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Abstract

Background

Direct-acting antivirals have improved treatment of chronic hepatitis C virus infection significantly. Direct-acting antivirals inhibit/induce and can also be substrates of drug-metabolising enzymes and transporters. This increases the risk for drug-drug interactions.

Objective

The purpose of this study was to predict drug-drug interactions with co-medication used by hepatitis C virus-infected patients.

Methods

We assembled a nationwide cohort of hepatitis C patients and collected cross-sectional data on co-medication use. We compiled a list of currently available direct-acting antiviral regimens and cross-checked for potential drug-drug interactions with used co-medication.

Results

The cohort included 461 patients of which 77% used co-medication. We identified 260 drugs used as co-medication. Antidepressants (7.4%), proton pump inhibitors (7.1%) and benzodiazepines (7.1%) were most frequently used. Of the patients, 60% were at risk for a clinically relevant drug-drug interaction with at least one of the direct-acting antiviral regimens. Interactions were most common with paritaprevir/ritonavir/ombitasvir/dasabuvir and least interactions were predicted with grazoprevir/elbasvir.

Conclusion

Co-medication use is rich in frequency and diversity in chronic hepatitis C patients. The majority of patients are at risk for drug-drug interactions which may affect efficacy or toxicity of direct-acting antivirals or co-medication. The most recently introduced direct-acting antivirals are associated with a lower risk of drug-drug interactions.

Keywords

Hepatitis C antiviral therapy pharmacokinetics drug-drug interactions NS5A-inhibitor NS5B-inhibitor Protease inhibitor

Introduction

Treatment of hepatitis C virus (HCV) infected patients has significantly improved with the introduction of direct-acting antivirals (DAAs). DAAs have the disadvantage that they can be involved in drug-drug interactions (DDIs) with patients’ co-medication. DDIs might increase the risk for toxicity or result in poorer efficacy.^1,2 The mechanism is twofold: DAAs can be both victim and/or perpetrator of DDIs. Drugs are victims of DDIs when their plasma concentration is affected by another drug. In contrast, drugs are perpetrators when they have the ability to influence plasma concentrations of drugs, for example by inhibiting or inducing metabolising enzymes and/or drug-transporters.^3,4 DAAs inhibit various cytochrome P450 (CYP) enzymes, responsible for drug metabolism (Table 1). The clinical importance of DDIs was illustrated by the interaction between sofosbuvir-based DAA therapy and amiodarone, resulting in severe bradycardia.^5,6 This report and other similar papers indicate that there is a genuine risk for relevant DDIs in patients treated with DAAs who use co-medication.^5–11

Table 1.

Overview of enzymes and drug transporters involved in the metabolism and transport of DAAs used for the treatment of hepatitis C.

		Perpetrator
Direct-acting antiviral	Victim (=substrate of)	Inhibitor	Inducer
Daclatasvir	CYP3A4/5, P-gp	P-gp, OATP1B1
Dasabuvir	CYP2C8, CYP3A4, P-gp, BCRP	UGT1A1, BCRP, P-gp
Elbasvir	CYP3A, P-gp
Grazoprevir	CYP3A, P-gp, OATP1B1/3	CYP3A (?)
Ledipasvir	Pg-p, BCRP	P-gp, BCRP
Ombitasvir	-	UGT1A1
Paritaprevir/ritonavir	CYP3A4/5, P-gp, OATP1B1/3, BCRP	CYP3A4/5, UGT1A1, CYP2D6(?),OATP1B1/3, OATP2B1, BCRP	CYP2C19
Simeprevir	CYP3A4/5	CYP3A4/5, CYP1A2, P-gp, OATP1B1/3
Sofosbuvir	P-gp, BCRP
Velpatasvir	P-gp, BCRP, CYP2B6, CYP2C8, CYP3A4	P-gp, BCRP, OATP1B1/2, OATP2B1

(?): Unknown of the inhibition/induction in clinically relevant; BCRP: breast cancer resistance protein; CYP: cytochrome P450; DAA: direct acting antiviral; OATP: organic anion-transporting polypeptide; P-gp: P-glycoprotein; UGT: uridine diphosphate glucuronosyltransferase.

