Medication-wide association studies

Data Interoperability and Harmonization in Cardiovascular Genomic and Precision Medicine

Despite advances in cardiovascular care and improved outcomes, fragmented healthcare systems, nonequitable access to health care, and nonuniform and unbiased collection and access to healthcare data have exacerbated disparities in healthcare provision and further delayed the technological-enabled implementation of precision medicine. Precision medicine relies on a foundation of accurate and valid omics and phenomics that can be harnessed at scale from electronic health records. Big data approaches in noncardiovascular healthcare domains have helped improve efficiency and expedite the development of novel therapeutics; therefore, applying such an approach to cardiovascular precision medicine is an opportunity to further advance the field. Several endeavors, including the American Heart Association Precision Medicine platform and public-private partnerships (such as BigData@Heart in Europe), as well as cloud-based platforms, such as Terra used for the National Institutes of Health All of Us, are attempting to temporally and ontologically harmonize data. This state-of-the-art review summarizes best practices used in cardiovascular genomic and precision medicine and provides recommendations for systems' requirements that could enhance and accelerate the integration of these platforms.

Drug use and acute kidney injury: a Drug-Wide Association Study (DWAS) in Denmark and Sweden

Background

Knowledge of which medications may lead to acute kidney injury (AKI) is limited, relying mostly on spontaneous reporting in pharmacovigilance systems. We here conducted an exploratory drug-wide association study (DWAS) to screen for associations between dispensed drugs and AKI risk.

Methods

Using two large Danish and Swedish data linkages, we identified AKI hospitalizations occurring between April 1997 and December 2021 in Denmark and between March 2007 and December 2021 in Sweden. We used a case-time control design comparing drug dispensing in the 3 months prior to the AKI with earlier periods for the same patient. Odds ratios (ORs) for the association between each drug and AKI were estimated using conditional logistic regression and adjusting for the presence of comorbidities. We sought replication of signals in both health systems and explored the plausibility of findings through pharmacovigilance system analysis in the US Food and Drug Administration Adverse Event Reporting System (FAERS) database, appearance in the RESCUE list of medications that report AKI as a side effect, PubMed evidence review and causality assessment through direct acyclic graphs.

Results

We included 20 622 adults in Denmark and 13 852 in Sweden hospitalized for AKI. In total, 16 unique medications were identified in both cohorts as associated to increased AKI occurrence. Of these, 10 medications had higher reporting ORs in the FAERS database, 9 were listed by RESCUE, and 7 appearing in PubMed. This analysis identified some medications with known AKI risks (i.e. likely true positives such as furosemide, penicillin, spironolactone and omeprazole), medications that may have initiated in response to conditions that lead to AKI (i.e. false positives like metoclopramide provided to treat nausea/vomiting) and other candidates (e.g. opioids) that warrant further evaluation in subsequent studies.

Conclusions

This hypothesis-generating study identifies medications with potential involvement in AKI that require confirmation and validation.

Value Set Hub: Software for developing and curating high-quality value sets

Terminology value sets are central to research studies using real-world patient data. We describe the considerable challenges that arise when researchers develop value sets for their studies or reuse those made by others. We present Value Set Hub (VS-Hub), software to overcome these challenges, describing its design, implementation, features, use in the field, lessons learned, and future directions. Over a five-month period, VS-Hub has been used by over 200 users and has been used in the development and curation of 95 recommended value sets for commonly studied conditions, treatments, and lab tests. Particular innovations include the presentation of multiple value sets on the same screen for easy comparison, the display of compared value sets in the context of vocabulary hierarchies, the integration of these analytic features and value set authoring, and value set browsing features that encourage users to review existing value sets that may be relevant to their needs.

Prescribed Drug Use and Aneurysmal Subarachnoid Hemorrhage Incidence: A Drug-Wide Association Study

BACKGROUND AND OBJECTIVES

Current benefits of invasive intracranial aneurysm treatment to prevent aneurysmal subarachnoid hemorrhage (aSAH) rarely outweigh treatment risks. Most intracranial aneurysms thus remain untreated. Commonly prescribed drugs reducing aSAH incidence may provide leads for drug repurposing. We performed a drug-wide association study (DWAS) to systematically investigate the association between commonly prescribed drugs and aSAH incidence.

METHODS

We defined all aSAH cases between 2000 and 2020 using International Classification of Diseases codes from the Secure Anonymised Information Linkage databank. Each case was matched with 9 controls based on age, sex, and year of database entry. We investigated commonly prescribed drugs (>2% in study population) and defined 3 exposure windows relative to the most recent prescription before index date (i.e., occurrence of aSAH): current (within 3 months), recent (3-12 months), and past (>12 months). A logistic regression model was fitted to compare drug use across these exposure windows vs never use, controlling for age, sex, known aSAH risk factors, and health care utilization. The family-wise error rate was kept at p < 0.05 through Bonferroni correction.

RESULTS

We investigated exposure to 205 commonly prescribed drugs between 4,879 aSAH cases (mean age 61.4, 61.2% women) and 43,911 matched controls. We found similar trends for lisinopril and amlodipine, with a decreased aSAH risk for current use (lisinopril odds ratio [OR] 0.63, 95% CI 0.44-0.90, amlodipine OR 0.82, 95% CI 0.65-1.04) and an increased aSAH risk for recent use (lisinopril OR 1.30, 95% CI 0.61-2.78, amlodipine OR 1.61, 95% CI 1.04-2.48). A decreased aSAH risk in current use was also found for simvastatin (OR 0.78, 95% CI 0.64-0.96), metformin (OR 0.58, 95% CI 0.43-0.78), and tamsulosin (OR 0.55, 95% CI 0.32-0.93). By contrast, an increased aSAH risk was found for current use of warfarin (OR 1.35, 95% CI 1.02-1.79), venlafaxine (OR 1.67, 95% CI 1.01-2.75), prochlorperazine (OR 2.15, 95% CI 1.45-3.18), and co-codamol (OR 1.31, 95% CI 1.10-1.56).

DISCUSSION

We identified several drugs associated with aSAH, of which 5 drugs (lisinopril and possibly amlodipine, simvastatin, metformin, and tamsulosin) showed a decreased aSAH risk. Future research should build on these signals to further assess the effectiveness of these drugs in reducing aSAH incidence.

CLASSIFICATION OF EVIDENCE

This study provides Class III evidence that some commonly prescribed drugs are associated with subsequent development of aSAH.

