Abstract and Introduction
Background & Aims: According to pivotal clinical trials, cure rates for sofosbuvir-based antiviral therapy exceed 96%. Treatment failure is usually assumed to be because of virological resistance-associated substitutions or clinical risk factors, yet the role of patient-specific genetic factors has not been well explored. We determined if patient-specific genetic factors help predict patients likely to fail sofosbuvir treatment in real-world treatment situations.
Methods: We recruited sofosbuvir-treated patients with chronic hepatitis C from five Canadian treatment sites, and performed a case-control pharmacogenomics study assessing both previously published and novel genetic polymorphisms. Specifically studied were variants predicted to impair CES1-dependent production of sofosbuvir's active metabolite, interferon-λ signalling variants expected to impact a patient's immune response to the virus and an HLA variant associated with increased spontaneous and treatment-induced viral clearance.
Results: Three hundred and fifty-nine sofosbuvir-treated patients were available for analyses after exclusions, with 34 (9.5%) failing treatment. We identified CES1 variants as novel predictors for treatment failure in European patients (rs115629050 or rs4513095; odds ratio (OR): 5.43; 95% confidence interval (CI): 1.64–18.01; P = .0057), replicated associations with IFNL4 variants predicted to increase interferon-λ signalling (eg rs12979860; OR: 2.25; 95% CI: 1.25–4.06; P = .0071) and discovered a novel association with a coding variant predicted to enhance the activity of IFNL4's receptor (rs2834167 in IL10RB; OR: 1.81; 95% CI: 1.01–3.24; P = .047).
Conclusions: Ultimately, this work demonstrates that patient-specific genetic factors could be used as a tool to identify patients at higher risk of treatment failure and allow for these patients to receive effective therapy sooner.
In this study, we asked whether patient-specific genetic factors help predict patients likely to fail sofosbuvir treatment, and we found that patients with certain genetic variants in CES1, which encodes an enzyme required for sofosbuvir activation, increase the rate of sofosbuvir failure. Additionally, a genetic variant that increases interferon lambda signalling, which is an important part of the immune response to the virus, increases the chance of sofosbuvir failure. In the future, identifying patients most likely to fail treatment with a predictive genetic test would allow for pre-emptive prediction of the need for retreatment, thus reducing the burden on both the healthcare system and the patient.
Sofosbuvir directly inhibits the hepatitis C viral polymerase and leads to sustained virological response-based cure rates greater than 96% according to pivotal clinical trials assessing relatively homogenous groups of chronic hepatitis C patients. When treatment failure does occur, virological resistance to antiviral treatment via resistance-associated substitutions is often assumed to be the culprit, yet the majority of patients with known resistance-associated substitutions still achieve a sustained virological response. Clinical risk factors can also increase the difficulty of eradicating the virus with sofosbuvir-based treatment (eg advanced liver disease, prior hepatitis C treatment experience and infection with viral genotype 3); however, these factors are not sufficient to predict who will fail treatment.
Significant attention has been given to investigating the role of viral genetic variation on treatment failure, yet apart from recently identified genetic variation specific to viral genotype 3a, viral genetic variation reducing sofosbuvir effectiveness is rarely seen clinically. In contrast, the potential role of patient-specific genetic factors (ie intrinsic genetic-based patient-specific factors) on treatment failure has generally been ignored.
However, the active triphosphorylated metabolite of sofosbuvir is formed in a five-step process involving five enzymes (carboxylesterase 1 [CES1], cathespin A [CTSA], histidine triad nucleotide binding protein 1 [HINT1], cytidine/uridine monophosphate kinase 1 [CMPK1] and NME1-NME2 readthrough [NME1-NME2]), where genetic variation that alters the enzymatic activity of any of these would change the rate of formation of the active metabolite and therefore affect the antiviral effectiveness of the drug. Notably, the first step of sofosbuvir (the prodrug) activation is mediated by CES1 that has known patient-specific genetic variation affecting drug outcome. In particular, CES1 variation predicts toxicity in patients taking capecitabine, drug plasma levels of dabigatran and increased antiplatelet activity of clopidogrel: all of which are metabolized by CES1-dependent metabolism similar to sofosbuvir.[5–10] Furthermore, patient-specific genetic factors that contribute to variable immune responses to viral infection (eg through activation of interferon lambda 4 [IFNL4] signalling) have been linked to treatment failure across various treatment regimens.[11–14]
In this work, we focused on the role of patient-specific genetic factors predicted to decrease CES1-based metabolism (and consequently predicted to reduce the production of sofosbuvir's active metabolite), increase interferon-λ signalling (through IFNL4 activation or upregulation of its receptor component interleukin 10 receptor subunit beta [IL10RB]), or defining a classical human leucocyte antigen (HLA ) allele associated with increased spontaneous[14–16] and treatment-induced viral clearance (HLA-DQB1*03:01).
Retreatment of patients with prior sofosbuvir-based treatment failure is largely successful when treatment regimens are modified (eg extended treatment duration to increase overall drug exposure, the addition of ribavirin to boost antiviral action, and/or the use of complementary direct-acting antivirals to reduce the impact of viral escape mutations). Therefore, further optimization of sofosbuvir-based treatment regimens using patient-specific genetic factors would ensure that patients are placed on effective therapy sooner, and before failure occurs, thus reducing the need for retreatment and leading to cost savings for the healthcare system. This is increasingly important given that outcomes are primarily predicted based on highly selected patients included in pivotal clinical trials, and real-world treatment situations will continue to encompass more heterogeneous patients, treated with a variety of regimens. Thus, identifying additional predictive factors for treatment failure will better allow for patient-centred decisions to increase the likelihood of treatment success for individual, and unique, patients.
Liver International. 2022;42(4):796-808. © 2022 Blackwell Publishing