The toxicity profiles of the currently used interferon-free DAA combinations, improved significantly relative to the DAAs combined with peginterferon and ribavirin. Nowadays, more HCV patients with complex co-morbidities and thus co-medication receive antiviral treatment.¹² The combination of DAAs and many other drugs obviously increases the risk for DDIs. To date, limited data is available about the extent of co-medication use by HCV patients and the risk of DDIs as a consequence. Therefore, the aim of this study is to identify co-medication use in a nationwide real-life HCV cohort in order to predict clinically relevant DDIs between co-medication and new DAA regimens.

Methods

We performed this research in three steps: (a) we identified which co-medication were used by HCV-infected patients in a real-world cohort; (b) in order to predict DDIs we cross-checked the co-medication with DAAs in the database of the University of Liverpool (www.hep-druginteractions.org); and (c) we assessed the risk for DDIs per patient. For this type of study (retrospective) formal consent was not required. Formal evaluation was waived by the Institutional Review Board, Arnhem-Nijmegen. Good clinical practice guidelines and the code of conduct for the use of data in health research were followed (www.federa.org).

Patients and use of co-medication

Data from a nationwide, real-life cohort were used.¹³ This cohort included Dutch patients treated for a HCV genotype 1 mono-infection. Patients were identified based on local databases present in 45 hepatitis treatment centres in the Netherlands. Data collection was performed between January 2014–July 2015. Baseline data were extracted from the patient’s medical record and included patient characteristics, medical history, HCV genotype and co-medication use prior to commencement of HCV treatment. Patients were excluded when data on co-medication use was missing and if patients had a co-infection with HIV or hepatitis B virus. In addition to prescribed medication, we included complementary and alternative medicine (CAM) when available in the medical record. Separate compounds of fixed-dose products were registered, except for CAMs, these were counted as one, even though they may have contained several chemical compounds. We did include drugs taken as part of a substance abuse disorder (e.g. methadone), although illicit drugs such as heroin or cocaine were not collected. We added Anatomical Therapeutic Chemical (ATC) codes to all co-medication reported in the patient’s medical record, and grouped the drugs by therapeutic/pharmacological subgroups (http://www.whocc.no/).

Predicted DDIs with DAAs

The co-medication was cross-checked with currently approved DAA regimens in Europe and USA through the University of Liverpool database in an effort to predict DDIs (July 2016). The University of Liverpool database is a commonly used resource to check for DDIs.^4,14 For cross-checking we included approved DAA regimens effective against HCV genotype 1: sofosbuvir plus simeprevir, sofosbuvir plus daclatasvir, sofosbuvir plus ledipasvir, paritaprevir/ritonavir, ombitasvir plus dasabuvir, elbasvir plus grazoprevir, and sofosbuvir plus velpatasvir. Ribavirin and first-generation protease inhibitors were not taken into account. Ribavirin is considered not to cause any DDIs in this population as it is not metabolized by or influencing any of the drug metabolizing enzymes and the included patients do not use nucleoside reverse transcriptase inhibitors (NRTIs).¹⁵ The first generation DAAs are considered outdated. We used four risk categories corresponding with the University of Liverpool database (http://www.hep-druginteractions.org):

Category 1. No clinically significant interaction;

Category 2. Potential interaction - may require close monitoring, alternation of drug dosage or timing of administration;

Category 3. Contraindication, i.e. drugs should not be co-administered;

Category 4. Unknown, as not available in the University of Liverpool database. For these unavailable drugs, the pharmacists (ES and DB) judged if there might be risk of a DDI. Pharmacokinetic parameters of these drugs were used (based on US Food and Drug Administration (FDA) prescribing information and MicroMedex) to evaluate these interactions.

Overall, we defined Categories 2 and 3 as the clinically relevant DDIs.¹⁶

Risk for DDIs per patient

To assess the number of patients at risk for a clinically relevant DDI, we counted the patients with at least one predicted DDI between co-medication and one of the DAA regimens. Further, we compared the risk for DDI between subgroups of patients: (a) patients aged <65 years vs. ≥65 years,¹⁶ and (b) in patients with vs. without cirrhosis. We used FIB-4 index >3.25 to classify patients as cirrhotic.¹⁷

Analyses

Descriptive analyses were performed with frequency counts and proportions. For the subgroup analyses we used chi-square tests. All analyses were performed in SPSS (IBM SPSS Statistics 20).

