Potential interactions between pangenotypic direct-acting antivirals and concomitant cardiovascular therapies in patients with chronic hepatitis C virus infection

Abstract

Objective

To identify potential drug interactions (DIs) between pangenotypic direct-acting antivirals (pDAAs) and concomitant cardiovascular (CV) therapies in patients with chronic hepatitis C (CHC).

Methods

A retrospective observational study was carried out. Patients ≥18 years of age diagnosed with CHC and treated with pDAAs during 2017 were included. Information was collected on concomitant CV therapies and potential DIs [www.hep-druginteractions.org]. The pDAAs analyzed were sofosbuvir/velpatasvir (SOF/VEL), glecaprevir/pibrentasvir (GLE/PIB) and sofosbuvir/velpatasvir/voxilaprevir (SOF/VEL/VOX). An analysis including lipid-lowering drugs was also performed.

Results

In total, 1286 patients (mean age 64.9 years, 56.6% men) were recruited. The percentages of potential DIs with CV drugs were 1.9% contraindications, 38.1% clinically significant and 2.4% weak. When lipid-lowering drugs were included, the percentages of potential DIs with CV drugs were 10.3% contraindications, 46.3% clinically significant and 3.2% weak. Potential DIs associated with each pDAA were as follows (contraindications; clinically significant; weak): SOF/VEL (1.4%; 23.0%; 0.9%), GLE/PIB (12.8%; 60.8%; 4.7%) and SOF/VEL/VOX (16.6%; 55.1%; 4.9%).

Conclusions

Approximately on third of patients with CHC are concomitantly treated with CV drugs. SOF/VEL may have fewer DIs with CV drugs than other pDAAs.

Keywords

Chronic hepatitis C cardiovascular drugs drug interactions pangenotypic direct-acting antivirals lipid-lowering drugs retrospective study

Introduction

Chronic hepatitis C virus (HCV) infection is a worldwide public health problem with an estimated prevalence of 1% to 2% among the general population.^1–4 Research into viral replication mechanisms has identified potential therapeutic targets.⁵ The development of new direct-acting antivirals (DAAs) against HCV was a great step forward in the treatment of this infection. The aim of these drugs is to achieve greater efficacy and safety, with fewer side effects, including potential drug interactions (DIs). Many DIs arise from drug metabolism mediated by cytochrome P450 (isoenzyme CYP3A) and/or P-glycoprotein transport.^6,7

For several years, treatment regimens were used according to the HCV genotype in each individual patient. However, current DAAs are pangenotypic (pDAAs) and are effective against all HCV genotypes.⁸ A single DAA alone cannot prevent HCV replication and mutation. For this reason, treatment should comprise 2 to 3 drugs from different inhibitor classes.⁹

In general, patients receiving pDAAs are older and have increased risks of comorbidities. Thus, they may be receiving several drugs, which can result in adverse effects arising from DIs.¹⁰ In addition, DIs with therapies used to treat comorbidities may contraindicate the use of pDAAs.^10–12 A recent study conducted in Spain reported high rates of comorbidities and concomitant medication rates in patients with HCV. The most commonly prescribed therapies with potential DIs were those related to the cardiovascular (CV) system and the central nervous system.¹³

Careful review of the medications used by patients with HCV is therefore advisable.²,^10–13 Little information is currently available on the true risks of DAAs based on patterns of concomitant medication use at the population level. The objective of our study was thus to identify DIs between pDAAs and concomitant CV therapies in patients with HCV in standard clinical practice in Spain.

