Towards personalized management of drug interactions: from drug-drug-interaction to drug- drug-gene-interaction
Bahar, Akbar
DOI:
10.33612/diss.112160601
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Bahar, A. (2020). Towards personalized management of drug interactions: from drug-drug-interaction to drug-drug-gene-interaction. University of Groningen. https://doi.org/10.33612/diss.112160601
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from drug-drug-interaction to drug-drug-gene-interaction
Muh. Akbar Bahar
Ivan Surya Pradipta Sylvi Irawati
Cover design: Muhammad Nur Amir
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ISBN (book): 978-94-034-2306-7
ISBN (electronic version): 978-94-034-2307-4
The work presented in this thesis was funded by DIKTI Scholarship, the Ministry of Research, Technology and Higher Education of Republic of Indonesia and the University of Groningen, the Netherlands. This thesis was conducted within the Groningen University Institute for Drug Exploration (GUIDE). Printing of this thesis was financially supported by Faculty of Science and Engineering and the University Library of the University of Groningen.
Copyright © 2019, Muh. Akbar Bahar
No part of this thesis may be reproduced or transmitted in any form by any means, electronically or mechanically by photocopying, recording, or otherwise, without written permission of the author.
The copy right of previously published articles of this thesis remains with the publishers or journals.
Towards personalized management of drug interactions:
from drug-drug-interaction to drug-drug-gene-interaction
PhD thesis
to obtain the degree of PhD at the University of Groningen
on the authority of the Rector Magnificus Prof. C. Wijmenga
and in accordance with the decision by the College of Deans.
This thesis will be defended in public on Tuesday 4 February 2020 at 11.00 hours
by
Muh. Akbar Bahar born on 16 May 1986 in Ujung Pandang, Indonesia
Prof. E. Hak
Assessment Committee
Prof. A. H. Maitland-Van Der Zee Prof. D. J. Touw
Prof. E. N. Van Roon
Chapter 1 General Introduction 9 PART A Burden and Management of Drug-Drug-Interaction
Chapter 2 Prevalence and Accuracy of Information on CYP2D6, CYP2C19, and 23 CYP2C9 related Substrate and Inhibitor Co-prescriptions in
the General Population: a Cross-Sectional Descriptive Study as Part of the PharmLines Initiative
Chapter 3 The Burden and Management of Cytochrome P450 2D6 (CYP2D6) 47 Mediated Drug-Drug-Interaction (DDI): Co-medication of
Metoprolol and Paroxetine or Fluoxetine in the Elderly
Chapter 4 Discontinuation and Dose Adjustments of Metoprolol after 73 Metoprolol- Paroxetine/Fluoxetine Co-prescriptions in Dutch Elderly
Chapter 5 The Impact of CYP2D6 mediated Drug-Drug-Interaction: 95 a Systematic Review on a Combination of Metoprolol and
Paroxetine/Fluoxetine
PART B Influence of CYP450 Polymorphisms on the Magnitude of Drug-Drug-Interaction and Drug-Drug-Gene-Interaction
Chapter 6 Pharmacogenetics of Drug-Drug-Interaction (DDI) and 119 Drug-Drug-Gene-Interaction (DDGI):
a Systematic Review on CYP2C9, CYP2C19, and CYP2D6
Chapter 7 Impact of Drug-Gene-Interaction, Drug-Drug-Interaction, 179 and Drug-Drug-Gene-Interaction on Switching, Dose Adjustment and
Early Discontinuation of (Es)citalopram: an Explorative Cohort Study within the PharmLines Initiative
Chapter 8 Discussion and Future Perspectives 225
Addendum Summary 237
Samenvatting 243 Acknowledgment 247
About the author 251
General Introduction
c h a p te r O N E
Introduction 1
An Adverse Drug Reaction (ADR) is still one of the main clinical factors causing excess morbidity and mortality as well as additional economic burden to the healthcare system1. According to the WHO, ADR is defined as ‘any noxious, unintended, and undesired effect of a drug, which occurs at doses used in humans for prophylaxis, diagnosis, or therapy’2. In the US, it was reported that ADR led to more than 100,000 deaths in 19943. Additionally, the prevalence of ADR causing hospitalization ranged from below 1% to almost 16%4. Importantly, patients in hospitals are also at risk of developing ADR. An epidemiological study of almost 3,700 hospitalized patients in the UK showed that about 15% of them experienced at least one ADR during their treatment and ADR extended the length of hospitalization of almost 27% of patients with ADR5. The estimated cost of ADR is about €706 million per year1. Another study reported that ADR may cost around €200 to €9000 per hospitalization6.
