GPC/SEC analysis reveals a multitude of molar mass information about unknown samples that are chemically simple or homogenous.
GPC/SEC analysis reveals a multitude of molar mass information about unknown samples that are chemically simple or homogenous.
However, results for copolymers may be compromised by the fact that fractions with different composition and different molar mass may co-elute.
There are different methods on how to characterize such samples:
• chemical composition analysis studied in GPC/SEC mode by concentration detectors (this issue)
• chemical heterogeneity analysis investigated by HPLC based on retention differences of samples with different composition (upcoming issue)
• comprehensive 2D analysis that allows samples to be separated by composition and molar mass simultaneously (upcoming issue)
This issue will discuss the chemical composition analysis with multiple detection, while the latter two will be discussed in the following two GPC/SEC: Tips & Tricks issues.
AI and GenAI Applications to Help Optimize Purification and Yield of Antibodies From Plasma
October 31st 2024Deriving antibodies from plasma products involves several steps, typically starting from the collection of plasma and ending with the purification of the desired antibodies. These are: plasma collection; plasma pooling; fractionation; antibody purification; concentration and formulation; quality control; and packaging and storage. This process results in a purified antibody product that can be used for therapeutic purposes, diagnostic tests, or research. Each step is critical to ensure the safety, efficacy, and quality of the final product. Applications of AI/GenAI in many of these steps can significantly help in the optimization of purification and yield of the desired antibodies. Some specific use-cases are: selecting and optimizing plasma units for optimized plasma pooling; GenAI solution for enterprise search on internal knowledge portal; analysing and optimizing production batch profitability, inventory, yields; monitoring production batch key performance indicators for outlier identification; monitoring production equipment to predict maintenance events; and reducing quality control laboratory testing turnaround time.