Active surveillance pharmacovigilance for Clostridioides difficile infection and gastrointestinal bleeding: an analytic framework based on case-control studies

BACKGROUND

Active surveillance pharmacovigilance is an emerging approach to identify medications with unanticipated effects. We previously developed a framework called pharmacopeia-wide association studies (PharmWAS) that limits false positive medication associations through high-dimensional confounding adjustment and set enrichment. We aimed to assess the transportability and generalizability of the PharmWAS framework by using medical claims data to reproduce known medication associations with Clostridioides difficile infection (CDI) or gastrointestinal bleeding (GIB).

METHODS

We conducted case-control studies using Optum's de-identified Clinformatics Data Mart Database of individuals enrolled in large commercial and Medicare Advantage health plans in the United States. Individuals with CDI (from 2010 to 2015) or GIB (from 2010 to 2021) were matched to controls by age and sex. We identified all medications utilized prior to diagnosis and analysed the association of each with CDI or GIB using conditional logistic regression adjusted for risk factors for the outcome and a high-dimensional propensity score.

FINDINGS

For the CDI study, we identified 55,137 cases, 220,543 controls, and 290 medications to analyse. Antibiotics with Gram-negative spectrum, including ciprofloxacin (aOR 2.83), ceftriaxone (aOR 2.65), and levofloxacin (aOR 1.60), were strongly associated. For the GIB study, we identified 450,315 cases, 1,801,260 controls, and 354 medications to analyse. Antiplatelets, anticoagulants, and non-steroidal anti-inflammatory drugs, including ticagrelor (aOR 2.81), naproxen (aOR 1.87), and rivaroxaban (aOR 1.31), were strongly associated.

INTERPRETATION

These studies demonstrate the generalizability and transportability of the PharmWAS pharmacovigilance framework. With additional validation, PharmWAS could complement traditional passive surveillance systems to identify medications that unexpectedly provoke or prevent high-impact conditions.

FUNDING

U.S. National Institute of Diabetes and Digestive and Kidney Diseases.

Natural Language Processing for Radiation Oncology: Personalizing Treatment Pathways

Natural language processing (NLP), a technology that translates human language into machine-readable data, is revolutionizing numerous sectors, including cancer care. This review outlines the evolution of NLP and its potential for crafting personalized treatment pathways for cancer patients. Leveraging NLP's ability to transform unstructured medical data into structured learnable formats, researchers can tap into the potential of big data for clinical and research applications. Significant advancements in NLP have spurred interest in developing tools that automate information extraction from clinical text, potentially transforming medical research and clinical practices in radiation oncology. Applications discussed include symptom and toxicity monitoring, identification of social determinants of health, improving patient-physician communication, patient education, and predictive modeling. However, several challenges impede the full realization of NLP's benefits, such as privacy and security concerns, biases in NLP models, and the interpretability and generalizability of these models. Overcoming these challenges necessitates a collaborative effort between computer scientists and the radiation oncology community. This paper serves as a comprehensive guide to understanding the intricacies of NLP algorithms, their performance assessment, past research contributions, and the future of NLP in radiation oncology research and clinics.

The association between prescription drugs and colorectal cancer prognosis: a nationwide cohort study using a medication-wide association study

BACKGROUND

With the availability of health insurance claim data, pharmacovigilance for various drugs has been suggested; however, it is necessary to establish an appropriate analysis method. To detect unintended drug effects and to generate new hypotheses, we conducted a hypothesis-free study to systematically examine the relationship between all prescription nonanticancer drugs and the mortality of colorectal cancer patients.

METHODS

We used the Korean National Health Insurance Service-National Sample Cohort database. A total of 2,618 colorectal cancer patients diagnosed between 2004 and 2015 were divided into drug discovery and drug validation sets (1:1) through random sampling. Drugs were classified using the Anatomical Therapeutic Chemical (ATC) classification system: 76 drugs classified as ATC level 2 and 332 drugs classified as ATC level 4 were included in the analysis. We used a Cox proportional hazard model adjusted for sex, age, colorectal cancer treatment, and comorbidities. The relationship between all prescription nonanticancer drugs and the mortality of colorectal cancer patients was analyzed, controlling for multiple comparisons with the false discovery rate.

RESULTS

We found that one ATC level-2 drug (drugs that act on the nervous system, including parasympathomimetics, addictive disorder drugs, and antivertigo drugs) showed a protective effect related to colorectal cancer prognosis. At the ATC level 4 classification, 4 drugs were significant: two had a protective effect (anticholinesterases and opioid anesthetics), and the other two had a detrimental effect (magnesium compounds and Pregnen [4] derivatives).

CONCLUSIONS

In this hypothesis-free study, we identified four drugs linked to colorectal cancer prognosis. The MWAS method can be useful in real-world data analysis.

A medication-wide association study (MWAS) on repurposed drugs for COVID-19 with Pre-pandemic prescription medication exposure and pregnancy outcomes

Information on effects of medication therapies during pregnancy is lacking as pregnant patients are often excluded from clinical trials. This retrospective study explores the potential of using electronic health record (EHR) data to inform safety profiles of repurposed COVID medication therapies on pregnancy outcomes using pre-COVID data. We conducted a medication-wide association study (MWAS) on prescription medication exposures during pregnancy and the risk of cesarean section, preterm birth, and stillbirth, using EHR data between 2010-2017 on deliveries at PennMedicine. Repurposed drugs studied for treatment of COVID-19 were extracted from ClinicalTrials.gov (n = 138). We adjusted for known comorbidities diagnosed within 2 years prior to birth. Using previously developed medication mapping and delivery-identification algorithms, we identified medication exposure in 2,830 of a total 63,334 deliveries; from 138 trials, we found 31 medications prescribed and included in our cohort. We found 21 (68%) of the 31 medications were not positively associated with increased risk of the outcomes examined. With caution, these medications warrant potential for inclusion of pregnant individuals in future studies, while drugs found to be associated with pregnancy outcomes require further investigation. MWAS facilitates hypothesis-driven evaluation of drug safety across all prescription medications, revealing potential drug candidates for further research.

Medication-Wide Association Study Using Electronic Health Record Data of Prescription Medication Exposure and Multifetal Pregnancies: Retrospective Study

Background

Medication-wide association studies (MWAS) have been applied to assess the risk of individual prescription use and a wide range of health outcomes, including cancer, acute myocardial infarction, acute liver failure, acute renal failure, and upper gastrointestinal ulcers. Current literature on the use of preconception and periconception medication and its association with the risk of multiple gestation pregnancies (eg, monozygotic and dizygotic) is largely based on assisted reproductive technology (ART) cohorts. However, among non-ART pregnancies, it is unknown whether other medications increase the risk of multifetal pregnancies.

Objective

This study aimed to investigate the risk of multiple gestational births (eg, twins and triplets) following preconception and periconception exposure to prescription medications in patients who delivered at Penn Medicine.