There are several main risk factors of ADR related hospitalization such as cognitive impairment, dependent living condition, kidney impairment, non-adherence, multiple comorbidities, and polypharmacy7. The latest is commonly defined as the use of five of more drugs daily8. The use of multiple medications can trigger the incidence of drug-drug interaction (DDI). The risk of DDI is reported to escalate linearly as the number of drugs consumed increased9. Consuming five to seven drugs and ten to fourteen drugs increased the risk of potentially clinically relevant DDI by about 20%
to 30% and 40% to 60%, respectively10. DDI is one example of ADR which often occurs, about 20% to 30% of ADRs are related to DDI, but it is mostly preventable and avoidable11.
Drug-Drug Interaction
A DDI occurs when the effect of one drug is interfered with, either enhanced or reduced, the presence of one or more other drugs. The prevalence of potential drug interaction varies widely. A systematic review of nineteen studies on this topic described that the prevalence ranged from 2.2% to 70.3%12. The variation in the proportion of reported DDI is caused by the discrepancy in study design, study population, definition of DDI, and method of measurement12.
The population which is particularly at risk to experience DDI and therefore needs special attention, is the elderly population. Elderly patients are more vulnerable to DDI because of gradual age-related physiologic changes, increased risk for disease associated with aging, and the consequent increase in the number of different medications13. The estimated frequency of elderly with four or more drugs was more than 50%14. It was also reported that about 46% of elderly patients had at least one potential clinically relevant DDI and the effects of 10% of the DDIs were considered to be severe15. This demonstrates the need to increase interventions aimed at reducing these DDIs.
DDIs are divided into pharmacodynamic DDI and pharmacokinetic DDI. A pharmacodynamic DDI occurs when drugs alter the effects of each other directly without changing their blood concentration16. This DDI may exhibit synergistic, additive, or antagonistic effects. Meanwhile, pharmacokinetic DDI take places when the absorption, distribution, metabolism, or excretion (ADME) of one drug is altered by another drug, and consequently, its blood concentration is either increased or decreased16. There are two possible main outcomes of this particular DDI i.e. serious
1
adverse effects or treatment failure which depends on the pharmacological properties of the drugs.As far as the pharmacokinetic interactions are concerned, a drug that inhibits or induces the phase I metabolic enzyme cytochrome P450 (CYP450) is commonly involved17,18.
Phase I metabolic enzyme CYP450
One of the main sources of pharmacokinetic variability of drugs is the family of CYP450 enzymes.
These heme-containing enzymes are mostly expressed in the centrilobular area of the liver and located in the membrane of the smooth endoplasmic reticulum19. The microsomal enzymes responsible for phase I drug metabolism are a major route of drug biosynthesis and degradation.
Based on amino acid sequence similarity, they are divided into 18 families and 44 subfamilies19,20. However, there are only several subtypes of CYP450 which are predominantly reported to actively catalyze more than half (70% to 80%) of marketed drugs i.e. CYP1A2, CYP2A6, CYP2B6, CYP2E1, CYP2D6, CYP2C and CYP3A420-22. They accounted for 70% of liver CYP450 enzymes with CYP2C and CYP3A4 as the dominant enzymes with a percentage of 20% and 30%, respectively21. CYP450 enzymes have inter-individually differing activity in human drug metabolism and the variation leads to different susceptibility in both pharmacological and adverse reactions of drugs.
An important factor that contributes to the different drug metabolism activity of CYP450 subtype enzymes is genetic polymorphism. It was estimated that genetic aspects are responsible for 20% to 90% of the variation in drug metabolism and response23. The genetic polymorphisms can be in the forms of loss of function, decreased or increased function of alleles of CYP genes20. This genetic variation could lead to different pharmacokinetic phenotypes of CYP450. For example in the case of CYP2C19, besides normal metabolic function (normal metabolizer/NM), combination of non-functional alleles refers to poor metabolizer (PM), a combination of a non-functional allele or a decreased function allele and a normal function allele refers to intermediate metabolizer (IM), combination of a normal function allele and an increased function allele refers to rapid metabolizer (RM) and combination of two or more increased function alleles refers to ultra-rapid metabolizer (UM) result in no metabolic activity, reduced metabolic activity, faster metabolic activity than NM, and faster metabolic activity than RM, respectively24. The clinical consequences of genetic polymorphisms is different among the CYP450 subfamilies with CYP2C9, CYP2C19, and CYP2D6 polymorphisms reported to have the most important clinically relevant implications20,25,26.
