Nicholas P. Tatonetti

Data-driven prediction of drug effects and interactions.

Two new databases—one of drug effects and a second of drug-drug interaction side effects—permit identification of drug targets, prediction of drug indications, and discovery of drug class interactions. Avoiding Adversity For some disease-therapy pairs, Francis Bacon was right: “The remedy is worse than the disease.” And when several drugs collide in an individual, the troubling effects can multiply. One goal of the developing field of pharmacogenomics is to make use of clinical data to predict adverse drug events so that future patients can be protected from the sometimes serious consequences. Now, Tatonetti et al. describe two new databases—one of drug effects and a second of drug-drug interaction side effects—that permit the identification of drug targets, prediction of drug indications, and discovery of drug-class interactions. Adverse drug events aren’t merely a nuisance; these toxic interactions can cause debilitating illness and death. The nature of clinical trials doesn’t allow the detection of all serious side effects and drug interactions before approval of the therapy, by regulatory agencies, for use in patients. But these agencies along with pharmaceutical companies, hospitals, and other institutions collect adverse event reports after the drugs are in use in the clinic. When delineated in databases and coupled with the impressive computing power now available, these reports have the potential to permit characterization of drug effects at the population level. However, even with the recent move toward electronic health records, adverse event data often lack crucial information about co-prescribed medications, patient demographics and medical histories, and the reasons that a given drug was prescribed in the first place. One can easily see how that lack of such information thwarts the ability to obtain meaningful analyses of drug side effects and interactions. To address this problem of omission and improve the ability to analyze drug effects, Tatonetti et al. use an adaptive data-driven approach to correct for the lack of such information—the so-called unknown “covariates.” Using this information, the authors developed two comprehensive databases—one of drug effects (Offsides) and another of drug-drug interaction side effects (Twosides)—and then used their new databases to pinpoint drug targets and discover drug-class interactions. Finally, the authors validated 47 of the drug-class interactions in an independent analysis of patient electronic health records. When prescribed together, widely used antidepressant drugs (selective serotonin reuptake inhibitors) and thiazide diuretics were associated with an increase in the incidence of prolonged QT, which indicates a delayed repolarization of the heart after a heartbeat. Prolonged QT can increase a patient’s risk of palpitations, fainting, and even sudden death resulting from ventricular fibrillation. Better than tarot cards or crystal balls, the authors show that intricate analyses of observational clinical data can improve physicians’ ability to predict the future—at least with respect to as yet uncharacterized adverse drug effects and interactions. Adverse drug events remain a leading cause of morbidity and mortality around the world. Many adverse events are not detected during clinical trials before a drug receives approval for use in the clinic. Fortunately, as part of postmarketing surveillance, regulatory agencies and other institutions maintain large collections of adverse event reports, and these databases present an opportunity to study drug effects from patient population data. However, confounding factors such as concomitant medications, patient demographics, patient medical histories, and reasons for prescribing a drug often are uncharacterized in spontaneous reporting systems, and these omissions can limit the use of quantitative signal detection methods used in the analysis of such data. Here, we present an adaptive data-driven approach for correcting these factors in cases for which the covariates are unknown or unmeasured and combine this approach with existing methods to improve analyses of drug effects using three test data sets. We also present a comprehensive database of drug effects (Offsides) and a database of drug-drug interaction side effects (Twosides). To demonstrate the biological use of these new resources, we used them to identify drug targets, predict drug indications, and discover drug class interactions. We then corroborated 47 (P < 0.0001) of the drug class interactions using an independent analysis of electronic medical records. Our analysis suggests that combined treatment with selective serotonin reuptake inhibitors and thiazides is associated with significantly increased incidence of prolonged QT intervals. We conclude that confounding effects from covariates in observational clinical data can be controlled in data analyses and thus improve the detection and prediction of adverse drug effects and interactions.

Detecting drug interactions from adverse-event reports: interaction between paroxetine and pravastatin increases blood glucose levels.

The lipid‐lowering agent pravastatin and the antidepressant paroxetine are among the most widely prescribed drugs in the world. Unexpected interactions between them could have important public health implications. We mined the US Food and Drug Administrations (FDAs) Adverse Event Reporting System (AERS) for side‐effect profiles involving glucose homeostasis and found a surprisingly strong signal for comedication with pravastatin and paroxetine. We retrospectively evaluated changes in blood glucose in 104 patients with diabetes and 135 without diabetes who had received comedication with these two drugs, using data in electronic medical record (EMR) systems of three geographically distinct sites. We assessed the mean random blood glucose levels before and after treatment with the drugs. We found that pravastatin and paroxetine, when administered together, had a synergistic effect on blood glucose. The average increase was 19 mg/dl (1.0 mmol/l) overall, and in those with diabetes it was 48 mg/dl (2.7 mmol/l). In contrast, neither drug administered singly was associated with such changes in glucose levels. An increase in glucose levels is not a general effect of combined therapy with selective serotonin reuptake inhibitors (SSRIs) and statins.

