Machine Learning and Accelerated Stress Approaches to Differentiate Potential Causes of Aggregation in Polyclonal Antibody Formulations During Shipping

Therapeutic proteins are among the most widely prescribed medications, with wide distribution and complex supply chains. Shipping exposes protein formulations to stresses that can trigger aggregation, although exact mechanism(s) responsible for aggregation unknown. To better understand how shipping causes we compared populations of aggregates were formed in a polyclonal antibody formulation during live studies observed accelerated stability designed mimic both sporadic high g-force continuous low encountered shipping. Additionally, effects on levels generated two types secondary packaging, one which was mitigate large stresses. Aggregation quantified using fluorescence intensity 4,4′-dianilino-1,1′-binaphthyl-5,5′-disulfonic acid (bis-ANS) dye, size exclusion performance liquid chromatography (SEC HPLC), flow imaging microscopy (FIM). FIM also combined machine learning methods analyze particle morphology distributions. These comparisons revealed distributions resemble result from events, but not those following suggesting play predominant role shipping-induced aggregation.