Methods

We used electronic health record data between 2010 and 2017 on patients who delivered babies at Penn Medicine, a health care system in the Greater Philadelphia area. We explored 3 logistic regression models: model 1 (no adjustment); model 2 (adjustment for maternal age); and model 3—our final logistic regression model (adjustment for maternal age, ART use, and infertility diagnosis). In all models, multiple births (MBs) were our outcome of interest (binary outcome), and each medication was assessed separately as a binary variable. To assess our MWAS model performance, we defined ART medications as our gold standard, given that these medications are known to increase the risk of MB.

Results

Of the 63,334 distinct deliveries in our cohort, only 1877 pregnancies (2.96%) were prescribed any medication during the preconception and first trimester period. Of the 123 medications prescribed, we found 26 (21.1%) medications associated with MB (using nominal P values) and 10 (8.1%) medications associated with MB (using Bonferroni adjustment) in fully adjusted model 3. We found that our model 3 algorithm had an accuracy of 85% (using nominal P values) and 89% (using Bonferroni-adjusted P values).

Conclusions

Our work demonstrates the opportunities in applying the MWAS approach with electronic health record data to explore associations between preconception and periconception medication exposure and the risk of MB while identifying novel candidate medications for further study. Overall, we found 3 novel medications linked with MB that could be explored in further work; this demonstrates the potential of our method to be used for hypothesis generation.

Mining Electronic Health Records for Drugs Associated With 28-day Mortality in COVID-19: Pharmacopoeia-wide Association Study (PharmWAS)

BACKGROUND

Patients hospitalized for a given condition may be receiving other treatments for other contemporary conditions or comorbidities. The use of such observational clinical data for pharmacological hypothesis generation is appealing in the context of an emerging disease but particularly challenging due to the presence of drug indication bias.

OBJECTIVE

With this study, our main objective was the development and validation of a fully data-driven pipeline that would address this challenge. Our secondary objective was to generate pharmacological hypotheses in patients with COVID-19 and demonstrate the clinical relevance of the pipeline.

METHODS

We developed a pharmacopeia-wide association study (PharmWAS) pipeline inspired from the PheWAS methodology, which systematically screens for associations between the whole pharmacopeia and a clinical phenotype. First, a fully data-driven procedure based on adaptive least absolute shrinkage and selection operator (LASSO) determined drug-specific adjustment sets. Second, we computed several measures of association, including robust methods based on propensity scores (PSs) to control indication bias. Finally, we applied the Benjamini and Hochberg procedure of the false discovery rate (FDR). We applied this method in a multicenter retrospective cohort study using electronic medical records from 16 university hospitals of the Greater Paris area. We included all adult patients between 18 and 95 years old hospitalized in conventional wards for COVID-19 between February 1, 2020, and June 15, 2021. We investigated the association between drug prescription within 48 hours from admission and 28-day mortality. We validated our data-driven pipeline against a knowledge-based pipeline on 3 treatments of reference, for which experts agreed on the expected association with mortality. We then demonstrated its clinical relevance by screening all drugs prescribed in more than 100 patients to generate pharmacological hypotheses.

RESULTS

A total of 5783 patients were included in the analysis. The median age at admission was 69.2 (IQR 56.7-81.1) years, and 3390 (58.62%) of the patients were male. The performance of our automated pipeline was comparable or better for controlling bias than the knowledge-based adjustment set for 3 reference drugs: dexamethasone, phloroglucinol, and paracetamol. After correction for multiple testing, 4 drugs were associated with increased in-hospital mortality. Among these, diazepam and tramadol were the only ones not discarded by automated diagnostics, with adjusted odds ratios of 2.51 (95% CI 1.52-4.16, Q=.1) and 1.94 (95% CI 1.32-2.85, Q=.02), respectively.

CONCLUSIONS

Our innovative approach proved useful in generating pharmacological hypotheses in an outbreak setting, without requiring a priori knowledge of the disease. Our systematic analysis of early prescribed treatments from patients hospitalized for COVID-19 showed that diazepam and tramadol are associated with increased 28-day mortality. Whether these drugs could worsen COVID-19 needs to be further assessed.

Drug prescriptions and dementia incidence: a medication-wide association study of 17000 dementia cases among half a million participants

BACKGROUND

Previous studies have suggested that some medications may influence dementia risk. We conducted a hypothesis-generating medication-wide association study to investigate systematically the association between all prescription medications and incident dementia.

METHODS

We used a population-based cohort within the Secure Anonymised Information Linkage (SAIL) databank, comprising routinely-collected primary care, hospital admissions and mortality data from Wales, UK. We included all participants born after 1910 and registered with a SAIL general practice at ≤60 years old. Follow-up was from each participant's 60th birthday to the earliest of dementia diagnosis, deregistration from a SAIL general practice, death or the end of 2018. We considered participants exposed to a medication if they received ≥1 prescription for any of 744 medications before or during follow-up. We adjusted for sex, smoking and socioeconomic status. The outcome was any all-cause dementia code in primary care, hospital or mortality data during follow-up. We used Cox regression to calculate hazard ratios and Bonferroni-corrected p values.

RESULTS

Of 551 344 participants, 16 998 (3%) developed dementia (median follow-up was 17 years for people who developed dementia, 10 years for those without dementia). Of 744 medications, 221 (30%) were associated with dementia. Of these, 217 (98%) were associated with increased dementia incidence, many clustering around certain indications. Four medications (all vaccines) were associated with a lower dementia incidence.

CONCLUSIONS

Almost a third of medications were associated with dementia. The clustering of many drugs around certain indications may provide insights into early manifestations of dementia. We encourage further investigation of hypotheses generated by these results.

Drug-Wide Association Study (DWAS): Challenges and Opportunities

Cancer risk associations with commonly prescribed medications have been mainly evaluated in hypothesis-driven studies that focus on one drug at a time. Agnostic drug-wide association studies (DWAS) offer an alternative approach to simultaneously evaluate associations between a large number of drugs with one or more cancers using large-scale electronic health records. Although cancer DWAS approaches are promising, a number of challenges limit their applicability. This includes the high likelihood of false positivity; lack of biological considerations; and methodological shortcomings, such as inability to tightly control for confounders. As such, the value of DWAS is currently restricted to hypothesis generation with detected signals needing further evaluation. In this commentary, we discuss those challenges in more detail and summarize the approaches to overcome them by using published cancer DWAS studies, including the accompanied article by Støer and colleagues. Despite current concerns, DWAS future is filled with opportunities for developing innovative analytic methods and techniques that incorporate pharmacology, epidemiology, cancer biology, and genetics.See related article by Støer et al., p. 682.

