' outcomes by using data from their own practice.

The Influence of Big (Clinical) Data and Genomics on Precision Medicine and Drug Development

Drug development continues to be costly and slow, with medications failing due to lack of efficacy or presence of toxicity. The promise of pharmacogenomic discovery includes tailoring therapeutics based on an individual's genetic makeup, rational drug development, and repurposing medications. Rapid growth of large research cohorts, linked to electronic health record (EHR) data, fuels discovery of new genetic variants predicting drug action, supports Mendelian randomization experiments to show drug efficacy, and suggests new indications for existing medications. New biomedical informatics and machine-learning approaches advance the ability to interpret clinical information, enabling identification of complex phenotypes and subpopulations of patients. We review the recent history of use of "big data" from EHR-based cohorts and biobanks supporting these activities. Future studies using EHR data, other information sources, and new methods will promote a foundation for discovery to more rapidly advance precision medicine.

The Cardiovascular Pharmacology of Nonsteroidal Anti-Inflammatory Drugs

The principal molecular mechanisms underlying the cardiovascular (CV) and renal adverse effects of nonsteroidal anti-inflammatory drugs (NSAIDs), such as myocardial infarction and hypertension, are understood in more detail than most side effects of drugs. Less is known, however, about differences in the CV safety profile between chemically distinct NSAIDs and their relative predisposition to complications. In review article, we discuss how heterogeneity in the pharmacokinetics and pharmacodynamics of distinct NSAIDs may be expected to affect their CV risk profile. We consider evidence afforded by studies in model systems, mechanistic clinical trials, a meta-analysis of randomized controlled trials, and two recent large clinical trials, Standard Care vs. Celecoxib Outcome Trial (SCOT) and Prospective Randomized Evaluation of Celecoxib Integrated Safety versus Ibuprofen or Naproxen (PRECISION), designed specifically to compare the CV safety of the cyclooxygenase-2-selective NSAID, celecoxib, with traditional NSAIDs. We conclude that SCOT and PRECISION have apparently not compared equipotent doses and have other limitations that bias them toward underestimation of the relative risk of celecoxib.

The Drug Data to Knowledge Pipeline: Large-Scale Claims Data Classification for Pharmacologic Insight

In biomedical informatics, assigning drug codes to categories is a common step in the analysis pipeline. Unfortunately, incomplete mappings are the norm rather than the exception with coverage values less than 85% not uncommon. Here, we perform this linking task on a nationwide insurance claims database with over 13 million members who were dispensed, according to National Drug Codes (NDCs), over 50,000 unique product forms of medication. The chosen approach employs Cerner Multum's VantageRx and the U.S. National Library of Medicine's RxMix. As a result, 94.0% of the NDCs were successfully mapped to categories used by common drug terminologies, e.g., Anatomical Therapeutic Chemical (ATC). Implemented as an SQL database and scripts, the approach is generic and can be setup for a new data set in a few hours. Thus, the method is a viable option for large-scale drug classification.

Supporting Multi-sourced Medication Information in i2b2

Postmarketing drug surveillance is critical to assessing adverse events associated with medications, because prelaunch clinical trials frequently miss negative drug effects. The Informatics for Integrating Biology and the Bedside platform (i2b2) has been used effectively for this. However, previous work suffers from incomplete medical data present in electronic health record (EHR) systems. Here, we develop a system to integrate non-traditional data sources with EHR data: pharmacy dispensing information and patient-reported data. We implement and validate a toolset to gather medication data from a Pharmacy Benefit Manager network, import it into an i2b2 EHR repository using a standard data format, merge it with the EHR data, and present it to for annotation with results returned to i2b2. This toolkit is enabling studies on medication list data quality, adherence, and adverse event detection.

