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Frequency of Prescription Claims for Drugs that May Interact with Janus Kinase Inhibitors Among Patients with Rheumatoid Arthritis in the US

Abstract

Introduction

This study describes the frequency of prescription claims for drugs that may interact with Janus kinase (JAK) inhibitors among adult patients with rheumatoid arthritis (RA) in a large US claims database.

Methods

This observational, retrospective, cross-sectional study of the IBM® MarketScan® Research Commercial and the Medicare Supplemental Database included adults (≥ 18 years) with ≥ 2 outpatient claims 30 or more days apart or ≥ 1 inpatient visit claim with an RA diagnosis between January 1, 2013 and March 31, 2017 (the index period). During the study period, from January 1, 2013 to March 31, 2018, strong organic anion transporter (OAT3) inhibitors, strong cytochrome P450 (CYP) 3A4 inhibitors, and moderate or strong CYP3A4 inhibitors in combination with strong CYP2C19 inhibitors, were identified as drugs with potential for drug–drug interactions (DDIs) with JAK inhibitors approved for RA treatment in the US. Descriptive statistics were conducted.

Results

A total of 152,853 patients met eligibility criteria. Approximately 76% were women and the median age was 57 years. Of these patients, < 0.1% had a claim for a strong OAT3 inhibitor, and 1% had claims for the combination of a strong CYP3A4 and strong CYP2C19 inhibitor; 3% of patients had a claim for a strong CYP3A4 inhibitor and almost 10% had claims for both a moderate CYP3A4 and a strong CYP2C19 inhibitor.

Conclusions

Up to 10% of RA patients have been prescribed a drug with a potential JAK interaction. Rheumatologists should consider potential DDIs when managing patients with RA.

FormalPara Key Summary Points
Why carry out this study?
To our knowledge, there are no studies assessing the relative frequency of prescription claims for drugs that may interact with Janus kinase (JAK) inhibitors in the US population of adult rheumatoid arthritis (RA) patients. Because of the rapidly changing treatment options for RA, information on the relative use of drugs with drug-drug interaction (DDI) potential may be informative in the shared decision making between the health care provider and patient.
The objective of this retrospective, cross-sectional study was to describe the frequency of prescription claims for drugs that may interact and require adjustment in therapy with JAK inhibitors, without documentation of concomitant administration, among adult patients with RA in a large, administrative US claims database.
What was learned from the study?
Up to 10% of patients with RA were prescribed a drug with the potential to cause a DDI with a JAK inhibitor.
This study highlights the need to consider the potential for DDIs for RA patients in the clinical setting.

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Introduction

Rheumatoid arthritis (RA) is an autoimmune disease characterized by chronic inflammation and subsequent joint pain and damage leading to functional impairment [1]. Among patients living with RA, polypharmacy is relatively common [2]. The concomitant exposure to multiple drugs may result in drug–drug interactions (DDIs) in which the efficacy and/or safety of one or more of the drugs are altered. The potential for DDIs is a clinical concern because they may be associated with increased morbidity, hospitalization, prolonged hospital stays, or worsened outcomes [3].

The development of biologic disease-modifying antirheumatic drugs (bDMARDs) and small molecule agents, also referred to as targeted synthetic DMARDs (tsDMARDs), has been a pivotal progression in the therapeutics for rheumatic diseases, such as RA. In contrast to conventional synthetic DMARDs (csDMARDs), bDMARDs and tsDMARDs are directed against specific targets in the immune cells and pathways important to RA pathogenesis. The bDMARDs can be classified by their mechanism of action as (1) anti-cytokines or cytokine receptor antagonists (i.e., tumor necrosis factor [TNF] α-, interleukin [IL]-6-, IL-1 inhibitors) and (2) cell function blockers (B- and T- cell modulators); whereas tsDMARDs target intracellular signaling pathways (i.e., Janus kinase [JAK] inhibitors), and have been successfully developed and implemented as RA therapies [4, 5]. Furthermore, bDMARDs have been formulated for the parenteral route (subcutaneous injections or intravenous infusions), while the JAK inhibitors are administered orally. In the US, the JAK inhibitors available as treatment options for patients with RA include baricitinib, tofacitinib, and upadacitinib [6].

These JAK inhibitors have demonstrated safety and efficacy in randomized controlled trials [7,8,9,10]. The safety profiles of JAK inhibitors are generally similar to bDMARDs, with the exception of an increased incidence of herpes zoster infection (with highest rates reported in Japan and Korea) and warnings for increased risk of thromboembolism [7, 9,10,11].