Evaluation of a Meta-Analysis of Ambient Air Quality as a Risk Factor for Asthma Exacerbation

Background: An irreproducibility crisis currently afflicts a wide range of scientific disciplines, including public health and biomedical science. A study was undertaken to assess the reliability of a meta-analysis examining whether air quality components (carbon monoxide, particulate matter 10 µm and 2.5 µm (PM10 and PM2.5), sulfur dioxide, nitrogen dioxide and ozone) are risk factors for asthma exacerbation. Methods: The number of statistical tests and models were counted in 17 randomly selected base papers from 87 used in the meta-analysis. Confidence intervals from all 87 base papers were converted to p-values. p-value plots for each air component were constructed to evaluate the effect heterogeneity of the p-values. Results: The number of statistical tests possible in the 17 selected base papers was large, median = 15,360 (interquartile range = 1536–40,960), in comparison to results presented. Each p-value plot showed a two-component mixture with small p-values < 0.001 while other p-values appeared random (p-values > 0.05). Given potentially large numbers of statistical tests conducted in the 17 selected base papers, p-hacking cannot be ruled out as explanations for small p-values. Conclusions: Our interpretation of the meta-analysis is that random p-values indicating null associations are more plausible and the meta-analysis is unlikely to replicate in the absence of bias.

Using indication embeddings to represent patient health for drug safety studies

Objective

The electronic health record is a rising resource for quantifying medical practice, discovering the adverse effects of drugs, and studying comparative effectiveness. One of the challenges of applying these methods to health care data is the high dimensionality of the health record. Methods to discover the effects of drugs in health data must account for tens of thousands of potentially relevant confounders. Our goal in this work is to reduce the dimensionality of the health data with the aim of accelerating the application of retrospective cohort studies to this data.

Materials and methods

Here, we develop indication embeddings, a way to reduce the dimensionality of health data while capturing information relevant to treatment decisions. We evaluate these embeddings using external data on drug indications. Then, we use the embeddings as a substitute for medical history to match patients and develop evaluation metrics for these matches.

Results

We demonstrate that these embeddings recover the therapeutic uses of drugs. We use embeddings as an informative representation of relationships between drugs, between health history events and drug prescriptions, and between patients at a particular time in their health history. We show that using embeddings to match cohorts improves the balance of the cohorts, even in terms of poorly measured risk factors like smoking.

Discussion and conclusion

Unlike other embeddings inspired by word2vec, indication embeddings are specifically designed to capture the medical history leading to the prescription of a new drug. For retrospective cohort studies, our low-dimensional representation helps in finding comparator drugs and constructing comparator cohorts.

High-Throughput Algorithm for Discovering New Drug Indications by Utilizing Large-Scale Electronic Medical Record Data

Drug repositioning is an effective way to mitigate the production problem in the pharmaceutical industry. Electronic medical record (EMR) databases harbor a large amount of data on drug prescriptions and laboratory test results and may thus be useful for finding new indications for existing drugs. Here, we present a novel high-throughput data-driven algorithm that identifies and prioritizes drug candidates that show significant effects on specific clinical indicators by utilizing large-scale EMR data. We chose four laboratory tests as clinical indicators: hemoglobin A1c (HbA1c), low-density lipoprotein (LDL) cholesterol, triglycerides (TGs), and high-density lipoprotein (HDL) cholesterol. From a 5-year EMR database, we generated datasets consisting of paired data with averaged measurement values during on and off each drug in each patient, adjusted for co-administered drug effects at each timepoint, and applied one sample t-test with the Bonferroni correction for statistical analysis. Among 1,774 drugs, 45 were associated with increases in HDL cholesterol, and 41, 146, and 65 were associated with reductions in HbA1c, LDL cholesterol, and TGs, respectively. We compared the list of candidate drugs with that of drugs indicated for relevant clinical conditions and found that the algorithm had high values for both sensitivity (range 0.95-1.00) and negative predictive value (range 0.95-1.00). Our algorithm was able to rediscover well-known drugs that are used for diabetes and dyslipidemia while revealing potential candidates without current indications but have shown promising results in the literature. Our algorithm may facilitate the repositioning of drugs with proven safety profiles.

Medication class enrichment analysis: a novel algorithm to analyze multiple pharmacologic exposures simultaneously using electronic health record data

Objective

Observational studies analyzing multiple exposures simultaneously have been limited by difficulty distinguishing relevant results from chance associations due to poor specificity. Set-based methods have been successfully used in genomics to improve signal-to-noise ratio. We present and demonstrate medication class enrichment analysis (MCEA), a signal-to-noise enhancement algorithm for observational data inspired by set-based methods.

Materials and Methods

We used The Health Improvement Network database to study medications associated with Clostridium difficile infection (CDI). We performed case-control studies for each medication in The Health Improvement Network to obtain odds ratios (ORs) for association with CDI. We then calculated the association of each pharmacologic class with CDI using logistic regression and MCEA. We also performed simulation studies in which we assessed the sensitivity and specificity of logistic regression compared to MCEA for ORs 0.1-2.0.

Results

When analyzing pharmacologic classes using logistic regression, 47 of 110 pharmacologic classes were identified as associated with CDI. When analyzing pharmacologic classes using MCEA, only fluoroquinolones, a class of antibiotics with biologically confirmed causation, and heparin products were associated with CDI. In simulation, MCEA had superior specificity compared to logistic regression across all tested effect sizes and equal or better sensitivity for all effect sizes besides those close to null.

Discussion

Although these results demonstrate the promise of MCEA, additional studies that include inpatient administered medications are necessary for validation of the algorithm.

Conclusions

In clinical and simulation studies, MCEA demonstrated superior sensitivity and specificity for identifying pharmacologic classes associated with CDI compared to logistic regression.

Evaluating statistical approaches to leverage large clinical datasets for uncovering therapeutic and adverse medication effects

Motivation

Phenome-wide association studies (PheWAS) have been used to discover many genotype-phenotype relationships and have the potential to identify therapeutic and adverse drug outcomes using longitudinal data within electronic health records (EHRs). However, the statistical methods for PheWAS applied to longitudinal EHR medication data have not been established.

Results

In this study, we developed methods to address two challenges faced with reuse of EHR for this purpose: confounding by indication, and low exposure and event rates. We used Monte Carlo simulation to assess propensity score (PS) methods, focusing on two of the most commonly used methods, PS matching and PS adjustment, to address confounding by indication. We also compared two logistic regression approaches (the default of Wald versus Firth's penalized maximum likelihood, PML) to address complete separation due to sparse data with low exposure and event rates. PS adjustment resulted in greater power than PS matching, while controlling Type I error at 0.05. The PML method provided reasonable P-values, even in cases with complete separation, with well controlled Type I error rates. Using PS adjustment and the PML method, we identify novel latent drug effects in pediatric patients exposed to two common antibiotic drugs, ampicillin and gentamicin.

