An exploratory study on the effect of mechanical stress on particle formation in monoclonal antibody infusions.

Monoclonal antibody infusions (mAb-i) are administered for the treatment of various diseases. They are often transported over long distances from the compounding site to the site of administration. However, transport studies are typically carried out with the original drug product but not with compounded mAb-i. To address this gap, the impact of mechanical stress on the formation of subvisible/nanoparticles in mAb-i was investigated by dynamic light scattering and flow imaging microscopy. Different mAb-i concentrations were subjected to vibrational orbital shaking and stored at 2-8°C up to 35 days. The screening revealed that pembrolizumab and bevacizumab infusions show the highest propensity for particle formation. Especially bevacizumab at low concentrations exhibited an increase in particle formation. Because of the unknown health risks associated with the long-term application of subvisible particles (SVPs)/nanoparticles in infusion bags, stability studies carried out in the frame of licensing application procedures should also focus on SVP formation in mAb-i. In general, pharmacists should minimize the time of storage and mechanical stress during transport, especially in the case of low-concentrated mAb-i. Moreover, if siliconized syringes are used, they should be washed once with saline solution to minimize particle entry.

Superstatistical analysis and modelling of heterogeneous random walks.

Stochastic time series are ubiquitous in nature. In particular, random walks with time-varying statistical properties are found in many scientific disciplines. Here we present a superstatistical approach to analyse and model such heterogeneous random walks. The time-dependent statistical parameters can be extracted from measured random walk trajectories with a Bayesian method of sequential inference. The distributions and correlations of these parameters reveal subtle features of the random process that are not captured by conventional measures, such as the mean-squared displacement or the step width distribution. We apply our new approach to migration trajectories of tumour cells in two and three dimensions, and demonstrate the superior ability of the superstatistical method to discriminate cell migration strategies in different environments. Finally, we show how the resulting insights can be used to design simple and meaningful models of the underlying random processes.