Web-scale pharmacovigilance: listening to signals from the crowd

Adverse drug events cause substantial morbidity and mortality and are often discovered after a drug comes to market. We hypothesized that Internet users may provide early clues about adverse drug events via their online information-seeking. We conducted a large-scale study of Web search log data gathered during 2010. We pay particular attention to the specific drug pairing of paroxetine and pravastatin, whose interaction was reported to cause hyperglycemia after the time period of the online logs used in the analysis. We also examine sets of drug pairs known to be associated with hyperglycemia and those not associated with hyperglycemia. We find that anonymized signals on drug interactions can be mined from search logs. Compared to analyses of other sources such as electronic health records (EHR), logs are inexpensive to collect and mine. The results demonstrate that logs of the search activities of populations of computer users can contribute to drug safety surveillance.

Pharmacological inhibition of cystine–glutamate exchange induces endoplasmic reticulum stress and ferroptosis

Exchange of extracellular cystine for intracellular glutamate by the antiporter system xc− is implicated in numerous pathologies. Pharmacological agents that inhibit system xc− activity with high potency have long been sought, but have remained elusive. In this study, we report that the small molecule erastin is a potent, selective inhibitor of system xc−. RNA sequencing revealed that inhibition of cystine–glutamate exchange leads to activation of an ER stress response and upregulation of CHAC1, providing a pharmacodynamic marker for system xc− inhibition. We also found that the clinically approved anti-cancer drug sorafenib, but not other kinase inhibitors, inhibits system xc− function and can trigger ER stress and ferroptosis. In an analysis of hospital records and adverse event reports, we found that patients treated with sorafenib exhibited unique metabolic and phenotypic alterations compared to patients treated with other kinase-inhibiting drugs. Finally, using a genetic approach, we identified new genes dramatically upregulated in cells resistant to ferroptosis. DOI: http://dx.doi.org/10.7554/eLife.02523.001

Genetic variants associated with warfarin dose in African-American individuals: a genome-wide association study

Summary Background VKORC1 and CYP2C9 are important contributors to warfarin dose variability, but explain less variability for individuals of African descent than for those of European or Asian descent. We aimed to identify additional variants contributing to warfarin dose requirements in African Americans. Methods We did a genome-wide association study of discovery and replication cohorts. Samples from African-American adults (aged ≥18 years) who were taking a stable maintenance dose of warfarin were obtained at International Warfarin Pharmacogenetics Consortium (IWPC) sites and the University of Alabama at Birmingham (Birmingham, AL, USA). Patients enrolled at IWPC sites but who were not used for discovery made up the independent replication cohort. All participants were genotyped. We did a stepwise conditional analysis, conditioning first for VKORC1 −1639G→A, followed by the composite genotype of CYP2C9*2 and CYP2C9*3. We prespecified a genome-wide significance threshold of p<5×10−8 in the discovery cohort and p<0·0038 in the replication cohort. Findings The discovery cohort contained 533 participants and the replication cohort 432 participants. After the prespecified conditioning in the discovery cohort, we identified an association between a novel single nucleotide polymorphism in the CYP2C cluster on chromosome 10 (rs12777823) and warfarin dose requirement that reached genome-wide significance (p=1·51×10−8). This association was confirmed in the replication cohort (p=5·04×10−5); analysis of the two cohorts together produced a p value of 4·5×10−12. Individuals heterozygous for the rs12777823 A allele need a dose reduction of 6·92 mg/week and those homozygous 9·34 mg/week. Regression analysis showed that the inclusion of rs12777823 significantly improves warfarin dose variability explained by the IWPC dosing algorithm (21% relative improvement). Interpretation A novel CYP2C single nucleotide polymorphism exerts a clinically relevant effect on warfarin dose in African Americans, independent of CYP2C9*2 and CYP2C9*3. Incorporation of this variant into pharmacogenetic dosing algorithms could improve warfarin dose prediction in this population. Funding National Institutes of Health, American Heart Association, Howard Hughes Medical Institute, Wisconsin Network for Health Research, and the Wellcome Trust.

High-Throughput Methods for Combinatorial Drug Discovery

Combinatorial drug discovery will rely on new approaches to analyzing highly dimensional data resources. Human disease is a complex network of interacting pathways. As such, it may be necessary to treat the system with a combination of drugs to boost efficacy and reduce side effects. In this Review, the authors discuss the current landscape of methodologies for using highly dimensional data resources and high-throughput biological measurements for combinatorial drug discovery. A more nuanced approach to drug design is to use multiple drugs in combination to target interacting or complementary pathways. Drug combination treatments have shown higher efficacy, fewer side effects, and less toxicity compared to single-drug treatment. In this Review, we focus on the use of high-throughput biological measurements (genetics, transcripts, and chemogenetic interactions) and the computational methods they necessitate to further combinatorial drug design (CDD). We highlight the state-of-the-art analytical methods, including network analysis, integrative informatics, and dynamic molecular modeling, that have been used successfully in CDD. Finally, we present an exhaustive list of the publicly available data and methodological resources available to the community. Such next-generation technologies that enable the measurement of millions of cellular data points simultaneously may usher in a new paradigm in drug discovery, where medicine is viewed as a system of interacting genes and pathways rather than the result of an individual protein or gene.