The prevalence of CYP2C9, CYP2C19, and CYP2D6 polymorphisms varies among ethnicities25. For example in the Caucasian population, it was reported that the prevalence of people with IM and PM genotypes was 40.5% (40% IM and 0.5% PM), 23% (20% IM and 3% PM), and 50% (40%
IM and 10% PM) for CYP2C9, CYP2C19, and CYP2D6, respectively22. Since these highly polymorphic enzymes metabolize about 40% of drugs used in daily clinical practice, the interaction between drug and variant CYP2C9, CYP2C19, and CYP2D6 alleles (drug-gene interaction/DGI) is prevalent27,28. US based pharmacoepidemiological studies reported that the prevalence of CYP2C9, CYP2C19, and CYP2D6 mediated DGI was about 15% to 25%28,29. Some other studies indicated that patients with deviating genotypes had a higher risk of experiencing adverse drug effects than the NM patients30-32. The clinical impact of DGI can be substantial ranging from therapeutic failures to death31,32.
The use of technology and pharmacogenetics to prevent adverse drug effects by DGI is therefore,
1
strongly recommended.
Genetic based therapy guidelines have been provided to deal with DGI in order to generally aid health practitioners to improve drug use and therapy outcomes33-35. In these guidelines, clinical interventions have been advised for many drugs. The Clinical Pharmacogenetics Implementation Consortium (CPIC) and the Dutch Pharmacogenetics Working Group (DPGW) are two main leading organizations that actively contribute in updating the guidelines by translating reliable peer‐
reviewed evidence to actionable recommendations33,36.
Drug-Drug-Gene-Interaction
To date, the available guidelines have not taken into account the interaction between internal (genetic polymorphisms) and external factors (CYP modulators) on the magnitude of drug interaction26,33. The magnitude of CYP-mediated DDIs can vary in patients with different metabolic phenotypes because the functionality of CYP enzymes is central in mediating these DDIs. Some DDIs can be expected to be clinically relevant for patients with normal metabolic phenotype but not clinically relevant for people with deviating phenotypes26. In this case, genetic variation of CYP enzymes may alter the impact of the DDIs. Additionally, it was also reported that CYP enzymes with a reduction-of-function allele are more prone to undergo a phenoconversion (discordance between genotype and phenotype) with a co-presence of a CYP inhibitor than those with active alleles37. Therefore, the addition of CYP modulator may also modify the clinical impact of DGI.
Furthermore, the genetic polymorphisms may also contribute in impairing the clearance of drugs with multiple metabolic pathways38. Combination of the existing non-functional alleles of CYP450 in one pathway and the presence of a CYP inhibitor for another pathway may produce a marked alteration in drug disposition. The interplay condition of DDI and DGI is called drug- drug-gene-interaction (DDGI)28. The complex and multimodal interaction of DDGI may produce a greater variability in person-to-person therapeutic drug reactions than bimodal DDI and DGI26. An example of DDGI is the interaction between voriconazole (a substrate of CYP2C19 and CYP3A4) and ritonavir (a strong inhibitor of CYP3A4) in a patient with CYP2C19 PM. The Area Under Curve (AUC) of voriconazole in an individual with CYP2C19 PM taking ritonavir was 9-fold, 17-fold, and 26-fold higher than in CYP2C19 PM without ritonavir, CYP2C19 NM with the combination and CYP2C19 NM without the combination, respectively39. DDGI is quite prevalent in clinical practice. In the US, it was reported that 19% of about a thousand clinically relevant interactions was deemed as DDGI28. Another US based study reported that 22% out of 16,924 severe drug interactions found from more than 20,000 patients was observed as DDGI29. Hence, it is important to understand the nature and magnitude of DDGI in order to screen and minimize the severity of this cumulative interaction.
