A discovery biotransformation strategy : combining in silico tools with high-resolution mass spectrometry and software-assisted data analysis for high-throughput metabolism
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.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2022 |
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| Erschienen: |
2022 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:52 |
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| Enthalten in: |
Xenobiotica; the fate of foreign compounds in biological systems - 52(2022), 8 vom: 13. Aug., Seite 928-942 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Weston, Daniel J [VerfasserIn] |
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| Links: |
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| Anmerkungen: |
Date Completed 21.12.2022 Date Revised 22.12.2022 published: Print-Electronic Citation Status MEDLINE |
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| doi: |
10.1080/00498254.2022.2136042 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM347428371 |
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| 100 | 1 | |a Weston, Daniel J |e verfasserin |4 aut | |
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| 520 | |a 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 | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Drug discovery | |
| 650 | 4 | |a biotransformation | |
| 650 | 4 | |a chemical design | |
| 650 | 4 | |a datamining software | |
| 650 | 4 | |a high-resolution mass spectrometry (HRMS) | |
| 650 | 4 | |a high-throughput | |
| 650 | 4 | |a in silico prediction | |
| 650 | 4 | |a intrinsic clearance | |
| 650 | 4 | |a in vitro translatability | |
| 650 | 4 | |a quan-qual | |
| 700 | 1 | |a Dave, Mehul |e verfasserin |4 aut | |
| 700 | 1 | |a Colizza, Kevin |e verfasserin |4 aut | |
| 700 | 1 | |a Thomas, Steve |e verfasserin |4 aut | |
| 700 | 1 | |a Tomlinson, Laura |e verfasserin |4 aut | |
| 700 | 1 | |a Gregory, Richard |e verfasserin |4 aut | |
| 700 | 1 | |a Beaumont, Claire |e verfasserin |4 aut | |
| 700 | 1 | |a Pirhalla, Jill |e verfasserin |4 aut | |
| 700 | 1 | |a Dear, Gordon J |e verfasserin |4 aut | |
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