Structure-based Prediction of Retention Times for Chromatography
Prediction systems for chromatographic retention times.
Chromatographic method development is a significant challenge in analytical chemistry. The chromatographer is asked to find an appropriate compromise between the often conflicting goals of resolution, robustness and run time. The problem of method development is typically divided into two stages. The first stage is the determination of the basic system on which development will proceed (type of chromatography, column choice etc.). The second stage is the optimization of continuous parameters (especially gradient and temperature) within that method space.
Traditionally, the first selection stage is based on a combination of the experience of the method development chromatographer and a limited number of screening experiments. The second stage is usually based on systematic optimization, commonly with software modelling. Software tools for optimization of separations have matured to the point where their use has become commonplace. More recently, software has also begun to be applied to the first stage of method development enabling chromatographers to select more suitable systems for optimization based on characteristics of the compounds of interest rather than by trial-and-error. However, there are many cases where systematic screening is impractical, especially when a method is not intended to become an organizational asset but rather will only be applied a few times. In these cases, the objective is usually to find an adequate method as fast as possible. When some or all of the chemical structures in the mixture are known with a high degree of confidence, it is feasible to perform selection between several “generic” methods based on chemical structures. This article will describe a new system for fast, effective selection between generic methods based on retention time prediction.
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.
