Viral kinetics analysis and virological characterization of treatment failures in patients with chronic hepatitis C treated with sofosbuvir and an NS5A inhibitor.

BACKGROUND: The combination of sofosbuvir (SOF) plus an NS5A inhibitor for 12 weeks is highly efficacious in patients with chronic hepatitis C. As the costs of generic production of sofosbuvir and NS5A inhibitor are rapidly decreasing, the combination of these DAAs will be the standard treatment in most low- to middle-income countries in the future. AIM: To identify key predictors of response that can be used to tailor treatment decisions. METHODS: A cohort of 216 consecutive patients infected with HCV genotype 1 (1a: n = 57; 1b: n = 77), 2 (n = 4), 3 (n = 33) or 4 (n = 44) were treated with sofosbuvir (SOF) + daclatasvir (n = 176) or SOF + ledipasvir (n = 40) for 12 weeks. The viral kinetics was analysed using the biphasic model and the cure boundary was used to predict time to clear HCV. RESULTS: The overall SVR rate was high (94.4%; n = 204), regardless of the time to viral suppression or low-level viraemia at the end of treatment. The model-based predicted HCV RNA levels at the end of treatment could not differentiate patients who did from those who did not achieve SVR. The presence of NS5A resistance-associated substitutions [position 28 (OR = 70.3, P<.001) and/or 31 (OR = 61.6, P = .002)] at baseline was predictive of virological failure in cirrhotic patients but was not associated with on-treatment viral kinetics. CONCLUSION: This real-world study confirms the excellent results of clinical trials with therapies based on a combination of SOF plus an NS5A inhibitor. It suggests that a personalized approach including baseline NS5A inhibitor resistance testing may inform treatment decisions in cirrhotic patients.

Model Evaluation of Continuous Data Pharmacometric Models: Metrics and Graphics.

This article represents the first in a series of tutorials on model evaluation in nonlinear mixed effect models (NLMEMs), from the International Society of Pharmacometrics (ISoP) Model Evaluation Group. Numerous tools are available for evaluation of NLMEM, with a particular emphasis on visual assessment. This first basic tutorial focuses on presenting graphical evaluation tools of NLMEM for continuous data. It illustrates graphs for correct or misspecified models, discusses their pros and cons, and recalls the definition of metrics used.

A pharmacokinetic-viral kinetic model describes the effect of alisporivir as monotherapy or in combination with peg-IFN on hepatitis C virologic response.

Alisporivir is a cyclophilin inhibitor with demonstrated in vitro and in vivo activity against hepatitis C virus (HCV). We estimated the antiviral effectiveness of alisporivir alone or in combination with pegylated interferon (peg-IFN) in 88 patients infected with different HCV genotypes treated for 4 weeks. The pharmacokinetics of the two drugs were modeled and used as driving functions for the viral kinetic model. Genotype was found to significantly affect peg-IFN effectiveness (ɛ = 86.3 and 99.1% for genotypes 1/4 and genotypes 2/3, respectively, P < 10(-7)) and the loss rate of infected cells (δ = 0.22 vs. 0.39 per day in genotype 1/4 and genotype 2/3 patients, respectively, P < 10(-6)). Alisporivir effectiveness was not significantly different across genotypes and was high for doses ≥600 mg q.d. We simulated virologic responses with other alisporivir dosing regimens in HCV genotype 2/3 patients using the model. Our predictions consistently matched the observed responses, demonstrating that this model could be a useful tool for anticipating virologic response and optimizing alisporivir-based therapies.

Influence of a priori Information, Designs, and Undetectable Data on Individual Parameters Estimation and Prediction of Hepatitis C Treatment Outcome.

Hepatitis C viral kinetic analysis based on nonlinear mixed effect models can be used to individualize treatment. For that purpose, it is necessary to obtain precise estimation of individual parameters. Here, we evaluated by simulation the influence on Bayesian individual parameter estimation and outcome prediction of a priori information on population parameters, viral load sampling designs, and methods for handling data below detection limit (BDL). We found that a precise estimation of both individual parameters and treatment outcome could be obtained using as few as six measurements in the first month of therapy. This result remained valid even when incorrect a priori information on population parameters was set as long as the parameters were identifiable and BDL data were properly handled. However, setting wrong values for a priori population parameters could lead to severe estimation/prediction errors if BDL data were ignored and not properly accounted in the likelihood function.CPT: Pharmacometrics & Systems Pharmacology (2013) 2, e56; doi:10.1038/psp.2013.31; published online 17 July 2013.