Rediscovering drug side effects: the impact of analytical assumptions on the detection of associations in EHR data

Large clinical datasets can be used to discover and monitor drug side effects. Many previous studies analyzed symptom data as discrete events. However, some drug side effects are inferred from continuous variables such as weight or blood pressure. These require additional assumptions for analysis. For example, we can define positive/negative thresholds and time windows within which we expect to see the side effect. In this paper, we discuss the impact of such assumptions on the ability to detect known continuous drug side effects using statistical and visualization techniques. Taking the case of prednisone exposure and weight gain reflected in real EHR data, we found that temporal windowing greatly affected the ability to detect the expected effect. Categorization of the exposure variable improved side effect detection but negatively impacted model fit. To avoid false positive and false negative conclusions from clinical data reuse, studies reusing clinical data should determine the sensitivity of their findings to alternative analytic assumptions.

Patient stratification and identification of adverse event correlations in the space of 1190 drug related adverse events

Purpose: New pharmacovigilance methods are needed as a consequence of the morbidity caused by drugs. We exploit fine-grained drug related adverse event information extracted by text mining from electronic medical records (EMRs) to stratify patients based on their adverse events and to determine adverse event co-occurrences.

Methods: We analyzed the similarity of adverse event profiles of 2347 patients extracted from EMRs from a mental health center in Denmark. The patients were clustered based on their adverse event profiles and the similarities were presented as a network. The set of adverse events in each main patient cluster was evaluated. Co-occurrences of adverse events in patients (p-value < 0.01) were identified and presented as well.

Results: We found that each cluster of patients typically had a most distinguishing adverse event. Examination of the co-occurrences of adverse events in patients led to the identification of potentially interesting adverse event correlations that may be further investigated as well as provide further patient stratification opportunities.

Conclusions: We have demonstrated the feasibility of a novel approach in pharmacovigilance to stratify patients based on fine-grained adverse event profiles, which also makes it possible to identify adverse event correlations. Used on larger data sets, this data-driven method has the potential to reveal unknown patterns concerning adverse event occurrences.

Making big data useful for health care: a summary of the inaugural mit critical data conference

With growing concerns that big data will only augment the problem of unreliable research, the Laboratory of Computational Physiology at the Massachusetts Institute of Technology organized the Critical Data Conference in January 2014. Thought leaders from academia, government, and industry across disciplines—including clinical medicine, computer science, public health, informatics, biomedical research, health technology, statistics, and epidemiology—gathered and discussed the pitfalls and challenges of big data in health care. The key message from the conference is that the value of large amounts of data hinges on the ability of researchers to share data, methodologies, and findings in an open setting. If empirical value is to be from the analysis of retrospective data, groups must continuously work together on similar problems to create more effective peer review. This will lead to improvement in methodology and quality, with each iteration of analysis resulting in more reliability.

Dynamic clinical data mining: search engine-based decision support

The research world is undergoing a transformation into one in which data, on massive levels, is freely shared. In the clinical world, the capture of data on a consistent basis has only recently begun. We propose an operational vision for a digitally based care system that incorporates data-based clinical decision making. The system would aggregate individual patient electronic medical data in the course of care; query a universal, de-identified clinical database using modified search engine technology in real time; identify prior cases of sufficient similarity as to be instructive to the case at hand; and populate the individual patient's electronic medical record with pertinent decision support material such as suggested interventions and prognosis, based on prior outcomes. Every individual's course, including subsequent outcomes, would then further populate the population database to create a feedback loop to benefit the care of future patients.

Reusing electronic patient data for dental clinical research: a review of current status

OBJECTIVES

The reuse of electronic patient data collected during clinical care has received increased attention as a way to increase our evidence base. The purpose of this paper was to review studies reusing electronic patient data for dental research.

DATA SOURCES

1527 citations obtained by searching MEDLINE and Embase databases, hand-searching seven dental and informatics journals, and snowball sampling.