Predicting drug side-effects by chemical systems biology

New approaches to predicting ligand similarity and protein interactions can explain unexpected observations of drug inefficacy or side-effects.

Birth month affects lifetime disease risk: a phenome-wide method

Objective An individual’s birth month has a significant impact on the diseases they develop during their lifetime. Previous studies reveal relationships between birth month and several diseases including atherothrombosis, asthma, attention deficit hyperactivity disorder, and myopia, leaving most diseases completely unexplored. This retrospective population study systematically explores the relationship between seasonal affects at birth and lifetime disease risk for 1688 conditions. Methods We developed a hypothesis-free method that minimizes publication and disease selection biases by systematically investigating disease-birth month patterns across all conditions. Our dataset includes 1 749 400 individuals with records at New York-Presbyterian/Columbia University Medical Center born between 1900 and 2000 inclusive. We modeled associations between birth month and 1688 diseases using logistic regression. Significance was tested using a chi-squared test with multiplicity correction. Results We found 55 diseases that were significantly dependent on birth month. Of these 19 were previously reported in the literature (P < .001), 20 were for conditions with close relationships to those reported, and 16 were previously unreported. We found distinct incidence patterns across disease categories. Conclusions Lifetime disease risk is affected by birth month. Seasonally dependent early developmental mechanisms may play a role in increasing lifetime risk of disease.

Toward a complete dataset of drug-drug interaction information from publicly available sources

Although potential drug-drug interactions (PDDIs) are a significant source of preventable drug-related harm, there is currently no single complete source of PDDI information. In the current study, all publically available sources of PDDI information that could be identified using a comprehensive and broad search were combined into a single dataset. The combined dataset merged fourteen different sources including 5 clinically-oriented information sources, 4 Natural Language Processing (NLP) Corpora, and 5 Bioinformatics/Pharmacovigilance information sources. As a comprehensive PDDI source, the merged dataset might benefit the pharmacovigilance text mining community by making it possible to compare the representativeness of NLP corpora for PDDI text extraction tasks, and specifying elements that can be useful for future PDDI extraction purposes. An analysis of the overlap between and across the data sources showed that there was little overlap. Even comprehensive PDDI lists such as DrugBank, KEGG, and the NDF-RT had less than 50% overlap with each other. Moreover, all of the comprehensive lists had incomplete coverage of two data sources that focus on PDDIs of interest in most clinical settings. Based on this information, we think that systems that provide access to the comprehensive lists, such as APIs into RxNorm, should be careful to inform users that the lists may be incomplete with respect to PDDIs that drug experts suggest clinicians be aware of. In spite of the low degree of overlap, several dozen cases were identified where PDDI information provided in drug product labeling might be augmented by the merged dataset. Moreover, the combined dataset was also shown to improve the performance of an existing PDDI NLP pipeline and a recently published PDDI pharmacovigilance protocol. Future work will focus on improvement of the methods for mapping between PDDI information sources, identifying methods to improve the use of the merged dataset in PDDI NLP algorithms, integrating high-quality PDDI information from the merged dataset into Wikidata, and making the combined dataset accessible as Semantic Web Linked Data.

Bridging Islands of Information to Establish an Integrated Knowledge Base of Drugs and Health Outcomes of Interest

The entire drug safety enterprise has a need to search, retrieve, evaluate, and synthesize scientific evidence more efficiently. This discovery and synthesis process would be greatly accelerated through access to a common framework that brings all relevant information sources together within a standardized structure. This presents an opportunity to establish an open-source community effort to develop a global knowledge base, one that brings together and standardizes all available information for all drugs and all health outcomes of interest (HOIs) from all electronic sources pertinent to drug safety. To make this vision a reality, we have established a workgroup within the Observational Health Data Sciences and Informatics (OHDSI, http://ohdsi.org) collaborative. The workgroup’s mission is to develop an open-source standardized knowledge base for the effects of medical products and an efficient procedure for maintaining and expanding it. The knowledge base will make it simpler for practitioners to access, retrieve, and synthesize evidence so that they can reach a rigorous and accurate assessment of causal relationships between a given drug and HOI. Development of the knowledge base will proceed with the measureable goal of supporting an efficient and thorough evidence-based assessment of the effects of 1,000 active ingredients across 100 HOIs. This non-trivial task will result in a high-quality and generally applicable drug safety knowledge base. It will also yield a reference standard of drug–HOI pairs that will enable more advanced methodological research that empirically evaluates the performance of drug safety analysis methods.