Evaluation of infectivity and reverse transcriptase real-time polymerase chain reaction assays for detection of xenotropic murine leukemia virus used in virus clearance validation.

Infectivity and reverse transcriptase quantitative real-time polymerase chain reaction (qRT-PCR) assays have been optimized and validated for xenotropic murine leukemia virus (X-MuLV) detection. We have evaluated the assays systematically with regard to specificity, linearity, lower limit of detection (LLOD), lower limit of quantification (LLOQ), and precision. Both assays are specific for X-MuLV detection, with a linear detection range of 0.6-5.6 log(10) TCID(50)/mL for the infectivity assay, and 1.4-6.5 log(10) particles/mL for the qRT-PCR assay. The LLOD and LLOQ of the infectivity and the qRT-PCR assays are determined as 0.5 and 1.0 log(10)/mL, and 1.4 and 2.2 log(10)/mL. The inter-assay repeatability of qRT-PCR assay (4.2% coefficient of variation [CV]) is higher than the infectivity assay (7.9% CV). We have shown that both assays are closely correlated (r = 0.85, P < 0.05, n = 22). The particle/infectivity ratio is determined as 66. Both assays were applied to evaluate virus removal using virus clearance samples of chromatographic and filtration processes. Here, we have demonstrated that the qRT-PCR assay is much faster in testing and is approximately 8-fold more sensitive than the infectivity assay. Therefore, the qRT-PCR assay can replace the infectivity assay in many cases, but both assays are complementary in elucidating the mechanism of virus inactivation and removal in virus clearance validation.

Application of machine learning methods to pathogen safety evaluation in biological manufacturing processes.

The production of recombinant therapeutic proteins from animal or human cell lines entails the risk of endogenous viral contamination from cell substrates and adventitious agents from raw materials and environment. One of the approaches to control such potential viral contamination is to ensure the manufacturing process can adequately clear the potential viral contaminants. Viral clearance for production of human monoclonal antibodies is achieved by dedicated unit operations, such as low pH inactivation, viral filtration, and chromatographic separation. The process development of each viral clearance step for a new antibody production requires significant effort and resources invested in wet laboratory experiments for process characterization studies. Machine learning methods have the potential to help streamline the development and optimization of viral clearance unit operations for new therapeutic antibodies. The current work focuses on evaluating the usefulness of machine learning methods for process understanding and predictive modeling for viral clearance via a case study on low pH viral inactivation.