Literature DB >> 23284866

Revisiting the stopping rule for hepatitis C genotype 1 patients treated with peginterferon plus ribavirin.

Ming-Lung Yu1, Chen-Hua Liu, Chung-Feng Huang, Tai-Chung Tseng, Jee-Fu Huang, Chia-Yen Dai, Zu-Yau Lin, Shinn-Cherng Chen, Liang-Yen Wang, Suh-Hang Hank Juo, Wan-Long Chuang, Jia-Horng Kao.   

Abstract

BACKGROUND: The current stopping rule for peginterferon/ribavirin therapy in hepatitis C virus genotype-1 (HCV-1) patients is based on an early virological response (EVR, defined as >2 log(10) viral reduction at treatment week 12). We aimed to explore rapid stopping rules at week 4.
METHODS: We randomly allocated 528 HCV-1 patients into training and validation sets (at a 1∶2 ratio). The interleukin-28B rs8099917 genotypes and on-treatment virological responses were evaluated to determine the negative predictive value (NPV) for achieving a sustained virological response (SVR, defined as undetectable HCV RNA 24 weeks after end-of-treatment). The study was approved by the ethics committees of the participating hospitals. All of the patients gave written informed consent before enrollment.
RESULTS: A poor week 4 response (W4R), defined as a HCV RNA reduction of <1 log(10) IU/mL at week 4 or a week 4 HCV RNA>10,000 IU/mL with interleukin-28B non-TT genotype, had the highest NPV (95%). In the complete sample, poor W4R could identify 43.4% (59/136) of the non-responders, with an NPV of 95% and a false negative rate of only 0.8% (3/396). The multivariate analysis revealed that a poor W4R was the most important negative predictor (odds ratio/95% confidence intervals: 49.01/13.70-175.37), followed by the lack of an EVR. In addition to HCV RNA<1 log(10) IU/mL reduction, using the criteria of HCV RNA>10,000 IU/mL/non-TT genotype helped identifying an additional one-third of non-SVR patients at W4.Using the strategy of sequential rapid stopping rule strategy could identify 53.7% (73/136) of the non-responders (43.4% at week 4 and an addition 11.3% at week 12), as compared to 40.4% for the classical week-12 early stopping rule.
CONCLUSIONS: Sequential rapid stopping rules using on-treatment virological responses and interleukin-28B genotype can rapidly identify additional peginterferon/ribavirin non-responders.

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Year:  2012        PMID: 23284866      PMCID: PMC3528729          DOI: 10.1371/journal.pone.0052048

Source DB:  PubMed          Journal:  PLoS One        ISSN: 1932-6203            Impact factor:   3.240


Introduction

Hepatitis C virus (HCV) infection, one of the leading causes of liver disease worldwide and in Taiwan [1], [2], frequently causes persistent infections leading to cirrhosis and hepatocellular carcinoma. [3], [4] Pegylated interferon (peginterferon) plus ribavirin has been the standard of care for chronic hepatitis C (CHC): 48-week and 24-week regimens for HCV genotype 1 or 4 (HCV-1/4) and HCV-2/3 infections, respectively [2]. Recent advances in response-guided therapy using on-treatment virological responses have greatly improved the benefit/risk ratio of peginterferon/ribavirin for HCV. Abbreviated regimens might be applied for patients achieving a rapid virological response (RVR) at week 4 without compromising the treatment efficacy.[5]–[10] By contrast, treatment should be stopped at week 12 in HCV-1 patients not achieving an early virological response (EVR) to avoid unnecessary treatment costs and adverse events, [2], [10] with and without recently approved protease inhibitors. [11] However, the week 12 HCV RNA levels are typically unavailable until week 14. There is an unmet need to predict treatment failure as rapid as possible. HCV RNA levels >10,000 IU/mL at treatment week 4 could predict detectable HCV RNA at week 12 and treatment failure. [12] Recently, genome-wide association studies (GWAS) on host genetics have demonstrated that favorable interleukin 28B (IL-28B) genotypes enhance early viral kinetics and sustained virological responses (SVR) in HCV-1 patients. [13], [14] Unfavorable IL-28B genotypes have been associated with slower viral decline and poor treatment efficacy, and this effect was particularly enhanced in patients who failed to achieve a RVR at week 4. [15]–[18] The collective and complementary negative impacts of the host factors and viral kinetics have rarely been studied. The current study aimed to explore the negative predictive values (NPV) of combined host IL28B genetic and viral factors in predicting treatment failure and to establish a predictive model capable of rapidly identifying therapeutic failure before treatment week 12 among HCV-1 patients with peginterferon/ribavirin therapy.