Results

Patients and use of co-medication

This cohort included 467 patients; we excluded six patients from the analysis because data on co-medication were missing. There were 313 males and the mean age was 51 years (Table 2). A total of 356 patients (77%) used co-medication at start of HCV therapy and 105 patients did not use any co-medication. The number of medications per patient ranged from 1–17 (median 2). Of the cohort, 12% used ≥6 medications at the start of HCV therapy. Overall, the 356 patients had a total number of 1329 prescriptions (including CAMs), which comprised 260 different drugs (Figure 1). Most frequently used co-medications were antidepressants, proton pump inhibitors (PPIs), benzodiazepine derivatives and drugs for opioid dependence (Table 3).

Table 2.

Patient characteristics.

Characteristic	Overall (n = 461)
Age – mean (range), years	51 (19–77)
Age ≥65 years – n (%)	30 (7)
Male sex – n (%)	313 (68)
Caucasian – n (%)^a	316 (90)
Treatment naive – n (%)^b	269 (58)
Decompensated liver disease – n (%)	23 (5)
FIB-4 index>3.25 (cirrhosis)^c – n (%)	115 (26)
Creatinine clearance <30 ml/min – n (%)^d	3 (1)

Race: available in 352 patients; ^bprevious response: available in 448 patients; ^cFIB-4 index¹⁷: available in 437 patients; ^dcreatinine clearance: available in 407 patients.

Figure 1.

Overview of concomitant medication and predicted number of drug-drug interactions (DDIs) between the direct-acting antiviral regimens and 260 different compounds.

Table 3.

Most frequently used (>2.0%) concomitant medications at start of hepatitis C treatment.

Drug class	ATC code (4^th level)	n (%)^a
Antidepressants (both tricyclic antidepressants and selective serotonin reuptake inhibitors e.g. amitriptyline, sertraline)	N06AA, N06AB, N06AX	98 (7.4)
Proton pump inhibitors (e.g. omeprazole)	A02BC	94 (7.1)
Benzodiazepine derivatives (e.g. diazepam, flurazepam)	N05BA, N05CD	94 (7.1)
Drugs used in opioid dependence (e.g. methadone)	N07BC	74 (5.6)
Selective beta-2-adrenoreceptor agonists (respiratory agents both systemic and inhalants e.g. salbutamol)	R03AC, R03CC	55 (4.2)
Antipsychotics (e.g. olanzapine, risperidon)	N05AA, N05AB, N05AD, N05AF, N05AH, N05AL, N05AN, N05AX	46 (3.5)
Vitamin D and analogues (e.g. colecalciferol)	A11CC	38 (2.9)
Thiazides (e.g. hydrochlorothiazide)	C03AA	34 (2.6)
Selective beta-blocking agents (e.g. metoprolol)	C07AB	32 (2.4)
ACE inhibitors (e.g. enalapril)	C09AA	32 (2.4)
Glucocorticoids (respiratory system e.g. beclometasone)	R03BA	32 (2.4)
Biguanides (e.g. metformin)	A10BA	27 (2.0)
Platelet aggregation inhibitors excluding heparin (e.g. acetylsalicylic acid)	B01AC	26 (2.0)
Dihydropyridine derivatives (calcium-channel blockers e.g. amlodipine)	C08CA	26 (2.0)

ACE: angiotensin-converting enzyme; ATC: Anatomical Therapeutic Chemical.

Percentage is calculated using the total number of prescriptions in this cohort (n = 1329).

Predicted DDIs with DAAs

We used our cohort to predict DDIs between co-medication and DAA regimens. Figure 1 presents the distribution of the DDI categories per DAA regimen for 260 different drugs. The combination of grazoprevir plus elbasvir and sofosbuvir plus velpatasvir had the lowest number of predicted DDIs in our mono-infected cohort. Grazoprevir plus elbasvir and sofosbuvir plus daclatasvir had no contraindicated drugs (Category 3) and no clinical significant interactions were predicted with 72% and 63%, respectively, of the concomitantly used drugs (Category 1).

