A Viral Dynamic Model for Treatment Regimens with Direct-acting Antivirals for Chronic Hepatitis C Infection

 A Viral Dynamic Model for Treatment Regimens with Direct-acting Antivirals for Chronic Hepatitis C Infection

Bambang S. Adiwijaya^1*, Tara L. Kieffer¹, Joshua Henshaw¹, Karen Eisenhauer¹, Holly Kimko², John J. Alam¹, Robert S. Kauffman¹, Varun Garg¹

1 Vertex Pharmaceuticals Incorporated, Cambridge, Massachusetts, United States of America, 2 Johnson & Johnson Pharmaceutical Research and Development, LLC, Raritan, New Jersey, United States of America

Abstract

We propose an integrative, mechanistic model that integrates in vitro virology data, pharmacokinetics, and viral response to a combination regimen of a direct-acting antiviral (telaprevir, an HCV NS3-4A protease inhibitor) and peginterferon alfa-2a/ribavirin (PR) in patients with genotype 1 chronic hepatitis C (CHC). This model, which was parameterized with on-treatment data from early phase clinical studies in treatment-naïve patients, prospectively predicted sustained virologic response (SVR) rates that were comparable to observed rates in subsequent clinical trials of regimens with different treatment durations in treatment-naïve and treatment-experienced populations. The model explains the clinically-observed responses, taking into account the IC50, fitness, and prevalence prior to treatment of viral resistant variants and patient diversity in treatment responses, which result in different eradication times of each variant. The proposed model provides a framework to optimize treatment strategies and to integrate multifaceted mechanistic information and give insight into novel CHC treatments that include direct-acting antiviral agents.

Discussion Only

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An integrated model of viral dynamic responses to treatment with telaprevir and PR has been developed and validated by comparing predictions against observed outcomes in late-phase clinical trials. It provides a framework to integrate multi-faceted information related to this novel CHC regimen, including in vitro resistance and fitness, pharmacokinetics, viral sequencing, and viral dynamics. The framework supported decisions pertaining to treatment strategies and optimizing regimens during clinical development. The model that was based on data from early-phase trials was predictive of observed SVR rates in subsequent studies that were not used in model building.
The model also aided understanding of a novel CHC treatment regimen consisting of telaprevir and PR. It provided a consolidated picture of the interplay between the fitness and resistance of variant populations, antiviral inhibition by telaprevir and by PR treatment, and patient diversity in PR responsiveness, and connected these factors to the ultimate treatment outcome of SVR. The model suggested that the primary role of telaprevir in a TPR regimen is to eradicate WT and lower-level telaprevir resistant variants, and the complementary role of PR is to eradicate higher-level telaprevir resistant variants. Accordingly, virologic failure during the telaprevir-treatment phase has been associated predominately with higher-level telaprevir resistant variants, indicating a failure of PR to inhibit higher-level telaprevir resistant variants in some patients [9], [29]. Modeling results and analysis of viral populations derived from patients who stopped treatment prior to viral eradication [28] have led to the working hypothesis that a successful regimen should have (1) a telaprevir treatment duration sufficient to eradicate WT and most lower-level telaprevir resistant variants, and (2) a PR treatment duration sufficient to eradicate any remaining variants, particularly higher-level telaprevir resistant variants. Once WT and lower-level telaprevir resistant variants have been eradicated and higher-level telaprevir resistant variants are the dominant residual viral population, telaprevir adds no additional antiviral effect. The PR duration required to eradicate higher-level telaprevir resistant variants depends greatly on the PR responsiveness of a given patient and likely the number of residual higher-level telaprevir resistant variants. Because higher-level telaprevir resistant variants pre-exist at lower frequency than WT and have reduced fitness, a greater percentage of patients can be treated with a shorter duration of PR treatment in the TPR regimen than in the PR regimen. The personalization of PR durations for patients treated with T12PR treatment has been demonstrated in those who achieved early virologic response in clinical trials [11], [12].
Data and modeling analyses suggest different eradication times for variants with varying fitness and resistance, leading to different optimal treatment durations of telaprevir and PR treatment. Modeling analysis showed that a higher percentage of patients would be expected to have virologic failure during PR treatment after the completion of 12 weeks of telaprevir treatment if simulated without viral eradication, a phenomenon that has rarely been observed in clinical trials: the virologic failure rates after 12-week of telaprevir treatment in treatment-naïve patients were 1% for the T12PR24 arm of Study PROVE2 [8] and 4.4% in the T12PR24-48 arms of ADVANCE [28], [29]. Moreover, the shorter eradication times of sensitive variants as compared to resistant variants are also consistent with the observed more rapid elimination of WT HCV in patients dosed with telaprevir as compared to those typically observed in PR treatment [23], [24].
The model produced consistently predictive results for different prior PR48-treatment-failure populations despite being trained only for the treatment-naïve population. This finding supports the hypothesis that a treatment-naive population contains several types of patients with differing PR responsiveness, and suggests that a model estimated from the treatment-naive population can be used to predict results for populations with different PR responsiveness. In the 2 studies in the treatment-experienced population (Studies PROVE3 and REALIZE), the predicted and observed SVR rates were generally consistent: comparable SVR rates in PROVE3 and slightly higher predicted SVR rates compared to those rates observed in REALIZE. The discrepancy is greatest in the prior nonresponder population. The discrepancy in the REALIZE study may arise from a limitation of the model: that the underlying parameters constituting PR responsiveness were assumed to be continuously distributed in treatment-naïve population, while the actual parameters may be more discrete and based on other factors such as the IL28B genotypes [30], which has been reported to produce different viral dynamics in response to PR treatment [31], [32]. Alternatively, the discrepancy may be attributed to a higher proportion of patients with adverse prognostic factors for achieving SVR (e.g., advanced liver disease) enrolled in REALIZE, whereas the predictions were generated from the dataset that contained treatment-naïve patients with fewer of these adverse factors. In the modeling described here, adverse factors were not formally examined as covariates because of the limited data available from the early studies.
In summary, the proposed model served as a framework in integrating information from multiple sources and was useful in supporting decision-making for the optimization of treatment strategies during clinical development. The model provided insights to help design novel treatment regimens of combination therapy with telaprevir, peginterferon alfa-2a and ribavirin for CHC treatment, and may be useful for evaluating future CHC treatment regimens that include direct-acting antiviral agents.
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