Drug-Drug Interaction Surveillance Study: Comparing Self-Controlled Designs in Five Empirical Examples in Real-World Data

Concomitant Use of Oral Anticoagulants With Oral Dipeptidyl Peptidase-4 Inhibitors and Serious Bleeding Events

In a prior screening study, saxagliptin, a dipeptidyl peptidase-4 inhibitor (DPP-4i), was found to have an increased rate of serious bleeding when used concomitantly with several oral anticoagulants (OACs). We aimed to confirm or refute the associations between concomitant use of individual OACs and DPP-4is and serious bleeding in a large US database, using self-controlled case series (SCCS) and case-crossover (CCO) designs. The study population was eligible Medicare beneficiaries co-exposed to a DPP-4i (precipitant) and either an OAC (object drug) or lisinopril (negative control object drug) in 2016-2020. For the SCCS, we used conditional Poisson regression to estimate adjusted rate ratios (RRs) between each co-exposure (vs. not) and serious bleeding and divided the RR by the adjusted RR for the corresponding lisinopril + precipitant pair to obtain ratios of RRs (RRRs). For the CCO, we estimated the adjusted odds ratios (ORs) of exposure to the precipitant in the focal window vs. referent window using multivariable conditional logistic regression and divided the ORs in the object drug-exposed cases over the ORs in negative object drug-exposed cases to obtain the ratios of ORs (RORs). The adjusted RRRs for serious bleeding ranged from 0.32 (0.05-1.91) for apixaban/lisinopril + saxagliptin to 3.49 (1.29-9.48) for warfarin/lisinopril + linagliptin. The adjusted RORs ranged from 0.01 (0.00-0.20) for rivaroxaban/lisinopril + saxagliptin to 2.99 (0.74-12.11) for apixaban/lisinopril + linagliptin. While we could not confirm previously identified signals because of statistical imprecision, several numerically elevated estimates still warrant caution in concomitant use and further examination.

Grace periods and exposure misclassification in self-controlled case-series studies of drug-drug interactions

The self-controlled case-series (SCCS) research design is increasingly used in pharmacoepidemiologic studies of drug-drug interactions (DDIs), with the target of inference being the incidence rate ratio (IRR) associated with concomitant exposure to the object plus precipitant drug vs the object drug alone. While day-level drug exposure can be inferred from dispensing claims, these inferences may be inaccurate, leading to biased IRRs. Grace periods (periods assuming continued treatment impact after days' supply exhaustion) are frequently used by researchers, but the impact of grace period decisions on bias from exposure misclassification remains unclear. Motivated by an SCCS study examining the potential DDI between clopidogrel (object) and warfarin (precipitant), we investigated bias due to precipitant or object exposure misclassification using simulations. We show that misclassified precipitant treatment always biases the estimated IRR toward the null, whereas misclassified object treatment may lead to bias in either direction or no bias, depending on the scenario. Further, including a grace period for each object dispensing may unintentionally increase the risk of misclassification bias. To minimize such bias, we recommend (1) avoiding the use of grace periods when specifying object drug exposure episodes and (2) including a washout period following each precipitant exposed period. This article is part of a Special Collection on Pharmacoepidemiology.

Potential interactions between antimicrobials and direct oral anticoagulants: Population-based cohort and case-crossover study

BACKGROUND

Although drug interactions between clarithromycin/erythromycin/fluconazole and direct oral anticoagulants (DOACs) are mechanistically plausible, it is uncertain whether they are clinically relevant.

OBJECTIVE

This study aims to investigate the association among coprescribed DOACs and antimicrobials and bleeding, cardiovascular disease and mortality.

METHODS

We identified DOAC users in the Clinical Practice Research Datalink Aurum from January 1, 2011 to March 29, 2021. We used a cohort design to estimate hazard ratios (HRs) for bleeding outcomes (intracranial bleeding, gastrointestinal bleeding, other bleeding), comparing DOACs + clarithromycin/erythromycin/fluconazole users with DOACs users not receiving these antimicrobials. Cardiovascular outcomes were ischaemic stroke, myocardial infarction, venous thromboembolism, cardiovascular mortality, and all-cause mortality. A 6-parameter case-crossover design comparing odds of exposure with different drug initiation patterns for all outcomes in hazard window vs referent window within an individual was also conducted.