STUDY SELECTION

We included studies reusing electronic patient data for research on dental and craniofacial topics, alone or in combination with medical conditions, medications and outcomes. Studies using administrative or research databases and systematic reviews were excluded. Three reviewers extracted data independently and performed analysis jointly

RESULTS

The 60 studies reviewed covered epidemiological (32 studies), outcomes (16), health services research (10) and other (2) topics; were primarily retrospective (58 studies); varied significantly in sample size (9-153,619 patients) and follow-up period (1-12 years); often drew on other data sources in addition to electronic ones (25); but rarely tapped electronic dental record (EDR) data in private practices (3). Type of research was not associated with data sources used, but research topics/questions were. The most commonly reported advantages of reusing electronic data were being able to study large samples and saving time, while data quality and the inability to capture study-specific data were identified as major limitations.

CONCLUSIONS

Dental research reusing electronic patient data is nascent but accelerating. Future EDR design should focus on enhancing data quality, begin to integrate research data collection and implement interoperability with electronic medical records to facilitate oral-systemic investigations.

CLINICAL SIGNIFICANCE

Measuring and improving the quality of dental care requires that we begin to reuse electronic patient data collected in practice for clinical research. Practice data can potentially serve as a useful complement to data collected in traditional research studies.

Medication-wide association studies

Undiscovered side effects of drugs can have a profound effect on the health of the nation, and electronic health-care databases offer opportunities to speed up the discovery of these side effects. We applied a “medication-wide association study” approach that combined multivariate analysis with exploratory visualization to study four health outcomes of interest in an administrative claims database of 46 million patients and a clinical database of 11 million patients. The technique had good predictive value, but there was no threshold high enough to eliminate false-positive findings. The visualization not only highlighted the class effects that strengthened the review of specific products but also underscored the challenges in confounding. These findings suggest that observational databases are useful for identifying potential associations that warrant further consideration but are unlikely to provide definitive evidence of causal effects.

Using electronic dental record data for research: a data-mapping study

Anecdotal evidence suggests that, during the clinical care process, many dental practices record some data that are also collected in dental practice based research network (PBRN) studies. Since the use of existing, electronically stored data for research has multiple benefits, we investigated the overlap between research data fields used in dental PBRN studies and clinical data fields typically found in general dental records. We mapped 734 unique data elements from the Dental Information Model (DIM) to 2,487 Common Data Elements (CDE) curated by the NIDCR's PBRNs in the Cancer Data Standards Registry and Repository (caDSR). Thirty-three percent of the DIM data elements matched at least one CDE completely and 9% partially, translating to about 9% and 2%, respectively, of all data elements used in PBRN studies. The most frequently used CDEs found in the DIM included data about dental anatomy, medications, and items such as oral biopsy and caries. Our study shows that a non-trivial number of data elements in general dental records can be mapped either completely or partially to data fields in research studies. Further studies should investigate the feasibility of electronic clinical data for research purposes.

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.

Effectiveness and safety of topical versus oral nonsteroidal anti-inflammatory drugs: a comprehensive review

INTRODUCTION

Topical nonsteroidal anti-inflammatory drugs (NSAIDs) represent a relatively recent alternative to oral NSAIDs. Topical NSAIDs are designed to target their therapeutic effect locally to damaged tissue while minimizing systemic exposure. To better inform patients considering topical NSAIDs as an alternative to oral NSAIDs, this is the first comprehensive review to present all available evidence comparing topical NSAIDs with oral NSAIDs in the treatment of both acute and chronic musculoskeletal injury.

METHODS

Six studies, including 600 subjects, compared the use of topical versus oral NSAIDs in the treatment of a variety of acute injuries. Nine trials, including 2403 subjects, studied topical versus oral NSAIDs for chronic injury treatment, almost exclusively for osteoarthritis (OA) of the knee. This review included all available comparative studies, the majority of which were well-designed, double-dummy, placebo-controlled trials. Relevant meta-analyses were also reviewed.

RESULTS

Topical and oral NSAIDs performed statistically better than placebo for chronic injury treatment. Limited evidence comparing topical NSAIDs with placebo for acute injury treatment was available in the included studies, but supported greater effectiveness for topical NSAIDs. In all head-to-head comparisons, topical and oral NSAIDs demonstrated similar efficacy for treatment of both acute and chronic injuries. There were more gastrointestinal side effects in patients receiving oral NSAIDs, while local skin reactions occurred more frequently in patients treated with topical NSAIDs.

