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AAPS J. 2012 September; 14(3): 571–580.
Published online 2012 May 26. doi:  10.1208/s12248-012-9368-z
PMCID: PMC3385833

Population Pharmacokinetics and Pharmacodynamics of Ribavirin in Patients with Chronic Hepatitis C Genotype 1 Infection


We report a population pharmacokinetic (PK) and pharmacodynamic (PD) model of orally administered ribavirin in patients with chronic hepatitis C virus (HCV) infection enrolled in a multicenter clinical trial, including the estimation of covariate effects on ribavirin PK parameters and sustained viral response (SVR). Ribavirin concentrations obtained from 144 patients, consisting of n = 71 African American (AA) and n = 73 Caucasian Americans (CA), during 24 weeks of therapy were best described by a two-compartment model with first-order absorption and elimination parameterized in terms of apparent oral clearance (CL/F), apparent central volume (Vc/F), apparent peripheral volume (Vp/F), and apparent intercompartmental clearance (Q/F). The typical population parameters were CL/F (19.0 L/h), Vc/F (1,130 L), Vp/F (4,020 L), and Q/F (38.6). The Vp/F was approximately 50% greater in AA compared to CA. Significant covariates in the SVR model included IL-28B genotype, homeostasis model assessment of insulin resistance, and ribavirin exposure during the first week (AUC0  7). The population PK and logistic regression models both described the observed ribavirin concentration data and SVR data well. These findings suggest that optimization of ribavirin plasma concentrations during the first week of ribavirin dosing is most critical in AA patients in order to increase the rate of SVR, especially those with the IL-28B TT genotype.

KEY WORDS: hepatitis C, pharmacodynamics, population pharmacokinetics, ribavirin


Over four million people in the USA are infected with hepatitis C virus (HCV), with nearly 3.2 million having chronic HCV infection (1). Chronic HCV is a leading cause of liver cirrhosis, end-stage liver disease, and hepatocellular carcinoma (2). Among the six known genotypes of HCV, the highly resistant genotype 1 is dominant (73–75%) in the USA (3,4). Treatment of chronic HCV infection includes weekly pegylated interferon alpha (PEGIFN) combined with weight-based ribavirin administered orally for up to 48 weeks (5). The use of ribavirin is essential for achieving sustained viral response (SVR), defined as undetectable serum HCV RNA at 24 weeks after completing therapy (68).

Significant racial differences in the prevalence and treatment response for HCV genotype 1 have been reported. African Americans (AA) are infected with HCV genotype 1 at a much higher rate than Caucasian Americans (CA) (91% vs. 70%, respectively) (4), and SVR rates are lower in AA (19–28%) compared to CA (39–52%) (911). These racial differences are not likely explained by differences in frequency of ribavirin dose reductions or PEGIFN systemic exposure (12,13). Although IL-28B polymorphism is a known predictor of SVR, the interaction between ribavirin exposure, race, IL-28B genotypes (CC, CT, and TT), and SVR has not been fully explored. We hypothesize that differences in ribavirin exposure by race may partly explain the low SVR reported in AA compared to CA, and that highly individualized ribavirin regimens may be required to maximize ribavirin exposure and minimize racial differences in SVR.

Our first objective was to investigate the racial difference in ribavirin plasma concentrations between AA and CA patients in the Virahep-C study using a population pharmacokinetic (PK) approach. The second objective was to explore the association between patient-related variables on the primary treatment outcome, SVR, using pharmacodynamic (PD) modeling. The population PK and PD model described here may serve as a useful tool to study ribavirin dose optimization to improve outcomes in patients with HCV genotype 1.


