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|Tanaka-parameter based approach for chromatographic column selection||K. Kassam, D. Tsarev and M. Euerby||View Poster|
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Booth # 18
Tanaka parameter-based approach for chromatographic column selection: How to locate “k” columns with the most dissimilar selectivity from a selection of “n” columns in a Tanka database
D.A. Tsarev, K. Kassam, and M. Euerby
Abstract: A common approach to chromatographic method development is to screen different combinations of columns and mobile phases that are expected to give good retention, good peak shape, as well as large differences in selectivity. Subsequently, gradient and temperature are optimised for the most promising combination, often using retention modelling.
The selection of columns to be included in a screen is not trivial. The best approach is probably to select a group of modern columns (i.e., n columns) that are expected to provide good stability, adequate retention, and good peak shape for the analytes in question, and thereafter select a subset (i.e., k columns, typically four or six columns) of these which provide large differences in selectivity for screening. Usually this is done by picking stationary phases with different ligands based on information from column manufacturers. However, two C18 columns can actually display larger differences in selectivity than a C18 column and a phenyl column.
Tanaka parameters combined with weighted and scaled Euclidean distance between two multidimensional points, referred to as Column Distance Factor (CDF), have been successfully used to identify the degree of similarity of two columns. This is useful for the identification of replacement/backup columns. It is less useful for the identification of the most diverse columns for screening. In this study, we will describe an approach that allows the selection of n columns of interest from the database and, subsequently, the identification of k columns among these with the most different selectivity by maximizing the CDF from any pair of columns.
A new method for analyzing MSe/All Ions Fragmentation in xenobiotic metabolism studies
Richard Lee, Vitaly Lashin, Alexandre Sakarov, Andrey Paramonov, Gabriela Cimpan
Abstract: During early drug discovery, the study of metabolism plays an essential role in determining which drug candidates move forward into development and later stages. Current methods for analysis to identify metabolic soft spots are through LC/MSn interpretation. The main challenge in this work has always been the structure elucidation of metabolites, and there have been a number of strategies developed to address these difficulties. Typically, the use of data dependent scanning has been the primary mode of data acquisition for structure elucidation, but in the past several years the use of MSe or All Ions Fragment (AIF) acquisition has become more prominent. Here we present a computational routine that automatically analyzes MSe/AIF data for LC/MSn based drug metabolism studies.
IntelliXtract 2.0: Simplified Intelligent Component Extraction and Detection
Richard Lee, Vitaly Lashin, Andrey Paramonov, Anne Marie Smith, Tim Salbert, Eduard Kolovanov
Abstract: The analysis of real-world samples is becoming increasingly complex and time-consuming. Scientists frequently use techniques, such as chromatography to aid in the separation of differing compounds and components. Liquid Chromatography-Mass Spectrometry (LC-MS) has been the primary platform for determination of differing components when chromatographic co-elution was inevitable.
Software can aid in simplifying the data-mining process and increase the speed of discovery. An earlier algorithm for component detection (IX), implemented a method of extracted ion chromatograms (XICs) that were automatically generated and assigned a component number, thereby simplifying the analysis process but was computationally inefficient. Here we describe a simplified and more optimized algorithm based on the use of ion threads vs XICs.
An Informatics Based Approach to Developing a Stability Indicating Method.
Albert Van Wyk, Dmitry Mityushev, Petr Kandalov, Andrew Anderson, Tim Salbert
Abstract: During drug development, a large amount of time and effort is spent on chromatographic method development. The work product of this development is a Stability Indicating Method (SIM)-a validated analytical procedure used to measure the active ingredients in the drug substance or drug product, separate from any process impurities, excipients or degradation products.
The efficient development of a Stability Indicating Method requires a complete understanding of the manufacturing process and efficient communication between various interrelated departments, from process chemistry through to analytical research and development. These groups are often located in different locations globally and have their own internal systems for capturing information, with the most common means of communication being Excel or PowerPoint documents. These documents are not purpose built for communicating such complex data and may leave results open to interpretation.
In this presentation, we will discuss an informatics system which captures structural, analytical and process related data in a structured and searchable manner to facilitate inter and intra departmental communications. Using such a system allows Quality by Design principles to be extended to the drug development process, leading to a safe, effective and well qualified product.