Methods

Patient Selection

The eligible subjects were previously untreated Taiwanese patients CHC who were seropositive for HCV antibodies by a third-generation enzyme immunoassay (Abbott Laboratories, North Chicago, IL, USA) and for HCV RNA by a polymerase chain reaction (PCR). Consecutive 528 HCV-1 patients who achieved 80/80/80 adherence during the assigned 48-week treatment were retrospectively selected at two medical centers and three regional core hospitals. The other inclusion and exclusion criteria were as previously described. [7] All of the patients received either peginterferon alfa-2a (180 µg/week) or peginterferon alfa-2b (1.5 µg/kg/week) subcutaneously plus weight-based ribavirin treatment (1000 mg/d for weight <75 kg and 1200 mg/d for weight >75 kg). The serum HCV RNA at the baseline, treatment week 4, week 12, the end-of-treatment, and 24 weeks after end-of-treatment were determined by standardized automated qualitative PCR (Cobas Amplicor Hepatitis C Virus Test, V.2·0; Roche Diagnostics, Branchburg, New Jersey, USA; detection limit: 50 IU/ml) or a real-time PCR assay (Roche Cobas Taqman HCV v2.0 Roche Diagnostics; limit of detection, 25 IU/mL). The study was approved by the ethics committees of the participating hospitals and conducted according to the guidelines of the International Conference on Harmonization for Good Clinical Practice. All of the patients gave written informed consent before enrollment.

Assessment of Efficacy

The primary efficacy end point was SVR, defined as PCR-seronegative for HCV RNA by the end-of-treatment and throughout the follow-up period. RVR was defined as PCR-seronegative for HCV RNA at treatment week 4. EVR was defined as HCV RNA undetectable or at least a 2-log10 decline from baseline at treatment week 12. The end-of-treatment virological response (EOTVR) was defined as PCR-seronegative for HCV RNA at the end-of-treatment. Relapse was defined as the reappearance of HCV RNA during the follow-up period in the patients achieving an ETOVR.

IL-28B Genotyping

Rs8105790, rs8099917, rs4803219, and rs10853728 have been found to be associated with antiviral treatment responses in GWAS and a replication study in ethnic Asians. [13] Based on our previous findings, [19] rs8099917 was selected as the candidate SNP in the current study. [16], [17] The genotypes of the patients were determined using methods that have been previously described [19].

Statistical Analyses

The study population was randomly divided into two groups at a 1∶2 ratio: 174 patients as the training set that provided the coefficients for the prediction equation; the remaining 352 patients as validation set for confirming the predictive power of the model. Several models with various combinations of the on-treatment viral kinetics and the rs8099917 genotype were constructed. The NPV for the treatment outcome was used to compare the prediction power of these models. Frequencies were compared between the groups using the χ2 test with the Yates correction or using Fisher’s exact test. The group means, presented as means and standard deviations, were compared using an analysis of variance model and the Student T or Mann-Whitney U tests. The serum HCV RNA levels were expressed after logarithmic transformation of the original values. The aspartate aminotransferase (AST)-to-platelet ratio index (APRI), representing the severity of the liver fibrosis, was calculated using the following equation: (AST level/upper limit of normal range)/platelet counts (109/L)×100. [20] The frequencies of the rare rs8099917 allele (G) were low; therefore, the rare homozygote (GG) and heterozygote (GT) genotypes were combined when analyzing the SNP frequency. All of the statistical analyses, using the SPSS 12.0 statistical package (SPSS, Chicago, IL, USA), were based on two-sided hypothesis tests with a significance level of p<0.05.

Results

Patient Profiles and Virological Responses

The basic demographical, virological, and clinical features were similar between the training and validation sets (Table 1). Overall, 427 (80.9%) of the 528 patients carried the rs8099917 TT genotype, and 101 (19.1%) patients carried the rs8099917 GT/GG genotype. The overall RVR, EVR, EOTVR, SVR and relapse rates were 45.6% (241/528), 89.0% (470/528), 91.1% (481/528), 74.2% (392/528) and 18.5% (89/481), respectively. Compared to the G allele carriers (GT/GG), those with the homozygous TT genotype had significantly higher rates of RVR (51.5% (220/427) vs. 20.8% (21/101), P<0.001), EVR (95.1% (406/427) vs. 63.4% (64/101), P<0·001), EOTVR (95.8% (409/427) vs. 71.3% (72/101), P<0.001) and SVR (82.9% (354/427) vs. 37.6% (38/101), P<0.001) and a lower relapse rate (13.4% (55/409) vs. 47.2% (34/72), P<0.001).
Table 1

Basic demographic, virological, and clinical features of the chronic hepatitis C genotype 1 patients.

All patients(N = 528)Training set(N = 176)Validation set(N = 352)P value
Age, years, mean(SD)52.8 (11.1)53.6 (10.5)52.4 (11.4)0.22
Male, n (%)275 (52.1)88(50.0)187 (53.1)0.50
Body weight, kg, mean (SD)65.9 (11.1)65.3 (11.0)66.3(11.0)0.32
Baseline HCV RNA, log IU/ml, mean (SD)5.97 (0.85)5.98 (0.82)5.96 (0.87)0.86
Baseline HCV RNA >400,000 IU/mL, n (%)389 (73.7)125 (71.0)264 (75.0)0.33
APRI, mean (SD)1.74 (1.64)1.84 (1.68)1.68 (1.61)0.30
AST, IU/l, mean (SD)88.0 (58.8)89.7 (60.9)87.2 (57.8)0.65
ALT, IU/l, mean (SD)128.6 (87.4)130.0 (89.5)125.8 (83.4)0.60
Rs8099917 TT genotype, n (%)427 (80.9)145 (82.4)282 (80.1)0.53

Note: SD: standard deviation; AST: aspartate aminotransferase; ALT: alanine aminotransferase; APRI: aspartate aminotransferase-to-platelet ratio index.

