A discovery biotransformation strategy: combining <i>in silico</i> tools with high-resolution mass spectrometry and software-assisted data analysis for high-throughput metabolism
Creators: name: Weston, Daniel J.
name: Dave, Mehul
name: Colizza, Kevin
name: Thomas, Steve
name: Tomlinson, Laura
name: Gregory, Richard
name: Beaumont, Claire
name: Pirhalla, Jill
name: Dear, Gordon J.
Publisher: name: Taylor & Francis
Year: 2022
Subjects: subject: Biochemistry
subject: Genetics
subject: FOS: Biological sciences
subject: Pharmacology
subject: 39999 Chemical Sciences not elsewhere classified
subject: FOS: Chemical sciences
subject: 69999 Biological Sciences not elsewhere classified
subject: 80699 Information Systems not elsewhere classified
subject: FOS: Computer and information sciences
subject: Inorganic Chemistry

description: 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. <i>In vitro</i> 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.Here, we describe a novel metabolism strategy increasing compound throughput using residual <i>in vitro</i> 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. <i>In silico</i> 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.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 <i>in vitro</i> metabolism data. 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. <i>In vitro</i> 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. Here, we describe a novel metabolism strategy increasing compound throughput using residual <i>in vitro</i> 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. <i>In silico</i> 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. 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 <i>in vitro</i> metabolism data.