RESULTS

Of 483,815 DOAC users, we identified 21,701 coprescribed clarithromycin, 4532 coprescribed erythromycin, and 4840 coprescribed fluconazole. We observed an increased risk of gastrointestinal bleeding over 7 days following coprescription of DOAC + erythromycin vs DOAC alone (HR 3.66; 99% confidence interval [CI] 1.27-10.51), with wide CIs in case-crossover analysis. No evidence of increased risk of bleeding outcomes was seen for DOAC + clarithromycin/fluconazole in cohort and case-crossover analyses. For cardiovascular outcomes, compared with DOAC alone, an increased risk of cardiovascular mortality with DOAC + clarithromycin (HR 3.36; 99% CI 1.73-6.52) and increased risk of all-cause mortality with DOAC + clarithromycin/erythromycin/fluconazole were observed in cohort analysis. However, similar risks were found when initiating erythromycin/fluconazole with and without DOACs.

CONCLUSION

We found no strong evidence of increased risks of bleeding and cardiovascular outcomes in DOACs + clarithromycin/fluconazole/erythromycin users except a possible short-term increased risk of gastrointestinal bleeding in DOACs + erythromycin users.

Evaluation of Drug-Drug Interactions in Pharmacoepidemiologic Research

Drug-drug interactions (DDIs) represent a significant concern for clinical care and public health, but the health consequences of many DDIs remain largely underexplored. This knowledge gap underscores the critical need for pharmacoepidemiologic research to evaluate real-world health outcomes of DDIs. In this review, we summarize the definitions commonly used in pharmacoepidemiologic DDI studies, discuss common sources of bias, and illustrate through examples how these biases can be mitigated.

Thinking Three-Dimensionally: A Self- and Externally-Controlled Approach to Screening for Drug-Drug-Drug Interactions Among High-Risk Populations

The global rise in polypharmacy has increased both the necessity and complexity of drug-drug interaction (DDI) assessments, given the growing potential for interactions involving more than two drugs. Leveraging large-scale healthcare claims data, we piloted a semi-automated, high-throughput case-crossover-based approach for drug-drug-drug interaction (3DI) screening. Cases were direct-acting oral anticoagulant (DOAC) users with either a major bleeding event during ongoing dispensings for potentially interacting, enzyme-inhibiting antihypertensive drugs (AHDs) (Study 1), or a thromboembolic event during ongoing dispensings for potentially interacting, enzyme-inducing antiseizure medications (ASMs) (Study 2). 3DI detection was based on screening for additional drug exposures that served as acute outcome triggers. To mitigate direct effects and confounding by concomitant drugs, self-controlled estimates were adjusted using negative cases (external "control" DOAC users with the same outcomes but co-dispensings for non-interacting AHDs or ASMs). Signal thresholds were set based on P-values and false discovery rate q-values to address multiple comparisons. Study 1: 285 drugs were examined among 3,306 episodes. Self-controlled assessments with q-value thresholds yielded 9 3DI signals (cases) and 40 DDI signals (negative cases). External adjustment generated 10 3DI signals from the P-value threshold and no signals from the q-value threshold. Study 2: 126 drugs were examined among 604 episodes. Assessments with P-value thresholds yielded 3 3DI and 26 DDI signals following self-control, as well as 4 3DI signals following adjustment. No 3DI signals met the q-value threshold. The presented self- and externally-controlled approach aimed to advance paradigms for real-world higher order drug interaction screening among high-susceptibility populations with pre-existent DDI risk.