Study Population and Design

Virahep-C was a multicenter clinical study conducted in the USA to evaluate the virologic response to combination therapy of PEGIFN and ribavirin in AA (n = 205) and CA (n = 196) patients with HCV genotype 1 (9). All patients received PEGIFN alpha-2a 180 μg/week subcutaneously with concurrent weight-based ribavirin (1,000 mg/day for weight <75 kg; 1,200 mg/day for weight ≥75 kg) for up to 48 weeks. Treatment was discontinued if the patient had a positive qualitative serum HCV RNA at week 24 and was considered a “non-responder” as described in the Virahep-C study. Patients completing 48 weeks of treatment underwent a 24-week follow-up period. Adherence to both drug regimens was monitored closely using the electronic Medication Event Management System (Aardex, Zug, Switzerland). Plasma samples were obtained at the end of weeks 1, 2, 4, 8, 12, and 24 during treatment. The collection time of ribavirin RBV plasma samples varied, with the majority of samples being collected at less than 4 h after last dose, nearly 25% being collected between 4 and 8 h after last dose, and less than 10% of the samples collected between 8 and 12 h after last dose. A total of 144 patients (n = 71 AA, n = 73 CA) who received at least 80% of the intended doses of PEGIFN and ribavirin during the first 24 weeks of treatment were included in the population PK analysis. Allelic discrimination for the IL28B SNP (rs12979860) was achieved using TaqMan (Applied Biosystems, Foster City, CA). The serum HCV RNA concentrations were quantified by a COBAS Amplicor Hepatitis C Virus Monitor Test (version 2.0, Roche Molecular Diagnostics, Alameda, CA). Negative results were confirmed using the qualitative Amplicor assay (Roche Molecular, Alameda, CA). The study protocol was approved by the Virahep-C Steering Committee and the Institutional Review Board at the University of Maryland.

Bioanalytical Methods

Ribavirin concentrations in plasma were determined using a high-performance liquid chromatography with tandem mass spectrometry method, which was modified from that reported by Liu et al. (14). Sample processing involved a liquid–liquid extraction method with acetonitrile/ethyl acetate. The analysis was performed on an Agilent 1100 LC binary pump and autosampler system equipped with an API 3000 triple quadrupole mass spectrometer (Applied Biosystems, Foster City, CA). Ribavirin and its internal standard (IS), 5-methyl-2′-deoxycytidine, were separated on an AQUASIL C18 reverse phase column (150 × 2.1 mm, 5-μm particle size, Thermo Fisher Scientific, San Jose, CA) using an isocratic mobile phase including 10% methanol and 90% 2 mM ammonium acetate. The multiple reactions monitoring analysis was used to monitor the transitions of both ribavirin and its IS in a positive electrospray ionization mode as follows: ribavirin, m/z 245→113, and IS, m/z 242→126. The within-day and between-day precision (percent coefficient of variability) and bias were within 10% for standards (20–5,000 ng/mL) and quality control samples.

Data Analysis

Pharmacokinetic Model

Modeling was performed using a mixed-effect approach with NONMEM version VI level 1.0 (ICON Development Solutions, Ellicott City, MD). Pharmacokinetic parameters were estimated using the first-order conditional estimation method with interaction. Models tested included a one-compartment and two-compartment oral model and a two-compartment infusion model. For the oral model, a first-order absorption rate constant (Ka) of 0.86 h−1 was selected, based on previously reported Ka for a standard (0.77 h−1) and high-fat meal (0.99 h−1). Structural model selection was driven by successful convergence, plausibility and precision of the parameter estimates, and the minimum objective function. The inter-individual variability (IIV) and inter-occasion variability (IOV) on each parameter were quantified using exponential error structures in the base model. The following exponential model was used to describe the IIV and IOV:

equation M1

where θ is an individual value of a model parameter, θpop is the population mean value of the parameter, η represents IIV, and κ represents IOV. It is assumed that η and κ are independent multivariate and distributed normally with mean 0 and variance ω2 and π2, respectively. A log-additive residual model was used to describe the residual variability. The influence of four physiologic covariates (race, gender, age, and body weight) were individually examined on each ribavirin PK parameter using a stepwise forward addition procedure followed by backward elimination. The impact of body weight (body weight) was described by a power model with the power factor set at 1.0 for V/F and 0.75 for oral clearance (CL/F), respectively, and body weight was centered to the typical population value (75 kg). The impact of age on each PK parameters was also described by a power model and age was standardized to the median value in the current population. Gender was coded as 0 = male and 1 = female and race was coded as 0 = CA and 1 = AA. The influence of either gender or race is described by the model as follows:

equation M2

where TVθ is the typical value of a PK parameter in current population, θ1 is population mean of the PK parameter, and θ2 is the fractional change after accounting for the covariate, and covariate is either gender or race here. Parameters of kidney function, such as serum creatinine and creatinine clearance (CRCL) estimated by the Cockcroft–Gault equation (15), were not included in the covariate examination because 85% of subjects had normal renal function (CRCL  90 mL/min) and only 19 subjects had CRCL values ranging from 67 to 89 mL/min.

Model Fit Assessment

Goodness-of-fit was determined by visualization of the scatter plots of the covariates vs. residuals and objective function (OF) value. A more complicated model was preferred when the decrease in OF value was more than 3.84 points (χ2, p < 0.05 with df = 1) when adding one covariate in forward addition and OF  10 points (χ2, p < 0.001 with df = 1) when deleting one covariate in backward elimination.