Availability and implementation

R packages PheWAS and EHR are available at https://github.com/PheWAS/PheWAS and at CRAN (https://www.r-project.org/), respectively. The R script for data processing and the main analysis is available at https://github.com/choileena/EHR.

Supplementary information

Supplementary data are available at Bioinformatics online.

A semi-automated approach for analyzing collages to inform the design of a family health information management system for Hispanic dementia caregivers

Culturally- and linguistically-tailored health communication is needed for vulnerable populations to manage their health and the health of their families. This presents a significant design challenge. The use of collages is an increasingly popular technique with the flexibility to capture the needs and experiences of individuals with various cultural and language preferences. Collage analysis has typically remained qualitative in nature. We introduce a novel, objective, semi-automated approach that enhances collage analysis to elucidate pattern differences that may not be detectable by natural perception. We present a case scenario of collage analysis based on the expressed experience and self-management needs of Hispanic dementia caregivers (n = 24). We demonstrate how our innovative approach may reveal cultural differences between language groups that could have otherwise been missed using traditional techniques.

Systematic assessment of prescribed medications and short-term risk of myocardial infarction - a pharmacopeia-wide association study from Norway and Sweden

Wholesale, unbiased assessment of Scandinavian electronic health-care databases offer a unique opportunity to reveal potentially important undiscovered drug side effects. We examined the short-term risk of acute myocardial infarction (AMI) associated with drugs prescribed in Norway or Sweden. We identified 24,584 and 97,068 AMI patients via the patient- and the cause-of-death registers and linked to prescription databases in Norway (2004-2014) and Sweden (2005-2014), respectively. A case-crossover design was used to compare the drugs dispensed 1-7 days before the date of AMI diagnosis with 15-21 days' time -window for all the drug individually while controlling the receipt of other drugs. A BOLASSO approach was used to select drugs that acutely either increase or decrease the apparent risk of AMI. We found 48 drugs to be associated with AMI in both countries. Some antithrombotics, antibiotics, opioid analgesics, adrenergics, proton-pump inhibitors, nitroglycerin, diazepam, metoclopramide, acetylcysteine were associated with higher risk for AMI; whereas angiotensin-II-antagonists, calcium-channel blockers, angiotensin-converting-enzyme inhibitors, serotonin-specific reuptake inhibitors, allopurinol, mometasone, metformin, simvastatin, levothyroxine were inversely associated. The results were generally robust in different sensitivity analyses. This study confirms previous findings for certain drugs. Based on the known effects or indications, some other associations could be anticipated. However, inverse associations of hydroxocobalamin, levothyroxine and mometasone were unexpected and needs further investigation. This pharmacopeia-wide association study demonstrates the feasibility of a systematic, unbiased approach to pharmacological triggers of AMI and other diseases with acute, identifiable onsets.

Signals Among Signals: Prioritizing Nongenetic Associations in Massive Data Sets

Massive data sets are often regarded as a panacea to the underpowered studies of the past. At the same time, it is becoming clear that in many of these data sets in which thousands of variables are measured across hundreds of thousands or millions of individuals, almost any desired relationship can be inferred with a suitable combination of covariates or analytic choices. Inspired by the genome-wide association study analysis paradigm that has transformed human genetics, X-wide association studies or "XWAS" have emerged as a popular approach to systematically analyzing nongenetic data sets and guarding against false positives. However, these studies often yield hundreds or thousands of associations characterized by modest effect sizes and miniscule P values. Many of these associations will be spurious and emerge due to confounding and other biases. One way of characterizing confounding in the genomics paradigm is the genomic inflation factor. An analogous "X-wide inflation factor," denoted λX, can be defined and applied to published XWAS. Effects that arise in XWAS may be prioritized using replication, triangulation, quantification of measurement error, contextualization of each effect in the distribution of all effect sizes within a field, and pre-registration. Criteria like those of Bradford Hill need to be reconsidered in light of exposure-wide epidemiology to prioritize signals among signals.

Bias in pharmacoepidemiologic studies using secondary health care databases: a scoping review

BACKGROUND

The availability of clinical and therapeutic data drawn from medical records and administrative databases has entailed new opportunities for clinical and epidemiologic research. However, these databases present inherent limitations which may render them prone to new biases. We aimed to conduct a structured review of biases specific to observational clinical studies based on secondary databases, and to propose strategies for the mitigation of those biases.

METHODS

Scoping review of the scientific literature published during the period 2000-2018 through an automated search of MEDLINE, EMBASE and Web of Science, supplemented with manually cross-checking of reference lists. We included opinion essays, methodological reviews, analyses or simulation studies, as well as letters to the editor or retractions, the principal objective of which was to highlight the existence of some type of bias in pharmacoepidemiologic studies using secondary databases.

RESULTS

A total of 117 articles were included. An increasing trend in the number of publications concerning the potential limitations of secondary databases was observed over time and across medical research disciplines. Confounding was the most reported category of bias (63.2% of articles), followed by selection and measurement biases (47.0% and 46.2% respectively). Confounding by indication (32.5%), unmeasured/residual confounding (28.2%), outcome misclassification (28.2%) and "immortal time" bias (25.6%) were the subcategories most frequently mentioned.

CONCLUSIONS

Suboptimal use of secondary databases in pharmacoepidemiologic studies has introduced biases in the studies, which may have led to erroneous conclusions. Methods to mitigate biases are available and must be considered in the design, analysis and interpretation phases of studies using these data sources.

A review of validation strategies for computational drug repositioning

Repositioning of previously approved drugs is a promising methodology because it reduces the cost and duration of the drug development pipeline and reduces the likelihood of unforeseen adverse events. Computational repositioning is especially appealing because of the ability to rapidly screen candidates in silico and to reduce the number of possible repositioning candidates. What is unclear, however, is how useful such methods are in producing clinically efficacious repositioning hypotheses. Furthermore, there is no agreement in the field over the proper way to perform validation of in silico predictions, and in fact no systematic review of repositioning validation methodologies. To address this unmet need, we review the computational repositioning literature and capture studies in which authors claimed to have validated their work. Our analysis reveals widespread variation in the types of strategies, predictions made and databases used as ‘gold standards’. We highlight a key weakness of the most commonly used strategy and propose a path forward for the consistent analytic validation of repositioning techniques.

