Pharmacogenetics of the anti-HCV drug sofosbuvir: a preliminary study

Pharmacogenetics of the anti-HCV drug sofosbuvir: a preliminary study Abstract Background Sofosbuvir is a potent nucleotide HCV NS5B polymerase inhibitor that is also a P-glycoprotein (encoded by the ABCB1 gene) and breast cancer resistance protein (encoded by the ABCG2 gene) substrate. Concerning previous anti-HCV therapies, pharmacogenetics had a significant impact, particularly considering the association of interleukin28B polymorphisms with dual-therapy (ribavirin + pegylated IFN) outcomes. Objectives In this work, we investigated the association between sofosbuvir and its prevalent metabolite (GS-331007) plasma concentrations at 1 month of therapy and genetic variants (SNPs) in genes encoding transporters and nuclear factors (ABCB1, ABCG2 and HNF4α) related to sofosbuvir transport. Patients and methods Allelic discrimination was performed through real-time PCR, whereas plasma concentrations were evaluated through liquid chromatography. One hundred and thirteen patients were enrolled. Results Sofosbuvir concentrations were below the limit of quantification since the drug was converted into its GS-331007 metabolite. ABCB1 2677 G>T (P = 0.044) and HNF4α 975 C>G (P = 0.049) SNPs were associated with GS-331007 metabolite plasma concentrations. In linear multivariate analysis, liver stiffness, insulin resistance, baseline haemoglobin and haematocrit and SNPs in the ABCB1 gene (3435 CT/TT and 1236 TT genotypes) were significant predictors of GS-331007 concentrations. Furthermore, we performed sub-analyses considering the anti-HCV concomitant drug and HCV genotype, identifying specific polymorphisms associated with GS-331007 plasma concentrations: ABCB1 3435 C>T and HNF4α975 C>G in patients treated with daclatasvir, ABCB1 2677 G>T with ledipasvir and ABCB1 3435 C>T, ABCB1 2677 G>T, ABCG2 421 C>A and ABCG2 1194 + 928 C>A with ribavirin. Conclusions In this study we suggested sofosbuvir GS-331007 metabolite plasma levels were affected by variants in the ABCB1 and HNFα genes. Introduction HCV infection affects about 170 million people worldwide and leads to several clinical consequences, including liver cirrhosis and hepatocellular carcinoma.1 For several years, the standard of care for chronic hepatitis C (CHC) treatment was dual therapy with ribavirin and pegylated IFN, but severe adverse effects were present. In 2011, the first two direct-acting antiviral drugs (DAAs) boceprevir and telaprevir were approved, achieving a higher percentage of sustained virological response (SVR), but also worsening the toxic effects.2,3 To date, new more effective DAAs, with pan-genotypic activity and with increased tolerability, have enhanced the probability of achieving SVR (nearly 100%) and led to a new scenario including shorter therapies and use of IFN- or ribavirin-free regimens.4 Sofosbuvir (SOF) is a potent nucleotide non-structural 5B polymerase inhibitor, approved for use in combination with ledipasvir, daclatasvir, simeprevir and ribavirin; the latter remains mandatory for specific groups as well as for chronic genotype 2 HCV infected, elderly and cirrhotic patients.5–8 Sofosbuvir is a low-molecular-weight prodrug that is stable in simulated gastric and intestinal fluids. Food is able to increase its mean plasma exposure <2-fold and its bioavailability following oral administration to dogs is 9.89%.9 Sofosbuvir is mainly metabolized in the liver and is intracellularly converted into different metabolites; among these, GS-331007 accounts for about 90% of the total systemic exposure and is excreted in urine. In vitro studies confirmed that sofosbuvir, but not the GS-331007 metabolite, is a P-glycoprotein (P-gp, encoded by the ABCB1 gene) substrate; furthermore sofosbuvir is transported by the breast cancer resistance protein (BCRP, encoded by the ABCG2 gene).10–13 It is known that variants in genes encoding transporters can affect their activity; pharmacogenetics is the study of inherited variability in drug response and it investigates SNPs, which are variations of a single DNA base present in at least 1% of the population. Pharmacogenetics had a significant impact on the contest between previous anti-HCV therapies; for instance, IL28B (encoding IFN-λ) and SLC28A2 (encoding concentrative nucleotide transporter 2) gene SNPs were associated with the probability of achieving SVR, whereas the ones in the ITPA gene (encoding inosine triphosphatase enzymes) added to the risk of developing the main adverse effect, anaemia. Similarly, ABCB1 and ABCB11 (encoding biliary salt export pump, BSEP) were associated with telaprevir and boceprevir exposure, whereas vitamin D-related genes influenced the therapeutic outcome, the onset of anaemia and cryoglobulinaemia.14–24 At present, few data are available in the literature concerning pharmacogenetics and DAAs; for example, Murakawa et al.25 showed that ITPA polymorphisms may still have a role in the prediction of anaemia in patients treated with sofosbuvir plus ribavirin; on the other hand, since the achievement of SVR with new drugs is near to 100%, the involvement of IL28B SNPs seems to be minor.26 In this scenario, we chose to investigate whether variants in genes encoding transporters and nuclear factors (involved in the regulation of transporter expression) could have an influence on the prediction of sofosbuvir and GS-331007 plasma concentrations at 1 month of therapy. Materials and methods Characteristics of enrolled patients In this study, 243 CHC adult patients treated with sofosbuvir (in combination with ribavirin and/or ledipasvir, daclatasvir or simeprevir) at the ‘Amedeo di Savoia’ hospital (Turin, Italy) from 2014 to 2016, were retrospectively analysed. Patients without other viral co-infections (hepatitis B or HIV) and without major contraindications to the treatment were included. All patients received sofosbuvir at 400 mg daily. The study was conducted in compliance with the Declaration of Helsinki and local review board regulations; all patients gave written informed consent, according to the local ethics committee standards (‘Kinetic-C protocol’, approved by Ethics Committee ‘A.O.U. S. Luigi Gonzaga, Orbassano, Turin’, no. 186/14 on 26 May 2015). Pharmacogenetic analyses Genomic DNA was isolated from blood samples (MagNA Pure Compact, Roche, Monza, Italy). Genotypes were assessed through a real-time PCR allelic discrimination system (LightCycler 96, Roche, Monza, Italy). Investigated gene SNPs were ABCB1 3435 C>T (rs1045642), ABCB1 1236C>T (rs1128503), ABCB1 2677G>T (rs2032582), ABCB11 1131T>C (rs2287622), ABCG2 421C>A (rs2231142), ABCG2 1194 + 928C>A (rs13120400) and HNF4α 975C>G (rs1884613). Pharmacokinetic analyses Blood sampling for the pharmacokinetic evaluation was performed at the end of the dosing interval (Ctrough), immediately before the new dose was taken. Blood samples were centrifuged at 1400 g for 10 min at 4°C, in order to obtain plasma samples. The quantification of sofosbuvir and GS-331007 in patients’ plasma was performed through a previously validated and published UHPLC-MS/MS method.27 Statistical analyses All the variables were tested for normality through the Shapiro–Wilk test. Non-normal variables were described as median values and IQR; categorical variables were described as numbers and percentages. All the polymorphisms were tested for Hardy–Weinberg equilibrium by the χ2 test, in order to determine the observed genotype frequencies. Kruskal–Wallis and Mann–Whitney tests were used to test differences in continuous variables between genetic groups, considering the level of statistical significance P < 0.05. The predictive power of the considered variables was finally evaluated through univariate (P < 0.2) and multivariate (P < 0.05) linear regression analysis. All the tests were performed with IBM SPSS Statistics 24.0 for Windows (Chicago, Illinois, USA). Results All the investigated data were available for 113 patients (pharmacogenetic, pharmacokinetic, HCV genotype or concomitant drug information were lacking for the other 130 patients). Characteristics of analysed patients are reported in Table 1. The variant allele frequencies of studied polymorphisms are shown in Table 2. Since sofosbuvir concentrations were always undetectable, only GS-331007 plasma concentrations were reported. Table 1. Baseline characteristics of the studied population Characteristic Value No. of patients 113 Age, years, median (IQR) 52 (48–60) Male sex, n (%) 23 (20.4) BMI, kg/m2, median (IQR) 22 (20–24) Cirrhotics, n (%) 40 (35.4) HCV-RNA at baseline, log IU/mL, median (IQR) 6.2 (5.7–6.7) ALT at baseline, IU/mL, median (IQR) 77 (49.5–110.5) Albumin at baseline, IU/mL, median (IQR) 39 (32.5–42) Haemoglobin at baseline, IU/mL, median (IQR) 14.8 (13.7–15.7) Haematocrit at baseline, IU/mL, median (IQR) 43.9 (40.8–45.8) Mean corpuscular volume at baseline, IU/mL, median (IQR) 90.7 (86.7–94.5) Estimated glomerular filtration rate (eGFR) at