Yeast cells, in particular Pichia pastoris, are the host cell of choice for manufacturing several protein therapeutic agents in the biopharmaceutical industry. Host cell DNA is an impurity of such manufacturing process and the residual DNA after the purification process of the drug must be monitored to ensure drug purity and safety. Currently, real-time PCR (qPCR) based methods are widely employed for quantification of host residual DNA. At the same time the digital PCR technology is coming into prominence with promise of higher sensitivity. Here we report a method where the protein drug is directly added to the droplet digital PCR (ddPCR) reaction including yeast-specific primers and fluorescent-tagged probe and nanoliter-sized droplets are generated. The droplets are then subjected to PCR followed by analysis for fluorescence. This Pichia residual DNA direct ddPCR method for yeast can be used to test higher amount of drug compared to the corresponding qPCR method thereby increasing sensitivity, retaining high precision and accuracy and has a wide linear range of determination. The method has been successfully tested with three batches of a recombinant human IgG1-Fc-based drug (RP-1) and with commercially available human insulin, both manufactured in yeast cells. This method simplifies the residual DNA quantification protocol by eliminating DNA extraction or protease digestion and eliminates use of DNA standards in day-to-day running of the method.

A major challenge to successful antiviral therapy is the emergence of drug-resistant viruses. Recent studies have developed several automated analyses of HIV sequence polymorphism based on calculations of selection pressure (K-a/K-s) to predict drug resistance mutations. Similar resistance analysis programs for HCV inhibitors are not currently available. Taking advantage of the recently available sequence data of patient HCV samples from a Phase II clinical study of protease inhibitor boceprevir, we calculated the selection pressure for all codons in the HCV protease region (amino acid 1181) to identify potential resistance mutations. The correlation between mutations was also calculated to evaluate linkage between any two mutations. Using this approach, we identified previously known major resistant mutations, including a recently reported mutation V55A. In addition, a novel mutation V158I was identified, and we further confirmed its resistance to boceprevir in protease enzyme and replicon assay. We also extended the approach to analyze potential interactions between individual mutations and identified three pairs of correlated changes. Our data suggests that selection pressure-based analysis and correlation mapping could provide useful tools to analyze large amount of sequencing data from clinical samples and to identify new drug resistance mutations as well as their linkage and correlations.

Pegylated interferon (PEG-IFN) has become standard therapy for hepatitis C virus (HCV) infection. We evaluated whether PEG-IFN pharmacodynamics and pharmacokinetics account for differences in treatment outcome and whether these parameters might be predictors of therapeutic outcome. Twenty-four IFN-naïve, HCV/human immunodeficiency virus–coinfected patients received PEG-IFN α-2b (1.5 μg/kg) once weekly plus daily ribavirin (1,000 or 1,200 mg) for up to 48 weeks. HCV RNA and PEG-IFN α concentrations were obtained from samples collected frequently after the first 3 PEG-IFN doses. We modeled HCV kinetics incorporating pharmacokinetic and pharmacodynamic parameters. Although PEG-IFN concentrations and pharmacokinetic parameters were similar in sustained virological responders (SVRs) and nonresponders (NRs), the PEG-IFN α-2b concentration that decreases HCV production by 50% (EC50) was lower in SVRs compared with NRs (0.04 vs. 0.45 μg/L [P = .014]). Additionally, the median therapeutic quotient (i.e., the ratio between average PEG-IFN concentration and EC50[C̄/EC50]), and the PEG-IFN concentration at day 7 divided by EC50 (C(7)/EC50) were significantly increased in SVRs compared with NRs after the first (10.1 vs. 1.0 [P = .012], 2.8 vs. 0.3 [P = .007], respectively) and second (14.0 vs. 1.1 [P = .016], 5.4 vs. 0.4 [P = .02], respectively) PEG-IFN doses. All 3 parameters may be used to identify NRs. In conclusion, PEG-IFN concentrations and pharmacokinetic parameters do not differ between SVRs and NRs. In contrast, pharmacodynamic measurements—namely EC50, the therapeutic quotient, and C(7)/EC50—are different in coinfected SVRs and NRs. These parameters might be useful predictors of treatment outcome during the first month of therapy. (HEPATOLOGY 2006;43:943–953.)

Type I interferon-α(IFN-α) has proven to be a clinically effective antiviral and antineoplastic drug for 20 yr (1). Recently, pegylated forms of IFN-α have been commercially produced and have shown superior clinical efficacy to unpegylated IFN-α for reducing HCV viral load with less frequent dosing required for the patient (2,3). The superior clinical efficacy is probably derived from the enhanced serum half-life of the pegylated IFN-α in patients. However, pegylation also reduces the in vitro activity of the core IFN-α protein (4,5). Understanding the structural implications of pegylation on IFN-α activity is critical for biologically characterizing the commercial drug product. In vitro characterization provides a basis for establishing consistency in the manufacturing process for the precursor IFN-α and the final pegylated product. The better characterized the product is, the higher the confidence is for assessing and demonstrating comparability between pegylated products when comparing changes in process of manufacture, site of manufacture, or like-product from different sources.