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. 2012 Mar 14;4(125):125ra31.
doi: 10.1126/scitranslmed.3003377.

Data-driven Prediction of Drug Effects and Interactions

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Free PMC article

Data-driven Prediction of Drug Effects and Interactions

Nicholas P Tatonetti et al. Sci Transl Med. .
Free PMC article

Abstract

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.

Figures

Fig. 1
Fig. 1
Synthetic associations in adverse event reports. (A) Disease indications are a significant source of synthetic associations. The more disproportionately a drug is reported with an indication (x axis), the more likely that drug will be synthetically associated with the indication’s effects (y axis) (for example, it is common for hypoglycemic agents to be synthetically associated with hyperglycemia). (B) Concomitantly taken drugs are another significant source of synthetic associations. The more disproportionately two drugs are reported together (x axis), the more likely they will be associated with the other drug’s effects (y axis). (C) Drugs that are preferentially reported with males are more likely to be synthetically associated with sex-related effects. (D) Similarly, drugs that are preferentially reported with relatively young or relatively old patients are more likely to be synthetically associated with age-related effects. (E to H) Application of SCRUB removes synthetic associations that result from disproportionate reporting with (E) disease indications, (F) concomitant drug use, (G) sex biases, and (H) age biases.
Fig. 2
Fig. 2
Systematic evaluation against three independent silver standards of drug-effect associations. (A) Side effects mined from the package inserts. (B) Drug-effect pairs reported to the AERS after the original download date. (C) Drug-effect pairs reported to the Canadian system MedEffect. ROC curves for the empirical Bayes geometric mean (EBGM) (black) and a model combining EBGM and the correction factor derived from the SCRUB algorithm (aqua). In each case, including the correction term substantially improves the predictive power of the algorithm.
Fig. 3
Fig. 3
Implicit matching of covariates. (A) Biases from age. Average age differences between cohorts of reports for those patients exposed to the drug and those who were not exposed (controls). The average difference for the uncorrected (solid squares) and corrected (open circles) nonexposed control reports is shown. Ideally, the difference between the two cohorts of reports is zero. (B) Biases from sex. Difference in the proportion of males reported to be exposed to the query drug versus those who were not exposed (controls). The difference for the uncorrected (solid squares) and corrected (open circles) nonexposed reports is shown. Ideally, this difference is zero.
Fig. 4
Fig. 4
Predicting shared protein targets using drug-effect similarities. (A) The side-effect similarity score between two drugs is linearly related to the number of targets that those drugs share. (B) A scatter plot showing the relationship between the side-effect similarity score and the number of shared targets for side effects derived from Offsides (blue), SIDER (red), and both combined (black). (C) ROC curve representing the ability of the side-effect similarity scores to predict which pairs of drugs share targets. The best performance is reached by combining both data sets.
Fig. 5
Fig. 5
Drug repurposing using drug-effect similarities in Offsides. (A) The side-effect similarity score between two drugs is linearly related to the number of indications those drugs share. (B) Scatter plot showing the relationship between the side-effect similarity score and the number of shared targets for side effects derived from Offsides (blue), SIDER (red), and both combined (black). (C) ROC curve representing the ability of the side-effect similarity scores to predict which pairs of drugs share indications. The best performance is reached by combining both data sets.
Fig. 6
Fig. 6
Interaction diagram depicting single-drug effects, drug-class effects, DDIs, and class-class interactions for cardiovascular adverse events. Drugs are sorted clockwise around the ring by the physiological system they treat. Drugs labeled by name are members of data-mined DDIs. Within each physiological system, drugs are grouped into lower-order drug classes according to structural similarity or treatment indication. These lower-order classes are colored by their class-wide association with adverse cardiovascular effects (red for most severe to blue for least severe). Each arc across the center represents one DDI according to the data mining. The arc is colored red if the drug interaction is corroborated with evidence from the EMRs and brown if the drugs are members of class-class interactions. The heat map around the interior of the ring indicates the individual drug effects with the top 10 cardiovascular adverse events (arteriosclerosis, decreased arteriole pressure, chest pain, difficulty breathing, heart attack, apoplexy, high blood pressure, coronary heart disease, edema in extremities, cardiac decompression) (dark red for strong associations to white for weak or no association).
Fig. 7
Fig. 7
Kaplan-Meier curves showing the proportion of patients that had prolonged QT corrected values after the start of drug therapy. The solid line represents patients who received both thiazides and SSRIs, the dashed line represents patients who received only thiazides, and the dotted line represents patients who received only SSRIs.

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