baseline, IU/mL, median (IQR) 108.8 (85.7–121) Hepatocellular carcinoma, n (%) 3 (2.7) Patients treated with ribavirin, n (%) 34 (30.1) Patients treated with simeprevir, n (%) 18 (15.9) Patients treated with daclatasvir, n (%) 44 (38.9) Patients treated with ledipasvir, n (%) 42 (37.2) GS-331007 concentrations at 1 month of therapy, ng/mL, median (IQR) 326 (213–488) Ribavirin concentrations at 1 month of therapy, ng/mL, median (IQR) 1637 (1321.5–2042) Simeprevir concentrations at 1 month of therapy, ng/mL, median (IQR) 860.5 (469.8–1535.5) Daclatasvir concentrations at 1 month of therapy, ng/mL, median (IQR) 188.5 (100–294) Ledipasvir concentrations at 1 month of therapy, ng/mL, median (IQR) 231.5 (168.8–332.3) Liver stiffness, kPa, median (IQR) 21.3 (12.1–33.2) Ribavirin dose, n (%)  600 mg 1 (2.9)  800 mg 8 (23.5)  1000 mg 15 (44.1)  1200 mg 10 (29.4) Genotype, n (%)  1 63 (55.8)  2 8 (7.1)  3 32 (28.3)  4 10 (8.8) Cryoglobulinaemia, n (%) 41 (36.3) Characteristic Value No. of patients 113 Age, years, median (IQR) 52 (48–60) Male sex, n (%) 23 (20.4) BMI, kg/m2, median (IQR) 22 (20–24) Cirrhotics, n (%) 40 (35.4) HCV-RNA at baseline, log IU/mL, median (IQR) 6.2 (5.7–6.7) ALT at baseline, IU/mL, median (IQR) 77 (49.5–110.5) Albumin at baseline, IU/mL, median (IQR) 39 (32.5–42) Haemoglobin at baseline, IU/mL, median (IQR) 14.8 (13.7–15.7) Haematocrit at baseline, IU/mL, median (IQR) 43.9 (40.8–45.8) Mean corpuscular volume at baseline, IU/mL, median (IQR) 90.7 (86.7–94.5) Estimated glomerular filtration rate (eGFR) at baseline, IU/mL, median (IQR) 108.8 (85.7–121) Hepatocellular carcinoma, n (%) 3 (2.7) Patients treated with ribavirin, n (%) 34 (30.1) Patients treated with simeprevir, n (%) 18 (15.9) Patients treated with daclatasvir, n (%) 44 (38.9) Patients treated with ledipasvir, n (%) 42 (37.2) GS-331007 concentrations at 1 month of therapy, ng/mL, median (IQR) 326 (213–488) Ribavirin concentrations at 1 month of therapy, ng/mL, median (IQR) 1637 (1321.5–2042) Simeprevir concentrations at 1 month of therapy, ng/mL, median (IQR) 860.5 (469.8–1535.5) Daclatasvir concentrations at 1 month of therapy, ng/mL, median (IQR) 188.5 (100–294) Ledipasvir concentrations at 1 month of therapy, ng/mL, median (IQR) 231.5 (168.8–332.3) Liver stiffness, kPa, median (IQR) 21.3 (12.1–33.2) Ribavirin dose, n (%)  600 mg 1 (2.9)  800 mg 8 (23.5)  1000 mg 15 (44.1)  1200 mg 10 (29.4) Genotype, n (%)  1 63 (55.8)  2 8 (7.1)  3 32 (28.3)  4 10 (8.8) Cryoglobulinaemia, n (%) 41 (36.3) Table 1. Baseline characteristics of the studied population Characteristic Value No. of patients 113 Age, years, median (IQR) 52 (48–60) Male sex, n (%) 23 (20.4) BMI, kg/m2, median (IQR) 22 (20–24) Cirrhotics, n (%) 40 (35.4) HCV-RNA at baseline, log IU/mL, median (IQR) 6.2 (5.7–6.7) ALT at baseline, IU/mL, median (IQR) 77 (49.5–110.5) Albumin at baseline, IU/mL, median (IQR) 39 (32.5–42) Haemoglobin at baseline, IU/mL, median (IQR) 14.8 (13.7–15.7) Haematocrit at baseline, IU/mL, median (IQR) 43.9 (40.8–45.8) Mean corpuscular volume at baseline, IU/mL, median (IQR) 90.7 (86.7–94.5) Estimated glomerular filtration rate (eGFR) at baseline, IU/mL, median (IQR) 108.8 (85.7–121) Hepatocellular carcinoma, n (%) 3 (2.7) Patients treated with ribavirin, n (%) 34 (30.1) Patients treated with simeprevir, n (%) 18 (15.9) Patients treated with daclatasvir, n (%) 44 (38.9) Patients treated with ledipasvir, n (%) 42 (37.2) GS-331007 concentrations at 1 month of therapy, ng/mL, median (IQR) 326 (213–488) Ribavirin concentrations at 1 month of therapy, ng/mL, median (IQR) 1637 (1321.5–2042) Simeprevir concentrations at 1 month of therapy, ng/mL, median (IQR) 860.5 (469.8–1535.5) Daclatasvir concentrations at 1 month of therapy, ng/mL, median (IQR) 188.5 (100–294) Ledipasvir concentrations at 1 month of therapy, ng/mL, median (IQR) 231.5 (168.8–332.3) Liver stiffness, kPa, median (IQR) 21.3 (12.1–33.2) Ribavirin dose, n (%)  600 mg 1 (2.9)  800 mg 8 (23.5)  1000 mg 15 (44.1)  1200 mg 10 (29.4) Genotype, n (%)  1 63 (55.8)  2 8 (7.1)  3 32 (28.3)  4 10 (8.8) Cryoglobulinaemia, n (%) 41 (36.3) Characteristic Value No. of patients 113 Age, years, median (IQR) 52 (48–60) Male sex, n (%) 23 (20.4) BMI, kg/m2, median (IQR) 22 (20–24) Cirrhotics, n (%) 40 (35.4) HCV-RNA at baseline, log IU/mL, median (IQR) 6.2 (5.7–6.7) ALT at baseline, IU/mL, median (IQR) 77 (49.5–110.5) Albumin at baseline, IU/mL, median (IQR) 39 (32.5–42) Haemoglobin at baseline, IU/mL, median (IQR) 14.8 (13.7–15.7) Haematocrit at baseline, IU/mL, median (IQR) 43.9 (40.8–45.8) Mean corpuscular volume at baseline, IU/mL, median (IQR) 90.7 (86.7–94.5) Estimated glomerular filtration rate (eGFR) at baseline, IU/mL, median (IQR) 108.8 (85.7–121) Hepatocellular carcinoma, n (%) 3 (2.7) Patients treated with ribavirin, n (%) 34 (30.1) Patients treated with simeprevir, n (%) 18 (15.9) Patients treated with daclatasvir, n (%) 44 (38.9) Patients treated with ledipasvir, n (%) 42 (37.2) GS-331007 concentrations at 1 month of therapy, ng/mL, median (IQR) 326 (213–488) Ribavirin concentrations at 1 month of therapy, ng/mL, median (IQR) 1637 (1321.5–2042) Simeprevir concentrations at 1 month of therapy, ng/mL, median (IQR) 860.5 (469.8–1535.5) Daclatasvir concentrations at 1 month of therapy, ng/mL, median (IQR) 188.5 (100–294) Ledipasvir concentrations at 1 month of therapy, ng/mL, median (IQR) 231.5 (168.8–332.3) Liver stiffness, kPa, median (IQR) 21.3 (12.1–33.2) Ribavirin dose, n (%)  600 mg 1 (2.9)  800 mg 8 (23.5)  1000 mg 15 (44.1)  1200 mg 10 (29.4) Genotype, n (%)  1 63 (55.8)  2 8 (7.1)  3 32 (28.3)  4 10 (8.8) Cryoglobulinaemia, n (%) 41 (36.3) Table 2. Variant allele frequencies Allele frequency (%) SNP homozygous wild type heterozygous homozygous mutant ABCB1 3435 C>T 28.3 CC 45.1 CT 26.5 TT ABCB1 1236 C>T 35.4 CC 42.5 CT 22.1 TT ABCB1 2677 G>T 23.9 CC 37.2 CT 38.9 TT ABCB11 1131 T>C 21.2 TT 40.7 TC 38.1 CC ABCG2 421 C>A 83.2 CC 16.8 CA – ABCG2 1194 + 928 T>C 61.1 TT 36.3 TC 2.7 CC HNF4α 975 C>G 62.8 CC 34.5 CG 2.7 GG Allele frequency (%) SNP homozygous wild type heterozygous homozygous mutant ABCB1 3435 C>T 28.3 CC 45.1 CT 26.5 TT ABCB1 1236 C>T 35.4 CC 42.5 CT 22.1 TT ABCB1 2677 G>T 23.9 CC 37.2 CT 38.9 TT ABCB11 1131 T>C 21.2 TT 40.7 TC 38.1 CC ABCG2 421 C>A 83.2 CC 16.8 CA – ABCG2 1194 + 928 T>C 61.1 TT 36.3 TC 2.7 CC HNF4α 975 C>G 62.8 CC 34.5 CG 2.7 GG Table 2. Variant allele frequencies Allele frequency (%) SNP homozygous wild type heterozygous homozygous mutant ABCB1 3435 C>T 28.3 CC 45.1 CT 26.5 TT ABCB1 1236 C>T 35.4 CC 42.5 CT 22.1 TT ABCB1 2677 G>T 23.9 CC 37.2 CT 38.9 TT ABCB11 1131 T>C 21.2 TT 40.7 TC 38.1 CC ABCG2 421 C>A 83.2 CC 16.8 CA – ABCG2 1194 + 928 T>C 61.1 TT 36.3 TC 2.7 CC HNF4α 975 C>G 62.8 CC 34.5 CG 2.7 GG Allele frequency (%) SNP homozygous wild type heterozygous homozygous mutant ABCB1 3435 C>T 28.3 CC 45.1 CT 26.5 TT ABCB1 1236 C>T 35.4 CC 42.5 CT 22.1 TT ABCB1 2677 G>T 23.9 CC 37.2 CT 38.9 TT ABCB11 1131 T>C 21.2 TT 40.7 TC 38.1 CC ABCG2 421 C>A 83.2 CC 16.8 CA – ABCG2 1194 + 928 T>C 61.1 TT 36.3 TC 2.7 CC HNF4α 975 C>G 62.8 CC 34.5 CG 2.7 GG ABCB1 2677G>T (P = 0.044) and HNF4α C>G (P = 0.049) SNPs were found to be associated with GS-331007 metabolite plasma concentrations at 1 month of therapy. Regarding ABCB1 2677G>T SNP (Figure 1), GG genotype patients showed median plasma levels of 260 ng/mL (IQR 203–384 ng/mL), whereas the GT/TT genotype patients had a median concentration of 345 ng/mL (IQR 221.8–506.3 ng/mL). Concerning the HNF4α C>G polymorphism (Figure 2), CC genotype patients had a median GS-331007 concentration of 366 ng/mL (IQR 237–503 ng/mL), whereas for GC/CC genotypes this was 270.5 ng/mL (IQR 196.8–407.3 ng/mL). Figure 1. View largeDownload slide ABCB1 2677 G>T SNP influence on GS-331007 metabolite concentrations at 1 month of therapy. Figure 1. View largeDownload slide ABCB1 2677 G>T SNP influence on GS-331007 metabolite concentrations at 1 month of therapy. Figure 2. View largeDownload slide HNF4α C>G SNP influence on GS-331007 metabolite concentrations at 1 month of therapy. Figure 2. View largeDownload slide HNF4α C>G SNP influence on GS-331007 metabolite concentrations at 1 month of therapy. Linear regression analysis was performed to clarify which factors (demographic, clinical or genetic ones) were able to predict the GS-331007 metabolite concentrations at 1 month of therapy (Table 3). In the multivariate analysis, liver stiffness, insulin resistance, baseline haemoglobin and haematocrit and SNPs in the ABCB1 gene (3435 CT/TT and 1236 TT genotypes) were significant predictors. In particular, baseline haemoglobin and ABCB1 3435 CT/TT genotypes were positive predictive factors, whereas the others were negative. Table 3. Linear regression analyses: factors able to predict GS-331007 metabolite concentrations at 1 month of therapy GS-331007 metabolite concentrations at 1 month of therapy univariate multivariate Characteristic P value OR (95% CI) P value OR (95% CI) BMI at baseline 0.047 −15.623 (−31.020; −0.226) 0.598 −9.796 (−48.907; 29.316) Age 0.051 6.180 (−0.024; 12.384) 0.547 4.348 (−10.629; 19.325) Liver stiffness 0.186 3.310 (−1.621; 8.240) 0.025 −10.048 (−18.690; −1.406) Viral genotype 0.252 −39.380 (−107.128; 28.367) Sex 0.045 180.908 (3.775; 358.041) 0.609 79.324 (−243.957; 402.604) Cirrhosis 0.688 31.796 (−124.828; 188.419) Child Score 0.094 91.170 (−15.723; 198.063) 0.614 −99.613 (−513.934; 314.707) MELD Score 0.010 43.647 (10.906; 76.389) 0.339 54.028 (−61.714; 169.770) Cryoglobulinaemia 0.444 62.761 (−99.293; 224.815) Insulin resistance 0.131 −142.219 (−328.092; 43.653) 0.018 −386.438 (−697.505; −75.371) Ribavirin treatment 0.035 −165.853 (−319.486; −12.220) 0.849 −56.051 (−687.391; 575.290) Simeprevir treatment 0.217 −123.361 (−320.452; 73.729) Daclatasvir treatment 0.265 83.673 (−64.430; 231.775) Ledipasvir treatment 0.389 65.373 (−84.403; 215.148) Baseline haemoglobin 0.048 −42.051 (−83.739; −0.364) <0.001 688.235 (350.813; 1025.657) Baseline haematocrit 0.078 −12.225 (−25.861; 1.411) <0.001 −295.316 (−427.391; −163.240) Baseline mean corpuscular volume 0.897 −0.728 (−11.825; 10.368) Baseline ALT 0.201 −0.459 (−1.168; 0.249) Baseline eGFR <0.001 −7.536 (−10.346; −4.726) 0.821 −0.545 (−5.687; 4.598) Baseline albumin 0.739 −1.400 (−9.737; 6.938) ABCB1 3435 CT/TT 0.155 −115.459 (−44.260; 275.179) 0.017 312.111 (63.344; 560.878) ABCB1 1236 TT 0.146 −127.901 (−301.200; 45.398) 0.087 −245.222 (−529.989; 39.545) ABCB1 2677 GT/TT 0.075 152.233 (−15.645; 320.112) 0.282 −162.453 (−470.044; 145.139) ABCB11 1131 TC/CC 0.866 −15.127 (−192.667; 162.413) ABCG2 421 CA 0.897 −12.770 (−206.939; 181.398) ABCG2 1194 + 928 TC/CC 0.319 74.851 (−73.418; 223.121) HNF4α 975 CG/GG 0.237 −89.690 (−239.019; 59.639) GS-331007 metabolite concentrations at 1 month of therapy univariate multivariate Characteristic P value OR (95% CI) P value OR (95% CI) BMI at baseline 0.047 −15.623 (−31.020; −0.226) 0.598 −9.796 (−48.907; 29.316) Age 0.051 6.180 (−0.024; 12.384) 0.547 4.348 (−10.629; 19.325) Liver stiffness 0.186 3.310 (−1.621; 8.240) 0.025 −10.048 (−18.690; −1.406) Viral genotype 0.252 −39.380 (−107.128; 28.367) Sex 0.045 180.908 (3.775; 358.041) 0.609 79.324 (−243.957; 402.604) Cirrhosis 0.688 31.796 (−124.828; 188.419) Child Score 0.094 91.170 (−15.723; 198.063) 0.614 −99.613 (−513.934; 314.707) MELD Score 0.010 43.647 (10.906; 76.389) 0.339 54.028 (−61.714; 169.770) Cryoglobulinaemia 0.444 62.761 (−99.293; 224.815) Insulin resistance 0.131 −142.219 (−328.092; 43.653) 0.018 −386.438 (−697.505; −75.371) Ribavirin treatment 0.035 −165.853 (−319.486; −12.220) 0.849 −56.051 (−687.391; 575.290) Simeprevir treatment 0.217 −123.361 (−320.452; 73.729) Daclatasvir treatment 0.265 83.673 (−64.430; 231.775) Ledipasvir treatment 0.389 65.373 (−84.403; 215.148) Baseline haemoglobin 0.048 −42.051 (−83.739; −0.364) <0.001 688.235 (350.813; 1025.657) Baseline haematocrit 0.078 −12.225 (−25.861; 1.411) <0.001 −295.316 (−427.391; −163.240) Baseline mean corpuscular volume 0.897 −0.728 (−11.825; 10.368) Baseline ALT 0.201 −0.459 (−1.168; 0.249) Baseline eGFR <0.001 −7.536 (−10.346; −4.726) 0.821 −0.545 (−5.687; 4.598) Baseline albumin 0.739 −1.400 (−9.737; 6.938) ABCB1 3435 CT/TT 0.155 −115.459 (−44.260; 275.179) 0.017 312.111 (63.344; 560.878) ABCB1 1236 TT 0.146 −127.901 (−301.200; 45.398) 0.087 −245.222 (−529.989; 39.545) ABCB1 2677 GT/TT 0.075 152.233 (−15.645; 320.112) 0.282 −162.453 (−470.044; 145.139) ABCB11 1131 TC/CC 0.866 −15.127 (−192.667; 162.413) ABCG2 421 CA 0.897 −12.770 (−206.939; 181.398) ABCG2 1194 + 928 TC/CC 0.319 74.851 (−73.418; 223.121) HNF4α 975 CG/GG 0.237 −89.690 (−239.019; 59.639) Statistically significant values are shown in bold. MELD, Model for End-Stage Liver Disease. Table 3. Linear regression analyses: factors able to predict GS-331007 metabolite concentrations at 1 month of therapy GS-331007 metabolite concentrations at 1 month of therapy univariate multivariate Characteristic P value OR (95% CI) P value OR (95% CI) BMI at baseline 0.047 −15.623 (−31.020; −0.226) 0.598 −9.796 (−48.907; 29.316) Age 0.051 6.180 (−0.024; 12.384) 0.547 4.348 (−10.629; 19.325) Liver stiffness 0.186 3.310 (−1.621; 8.240) 0.025 −10.048 (−18.690; −1.406) Viral genotype 0.252 −39.380 (−107.128; 28.367) Sex 0.045 180.908 (3.775; 358.041) 0.609 79.324 (−243.957; 402.604) Cirrhosis 0.688 31.796 (−124.828; 188.419) Child Score 0.094 91.170 (−15.723; 198.063) 0.614 −99.613 (−513.934; 314.707) MELD Score 0.010 43.647 (10.906; 76.389) 0.339 54.028 (−61.714; 169.770) Cryoglobulinaemia 0.444 62.761 (−99.293; 224.815) Insulin resistance 0.131 −142.219 (−328.092; 43.653) 0.018 −386.438 (−697.505; −75.371) Ribavirin treatment 0.035 −165.853 (−319.486; −12.220) 0.849 −56.051 (−687.391; 575.290) Simeprevir treatment 0.217 −123.361 (−320.452; 73.729) Daclatasvir treatment 0.265 83.673 (−64.430; 231.775) Ledipasvir treatment 0.389 65.373 (−84.403; 215.148) Baseline haemoglobin 0.048 −42.051 (−83.739; −0.364) <0.001 688.235 (350.813; 1025.657) Baseline haematocrit 0.078 −12.225 (−25.861; 1.411) <0.001 −295.316 (−427.391; −163.240) Baseline mean corpuscular volume 0.897 −0.728 (−11.825; 10.368) Baseline ALT 0.201 −0.459 (−1.168; 0.249) Baseline eGFR <0.001 −7.536 (−10.346; −4.726) 0.821 −0.545 (−5.687; 4.598) Baseline albumin 0.739 −1.400 (−9.737; 6.938) ABCB1 3435 CT/TT 0.155 −115.459 (−44.260; 275.179) 0.017 312.111 (63.344; 560.878) ABCB1 1236 TT 0.146 −127.901 (−301.200; 45.398) 0.087 −245.222 (−529.989; 39.545) ABCB1 2677 GT/TT 0.075 152.233 (−15.645; 320.112) 0.282 −162.453 (−470.044; 145.139) ABCB11 1131 TC/CC 0.866 −15.127 (−192.667; 162.413) ABCG2 421 CA 0.897 −12.770 (−206.939; 181.398) ABCG2 1194 + 928 TC/CC 0.319 74.851 (−73.418; 223.121) HNF4α 975 CG/GG 0.237 −89.690 (−239.019; 59.639) GS-331007 metabolite concentrations at 1 month of therapy univariate multivariate Characteristic P value OR (95% CI) P value OR (95% CI) BMI at baseline 0.047 −15.623 (−31.020; −0.226) 0.598 −9.796 (−48.907; 29.316) Age 0.051 6.180 (−0.024; 12.384) 0.547 4.348 (−10.629; 19.325) Liver stiffness 0.186 3.310 (−1.621; 8.240) 0.025 −10.048 (−18.690; −1.406) Viral genotype 0.252 −39.380 (−107.128; 28.367) Sex 0.045 180.908 (3.775; 358.041) 0.609 79.324 (−243.957; 402.604) Cirrhosis 0.688 31.796 (−124.828; 188.419) Child Score 0.094 91.170 (−15.723; 198.063) 0.614 −99.613 (−513.934; 314.707) MELD Score 0.010 43.647 (10.906; 76.389) 0.339 54.028 (−61.714; 169.770) Cryoglobulinaemia 0.444 62.761 (−99.293; 224.815) Insulin resistance 0.131 −142.219 (−328.092; 43.653) 0.018 −386.438 (−697.505; −75.371) Ribavirin treatment 0.035 −165.853 (−319.486; −12.220) 0.849 −56.051 (−687.391; 575.290) Simeprevir treatment 0.217 −123.361 (−320.452; 73.729) Daclatasvir treatment 0.265 83.673 (−64.430; 231.775) Ledipasvir treatment 0.389 65.373 (−84.403; 215.148) Baseline haemoglobin 0.048 −42.051 (−83.739; −0.364) <0.001 688.235 (350.813; 1025.657) Baseline haematocrit 0.078 −12.225 (−25.861; 1.411) <0.001 −295.316 (−427.391; −163.240) Baseline mean corpuscular volume 0.897 −0.728 (−11.825; 10.368) Baseline ALT 0.201 −0.459 (−1.168; 0.249) Baseline eGFR <0.001 −7.536 (−10.346; −4.726) 0.821 −0.545 (−5.687; 4.598) Baseline albumin 0.739 −1.400 (−9.737; 6.938) ABCB1 3435 CT/TT 0.155 −115.459 (−44.260; 275.179) 0.017 312.111 (63.344; 560.878) ABCB1 1236 TT 0.146 −127.901 (−301.200; 45.398) 0.087 −245.222 (−529.989; 39.545) ABCB1 2677 GT/TT 0.075 152.233 (−15.645; 320.112) 0.282 −162.453 (−470.044; 145.139) ABCB11 1131 TC/CC 0.866 −15.127 (−192.667; 162.413) ABCG2 421 CA 0.897 −12.770 (−206.939; 181.398) ABCG2 1194 + 928 TC/CC 0.319 74.851 (−73.418; 223.121) HNF4α 975 CG/GG 0.237 −89.690 (−239.019; 59.639) Statistically significant values are shown in bold. MELD, Model for End-Stage Liver Disease. Sub-analysis for concomitant drugs We performed another analysis, stratifying patients depending on their concomitant anti-HCV administered drug: 18 patients with simeprevir, 44 with daclatasvir, 42 with ledipasvir and 34 with ribavirin. ABCB1 3435C>T SNP influenced GS-331007 metabolite levels at 1 month of therapy in patients treated with daclatasvir (P = 0.031); median GS-331007 concentrations were 203.5 ng/mL (IQR 134.8–435.8 ng/mL) for CC patients and 453 ng/mL (IQR 184–/ ng/mL) for CT/TT. ABCB1 2677G>T polymorphism was associated with GS-331007 concentrations in the ledipasvir treatment group (P = 0.025); for GG and GT/TT genotypes respectively, GS-331007 levels were 340 ng/mL (IQR 154.5–412.5 ng/mL) and 299.5 ng/mL (181.8–455.3 ng/mL). HNF4α 975C>G SNP was able to affect sofosbuvir metabolite exposure at 1 month of treatment (P = 0.003) in patients treated with daclatasvir. In fact, median concentrations were 453 ng/mL (IQR 155–741 ng/mL) and 203 ng/mL (IQR 124–303 ng/mL) for CC versus CG/GG genotypes. Concerning ribavirin use, ABCB1 3435C>T (P = 0.008), ABCB1 2677G>T (P = 0.039), ABCG2 421C>A (P = 0.008) and ABCG2 1194+928C>A (P = 0.005) variants were associated with GS-331007 plasma concentrations. In detail, the median