Baricitinib, tofacitinib, and upadacitinib have unique pharmacokinetic profiles, and subsequently, drug interaction profiles. While the JAK inhibitors are metabolized through the cytochrome P450 (CYP) system, primarily via the CYP3A4 metabolizing enzyme, the extent of metabolism by this and other enzymes varies by agent, as does the extent of renal excretion [7, 9, 10]. Concomitant administration of baricitinib and a strong organic anion transporter (OAT3) inhibitor, such as probenecid, may increase the serum concentration of baricitinib and thus a dose reduction of baricitinib is recommended [9]. Tofacitinib labeling advises dose adjustment with concomitant strong CYP3A4 inhibitors (e.g., ketoconazole) or when moderate CYP3A4 inhibitors are used in combination with strong CYP2C19 inhibitors (e.g., fluconazole) [10]. Upadacitinib labeling recommends using concomitant strong CYP3A4 inhibitors with caution; no specific dosing recommendations are included [7].

To our knowledge, there are no studies assessing the relative frequency of prescription claims for drugs that may interact with JAK inhibitors in the US population of adult RA patients. Because of the rapidly changing treatment options for RA, information on the relative use of drugs with DDI potential may be informative in the shared decision making between the health care provider and patient.

The objective of this retrospective, cross-sectional study was to describe the frequency of prescription claims for drugs that may interact and require adjustment in therapy with JAK inhibitors, without documentation of concomitant administration, among adult patients with RA in a large, administrative US claims database.

Methods

Data

This study used data from the IBM® MarketScan® Research Commercial and the Medicare Supplemental Database, which contains patient-level information on clinical utilization, expenditures, and enrollment across inpatient, outpatient, prescription drug, and carve-out services from large US employers, health plans, government, and public organizations. The annual medical databases capture private sector health data from approximately 100 payers, with over 4.6 billion claim records available.

The database is compliant with the Health Insurance Portability and Accountability Act of 1996 and patient identifiers were removed to ensure patient privacy. The database is commercially available, therefore approval from an institutional review board was not required.

Study Population

We included patients who were ≥ 18 years of age and had either ≥ 2 outpatient claims at least 30 or more days apart or ≥ 1 inpatient visit claim with an RA diagnosis between January 1, 2013 and March 31, 2017 (index period). The first RA claim, determined by International Classification of Diseases Ninth (ICD-9) or Tenth Revision (ICD-10) codes (714.0x, 714.1x, 714.2x, M05.x%, M06.0%, M06.8%) was set as the index date. Patients were also required to have ≥ 12 months of continuous enrollment with medical and prescription benefits and were allowed a maximum 29-day gap.

We excluded patients with one or more claims for the following diagnoses: juvenile idiopathic arthritis (714.3x, M08.%), ankylosing spondylitis (720.0x, M45.%), psoriatic arthritis (696.0x, L40.5%), systemic lupus erythematosus (710.0x, M32.%), and lupus nephritis (583.81, M32.14).

Prescription claims for drugs with potential DDIs (described below) were quantified from January 1, 2013 to March 31, 2018.

Frequency of Variables of Interest

To characterize the patient sample, we evaluated the number and percentage of the following variables: demographic characteristics, such as sex and age; insurance type (commercial or Medicare supplement); US geographic region (Midwest, West, Northeast, South); comorbidities as quantified by the Charlson Comorbidity Index (CCI)(12, 13); and claims for csDMARDs (i.e., hydroxychloroquine, leflunomide, methotrexate, or sulfasalazine), bDMARDS (i.e., abatacept, adalimumab, anakinra, certolizumab pegol, etanercept, golimumab, infliximab, rituximab, sarilumab, or tocilizumab) or tsDMARDs (i.e., tofacitinib; baricitinib and upadacitinib were not available during the study period).

We assessed the number and percentage of patients with claims at any time during the study period for strong OAT3 inhibitors, strong CYP3A4 inhibitors, and moderate or strong CYP3A4 inhibitors with strong CYP2C19 inhibitors in combination. Definitions of moderate and strong followed the classifications on DrugBank, an online, publicly available database, as described by Wishart et al. [12]. The drugs evaluated for potential DDI are listed in Table 1. Two drugs were identified as strong OAT3 inhibitors, 47 as strong CYP3A4 inhibitors, 37 as moderate CYP3A4 inhibitors, and 16 as strong CYPC19 inhibitors.

Table 1 Drug list [12]

Statistical Analyses

Descriptive statistics were conducted. Sample selection and the creation of analytic variables were performed using the Instant Health Data (IHD) platform (BHE, Boston, MA). Statistical analyses were conducted with R, version 3.2.1 (R Foundation for Statistical Computing, Vienna, Austria).

