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A machine learning and live-cell imaging tool kit uncovers small molecules induced phospholipidosis.
Hu, Huabin; Tjaden, Amelie; Knapp, Stefan; Antolin, Albert A; Müller, Susanne.
Affiliation
  • Hu H; Centre for Cancer Drug Discovery, Division of Cancer Therapeutics, The Institute of Cancer Research, London, UK; Science for Life Laboratory, Department of Cell and Molecular Biology, Uppsala University, BMC, Box 596, SE-751 24 Uppsala, Sweden.
  • Tjaden A; Institute of Pharmaceutical Chemistry, Johann Wolfgang Goethe University, 60438 Frankfurt am Main, Germany; Structural Genomics Consortium (SGC), Buchmann Institute for Life Sciences, Johann Wolfgang Goethe University, 60438 Frankfurt am Main, Germany.
  • Knapp S; Institute of Pharmaceutical Chemistry, Johann Wolfgang Goethe University, 60438 Frankfurt am Main, Germany; Structural Genomics Consortium (SGC), Buchmann Institute for Life Sciences, Johann Wolfgang Goethe University, 60438 Frankfurt am Main, Germany.
  • Antolin AA; Centre for Cancer Drug Discovery, Division of Cancer Therapeutics, The Institute of Cancer Research, London, UK; ProCURE, Catalan Institute of Oncology, Oncobell, Bellvitge Institute for Biomedical Research (IDIBELL), L'Hospitalet del Llobregat, Catalonia Barcelona, Spain. Electronic address: aantol
  • Müller S; Institute of Pharmaceutical Chemistry, Johann Wolfgang Goethe University, 60438 Frankfurt am Main, Germany; Structural Genomics Consortium (SGC), Buchmann Institute for Life Sciences, Johann Wolfgang Goethe University, 60438 Frankfurt am Main, Germany. Electronic address: susanne.mueller-knapp@bmls.
Cell Chem Biol ; 30(12): 1634-1651.e6, 2023 12 21.
Article in En | MEDLINE | ID: mdl-37797617
Drug-induced phospholipidosis (DIPL), characterized by excessive accumulation of phospholipids in lysosomes, can lead to clinical adverse effects. It may also alter phenotypic responses in functional studies using chemical probes. Therefore, robust methods are needed to predict and quantify phospholipidosis (PL) early in drug discovery and in chemical probe characterization. Here, we present a versatile high-content live-cell imaging approach, which was used to evaluate a chemogenomic and a lysosomal modulation library. We trained and evaluated several machine learning models using the most comprehensive set of publicly available compounds and interpreted the best model using SHapley Additive exPlanations (SHAP). Analysis of high-quality chemical probes extracted from the Chemical Probes Portal using our algorithm revealed that closely related molecules, such as chemical probes and their matched negative controls can differ in their ability to induce PL, highlighting the importance of identifying PL for robust target validation in chemical biology.
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Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Lysosomal Storage Diseases / Lipidoses Type of study: Prognostic_studies Limits: Humans Language: En Journal: Cell Chem Biol Year: 2023 Document type: Article

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Lysosomal Storage Diseases / Lipidoses Type of study: Prognostic_studies Limits: Humans Language: En Journal: Cell Chem Biol Year: 2023 Document type: Article