The combination of paritaprevir/ritonavir, ombitasvir plus dasabuvir had the most contraindications (4%), followed by simeprevir (2%) and velpatasvir (1%). Category 2 interactions were also mainly predicted with the regimen containing paritaprevir/ritonavir, ombitasvir plus dasabuvir (33%) and sofosbuvir plus simeprevir (26%). Interestingly, ∼90% of these Category 2 DDIs have not been studied in vivo. These potential interactions were predicted by the pharmacologist of the University of Liverpool database, based on the characteristics of the drugs. The top five medications which can cause clinically relevant DDIs with at least one of the antiviral regimens are shown in Table 4.

Table 4.

Top five concomitant medication causing clinically relevant interactions with at least one of the antiviral regimens.

	Drug class	ATC code	n
DDI Category 2	1. Benzodiazepines	N05BA	61
	2. Antidepressants	N06A	43
	3. Proton pump inhibitors (omeprazole)	A02BC	42
	4. Glucocorticoids respiratory system	R03BA	30
	5. Selective beta-blocking agents	C07AB	29
DDI Category 3	1. Proton pump inhibitors (esomeprazole, pantoprazole)	A02BC	52
	2. HMG CoA reductase inhibitors (statins)	C10AA	19
	3. Antipsychotics	N05A	13
	4. Selective beta-2-adrenoreceptor agonists respiratory system	R03AC/R03CC	12
	5. CAM	no ATC	2

ATC: Anatomical Therapeutic Chemical; CAM: complementary and alternative medicine; DDI: drug-drug interaction; HMG CoA: 3-hydroxy-3-methyl-glutaryl-coenzyme A reductase.

The risk of DDIs could not be assessed in 60 of the 260 different drugs (Category 4), because the drugs were not listed in the University of Liverpool database (July 2016). The top three of the therapeutic subgroup (2nd ATC level) in Category 4 were antihaemorrhagics (B02; e.g. coagulation factors), vitamins (A11; e.g. colecalciferol), and psycholeptics (N05; e.g. flunitrazepam) which were used by a total of 17, 33 and 13 patients, respectively.

The pharmacists (ES and DB) judged if there were potential interactions with these 60 drugs and DAAs. We predicted that 11 drugs had a potential interaction, 30 drugs would not cause interactions, and for 19 drugs it is unknown if there is a potential interaction (for example: metabolism not known of the co-medication), data is shown in Table 5.

Table 5.

Predicted drug-drug interactions and recommendations of drugs not available in the University of Liverpool database.