Note: SD: standard deviation; AST: aspartate aminotransferase; ALT: alanine aminotransferase; APRI: aspartate aminotransferase-to-platelet ratio index.

The Host IL-28B Genotype and Week 4 HCV Viral Loads in Rapidly Predicting Non-responders

The week 4 viral loads and IL-28B genotypes have been demonstrated to be key determinants of treatment failure. [12], [15], [16], [21] We sought to analyze the simultaneous contribution of these two factors and to determine whether there is a predictive model for a rapid stopping rule before treatment week 12. In the training set (Table 2), the patients with higher HCV RNA levels at week 4 (viral loads >50 IU/mL, >1000 IU/mL or >10,000 IU/mL) had significantly lower SVR rates than their counterparts (all P<0.001). Adding the unfavorable rs8099917 non-TT genotype to the week 4 viral loads (at any cut-off value) greatly improved the NPV. The NPV reached 94% in the non-TT patients for week 4 viral loads >10,000 IU/mL. Similarly, the patients with a smaller HCV RNA decline at treatment week 4 (<1-log10, 2-log10, or 3-log10 decrease in HCV RNA levels from baseline) had significantly lower SVR rates than did their counterparts (all P<0.001, Table 2). The NPV reached 92% among the patients with an HCV RNA decline <1 log10 IU/mL at week 4. However, adding the unfavorable IL28B genotype did not improve the negative predictive power in this clinical setting.
Table 2

Week 4 viral loads and IL28B rs8099917 genotype in predicting SVR in the training set.

Week 4 viral loads andIL28B rs8099917 genotypeNon-SVR(N = 46)SVR (N = 130) P value SENSPEPPVNPVACC
n(%)n(%)%%%%%
>50 IU/mL44 (96)51 (39)<0.0016196984670
>50 IU/mL+non TT21 (46)5 (04)<0.0019646838183
>1000 IU/mL37 (80)22 (17)<0.0018380926382
>1000 IU/mL+non TT19 (41)5 (04)<0.0019641827982
>10,000 IU/mL25 (54)7 (05)<0.0019554857884
>10,000 IU/mL+non TT15 (33)1 (01)<0.0019933819482
<1 log IU/mL drop12 (26)1 (01)<0.0019926799280
<1 log IU/mL drop+non TT9 (20)1 (01)<0.0019920789078
<2 logs IU/mL drop28 (61)8 (06)<0.0019461877885
<2 logs IU/mL drop+non TT15 (33)2 (02)<0.0019833818881
<3 logs IU/mL drop37 (80)24 (18)<0.0018280926181
<3 logs IU/mL drop+non TT19 (41)3 (02)<0.0019841828683

Note: IL28B; interleukin-28B; SVR, sustained virological response; TT, rs8099917 TT genotype; SEN, sensitivity; SPE, specificity; PPV, positive predictive value; NPV, negative predictive value; ACC, accuracy.

Note: IL28B; interleukin-28B; SVR, sustained virological response; TT, rs8099917 TT genotype; SEN, sensitivity; SPE, specificity; PPV, positive predictive value; NPV, negative predictive value; ACC, accuracy. We then applied these results to the validation set to determine if the two factors (week 4 viral load reduction <1 log10 IU/mL and week 4 viral load >10,000 IU/mL with the non-TT genotype), alone or in combination, consistently provided the highest NPV and to evaluate their coverage rates (Table 3). We considered either patients with a week 4 viral load reduction of <1 log10 IU/mL (Group A) or a week 4 viral load >10,000 IU/mL with the non-TT genotype (Group B) as having a poor week 4 response (W4R). In the validation set, the NPVs were 100%, 94% and 95% in the patients with a <1 log10 IU/mL reduction, a week 4 viral load >10,000 IU/mL with the non-TT genotype, and a poor W4R, respectively. For all of the patients, the NPVs were 98%, 94%, and 95% in the patients with a <1 log10 IU/mL reduction, a week 4 viral load >10,000 IU/mL with the non-TT genotype and a poor W4R, respectively. Using the criteria of poor W4R as the rapid stopping rule, the NPV was as high as 95.2%, with the highest coverage rate at 43.4% (Table 3 and Figure 1). In contrast, with the classical 12-week stopping rule, lack of an EVR at week 12 had an NPV of 94.8% (55/58), with a coverage rate of 40.4% (Figure 1).
Table 3

Negative predictors at week 4 in predicting SVR in the training set, validation set and all patients.

