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Drug discovery is a multifaceted process involving extensive screening, testing, and optimization of lead compounds to identify potential new drugs for therapeutic use. It combines several approaches, including screening large numbers of natural products, chemical modification of known active molecules, identification of new drug targets, and rational design based on biological mechanisms and drug-receptor structure. These approaches are carried out in both academic research laboratories and...
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Metabolite Identification Data in Drug Discovery, Part 1: Data Generation and Trend Analysis.

Marie Ahlqvist1, Isabella Bonner Karlsson1, Anja Ekdahl1

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Drug discovery utilizes metabolite identification data to pinpoint metabolic liabilities in molecules. This study shares AstraZeneca

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Area of Science:

  • Drug Discovery and Development
  • Metabolomics and Cheminformatics

Background:

  • Metabolite identification data are crucial in drug discovery for optimizing molecular design and assessing safety.
  • Understanding metabolite structures helps identify potential risks from active, reactive, or toxic metabolites.

Purpose of the Study:

  • To demonstrate how metabolite identification data are generated and applied within AstraZeneca's drug discovery programs.
  • To share a dataset of metabolite transformation schemes from human hepatocyte incubations for 120 compounds.
  • To explore the utility of this data for machine learning-based drug metabolism prediction.

Main Methods:

  • Generation of metabolite transformation scheme data from in vitro human hepatocyte incubations.
  • Chemical space analysis and comparison of in vitro metabolite identification data.
  • Characterization of observed metabolic pathways and correlation of in vitro with in vivo data.

Main Results:

  • A dataset of metabolite transformation schemes for 120 compounds is presented.
  • Analysis includes chemical space comparisons and characterization of metabolic pathways.
  • Correlation between in vitro and in vivo metabolite data is shown for selected compounds.

Conclusions:

  • Metabolite identification data are valuable for guiding drug design and safety assessments.
  • The shared data can be leveraged for advanced drug metabolism prediction using AI/ML.
  • This exemplifies a data-driven approach to enhance drug discovery efficiency and safety.