Hepatitis C virus (HCV) infection is a significant public health concern that affects nearly 170 million people worldwide. Since a vaccine or prophylactic therapeutic regimen to protect against HCV does not currently exist, infected individuals must rely on antiviral therapy to manage HCV infection. The current standard of care for HCV infection is a combination of pegylated interferon and ribavirin. Unfortunately, this therapeutic regimen is riddled with side effects and is effective in less than 50% of patients infected with genotype 1 HCV, the most prevalent genotype in the United States. Therefore, there is a great medical need for new therapeutic regimens for the treatment of HCV. In an attempt to fulfill this need for new HCV therapeutics, Gilead Sciences, Inc., Merck and Co., and Bristol-Myers Squibb have focused on developing new compounds that inhibit specific processes in the viral replication cycle, referred to as direct acting antiviral agents (DAAs). DAAs offer several advantages to the current standard of care regimen, including increased efficacy and improved tolerability. However, drug resistance has been a major challenge in the development of these new compounds. Combination therapy with two or more DAAs that act on different target sites is a promising strategy to prevent the emergence of resistance. In order for combination therapy to be effective in man, one must elucidate the optimal dose (how much?) and dosing interval (how often?) for each compound that will maximize resistance suppression and viral inhibition. For this proposal we will evaluate a total of 9 DAAs that represent each of the four anti-HCV drug classes. We will first determine the optimal dose and dosing interval for the 9 compounds as monotherapy (Aim #1) against a genotype 1b HCV replicon using the state-of-the-art BelloCell pharmacodynamic (PD) model system in which human PK profiles are simulated. The results from monotherapy studies will be applied to design dosage regimens for the most promising 2-drug combinations of DAAs and these regimens will be evaluated in the BelloCell PD system (Aim #2). A novel and innovative mathematical mixture model for combination therapy will be fit to the data generated from these experiments. This model delineates the impact of combination therapy upon susceptible replicons as well as mutant replicons resistant to either drug in the combination. The use of this model together with Monte Carlo simulation allows for the identification of population-based optimal regimens for combination chemotherapy. Finally, triple combination therapy will also be assessed in the BelloCell PD system. As part of this application, new computer programs will be developed (Specific Aim #3) to improve the run time and efficiency of the mathematical models. Completing this research will result in intelligently designed combination therapeutic regimens that have the greatest likelihood of clinical success. These regimens can be directly applied to the design protocol for human clinical trials.
Antiviral resistance is a major obstacle in the treatment of Hepatitis C virus (HCV). Combination therapy with two or more antivirals from different drug classes is a promising strategy to prevent the emergence of resistance, thereby improving therapeutic outcomes. Therefore, we will employ an in vitro pharmacodynamic model for HCV infection, in conjunction with novel and innovative mathematical models, to elucidate optimal dosage regimens for combinations of direct-acting antiviral agents against HCV that will maximize resistance suppression and HCV inhibition.
| Pomeroy, Justin J; Drusano, George L; Rodriquez, Jaime L et al. (2017) Searching for synergy: Identifying optimal antiviral combination therapy using Hepatitis C virus (HCV) agents in a replicon system. Antiviral Res 146:149-152 |
| Brown, Ashley N; Liu, Lin; Rodriquez, Jaime L et al. (2017) Sofosbuvir (SOF) Suppresses Ledipasvir (LDV)-resistant Mutants during SOF/LDV Combination Therapy against Genotype 1b Hepatitis C Virus (HCV). Sci Rep 7:14421 |