Week 4 viral loads and IL28B rs8099917 genotypeNon-SVRSVR P value SENSPEPPVNPVACC
n(%)n(%)%%%%%
Training setN = 46N = 130
Group A: <1 log10 IU/mL decline12 (26)1 (1)<0.0019926799280
Group B: >10,000 IU/mL/non-TT genotype15 (33)1 (1)<0.0019933819482
Poor week 4 responses: Group A or B19 (41)1 (1)<0.0019941839584
Validation setN = 90N = 262
Group A: <1 log10 IU/mL decline28 (31)0 (0)<0.001100318110082
Group B: >10,000 IU/mL/non-TT genotype32 (36)2 (1)<0.0019936829483
Poor week 4 responses: Group A or B40 (44)2 (1)<0.0019944849585
All patientsN = 136N = 392
Group A: <1 log10 IU/mL decline40 (29)1 (0.3)<0.00199.729809882
Group B: >10,000 IU/mL/non-TT genotype47 (35)3 (1)<0.0019935819483
Poor week 4 responses: Group A or B59 (43)3 (1)<0.0019943849585

Note: SVR, sustained virological response; IL28B, interleukin-28B; SEN, sensitivity; SPE, specificity; PPV, positive predictive value; NPV, negative predictive value; ACC, accuracy.

Figure 1

A flow chart of the on-treatment responses and final outcomes.

W4R = week 4 response, defined as a week 4 HCV RNA reduction of <1 log10 IU/mL (Group A) or a week 4 HCV RNA>10,000 IU/mL+the non-TT genotype (Group B); EVR = early virological response, defined as an HCV RNA reduction of >2 log10 IU/mL at week 12.

A flow chart of the on-treatment responses and final outcomes.

W4R = week 4 response, defined as a week 4 HCV RNA reduction of <1 log10 IU/mL (Group A) or a week 4 HCV RNA>10,000 IU/mL+the non-TT genotype (Group B); EVR = early virological response, defined as an HCV RNA reduction of >2 log10 IU/mL at week 12. Note: SVR, sustained virological response; IL28B, interleukin-28B; SEN, sensitivity; SPE, specificity; PPV, positive predictive value; NPV, negative predictive value; ACC, accuracy.

Factors Predicting Non-responders

In the univariate analysis, having the rs8099917 non-TT genotype, female gender, older age, higher baseline viral loads, lack of an EVR, and poor W4R were significantly associated with treatment failure (Table 4). The multivariate analysis revealed that poor W4R was the most important factor for predicting treatment failure, followed by lack of an EVR, female gender, older age, and increased body weight (Table 4).
Table 4

Univariate and logistic regression analysis for factors associated with treatment failure in 528 chronic hepatitis C genotype 1 patients.

SVR (+)(n = 392)SVR (−)(n = 136) P valueLogistic regression analysis fortreatment failure
OR95% CI P value
IL28B rs8099917 genotype TT/GT+GG, n (%)354/38 (90.3/9.7)73/63 (53.7/46.3)<0.001
Male sex, n (%)221 (56.4)54 (39.7)0.0010.380.21–0.700.002
Age, yrs, mean (SD)51.5 (11.0)56.6 (10.3)<0.0011.051.02–1.080.002
Body weight, kg, mean (SD)65.8 (11.1)66.4 (10.7)0.511.031.00–1.060.03
Baseline HCV RNA, log IU/ml, mean (SD)5.92 (0.89)6.10 (0.72)0.02
APRI, mean (SD)1.72 (1.58)1.79 (1.80)0.68
Ribavirin per body weight (mg/kg/d, mean(SD))15.4 (2.5)15.1(2.5)0.26
EVR (+), n (%)389 (99.2)81 (59.6)<0.00134.79.32–128.9<0.001
Poor week 4 responses*, n (%)3 (0.8)59 (43.4)<0.00149.013.7–175.4<0.001

Note: IL28B, interleukin-28B gene; SD: standard deviation; OR: odds ratio; CI: confidence intervals; SVR: sustained virological response; EVR: early virological response; AST: aspartate aminotransferase; ALT: alanine aminotransferase; APRI: aspartate aminotransferase-to-platelet ratio index.

defined as W4<1 log IU/mL reduction or >10,000 IU/mL/non-TT genotype. Odds ratio of treatment failure are for age (per year increase), sex (male vs. female), body weight (per kilogram increase), EVR status (no vs. yes) and poor week 4 responses, treatment week 4 HCV RNA<1 log10 IU/mL decline or >10,000 IU/mL combined with IL28B non-TT genotype (yes vs. no).