Patients and methods

An observational, retrospective study based on review of medical records (from electronic databases) was carried out. The study population was obtained from the records of healthcare providers corresponding to different Spanish centers (unified in the BIG-PAC anonymized database).¹⁴ Data were derived from electronic medical records and other supplementary databases from seven Spanish Autonomous Communities (1.8 million patients). Before exporting from the BIG-PAC database, all electronic records were rigorously anonymized by the centers/hospitals of origin in compliance with Organic Law 3/2018 on the Protection of Personal Data and Guarantee of Digital Rights. Patients ≥18 years of age diagnosed with HCV infection (ICD-10-MC [B18.2]) and treated with pDAAs during 2017 were consecutively recruited. The patients were required to meet the following criteria: (i) age ≥18 years; (ii) diagnosis of HCV infection at least 12 months prior to the start of the study (patients active in the database); (iii) enrolment in the chronic medication program for obtaining medical prescriptions (with a documented record of the daily dose and duration of each treatment and ≥2 prescriptions over the follow-up period for any drug administered); and (iv) regular monitoring throughout the study (≥2 healthcare registries in the electronic system). Patients who transferred to other sites, as well as those who moved outside the study area, were excluded.

The study variables included demographic data, body mass index (BMI, kg/m²), HCV duration, personal history (detailed in Table 1) and comorbidities (ICD-10-MC). The Charlson comorbidity index was used as a summary variable of general comorbidity¹⁵ to assess severity. Among patients with HCV infection, we identified those receiving concomitant treatment with one of the following CV drug classes: anticoagulants, cardiac therapy, antihypertensive agents, diuretics, beta-blockers, calcium channel blockers, and agents acting on the renin–angiotensin system.¹³ Treatment description was based on the Anatomical Therapeutic Chemical Classification System.¹⁶ Patient assignment to a concrete treatment regimen was determined according to standard clinical practice. The pDAAs selected were sofosbuvir/velpatasvir (SOF/VEL), glecaprevir/pibrentasvir (GLE/PIB) and sofosbuvir/velpatasvir/voxilaprevir (SOF/VEL/VOX). Concomitant medications were analyzed during the antiviral treatment period, and only chronic or regular medications administered to patients were assessed. An additional analysis was performed including lipid-lowering medications. Contraindicated drugs and major interactions were described.

Table 1.

General characteristics of study patients.

Patients receiving CV medication	n = 1286
Mean age, years	64.9 (12.5)
Gender (male)	55.6%
Mean Charlson index	1.6 (1.1)
Specific comorbidities
Arterial hypertension	68.1%
Diabetes mellitus	29.8%
Dyslipidemia	41.6%
Obesity	17.6%
Active smokers	11.2%
Alcoholism	2.9%
Ischemic heart disease	11.2%
Stroke	8.1%
Heart failure	6.8%
Renal failure	7.3%
Bronchial asthma	6.3%
COPD	15.4%
Neuropathies	3.5%
Dementia (all types)	7.6%
Depressive syndrome	12.7%
Anxiety disorder	36.5%
Organic psychosis	3.4%
HIV/AIDS	1.7%
Addictions	5.7%
Osteoporosis	14.1%
Cirrhosis	3.3%
Other characteristics
Time since diagnosis, years	23.5 (4.2)
BMI, kg/m²	28.1 (5.3)
Genotype
G1a	28.2%
G1b	31.2%
G2	4.3%
G3	14.2%
G4	8.5%
G5 or G6	3.5%
Unknown	10.1%

Values reported as mean (standard deviation) or percentages.

CV, cardiovascular; BMI, body mass index; COPD, chronic obstructive pulmonary disease; HIV/AIDS, human immunodeficiency virus/acquired immunodeficiency syndrome.

To identify potential DIs, the guidelines of the University of Liverpool¹⁷ were followed. This resource is recommended by the European Association for the Study of the Liver.² Potential DIs were classified as contraindications, clinically significant, or weak. In addition, we identified the main indications for prescription of drugs such as enalapril, atorvastatin, amiodarone and digoxin.

Data were anonymized and all analyses were confidential, in accordance with Spanish legislation on personal data protection. The study was classified by the Spanish Agency for Medicines and Health Products (EPA-OD) and was subsequently approved by the Clinical Research Ethics Committee of the Unió Catalana Balears de Hospitals de Barcelona, Spain (reference: 15/49). According to Spanish legislation, retrospective studies of secondary data do not require written informed consent of the patient. Data were validated to ensure quality and consistency of the records. Descriptive univariate statistical analyses were performed. The SPSSWIN version 23 statistical package was used for all analyses. Values of p < 0.05 were considered statistically significant.

