A cellular replicon-based phenotyping assay to determine susceptibility of hepatitis C virus clinical isolates to NS3/4A protease inhibitors.

A hepatitis C virus (HCV) replicon-based protease phenotyping assay has been developed that allows determining the susceptibility of a patient's HCV protease sequence to HCV protease inhibitors. In brief, HCV protease sequences amplified from clinical samples are cloned in a transient HCV genotype 1b replicon backbone, containing a luciferase reporter gene. These protease chimeric replicons are replication-competent when electroporated into susceptible Huh7-Lunet cells. Replication can be quantified by measuring the enzymatic activity of the luciferase protein. This assay is reproducible and robust, and has a high overall success rate for determining the phenotypic susceptibility of HCV genotype 1a and 1b patient-derived protease domains to HCV protease inhibitors. In addition, the HCV genotype 1b protease shuttle backbone also supports efficient replication of HCV genotype 4 protease sequences.

Development of a high-throughput antiviral assay for screening inhibitors of chikungunya virus and generation of drug-resistant mutations in cultured cells.

Chikungunya virus (CHIKV) is a mosquito-borne Alphavirus that has already infected millions of people in recent large-scale epidemics in Africa, the islands of the Indian Ocean, South and Southeast Asia, and northern Italy. The infection is still ongoing in many countries, such as India. Although the fatal rate is approximately 0.1% in the La Réunion outbreak, it causes painful arthritis-like symptoms that can last for months or even years. Currently, neither vaccine nor approved antiviral therapy exists to protect humans from chikungunya infection. Therefore, there is an urgent unmet medical need for the development of antiviral drugs for pre-exposure prophylaxis and/or treatment of chikungunya infections. In this chapter, we describe a fully validated ATP/luminescence assay that is effective for high-throughput screening of CHIKV inhibitors. Protocols for growing CHIKV stocks and generating drug-resistant viral variants for modes of action studies of compounds are also described.

Cross-validated stepwise regression for identification of novel non-nucleoside reverse transcriptase inhibitor resistance associated mutations.

BACKGROUND: Linear regression models are used to quantitatively predict drug resistance, the phenotype, from the HIV-1 viral genotype. As new antiretroviral drugs become available, new resistance pathways emerge and the number of resistance associated mutations continues to increase. To accurately identify which drug options are left, the main goal of the modeling has been to maximize predictivity and not interpretability. However, we originally selected linear regression as the preferred method for its transparency as opposed to other techniques such as neural networks. Here, we apply a method to lower the complexity of these phenotype prediction models using a 3-fold cross-validated selection of mutations. RESULTS: Compared to standard stepwise regression we were able to reduce the number of mutations in the reverse transcriptase (RT) inhibitor models as well as the number of interaction terms accounting for synergistic and antagonistic effects. This reduction in complexity was most significant for the non-nucleoside reverse transcriptase inhibitor (NNRTI) models, while maintaining prediction accuracy and retaining virtually all known resistance associated mutations as first order terms in the models. Furthermore, for etravirine (ETR) a better performance was seen on two years of unseen data. By analyzing the phenotype prediction models we identified a list of forty novel NNRTI mutations, putatively associated with resistance. The resistance association of novel variants at known NNRTI resistance positions: 100, 101, 181, 190, 221 and of mutations at positions not previously linked with NNRTI resistance: 102, 139, 219, 241, 376 and 382 was confirmed by phenotyping site-directed mutants. CONCLUSIONS: We successfully identified and validated novel NNRTI resistance associated mutations by developing parsimonious resistance prediction models in which repeated cross-validation within the stepwise regression was applied. Our model selection technique is computationally feasible for large data sets and provides an approach to the continued identification of resistance-causing mutations.