Note: IL28B, interleukin-28B gene; SD: standard deviation; OR: odds ratio; CI: confidence intervals; SVR: sustained virological response; EVR: early virological response; AST: aspartate aminotransferase; ALT: alanine aminotransferase; APRI: aspartate aminotransferase-to-platelet ratio index. defined as W4<1 log IU/mL reduction or >10,000 IU/mL/non-TT genotype. Odds ratio of treatment failure are for age (per year increase), sex (male vs. female), body weight (per kilogram increase), EVR status (no vs. yes) and poor week 4 responses, treatment week 4 HCV RNA<1 log10 IU/mL decline or >10,000 IU/mL combined with IL28B non-TT genotype (yes vs. no).

Relationships between W4R, EVR, and Final Outcome

To compare the newly developed week-4 stopping rule with the classical week-12 stopping rule, we further evaluated the relationship between poor W4R, subsequent EVR status, and final treatment outcome (Figure 1). Of the 62 patients with poor W4R, 18 of the 21 EVR patients and all of the 41 non-EVR patients experienced treatment failure (13.2% and 30.1% coverage rates for non-SVR, respectively). A poor W4R could identify 70.7% (41/58) of the non-EVR patients at week 4. In contrast, in the 466 patients with adequate W4R three of the 17 non-EVR patients had an SVR. Using a poor W4R had a false negative rate of only 0.8% (3/396), which was comparable to those with the week-12 stopping rule (0.8%, 3/396). A week 4 viral load reduction of <1 log10 IU/mL has been suggested as a benchmark for treatment decisions. [22] We therefore tried to clarify the individual relationships between the week 4 responses, either a <1 log10 IU/mL reduction or HCV RNA>10,000 IU/mL with the non-TT genotype, and the subsequent week 12 response in the non-SVR patients. Eighty percent (32/40) of the patients with a <1 log10 IU/mL reduction and 68.1% (32/47) of the non-TT genotype patients with an HCV RNA>10,000 IU/mL at week 4 failed to achieve an EVR (Table 5). In addition to the <1 log10 IU/mL reduction criteria, a >10,000 IU/mL at week 4 and non-TT genotype criteria identified an additional 32.2% (19/59) of the non-SVR patients. It was noteworthy that a significantly higher proportion of the patients (52.6%, 10/19) had an EVR at week 12 but failed to achieve an SVR.
Table 5

Relationship between week 4 responses and EVR status in non-SVR patients.

No.EVR(+)EVR(−)
Group A, week 4<1 log10 IU/mL viral reduction n (%)* # 408 (20.0)32 (80.0)
Group B, week 4>10,000 IU/mL & IL28B rs8099917 non-TT genotype n (%)* & 4715 (31.9)32 (68.1)
Group A, but exclude Group B, n (%)& 123 (25.0)9 (75.0)
Group B, but exclude Group A, n (%)# 1910 (52.6)9 (47.4)

Note: EVR, early virological response, defined as HCV RNA>2 log10 IU/mL reduction at week 12.

P = 0.21.

P = 0.01.

P = 0.74.

Note: EVR, early virological response, defined as HCV RNA>2 log10 IU/mL reduction at week 12. P = 0.21. P = 0.01. P = 0.74.

Sequential Stopping Rules for Predicting Treatment Failure

We combined the newly developed rapid stopping rules and the classical early stopping rule to identify non-responders (Figure 2). Fifty-nine of the 136 non-responders could be identified at week 4, and 14 others could be identified at week 12. Using the sequential stopping rule strategy, 53.7% of the non-responders could be identified at week 12, as compared to 40.4% when using the classical 12-week stopping rule. Noteworthily, 43.4% (59/136) of the non-responders could be identified at week 4.
Figure 2

The sequential stopping rule.

IL-28B genotype: rs8099917. % was used for the coverage rate of the predicting non-responders.

The sequential stopping rule.

IL-28B genotype: rs8099917. % was used for the coverage rate of the predicting non-responders.

