相似性(几何)
生物系统
主成分分析
正规化(语言学)
计算机科学
动态光散射
产品(数学)
签名(拓扑)
航程(航空)
点积
光散射
降级(电信)
数据挖掘
实验数据
散射
相似
动态范围
相似解
数学
基础(线性代数)
材料科学
动态数据
人工智能
模式识别(心理学)
热点(计算机编程)
动态相似性
化学相似性
数据建模
作者
Ashwinkumar Bhirde,Siri Harish,Nicholas Trunfio,Isabella F de Luna,William Smith,Qiong Fu
标识
DOI:10.1038/s41598-025-97377-6
摘要
Abstract Comparative analytical assessment (CAA) between the reference product and the proposed product forms the basis of the biosimilarity demonstration. Though Dynamic Light Scattering (DLS) has been implemented for CAA, its capability beyond signature peak for similarity assessments has remained unexplored. Herein, we have developed a innovative forced degradation based sweet spot method consisting of signature peak, temperature range, increment and hold time using high throughput-DLS (HT-DLS) to show similarity in hydrodynamic size between products. In our study, we used rituximab, its biosimilars, and insulin analogs as model products to demonstrate product similarity in hydrodynamic size (D h ) size through the HT-DLS sweet spot approach. Our data indicate that temperature range, temperature increment, hold time, and regularization algorithm, all play a role in showing analytical similarity in D h size. Our data also indicate that establishing DLS signature peaks of the products is insufficient to show analytical similarity in D h size distribution. Additionally, the temperature range (sweet spot) varies from product to product. Principal component analysis modeling was used for detailed data interpretation. Overall, our HT-DLS based sweet spot method provided informative data to support similarity in D h size distribution.
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