Statistical evaluations of viral clearance studies for biological products

Since most biological products are derived from living cell culture, it is possible that viral contaminants be transmitted to the final product. Regulatory guidance requires that viral clearance studies be conducted to demonstrate the capacity of the production process in viral removal and inactivation. The key is accurate estimation of viral titer and reduction factor (RF), defined as the difference in log(10) virus titers before and after each step of purification. Darling et al. (1998) [1] suggested a method for analysis of clearance studies. However it is unable to establish an estimate of RF when the post-process viral counts are zero. In this paper, we provide theoretical justification of the method based on normal distribution and discuss the caveats regarding the degrees of freedom. We propose two alternative methods under the assumption that the number of plaques follows a Poisson distribution. Through simulation studies, the Poisson-based methods are shown to provide better estimates of viral titers. Under the Poisson model, we also derive a method to calculate the exact confidence limits for the viral titer and reduction factor even if the post-process viral counts are zero. The use of the methods is illustrated through numerical examples.