CONCLUSION

Overall, topical NSAIDs may be considered as comparable alternatives to oral NSAIDs and are associated with fewer serious adverse events (specifically GI reactions) when compared with oral NSAIDs. Caution should be exercised with the use of both topical and oral NSAIDs, including close adherence to dosing regimens and monitoring, particularly for patients with previous adverse reactions to NSAIDs.

Combing signals from spontaneous reports and electronic health records for detection of adverse drug reactions

OBJECTIVE

Data-mining algorithms that can produce accurate signals of potentially novel adverse drug reactions (ADRs) are a central component of pharmacovigilance. We propose a signal-detection strategy that combines the adverse event reporting system (AERS) of the Food and Drug Administration and electronic health records (EHRs) by requiring signaling in both sources. We claim that this approach leads to improved accuracy of signal detection when the goal is to produce a highly selective ranked set of candidate ADRs.

MATERIALS AND METHODS

Our investigation was based on over 4 million AERS reports and information extracted from 1.2 million EHR narratives. Well-established methodologies were used to generate signals from each source. The study focused on ADRs related to three high-profile serious adverse reactions. A reference standard of over 600 established and plausible ADRs was created and used to evaluate the proposed approach against a comparator.

RESULTS

The combined signaling system achieved a statistically significant large improvement over AERS (baseline) in the precision of top ranked signals. The average improvement ranged from 31% to almost threefold for different evaluation categories. Using this system, we identified a new association between the agent, rasburicase, and the adverse event, acute pancreatitis, which was supported by clinical review.

CONCLUSIONS

The results provide promising initial evidence that combining AERS with EHRs via the framework of replicated signaling can improve the accuracy of signal detection for certain operating scenarios. The use of additional EHR data is required to further evaluate the capacity and limits of this system and to extend the generalizability of these results.

SHRINE: enabling nationally scalable multi-site disease studies

Results of medical research studies are often contradictory or cannot be reproduced. One reason is that there may not be enough patient subjects available for observation for a long enough time period. Another reason is that patient populations may vary considerably with respect to geographic and demographic boundaries thus limiting how broadly the results apply. Even when similar patient populations are pooled together from multiple locations, differences in medical treatment and record systems can limit which outcome measures can be commonly analyzed. In total, these differences in medical research settings can lead to differing conclusions or can even prevent some studies from starting. We thus sought to create a patient research system that could aggregate as many patient observations as possible from a large number of hospitals in a uniform way. We call this system the ‘Shared Health Research Information Network’, with the following properties: (1) reuse electronic health data from everyday clinical care for research purposes, (2) respect patient privacy and hospital autonomy, (3) aggregate patient populations across many hospitals to achieve statistically significant sample sizes that can be validated independently of a single research setting, (4) harmonize the observation facts recorded at each institution such that queries can be made across many hospitals in parallel, (5) scale to regional and national collaborations. The purpose of this report is to provide open source software for multi-site clinical studies and to report on early uses of this application. At this time SHRINE implementations have been used for multi-site studies of autism co-morbidity, juvenile idiopathic arthritis, peripartum cardiomyopathy, colorectal cancer, diabetes, and others. The wide range of study objectives and growing adoption suggest that SHRINE may be applicable beyond the research uses and participating hospitals named in this report.

Interpreting noncoding genetic variation in complex traits and human disease

Association studies provide genome-wide information about the genetic basis of complex disease, but medical research has primarily focused on protein-coding variants, due to the difficulty of interpreting non-coding mutations. This picture has changed with advances in the systematic annotation of functional non-coding elements. Evolutionary conservation, functional genomics, chromatin state, sequence motifs, and molecular quantitative trait loci all provide complementary information about non-coding function. These functional maps can help prioritize variants on risk haplotypes, filter mutations encountered in the clinic, and perform systems-level analyses to reveal processes underlying disease associations. Advances in predictive modeling can enable dataset integration to reveal pathways shared across loci and alleles, and richer regulatory models can guide the search for epistatic interactions. Lastly, new massively parallel reporter experiments can systematically validate regulatory predictions. Ultimately, advances in regulatory and systems genomics can help unleash the value of whole-genome sequencing for personalized genomic risk assessment, diagnosis, and treatment.