Generic name	ATC	Metabolism	Proposed interaction mechanism	Recommendation	References
Barnidipine	C08CA12	CYP3A4 substrate	CYP3A4 inhibition by grazoprevir (?), paritaprevir/ritonavir, simeprevir.	Barnidipine levels may increase, monitor for adverse reactions or monitor blood pressure.	^a
Calcitriol	A11CC04	CYP3A4 substrate	CYP3A4 inhibition by grazoprevir (?), paritaprevir/ritonavir, simeprevir.	Calcitriol levels may increase, monitor for adverse reactions.	^b
Chlordiazepoxide	N05BA02	CYP3A4 substrate	CYP3A4 inhibition by grazoprevir (?), paritaprevir/ritonavir, simeprevir.	Chlordiazepoxide levels may increase, monitor adverse reactions.	^b
Deferasirox	V03AC03	UGT1A1 and UGT1A3 substrate (CYP450-catalysed metabolism: minor (8%). MRP2, BCRP substrate CYP3A4, CYP2C8, CYP1A2 inhibitor	UGT1A1 inhibition by ombitasvir, paritaprevir/ritonavir. BCRP inhibition by ledipasvir, paritaprevir/ritonavir, velpatasvir. Daclatasvir, dasabuvir, elbasvir, grazoprevir, paritaprevir/ritonavir, simeprevir, velpatasvir are substrate of CYP3A4. Dasabuvir is a substrate of CYP2C8.	Adverse reactions of deferasirox are dose-dependent and frequently reported, therefore we advise to minimise the use of deferasirox during DAA therapy as increased plasma levels of deferasirox might occur. Deferasirox inhibits CYP3A4 and CYP2C8 which might increase DAA levels, monitor adverse events.	^c
Phenprocoumon	B01AA04	CYP2C9, CYP2C19, CYP1A2, and CYP3A4 substrate	CYP2C19 inhibition by paritaprevir/ritonavir. CYP1A2 inhibition by simeprevir. CYP3A4 inhibition by grazoprevir (?), paritaprevir/ritonavir, simeprevir.	Inhibition of the various CYP enzymes may increase the phenprocoumon concentration, therefore monitor INR more frequently.	^b
Isosorbide mononitrate	C01DA14	CYP3A4 substrate	CYP3A4 inhibition by grazoprevir (?), paritaprevir/ritonavir, simeprevir.	Isosorbide mononitrate levels may increase, monitor for adverse reactions.	^b
Levonorgestrel	G02BA03	CYP3A4 substrate	CYP3A4 inhibition by grazoprevir (?), paritaprevir/ritonavir, simeprevir.	Levonorgestrel levels may increase, monitor for adverse reactions.	^b
Medroxyprogesteron	G03AC06	CYP3A4 substrate	CYP3A4 inhibition by grazoprevir (?), paritaprevir/ritonavir, simeprevir.	Medroxyprogesteron levels may increase, monitor for adverse reactions.	^b
Misoprostol	A02BB01	Unknown	Pharmacodynamic interaction with ledipasvir. Misoprostol decreases gastrointestinal pH.	Based on studies with antacids: separate misoprostol and ledipasvir/sofosbuvir intake by 4 h.	^b
Oral contraceptives	G03A	Dependent on compounds. CYP3A4 metabolises e.g. levonorgestrel and cyproteron	CYP3A4 inhibition by grazoprevir (?), paritaprevir/ritonavir, simeprevir.	Dependent on the oral contraceptive agent there may be an interaction. Due to CYP inhibition of the DAAs the plasma levels of the contraceptive agent may increase with risk on toxicity. Switch to non-hormonal contraception during HCV therapy.	^b
Solifenacin	G04BD08	CYP3A4 substrate	CYP3A4 inhibition by grazoprevir (?), paritaprevir/ritonavir, simeprevir.	Adverse reactions of solifenacin are dose dependent and solifenacin levels may increase when combined with CYP3A4 inhibitors. Adverse reactions are mostly anticholinergic. Preferably do not use during therapy with CYP3A4 inhibitors.	^b

(?): Unknown whether the interaction is clinically relevant; ATC: Anatomical Therapeutic Chemical; BCRP: breast cancer resistance protein; CYP: cytochrome P450; DAA: direct-acting antiviral; HCV: hepatitis C virus; INR: international normalised ratio; MRP2: multidrug resistance-associated protein 2; UGT: uridine diphosphate glucuronosyltransferase.

Astellas. Cyress: Samenvatting van de productkenmerken, http://www.astellas.nl/sites/default/files/SmPC%20Cyress%2010mg.pdf.

The product label and other sources such as Micromedex Solutions and Lexicomp database (available via www.uptodate.com) were used to determine the pharmacokinetic profile and metabolism of the drugs. This information is used to predict theoretical interactions between the concomitant medication and DAAs (expert opinion ES and DB).

European Medicines Agency. Exjade: Summary of product characteristics, http://www.ema.europa.eu/docs/en_GB/document_library/EPAR_-_Product_Information/human/000670/WC500033925.pdf.

Risk for DDIs per patient

The majority of the patients in our cohort (60%) was at risk for a clinically relevant DDI with at least one of the DAA regimens: 93 patients (20%) used a drug that would be contraindicated (Category 3) and 184 patients (40%) had co-medication leading to a possible interaction (Category 2), which would require close monitoring, alternation of drug dosage or timing of administration. Figure 2 shows the risk of a DDI per DAA regimen per patient. The risk for DDIs per patient did not differ in patients aged below or above 65 years (60% vs. 67%, p = 0.45), nor between patients without cirrhosis and with cirrhosis (60% vs. 64%, p = 0.50).

Figure 2.

Risk of a clinically relevant drug-drug interaction (DDI) per patient (n = 461), grouped per direct-acting antiviral regimen. DCV: daclatasvir; DSV: dasabuvir; EBR: elbasvir; GZR: grazoprevir; LDV: ledipasvir; OBV: ombitasvir; PTV: paritaprevir; r: ritonavir; SIM: simeprevir; SOF: sofosbuvir; VEL: velpatasvir.