Model Simulation and Evaluation

In order to evaluate the effect of racial difference in PK parameters on ribavirin exposure, trough plasma concentrations were simulated from 500 CA and 500 AA patients using the final population PK model of ribavirin. A body weight of 75 kg and ribavirin dose of 600 mg twice daily for 24 weeks were used in the model, and ribavirin trough concentrations were simulated at weeks 1 and 24. Areas under the ribavirin plasma concentration vs. time curve [area under the curve (AUC)] for week 1 (AUC0  7) and week 24 (AUC161  168) were calculated, and simulated AUC0  7 and AUC161  168 were compared between CA and AA. Leverage analysis and bootstrapping were performed to test the predictability and stability of the final model. The database was split into 70% index and 30% reference for 25 times in leverage analysis. The ratio of AA to CA was set at 1:1 in both index and reference data. The bootstrap data were constructed using resampling with replacement, and the average value of each model parameter from 500 bootstrap runs was compared to the population mean value in the final model. The 95% confidence intervals were calculated for each model parameter obtained from bootstrapping.

Pharmacodyamic Model

A logistic regression model was applied to investigate the relationship between SVR and selected independent variables related patient and treatment. A Bayesian approach with a Markov Chain Monte Carlo (MCMC) algorithm was applied to develop the logistic regression model using OpenBUGS (16,17). The parameter estimates were produced from 200,000 MCMC simulations after a burn-in period of 200,000 iterations. The prior information applied to the calculation of the posterior distributions was deemed low informative but biologically plausible. Convergence of the MCMC algorithms was assessed by trace plots and Monte Carlo (MC) error ratios corresponding to posterior standard deviations of each model parameter. The SVR was coded as a binary dependent variable with ten independent variables including race, age, gender, body mass index (BMI), baseline HCV RNA load, fibrosis (Ishak score), homeostasis model assessment of insulin resistance (HOMA-IR), IL28B genotype (CC/CT/TT), PEGIFN serum concentration at steady state (Css), and ribavirin AUC during the first 7 days of therapy (AUC0  7). Insulin resistance is commonly used as an index of glucose tolerance for obesity and diabetes and has been reported as an independent predictor of SVR for patients with HCV (18).

The logistic regression model is expressed as follows:

equation M3

where pr(Y = 1) represents the probability of SVR, X1  k are the independent variables of interest, β1  k are the coefficients for the corresponding X1  k, and β0 is the intercept. The variables were screened individually by a univariate model, and those with coefficients having a 95% confidence interval excluding 0 were included in a multivariate model for further variable selection. Deviance information criterion (DIC) was applied for variable selection using a stepwise forward addition and backward elimination (16,19). The predicted probability of SVR was calculated based on the final logistic regression model:

equation M4

where equation M5 is the predicted probability of SVR, the X1…n are the identified variables, β1… n are the coefficients for the corresponding identified variables, and β0 is the intercept of the logistic function.

Statistical Analysis

All continuous variables were compared using a t test or nonparametric Wilcoxon rank-sum test, and categorical variables were compared using the Pearson's chi-squared test. A p value of 0.05 was considered statistically significant. All statistics were performed using SAS (version 9.1, SAS institute, Inc., Cary, NC). The sample size of 71 subjects per group provided at least 80% power to detect a 20% difference in ribavirin AUC assuming a 40% of inter-patient variation in AUC (PS software, v.3.0) (20).


Patient Characteristics

The demographic data for subjects included in the pharmacokinetic and logistic regression model sets are presented in Tables I and andII,II, respectively. Both AA and CA had similarly high adherence to combination treatment (>90%). As shown in Table II, the rates of SVR in CA and AA were 57% and 37%, respectively (p = 0.03). Compared to non-SVR, patients with SVR had lower fibrosis and HOMA-IR scores, higher ribavirin AUC0  7, and higher frequency of CC genotype. The AA group tended to have a lower frequency of CC genotype than CA (7 vs. 29) but more TT genotype (13 vs. 7). Figures 1 and and22 show the observed ribavirin plasma concentration profiles during 24 weeks of ribavirin therapy. The AA group had significantly lower ribavirin plasma concentrations at weeks 1, 2, and 4 (p < 0.05) compared to CA; however, the steady-state concentrations were only marginally lower in AA compared to CA.