Phenome based analysis as a means for discovering context dependent clinical reference ranges

Robust electronic medical records (EMR's) have made large-scale phenome-based analysis feasible. The context-dependent phenome of a large ICU-based EMR database (MIMIC II) was explored, as a function of a clinical feature: white blood cell count (WBC). Phenome visualization led to the discovery that peak WBC in the range 15-45 K/μl was highly associated with the diagnoses of Clostridium difficile and bacterial sepsis; thus, it is conceivable that clinicians might delay ordering targeted antimicrobials towards C. difficile for patients with peak WBC in this range. This hypothesis was confirmed, with significant delays in this group (median 135 vs. 85 hours, p = 0.002). These delays could be associated with adverse effects on patient health and high hospitalization costs (e.g. an additional $3,000,000 for the MIMIC II cohort). In conclusion, context-dependent clinical reference ranges are critical to clinical decision making; furthermore, important findings can be discovered through EMR-driven phenome association studies.

Electronic medical records as a tool in clinical pharmacology: opportunities and challenges

The development and increasing sophistication of electronic medical record (EMR) systems hold the promise of not only improving patient care but also providing unprecedented opportunities for discovery in the fields of basic, translational, and implementation sciences. Clinical pharmacology research in the EMR environment has only recently started to become a reality, with EMRs becoming increasingly populated, methods to mine drug response and other phenotypes becoming more sophisticated, and links being established with DNA repositories.

A pharmacoepidemiological network model for drug safety surveillance: statins and rhabdomyolysis

BACKGROUND

Recent withdrawals of major drugs have highlighted the critical importance of drug safety surveillance in the postmarketing phase. Limitations of spontaneous report data have led drug safety professionals to pursue alternative postmarketing surveillance approaches based on healthcare administrative claims data. These data are typically analysed by comparing the adverse event rates associated with a drug of interest to those of a single comparable reference drug.

OBJECTIVE

The aim of this study was to determine whether adverse event detection can be improved by incorporating information from multiple reference drugs. We developed a pharmacological network model that implemented this approach and evaluated its performance.

METHODS

We studied whether adverse event detection can be improved by incorporating information from multiple reference drugs, and describe two approaches for doing so. The first, reported previously, combines a set of related drugs into a single reference cohort. The second is a novel pharmacoepidemiological network model, which integrates multiple pair-wise comparisons across an entire set of related drugs into a unified consensus safety score for each drug. We also implemented a single reference drug approach for comparison with both multi-drug approaches. All approaches were applied within a sequential analysis framework, incorporating new information as it became available and addressing the issue of multiple testing over time. We evaluated all these approaches using statin (HMG-CoA reductase inhibitors) safety data from a large healthcare insurer in the US covering April 2000 through March 2005.

RESULTS

We found that both multiple reference drug approaches offer earlier detection (6-13 months) than the single reference drug approach, without triggering additional false positives.

CONCLUSIONS

Such combined approaches have the potential to be used with existing healthcare databases to improve the surveillance of therapeutics in the postmarketing phase over single-comparator methods. The proposed network approach also provides an integrated visualization framework enabling decision makers to understand the key high-level safety relationships amongst a group of related drugs.

The future of genomics in pathology

The recent advances in technology and the promise of cheap and fast whole genomic data offer the possibility to revolutionise the discipline of pathology. This should allow pathologists in the near future to diagnose disease rapidly and early to change its course, and to tailor treatment programs to the individual. This review outlines some of these technical advances and the changes needed to make this revolution a reality.