concentrations were 319 ng/mL (IQR 193–427 ng/mL) and 149 ng/mL (IQR 105–262 ng/mL) for ABCB1 3435 CC versus CT/TT genotypes, 282 ng/mL (IQR 184–375 ng/mL) and 216 ng/mL (IQR 149–405 ng/mL) for ABCB1 2677 GG versus GT/TT genotypes, respectively, 274 ng/mL (IQR 133–496 ng/mL) and 259 ng/mL (IQR 179.5–373 ng/mL) for ABCG2 421 CC versus CA/AA genotypes, and 250 ng/mL (IQR 172–419 ng/mL) and 272 ng/mL (IQR 158–373 ng/mL) for ABCG2 1194+928 CC versus CA/AA genotypes. Sub-analysis for HCV genotypes We also analysed dissimilarities between the different HCV genotypes: 64 patients belonged to HCV-1, 8 to HCV-2, 32 to HCV-3 and 9 to HCV-4. The following differences were suggested: ABCB1 3435C>T SNP was able to influence GS-331007 metabolite plasma concentrations at 1 month of therapy in patients affected by the HCV-1 genotype (P = 0.030); median concentrations were 264 ng/mL (IQR 209–486.5 ng/mL) for CC patients and 363 ng/mL (IQR 286.3–587 ng/mL) for CT/TT ones. ABCB1 2677G>T polymorphism was associated with GS-331007 concentrations in all the analysed HCV genotypes. For GG versus GT/TT patients respectively, plasma levels were 252 (IQR 214.3–357) and 380 (291–357) for genotype 1 (P = 0.003); 216 (IQR/–/) and 191 (146–722) for genotype 2 (P = 0.048); 340 (IQR 154.3–412.5 ng/mL) and 299.5 ng/mL (181.8–455.3 ng/mL) for genotype 3 (P = 0.030); and 287.5 ng/mL (IQR 191–/ ng/mL) and 282 ng/mL (110–395 ng/mL) for genotype 4 (P < 0.001). Also HNF4α C>G was able to influence GS-331007 concentrations. In genotype 4, levels were 333 ng/mL (IQR 90–396.8 ng/mL) and 191 ng/mL (IQR 160.5–293 ng/mL) for CC and CG/GG genotypes, respectively (P < 0.001). Discussion The aim of this study was to investigate the role of SNPs in genes encoding transporters and the factors regulating them, in affecting sofosbuvir and its GS-331007 metabolite plasma concentrations at 1 month of therapy. Pharmacokinetic results showed that sofosbuvir concentrations were undetectable in all the analysed patients and only concentrations of GS-331007 were reported; this was owing to the extensive conversion of sofosbuvir into its metabolite.6 The following associations with GS-331007 metabolite plasma levels at 1 month of therapy were observed: ABCB1 2677G>T and HNF4α C>G SNPs in all the analysed patients, ABCB1 3435C>T and HNF4α 975C>G SNPs in patients treated with daclatasvir, ABCB1 2677G>T polymorphism in the ledipasvir treatment group and ABCB1 3435C>T, ABCB1 2677G>T, ABCG2 421C>A and ABCG2 1194+928C>A variants with ribavirin use. Regarding HCV genotypes, the ABCB1 3435C>T SNP in patients affected by HCV-1 genotype, ABCB1 2677G>T variant in all the analysed HCV genotypes and HNF4α C>G in genotype 4, were able to influence GS-331007 concentrations at 1 month of treatment. Linear regression analysis performed in all the patients, without stratification, showed that liver stiffness, insulin resistance, baseline haemoglobin and haematocrit and SNPs in the ABCB1 gene (3435 CT/TT and 1236 TT genotypes) were predictors of GS-331007 concentrations at 1 month of therapy. Particularly, baseline haemoglobin and ABCB1 3435 CT/TT genotypes were positive predictive factors, whereas others were negative. The effect of haematocrit on the plasma exposure of nucleoside analogues is explainable by the strong activity of red blood cells in absorbing nucleosides from plasma (‘nucleoside salvage’ mechanisms), as already reported in several works for different drugs.28,29 Few data are available in the literature concerning pharmacogenetics and DAAs; concerning the treatment of HCV infection, pharmacogenetics is considered an important factor that can be applied to assess benefits and risks.30 The genetics of IL28B and ITPA have played an important role in predicting outcome and toxicity in dual therapy, as well as the pharmacokinetics of ribavirin.17,23 The ABCB1 gene is expressed in many tissues and encodes the P-gp transporter, which removes chemical toxins and metabolites from cells into bile, urine and the intestinal lumen. Alterations in P-gp function may affect the bioavailability, distribution and clearance of many drugs.31–33 To date, more than 50 SNPs in the ABCB1 gene have been reported. ABCB1 3435, a synonymous SNP in exon 26 has been associated with reduced functionality of this transporter.34 In a 2007 study it was discovered that this silent SNP could alter the substrate specificity of the protein influencing protein folding and function.35 However, Soranzo et al.36 showed that, within and surrounding the ABCB1 gene, there is a region of high linkage disequilibrium and that other SNPs can potentially alter ABCB1 gene function, independently of the ABCB1 3435 variant. Regarding this SNP, in 2015 our group described a trend in telaprevir plasma concentrations at 1 month of therapy: CT and TT genotypes showed higher levels than the CC one. This analysis confirmed data present in the literature, but it was not statistically significant, probably owing to the small number of patients analysed.20 Our results suggest that the ABCB1 3435 CT/TT genotype group is a predictive factor of higher GS-331007 concentrations, thus confirming the results observed for telaprevir.20 ABCB1 1236 SNP (exon 12) is a synonymous SNP and several studies observed increased drug levels or treatment outcome associated with the CC genotype, or with the TT one, or neither genetic effect was found.34,37–40 We analysed this variant in a group of boceprevir-treated patients and a trend in the intracellular disposition of its S isomer was observed: CC and CT genotypes showed stronger penetrance than the TT one, thus we supposed that CC and CT genotypes were associated with a decreased P-gp activity.22 In this study, we found that TT genotype is predictive of lower GS-331007 plasma concentrations at 1 month of therapy, thus suggesting decreased P-gp activity.22 Anyway, we have to highlight that sofosbuvir, but not GS-331007, is a P-gp substrate. Therefore, the results of this work, suggesting an influence of ABCB1 gene SNPs on sofosbuvir metabolite concentrations, could be related to an indirect P-gp action; this carrier transports sofosbuvir in amounts dependent on P-gp activity, which is regulated by ABCB1 gene SNPs. Then, sofosbuvir is intracellularly converted into its GS-331007 metabolite in concentrations dependent on, as suggested, P-gp activity and gene regulation. This could be the reason why we observed a trend in GS-331007 concentrations, although it is not a P-gp substrate. Sofosbuvir metabolite plasma concentrations have been associated with SVR; for this reason, the ability to predict higher or lower levels, through genetic analysis, could be useful to clinicians to better manage HCV-infected patients.41 Conclusions Data on the relationship between pharmacogenetic and pharmacokinetic profiles of sofosbuvir are lacking in the literature. This is the first study, to our knowledge, reporting this kind of analysis, but further studies on larger cohorts are required to confirm these preliminary data. Acknowledgements We thank CoQua Lab (www.coqualab.it) for its methodological support and assistance in the preparation and execution of the study and analysis. Funding This study was supported by internal funding. Transparency declarations None to declare. References 1 Alter MJ. The epidemiology of acute and chronic hepatitis C . Clin Liver Dis 1997 ; 1 : 559 – 68 . vi–vii. Google Scholar CrossRef Search ADS PubMed 2 De Nicolo A , Boglione L , Ciancio A et al. Telaprevir-S isomer enhances ribavirin exposure and the ribavirin-related haemolytic anaemia in a concentration-dependent manner . Antiviral Res 2014 ; 109 : 7 – 14 . Google Scholar CrossRef Search ADS PubMed 3 Boglione L , De Nicolo A , Cusato J et al. 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Google Scholar PubMed 40 Zhang YT , Yang LP , Shao H et al. ABCB1 polymorphisms may have a minor effect on ciclosporin blood concentrations in myasthenia gravis patients . Br J Clin Pharmacol 2008 ; 66 : 240 – 6 . Google Scholar CrossRef Search ADS PubMed 41 de Kanter CT , Drenth JP , Arends JE et al. Viral hepatitis C therapy: pharmacokinetic and pharmacodynamic considerations . Clin Pharmacokinet 2014 ; 53 : 409 – 27 . Google Scholar CrossRef Search ADS PubMed © The Author(s) 2018. Published by Oxford University Press on behalf of the British Society for Antimicrobial Chemotherapy. All rights reserved. For Permissions, please email: journals.permissions@oup.com. This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/about_us/legal/notices) http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Antimicrobial Chemotherapy Oxford University Press

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Oxford University Press
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© The Author(s) 2018. Published by Oxford University Press on behalf of the British Society for Antimicrobial Chemotherapy. All rights reserved. For Permissions, please email: journals.permissions@oup.com.
