LCGC Columnist Receives Medal
Ronald E. Majors, a senior chemist at Agilent and LCGC's "Column Watch" and "Sample Preperation Perspectives" columnist, received the Chromatographic Society's 2007 Martin Gold Medal at HPLC 2008.
Ronald E. Majors, a senior chemist at Agilent and LCGC's "Column Watch" and "Sample Preperation Perspectives" columnist, received the Chromatographic Society's 2007 Martin Gold Medal at HPLC 2008. The award, named after the Nobel Prize winner A.J.P. Martin, was presented by Professor W. John Lough, president of the Chromatographic Society of the United Kingdom.
Majors has contributed significantly to separation science research through the years, including his work in HPLC column technology, particle size studies, packing methodologies, and chemically bonded phases. He was the first to efficiently pack 5-μm particles into high performance columns and was responsible for the introduction of the first commercial microparticulate column.
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.