Novel data-mining methodologies for adverse drug event discovery and analysis

An important goal of the health system is to identify new adverse drug events (ADEs) in the postapproval period. Datamining methods that can transform data into meaningful knowledge to inform patient safety have proven essential for this purpose. New opportunities have emerged to harness data sources that have not been used within the traditional framework. This article provides an overview of recent methodological innovations and data sources used to support ADE discovery and analysis.

Effect of evening primrose oil and ω-3 polyunsaturated fatty acids on the cardiovascular risk of celecoxib in rats

Experimental data raised the specter of increased cardiovascular risk with selective cyclooxygenase-2 inhibitors. The study aimed to investigate the cardiovascular risk caused by celecoxib by studying its effect on blood pressure (BP) and thrombogenesis in rats. We tested the possible protective effects of evening primrose oil (EPO) or ω-3 polyunsaturated fatty acids (n-3 PUFAs). Male Wistar rats were assigned to the following groups: vehicle, celecoxib, celecoxib/n-3 PUFAs, celecoxib/EPO, n-3 PUFAs, and EPO. The rats were treated with celecoxib (20 mg·kg(-1)·d(-1)) by gastric gavage for 6 weeks. The mean BP was recorded, and blood samples were collected for testing prothrombin time and activated partial thromboplastin time. Platelet aggregation assay and collagen-induced platelet consumption test were used as models of thrombogenesis. Celecoxib increased the BP without affecting coagulation parameters and accelerated thrombogenesis by increasing platelet aggregation and collagen-induced thrombocytopenia. EPO and n-3 PUFAs decreased the celecoxib-induced elevation in BP. Although EPO significantly decreased platelet aggregation and collagen-induced thrombocytopenia, n-3 PUFAs did not. Celecoxib elevated BP and increased the risk of thrombogenesis in rats. A combination of celecoxib and the selected natural supplements is suggested as a novel approach to minimize cardiovascular risk caused by celecoxib.

Integrative genomics strategies to elucidate the complexity of drug response

Pharmacogenomic investigation from both genome-wide association studies and experiments focused on candidate loci involved in drug mechanism and metabolism has yielded a substantial and increasing list of robust genetic effects on drug therapy in humans. At the same time, reasonably comprehensive molecular data such as gene expression, proteomic and metabolomic data are now available for collections of hundreds to thousands of individuals. If these data are structured in a statistically robust and computationally tractable way, such as a network model, they can aid in the analysis of new pharmacogenomics studies by suggesting novel hypotheses for the regulation of genes involved in drug metabolism and response. Similarly, hypotheses taken from these same models can direct genome-wide association studies by focusing the genome-wide association studies analysis on a number of specific hypotheses informed by the relationships customarily seen between a gene's expression or protein activity and genetic variation at a particular locus. Network models based on other sorts of systematic biological data such as cell-based surveys of drug effect on gene expression and mining of literature and electronic medical records for associations between clinical and molecular phenotypes also promise similar utility. Although surely primitive in comparison with what will be developed, these model-based approaches to leveraging the increasing volume of data generated in the course of patient care and medical research nevertheless suggest a huge opportunity to improve our understanding of biological systems involved in pharmacogenomics and apply them to questions of medical relevance.

Translational bioinformatics embraces big data

We review the latest trends and major developments in translational bioinformatics in the year 2011-2012. Our emphasis is on highlighting the key events in the field and pointing at promising research areas for the future. The key take-home points are: • Translational informatics is ready to revolutionize human health and healthcare using large-scale measurements on individuals. • Data-centric approaches that compute on massive amounts of data (often called "Big Data") to discover patterns and to make clinically relevant predictions will gain adoption. • Research that bridges the latest multimodal measurement technologies with large amounts of electronic healthcare data is increasing; and is where new breakthroughs will occur.

A translational engine at the national scale: informatics for integrating biology and the bedside

Informatics for integrating biology and the bedside (i2b2) seeks to provide the instrumentation for using the informational by-products of health care and the biological materials accumulated through the delivery of health care to conduct discovery research and to study the healthcare system in vivo. This complements existing efforts such as prospective cohort studies or trials outside the delivery of routine health care. i2b2 has been used to generate genome-wide studies at less than one tenth the cost and one tenth the time of conventionally performed studies as well as to identify important risk from commonly used medications. i2b2 has been adopted by over 60 academic health centers internationally.