Results

Patient Selection and Demographics

We identified 508,243 unique patients with a diagnosis code for RA in the database. After applying the study inclusion and exclusion criteria, 152,853 patients were eligible for the analysis dataset (Fig. 1). Approximately 75% of the patients in this dataset were women and 60% were between 45 and 64 years of age. About 67% of patients had a claim for a csDMARD and 34% had a claim for a bDMARD; less than 2% had a claim for a tsDMARD (Table 2).

Fig. 1
figure 1

RA patient population identification

Table 2 Patient demographics

Potential for Drug–Drug Interactions

Less than 0.1% of patients had a claim for a strong OAT3 inhibitor, and 1% had claims for the combination of a strong CYP3A4 and strong CYP2C19 inhibitor. Claims for a strong CYP3A4 inhibitor alone were identified in 3% of patients, and almost 10% had claims for the combination of a moderate CYP3A4 and strong CYP2C19 inhibitor (Table 3). The most common drugs identified in each metabolic pathway and the combination of metabolic pathways are described in Table 3.

Table 3 Number and percentage of patients by metabolic pathway, with the five most commonly prescribed drugs, and for the combination of metabolic pathways

Claims stratified by patient age group showed similar trends to the overall population (Table 4). Of note, patients from the ages of 18 to 44 tended to have more prescription claims for moderate CYP3A4 and strong CYP2C19 inhibitors than older patients. Fluconazole was more likely to be prescribed for the 18 to 44-year-old age group.

Table 4 Number and percentage of patients by age group and their prescription claims by metabolic pathway

Discussion

This analysis investigated the frequency of prescription claims for drugs that have DDI potential when co-administered with JAK inhibitors. Among the JAK inhibitors currently indicated for RA in the US, there is great variability in the number of potentially interacting drugs by metabolic pathway, with the highest number associated with strong CYP3A4 inhibition (described in the product labeling for tofacitinib and upadacitinib) and the lowest number associated with strong OAT3 inhibition (described in the product labeling for baricitinib). In this large US claims database, we observed that up to 10% of RA patients had claims for drugs with DDI potential. These results confirm that DDIs are a risk for RA patients and, subsequently, there is a need to recognize and manage DDIs to minimize the risk of therapeutic failure or of adverse drug effects [13].

The recognition and management of DDIs is difficult. Prescribers may not identify potential DDIs without the assistance of reference materials [14]. Additionally, drugs identified with potential DDI are often prescribed by primary care providers; whereas DMARDs, such as JAK inhibitors, are more likely prescribed by rheumatologists [15]. Thus, DDIs may be missed at the point of prescribing.

There are limitations to this analysis. First, there are inherent limitations when working with an administrative claims database. Namely, the claims reflect prescription of a drug, but not necessarily subsequent consumption. Second, the database does not provide information on over-the-counter drugs, supplements, or foods that may affect DDIs. Some interacting drugs, such as antibiotics and antifungals, may be used for a short duration, and temporary interruptions or dose adjustments may be employed to address potential DDIs. The co-administration of JAK inhibitors and the drugs in this analysis could not be evaluated because of the limited number of JAK inhibitor prescription claims during the study period; this is a potential analysis for future study. Clinical outcomes were not evaluated, so we are unable to determine if RA patients encountered any adverse clinical outcomes with the administration of drugs with DDI potential.

To our knowledge, no other administrative claims database analysis has investigated the use of drugs that have the potential to cause DDIs among RA patients in a real-world setting. With commonly used administrative claims database methodologies, this analysis provides a new perspective on the treatment experience for a large, representative sample of RA patients in the US.

Conclusions

In this analysis, up to 10% of patients with RA may be prescribed a drug with the potential to cause a DDI with a JAK inhibitor. As treatment options and polypharmacy management expand, rheumatologists should consider potential DDIs and adjust treatment according to the respective drug-label recommendations. Many of the potentially interacting drugs observed in this real-word analysis may be used acutely (e.g., antifungals), which may require a temporary treatment interruption or dose adjustment of the chronic RA therapy. This study highlights the need to consider the potential for DDIs for RA patients in the clinical setting.

References

  1. Kvien TK. Epidemiology and burden of illness of rheumatoid arthritis. Pharmacoeconomics. 2004;22:1–12.

    Article  Google Scholar 

  2. Treharne GJ, Douglas KM, Iwaszko J, et al. Polypharmacy among people with rheumatoid arthritis: the role of age, disease duration and comorbidity. Musculoskelet Care. 2007;5:175–90.