Results

We identified 1286 HCV patients (37.5%) receiving chronic CV medications (Figure 1). Table 1 shows the general characteristics of the patients. Patients were 64.9 years old on average (standard deviation [SD]: 12.5 years) and 56.6% were men. Their mean BMI was 28.1 kg/m², and the mean Charlson index was 1.6 (SD: 1.1). The most common comorbidities were arterial hypertension (68.1%), dyslipidemia (41.6%), anxiety disorders (36.5%) and diabetes (29.8%). The mean number of medications received was 3.6 (SD: 2.5) per patient per year.

Figure 1.

Study flow chart.

The drugs administered for CV therapy (excluding lipid-lowering drugs) showing potential DIs with pDAAs included enalapril (n = 365), amlodipine (n = 150), acenocoumarol (n = 77), olmesartan (n = 73) and valsartan (n = 60) (Table 2). The percentages of potential DIs associated with CV drugs were 1.9% contraindications, 38.1% clinically significant and 2.4% weak. The percentages of DIs associated with each class of pDAAs were as follows (contraindications; clinically significant; weak): SOF/VEL (1.4%; 8.1%; 0.9%), GLE/PIB (0.2%; 55.3%; 4.7%) and SOF/VEL/VOX (1.6%; 53.0%; 4.0%) (Figure 2a).

Table 2.

Potential drug interactions of pangenotypic direct-acting antivirals (pDAAs).

pDAA	n	CVS+LL (%)	CV drug	Magnitude
SOF/VEL	18	1.4%	Amiodarone	Contraindicated
	85	6.6%	Atorvastatin	Significant
	77	6.0%	Simvastatin
	38	3.0%	Carvedilol
	25	1.9%	Diltiazem
	19	1.5%	Rosuvastatin
	13	1.0%	Digoxin
	11	0.9%	Pitavastatin
	28	2.2%	Others
	12	0.9%	colestyramine	Weak

GLE/PIB	85	6.6%	Atorvastatin	Contraindicated
	77	6.0%	Simvastatin
	3	0.2%	Dabigatran
	365	28.4%	Enalapril	Significant
	77	6.0%	Acenocoumarol
	73	5.7%	Olmesartan
	39	3.0%	Irbesartan
	38	3.0%	Carvedilol
	26	2.0%	Telmisartan
	25	1.9%	Diltiazem
	19	1.5%	Rosuvastatin
	18	1.4%	Amiodarone
	15	1.2%	Gemfibrozil
	15	1.2%	Pravastatin
	13	1.0%	Digoxin
	11	0.9%	Ezetimibe
	11	0.9%	Pitavastatin
	37	2.9%	Others
	49	3.8%	Candesartan	Weak
	12	0.9%	Cholestyramine	Weak

SOF/VEL/VOX	85	6.6%	Atorvastatin	Contraindicated
	77	6.0%	Simvastatin
	19	1.5%	Rosuvastatin
	18	1.4%	Amiodarone
	11	0.9%	Pitavastatin
	3	0.2%	Dabigatran
	365	28.4%	Enalapril	Significant
	73	5.7%	Olmesartan
	60	4.7%	Valsartan
	49	3.8%	Candesartan
	38	3.0%	Carvedilol
	26	2.0%	Telmisartan
	25	1.9%	Diltiazem
	15	1.2%	Pravastatin
	13	1.0%	Digoxin
	11	0.9%	Ezetimibe
	33	2.6%	Others
	39	3.0%	Irbesartan	Weak
	12	0.9%	Cholestyramine	Weak

Values reported as percentage of patients.

pDAAs, pangenotypic direct-acting antivirals; CVS, cardiovascular system; SOF/VEL, sofosbuvir/velpatasvir; GLE/PIB, glecaprevir/pibrentasvir; SOF/VEL/VOX, sofosbuvir/velpatasvir/voxilaprevir; LL, lipid-lowering drugs.

Figure 2.