Prevention and Management of DDI
To ensure medication safety, a computerized DDI surveillance system is embedded within most of the electronic prescriptions and health record systems40. The tool works as a screening and early warning system to timely identify or detect potential clinically relevant DDIs41. When a potentially
1
harmful medication combination is prescribed or dispensed, an alert will generally pop up to remind the prescriber or pharmacist about the DDI severity and its clinical consequences. The automated medication surveillance system is also usually completed with actionable management recommendations (replacement, dose adjustment, monitoring, etc) to avoid or minimize the detrimental risk of the DDI40.Some studies reported that the computerized DDI alerts effectively decrease the ADR related to DDI substantially, and therefore, produce a cost-saving DDI management42-45. Furthermore, it was also reported that DDI alerts enhanced the ability of physicians to practice safe prescribing42. However, the DDI alert systems are not without limitations. It could produce an ‘alert fatigue’ problem which leads medical doctors or pharmacists to override the alerts40,46. It was reported that 22% of general practitioners using the DDI decision support system frequently overlooked the potential DDI related alerts47. The excessive nuisance alerts produced by the safety systems might be due in part to the fact that some DDI decision support systems still alert DDIs with questionable clinical relevance40,47,48. The prevalence of actual DDIs is much lower than of potential DDIs49. Additionally, another study reported that the safe pharmacotherapy and cost-saving attributed to a DDI alert application is provided by only small parts of the generated alerts45. Therefore, it seems that most of the current DDI alerts systems overestimate the impact of some potential DDIs40.
Moreover, DDI knowledge databases used to support the DDI alerts have different assessment regarding the clinical relevance of specific DDIs40,50. Differences in severity ranking and quality of evidence rating systems might produce conflicting conclusions whether to signal a DDI or not40,50. Collectively, they can decrease the confidence of health practitioners on the ability of DDI alerts to improve medication safety. However, the decision to ignore the DDI signals may undermine the quality of patient safety and care because it can lead to serious medical consequences46. Grizzle at al. reported that 72% of 291,890 overridden DDI alerts in six veterans affairs medical facilities in the US was identified as clinically significant DDIs51. Meanwhile, Weingart et al. reported that 89.4%
of overridden signals in primary care settings were deemed as alerts for clinically significant DDIs48. Therefore, the efforts to provide high quality evidence for DDIs with unclear clinical impact and only supported by weak evidence are critical and should be encouraged in order to improve the consistency, sensitivity and specificity of DDI decision supports. Prescription databases can be used to study the quantity and the clinical relevance of particular omitted DDIs because they can provide real world drug utilization data52,53. These prescription repositories have been used as reliable and valid source of data for a wide range of pharmacoepidemiological studies54-56.
Another limitation is that most of the DDI alerts are only designed to detect binary drug–drug interactions. Multimodal DDIs involving multiple elimination pathways are hardly considered.
Moreover, since genotyping is still not part of regular clinical laboratory testing, most of the DDI alerts have also not been upgraded to include detection of gene related interactions such as DGI and DDGI57. Most of the current DDI alert systems might not detect more than a third of the potential drug interactions because of the unknown patient genetic status28,57. Therefore, there should also be efforts to link patients’ genetics data to the DDI alert system and collect evidence especially for DDGI to support more advanced and personalized DDI clinical decision supports.
Thesis objective 1
The objective of this thesis was to evaluate the burden, management, and impact of DDI as well as to investigate the influence of CYP2D6, CYP2C19, and CYP2C9 polymorphisms in mediating DDGI.
Thesis outline
This thesis consists of two main parts (A and B). Part A focuses on studies with the aim to determine the frequency of CYP2C9, CYP2C19, and CYP2D6 mediated DDIs, as well as to evaluate the concordance between two sources of assessing the burden of DDI i.e. self-reported questionnaire and prescription database (chapter 2). Additionally, this part also includes a study evaluating the burden and management of one of the most frequently ignored potential DDIs in the presence of DDI alerts (chapter 3).
Chapter 4 describes a study which was intended to add evidence regarding the impact of the frequently omitted DDI and then, used this study and other relevant studies to systematically evaluate the potential impact of the DDIs (chapter 5).
Part B depicts the impact of pharmacogenetics on DDIs and DDGIs involving CYP2D6, CYP2C19, and CYP2C9 (chapter 6) and tested the influence of DGIs, DDIs and DDGIs on the prescription profiles of citalopram and escitalopram (chapter 7). Lastly, the main findings and future perspectives in the field of DDI and DDGI were discussed and summarized in Chapter 8.
