Peptide metabolite identification

Peptide metabolite identification

Metabolite identification is one of the routine tasks that are undertaken in the Drug

Metabolite identification is one of the routine tasks that are undertaken in the Drug Metabolism and Pharmacokinetic departments during the Drug Discovery and development. These efforts can help to understand the structural elements that makes a compound to have high clearance and therefore to guide compound design to modulate this property. Also, it is crucial to compare the metabolism of the potential drug across multiple species helping to translate the metabolism across species. It can be applied in both in-vitro and in-vivo metabolism and to identify potential toxic effects due to metabolism.

Due to the small of the amount metabolite formed and the complexity of the biological matrix, the Liquid Chromatography coupled to Mass Spectrometry (LC-MS) is the bioanalysis technique of choice. This technique has two fundamental steps: one is the separation of the multiple components of the complex biological matrix and a second one is the structure elucidation. Over the years we have developed a number of software and strategies to facilitate these steps: optimizing and automatizing the chromatographic peak detection and the metabolites structure identification starting from data obtained in different instrument and acquisition modes. Both technological improvements has lead to a reduction on the time needed to analyze a sample, and the dependency on instrumentation having a single workflow for any kind of instrument.

Cecropin, an antimicrobial peptide. A main part of the cell-free immunity of insects. 3d rendering.

Most of the metabolism workflows imply the analysis of multiple samples, like multiple incubation times, multiple matrix or multiple conditions. Therefore, in addition of the automation of the single analysis steps (peak detection and metabolite structure elucidation) we have also developed tools that automatize the parsing and clustering of the results from multiple sample experiments storing the results that ensure the integrity of the original data as well as the steps used to get to the final reports. The overall process now can be now done without human intervention, reducing the demand of experts’ time of the analysis of a single analysis and increasing the throughput of the experiments providing more information to the projects that can take better and faster drug design decisions.


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