Taking Backgrounded Membrane Imaging (BMI) for particle analysis in biopharmaceutics to the next level: Statistical...

The aggregation of proteins is a major threat to the integrity of biopharmaceutical products. Typically the state of aggregation at a specific timepoint is evaluated via particle analysis and counting or turbidity. Backgrounded Membrane Imaging (BMI) is a recently introduced methodology that provides a low-volume, high-throughput alternative to be used in biopharmaceutical development. Recent work has successfully evaluated BMI as an orthogonal method regarding its counting and sizing accuracy for subvisible particle analysis. The work at hand shows that apart from background noise, stochastic variations need to be considered to define the lower limit of detection. A systematic evaluation of particle identification robustness shows that particles at the lower and upper size limit of the technique are not reliably detected. To overcome potential biases due to particle crowding and overlapping, novel evaluation parameters are introduced: the Total Area, the Total Intensity and the BMI-Z-Average to be reported alongside the particle count. Overall, we were able to refine root causes for loss in data quality in BMI and to showcase the use of additional reporting parameters to shift focus to more robustly-identified and quantified larger particles.