Discussion

A substantial proportion of the HCV-1 patients failed to attain treatment success with 48-week peginterferon/ribavirin. [2], [10], [23] Early and reliable strategies based on negative predictors are urgently needed to allow clinicians to identify non-responders, stop or modify treatment, and reduce the adverse events and costs. In the current study, we demonstrated that the IL28B genotype combined with the week 4 viral loads could predict treatment failure of HCV-1 patients with 48-week peginterferon/ribavirin with an NPV as high as 95%. With a sequential stopping rule, including the week-4 rapid stopping rule (an HCV RNA decline <1 log10 IU/mL at week 4 or an HCV RNA>10,000 IU/mL at week 4 plus the IL -28B rs8099917 non-TT genotype) and the classical week-12 early stopping rule, 53.7% of the HCV-1 non-responders could be identified at week 12. Of the early identified non-responders, 81% could be identified as early as at treatment week 4. The IL-28B genetic variants are strongly associated treatment response in HCV-1 patients. The major impact of the favorable genetic variants was to increase the rate of early viral decline, leading to higher SVR rates. These delayed viral kinetics have been observed in patients with poor responder alleles as early as the first phase [24] to week 12 [15]. The effect was enhanced in the patients who failed to achieve a RVR at week 4, particularly in those with high baseline viral loads. [15], [16], [18], [21] These observations implied an interactive effect between unfavorable host and viral factors in predicting treatment failure. In the current study, we further demonstrated that the HCV-1 patients with the unfavorable IL28B genotype and high HCV RNA levels at treatment week 4 had little chance of attaining an SVR, even if they achieved an EVR at week 12. Similar to our previous findings, [12] the week 4 HCV RNA levels were strongly associated with treatment failure in the non-RVR patients in the current study. Patients with a flat phase II response after interferon therapy are unlikely to achieve an SVR. [25] The classical stopping rule, failure to achieve an EVR, is based on week 12 HCV RNA levels. [26] In the current study, we tried to incorporate the host genetic factors to identify the non-responders as rapid as possible. We found that the rs8099917 non-TT genotype provided an NPV of only 62%, which was similar to the 68% offered by the rs12979860 non-CC genotype in Caucasians. [15] However, considering unfavorable viral results at week 4 in addition to the patientsIL-28B genotype increased the NPV to 95%, which was essentially identical to that of the classical week-12 stopping rule in the current study. Importantly, 40% of the non-responders could not be identified until week 12 when using the classical stopping rule. This coverage rate could be achieved as early as week 4 by recognizing the patients with poor W4R, and more than half of the patients who experienced treatment failure could be identified at week 12 by applying the newly developed sequential stopping rule. An HCV RNA reduction of <1 log10 at week 4 is one component of the poor W4R and has been suggested as a surrogate marker for treatment failure. [22], [27] The current study supports its utility, regardless of the host IL-28B genotype. Applying the other component of poor W4R, HCV RNA>10,000 IU/mL in the non-TT genotype carriers, helped to identify an extra third of the non-responders at week 4. As in the current study, it was not surprising, given viral kinetics, that the patients who had a <1 log10 reduction at week 4 were more likely to have a <2 log10 reduction at week 12. Nevertheless, it was noteworthy that applying the other component of a poor W4R, an HCV RNA>10,000 IU/mL at week 4 with the non-TT genotype, was critical because more than half of the extra third of the non-responders disclosed by this criteria had an EVR, which may lead to inappropriate continuation of treatment. Recently, promising data have shown that potent direct antiviral agents (DAAs) combined with peginterferon/ribavirin markedly increased the SVR rates for HCV-1 patients. [11], [27] However, the patients with an HCV RNA reduction of <1 log10 after 4 weeks of peginterferon/ribavirin lead in therapy had much lower SVR rates (28%–38% vs. 79%–81%) with boceprevir-containing triple therapy, and much higher rates of boceprevir-resistance-associated variants (40%–52% vs. 4%–6%) than those with an HCV RNA reduction of >1 log10 after 4-week lead in. [27] Nevertheless, adding boceprevir still leads to an SVR in about a third of the patients with an HCV RNA reduction of <1 log10 during the lead in, as compared to only 4% if boceprevir is not added. Thus, recently updated practice guidelines have suggested that a poor response after the therapy lead in should not be used to deny patients access to protease inhibitor therapy. [28] Developing more potent antiviral regimens is urgent for “difficult-to-treat” populations. [29] Similar to most of the studies on the IL-28B genotype, a limitation of the current study was its retrospective nature. Although the applicability of current study to patients of other ethnicities needs further evaluation, IL-28B genotype has been associated with, and probably was the most important baseline predictor of peginterferon/ribavirin therapy for HCV, spontaneous HCV clearance, and outcome of HCV-related liver transplantation across different ethnicities worldwide. [30]–[36] Of particular noted was that the current study shed light for individualized therapy in the era of DAA based on the interplay of early viral kinetics and host IL-28B genetic variants. In conclusion, we demonstrated that rapid sequential stopping rules using on-treatment virological responses and the host IL28B genotype can rapidly identify additional patients who do not respond to peginterferon/ribavirin with a high negative predictive value. The introduction of a week-4 rapid stopping rule may inform the decision to immediately discontinue peginterferon/ribavirin treatment in countries where DAAs are not currently available or in patients for whom DAAs are contraindicated. Furthermore, selected patients could be designated for therapy with new direct antiviral agents that may become available in the near future.
  36 in total

1.  Interleukin 28B genetic polymorphisms and viral factors help identify HCV genotype-1 patients who benefit from 24-week pegylated interferon plus ribavirin therapy.

Authors:  Chen-Hua Liu; Cheng-Chao Liang; Chun-Jen Liu; Tai-Chung Tseng; Chih-Lin Lin; Sheng-Shun Yang; Tung-Hung Su; Shih-Jer Hsu; Jou-Wei Lin; Jun-Herng Chen; Pei-Jer Chen; Ding-Shinn Chen; Jia-Horng Kao
Journal:  Antivir Ther       Date:  2011-12-20

2.  EASL Clinical Practice Guidelines: management of hepatitis C virus infection.

Authors: 
Journal:  J Hepatol       Date:  2011-03-01       Impact factor: 25.083

3.  Interleukin-28B polymorphism improves viral kinetics and is the strongest pretreatment predictor of sustained virologic response in genotype 1 hepatitis C virus.

