June 5, 2005, ASMS 2005, San Antonio, TX, USA

Optimizing the Input Parameters for CODA and COMPARELCMS: A Technical Presentation with Illustrations for Drug Metabolism Studies

Margaret Antler, Mark Bayliss

Abstract

Identification of low-intensity analytes from within an LC/MS dataset can be challenging. These datasets are commonly large, with an abundance of chemical noise that can change under the influence of a chromatographic gradient. As a result, an analyst may take four hours or more to manually extract trace components such as metabolites or impurities from a LC/MS dataset.

Component Detection Algorithm (CODA) is a highly effective, generic tool for quickly extracting meaningful chromatographic peaks from LC/MS or GC/MS datasets. This session will review the fundamentals of CODA and present recent improvements made by ACD/Labs. Optimal parameter selection will be discussed, including an illustration of the benefits of baseline correction and peak picking within CODA.

Detecting small changes between two or more LC/MS datasets can be an even greater challenge, especially if searching for unexpected metabolites in a complex biomatrix or for degradation products or contaminants. COMPARELCMS is an algorithm used to identify differences between two or more datasets using CODA as part of its processing action. A review of COMPARELCMS similar to that of CODA will be given. Both CODA and COMPARELCMS will be illustrated with examples of drug metabolism studies.

Download the presentation in MS PowerPoint (510 Kb ZIP file) or Adobe Acrobat format (336 Kb PDF file).

Relevant Products: ACD/MS Manager, ACD/MS Processor, ACD/MS Manager Suite

Relevant Solutions: Metabolism Studies, Impurity and Degradant Studies, Analytical Laboratory