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part A
DRUG-DRUG-INTERACTION
Prevalence and Accuracy of Information on CYP2D6, CYP2C19, and CYP2C9 related Substrate and Inhibitor Co-prescriptions in the General Population:
a Cross-Sectional Descriptive Study as Part of the PharmLines Initiative
Muh. Akbar Bahar Jens H.J. Bos Sander D. Borgsteede Aafje Dotinga Rolinde A. Alingh Bob Wilffert Eelko Hak
Submitted
c h a p te r T WO
Objectives
To study the frequency and concordance on self-reported CYP2D6, CYP2C19 and CYP2C9 (CYP2D6/2C19/2C9)-related co-prescriptions that may lead to DDIs at entry of the Lifelines cohort and linked data from a prescription database.
Design
A cross-sectional descriptive study.
Setting
As part of the University of Groningen PharmLines Initiative, data were collected on substrate/
inhibitors of CYP2D6/2C19/2C9 from cohort entry questionnaires of Lifelines participants and linked information from the community pharmacy database IADB.nl.
Participants
Among 80,837 adult self-reported medicine users in the Lifelines cohort, 25,387 participants had linked pharmacy database information.
Outcome measures
Frequency of potential CYP2D6/2C19/2C9 mediated DDIs as well as the levels of agreement between the information from the self-reported Lifelines cohort and the IADB.nl prescription data on these potential DDI. CYP2D6/2C19/2C9 related co-prescriptions were divided based on the type of drugs i.e. chronically used medication (CM) or occasionally used medication (OM). This resulted in the combinations of two chronically used drugs (CM-CM), chronically and occasionally used drugs (CM-OM), and two occasionally used drugs (OM-OM). To measure the agreement level, cohen’s kappa statistics and test characteristics were used. Results were stratified by time window, sex, and age.
Results
About 1-2 per hundred participants were exposed to a potential CYP2D6/2C19/2C9-mediated DDI.
Overall, the overlapping time window of three months produced the highest mean kappa values among potential CYP2D6/2C19/2C9-mediated DDIs i.e. 0.545 (95% CI: 0.544-0.545), 0.512 (95% CI:
0.511-0.512), and 0.374 (95% CI: 0.373-0.375), respectively. CM-CM had a better level of agreement (good) than CM-OM (fair to moderate) and OM-OM combination (poor to moderate). The influence of sex on concordance values was different for different CYPs. Among older persons, agreement levels were higher than for younger population.
Conclusions
CYP2D6/2C19/2C9-mediated potential DDIs were frequent and concordance of data varied by time window, type of interacting medication, sex and age. Subsequent studies should rather use
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Introduction
Drug-drug interactions (DDIs) are an important contributor to adverse drug reactions leading to hospitalization or mortality.1,2 CYP2D6, CYP2C19 and CYP2C9 (CYP2D6/2C19/2C9), subtypes of CYP450 drug metabolizing enzymes, are commonly involved in mediating partly inappropriate DDIs as these enzymes metabolize a wide variety of drugs in clinical practice.3,4 Since these enzymes are highly polymorphic, the clinical impact of DDIs is variable from person to person and depends on his/her genetics profile.5,6 Consequently, the strategy to manage the DDI cannot be generalized across different CYP2D6/2C19/2C9 polymorphisms, hindering the development of guidelines.5 Therefore, it is important to study the prevalence and type of DDI mediated by these enzymes using a combination of exposure measurements.
Estimation of the prevalence rate of a potential DDI is commonly performed using self-reporting methods in which patients are interviewed or filled out a self-administered questionnaire.7-9 However, this kind of assessment is prone to information bias, because of inaccurate recall, which may influence the validity of results.10,11 Hence, it is important to validate drug information collected with self-reporting methods.12,13
The Lifelines cohort is a Dutch three-generation population cohort that provides a wide variety of medical and non-medical data, genomic information, and data on medication use.14,15 The Lifelines cohort, as a prospective and long-term database, offers possibilities in pharmaco- epidemiological studies, such as assessing the impact of gene polymorphism on the magnitude of DDIs in the population. However, currently not much is known about the frequency, type and validity of potential DDIs in the open population.
