Infectivity assays are the key analytical technology for the development and manufacturing of virus-based therapeutics. Here, we introduce a novel assay format that utilizes label-free bright-field images to determine the kinetics of infection-dependent changes in cell morphology. In particular, cell rounding is directly proportional to the amount of infectious virus applied, enabling rapid determination of viral titers in relation to a standard curve. Our kinetic infectious virus titer (KIT) assay is stability-indicating and, due to its sensitive readout method, provides results within 24 h post-infection. Compared to traditional infectivity assays, which depend on a single readout of an infection endpoint, cumulated analysis of kinetic data by a fit model results in precise results (CV < 20%) based on only three wells per sample. This approach allows for a high throughput with ~400 samples processed by a single operator per week. We demonstrate the applicability of the KIT assay for the genetically engineered oncolytic VSV-GP, Newcastle disease virus (NDV), and parapoxvirus ovis (ORFV), but it can potentially be extended to a wide range of viruses that induce morphological changes upon infection. The versatility of this assay, combined with its independence from specific instruments or software, makes it a promising solution to overcome the analytical bottleneck in infectivity assays within the pharmaceutical industry and as a routine method in academic research.

For process optimisation Design of Experiments (DoE) has long been established as a more powerful strategy than a One Factor at a Time approach. Nevertheless, DoE is not widely used especially in the field of cell-based bioassay development although it is known that complex interactions often exist. We believe that biopharmaceutical manufacturers are reluctant to move beyond standard practices due to the perceived costs, efforts, and complexity. We therefore introduce the integrated DoE (ixDoE) approach to target a smarter use of DoEs in the bioassay setting, specifically in optimising resources and time. Where in a standard practice 3 to 4 separate DoEs would be performed, our ixDoE approach includes the necessary statistical inference from only a single experimental set. Hence, we advocate for an innovative, ixDoE approach accompanied by a suitable statistical analysis strategy and present this as a practical guide for a typical bioassay development from basic research to biopharmaceutical industry.

Aim/Materials & methods: Guidelines like United States Pharmacopeia 1032 [1] and pharm.Eur. [2] acknowledge that cell-based bioassays are complex methods and thus prone to outliers. However, invest...

Infectivity assays are the key analytical technology for development of manufacturing processes for virus-based therapeutics. Here, we introduce a novel assay format that utilizes label-free bright field images to determine the kinetics of infection-dependent changes in cell morphology. In particular, cell rounding is directly proportional to the amount of infectious virus applied, enabling rapid determination of viral titers in relation to a standard curve. Our kinetic infectious virus titer (KIT) assay is stability-indicating and, due to its sensitive readout method, provides results within 24 hours post-infection. Compared to traditional infectivity assays, which depend on a single readout of an infection endpoint, cumulated analysis of kinetic data by a fit model results in precise results (CV < 20%) based on only three wells per sample. This approach allows for a high throughput with ~400 samples processed by a single operator per week. We demonstrate the applicability of the KIT assay for VSV-GP and NDV, but it can potentially be extended to a wide range of viruses that induce morphological changes upon infection. The versatility of this assay, combined with its independence from specific instruments or software, makes it a promising solution to overcome the analytical bottleneck in infectivity assays within the pharmaceutical industry and as a routine method in academic research.