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Abstract

Abstract Background Sofosbuvir is a potent nucleotide HCV NS5B polymerase inhibitor that is also a P-glycoprotein (encoded by the ABCB1 gene) and breast cancer resistance protein (encoded by the ABCG2 gene) substrate. Concerning previous anti-HCV therapies, pharmacogenetics had a significant impact, particularly considering the association of interleukin28B polymorphisms with dual-therapy (ribavirin + pegylated IFN) outcomes. Objectives In this work, we investigated the association between sofosbuvir and its prevalent metabolite (GS-331007) plasma concentrations at 1 month of therapy and genetic variants (SNPs) in genes encoding transporters and nuclear factors (ABCB1, ABCG2 and HNF4α) related to sofosbuvir transport. Patients and methods Allelic discrimination was performed through real-time PCR, whereas plasma concentrations were evaluated through liquid chromatography. One hundred and thirteen patients were enrolled. Results Sofosbuvir concentrations were below the limit of quantification since the drug was converted into its GS-331007 metabolite. ABCB1 2677 G>T (P = 0.044) and HNF4α 975 C>G (P = 0.049) SNPs were associated with GS-331007 metabolite plasma concentrations. In linear multivariate analysis, liver stiffness, insulin resistance, baseline haemoglobin and haematocrit and SNPs in the ABCB1 gene (3435 CT/TT and 1236 TT genotypes) were significant predictors of GS-331007 concentrations. Furthermore, we performed sub-analyses considering the anti-HCV concomitant drug and HCV genotype, identifying specific polymorphisms associated with GS-331007 plasma concentrations: ABCB1 3435 C>T and HNF4α975 C>G in patients treated with daclatasvir, ABCB1 2677 G>T with ledipasvir and ABCB1 3435 C>T, ABCB1 2677 G>T, ABCG2 421 C>A and ABCG2 1194 + 928 C>A with ribavirin. Conclusions In this study we suggested sofosbuvir GS-331007 metabolite plasma levels were affected by variants in the ABCB1 and HNFα genes. Introduction HCV infection affects about 170 million people worldwide and leads to several clinical consequences, including liver cirrhosis and hepatocellular carcinoma.1 For several years, the standard of care for chronic hepatitis C (CHC) treatment was dual therapy with ribavirin and pegylated IFN, but severe adverse effects were present. In 2011, the first two direct-acting antiviral drugs (DAAs) boceprevir and telaprevir were approved, achieving a higher percentage of sustained virological response (SVR), but also worsening the toxic effects.2,3 To date, new more effective DAAs, with pan-genotypic activity and with increased tolerability, have enhanced the probability of achieving SVR (nearly 100%) and led to a new scenario including shorter therapies and use of IFN- or ribavirin-free regimens.4 Sofosbuvir (SOF) is a potent nucleotide non-structural 5B polymerase inhibitor, approved for use in combination with ledipasvir, daclatasvir, simeprevir and ribavirin; the latter remains mandatory for specific groups as well as for chronic genotype 2 HCV infected, elderly and cirrhotic patients.5–8 Sofosbuvir is a low-molecular-weight prodrug that is stable in simulated gastric and intestinal fluids. Food is able to increase its mean plasma exposure <2-fold and its bioavailability following oral administration to dogs is 9.89%.9 Sofosbuvir is mainly metabolized in the liver and is intracellularly converted into different metabolites; among these, GS-331007 accounts for about 90% of the total systemic exposure and is excreted in urine. In vitro studies confirmed that sofosbuvir, but not the GS-331007 metabolite, is a P-glycoprotein (P-gp, encoded by the ABCB1 gene) substrate; furthermore sofosbuvir is transported by the breast cancer resistance protein (BCRP, encoded by the ABCG2 gene).10–13 It is known that variants in genes encoding transporters can affect their activity; pharmacogenetics is the study of inherited variability in drug response and it investigates SNPs, which are variations of a single DNA base present in at least 1% of the population. Pharmacogenetics had a significant impact on the contest between previous anti-HCV therapies; for instance, IL28B (encoding IFN-λ) and SLC28A2 (encoding concentrative nucleotide transporter 2) gene SNPs were associated with the probability of achieving SVR, whereas the ones in the ITPA gene (encoding inosine triphosphatase enzymes) added to the risk of developing the main adverse effect, anaemia. Similarly, ABCB1 and ABCB11 (encoding biliary salt export pump, BSEP) were associated with telaprevir and boceprevir exposure, whereas vitamin D-related genes influenced the therapeutic outcome, the onset of anaemia and cryoglobulinaemia.14–24 At present, few data are available in the literature concerning pharmacogenetics and DAAs; for example, Murakawa et al.25 showed that ITPA polymorphisms may still have a role in the prediction of anaemia in patients treated with sofosbuvir plus ribavirin; on the other hand, since the achievement of SVR with new drugs is near to 100%, the involvement of IL28B SNPs seems to be minor.26 In this scenario, we chose to investigate whether variants in genes encoding transporters and nuclear factors (involved in the regulation of transporter expression) could have an influence on the prediction of sofosbuvir and GS-331007 plasma concentrations at 1 month of therapy. Materials and methods Characteristics of enrolled patients In this study, 243 CHC adult patients treated with sofosbuvir (in combination with ribavirin and/or ledipasvir, daclatasvir or simeprevir) at the ‘Amedeo di Savoia’ hospital (Turin, Italy) from 2014 to 2016, were retrospectively analysed. Patients without other viral co-infections (hepatitis B or HIV) and without major contraindications to the treatment were included. All patients received sofosbuvir at 400 mg daily. The study was conducted in compliance with the Declaration of Helsinki and local review board regulations; all patients gave written informed consent, according to the local ethics committee standards (‘Kinetic-C protocol’, approved by Ethics Committee ‘A.O.U. S. Luigi Gonzaga, Orbassano, Turin’, no. 186/14 on 26 May 2015). Pharmacogenetic analyses Genomic DNA was isolated from blood samples (MagNA Pure Compact, Roche, Monza, Italy). Genotypes were assessed through a real-time PCR allelic discrimination system (LightCycler 96, Roche, Monza, Italy). Investigated gene SNPs were ABCB1 3435 C>T (rs1045642), ABCB1 1236C>T (rs1128503), ABCB1 2677G>T (rs2032582), ABCB11 1131T>C (rs2287622), ABCG2 421C>A (rs2231142), ABCG2 1194 + 928C>A (rs13120400) and HNF4α 975C>G (rs1884613). Pharmacokinetic analyses Blood sampling for the pharmacokinetic evaluation was performed at the end of the dosing interval (Ctrough), immediately before the new dose was taken. Blood samples were centrifuged at 1400 g for 10 min at 4°C, in order to obtain plasma samples. The quantification of sofosbuvir and GS-331007 in patients’ plasma was performed through a previously validated and published UHPLC-MS/MS method.27 Statistical analyses All the variables were tested for normality through the Shapiro–Wilk test. Non-normal variables were described as median values and IQR; categorical variables were described as numbers and percentages. All the polymorphisms were tested for Hardy–Weinberg equilibrium by the χ2 test, in order to determine the observed genotype frequencies. Kruskal–Wallis and Mann–Whitney tests were used to test differences in continuous variables between genetic groups, considering the level of statistical significance P < 0.05. The predictive power of the considered variables was finally evaluated through univariate (P < 0.2) and multivariate (P < 0.05) linear regression analysis. All the tests were performed with IBM SPSS Statistics 24.0 for Windows (Chicago, Illinois, USA). Results All the investigated data were available for 113 patients (pharmacogenetic, pharmacokinetic, HCV genotype or concomitant drug information were lacking for the other 130 patients). Characteristics of analysed patients are reported in Table 1. The variant allele frequencies of studied polymorphisms are shown in Table 2. Since sofosbuvir concentrations were always undetectable, only GS-331007 plasma concentrations were reported. Table 1. Baseline characteristics of the studied population Characteristic Value No. of patients 113 Age, years, median (IQR) 52 (48–60) Male sex, n (%) 23 (20.4) BMI, kg/m2, median (IQR) 22 (20–24) Cirrhotics, n (%) 40 (35.4) HCV-RNA at baseline, log IU/mL, median (IQR) 6.2 (5.7–6.7) ALT at baseline, IU/mL, median (IQR) 77 (49.5–110.5) Albumin at baseline, IU/mL, median (IQR) 39 (32.5–42) Haemoglobin at baseline, IU/mL, median (IQR) 