This study has both a methodological and an epidemiological aim: we studied the frequency of potentially interacting substrates and inhibitors of the CYP2D6/2C19/2C9 and the concordance level of the information derived by self-reported drug use and an analysis of data from a drug-use database. For the latter aim, information as observed in the Lifelines cohort was compared with data from a prescription database, the University of Groningen prescription database IADB.nl, across type of medications, sex and age.16,17 A prescription database is regarded as an accurate database and not to be influenced by so-called recall bias.18,19 Additionally, IADB.nl has been proven a reliable database in many pharmaco-epidemiological studies.20-22
Materials and Methods
The PharmLines Initiative is a university wide project in which the data of the Lifelines Cohort study have been linked to the University Groningen prescription database IADB.nl.17 The project was started in 2017 by the Groningen Research Institute of Pharmacy, Departments of Epidemiology and Clinical Pharmacy, Department of Pharmacology of the University Medical Center Groningen and the Lifelines Cohort Study (see https://www.lifelines.nl/researcher/cohort-and-biobank).17
The Lifelines cohort
The Lifelines cohort covers 167,729 participants from the Northern part of the Netherlands, aged 6 months until 93 years old, which were recruited from 2006 until 2013.14,15 It is an observational
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environmental, genetic, and phenotypic aspects in the development of chronic diseases and healthy aging.14,15 The recruited participants will be followed for at least 30 years and are asked to complete a questionnaire every 1.5 years. In addition, once every five years, the participants have a comprehensive physical examination.14,15 Baseline questionnaires included questions about general information, lifestyle and environment, psychosocial aspects, and health including medication
use.14,15,23 The medication use information were collected in two ways i.e. (a) patients filled out
a questionnaire or (b) patients carried the medication at the time of interview.17 The medication data regarding their current prescription and dose were recorded and classified using the Anatomical Therapeutic Chemical (ATC) coding scheme.14,15 The Lifelines population is generally representative of the Dutch population resided in the Northern part of the Netherlands.23
University of Groningen IADB.nl database
The University of Groningen prescription database IADB.nl has recorded prescriptions from community pharmacies in the Netherlands since 1994, and is updated annually.16,17 In 2017, it contained prescription data of approximately 700,000 individuals from around 72 pharmacies that are located in most of the area where the Lifelines cohort is also resident. The study population was reported to represent the general population in the Netherlands.16,17 In the IADB.nl, each patient has a unique and anonymous identifier. Each record contains information about patient’s sex, date of birth, and information about his/her prescribed medication such as ATC code, duration, daily dose, amount prescribed, and dispensing date.16,17 The IADB.nl has no information about over-the-counter (OTC) drugs and prescriptions from the hospital.
Study population and linkage of databases
The study population consists of all medicine users (≥18 years) in the Lifelines cohort. A Trusted Third Party, Statistics Netherlands (Dutch: Centraal Bureau voor de Statistiek; CBS), carried out the linkage of the Lifelines and the IADB.nl records at the patient level based on postal code in combination with sex and date of birth. The unique identifiers from both databases were removed, and once the linkage was completed, each patient was assigned a new unique code that cannot be traced back to their previous identifier. Using the new identifier, the data from both databases could be combined. The complete linking process was described in more detail by Sediq et al.17
Exposures
Exposures were defined as substrates and inhibitors of CYP2D6/2C19/2C9. We defined a potential DDI as each combination of a substrate and inhibitor listed in the international standard and local guideline, Flockhart Table for CYP-mediated drug interactions and the Dutch Commentaren Medicatiebewaking book, respectively.24,25 Based on the main indication according to the official product information, the exposures were classified as: 1) chronically used medication (CM) for example CYP2D6 substrates such as beta-blockers (metoprolol), and 2) occasionally used medication (OM) for example CYP2D6 substrates such as opioids (tramadol). The full list of medications including their classification can be found in supplementary 1.
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Outcomes
Outcome measures were defined as frequency of potential CYP2D6/2C19/2C9 mediated DDIs as well as the levels of agreement between information from the self-reported Lifelines cohort and the IADB.nl prescription data on these potential DDIs across type of medications, age and sex.
If the potential DDI was only found in the Lifelines cohort records, it was categorized as over- reporting (false positive). If the potential DDI was only found in the IADB.nl, it was categorized as under-reporting (false negative). We also provided data on test characteristics as sensitivity and specificity for the top five potential DDIs detected in the overlapping population of the two databases. Different overlapping time windows (i.e. 1 month, 3 months, 6 months, 9 months, and 1 year) between baseline date of self-reporting medication in the Lifelines cohort and dispensing date of prescription in the IADB.nl were applied to determine the optimum time window for assessing the agreement of both databases. Subgroup analyses by the type of medication (CM vs OM), age, and sex were performed to observe the potential influence of these factors on the agreement.