Table I
Baseline Patient Characteristics
Table II
Comparisons of Patient Characteristics Between SVR and Non-SVR
Fig. 1
Comparison of dose-adjusted ribavirin plasma concentration between AA and CA during 24-week treatment phase (***p = 0.003, **p = 0.016, *p = 0.04; AA vs. CA)
Fig. 2
a, b RBV concentration time profile in AA and CA for 1,000 mg per day (n = 21, a) and 1,200 mg per day (n = 123, b)

Pharmacokinetic Model

Among three tested structure models, the two-compartment oral model showed the best fit to the observed ribavirin plasma concentration vs. time profile. The addition of IIV on apparent clearance (CL/F), apparent central volume of distribution (Vc/F), and apparent peripheral volume of distribution (Vp/F) improved the model. The IOV was significant for CL/F and Vc/F, but not for Vp/F. As a result, the base model consisted of a two-compartment oral model with first-order absorption and elimination, with IIV on CL/F, Vc/F, and Vp/F and IOV on CL/F and Vc/F.

The significant relationships between covariates and parameters identified in the forward addition selection were body weight on Vp/F, body weight on CL/F, and race on Vp/F. The incorporation of BW in the base model explained part of the IIV of Vp/F with a moderate decrease in IIV of 6% (75% to 69%). In addition, the positive relationship between body weight and inter-subject variability of Vp/F was corrected after adding body weight on Vp/F. The incorporation of BW on CL/F in the base model explained 2% of variability in CL/F with a decrease in the OF by 7 points. The bias in the residuals of CL/F was eliminated for those with body weight <75 kg using the covariate model compared to the base model. After accounting for race on Vp/F, the IIV of Vp/F was reduced by an additional 2%. The average post hoc estimate of Vp/F in AA was approximately 1.5-fold higher than in CA (7,946 vs. 5,445 L). In the backward elimination selection, three reduced models were formed after removing body weight from Vp/F and CL/F and race from Vp/F, respectively. Race and body weight remained as covariates on Vp/F and CL/F in the final model based on improvements in residual plots, decrease in IIV, and the significant racial difference in Vp/F. The identified relationships were expressed as:

equation M6

where TVCL/F was the typical value of CL/F in liters per hour for an individual knowing body weight in kilograms, TVVp/F was the typical value of Vp/F in liters for an individual knowing body weight and race, CL/Fpop was the population mean value of CL/F, Vp/Fpop was the population mean value of Vp/F, and the race factor was 1.0 for CA and 1.5 for AA. Age and gender did not significantly impact CL/F, Vc/F, or Vp/F. The summary of parameters in the final model and corresponding IIV and IOV is presented in Table III. The population mean of CL/F of ribavirin was 19 L/h with an IIV at 37% and an IOV at 8%. The population mean of Vc/F of ribavirin was 1,130 L with an IIV at 32% and IOV at 64%. The population mean of Vp/F of ribavirin was 4,020 L with an IIV at 68%. The population means of Q/F and Ka were 38.6 L/h and 0.86 h−1, respectively. Figure 3 shows the goodness-of-fit and residual plots of the final model. The population-predicted concentrations and the individual-predicted concentrations were in good agreements with the observed ribavirin concentrations (Fig. 3a, ,b).b). No systematic bias was identified in the weighted residuals when plotted with the population-predicted concentrations and time (Fig. 3c, ,dd).

Table III
Population Means of Parameters in Final Ribavirin PK Model and Summaries of Parameter Evaluation of Leverage Analysis and Bootstrapping
Fig. 3
Diagnostic plots of final population PK model of ribavirin

AUC Simulations

The parameters estimated from the final model were used to simulate ribavirin trough concentrations during 24 weeks of ribavirin treatment. The simulated AUC0  7 and AUC161  168 in 500 AA and 500 CA subjects were compared using boxplots as shown in Fig. 4. In the first week, the AUCs in AA were lower than in CA although there was overlap between the groups for AUC0  7. At steady state (days 161–168), there was no apparent racial difference in AUC. The diminished racial disparity in simulated AUCs from week 1 to steady state was similar to that observed when comparing ribavirin plasma concentration between AA and CA before and after week 4.