Using electronic health records to drive discovery in disease genomics

If genomic studies are to be a clinically relevant and timely reflection of the relationship between genetics and health status--whether for common or rare variants--cost-effective ways must be found to measure both the genetic variation and the phenotypic characteristics of large populations, including the comprehensive and up-to-date record of their medical treatment. The adoption of electronic health records, used by clinicians to document clinical care, is becoming widespread and recent studies demonstrate that they can be effectively employed for genetic studies using the informational and biological 'by-products' of health-care delivery while maintaining patient privacy.

The emerging role of electronic medical records in pharmacogenomics

Health-care information technology and genotyping technology are both advancing rapidly, creating new opportunities for medical and scientific discovery. The convergence of these two technologies is now facilitating genetic association studies of unprecedented size within the context of routine clinical care. As a result, the medical community will soon be presented with a number of novel opportunities to bring functional genomics to the bedside in the area of pharmacotherapy. By linking biological material to comprehensive medical records, large multi-institutional biobanks are now poised to advance the field of pharmacogenomics through three distinct mechanisms: (i) retrospective assessment of previously known findings in a clinical practice-based setting, (ii) discovery of new associations in huge observational cohorts, and (iii) prospective application in a setting capable of providing real-time decision support. This review explores each of these translational mechanisms within a historical framework.

The development of a dental diagnostic terminology

There is no commonly accepted standardized terminology for oral diagnoses. The purpose of this article is to report the development of a standardized dental diagnostic terminology by a work group of dental faculty members. The work group developed guiding principles for decision making and adhered to principles of terminology development. The members used an iterative process to develop a terminology incorporating concepts represented in the Toronto/University of California, San Francisco/Creighton University and International Classification of Diseases (ICD)-9/10 codes and periodontal and endodontic diagnoses. Domain experts were consulted to develop a final list of diagnostic terms. A structure was developed, consisting of thirteen categories, seventy-eight subcategories, and 1,158 diagnostic terms, hierarchically organized and mappable to other terminologies and ontologies. Use of this standardized diagnostic terminology will reinforce the diagnosis-treatment link and will facilitate clinical research, quality assurance, and patient communication. Future work will focus on implementation and approaches to enhance the validity and reliability of diagnostic term utilization.

Potential for electronic health records and online social networking to redefine medical research

BACKGROUND

Recent legislation in the US requires that all medical records become electronic over the next decade. In addition, ongoing developments in patient-oriented care, most notably with the advent of health social networking and personal health records, provide a plethora of new information sources for research.

CONTENT

Electronic health records (EHRs) show great potential for use in observational studies to examine drug safety via pharmacovigiliance methods that can find adverse drug events as well as expand drug safety profiles. EHRs also show promise for head-to-head comparative effectiveness trials and could play a critical role in secondary and tertiary diabetes prevention efforts. A growing subset of EHRs, personal health records (PHRs), opens up the possibility of engaging patients in their care, as well as new opportunities for participatory research and personalized medicine. Organizations nationwide, from providers to employers, are already investing heavily in PHR systems. Additionally, the explosive use of online social networking sites and mobile technologies will undoubtedly play a role in future research efforts by making available a veritable flood of information, such as real-time exercise monitoring, to health researchers.

SUMMARY

The future confluence of health information technologies will enable researchers and clinicians to reveal novel therapies and insights into treatments and disease management, as well as environmental and genomic interactions, at an unprecedented population scale.