Tissue-Specific Analysis of Pharmacological Pathways

Understanding the downstream consequences of pharmacologically targeted proteins is essential to drug design. Current approaches investigate molecular effects under tissue‐naïve assumptions. Many target proteins, however, have tissue‐specific expression. A systematic study connecting drugs to target pathways in in vivo human tissues is needed. We introduced a data‐driven method that integrates drug‐target relationships with gene expression, protein‐protein interaction, and pathway annotation data. We applied our method to four independent genomewide expression datasets and built 467,396 connections between 1,034 drugs and 954 pathways in 259 human tissues or cell lines. We validated our results using data from L1000 and Pharmacogenomics Knowledgebase (PharmGKB), and observed high precision and recall. We predicted and tested anticoagulant effects of 22 compounds experimentally that were previously unknown, and used clinical data to validate these effects retrospectively. Our systematic study provides a better understanding of the cellular response to drugs and can be applied to many research topics in systems pharmacology.

Standard-based comprehensive detection of adverse drug reaction signals from nursing statements and laboratory results in electronic health records

Objective. We propose 2 Medical Dictionary for Regulatory Activities–enabled pharmacovigilance algorithms, MetaLAB and MetaNurse, powered by a per-year meta-analysis technique and improved subject sampling strategy.

Matrials and methods. This study developed 2 novel algorithms, MetaLAB for laboratory abnormalities and MetaNurse for standard nursing statements, as significantly improved versions of our previous electronic health record (EHR)–based pharmacovigilance method, called CLEAR. Adverse drug reaction (ADR) signals from 117 laboratory abnormalities and 1357 standard nursing statements for all precautionary drugs (n  = 101) were comprehensively detected and validated against SIDER (Side Effect Resource) by MetaLAB and MetaNurse against 11 817 and 76 457 drug-ADR pairs, respectively.

Results. We demonstrate that MetaLAB (area under the curve, AUC = 0.61 ± 0.18) outperformed CLEAR (AUC = 0.55 ± 0.06) when we applied the same 470 drug-event pairs as the gold standard, as in our previous research. Receiver operating characteristic curves for 101 precautionary terms in the Medical Dictionary for Regulatory Activities Preferred Terms were obtained for MetaLAB and MetaNurse (0.69 ± 0.11; 0.62 ± 0.07), which complemented each other in terms of ADR signal coverage. Novel ADR signals discovered by MetaLAB and MetaNurse were successfully validated against spontaneous reports in the US Food and Drug Administration Adverse Event Reporting System database.

Discussion. The present study demonstrates the symbiosis of laboratory test results and nursing statements for ADR signal detection in terms of their system organ class coverage and performance profiles.

Conclusion. Systematic discovery and evaluation of the wide spectrum of ADR signals using standard-based observational electronic health record data across many institutions will affect drug development and use, as well as postmarketing surveillance and regulation.

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.

MeSHDD: Literature-based drug-drug similarity for drug repositioning

OBJECTIVE

Drug repositioning is a promising methodology for reducing the cost and duration of the drug discovery pipeline. We sought to develop a computational repositioning method leveraging annotations in the literature, such as Medical Subject Heading (MeSH) terms.

METHODS

We developed software to determine significantly co-occurring drug-MeSH term pairs and a method to estimate pair-wise literature-derived distances between drugs.

RESULTS

We found that literature-based drug-drug similarities predicted the number of shared indications across drug-drug pairs. Clustering drugs based on their similarity revealed both known and novel drug indications. We demonstrate the utility of our approach by generating repositioning hypotheses for the commonly used diabetes drug metformin.

CONCLUSION

Our study demonstrates that literature-derived similarity is useful for identifying potential repositioning opportunities. We provided open-source code and deployed a free-to-use, interactive application to explore our database of similarity-based drug clusters (available at http://apps.chiragjpgroup.org/MeSHDD/ ).

A standard database for drug repositioning

Drug repositioning, the process of discovering, validating, and marketing previously approved drugs for new indications, is of growing interest to academia and industry due to reduced time and costs associated with repositioned drugs. Computational methods for repositioning are appealing because they putatively nominate the most promising candidate drugs for a given indication. Comparing the wide array of computational repositioning methods, however, is a challenge due to inconsistencies in method validation in the field. Furthermore, a common simplifying assumption, that all novel predictions are false, is intellectually unsatisfying and hinders reproducibility. We address this assumption by providing a gold standard database, repoDB, that consists of both true positives (approved drugs), and true negatives (failed drugs). We have made the full database and all code used to prepare it publicly available, and have developed a web application that allows users to browse subsets of the data (http://apps.chiragjpgroup.org/repoDB/).

Systematic assessment of pharmaceutical prescriptions in association with cancer risk: a method to conduct a population-wide medication-wide longitudinal study

It is a public health priority to identify the adverse and non-adverse associations between pharmaceutical medications and cancer. We search for and evaluate associations between all prescribed medications and longitudinal cancer risk in participants of the Swedish Cancer Register (N = 9,014,975). We associated 552 different medications with incident cancer risk (any, breast, colon, and prostate) during 5.5 years of follow-up (7/1/2005-12/31/2010) in two types of statistical models, time-to-event and case-crossover. After multiple hypotheses correction and replication, 141 (26%) drugs were associated with any cancer in a time-to-event analysis constraining drug exposure to 1 year before first cancer diagnosis and adjusting for history of medication use. In a case-crossover analysis, 36 drugs (7%) were associated with decreased cancer risk. 12 drugs were found in common in both analyses with concordant direction of association. We found 14, 10, 7% of all drugs associated with colon, prostate, and breast cancers in time-to-event models. We only found 1, 2%, and 0% for these cancers, respectively, in case-crossover analyses. Pharmacoepidemiologic analyses of cancer risk are sensitive to modeling choices and false-positive findings are a threat. Medication-wide analyses using different analytical models may help suggest consistent signals of increased cancer risk.

Drug-induced adverse events prediction with the LINCS L1000 data

MOTIVATION

Adverse drug reactions (ADRs) are a central consideration during drug development. Here we present a machine learning classifier to prioritize ADRs for approved drugs and pre-clinical small-molecule compounds by combining chemical structure (CS) and gene expression (GE) features. The GE data is from the Library of Integrated Network-based Cellular Signatures (LINCS) L1000 dataset that measured changes in GE before and after treatment of human cells with over 20 000 small-molecule compounds including most of the FDA-approved drugs. Using various benchmarking methods, we show that the integration of GE data with the CS of the drugs can significantly improve the predictability of ADRs. Moreover, transforming GE features to enrichment vectors of biological terms further improves the predictive capability of the classifiers. The most predictive biological-term features can assist in understanding the drug mechanisms of action. Finally, we applied the classifier to all  >20 000 small-molecules profiled, and developed a web portal for browsing and searching predictive small-molecule/ADR connections.

