Search

Various high-throughput systems and strategies are employed by the biopharmaceutical industry for early to late-stage process development for biologics manufacturing. The associated increases to experiment productivity and reduction in material consumption makes high throughput tools integral for bioprocess development. While these high-throughput systems have been successfully leveraged to generate high quality data representative of manufacturing scale processes, their data interpretation often requires complex data transformation and time-intensive system characterization. With respect to high throughput purification development, RoboColumns by Repligen operated on Tecan automated liquid handling systems offer superior performance scalability, but lack an optimized liquid delivery system that is representative of preparative chromatography. Particularly, stock Tecan liquid handling systems lack the capability to provide high-capacity continuous liquid flow and ideal linear gradient chromatography conditions. These limitations impact protein chromatography performance and hinder the application of high-throughput gradient elution experiments. In this work, we describe a Tecan Freedom EVO high-throughput purification tool that provides more continuous liquid delivery enabling continuous gradient elution capability for RoboColumn experiments as demonstrated by generation of highly linear conductivity gradients. Results demonstrate that the tool can provide RoboColumn performance and product quality data that is in agreement with larger, bench scale chromatography formats for two model purification methods. The described gradient purification method also provides more consistent performance between RoboColumns and larger column formats compared to step elution methods using the same optimized Tecan system. Lastly, new insights into the impact of discontinuous flow on RoboColumn elution performance are introduced, which may help further improve application of these data towards bioprocess development. [ABSTRACT FROM AUTHOR]

Abstract: A novel isolation and purification method was developed for producing 1,3-propanediol with high purity and high yield from the mixture containing 1,3-propanediol, 1,2-propanediol, glycerol, and glucose. The developed method, which consisted of phase separation and chromatography, was found to be a simple and efficient procedure for the isolation and purification of 1,3-propanediol from the other components in the mixture. The use of a phase separation process allows rapid separation of 1,3-propanediol from interfering compounds and dramatically reduces solvent usage compared to alternative methods. The overall purity and yield of 1,3-propanediol in this study were 98% and 82% in the purification process, respectively. [Copyright &y& Elsevier]

In this research, a process for developing normal-phase liquid chromatography solvent systems has been proposed. In contrast to the development of conditions via thin-layer chromatography (TLC), this process is based on the architecture of two hierarchically connected neural network-based components. Using a large database of reaction procedures allows those two components to perform an essential role in the machine-learning-based prediction of chromatographic purification conditions, i.e., solvents and the ratio between solvents. In our paper, we build two datasets and test various molecular vectorization approaches, such as extended-connectivity fingerprints, learned embedding, and auto-encoders along with different types of deep neural networks to demonstrate a novel method for modeling chromatographic solvent systems employing two neural networks in sequence. Afterward, we present our findings and provide insights on the most effective methods for solving prediction tasks. Our approach results in a system of two neural networks with long short-term memory (LSTM)-based auto-encoders, where the first predicts solvent labels (by reaching the classification accuracy of 0.950 ± 0.001) and in the case of two solvents, the second one predicts the ratio between two solvents (R2 metric equal to 0.982 ± 0.001). Our approach can be used as a guidance instrument in laboratories to accelerate scouting for suitable chromatography conditions. [ABSTRACT FROM AUTHOR]