Potential cardiovascular system drug interactions according to pangenotypic direct-acting antiviral (pDAA) regimen. (a) Not including lipid-lowering agents and (b) Including lipid-lowering agents.

Inclusion of lipid-lowering drugs (particularly atorvastatin and simvastatin) in the analysis yielded the following percentages of potential DIs: 10.3% contraindications, 46.3% clinically significant and 3.2% weak. The percentages of DIs associated with each class of pDAAs were as follows (contraindications; clinically significant; weak): SOF/VEL (1.4%; 23.0%; 0.9%), GLE/PIB (12.8%; 60.8%; 4.7%) and SOF/VEL/VOX (16.6%; 55.1%; 4.9%) (Table 2 and Figure 2b).

The reasons for prescribing CV drugs were as follows: (i) enalapril (n = 365): arterial hypertension (n = 195, 53%), heart failure (n = 95, 26%) and renal failure (n = 75, 21%); (ii) atorvastatin (n = 85): secondary prevention (n = 52, 61%) and dyslipidemia (n = 33, 39%); (iii) amiodarone (n = 18): tachycardia (n = 8, 44%) and ventricular fibrillation (n = 10, 56%); and (iv) digoxin (n = 13): heart failure with atrial fibrillation (n = 13, 100%).

Atorvastatin was the drug with the highest potential for DIs (contraindicated: n = 85, 6.6%). During the administration of pDAAs, reasonable alternatives determined by physicians included the following: (i) discontinuation of atorvastatin (n = 14, 16.5%); (ii) dose reduction (n = 33, 38.8%); and (iii) substitution with another drug (n = 38, 44.7%).

Discussion

The present study showed that more than one third of patients with HCV infection received concomitant medications acting upon the CV system. Statins such as atorvastatin and simvastatin had increased risks of potential DIs. SOF/VEL had a lower risk of DIs than other pDAAs.

Patients with HCV infection tend to have many comorbidities. For example, Basseri¹⁸ found that kidney disease, diabetes and obesity were more prevalent in patients with HCV infection compared with the general population in the United States. McKibben¹⁹ concluded that HCV infection was associated with CV disease risk. Notwithstanding its methodological limitations, the findings of our study agree with those reported in the literature.²

When lipid-lowering drugs were included in our analyses, the percentages of potential DIs were 10.2% contraindications, 46.3% clinically significant, and 3.2% weak. Some studies have found that two-thirds of patients experience potential interactions with DAAs, and that approximately 20% of concurrent drugs are contraindicated.^10,11,13 The PITER study²⁰ found that 30% to 44% of all patients receiving DAAs were at risk of clinically significant interactions. The authors underscored the need for greater care when these drugs are administered, particularly to patients with moderate to severe liver disease. Other studies reported similar or substantially higher percentages of DIs.^10–13,21 Our results agree with those of previous studies, although we recorded a lower proportion of clinically relevant drug interactions. This may be because our study was conducted with the more recently marketed pDAAs. The literature available for comparison is relatively limited.

We found that SOF/VEL exhibited a lower proportion of DIs compared with other regimens. SOF is a NS5B polymerase inhibitor, while VEL is an NS5 replication complex inhibitor.⁸ The intracellular metabolic activation of SOF is mediated by nucleotide phosphorylation and hydrolase activity (generally of low affinity and high capacity), and is therefore unlikely to be affected by concomitant medications.²² Recent reviews reported that administration of drugs concomitantly with SOF generally resulted in fewer DIs than with protease inhibitor-based regimens. However, the analysis of each DI was theoretical, and further interaction studies would be required to confirm the real effect.²² Interactions may occur at the phases of drug absorption, distribution, or metabolism. The P450 enzyme system (CYP450), and particularly isoenzyme CYP3A4, is responsible for most drug oxidation. The administration of inducers, inhibitors or substrates of this enzyme system may alter the plasma concentrations of drugs, and thus the pharmacological response.^23,24