Discussion

In this nationwide, real-life cohort study, we show that the majority of HCV-infected patients is at risk for having a clinically relevant DDI with new DAAs. This can have a negative influence on treatment outcomes and could potentially harm the patient.¹ In this cohort, patients with cirrhosis or ≥65 years old did not have a higher risk for a DDI when compared with patients <65 years old or without cirrhosis. This contrasts with a recently published study¹⁶and might be explained due to low number of elderly patients in our cohort and the lower mean age of patients ≥65 years (68 years, standard deviation (SD) 3). This shows that not only the elderly are at risk for a DDI. The psycho-active agents such as antidepressants (7.4%) and benzodiazepines (7.1%) were the most frequently used drugs in our cohort, as well as in the literature.³ This is relevant because these drugs increase the risk for DDIs: antidepressants and benzodiazepines are extensively metabolised through CYP enzymes, which can be inhibited by DAAs.^18,19 This causes increased plasma concentrations of psycho-active agents increasing the likelihood of toxicity.

PPIs were also responsible for many clinically relevant DDIs in our cohort, both as victim and perpetrator.¹⁴ Omeprazole is a victim of paritaprevir/ritonavir, ombitasvir plus dasabuvir due to CYP2C19 induction of ritonavir, decreasing omeprazole exposure by 40–50%.²⁰ In contrast, PPIs are the perpetrators of a DDI with ledipasvir and velpatasvir. PPIs increase gastric pH, which decreases exposure to DAAs due to its insolubility at higher pH ranges.^21,22 The clinical relevance for DDIs between PPIs and ledipasvir is under debate.^23,24 For velpatasvir, the product label states that co-administration of omeprazole or other PPIs is not recommended, and that esomeprazole and pantoprazole are contraindicated.²²

Most frequently predicted DDIs were found with paritaprevir/ritonavir, ombitasvir plus dasabuvir, which fits with data from the published literature.¹¹ These interactions are predominantly caused by ritonavir, which strongly inhibits the most important drug-metabolising enzyme (CYP3A4) and various other enzymes and drug-transporters are influenced (e.g. CYP2D6, P-glycoprotein (P-gp)).^25,26 The fewest interactions were seen with the newest regimens: sofosbuvir plus velpatasvir and elbasvir plus grazoprevir. Elbasvir and grazoprevir are substrates of P-gp and CYP3A and only strong CYP3A inhibitors or inducers lead to clinically relevant DDIs. Grazoprevir is also a (weak) CYP3A inhibitor, but no DDIs between this combination and CYP3A substrates are listed.²⁷ However, we recommend caution when combining elbasvir and grazoprevir with CYP3A substrates with a narrow therapeutic range, such as tacrolimus.²⁸

The contraindicated drugs count for up to 4% of the predicted interactions. This is a very clear signal to the physician: do not combine the co-medication with this DAA regimen. The dilemma is mostly present in the drugs in Categories 2 and 4. In our study, ∼90% of Category 2 DDIs have not been studied, but were predicted by the University of Liverpool group. However, some DDIs cannot be predicted on theoretical grounds but do occur in clinical practice. For example, the unexpected severe bradycardia that occurred in nine patients who were on amiodarone treatment and received a sofosbuvir-containing regimen. The mechanism of this DDI and the role of sofosbuvir is still unclear.^6,29–31 Further, 23% (n = 60) of drugs used by patients from our cohort were not listed in the University of Liverpool database (Category 4). We judged that 11 of these drugs might cause an enzymatic interaction with currently used DAAs. Prescribers should be aware that when the drug is not mentioned in the database, it does not mean there is no interaction.

A strength of this study is that it is a nationwide multicentre cohort with a large number of patients. This cohort provides a representative overview of co-medication use in the Dutch HCV genotype 1 population with a treatment indication. Genotype 1 is globally the main genotype (60%) and we expect that the patients of the cohort reflect the patients who will be subjected to therapy.^32,33 Further, we provide a risk assessment for drugs not available in the University of Liverpool database. Limitations of our study are the retrospective design and that our study describes predicted DDIs and not observed DDIs. Finally, the research question that led to this study was not the primary objective of data collection.