AVAILABILITY AND IMPLEMENTATION

The interface for the adverse event predictions for the  >20 000 LINCS compounds is available at http://maayanlab.net/SEP-L1000/ CONTACT: avi.maayan@mssm.edu

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Leveraging 3D chemical similarity, target and phenotypic data in the identification of drug-protein and drug-adverse effect associations

BACKGROUND

Drug-target identification is crucial to discover novel applications for existing drugs and provide more insights about mechanisms of biological actions, such as adverse drug effects (ADEs). Computational methods along with the integration of current big data sources provide a useful framework for drug-target and drug-adverse effect discovery.

RESULTS

In this article, we propose a method based on the integration of 3D chemical similarity, target and adverse effect data to generate a drug-target-adverse effect predictor along with a simple leveraging system to improve identification of drug-targets and drug-adverse effects. In the first step, we generated a system for multiple drug-target identification based on the application of 3D drug similarity into a large target dataset extracted from the ChEMBL. Next, we developed a target-adverse effect predictor combining targets from ChEMBL with phenotypic information provided by SIDER data source. Both modules were linked to generate a final predictor that establishes hypothesis about new drug-target-adverse effect candidates. Additionally, we showed that leveraging drug-target candidates with phenotypic data is very useful to improve the identification of drug-targets. The integration of phenotypic data into drug-target candidates yielded up to twofold precision improvement. In the opposite direction, leveraging drug-phenotype candidates with target data also yielded a significant enhancement in the performance.

CONCLUSIONS

The modeling described in the current study is simple and efficient and has applications at large scale in drug repurposing and drug safety through the identification of mechanism of action of biological effects.

Systems biology approaches for identifying adverse drug reactions and elucidating their underlying biological mechanisms

Small molecules are indispensable to modern medical therapy. However, their use may lead to unintended, negative medical outcomes commonly referred to as adverse drug reactions (ADRs). These effects vary widely in mechanism, severity, and populations affected, making ADR prediction and identification important public health concerns. Current methods rely on clinical trials and postmarket surveillance programs to find novel ADRs; however, clinical trials are limited by small sample size, whereas postmarket surveillance methods may be biased and inherently leave patients at risk until sufficient clinical evidence has been gathered. Systems pharmacology, an emerging interdisciplinary field combining network and chemical biology, provides important tools to uncover and understand ADRs and may mitigate the drawbacks of traditional methods. In particular, network analysis allows researchers to integrate heterogeneous data sources and quantify the interactions between biological and chemical entities. Recent work in this area has combined chemical, biological, and large-scale observational health data to predict ADRs in both individual patients and global populations. In this review, we explore the rapid expansion of systems pharmacology in the study of ADRs. We enumerate the existing methods and strategies and illustrate progress in the field with a model framework that incorporates crucial data elements, such as diet and comorbidities, known to modulate ADR risk. Using this framework, we highlight avenues of research that may currently be underexplored, representing opportunities for future work.

A method for systematic discovery of adverse drug events from clinical notes

OBJECTIVE

Adverse drug events (ADEs) are undesired harmful effects resulting from use of a medication, and occur in 30% of hospitalized patients. The authors have developed a data-mining method for systematic, automated detection of ADEs from electronic medical records.

MATERIALS AND METHODS

This method uses the text from 9.5 million clinical notes, along with prior knowledge of drug usages and known ADEs, as inputs. These inputs are further processed into statistics used by a discriminative classifier which outputs the probability that a given drug-disorder pair represents a valid ADE association. Putative ADEs identified by the classifier are further filtered for positive support in 2 independent, complementary data sources. The authors evaluate this method by assessing support for the predictions in other curated data sources, including a manually curated, time-indexed reference standard of label change events.

RESULTS

This method uses a classifier that achieves an area under the curve of 0.94 on a held out test set. The classifier is used on 2,362,950 possible drug-disorder pairs comprised of 1602 unique drugs and 1475 unique disorders for which we had data, resulting in 240 high-confidence, well-supported drug-AE associations. Eighty-seven of them (36%) are supported in at least one of the resources that have information that was not available to the classifier.

CONCLUSION

This method demonstrates the feasibility of systematic post-marketing surveillance for ADEs using electronic medical records, a key component of the learning healthcare system.

Crowdsourcing in biomedicine: challenges and opportunities

The use of crowdsourcing to solve important but complex problems in biomedical and clinical sciences is growing and encompasses a wide variety of approaches. The crowd is diverse and includes online marketplace workers, health information seekers, science enthusiasts and domain experts. In this article, we review and highlight recent studies that use crowdsourcing to advance biomedicine. We classify these studies into two broad categories: (i) mining big data generated from a crowd (e.g. search logs) and (ii) active crowdsourcing via specific technical platforms, e.g. labor markets, wikis, scientific games and community challenges. Through describing each study in detail, we demonstrate the applicability of different methods in a variety of domains in biomedical research, including genomics, biocuration and clinical research. Furthermore, we discuss and highlight the strengths and limitations of different crowdsourcing platforms. Finally, we identify important emerging trends, opportunities and remaining challenges for future crowdsourcing research in biomedicine.

Development and validation of a classification approach for extracting severity automatically from electronic health records

Background

Electronic Health Records (EHRs) contain a wealth of information useful for studying clinical phenotype-genotype relationships. Severity is important for distinguishing among phenotypes; however other severity indices classify patient-level severity (e.g., mild vs. acute dermatitis) rather than phenotype-level severity (e.g., acne vs. myocardial infarction). Phenotype-level severity is independent of the individual patient’s state and is relative to other phenotypes. Further, phenotype-level severity does not change based on the individual patient. For example, acne is mild at the phenotype-level and relative to other phenotypes. Therefore, a given patient may have a severe form of acne (this is the patient-level severity), but this does not effect its overall designation as a mild phenotype at the phenotype-level.

Methods

We present a method for classifying severity at the phenotype-level that uses the Systemized Nomenclature of Medicine – Clinical Terms. Our method is called the Classification Approach for Extracting Severity Automatically from Electronic Health Records (CAESAR). CAESAR combines multiple severity measures – number of comorbidities, medications, procedures, cost, treatment time, and a proportional index term. CAESAR employs a random forest algorithm and these severity measures to discriminate between severe and mild phenotypes.

Results

Using a random forest algorithm and these severity measures as input, CAESAR differentiates between severe and mild phenotypes (sensitivity = 91.67, specificity = 77.78) when compared to a manually evaluated reference standard (k = 0.716).

Conclusions

CAESAR enables researchers to measure phenotype severity from EHRs to identify phenotypes that are important for comparative effectiveness research.