According to the indications for administration of some CV medications such as enalapril, atorvastatin, amiodarone and digoxin, it would be difficult to adopt a single strategy during the period of pDAA administration. For example, it is advisable to monitor blood pressure and heart rate and/or to reduce the dose of antihypertensive treatments. In the case of amiodarone, one should consider replacement with another drug or dose reduction in the face of DIs, and cardiac function should be monitored. In the case of statins, it is advisable to reduce the dose and monitor potential side effects because of the increased risk of myopathy and rhabdomyolysis. Nevertheless, the safest pDAA should be chosen based on the concomitant medications used in each patient.²⁵ The results of our study regarding potential DIs with statins have also been reported in other published studies.²⁶

The present study had several limitations inherent to retrospective studies. These included underreporting of the disease and potential variability of professionals and patients, because an observational design was used. It should be noted that this study design is not free from biases, including those based on socioeconomic background, cultural or educational level, drug doses used, treatment duration, treatment adherence, or treatment suitability. In addition, over-the-counter drugs, patient self-medication or prescriptions issued at other health institutions (public or private) were not considered in our study. Our study could not account for over-the-counter drugs (not funded by the Spanish National Health System). However, we believe that this circumstance is rare. However, these factors may together have led to underestimation of DIs in our study. Our study demonstrated several potential DIs in patients with HCV. Other limitations of the study included bias in selection of the drug to be administered by the attending physician, as well as potentially limited external validity. Our results therefore should be interpreted with caution. Potential DIs are a serious problem in clinical practice, but many of them can be avoided by adjusting drug dosage or selecting a safer alternative drug. New care models, including e-prescription support tools or clinical decision support systems, may be of considerable help for clinicians in this respect. Early detection and treatment are important for prevention of the most severe consequences of HCV infection.

Conclusions

About one third of patients with HCV infection were concomitantly treated with CV drugs. Protease inhibitor pDAAs may have increased numbers of DIs with CV drugs. SOF/VEL may have fewer DIs than other pDAAs.

Footnotes

Author contributions

The study was conceived and designed by A. Sicras. Data compilation and statistical analysis was performed by A. Sicras. All authors participated in data interpretation and in manuscript writing, review, and approval.

Declaration of conflicting interest

A. Sicras was an independent consultant for the development of this manuscript. R. Morillo has no conflicts of interest.

Funding

This study was sponsored by Gilead Sciences.

ORCID iD

Antoni Sicras-Mainar

References

Pineda

Climent

García

, et al. Executive summary: Consensus document of GEHEP of the Spanish Society of Infectious Diseases and Clinical Microbiology (SEIMC), along with SOCIDROGALCOHOL, SEPD and SOMAPA on hepatitis C virus infection management in drug users. Enferm Infecc Microbiol Clin 2020; 38: 127–131.

European Association for the Study of the Liver. EASL recommendations on treatment of hepatitis C 2018. J Hepatol 2018; 69: 461–511.

Pérez

Chueca

Macías

, et al. Prevalence of resistance associated substitutions and efficacy of baseline resistance-guided chronic hepatitis C treatment in Spain from the GEHEP-004 cohort. PLoS One 2019; 14: e0221231.

Han

Zhou

François

, et al. Prevalence of hepatitis C infection among the general population and high-risk groups in the EU/EEA: A systematic review update. BMC Infect Dis 2019; 19: 655.

Romero-López

Berzal-Herranz

The role of the RNA-RNA interactome in the hepatitis C virus life cycle. Int J Mol Sci 2020; 21: 1479.

Mohanty

Salameh

Butt

AA.

Impact of direct acting antiviral agent therapy upon extrahepatic manifestations of hepatitis C virus infection.

Curr HIV/AIDS Rep 2019; 16: 389–394.

Misra

Dieterich

Saberi

, et al. Direct-acting antiviral treatment of acute hepatitis C virus infections. Expert Rev Anti Infect Ther 2018; 16: 599–610.

Paolucci

Novazzi

Piralla

, et al. Viral dynamics among HCV infected patients with different genotypes treated with genotypic specific or pan-genotypic direct-acting antiviral agent combinations. Infect Drug Resist 2019; 12: 1975–1984.