    CAS  Article  Google Scholar 

  3. Hines LE, Murphy JE, Grizzle AJ, Malone DC. Critical issues associated with drug–drug interactions: highlights of a multistakeholder conference. Am J Health Syst Pharm. 2011;68:941–6.

    Article  Google Scholar 

  4. Fleischmann R, Schiff M, van der Heijde D, et al. Baricitinib, methotrexate, or combination in patients with rheumatoid arthritis and no or limited prior disease-modifying antirheumatic drug treatment. Arthritis Rheumatol. 2017;69:506–17.

    CAS  Article  Google Scholar 

  5. Lee EB, Fleischmann R, Hall S, et al. Tofacitinib versus methotrexate in rheumatoid arthritis. N Engl J Med. 2014;370:2377–86.

    Article  Google Scholar 

  6. Jegatheeswaran J, Turk M, Pope JE. Comparison of Janus kinase inhibitors in the treatment of rheumatoid arthritis: a systemic literature review. Immunotherapy. 2019;11:737–54.

    CAS  Article  Google Scholar 

  7. Rinvoq [package insert]. North Chicago. AbbVie Inc., IL 2019

  8. Yamaoka K. Janus kinase inhibitors for rheumatoid arthritis. Curr Opin Chem Biol. 2016;32:29–33.

    CAS  Article  Google Scholar 

  9. Olumiant [package insert]. Eli Lilly & Co, Indianapolis 2019

  10. Xeljanz [package insert]. Pfizer Labs, New York 2019

  11. Winthrop KL, Yamanaka H, Valdez H, et al. Herpes zoster and tofacitinib therapy in patients with rheumatoid arthritis. Arthritis Rheumatol. 2014;66:2675–84.

    CAS  Article  Google Scholar 

  12. Wishart DS, Feunang YD, Guo AC, et al. DrugBank 5.0: a major update to the DrugBank database for 2018. Nucleic Acids Res. 2018;46:D1074–82.

    CAS  Article  Google Scholar 

  13. van Roon EN, van den Bemt PM, Jansen TL, et al. An evidence-based assessment of the clinical significance of drug–drug interactions between disease-modifying antirheumatic drugs and non-antirheumatic drugs according to rheumatologists and pharmacists. Clin Ther. 2009;31:1737–46.

    Article  Google Scholar 

  14. Ko Y, Malone DC, Skrepnek GH, et al. Prescribers’ knowledge of and sources of information for potential drug–drug interactions: a postal survey of US prescribers. Drug Saf. 2008;31:525–36.

    Article  Google Scholar 

  15. Kern DM, Chang L, Sonawane K, et al. Treatment patterns of newly diagnosed rheumatoid arthritis patients from a commercially insured population. Rheumatol Ther. 2018;5:355–69.

    Article  Google Scholar 

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Acknowledgements

Funding

Sponsorship for this study and Rapid Service Fee were funded by Eli Lilly and Company (Indianapolis, IN, US).

Authorship

All named authors meet the International Committee of Medical Journal Editors (ICMJE) criteria for authorship for this article, take responsibility for the integrity of the work as a whole, and have given their approval for this version to be published.

Medical Writing, Editorial, and Other Assistance

Editorial assistance in the preparation of this article was provided by Molly E. Tomlin, MS, MEd, of Eli Lilly and Company.

Disclosures

Alison Walton and Justin Owensby declare that they have no conflicts of interest. Jim Paik, Amanda Quebe, Carol Kannowski, and Casey Choong are employees and shareholders of Eli Lilly and Company. At the time of this analysis, Seth Anderson was an employee of Eli Lilly and Company.

Compliance with Ethics Guidelines

The database is compliant with the Health Insurance Portability and Accountability Act of 1996 and patient identifiers were removed to ensure patient privacy. The database is commercially available and the data are de-identified, therefore approval from an institutional review board was not required.

Data Availability

Lilly provides access to relevant anonymized patient-level data from studies on approved medicines and indications as defined by the sponsor-specific information on www.vivli.org. For details on submitting a request, see the instructions provided at www.vivli.org.

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Correspondence to Alison Walton.

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Walton, A., Paik, J., Quebe, A. et al. Frequency of Prescription Claims for Drugs that May Interact with Janus Kinase Inhibitors Among Patients with Rheumatoid Arthritis in the US. Rheumatol Ther 8, 599–607 (2021). https://doi.org/10.1007/s40744-020-00275-8

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Keywords

  • Baricitinib
  • Drug–drug interaction
  • Janus kinase inhibitors
  • Real-world study
  • Rheumatoid arthritis
  • Tofacitinib
  • Upadacitinib