Additionally, we also presented information about the clinical relevance of the potential DDIs based on the suggested management provided by Epocrates® i.e. ‘contraindicated, avoid combination/
use alternative, modify treatment/monitor and caution’. If Epocrates® had no recommendation for the potential DDI, we checked whether Drugs.com, another online drug interactions screening software, provided suggestions for the potential DDIs. Both of them were reported to have a high sensitivity for detection of potential DDIs.26,27
Statistical methods
To determine the agreement values between the databases on the DDIs, we used Cohen’s kappa statistics and 95% confidence interval (CI). Altman et al. provided some guidelines to define the Cohen’s kappa values i.e. poor (<0.20), fair (0.20-0.40), moderate (0.41-0.60), good (0.61-0.80), and very good (0.81-1.00).28
Results
Among of 167,729 Lifelines participants, 80,837 adults were recorded with self-reported medicine use (mean age 46 years and 68.5% women) in the cohort at entry (table 1). Among the subjects, there were 1,125 (1.4%) self-reported medicine users exposed to 1,199 potential CYP2D6/2C19/2C9- mediated DDIs (figure 1). Most of the potential DDIs were mediated by CYP2C19 (513 events), followed by CYP2D6 (488 events) and CYP2C9 (198 events) (table 2). Women were exposed more to potential CYP2D6 and CYP2C9 mediated DDIs than men. Older subjects were more exposed to potential CYP2C19 mediated DDIs than younger subjects (table 2). There were 24% and 47%
of CYP2D6 and CYP2C19 mediated co-prescriptions, respectively, which were in the category of
‘avoid combination/use alternative’. Additionally, about 65%, 43% and 93% of CYP2D6/2C19/2C9- mediated combinations were in the category of ‘modify treatment/monitor’ according to the knowledgebase (figure 2).
Information from 45,160 Lifelines participants could be linked to the IADB.nl database. Among this linked population, there were 25,387 self-reported medicine users with comparable age and
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Table 1. Characteristics of participants with self-reported medication use at entry in the Lifelines cohort database and overlap with IADB.nl.
Characteristics Number of participants (n= 80,837)
Age in year, mean (±SD) 46.13 (±14.21)
18-59 years old, N (%) 64,807 (80.17%)
>= 60 years old, N (%) 16,030 (19.83%)
Sex, N women (%) 55,352 (68.50%)
Total participants with CYP2D6/2C19/2C9 mediated DDI, N(%) 1,125 (1.40%) Total participants overlapped with IADB database, N(%) 25,387 (31.41%)
• Age in year, mean (±SD) 45.54 (±14.62)
• 18-59 years old, N (%) 20,277 (79.90%)
• >= 60 years old, N( %) 5,110 (20.10%)
• Sex, N women (%) 17,416 (68.60%)
• Total participants with CYP2D6/2C19/2C9 mediated DDI, N(%) 366 (1.44%)
sex distribution (mean age 45.5 years and 68.6% women) as observed in the total medicine users in the Lifelines cohort (table 1). Metoprolol-paroxetine (83 events), citalopram-omeprazole (173 events), and diclofenac-paroxetine (51 events) were the most prevalent potential DDIs mediated by CYP2D6/2C19/2C9, with good, moderate, and fair agreement of questionnaire and prescription data, respectively. Data on kappa, sensitivity and specificity values of the top five most frequent potential DDIs in the Lifelines database can be found in table 3. Information on self-reported combinations of chronically used medications such as metoprolol-fluoxetine and metoprolol-duloxetine had very good agreement and high sensitivity as well as specificity. Meanwhile information on self-reported combinations with occasionally used medication such as ibuprofen-paroxetine and diclofenac- fluoxetine tended to have fair kappa and sensitivity values, but their specificity is high because of the low prevalence of the DDI.
The application of different time windows resulted in different agreement levels of the potential DDIs (figure 3). Overall, the time window of three months produced the highest mean kappa values among potential CYP2D6/2C19/2C9-mediated DDIs i.e. moderate [0.545 (95% CI: 0.544-0.545)], moderate [0.512 (95% CI: 0.511-0.512)], and fair [0.374 (95% CI: 0.373-0.375)], respectively. Extension of the time windows to 6, 9, and 12 months decreased the mean kappa values. The time window of 1 month also produced a low kappa value. For the time window of three months, subgroup analysis for the type of medication indicated the potential DDIs in CM-CM had better levels of agreement (good) than CM-OM (fair to moderate) and OM-OM (poor to moderate). For the CYP2D6 and CYP2C9 mediated DDIs, CM-OM combination had better kappa values (fair agreement) than OM-OM combination (poor agreement). Meanwhile, for the CYP2C19 mediated DDIs, both CM-OM and OM-OM combination had comparable agreement level (moderate). The summary of the results can be found in supplementary material 2.
