English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

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.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.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. 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. 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. 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.

xenobiotica

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