14.8 (13.7–15.7) Haematocrit at baseline, IU/mL, median (IQR) 43.9 (40.8–45.8) Mean corpuscular volume at baseline, IU/mL, median (IQR) 90.7 (86.7–94.5) Estimated glomerular filtration rate (eGFR) at baseline, IU/mL, median (IQR) 108.8 (85.7–121) Hepatocellular carcinoma, n (%) 3 (2.7) Patients treated with ribavirin, n (%) 34 (30.1) Patients treated with simeprevir, n (%) 18 (15.9) Patients treated with daclatasvir, n (%) 44 (38.9) Patients treated with ledipasvir, n (%) 42 (37.2) GS-331007 concentrations at 1 month of therapy, ng/mL, median (IQR) 326 (213–488) Ribavirin concentrations at 1 month of therapy, ng/mL, median (IQR) 1637 (1321.5–2042) Simeprevir concentrations at 1 month of therapy, ng/mL, median (IQR) 860.5 (469.8–1535.5) Daclatasvir concentrations at 1 month of therapy, ng/mL, median (IQR) 188.5 (100–294) Ledipasvir concentrations at 1 month of therapy, ng/mL, median (IQR) 231.5 (168.8–332.3) Liver stiffness, kPa, median (IQR) 21.3 (12.1–33.2) Ribavirin dose, n (%)  600 mg 1 (2.9)  800 mg 8 (23.5)  1000 mg 15 (44.1)  1200 mg 10 (29.4) Genotype, n (%)  1 63 (55.8)  2 8 (7.1)  3 32 (28.3)  4 10 (8.8) Cryoglobulinaemia, n (%) 41 (36.3) Characteristic Value No. of patients 113 Age, years, median (IQR) 52 (48–60) Male sex, n (%) 23 (20.4) BMI, kg/m2, median (IQR) 22 (20–24) Cirrhotics, n (%) 40 (35.4) HCV-RNA at baseline, log IU/mL, median (IQR) 6.2 (5.7–6.7) ALT at baseline, IU/mL, median (IQR) 77 (49.5–110.5) Albumin at baseline, IU/mL, median (IQR) 39 (32.5–42) Haemoglobin at baseline, IU/mL, median (IQR) 14.8 (13.7–15.7) Haematocrit at baseline, IU/mL, median (IQR) 43.9 (40.8–45.8) Mean corpuscular volume at baseline, IU/mL, median (IQR) 90.7 (86.7–94.5) Estimated glomerular filtration rate (eGFR) at baseline, IU/mL, median (IQR) 108.8 (85.7–121) Hepatocellular carcinoma, n (%) 3 (2.7) Patients treated with ribavirin, n (%) 34 (30.1) Patients treated with simeprevir, n (%) 18 (15.9) Patients treated with daclatasvir, n (%) 44 (38.9) Patients treated with ledipasvir, n (%) 42 (37.2) GS-331007 concentrations at 1 month of therapy, ng/mL, median (IQR) 326 (213–488) Ribavirin concentrations at 1 month of therapy, ng/mL, median (IQR) 1637 (1321.5–2042) Simeprevir concentrations at 1 month of therapy, ng/mL, median (IQR) 860.5 (469.8–1535.5) Daclatasvir concentrations at 1 month of therapy, ng/mL, median (IQR) 188.5 (100–294) Ledipasvir concentrations at 1 month of therapy, ng/mL, median (IQR) 231.5 (168.8–332.3) Liver stiffness, kPa, median (IQR) 21.3 (12.1–33.2) Ribavirin dose, n (%)  600 mg 1 (2.9)  800 mg 8 (23.5)  1000 mg 15 (44.1)  1200 mg 10 (29.4) Genotype, n (%)  1 63 (55.8)  2 8 (7.1)  3 32 (28.3)  4 10 (8.8) Cryoglobulinaemia, n (%) 41 (36.3) Table 1. Baseline characteristics of the studied population Characteristic Value No. of patients 113 Age, years, median (IQR) 52 (48–60) Male sex, n (%) 23 (20.4) BMI, kg/m2, median (IQR) 22 (20–24) Cirrhotics, n (%) 40 (35.4) HCV-RNA at baseline, log IU/mL, median (IQR) 6.2 (5.7–6.7) ALT at baseline, IU/mL, median (IQR) 77 (49.5–110.5) Albumin at baseline, IU/mL, median (IQR) 39 (32.5–42) Haemoglobin at baseline, IU/mL, median (IQR) 14.8 (13.7–15.7) Haematocrit at baseline, IU/mL, median (IQR) 43.9 (40.8–45.8) Mean corpuscular volume at baseline, IU/mL, median (IQR) 90.7 (86.7–94.5) Estimated glomerular filtration rate (eGFR) at baseline, IU/mL, median (IQR) 108.8 (85.7–121) Hepatocellular carcinoma, n (%) 3 (2.7) Patients treated with ribavirin, n (%) 34 (30.1) Patients treated with simeprevir, n (%) 18 (15.9) Patients treated with daclatasvir, n (%) 44 (38.9) Patients treated with ledipasvir, n (%) 42 (37.2) GS-331007 concentrations at 1 month of therapy, ng/mL, median (IQR) 326 (213–488) Ribavirin concentrations at 1 month of therapy, ng/mL, median (IQR) 1637 (1321.5–2042) Simeprevir concentrations at 1 month of therapy, ng/mL, median (IQR) 860.5 (469.8–1535.5) Daclatasvir concentrations at 1 month of therapy, ng/mL, median (IQR) 188.5 (100–294) Ledipasvir concentrations at 1 month of therapy, ng/mL, median (IQR) 231.5 (168.8–332.3) Liver stiffness, kPa, median (IQR) 21.3 (12.1–33.2) Ribavirin dose, n (%)  600 mg 1 (2.9)  800 mg 8 (23.5)  1000 mg 15 (44.1)  1200 mg 10 (29.4) Genotype, n (%)  1 63 (55.8)  2 8 (7.1)  3 32 (28.3)  4 10 (8.8) Cryoglobulinaemia, n (%) 41 (36.3) Characteristic Value No. of patients 113 Age, years, median (IQR) 52 (48–60) Male sex, n (%) 23 (20.4) BMI, kg/m2, median (IQR) 22 (20–24) Cirrhotics, n (%) 40 (35.4) HCV-RNA at baseline, log IU/mL, median (IQR) 6.2 (5.7–6.7) ALT at baseline, IU/mL, median (IQR) 77 (49.5–110.5) Albumin at baseline, IU/mL, median (IQR) 39 (32.5–42) Haemoglobin at baseline, IU/mL, median (IQR) 14.8 (13.7–15.7) Haematocrit at baseline, IU/mL, median (IQR) 43.9 (40.8–45.8) Mean corpuscular volume at baseline, IU/mL, median (IQR) 90.7 (86.7–94.5) Estimated glomerular filtration rate (eGFR) at baseline, IU/mL, median (IQR) 108.8 (85.7–121) Hepatocellular carcinoma, n (%) 3 (2.7) Patients treated with ribavirin, n (%) 34 (30.1) Patients treated with simeprevir, n (%) 18 (15.9) Patients treated with daclatasvir, n (%) 44 (38.9) Patients treated with ledipasvir, n (%) 42 (37.2) GS-331007 concentrations at 1 month of therapy, ng/mL, median (IQR) 326 (213–488) Ribavirin concentrations at 1 month of therapy, ng/mL, median (IQR) 1637 (1321.5–2042) Simeprevir concentrations at 1 month of therapy, ng/mL, median (IQR) 860.5 (469.8–1535.5) Daclatasvir concentrations at 1 month of therapy, ng/mL, median (IQR) 188.5 (100–294) Ledipasvir concentrations at 1 month of therapy, ng/mL, median (IQR) 231.5 (168.8–332.3) Liver stiffness, kPa, median (IQR) 21.3 (12.1–33.2) Ribavirin dose, n (%)  600 mg 1 (2.9)  800 mg 8 (23.5)  1000 mg 15 (44.1)  1200 mg 10 (29.4) Genotype, n (%)  1 63 (55.8)  2 8 (7.1)  3 32 (28.3)  4 10 (8.8) Cryoglobulinaemia, n (%) 41 (36.3) Table 2. Variant allele frequencies Allele frequency (%) SNP homozygous wild type heterozygous homozygous mutant ABCB1 3435 C>T 28.3 CC 45.1 CT 26.5 TT ABCB1 1236 C>T 35.4 CC 42.5 CT 22.1 TT ABCB1 2677 G>T 23.9 CC 37.2 CT 38.9 TT ABCB11 1131 T>C 21.2 TT 40.7 TC 38.1 CC ABCG2 421 C>A 83.2 CC 16.8 CA – ABCG2 1194 + 928 T>C 61.1 TT 36.3 TC 2.7 CC HNF4α 975 C>G 62.8 CC 34.5 CG 2.7 GG Allele frequency (%) SNP homozygous wild type heterozygous homozygous mutant ABCB1 3435 C>T 28.3 CC 45.1 CT 26.5 TT ABCB1 1236 C>T 35.4 CC 42.5 CT 22.1 TT ABCB1 2677 G>T 23.9 CC 37.2 CT 38.9 TT ABCB11 1131 T>C 21.2 TT 40.7 TC 38.1 CC ABCG2 421 C>A 83.2 CC 16.8 CA – ABCG2 1194 + 928 T>C 61.1 TT 36.3 TC 2.7 CC HNF4α 975 C>G 62.8 CC 34.5 CG 2.7 GG Table 2. Variant allele frequencies Allele frequency (%) SNP homozygous wild type heterozygous homozygous mutant ABCB1 3435 C>T 28.3 CC 45.1 CT 26.5 TT ABCB1 1236 C>T 35.4 CC 42.5 CT 22.1 TT ABCB1 2677 G>T 23.9 CC 37.2 CT 38.9 TT ABCB11 1131 T>C 21.2 TT 40.7 TC 38.1 CC ABCG2 421 C>A 83.2 CC 16.8 CA – ABCG2 1194 + 928 T>C 61.1 TT 36.3 TC 2.7 CC HNF4α 975 C>G 62.8 CC 34.5 CG 2.7 GG Allele frequency (%) SNP homozygous wild type heterozygous homozygous mutant ABCB1 3435 C>T 28.3 CC 45.1 CT 26.5 TT ABCB1 1236 C>T 35.4 CC 42.5 CT 22.1 TT ABCB1 2677 G>T 23.9 CC 37.2 CT 38.9 TT ABCB11 1131 T>C 21.2 TT 40.7 TC 38.1 CC ABCG2 421 C>A 83.2 CC 16.8 CA – ABCG2 1194 + 928 T>C 61.1 TT 36.3 TC 2.7 CC HNF4α 975 C>G 62.8 CC 34.5 CG 2.7 GG ABCB1 2677G>T (P = 0.044) and HNF4α C>G (P = 0.049) SNPs were found to be associated with GS-331007 metabolite plasma concentrations at 1 month of therapy. Regarding ABCB1 2677G>T SNP (Figure 1), GG genotype patients showed median plasma levels of 260 ng/mL (IQR 203–384 ng/mL), whereas the GT/TT genotype patients had a median concentration of 345 ng/mL (IQR 221.8–506.3 ng/mL). Concerning the HNF4α C>G polymorphism (Figure 2), CC genotype patients had a median GS-331007 concentration of 366 ng/mL (IQR 237–503 ng/mL), whereas for GC/CC genotypes this was 270.5 ng/mL (IQR 196.8–407.3 ng/mL). Figure 1. View largeDownload slide ABCB1 2677 G>T SNP influence on GS-331007 metabolite concentrations at 1 month of therapy. Figure 1. View largeDownload slide ABCB1 2677 G>T SNP influence on GS-331007 metabolite concentrations at 1 month of therapy. Figure 2. View largeDownload slide HNF4α C>G SNP influence on GS-331007 metabolite concentrations at 1 month of therapy. Figure 2. View largeDownload slide HNF4α C>G SNP influence on GS-331007 metabolite concentrations at 1 month of therapy. Linear regression analysis was performed to clarify which factors (demographic, clinical or genetic ones) were able to predict the GS-331007 metabolite concentrations at 1 month of therapy (Table 3). In the multivariate analysis, liver stiffness, insulin resistance, baseline haemoglobin and haematocrit and SNPs in the ABCB1 gene (3435 CT/TT and 1236 TT genotypes) were significant predictors. In particular, baseline haemoglobin and ABCB1 3435 CT/TT genotypes were positive predictive factors, whereas the others were negative. Table 3. Linear regression analyses: factors able to predict GS-331007 metabolite concentrations at 1 month of therapy GS-331007 metabolite concentrations at 1 month of therapy univariate multivariate Characteristic P value OR (95% CI) P value OR (95% CI) BMI at baseline 0.047 −15.623 (−31.020; −0.226) 0.598 −9.796 (−48.907; 29.316) Age 0.051 6.180 (−0.024; 12.384) 0.547 4.348 (−10.629; 19.325) Liver stiffness 0.186 3.310 (−1.621; 8.240) 0.025 −10.048 (−18.690; −1.406) Viral genotype 0.252 −39.380 (−107.128; 28.367) Sex 0.045 180.908 (3.775; 358.041) 0.609 79.324 (−243.957; 402.604) Cirrhosis 0.688 31.796 (−124.828; 188.419) Child Score 0.094 91.170 (−15.723; 198.063) 0.614 −99.613 (−513.934; 314.707) MELD Score 0.010 43.647 (10.906; 76.389) 0.339 54.028 (−61.714; 169.770) Cryoglobulinaemia 0.444 62.761 (−99.293; 224.815) Insulin resistance 0.131 −142.219 (−328.092; 43.653) 0.018 −386.438 (−697.505; −75.371) Ribavirin treatment 0.035 −165.853 (−319.486; −12.220) 0.849 −56.051 (−687.391; 575.290) Simeprevir treatment 0.217 −123.361 (−320.452; 73.729) Daclatasvir treatment 0.265 83.673 (−64.430; 231.775) Ledipasvir treatment 0.389 65.373 (−84.403; 215.148) Baseline haemoglobin 0.048 −42.051 (−83.739; −0.364) <0.001 688.235 (350.813; 1025.657) Baseline haematocrit 0.078 −12.225 (−25.861; 1.411) <0.001 −295.316 (−427.391; −163.240) Baseline mean corpuscular volume 0.897 −0.728 (−11.825; 10.368) Baseline ALT 0.201 −0.459 (−1.168; 0.249) Baseline eGFR <0.001 −7.536 (−10.346; −4.726) 0.821 −0.545 (−5.687; 4.598) Baseline albumin 0.739 −1.400 (−9.737; 6.938) ABCB1 3435 CT/TT 0.155 −115.459 (−44.260; 275.179) 0.017 312.111 (63.344; 560.878) ABCB1 1236 TT 0.146 −127.901 (−301.200; 45.398) 0.087 −245.222 (−529.989; 39.545) ABCB1 2677 GT/TT 0.075 152.233 (−15.645; 320.112) 0.282 −162.453 (−470.044; 145.139) ABCB11 1131 TC/CC 0.866 −15.127 (−192.667; 162.413) ABCG2 421 CA 0.897 −12.770 (−206.939; 181.398) ABCG2 1194 + 928 TC/CC 0.319 74.851 (−73.418; 223.121) HNF4α 975 CG/GG 0.237 −89.690 (−239.019; 59.639) GS-331007 metabolite concentrations at 1 month of therapy univariate multivariate Characteristic P value OR (95% CI) P value OR (95% CI) BMI at baseline 0.047 −15.623 (−31.020; −0.226) 0.598 −9.796 (−48.907; 29.316) Age 0.051 6.180 (−0.024; 12.384) 0.547 4.348 (−10.629; 19.325) Liver stiffness 0.186 3.310 (−1.621; 8.240) 0.025 −10.048 (−18.690; −1.406) Viral genotype 0.252 −39.380 (−107.128; 28.367) Sex 0.045 180.908 (3.775; 358.041) 0.609 79.324 (−243.957; 402.604) Cirrhosis 0.688 31.796 (−124.828; 188.419) Child Score 0.094 91.170 (−15.723; 198.063) 0.614 −99.613 (−513.934; 314.707) MELD Score 0.010 43.647 (10.906; 76.389) 0.339 54.028 (−61.714; 169.770) Cryoglobulinaemia 0.444 62.761 (−99.293; 224.815) Insulin resistance 0.131 −142.219 (−328.092; 43.653) 0.018 −386.438 (−697.505; −75.371) Ribavirin treatment 0.035 −165.853 (−319.486; −12.220) 0.849 −56.051 (−687.391; 575.290) Simeprevir treatment 0.217 −123.361 (−320.452; 73.729) Daclatasvir treatment 0.265 83.673 (−64.430; 231.775) Ledipasvir treatment 0.389 65.373 (−84.403; 215.148) Baseline haemoglobin 0.048 −42.051 (−83.739; −0.364) <0.001 688.235 (350.813; 1025.657) Baseline haematocrit 0.078 −12.225 (−25.861; 1.411) <0.001 −295.316 (−427.391; −163.240) Baseline mean corpuscular volume 0.897 −0.728 (−11.825; 10.368) Baseline ALT 0.201 −0.459 (−1.168; 0.249) Baseline eGFR <0.001 −7.536 (−10.346; −4.726) 0.821 −0.545 (−5.687; 4.598) Baseline albumin 0.739 −1.400 (−9.737; 6.938) ABCB1 3435 CT/TT 0.155 −115.459 (−44.260; 275.179) 0.017 312.111 (63.344; 560.878) ABCB1 1236 TT 0.146 −127.901 (−301.200; 45.398) 0.087 −245.222 (−529.989; 39.545) ABCB1 2677 GT/TT 0.075 152.233 (−15.645; 320.112) 0.282 −162.453 (−470.044; 145.139) ABCB11 1131 TC/CC 0.866 −15.127 (−192.667; 162.413) ABCG2 421 CA 0.897 −12.770 (−206.939; 181.398) ABCG2 1194 + 928 TC/CC 0.319 74.851 (−73.418; 223.121) HNF4α 975 CG/GG 0.237 −89.690 (−239.019; 59.639) Statistically significant values are shown in bold. MELD, Model for End-Stage Liver Disease. Table 3. Linear regression analyses: factors able to predict GS-331007 metabolite concentrations at 1 month of therapy GS-331007 metabolite concentrations at 1 month of therapy univariate multivariate Characteristic P value OR (95% CI) P value OR (95% CI) BMI at baseline 0.047 −15.623 (−31.020; −0.226) 0.598 −9.796 (−48.907; 29.316) Age 0.051 6.180 (−0.024; 12.384) 0.547 4.348 (−10.629; 19.325) Liver stiffness 0.186 3.310 (−1.621; 8.240) 0.025 −10.048 (−18.690; −1.406) Viral genotype 0.252 −39.380 (−107.128; 28.367) Sex 0.045 180.908 (3.775; 358.041) 0.609 79.324 (−243.957; 402.604) Cirrhosis 0.688 31.796 (−124.828; 188.419) Child Score 0.094 91.170 (−15.723; 198.063) 0.614 −99.613 (−513.934; 314.707) MELD Score 0.010 43.647 (10.906; 76.389) 0.339 54.028 (−61.714; 169.770) Cryoglobulinaemia 0.444 62.761 (−99.293; 224.815) Insulin resistance 0.131 −142.219 (−328.092; 43.653) 0.018 −386.438 (−697.505; −75.371) Ribavirin treatment 0.035 −165.853 (−319.486; −12.220) 0.849 −56.051 (−687.391; 575.290) Simeprevir treatment 0.217 −123.361 (−320.452; 73.729) Daclatasvir treatment 0.265 83.673 (−64.430; 231.775) Ledipasvir treatment 0.389 65.373 (−84.403; 215.148) Baseline haemoglobin 0.048 −42.051 (−83.739; −0.364) <0.001 688.235 (350.813; 1025.657) Baseline haematocrit 0.078 −12.225 (−25.861; 1.411) <0.001 −295.316 (−427.391; −163.240) Baseline mean corpuscular volume 0.897 −0.728 (−11.825; 10.368) Baseline ALT 0.201 −0.459 (−1.168; 0.249) Baseline eGFR <0.001 −7.536 (−10.346; −4.726) 0.821 −0.545 (−5.687; 4.598) Baseline albumin 0.739 −1.400 (−9.737; 6.938) ABCB1 3435 CT/TT 0.155 −115.459 (−44.260; 275.179) 0.017 312.111 (63.344; 560.878) ABCB1 1236 TT 0.146 −127.901 (−301.200; 45.398) 0.087 −245.222 (−529.989; 39.545) ABCB1 2677 GT/TT 0.075 152.233 (−15.645; 320.112) 0.282 −162.453 (−470.044; 145.139) ABCB11 1131 TC/CC 0.866 −15.127 (−192.667; 162.413) ABCG2 421 CA 0.897 −12.770 (−206.939; 181.398) ABCG2 1194 + 928 TC/CC 0.319 74.851 (−73.418; 223.121) HNF4α 975 CG/GG 0.237 −89.690 (−239.019; 59.639) GS-331007 metabolite concentrations at 1 month of therapy univariate multivariate Characteristic P value OR (95% CI) P value OR (95% CI) BMI at baseline 0.047 −15.623 (−31.020; −0.226) 0.598 −9.796 (−48.907; 29.316) Age 0.051 6.180 (−0.024; 12.384) 0.547 4.348 (−10.629; 19.325) Liver stiffness 0.186 3.310 (−1.621; 8.240) 0.025 −10.048 (−18.690; −1.406) Viral genotype 0.252 −39.380 (−107.128; 28.367) Sex 0.045 180.908 (3.775; 358.041) 0.609 79.324 (−243.957; 402.604) Cirrhosis 0.688 31.796 (−124.828; 188.419) Child Score 0.094 91.170 (−15.723; 198.063) 0.614 −99.613 (−513.934; 314.707) MELD Score 0.010 43.647 (10.906; 76.389) 0.339 54.028 (−61.714; 169.770) Cryoglobulinaemia 0.444 62.761 (−99.293; 224.815) Insulin resistance 0.131 −142.219 (−328.092; 43.653) 0.018 −386.438 (−697.505; −75.371) Ribavirin treatment 0.035 −165.853 (−319.486; −12.220) 0.849 −56.051 (−687.391; 575.290) Simeprevir treatment 0.217 −123.361 (−320.452; 73.729) Daclatasvir treatment 0.265 83.673 (−64.430; 231.775) Ledipasvir treatment 0.389 65.373 (−84.403; 215.148) Baseline haemoglobin 0.048 −42.051 (−83.739; −0.364) <0.001 688.235 (350.813; 1025.657) Baseline haematocrit 0.078 −12.225 (−25.861; 1.411) <0.001 −295.316 (−427.391; −163.240) Baseline mean corpuscular volume 0.897 −0.728 (−11.825; 10.368) Baseline ALT 0.201 −0.459 (−1.168; 0.249) Baseline eGFR <0.001 −7.536 (−10.346; −4.726) 0.821 −0.545 (−5.687; 4.598) Baseline albumin 0.739 −1.400 (−9.737; 6.938) ABCB1 3435 CT/TT 0.155 −115.459 (−44.260; 275.179) 0.017 312.111 (63.344; 560.878) ABCB1 1236 TT 0.146 −127.901 (−301.200; 45.398) 0.087 −245.222 (−529.989; 39.545) ABCB1 2677 GT/TT 0.075 152.233 (−15.645; 320.112) 0.282 −162.453 (−470.044; 145.139) ABCB11 1131 TC/CC 0.866 −15.127 (−192.667; 162.413) ABCG2 421 CA 0.897 −12.770 (−206.939; 181.398) ABCG2 1194 + 928 TC/CC 0.319 74.851 (−73.418; 223.121) HNF4α 975 CG/GG 0.237 −89.690 (−239.019; 59.639) Statistically significant values are shown in bold. MELD, Model for End-Stage Liver Disease. Sub-analysis for concomitant drugs We performed another analysis, stratifying patients depending on their concomitant anti-HCV administered drug: 18 patients with simeprevir, 44 with daclatasvir, 42 with ledipasvir and 34 with ribavirin. ABCB1 3435C>T SNP influenced GS-331007 metabolite levels at 1 month of therapy in patients treated with daclatasvir (P = 0.031); median GS-331007 concentrations were 203.5 ng/mL (IQR 134.8–435.8 ng/mL) for CC patients and 453 ng/mL (IQR 