Authors:  Alexander J Thompson; Andrew J Muir; Mark S Sulkowski; Dongliang Ge; Jacques Fellay; Kevin V Shianna; Thomas Urban; Nezam H Afdhal; Ira M Jacobson; Rafael Esteban; Fred Poordad; Eric J Lawitz; Jonathan McCone; Mitchell L Shiffman; Greg W Galler; William M Lee; Robert Reindollar; John W King; Paul Y Kwo; Reem H Ghalib; Bradley Freilich; Lisa M Nyberg; Stefan Zeuzem; Thierry Poynard; David M Vock; Karen S Pieper; Keyur Patel; Hans L Tillmann; Stephanie Noviello; Kenneth Koury; Lisa D Pedicone; Clifford A Brass; Janice K Albrecht; David B Goldstein; John G McHutchison
Journal:  Gastroenterology       Date:  2010-04-24       Impact factor: 22.682

4.  Early identification of achieving a sustained virological response in chronic hepatitis C patients without a rapid virological response.

Authors:  Chung-Feng Huang; Jeng-Fu Yang; Jee-Fu Huang; Chia-Yen Dai; Chang-Fu Chiu; Nai-Jen Hou; Ming-Yen Hsieh; Zu-Yau Lin; Shinn-Cherng Chen; Ming-Yuh Hsieh; Liang-Yen Wang; Wen-Yu Chang; Wan-Long Chuang; Ming-Lung Yu
Journal:  J Gastroenterol Hepatol       Date:  2010-04       Impact factor: 4.029

5.  Early virologic response to treatment with peginterferon alfa-2b plus ribavirin in patients with chronic hepatitis C.

Authors:  Gary L Davis; John B Wong; John G McHutchison; Michael P Manns; Joann Harvey; Janice Albrecht
Journal:  Hepatology       Date:  2003-09       Impact factor: 17.425

6.  Pegylated interferon-alpha-2a plus ribavirin for treatment-naive Asian patients with hepatitis C virus genotype 1 infection: a multicenter, randomized controlled trial.

Authors:  Chen-Hua Liu; Chun-Jen Liu; Chih-Lin Lin; Cheng-Chao Liang; Shih-Jer Hsu; Sheng-Shun Yang; Ching-Sheng Hsu; Tai-Chung Tseng; Chia-Chi Wang; Ming-Yang Lai; Jun-Herng Chen; Pei-Jer Chen; Ding-Shinn Chen; Jia-Horng Kao
Journal:  Clin Infect Dis       Date:  2008-11-15       Impact factor: 9.079

7.  Diagnosis, management, and treatment of hepatitis C: an update.

Authors:  Marc G Ghany; Doris B Strader; David L Thomas; Leonard B Seeff
Journal:  Hepatology       Date:  2009-04       Impact factor: 17.425

8.  Efficacy and safety of pegylated interferon combined with ribavirin for the treatment of older patients with chronic hepatitis C.

Authors:  Chung-Feng Huang; Jeng-Fu Yang; Chia-Yen Dai; Jee-Fu Huang; Nai-Jen Hou; Ming-Yen Hsieh; Zu-Yau Lin; Shinn-Cherng Chen; Ming-Yuh Hsieh; Liang-Yen Wang; Wen-Yu Chang; Wan-Long Chuang; Ming-Lung Yu
Journal:  J Infect Dis       Date:  2010-03       Impact factor: 5.226

9.  An update on treatment of genotype 1 chronic hepatitis C virus infection: 2011 practice guideline by the American Association for the Study of Liver Diseases.

Authors:  Marc G Ghany; David R Nelson; Doris B Strader; David L Thomas; Leonard B Seeff
Journal:  Hepatology       Date:  2011-09-26       Impact factor: 17.425

10.  Association of IL28B SNP With Progression of Egyptian HCV Genotype 4 Patients to End Stage Liver Disease.

Authors:  Mostafa K El-Awady; Lotiaf Mostafa; Ashraf A Tabll; Tawfeek H Abdelhafez; Noha G Bader El Din; Naglaa Zayed; Reem El Shenawy; Yasmin El Abd; Reham M Hasan; Hosam Zaghlol; Hesham El Khayat; Ashraf O Abdel Aziz
Journal:  Hepat Mon       Date:  2012-04-30       Impact factor: 0.660

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  10 in total

1.  Hepatitis C viremia interferes with serum hepatitis B virus surface antigen and DNA levels in hepatitis B uremics.

Authors:  Chung-Feng Huang; Ming-Lun Yeh; Jia-Jung Lee; Mei-Chin Chen; Chia-Yen Dai; Jee-Fu Huang; Jer-Ming Chang; Hung-Chun Chen; Shang-Jyh Hwang; Wan-Long Chuang; Ming-Lung Yu
Journal:  Hepatol Int       Date:  2014-03-19       Impact factor: 6.047

2.  Huge gap between clinical efficacy and community effectiveness in the treatment of chronic hepatitis C: a nationwide survey in Taiwan.

