Closed, one-stop intelligent and accurate particle characterization based on micro-Raman spectroscopy and digital microfluidics

Abstract

Monoclonal antibodies are prone to form protein particles through aggregation, fragmentation, and oxidation under varying stress conditions during the manufacturing, shipping, and storage of parenteral drug products. According to pharmacopeia requirements, sub-visible particle levels need to be controlled throughout the shelf life of the product. Therefore, in addition to determining particle counts, it is crucial to accurately characterize particles in drug product to understand the stress condition of exposure and to implement appropriate mitigation actions for a specific formulation. In this study, we developed a new method for intelligent characterization of protein particles using micro-Raman spectroscopy on a digital microfluidic chip (DMF). Several microliters of protein particle solutions induced by stress degradation were loaded onto a DMF chip to generate multiple droplets for Raman spectroscopy testing. By training multiple machine learning classification models on the obtained Raman spectra of protein particles, eight types of protein particles were successfully characterized and predicted with high classification accuracy (93%–100%). The advantages of the novel particle characterization method proposed in this study include a closed system to prevent particle contamination, one-stop testing of morphological and chemical structure information, low sample volume consumption, reusable particle droplets, and simplified data analysis with high classification accuracy. It provides great potential to determine the probable root cause of the particle source or stress conditions by a single testing, so that an accurate particle control strategy can be developed and ultimately extend the product shelf-life.