184–/ ng/mL) for CT/TT. ABCB1 2677G>T polymorphism was associated with GS-331007 concentrations in the ledipasvir treatment group (P = 0.025); for GG and GT/TT genotypes respectively, GS-331007 levels were 340 ng/mL (IQR 154.5–412.5 ng/mL) and 299.5 ng/mL (181.8–455.3 ng/mL). HNF4α 975C>G SNP was able to affect sofosbuvir metabolite exposure at 1 month of treatment (P = 0.003) in patients treated with daclatasvir. In fact, median concentrations were 453 ng/mL (IQR 155–741 ng/mL) and 203 ng/mL (IQR 124–303 ng/mL) for CC versus CG/GG genotypes. Concerning ribavirin use, ABCB1 3435C>T (P = 0.008), ABCB1 2677G>T (P = 0.039), ABCG2 421C>A (P = 0.008) and ABCG2 1194+928C>A (P = 0.005) variants were associated with GS-331007 plasma concentrations. In detail, the median concentrations were 319 ng/mL (IQR 193–427 ng/mL) and 149 ng/mL (IQR 105–262 ng/mL) for ABCB1 3435 CC versus CT/TT genotypes, 282 ng/mL (IQR 184–375 ng/mL) and 216 ng/mL (IQR 149–405 ng/mL) for ABCB1 2677 GG versus GT/TT genotypes, respectively, 274 ng/mL (IQR 133–496 ng/mL) and 259 ng/mL (IQR 179.5–373 ng/mL) for ABCG2 421 CC versus CA/AA genotypes, and 250 ng/mL (IQR 172–419 ng/mL) and 272 ng/mL (IQR 158–373 ng/mL) for ABCG2 1194+928 CC versus CA/AA genotypes. Sub-analysis for HCV genotypes We also analysed dissimilarities between the different HCV genotypes: 64 patients belonged to HCV-1, 8 to HCV-2, 32 to HCV-3 and 9 to HCV-4. The following differences were suggested: ABCB1 3435C>T SNP was able to influence GS-331007 metabolite plasma concentrations at 1 month of therapy in patients affected by the HCV-1 genotype (P = 0.030); median concentrations were 264 ng/mL (IQR 209–486.5 ng/mL) for CC patients and 363 ng/mL (IQR 286.3–587 ng/mL) for CT/TT ones. ABCB1 2677G>T polymorphism was associated with GS-331007 concentrations in all the analysed HCV genotypes. For GG versus GT/TT patients respectively, plasma levels were 252 (IQR 214.3–357) and 380 (291–357) for genotype 1 (P = 0.003); 216 (IQR/–/) and 191 (146–722) for genotype 2 (P = 0.048); 340 (IQR 154.3–412.5 ng/mL) and 299.5 ng/mL (181.8–455.3 ng/mL) for genotype 3 (P = 0.030); and 287.5 ng/mL (IQR 191–/ ng/mL) and 282 ng/mL (110–395 ng/mL) for genotype 4 (P < 0.001). Also HNF4α C>G was able to influence GS-331007 concentrations. In genotype 4, levels were 333 ng/mL (IQR 90–396.8 ng/mL) and 191 ng/mL (IQR 160.5–293 ng/mL) for CC and CG/GG genotypes, respectively (P < 0.001). Discussion The aim of this study was to investigate the role of SNPs in genes encoding transporters and the factors regulating them, in affecting sofosbuvir and its GS-331007 metabolite plasma concentrations at 1 month of therapy. Pharmacokinetic results showed that sofosbuvir concentrations were undetectable in all the analysed patients and only concentrations of GS-331007 were reported; this was owing to the extensive conversion of sofosbuvir into its metabolite.6 The following associations with GS-331007 metabolite plasma levels at 1 month of therapy were observed: ABCB1 2677G>T and HNF4α C>G SNPs in all the analysed patients, ABCB1 3435C>T and HNF4α 975C>G SNPs in patients treated with daclatasvir, ABCB1 2677G>T polymorphism in the ledipasvir treatment group and ABCB1 3435C>T, ABCB1 2677G>T, ABCG2 421C>A and ABCG2 1194+928C>A variants with ribavirin use. Regarding HCV genotypes, the ABCB1 3435C>T SNP in patients affected by HCV-1 genotype, ABCB1 2677G>T variant in all the analysed HCV genotypes and HNF4α C>G in genotype 4, were able to influence GS-331007 concentrations at 1 month of treatment. Linear regression analysis performed in all the patients, without stratification, showed that liver stiffness, insulin resistance, baseline haemoglobin and haematocrit and SNPs in the ABCB1 gene (3435 CT/TT and 1236 TT genotypes) were predictors of GS-331007 concentrations at 1 month of therapy. Particularly, baseline haemoglobin and ABCB1 3435 CT/TT genotypes were positive predictive factors, whereas others were negative. The effect of haematocrit on the plasma exposure of nucleoside analogues is explainable by the strong activity of red blood cells in absorbing nucleosides from plasma (‘nucleoside salvage’ mechanisms), as already reported in several works for different drugs.28,29 Few data are available in the literature concerning pharmacogenetics and DAAs; concerning the treatment of HCV infection, pharmacogenetics is considered an important factor that can be applied to assess benefits and risks.30 The genetics of IL28B and ITPA have played an important role in predicting outcome and toxicity in dual therapy, as well as the pharmacokinetics of ribavirin.17,23 The ABCB1 gene is expressed in many tissues and encodes the P-gp transporter, which removes chemical toxins and metabolites from cells into bile, urine and the intestinal lumen. Alterations in P-gp function may affect the bioavailability, distribution and clearance of many drugs.31–33 To date, more than 50 SNPs in the ABCB1 gene have been reported. ABCB1 3435, a synonymous SNP in exon 26 has been associated with reduced functionality of this transporter.34 In a 2007 study it was discovered that this silent SNP could alter the substrate specificity of the protein influencing protein folding and function.35 However, Soranzo et al.36 showed that, within and surrounding the ABCB1 gene, there is a region of high linkage disequilibrium and that other SNPs can potentially alter ABCB1 gene function, independently of the ABCB1 3435 variant. Regarding this SNP, in 2015 our group described a trend in telaprevir plasma concentrations at 1 month of therapy: CT and TT genotypes showed higher levels than the CC one. This analysis confirmed data present in the literature, but it was not statistically significant, probably owing to the small number of patients analysed.20 Our results suggest that the ABCB1 3435 CT/TT genotype group is a predictive factor of higher GS-331007 concentrations, thus confirming the results observed for telaprevir.20 ABCB1 1236 SNP (exon 12) is a synonymous SNP and several studies observed increased drug levels or treatment outcome associated with the CC genotype, or with the TT one, or neither genetic effect was found.34,37–40 We analysed this variant in a group of boceprevir-treated patients and a trend in the intracellular disposition of its S isomer was observed: CC and CT genotypes showed stronger penetrance than the TT one, thus we supposed that CC and CT genotypes were associated with a decreased P-gp activity.22 In this study, we found that TT genotype is predictive of lower GS-331007 plasma concentrations at 1 month of therapy, thus suggesting decreased P-gp activity.22 Anyway, we have to highlight that sofosbuvir, but not GS-331007, is a P-gp substrate. Therefore, the results of this work, suggesting an influence of ABCB1 gene SNPs on sofosbuvir metabolite concentrations, could be related to an indirect P-gp action; this carrier transports sofosbuvir in amounts dependent on P-gp activity, which is regulated by ABCB1 gene SNPs. Then, sofosbuvir is intracellularly converted into its GS-331007 metabolite in concentrations dependent on, as suggested, P-gp activity and gene regulation. This could be the reason why we observed a trend in GS-331007 concentrations, although it is not a P-gp substrate. Sofosbuvir metabolite plasma concentrations have been associated with SVR; for this reason, the ability to predict higher or lower levels, through genetic analysis, could be useful to clinicians to better manage HCV-infected patients.41 Conclusions Data on the relationship between pharmacogenetic and pharmacokinetic profiles of sofosbuvir are lacking in the literature. This is the first study, to our knowledge, reporting this kind of analysis, but further studies on larger cohorts are required to confirm these preliminary data. Acknowledgements We thank CoQua Lab (www.coqualab.it) for its methodological support and assistance in the preparation and execution of the study and analysis. Funding This study was supported by internal funding. Transparency declarations None to declare. References 1 Alter MJ. The epidemiology of acute and chronic hepatitis C . Clin Liver Dis 1997 ; 1 : 559 – 68 . vi–vii. Google Scholar CrossRef Search ADS PubMed 2 De Nicolo A , Boglione L , Ciancio A et al. Telaprevir-S isomer enhances ribavirin exposure and the ribavirin-related haemolytic anaemia in a concentration-dependent manner . Antiviral Res 2014 ; 109 : 7 – 14 . Google Scholar CrossRef Search ADS PubMed 3 Boglione L , De Nicolo A , Cusato J et al. 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Journal

Journal of Antimicrobial ChemotherapyOxford University Press

Published: Mar 2, 2018

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