Use of Real-World Claims Data to Assess the Prevalence of Concomitant Medications to Inform Drug-Drug Interaction Risk in Target Patient Populations

Abstract

A common issue in clinical drug development involves drug-drug interactions (DDI) that may lead to altered drug exposure and subsequent altered safety and efficacy of an investigational drug or concomitant medications (conmeds) in the target patient population. The drug development pipeline therefore involves DDI risk assessment of the investigational drug based on in vitro studies, in silico modeling, and clinical trials. Real-world data (RWD), particularly claims databases with reliable information on pharmacy dispensing, provide an opportunity to understand conmeds usage in the target indication in a real-world setting as one approach to assess potential DDI risk. We describe two cases of characterizing DDI-related conmeds usage with a large closed US-based claims database, IQVIA PharMetrics® Plus, and identified potential DDI risk for multiple sclerosis and hormone receptor-positive breast cancer. For example, prevalent and chronic use of statins (atorvastatin and simvastatin), which are CYP3A4 substrates, were identified among both disease cases. Further examples, limitations, and future directions are also discussed. These insights can therefore help augment decision-making during clinical drug research and development.

Figure 1

Selection of multiple sclerosis and hormone receptor‐positive breast cancer cohorts. (a) Multiple sclerosis attrition table based upon algorithm from Culpepper et al. (b) Age and sex distribution for final MS cohort. Age distribution is also further stratified by sex recorded in the database. Note that patients over 85 in 2022 are represented as being 85 for de‐identification purposes. (c) Hormone receptor‐positive breast cancer (includes both estrogen receptor‐positive and/or progesterone receptor‐positive cancer) cohort attrition table. (d) Age distribution for final HR+ breast cancer cohort. Note that patients over 85 in 2022 are represented as being 85 for de‐identification purposes. Patients of age 85+ in 2022 have their date of birth shifted so they are represented as being 80 years old.

Figure 2

Use of MS‐specific DMTs, PPIs, CYP3A4 inhibitors, inducers, and substrates, and P‐gp/BCRP inhibitors in MS patients. (a) Use of disease‐modifying treatments among the MS population within the year 2022. (b) Use of any proton pump inhibitor among the MS population within the year 2022. (c) CYP3A4 inhibitors and inducers use among the MS population, as well as within age and sex strata. Further details on dosing, route, duration, and indication diagnoses (from DrugBank) are shown. (d) CYP3A4 substrates use among the MS population. (e) P‐gp inhibitors use among the MS population. (f) BCRP inhibitors use among the MS population.

Figure 3

Use of BC‐specific DMTs, PPIs, CYP3A4 inhibitors, inducers, and substrates, and P‐gp/BCRP inhibitors in HR+ BC patients. (a) Exposure to any breast cancer‐related treatment within the year 2022 among the HR+ breast cancer cohort. (b) Use of any proton pump inhibitor among the HR+ BC population within the year 2022. (c) CYP3A4 inhibitors and inducers use among the HR+ BC population, as well as within age and sex strata. Further details on dosing, route, duration, and indication diagnoses (from DrugBank) are shown. (d) CYP3A4 substrates use among the HR+ BC population. (e) P‐gp inhibitors use among the HR+ BC population. (f) BCRP inhibitors use among the HR+ BC population.