Metabolite Identification

Characterize and quantify metabolites with speed, efficiency, and confidence.

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Improving Peptide Catabolism Interpretation Using Ion Mobility Data and Server-Based Data Review With HELM Integration

A unique challenge in peptide-based drug metabolism for evaluating preclinical candidates is the need for processing and visualization software tools that accommodate both small molecule rules (such as oxidations, synthetic modifications), as well as large molecule modifications (such as amide hydrolysis and oxidative deamination).

Ion Mobility-Enabled Metabolite Identification of Tienilic Acid and Tienilic Acid Isomer

Implementation of IMS-HRMS for TA and TAI metabolite identification and assessment of CCS modelling techniques for metabolite identification support.

Comprehensive Drug Metabolite Analysis Using Ion Mobility and Advanced Data Interpretation Tools

This webinar will outline the common technical and workflow challenges for metabolite identification and characterization throughout the drug development process.

Capitalizing on the Benefits of Ion Mobility for Metabolite Identification

Metabolite identification plays a crucial role in drug development, leading to a constant need to evaluate new technologies that can improve data quality or increase efficiencies. The recent example of a multiple sclerosis drug submitted for FDA approval, and the agency’s subsequent refusal to consider the application, illustrates the risk of incomplete characterization of clinically relevant metabolites.

Ion Mobility: Uncover the Unknown - Comprehensive Drug Metabolite Analysis Using Ion Mobility and Advanced Data Mining

View the latest webinar on the use of Ion Mobility for Comprehensive Drug Metabolite Analysis conducted on September 25th, 2018.

Catabolism of Peptides Using Ion Mobility Enabled High Resolution Mass Spectrometry Coupled with Mass-Metasite Data Processing

This study outlines and focuses on the benefits and ease of use of ion mobility for the application of catabolite identification, as well as the use of Mass-MetaSite and WebMetabase for processing these complex data sets.

Characterising the Catabolism of Peptides Using Ion Mobility Enabled High Resolution Mass Spectrometry with Mass-Metasite Integration for Data Processing

The pharmaceutical industry is increasingly exploring biotherapeutic molecules as an alternative to conventional small molecule drugs, with the number of biotherapeutic drugs approved each year continuing to outpace that of small molecule compounds. In response, a growing need exists to characterize and optimize the absorption, distribution, metabolism and excretion (ADME) properties of these diverse biotherapeutics, and with DMPK groups looking to apply similar principles from their small molecule experience to the ADME of diverse biotherapeutics. The challenge is that few software packages available to characterise the clearance and metabolic fate of biotherapeutics. Here, we mine ion mobility high resolution mass spectrometric (DIA) data for the analysis of biotherapeutic drug metabolism using the Mass-MetaSite and WebMetabase software platform for processing.

Development of a Workflow for the Metabolite Profiling and Identification of Peptide-Based Large Molecules

Helen Robinson, Ph.D Associate Scientist Covance Laboratories

There is a growing interest within the pharmaceutical industry in the use of large molecules and biotherapeutics as drug compounds. Incubating a variety of known peptides (ca. 25-50 amino acids) with rat kidney homogenate or cryo-preserved rat hepatocytes prior to analysis using a Waters Vion IMS-QTof, Covance shows how UNIFI workflows can be used to interrogate peptide metabolism data.

Using Mass-MetaSite and WebMetabase to Process HDMSE Data Acquired on the Vion IMS QTof Mass Spectrometer

This technology brief describes the integration of third party applications to read and process data acquired on instruments using the UNIFI platform.

Peptide Catabolite Identification Using HDMSE data and Mass-MetaSite Processing

This poster was presented at the 2018 ASMS annual conference from June 3-7 in San Diego, CA.