In conclusion, co-medication use is rich in both frequency and diversity in chronic HCV-infected patients. DDIs may result in subtherapeutic or increased drug concentrations of DAAs or co-medication, and can cause treatment failure or toxicity. Physicians should be aware that the majority of patients are at risk for clinically relevant DDIs. In that case, co-medication can be adjusted prior to DAA therapy or DAA treatment can be aligned with co-medication use.

Footnotes

Acknowledgements

Elise J Smolders and Floor AC Berden designed the study,performed research,analysed data and wrote the paper. Clara TMM de Kanter critically revised the manuscript for important intellectual content. Wietske Kievit critically revised the manuscript for important intellectual content. Joost PH Drenth and David M Burger supervised the study,analysed data,and critically revised the manuscript for important intellectual content. The authors would like to thank all the hepatitis treatment centres in the Netherlands for their participation and cooperation in this study. The authors also thank Wolfram Huizinga for his contribution to this study. The authors thank the following physicians,nurses and participating centres for their contribution and collaboration in this study: J Vrolijk and C Richter,Rijnstate Hospital,Arnhem;J Kamphuis and B Blank,Maxima Medical Centre Eindhoven;T Römkens and M van Ijzendoorn,Jeroen Bosch Hospital,‘s Hertogenbosch;T Dofferhoff and K Kok,Canisius Wilhelmina Hospital,Nijmegen;I Gisbertz,Hospital Bernhoven,Uden;M Verhagen,Diakonesse Hospital,Utrecht;R Adang,VieCurie Medical Center,Venlo;M Klemt-Kropp,Medical Center Alkmaar,Alkmaar;A Vrij,ZGT Twente,Almelo;H Telleman,Flevoziekenhuis,Almere;P Friederich,Meander Medical Centre,Amersfoort;B Baak,Onze Lieve Vrouwe Gasthuis,Amsterdam;A Depla,Slotervaart Hospital,Amsterdam;W Erkelens,Gelre Hospital,Apeldoorn;M Wagtmans,Rode Kruis Hospital,Beverwijk;P van Wijngaarden,Amphia Hospital,Breda;J Brouwer,Reinier de Graaf Gasthuis,Delft;H van Soest,Medical Center Haaglanden,Den Haag;F ter Borg,Deventer Hospital,Deventer;P Honkoop,Albert Schweitzer Hospital,Dordrecht;M Kerbert-Dreteler,Medisch Spectrum Twente,Enschede;J Kuyvenhoven,Kennemer Gasthuis,Haarlem;C Bakker,Atrium Medical Centre Heerlen,Heerlen;J Tjhie-Wensing,Elkerliek Hospital,Helmond;R Lieverse,Bethesda Hospital,Hoogeveen;S Abraham,Rijnland Hospital and Diaconessenhuis,Leiderdorp;G Koek,Maastricht University Medical Center,Maastricht;P Stadhouders,St Antonius Hospital,Nieuwegein;P Bus,Laurentius Hospital,Roermond;L Berk,St Franciscus Gasthuis,Rotterdam;J Otte,Hospital Zeeuws Vlaanderen,Terneuzen;M van Kasteren,St Elisabeth Hospital,Tilburg;M van den Berge,Admiraal de Ruyter Hospital,Vlissingen;P Groeneveld,Isala Klinieken,Zwolle.

Conflict of interests

EJ Smolders,FAC Berden,CTMM de Kanter,and W Kievit declare no conflicts of interest that are directly relevant to the content of this article. JPH Drenth joins advisory boards of Abbvie,BMS,Gilead,Janssen and Merck and received sponsorship/research grants from Abbvie and Janssen. DM Burger joins advisory boards of Abbvie,BMS,Gilead,Janssen,Merck and ViiV,and received sponsorship/research grants from BMS,Janssen,Merck,and ViiV. However,these conflicts of interest did not influence the preparation of this article.

Funding

No funding was received for this study.

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The majority of hepatitis C patients treated with direct acting antivirals are at risk for relevant drug-drug interactions

Abstract

Background

Objective

Methods

Results

Conclusion

Keywords

Introduction

Methods

Patients and use of co-medication

Predicted DDIs with DAAs

Risk for DDIs per patient

Analyses

Results

Patients and use of co-medication

Predicted DDIs with DAAs

Risk for DDIs per patient

Discussion

Footnotes

Acknowledgements

Conflict of interests

Funding

References