Are All Vaccines Created Equal? Using Electronic Health Records to Discover Vaccines Associated With Clinician-Coded Adverse Events

Adverse drug events (ADEs) are responsible for unnecessary patient deaths making them a major public health issue. Literature estimates 1% of ADEs recorded in Electronic Health Records (EHRs) are reported to federal databases making EHRs a vital source of ADE-related information. Using Columbia University Medical Center (CUMC)'s EHRs, we developed an algorithm to mine for vaccine-related ADEs occurring within 3 months of vaccination. In phase one, we measured the association between vaccinated patients with an ADE (cases) against those vaccinated without an ADE. To adjust for healthcare-process effects, phase two compared cases against those who returned to CUMC within 3 months without an ADE. We report 7 results passing multiplicity correction after demographic confounder adjustment. We observed an association, having some literature support, between swine flu vaccination and ADEs (H1N1v-like, OR=9.469, p<0.001; H1N1/H3N2, OR=3.207, p<0.001). Our algorithm could inform clinicians of the risks/benefits of vaccinations towards improving clinical care.

3D pharmacophoric similarity improves multi adverse drug event identification in pharmacovigilance

Adverse drugs events (ADEs) detection constitutes a considerable concern in patient safety and public health care. For this reason, it is important to develop methods that improve ADE signal detection in pharmacovigilance databases. Our objective is to apply 3D pharmacophoric similarity models to enhance ADE recognition in Offsides, a pharmacovigilance resource with drug-ADE associations extracted from the FDA Adverse Event Reporting System (FAERS). We developed a multi-ADE predictor implementing 3D drug similarity based on a pharmacophoric approach, with an ADE reference standard extracted from the SIDER database. The results showed that the application of our 3D multi-type ADE predictor to the pharmacovigilance data in Offsides improved ADE identification and generated enriched sets of drug-ADE signals. The global ROC curve for the Offsides ADE candidates ranked with the 3D similarity score showed an area of 0.7. The 3D predictor also allows the identification of the most similar drug that causes the ADE under study, which could provide hypotheses about mechanisms of action and ADE etiology. Our method is useful in drug development, screening potential adverse effects in experimental drugs, and in drug safety, applicable to the evaluation of ADE signals selected through pharmacovigilance data mining.

Observational Health Data Sciences and Informatics (OHDSI): Opportunities for Observational Researchers

The vision of creating accessible, reliable clinical evidence by accessing the clincial experience of hundreds of millions of patients across the globe is a reality. Observational Health Data Sciences and Informatics (OHDSI) has built on learnings from the Observational Medical Outcomes Partnership to turn methods research and insights into a suite of applications and exploration tools that move the field closer to the ultimate goal of generating evidence about all aspects of healthcare to serve the needs of patients, clinicians and all other decision-makers around the world.

Systems pharmacology augments drug safety surveillance

Small molecule drugs are the foundation of modern medical practice yet their use is limited by the onset of unexpected and severe adverse events (AEs). Regulatory agencies rely on post-marketing surveillance to monitor safety once drugs are approved for clinical use. Despite advances in pharmacovigilance methods that address issues of confounding bias, clinical data of AEs are inherently noisy. Systems pharmacology– the integration of systems biology and chemical genomics – can illuminate drug mechanisms of action. We hypothesize that these data can improve drug safety surveillance by highlighting drugs with a mechanistic connection to the target phenotype (enriching true positives) and filtering those that do not (depleting false positives). We present an algorithm, the modular assembly of drug safety subnetworks (MADSS), to combine systems pharmacology and pharmacovigilance data and significantly improve drug safety monitoring for four clinically relevant adverse drug reactions.

Observational study of upper gastrointestinal tract bleeding events in patients taking duloxetine and nonsteroidal anti-inflammatory drugs: a case-control analysis

Purpose

To determine whether the concomitant use of duloxetine with prescription nonsteroidal anti-inflammatory drugs (NSAIDs) or aspirin was associated with an increased risk for upper gastrointestinal (UGI) bleeding compared with taking these analgesics alone.

Methods

Truven Health Analytics MarketScan Research Databases were examined for hospital admissions of adult patients indexed from January 1, 2007–December 31, 2011. Cases were patients with UGI hemorrhage or peptic ulcer disease. Controls were randomly selected from the remaining admissions to match 10:1 with cases based on age, sex, and admission date. Prescription medication exposure groups of interest were: 1) no exposure to duloxetine, NSAIDs or aspirin; 2) duloxetine only; 3) NSAIDs or aspirin only; 4) duloxetine plus NSAIDs or aspirin. Logistic regression and relative excess risk due to interaction was utilized to estimate any increased risk of UGI bleeding for patients prescribed these medications across these groups.

Results

There were 33,571 cases and 335,710 controls identified. Comparing exposure group 2 and group 4, the adjusted odds ratio was 1.03 (95% confidence interval [CI], 0.94, 1.12), and the adjusted relative excess risk due to interaction was 0.352 (95% CI: –0.18, 0.72) for risk of UGI bleeding, neither of which support an increased risk or an interaction between duloxetine and prescription NSAID or aspirin for these events.

Conclusion

There was no evidence of an increased risk for UGI bleeding when duloxetine was taken with prescription NSAIDs or aspirin. In addition, there was no evidence of an interaction between duloxetine and prescription NSAIDs or aspirin for an increased risk of these events.

Similarity-based modeling applied to signal detection in pharmacovigilance

One of the main objectives in pharmacovigilance is the detection of adverse drug events (ADEs) through mining of healthcare databases, such as electronic health records or administrative claims data. Although different approaches have been shown to be of great value, research is still focusing on the enhancement of signal detection to gain efficiency in further assessment and follow-up. We applied similarity-based modeling techniques, using 2D and 3D molecular structure, ADE, target, and ATC (anatomical therapeutic chemical) similarity measures, to the candidate associations selected previously in a medication-wide association study for four ADE outcomes. Our results showed an improvement in the precision when we ranked the subset of ADE candidates using similarity scorings. This method is simple, useful to strengthen or prioritize signals generated from healthcare databases, and facilitates ADE detection through the identification of the most similar drugs for which ADE information is available.

Surveying Recent Themes in Translational Bioinformatics: Big Data in EHRs, Omics for Drugs, and Personal Genomics

OBJECTIVE

To provide a survey of recent progress in the use of large-scale biologic data to impact clinical care, and the impact the reuse of electronic health record data has made in genomic discovery.

METHOD

Survey of key themes in translational bioinformatics, primarily from 2012 and 2013.

RESULT

This survey focuses on four major themes: the growing use of Electronic Health Records (EHRs) as a source for genomic discovery, adoption of genomics and pharmacogenomics in clinical practice, the possible use of genomic technologies for drug repurposing, and the use of personal genomics to guide care.

CONCLUSION

Reuse of abundant clinical data for research is speeding discovery, and implementation of genomic data into clinical medicine is impacting care with new classes of data rarely used previously in medicine.