Creating a High-Throughput Workflow for Automated Peptide Characterization using LC-MS
Abstract: In the early stage of a drug discovery project, there is a need for efficient methods that can analyse peptides in short time. This includes methods that confirm the peptide’s identity and estimates its relative purity in an efficient and reliable way. The aim was to create a workflow for peptide characterization for AstraZeneca’s in-house peptides that was applicable for a high-throughput analysis. The approach was to develop and optimise an LC-MS method based on 20 therapeutic peptides considering stationary phase, gradient of mobile phase and flow rate. Also, to evaluate different peptide descriptors to see if they could be used to predict the suitability of a peptide for this workflow and to consider the usefulness and different features of software for processing of raw data. The result was that an LC-MS method with an acquisition time of 5 minutes was developed. The method comprised a CSH column (1.7 µm, 2.1 x 50 mm), 0.5 mL/minute flow rate and gradient time of 3.5 minutes (slope 14%B/minutes). The descriptors ClogKD and aqueous solubility were useful to predict if this method was applicable for the peptides in question. Three software for processing of raw data was considered and the software Waters Connect with its application Intact Mass was chosen. Intact Mass can deconvolve the neutral mass, yield a purity as a combined UV and mass spectral purity (UVxMS) and perform a simple impurity profiling. In conclusion, an LC-MS method adapted for a high-throughput workflow was accomplished that succeeded to obtain adequate results regarding retention time and separation of potential impurities for a range of peptides. Furthermore, descriptors turned out to be useful for predicting suitability of this workflow and an appropriate software could be applied for processing of raw data.
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