June 4-8, 2017
Indiana Convention Center, Indianapolis, IN, USA
Related presentations, posters, and scientific talks from this event have been posted here for your reference. Please click the associated link to download.
A new biotransformation prediction engine integrated into a metabolite identification solution, Richard Lee, Rytis Kubilius, Vitaly Lashin, Alexandr SakharovView Poster
A new method for analyzing MSE/All Ions Fragmentation in Xenobiotic metabolism studies, Richard Lee, Vitaly Lashin, Andrey Paramonov, Alexandr SakharovView Poster
IntelliXtract 2.0: Simplified Intelligent Component Extraction and Detection, A.M. Smith, R. Lee, V. Lashin, A. ParamonovView Poster
Join the ACD/Labs team at Booth # 711 throughout the show.
Monday, June 5th, 6:45–8:15 AM
Indiana Convention Center, Room 140
|6:45–7:15 AM||Breakfast Buffet and Registration|
|7:15‐7:25 AM||General Introduction
Graham McGibbon, Director, Strategic Partnerships (ACD/Labs)
|7:25‐7:45 AM||Implementing Method Development Software for Complex Chromatography Problems
Erin Gemperline, Senior Analytical Scientist (Dow AgroSciences)
|7:45‐8:00 AM||MetaSense: A New Platform for Metabolite Identification and Visualization
Richard Lee, Solution Area Manager—MetaSense (ACD/Labs)
|8:00‐8:10 AM||What's New in Mass Spectrometry at ACD/Labs
Anne Marie Smith, Technical Specialist (ACD/Labs)
|8:10‐8:15 AM||Concluding Remarks
Jeffrey Hendrycks, Account Manager (ACD/Labs)
Tuesday, June 6th, 4:10 PM
Location: 500 Room Abstract #: 290108
A Unifying, Informatics-Based Approach to Life Cycle Management of Impurity Data in Pharmaceutical Development
Graham A. McGibbon, Albert van Wyk
Abstract: Applying Quality by Design (QbD) principles to impurity investigations and control strategies requires a thorough understanding of the manufacturing process. The drug development lifecycle is lengthy and complicated, with multiple stakeholders, often in disparate locations around the globe. During the process, a vast array of complex and heterogeneous data is collected and transferred between the various project teams. Mass spectrometry data, especially LC/UV/MS data along with associated identifications, inevitably comprises a substantial extent of this essential data. Many groups have their own systems for capturing data, but no single centralized system linking information together. There is a significant need for informatics systems that manage impurity information including LC/UV/MS data.
Wednesday, June 7th
A new method for analyzing MSe/All Ions Fragmentation in xenobiotic metabolism studies
Richard Lee, Vitaly Lashin, Alexandre Sakarov, Andrey Paramonov
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.
A new biotransformation prediction engine integrated into a metabolite identification solution.
Richard Lee, Rytis Kubilius, Vitaly Lashin, Alexandre Sakarov
Abstract: In the analysis of xenobiotic metabolism, the main analytical platform for studying experiments is liquid chromatography coupled to a high resolution mass spectrometer. There have been a number of advancements in the hardware used, as well as the software that processes these data. However, bottlenecks remain in the workflow and especially in the structure elucidation phase. A new prediction algorithm was developed to aid in the identification of possible metabolites within an LC/MS dataset. The work presented here describes the integration of new prediction methods that determine the likelihood of biotransformation reactions, and subsequent metabolite identification within an automated processing routine.
Thursday, June 8th
IntelliXtract 2.0: Simplified Intelligent Component Extraction and Detection
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.
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