Subgroup analysis of agreement by sex showed mixed results (figure 4). In CYP2D6 mediated potential DDIs, females appeared to have a better level of agreement than males. The opposite result was observed in CYP2C19 and CYP2C9 mediated potential DDIs where males mostly had
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Figure 1. Selection of the population study.
Lifelines population (n= 167,729)Medicine users (n= 80,837)
Excluded: Without drug information, less than 18 years old Overlapping population between Lifelines and IADB.nl databases (n= 45,160)
Medicine users within overlapping population (n= 25,387)
Excluded: Not detected in the IADB.nl Participants with CYP2D6/2C19/2C9 mediated DDI (n= 1,125)
Participants with CYP2D6 mediated DDI (n= 448) Participants with CYP2C1 mediated DDI (n= 490) Participants with CYP2C9 mediated DDI (n= 187) Participants with CYP2D6/2C19/2C9 mediated DDI (n= 366) Participants with CYP2D6 mediated DDI (n= 192)Participants with CYP2C19 mediated DDI (n= 125)Participants with CYP2C9 mediated DDI (n= 49)
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Table 2. Prevalence and participants with potential DDIs in the Lifelines cohort. Variables
Prevalence of potential DDIs (n = 1,199) Variables
Participants with potential DDIs (n = 1,125) Age in years [mean (SD)] P-value
Sex [mean (SD)] P-value
Age in years [n (%)] P-value
Sex [n (%)] P-value18-59 >=60MenWomen18-59 >=60MenWomen CYP2D6 (n= 488)0.006 (0.09)0.006 (0.01)0.5190.005 (0.08)0.006 (0.08)0.048CYP2D6 (n = 448)349 (0.54)99 (0.62)0.227118 (0.46)330 (0.59)0.018 CYP2C19 (n = 513)0.006 (0.08)0.009 (0.09)0.00020.006 (0.08)0.007 (0.08)0.428CYP2C19 (n=490)351 (0.54)139 (0.87)0.000002148 (0.58)342 (0.62)0.527 CYP2C9 (n= 198)0.003 (0.05)0.002 (0.05)0.1780.002 (0.04)0.003 (0.05)0.037CYP2C9 (n = 187)156 (0.24)31 (0.19)0.26447 (0.18)140 (0.25)0.060
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a better kappa value compared to females. Stratification by age indicated that people aged 60 years or older had a generally better kappa value than the younger population in CYP2D6/2C19/2C9 mediated potential DDIs (figure 4).
Discussion
In this cross-sectional study, CYP2D6/2C19/2C9-mediated potential DDIs were frequent and concordance of data varied by time window, type of medication, sex and age. We found that one to two per hundred drug users in the Lifelines cohort were exposed to a potential CYP2D6/2C19/2C9- mediated DDI at a short moment in life time. Some of these potential DDIs are regarded as clinically relevant DDIs such as metoprolol and CYP2D6 inhibitors combinations. The DDIs may lead to bradycardia, hypotension and atrioventricular block.20,29-32 Other relevant DDIs were the combination of CYP2C9 inhibitors, that consists of selective serotonin inhibitors (SSRIs), and nonsteroidal anti- inflammatory drugs (NSAIDs). The combination of SSRIs and NSAIDs was reported to increase risk of gastrointestinal bleedings.33,34 Yet, the interaction between SSRIs and NSAIDs might be not solely a pharmacokinetic interaction but also involves a pharmacodynamic interaction.35 Our findings on the burden of DDI might have potential clinical as well as economic implications. A DDI is one of the main contributors of an adverse drug reaction (ADR) which is one of the leading causes of hospitalisation and it can cost at minimum around €200 to €9,000 per hospitalisation.36
The influence of age and sex on the estimated burden was not consistent. CYP2D6 and CYP2C9 mediated potential DDIs were more frequent in females than males but their distributions were comparable among old and young population. In contrast, CYP2C19 mediated potential DDIs were more common in the old than young population, but their distribution was comparable between sex.
Figure 2. Proportion of potential DDIs based on the suggested managements provided by Epocrates® and Drugs.com.
11 10 7
65 43
93
24
47
0 20 40 60 80 100
CYP2D6 CYP2C19 CYP2C9
Percentage (%)
avoid combination/use alternative modify treatment/monitor caution