Emotion recollected in tranquility: lessons learned from the COX-2 saga

Nonsteroidal antinflammatory drugs (NSAIDs) inhibit prostaglandin formation by cyclooxygenases (COX) 1 and 2. NSAIDs selective for inhibition of COX-2 are less likely than traditional drugs to cause serious gastrointestinal adverse effects, but predispose to adverse cardiovascular events, such as heart failure, myocardial infarction, and stroke. Evidence from human pharmacology and genetics, genetically manipulated rodents, and other animal models and randomized trials indicates that this is consequent to suppression of COX-2-dependent cardioprotective prostagladins, particularly prostacyclin. Lessons drawn from how this saga unfolded are relevant to how we approach drug surveillance and regulation, integrate diversifed forms of information and might pursue a more personalized approach to drug efficacy and risk.

Mechanisms of Kv2.1 channel inhibition by celecoxib--modification of gating and channel block

BACKGROUND AND PURPOSE

Selective cyclooxygenase-2 (COX-2) inhibitors such as rofecoxib (Vioxx) and celecoxib (Celebrex) were developed as NSAIDs with reduced gastric side effects. Celecoxib has now been shown to affect cellular physiology via an unexpected, COX-independent, pathway - by inhibiting K(v)2.1 and other ion channels. In this study, we investigated the mechanism of the action of celecoxib on K(v)2.1 channels.

EXPERIMENTAL APPROACH

The mode of action of celecoxib on rat K(v)2.1 channels was studied by whole-cell patch-clamping to record currents from channels expressed in HEK-293 cells.

KEY RESULTS

Celecoxib reduced current through K(v)2.1 channels when applied from the extracellular side. At low concentrations (<or=3 microM), celecoxib accelerated kinetics of activation, deactivation and inactivation. Recovery of rat K(v)2.1 channels from inactivation could be characterized by two components, with celecoxib selectively accelerating the slow component of recovery at <or=10 microM. At >3 microM, celecoxib led to closed-channel block with relative slowing of activation. At 30 microM, it additionally induced open-channel block that manifested in use-dependent inhibition and slower recovery from inactivation.

CONCLUSIONS AND IMPLICATIONS

Celecoxib reduced current through K(v)2.1 channels by modifying gating and inducing closed- and open-channel block, with the three effects manifesting at different concentrations. These data will help to elucidate the mechanisms of action of this widely prescribed drug on ion channels and those underlying its neurological, cardiovascular and other effects.

Measuring population health using personal health messages

Personal health messages - inter patient communications within online communities; represent a new path towards providing continuous information about patient derived health status. We apply natural language processing techniques to personal health messages from online message boards to demonstrate the ability to track trends in people's positive or negative opinion (sentiment) regarding particular drugs over time. The significant changes in sentiment correspond to FDA announcements and other publicity. We envision such analysis as a scalable tool for pharmacovigilance hypothesis generation for possible adverse drug reactions.

Prostaglandin E(2) synthase inhibition as a therapeutic target

BACKGROUND

Most NSAIDs function by inhibiting biosynthesis of PGE(2) by inhibition of COX-1 and/or COX-2. Since COX-1 has a protective function in the gastro-intestinal tract (GIT), non-selective inhibition of both cycloxy genases leads to moderate to severe gastro-intestinal intolerance. Attempts to identify selective inhibitors of COX-2, led to the identification of celecoxib and rofecoxib. However, long-term use of these drugs has serious adverse effects of sudden myocardial infarction and thrombosis. Drug-mediated imbalance in the levels of prostaglandin I(2) (PGI(2)) and thromboxane A(2) (TXA(2)) with a bias towards TXA(2) may be the primary reason for these events. This resulted in the drugs being withdrawn from the market, leaving a need for an effective and safe anti-inflammatory drug.

METHODS

Recently, the focus of research has shifted to enzymes downstream of COX in the prosta glandin biosynthetic pathway such as prostaglandin E(2) synthases. Microsomal prostaglandin E(2) synthase-1 (mPGES-1) specifically isomerizes PGH(2) to PGE(2), under inflammatory conditions. In this review, we examine the biology of mPGES-1 and its role in disease. Progress in designing molecules that can selectively inhibit mPGES-1 is reviewed.

CONCLUSION

mPGES-1 has the potential to be a target for anti-inflammatory therapy, devoid of adverse GIT and cardiac effects and warrants further investigation.