Proposing a framework to quantify the potential impact of pharmacokinetic drug-drug interactions caused by a new drug candidate by using real world data about the target patient population

Drug development teams must evaluate the risk/benefit profile of new drug candidates that perpetrate drug-drug interactions (DDIs). Real-world data (RWD) can inform this decision. The purpose of this study was to develop a predicted impact score for DDIs perpetrated by three hypothetical drug candidates via CYP3A, CYP2D6, or CYP2C9 in type 2 diabetes mellitus (T2DM), obesity, or migraine. Optum Market Clarity was analyzed to estimate use of CYP3A, CYP2D6, or CYP2C9 substrates classified in the University of Washington Drug Interaction Database as moderate sensitive, sensitive, narrow therapeutic index, or QT prolongation. Scoring was based on prevalence of exposure to victim substrates and characteristics (age, polypharmacy, duration of exposure, and number of prescribers) of those exposed. The study population of 14,163,271 adults included 1,579,054 with T2DM, 3,117,753 with obesity, and 410,436 with migraine. For T2DM, 71.3% used CYP3A substrates, 44.3% used CYP2D6 substrates, and 44.3% used CYP2C9 substrates. For obesity, 57.1% used CYP3A substrates, 34.6% used CYP2D6 substrates, and 31.0% used CYP2C9 substrates. For migraine, 64.1% used CYP3A substrates, 44.0% used CYP2D6 substrates, and 28.9% used CYP2C9 substrates. In our analyses, the predicted DDI impact scores were highest for DDIs involving CYP3A, followed by CYP2D6, and CYP2C9 substrates, and highest for T2DM, followed by migraine, and obesity. Insights from RWD can be used to estimate a predicted DDI impact score for pharmacokinetic DDIs perpetrated by new drug candidates currently in development. This score can inform the risk/benefit profile of new drug candidates in a target patient population.

Bleeding events among patients concomitantly treated with direct oral anticoagulants and macrolide or fluoroquinolone antibiotics

Fluoroquinolones and macrolides may, due to a potential drug-drug interaction, increase the concentration of any concomitantly administered direct oral anticoagulant (DOAC) and thereby increase the risk of severe bleeding. However, clinical evidence for such an effect is scarce. The present study aimed to evaluate the association between the use of fluoroquinolones or macrolides and bleeding events in patients with concomitant DOAC use. This was a nationwide cohort study including 19 288 users of DOACs in 2008-2018 using information from Swedish national health registers. We compared the incidence of bleeding events associated with use of fluoroquinolones or macrolides using doxycycline as a negative control. Cox regression was used to calculate crude and adjusted hazard ratios (aHRs) in time windows of various length of follow-up after the start of antibiotic use. The incidence rates for fluoroquinolones and macrolides ranged from 12 to 24 and from 12 to 53 bleeding events per 100 000 patients in the investigated time windows. The aHRs (95% confidence interval) for use of fluoroquinolones and macrolides were 1.29 (0.69-2.44) and 2.60 (0.74-9.08) at the concomitant window, 1.31 (0.84-2.03) and 1.79 (0.75-4.29) at 30 days, and 1.34 (0.99-1.82) and 1.28 (0.62-2.65) at 150 days, respectively. With regard to fluoroquinolones, the present study suggests that the risk of bleeding when combined with DOACs, if any, is small. Codispensation of macrolides in patients on DOACs was not associated with an increased risk of bleeding. However, due to the small number of macrolide users, the results must be interpreted with caution.

Artificial Intelligence and Data Mining for the Pharmacovigilance of Drug-Drug Interactions

Despite increasing mechanistic understanding, undetected and underrecognized drug-drug interactions (DDIs) persist. This elusiveness relates to an interwoven complexity of increasing polypharmacy, multiplex mechanistic pathways, and human biological individuality. This persistent elusiveness motivates development of artificial intelligence (AI)-based approaches to enhancing DDI detection and prediction capabilities. The literature is vast and roughly divided into "prediction" and "detection." The former relatively emphasizes biological and chemical knowledge bases, drug development, new drugs, and beneficial interactions, whereas the latter utilizes more traditional sources such as spontaneous reports, claims data, and electronic health records to detect novel adverse DDIs with authorized drugs. However, it is not a bright line, either nominally or in practice, and both are in scope for pharmacovigilance supporting signal detection but also signal refinement and evaluation, by providing data-based mechanistic arguments for/against DDI signals. The wide array of intricate and elegant methods has expanded the pharmacovigilance tool kit. How much they add to real prospective pharmacovigilance, reduce the public health impact of DDIs, and at what cost in terms of false alarms amplified by automation bias and its sequelae are open questions. (Clin Ther. 2023;45:XXX-XXX) © 2023 Elsevier HS Journals, Inc.