[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126273-en":3,"doc-seo-126273-105":31,"detail-sidebar-cat-0-en-105":93},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},126273,2336475104362,"Eden","https://ap-avatar.wpscdn.com/avatar/22000c4c46a41b752dd?x-image-process=image/resize,m_fixed,w_180,h_180&k=1786595829695023868",8,"Research & Report","Advancing Anticancer Drug Discovery - Leveraging Metabolomics and Machine Learning for Mode of Action Prediction","A bottleneck in developing new anti-cancer drugs is determining their mode of action (MoA) early and accurately. By combining metabolomics with machine learning, the study predicts MoAs of novel anti-proliferative candidates using human prostate cancer cells (PC-3). Low molecular weight metabolic intermediates are profiled by LC-MS/MS across multiple cancer-relevant processes, revealing metabotype patterns that enable MoA prediction for new agents. Transferability is assessed across other cancer cell models, and metabolic signatures provide mechanistic insights, including mitochondrial dysfunction and phospholipid biosynthesis effects.","RESEARCH ARTICLE  \n[www.advancedscience.com](www.advancedscience.com)  \nAdvancing Anticancer Drug Discovery: Leveraging Metabolomics and Machine Learning for Mode ofAction Prediction by Pattern Recognition  \nMohamad Saoud, Jan Grau, Robert Rennert, Thomas Mueller, Mohammad Yousefi, Mehdi D. Davari, Bettina Hause, René Csuk, Luay Rashan, Ivo Grosse, Alain Tissier, LudgerA. Wessjohann,* and Gerd U. Balcke*  \nA bottleneck in the development of new anti-cancer drugs is the recognition of their mode of action (MoA). Metabolomics combined with machine learning allowed to predict MoAs of novel anti-proliferative drug candidates, focusing on human prostate cancer cells (PC-3). As proof of concept, 38 drugs are studied with known effects on 16 key processes of cancer metabolism, profiling low molecular weight intermediates ofthe central carbon and cellular energy metabolism (CCEM) by LC-MS/MS. These metabolic patterns unveiled distinct MoAs, enabling accurate MoA predictions for novel agents by machine learning. The transferability of MoA predictions based on PC-3 cell treatments is validated with two other cancer cell models, i.e., breast cancer and Ewing’s sarcoma, and show that correct MoA predictions for alternative cancer cells are possible, but still at some expense of prediction quality. Furthermore, metabolic profiles of treated cells yield insights into intracellular processes, exemplified for drugs inducing different types of mitochondrial dysfunction. Specifically, it is predicted that pentacyclic triterpenes inhibit oxidative phosphorylation and affect phospholipid biosynthesis, as confirmed by respiration parameters, lipidomics, and molecular docking. Using biochemical insights from individual drug treatments, this approach offers new opportunities, including the optimization of combinatorial drug applications.  \n1. Introduction  \nAnti-cancer drug discovery usually starts with in vitro viability assays using cancer cell line models, which identify cytotoxic chemicals with high anti-proliferative capacity. At that early stage, knowledge regarding the mechanisms by which cell growth is inhibited is limited. Therefore, early characterization of the mechanism of action (MeA) or mode of action (MoA) of a drug has the potential to advance the drug development process. [1] MeA refers to the specific biochemical interaction between a compound and its molecular targets, while MoA refers to the physiological effects caused by the compound of study. Metabolomics can help delineating MoAs by identifying metabolic changes in cells upon drug exposure, which are reflected in complex metabolic patterns (metabotypes) . Prediction of the MoA for uncharacterized compounds is based on the hypothesis that drugs with similar targets will have similar effects on the metabolome. [2] Machine learning (ML) can then be employed to  \nM. Saoud, R. Rennert, M. Yousefi, M. D. Davari, L. A. Wessjohann  \nLeibniz Institute of Plant Biochemistry Dept. of Bioorganic Chemistry  \nWeinberg 3, 06120 Halle (Saale), Germany E-mail: [ludger.wessjohann@ipb-halle.de](ludger.wessjohann@ipb-halle.de)  \nJ. Grau, I. Grosse  \nMartin Luther University Halle-Wittenberg Institute of Computer Science  \n06120 Halle (Saale), Germany  \nThe ORCID identification number(s) for the author(s) of this article  \ncan be found under [https://doi.org/10.1002/advs.202404085](https://doi.org/10.1002/advs.202404085)[ ](https://doi.org/10.1002/advs.202404085)© 2024 The Author(s). Advanced Science published by Wiley-VCH GmbH. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.  \nDOI: 10.1002/advs.202404085  \nT. Mueller  \nMartin Luther University Halle-Wittenberg Medical Faculty  \nUniversity Clinic for Internal Medicine IV (Hematology/Oncology) 06120 Halle (Saale), Germany  \nB. Hause, A. Tissier, G. U. Balcke Leibniz Institute of Plant Biochemistry Dept. ofCell a","cbCaihITTkMAWQ4K","https://ap.wps.com/l/cbCaihITTkMAWQ4K","pdf",4537546,5,1,14,"English","en",105,"# Introduction\n## Mode of action and metabolomics rationale\n## Machine learning approach and prior related work\n## Cancer cell models and metabolic profiling","[{\"question\":\"Why is early mode-of-action identification important in anti-cancer drug discovery?\",\"answer\":\"Early characterization of mode of action can advance drug development because initial in vitro viability assays provide limited knowledge about the mechanisms behind inhibited cell growth.\"},{\"question\":\"How does metabolomics contribute to mode-of-action prediction in this study?\",\"answer\":\"Metabolomics identifies metabolic changes induced by drug exposure, forming complex metabolic patterns (metabotypes) that reflect the effects of drugs on cellular metabolism.\"},{\"question\":\"How is the method validated for predicting MoA in different cancer cell types?\",\"answer\":\"MoA predictions based on PC-3 cell treatments are validated using additional cancer cell models, including breast cancer and Ewing’s sarcoma, showing correct MoA prediction is possible with some reduction in quality.\"}]","Advancing Anticancer Drug Discovery - Leveraging Metabolomics and Machine Learning for Mode of Action Prediction | PDF",1785904191,35,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":88,"head_meta":90,"extra_data":92,"updated_unix":29},"advancing-anticancer-drug-discovery-leveraging-metabolomics-and-machine-learning-for-mode-of-action-prediction","",{"@graph":37,"@context":87},[38,55,70],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,49,52],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":48},"https://docshare.wps.com/document/","Document",2,{"item":50,"name":12,"@type":44,"position":51},"https://docshare.wps.com/document/research-report/",3,{"item":53,"name":13,"@type":44,"position":54},"https://docshare.wps.com/document/advancing-anticancer-drug-discovery-leveraging-metabolomics-and-machine-learning-for-mode-of-action-prediction/126273/",4,{"url":53,"name":13,"@type":56,"author":57,"headline":13,"publisher":59,"fileFormat":62,"inLanguage":24,"description":14,"dateModified":63,"datePublished":64,"encodingFormat":62,"isAccessibleForFree":65,"interactionStatistic":66},"DigitalDocument",{"name":9,"@type":58},"Person",{"url":42,"name":60,"@type":61},"DocShare","Organization","application/pdf","2026-08-23","2026-08-05",true,{"@type":67,"interactionType":68,"userInteractionCount":20},"InteractionCounter",{"@type":69},"ViewAction",{"@type":71,"mainEntity":72},"FAQPage",[73,79,83],{"name":74,"@type":75,"acceptedAnswer":76},"Why is early mode-of-action identification important in anti-cancer drug discovery?","Question",{"text":77,"@type":78},"Early characterization of mode of action can advance drug development because initial in vitro viability assays provide limited knowledge about the mechanisms behind inhibited cell growth.","Answer",{"name":80,"@type":75,"acceptedAnswer":81},"How does metabolomics contribute to mode-of-action prediction in this study?",{"text":82,"@type":78},"Metabolomics identifies metabolic changes induced by drug exposure, forming complex metabolic patterns (metabotypes) that reflect the effects of drugs on cellular metabolism.",{"name":84,"@type":75,"acceptedAnswer":85},"How is the method validated for predicting MoA in different cancer cell types?",{"text":86,"@type":78},"MoA predictions based on PC-3 cell treatments are validated using additional cancer cell models, including breast cancer and Ewing’s sarcoma, showing correct MoA prediction is possible with some reduction in quality.","https://schema.org",{"og:url":53,"og:type":89,"og:title":13,"og:site_name":60,"og:description":14},"article",{"robots":91,"canonical":53},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":94},[95,99,103,107,111,116,121,124,129,132,136],{"id":21,"doc_module":4,"doc_module_name":47,"category_name":96,"show_sort_weight":97,"slug":98},"Story & Novel",90,"story-novel",{"id":48,"doc_module":4,"doc_module_name":47,"category_name":100,"show_sort_weight":101,"slug":102},"Literature",80,"literature",{"id":54,"doc_module":4,"doc_module_name":47,"category_name":104,"show_sort_weight":105,"slug":106},"Exam",70,"exam",{"id":20,"doc_module":4,"doc_module_name":47,"category_name":108,"show_sort_weight":109,"slug":110},"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":47,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":47,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":47,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":125,"doc_module":4,"doc_module_name":47,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":47,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":133,"doc_module":4,"doc_module_name":47,"category_name":134,"show_sort_weight":133,"slug":135},10,"Lifestyle","lifestyle",{"id":137,"doc_module":4,"doc_module_name":47,"category_name":138,"show_sort_weight":20,"slug":139},19,"General","general"]