Fig. 4
Simulated AUCs in 500 CA and 500 AA from day 0 to day 7 (a) and day 161 to day 168 (b)

Internal Evaluation of Population Model

As shown in Table III, the average value of each parameter from 25 leverage runs was similar to those obtained from the final model. The estimations of IIV on Vc/F and IOV on Vc/F were less precise with CV values of 99% and 83%, respectively. The stability evaluation of the final model using 500 bootstrap data sets is summarized in Table III. The geometric mean estimates of the model parameters from the 500 bootstrap sets were consistent with those obtained from the final model, and the mean of each parameter was within the 95% CI calculated from bootstrap analysis. However, the 95% CI of IIV on Vc/F, IOV on CL/F, and Vc/F produced by the bootstrap analysis were quite wide, indicating lack of precision in these estimates. Overall, the internal evaluation suggests that the final population model describes the current data adequately and robustly.

Probabilistic Model Evaluation

After the preliminary variable screen using a univariate model, race, age, HOMA-IR, ribavirin AUC0  7, and IL28B genotype showed significant relationships with SVR (Table IV). These variables were used in the multivariate model for variable selection by stepwise forward addition and backward elimination. The one-variable model with IL28B genotype had the largest decrease in DIC value among all one-variable models in the first step of forward addition selection. This indicated that IL28B had the strongest association with SVR among five variables. The final multivariate model included IL28B genotype, HOMA-IR, and ribavirin AUC0  7, as shown in the probabilistic model equation below:

equation M7
Table IV
Summary of PD Variable Screen in Univariate Model

The parameter estimates, corresponding independent variable in the final model, corresponding posterior distribution, and MC errors are presented in Table V. The probability of attaining SVR decreased by 25% for each increasing unit of HOMA-IR score after controlling for IL28B genotype and ribavirin AUC0  7. On average, the increase in ribavirin AUC0  7 by each 1,000 ng*day/mL increased the probability of SVR by 1.31-fold after adjusting for HOMA-IR and IL28B genotype. In this model, patients with the TT allele have fivefold lower probability of reaching SVR than patients with CC or CT alleles, regardless of the HOMA-IR and ribavirin AUC0  7.

Table V
PD Parameters Included in Final PD Model

The predicted probabilities of SVR as a function of HOMA-IR and ribavirin AUC0  7 are shown for each IL28B genotype in Fig. 5. The range of ribavirin AUC0  7 is from 0 to 15,000 ng*day/mL, which covers ribavirin concentrations from 0 to 4,286 ng/mL at day 7 in the study. HOMA-IR values were between 0 and 15, which represents 95% of all subjects in the current data set. Overall, the CC genotype had the highest predicted probabilities of SVR among all three IL28B genotypes given similar HOMA-IR and ribavirin AUC0  7 values.

Fig. 5
Predicted probability of SVR stratified by IL28B genotype. (a) CC genotype. (b) CT genotype. (c) TT genotype


In the present study, we applied population analysis to investigate factors associated with ribavirin exposure in patients enrolled in the Virahep-C study. We developed a logistic regression model to examine treatment efficacy (SVR) as a function of systemic exposure and patient-related variables. We have shown that ribavirin exposure is significantly lower in AA compared to CA patients during the first 4 weeks of therapy. Although prior investigations of racial differences in ribavirin exposure in HCV are lacking, our results are consistent with a single unpublished pilot study comparing ribavirin exposure among AA (n = 17), CA (n = 16), and Hispanic (n = 14) patients with HCV genotype 1, where the ribavirin maximum concentration (Cmax) and AUCs were 20–30% lower in AA compared to CA (21).

We describe the first population model of ribavirin including race as a covariate based on a large clinical trial enrolling both AA and CA patients who were highly adherent to therapy. The two-compartment oral model with first-order absorption and elimination produced a ribavirin CL/F of 19.0 L/h, which is consistent with the values reported by other investigators (22,23). The average Vp/F (4,020 L) is similar to the second compartment volume reported by Wade et al. (4,910 L), confirming extensive distribution into non-plasma compartments (23).

Body weight was found to have a significant impact on both CL/F and Vp/F, which was also consistent with previous reports (2225). Using a constant infusion model during 4 weeks of treatment, Jen et al. reported that CL/F was dependent on body weight, age, gender, and serum creatinine (22). Two other population PK analyses included patients with mild to severe renal impairment, where CRCL had a significant impact on ribavirin CL/F (24,25). Utilizing a three-compartment model with a sequential zero-order and first-order absorption, Wade et al. reported that lean body weight was the only covariate that had a significant effect on both CL/F and Vp/F at steady state (23). Here, CRCL had no effect on CL/F, which is similar to our findings.

