A discovery biotransformation strategy: combining in silico tools with high-resolution mass spectrometry and software-assisted data analysis for high-throughput metabolism

Abstract

1. Understanding compound metabolism in early drug discovery aids medicinal chemistry in designing molecules with improved safety and ADME properties. While advancements in metabolite prediction brings increased confidence, structural decisions require experimental data. In vitro metabolism studies using liquid chromatography and high-resolution mass spectrometry (LC–MS) are generally resource intensive and performed on very few compounds, limiting the chemical space that can be examined.

2. Here, we describe a novel metabolism strategy increasing compound throughput using residual in vitro clearance samples conducted at drug concentrations of 0.5 µM. Analysis by robust ultra high-performance liquid chromatography separation and accurate-mass MS detection ensures major metabolites are identified from a single injection. In silico prediction (parent cLogD) tailors chromatographic conditions, with data-dependent tandem mass spectroscopy targeting predicted metabolites. Software-assisted data mining, structure elucidation and automatic reporting are used.

3. Confidence in the globally aligned workflow is demonstrated with 16 marketed drugs. The approach is now implemented routinely across our laboratories. To date, the success rate for identification of at least one major metabolite is 85%. The utility of these data has been demonstrated across multiple projects, allowing earlier medicinal chemistry decisions to increase efficiency and impact of the design–make–test cycle thus improving the translatability of early in vitro metabolism data.

Keywords:

• Drug discovery
• quan-qual
• high-throughput
• intrinsic clearance
• in vitro translatability
• chemical design
• in silico prediction
• biotransformation
• high-resolution mass spectrometry (HRMS)
• datamining software

Acknowledgements

The authors wish to extend their thanks to the many colleagues across GSK Global DMPK for their assistance in delivering this workflow and their support in the analysis of real-world samples.

Disclosure statement

No potential conflict of interest was reported by the author(s).