Asselah

Marcellin

Schinazi

RF.

Treatment of hepatitis C virus infection with direct-acting antiviral agents: 100% cure? Liver Int 2018; 38: 7–13.

10.

Keast

Holderread

Cothran

, et al. Hepatitis C direct-acting antiviral treatment selection, treatment failure, and use of drug-drug interactions in a state Medicaid program. J Manag Care Spec Pharm 2019; 25: 1261–1267.

11.

Talavera Pons

Boyer

Lamblin

, et al. Managing drug-drug interactions with new direct-acting antiviral agents in chronic hepatitis C. Br J Clin Pharmacol 2017; 83: 269–293.

12.

Cohen

Liapakis

Pharmacokinetics and important drug-drug interactions to remember when treating advanced chronic kidney disease patients with hepatitis C direct acting anti-viral therapy.

Semin Dial 2019; 32: 141–151.

13.

Sicras Mainar

Navarro Artieda

Hernández

, et al. Prevalence of the potential drug-drug interactions between pangenotypic direct-acting antivirals and the concomitant medications associated with patients with chronic hepatitis C virus infection in Spain. Gastroenterol Hepatol 2019; 42: 465–475.

14.

Sicras-Mainar

Enríquez

Hernández

, et al. Validation and representativeness of the Spanish BIG-PAC database: integrated computerized medical records for research into epidemiology, medicines and health resource use (real word evidence). Value Health 2019; 22: S734.

15.

Charlson

Pompei

Ales

, et al. A new method of classifying prognostic comorbidity in longitudinal studies: Development and validation. J Chronic Dis 1987; 40: 373–383.

16.

World Health Organization. The Anatomical Therapeutic Chemical Classification System with Defined Daily Doses (ATC/DDD). http: //www.who.int/classifications/atcddd/en/ (2003, accessed 28 September 2020).

17.

University of Liverpool. University of Liverpool HIV & Hepatitis Pharmacology Group Drug Interaction Charts. http: //www.hep-druginteractions.org/Interactions.aspx (2020, accessed 28 September 2020).

18.

Basseri

Yamini

Chee

, et al. Comorbidities associated with the increasing burden of hepatitis C infection. Liver Int 2010; 30: 1012–1018.

19.

McKibben

Haberlen

Post

, et al. A cross-sectional study of the association between chronic hepatitis C virus infection and subclinical coronary atherosclerosis among participants in the multicenter AIDS cohort study. J Infect Dis 2016; 213: 257–265.

20.

Kondili

Gaeta

Ieluzzi

, et al. Real-life data on potential drug-drug interactions in patients with chronic hepatitis C viral infection undergoing antiviral therapy with interferon-free DAAs in the PITER Cohort Study. PLoS One 2017; 12: e0172159.

21.

Kawabe

Hashimoto

Yoshioka

Editorial: Comorbidities, concomitant medications, and potential drug-drug interactions with interferon-free direct-acting anti-viral agents in chronic hepatitis C.

Aliment Pharmacol Ther 2019; 49: 116–117.

22.

Roncero

Villegas

Martínez-Rebollar

, et al. The pharmacological interactions between direct-acting antivirals for the treatment of chronic hepatitis C and psychotropic drugs. Expert Rev Clin Pharmacol 2018; 11: 999–1030.

23.

Dick

Lindberg

Ramírez

, et al. A clinician’s guide to drug-drug interactions with direct-acting antiviral agents for the treatment of hepatitis C viral infection. Hepatol 2016; 63: 634–643.

24.

Crouchet

Baumert

Schuster

Hepatitis C virus apolipoprotein interactions: Molecular mechanisms and clinical impact.

Expert Rev Proteomics 2017; 14: 593–606.

25.

Casas Arrate

Lázaro López

De Miguel Cascón

. Interacciones farmacológicas.

El Farmacéutico Hospitales 2017; 209: 33–38.

26.

Schulte

Wübbolding

Marra

, et al. Frequency of potential drug-drug interactions in the changing field of HCV therapy. Open Forum Infect Dis 2020; 7: ofaa040.