This is the first report demonstrating the influence of race on Vp/F after accounting for the impact of body weight on Vp/F. We found that Vp/F was nearly 1.5-fold greater in AA compared to CA. This finding is important because the racial difference in Vp/F likely plays a significant role on the ribavirin plasma concentration during the early stage of treatment and prior to the 8 weeks required to reach steady state. Since ribavirin is not bound to plasma proteins, it is unlikely that racial differences in protein binding would explain this observation. Ribavirin is rapidly phosphorylated in all cells with a slow dephosporylation occurring in nucleated cells. Thus, a plausible explanation for a racial difference in distribution may be altered expression or activity of the equilibrative nucleoside transporter (ENT1, SLC29A1), which is involved in ribavirin cellular uptake. A recent study reported that patients with an ENT1 transporter mutation (−706, G>C) had reduced clearance of gemcitibine which was likely related to hepatic uptake, although racial differences were not evaluated (26). A limitation of the present study was uncontrolled dietary intake, since it has been reported that AUC and Cmax were increased by up to 70% and bioavailability increased by 46% after a high-fat meal (23,27,28).

A logistic regression model of SVR was developed in the present study using a Bayesian approach. As expected, race was not a significant predictor of SVR after accounting for ribavirin AUC0  7 and IL28B genotype in the model. Our finding that HOMA-IR and IL28B genotype had a significant effect on SVR is consistent with recent reports (2931). Hepatic fibrosis showed a weak association with SVR in the univariate model, which may be explained by the majority of patients (85%) having mild to moderate Ishak scores. Other covariates including gender, age, baseline HCV RNA load, BMI, and PEGIFN concentration at steady state did not have a significant impact on SVR.

Our finding that ribavirin AUC in the first week had a positive relationship with SVR both in univariate and multivariate PD model, after adjusting for HOMA and IL28B genotype, has not been previously reported. Other studies have reported an association between ribavirin exposure after the first dose, or at steady state, as a predictor of SVR rate (32,33). Loustaud-Ratti et al. reported that the probability of SVR increased with an increasing in ribavirin AUC0  12h and AUC0  4h after the first dose at a minimum threshold of 3,014 and 1,755 ng*h/mL, respectively. Snoeck et al. suggested that the rise in SVR was initially rapid, followed by a slower increase when ribavirin AUC0  24 h at steady state was within the range of 30,000 to 80,000 ng*h/mL. The results of our probabilistic model of SVR suggest that although patients with CT and TT genotype have a lower probability of SVR than those with CC genotype, the probability of SVR could be increased when ribavirin AUC0  7 is optimized. Similar to Snoeck et al., the increase in the predicted probabilities of SVR was initially rapid as RBV AUC0  7 increased, and then slowed when AUC0  7 approached 15,000 ng*day/mL. We also observed that patients having the CC genotype and a low level of HOMA-IR have the highest SVR. Here, an increase in ribavirin AUC0  7 would have a minimal impact on SVR when compared to patients with the TT genotype and a high HOMA-IR. The probability of SVR decreased at the highest levels of HOMA-IR, which remained relatively insensitive to changes in ribavirin AUC0  7 and IL28B genotype. Here, the presence of a TT genotype at a ribavirin AUC0  7 of 15,000 ng*day/mL yields a steeper decrease in the predicted probability of SVR (change from 0.93 to 0.18) compared to individuals with the absence of ribavirin and HOMA-IR values ranging from 0 to 15 (change from 0.20 to 0.004). The probability of SVR was further reduced in patients with insulin resistance when compared to individuals with the CC genotype and maximum ribavirin exposure. Taken together, these results indicate that patients with the TT genotype can have an increased likelihood of SVR with improvements in insulin resistance and ribavirin exposure.

In conclusion, a population model of ribavirin and logistic regression model of SVR were successfully developed to fit data obtained in a large HCV clinical trial. Our findings suggest that optimization of ribavirin plasma concentrations during the first week of ribavirin dosing is critical in order to increase the rate of SVR, especially in AA and those with the IL28B TT genotype. Our primary finding that dose-adjusted ribavirin plasma concentrations were significantly lower in AA than in CA despite similar rates of drug adherence (>90%) strongly suggests that there is a racial difference in ribavirin pharmacokinetics.


This work was supported by 1R21DK078100-01, 1K24DK072036-01, and the Virahep-C study (cooperative agreement funded by the NIDDK). The authors wish to thank Dr. Michelle Rudek and Dr. Ming Zhao for their assistance with analytical methods development.


This work was presented at the 2010 American College of Clinical Pharmacology 39th Annual Meeting and received the 2010 Wayne A. Colburn Memorial Award (RJ).


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