Authors:  Ming-Lung Yu; Ming-Lun Yeh; Pei-Chien Tsai; Ching-I Huang; Jee-Fu Huang; Chung-Feng Huang; Meng-Hsuan Hsieh; Po-Cheng Liang; Yi-Hung Lin; Ming-Yen Hsieh; Wen-Yi Lin; Nai-Jen Hou; Zu-Yau Lin; Shinn-Cherng Chen; Chia-Yen Dai; Wan-Long Chuang; Wen-Yu Chang
Journal:  Medicine (Baltimore)       Date:  2015-04       Impact factor: 1.889

3.  Host genetic variations are associated with virological response to interferon therapy of chronic HCV in Han Chinese patients.

Authors:  Hongbo Chen; Yuanyuan Zhang; Peng Huang; Yin Xu; Jie Wang; Jing Su; Rongbin Yu
Journal:  J Biomed Res       Date:  2014-11

4.  Elevated on-treatment levels of serum IFN-gamma is associated with treatment failure of peginterferon plus ribavirin therapy for chronic hepatitis C.

Authors:  Ming-Ying Lu; Ching-I Huang; Chia-Yen Dai; Shu-Chi Wang; Ming-Yen Hsieh; Meng-Hsuan Hsieh; Po-Cheng Liang; Yi-Hung Lin; Nai-Jen Hou; Ming-Lun Yeh; Chung-Feng Huang; Zu-Yau Lin; Shinn-Cherng Chen; Jee-Fu Huang; Wan-Long Chuang; Ming-Lung Yu
Journal:  Sci Rep       Date:  2016-03-11       Impact factor: 4.379

Review 5.  Host factors determining the efficacy of hepatitis C treatment.

Authors:  Wan-Long Chuang; Ming-Lung Yu
Journal:  J Gastroenterol       Date:  2012-10-27       Impact factor: 7.527

6.  Rapid Prediction of Treatment Futility of Boceprevir with Peginterferon-Ribavirin for Taiwanese Treatment Experienced Hepatitis C Virus Genotype 1-Infected Patients.

Authors:  Chi-Chieh Yang; Wei-Lun Tsai; Wei-Wen Su; Chung-Feng Huang; Pin-Nan Cheng; Ching-Chu Lo; Kuo-Chih Tseng; Lein-Ray Mo; Chun-Hsiang Wang; Shih-Jer Hsu; Hsueh-Chou Lai; Chien-Wei Su; Chun-Jen Liu; Cheng-Yuan Peng; Ming-Lung Yu
Journal:  PLoS One       Date:  2015-09-14       Impact factor: 3.240

Review 7.  Nanomedicines in the treatment of hepatitis C virus infection in Asian patients: optimizing use of peginterferon alfa.

Authors:  Chen-Hua Liu; Jia-Horng Kao
Journal:  Int J Nanomedicine       Date:  2014-04-25

8.  Hepatitis C virus infection increases risk of developing end-stage renal disease using competing risk analysis.

Authors:  Jia-Jung Lee; Ming-Yen Lin; Jung-San Chang; Chi-Chih Hung; Jer-Ming Chang; Hung-Chun Chen; Ming-Lung Yu; Shang-Jyh Hwang
Journal:  PLoS One       Date:  2014-06-27       Impact factor: 3.240

Review 9.  APASL consensus statements and recommendations for hepatitis C prevention, epidemiology, and laboratory testing.

Authors:  Masao Omata; Tatsuo Kanda; Lai Wei; Ming-Lung Yu; Wang-Long Chuang; Alaaeldin Ibrahim; Cosmas Rinaldi Adithya Lesmana; Jose Sollano; Manoj Kumar; Ankur Jindal; Barjesh Chander Sharma; Saeed S Hamid; A Kadir Dokmeci; Mamun Al-Mahtab; Geofferey W McCaughan; Jafri Wasim; Darrell H G Crawford; Jia-Horng Kao; Osamu Yokosuka; George K K Lau; Shiv Kumar Sarin
Journal:  Hepatol Int       Date:  2016-05-26       Impact factor: 6.047

10.  Identification of groups with poor cost-effectiveness of peginterferon plus ribavirin for naïve hepatitis C patients with a real-world cohort and database.

Authors:  Pei-Chien Tsai; Ta-Wei Liu; Yi-Shan Tsai; Yu-Min Ko; Kuan-Yu Chen; Ching-Chih Lin; Ching-I Huang; Po-Cheng Liang; Yi-Hung Lin; Ming-Yen Hsieh; Nai-Jen Hou; Chung-Feng Huang; Ming-Lun Yeh; Zu-Yau Lin; Shinn-Cherng Chen; Chia-Yen Dai; Wan-Long Chuang; Jee-Fu Huang; Ming-Lung Yu
Journal:  Medicine (Baltimore)       Date:  2017-06       Impact factor: 1.889

  10 in total

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