[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124751-en":3,"doc-seo-124751-105":30,"detail-sidebar-cat-0-en-105":91},{"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":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},124751,16904993612988,"Olivia Brown","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",7,"Healthcare","AMALPHI - A Machine Learning Platform for Predicting Drug-Induced Phospholipidosis","Drug-induced phospholipidosis (PLD) is driven by prolonged exposure to druglike compounds, especially cationic amphiphilic drugs (CADs), leading to phospholipid accumulation in multiple tissues, notably lysosomes. PLD can confound antiviral conclusions in drug-repurposing efforts, including SARS-CoV-2 studies, making early identification of safer candidates critical. The work develops machine learning classifiers to predict PLD-inducing potential from a curated set of 545 small molecules from ChEMBL v30, achieving strong validation performance (balanced random forest, AUC up to 0.90).","This article is licensed under CC-BY-NC-ND 4.0  \n[pubs.acs.org/molecularpharmaceutics](pubs.acs.org/molecularpharmaceutics)  Article   \nAMALPHI: A Machine Learning Platform for Predicting Drug-Induced PhospholIpidosis  \nMaria Cristina Lomuscio, Carmen Abate, Domenico Alberga, Antonio Laghezza, Nicola Corriero, Nicola Antonio Colabufo, Michele Saviano, Pietro Delre, * and Giuseppe Felice Mangiatordi *  \n Cite This: Mol. Pharmaceutics 2024, 21, 864−872  \nRead Online  \nDownloaded via UNIV DEGLI STUDI DI BARI on December 6, 2024 at 16:03:59 (UTC) . See [https://pubs.acs.org/sharingguidelines](https://pubs.acs.org/sharingguidelines) for options on how to legitimately share published articles.  \nACCESS  \n Metrics & More  \n Article Recommendations  \n*sı   \nSupporting Information  \nABSTRACT: Drug-induced phospholipidosis (PLD) involves the accumulation of phospholipids in cells of multiple tissues, particularly within lysosomes, and it is associated with prolonged exposure to druglike compounds, predominantly cationic amphiphilic drugs (CADs). PLD affects a significant portion of drugs currently in development and has recently been proven to be responsible for confounding antiviral data during drug repurposing for SARSCoV-2. In these scenarios, it has become crucial to identify potential safe drug candidates in advance and distinguish them from those that may lead to false in vitro antiviral activity. In this work, we developed a series of machine learning classifiers with the aim of predicting the PLD-inducing potential of drug candidates. The models were built on a high-quality chemical collection comprising 545 curated small molecules extracted from ChEMBL v30. The most effective model, obtained using the balanced random forest algorithm, achieved high performance, including an AUC value computed in validation as high as 0.90. The model was made freely available through a user-friendly web platform named AMALPHI ([https://www.ba.ic.cnr.it/softwareic/amalphiportal/](https://www.ba.ic.cnr.it/softwareic/amalphiportal/)), which can represent a valuable tool for medicinal chemists interested in conducting an early evaluation of PLD inducer potential.  \nKEYWORDS: phospholipidosis, ligand-based classifiers, machine learning, SARS-CoV-2  \n■ INTRODUCTION  \nPhospholipidosis (PLD) is a lysosomal storage disorder characterized by excessive accumulation of phospholipids in liver, kidney, brain, cornea, lung, and other organs.1 While it is widely recognized that this phenomenon can arise from prolonged treatment with cationic amphiphilic drugs (CADs), the exact mechanism behind this process remains unclear. Various hypotheses have been explored in the literature, including direct inhibition of lysosomal phospholipases,2 binding to phospholipids,3 the potential regulation of phospholipid synthesis,4 and the enhanced cholesterol biosynthesis.5 For a comprehensive review on this topic, the reader is referred to the recenpaper by Breiden et al.6 Given that a  \nnotable proportion ( 5%7) of drugs can induce PLD, there has been a growing interest in recent years to assess the potential of drug candidates to be inducers of PLD during the early stages of a drug discovery (DD) process. This proactive evaluation is recognized as valuable, as compounds that lead to PLD have a reduced likelihood ofbeing successfully brought to market.8 Recently, highly significant correlations have been demonstrated between lipophilicity, the ability of CADs to induce PLD, and the antiviral activity that these cationic amphiphilic drugs have shown against multiple viruses such as hepatitis C virus (HCV), Japanese encephalitis virus (JEV), severe acute respiratory syndrome coronavirus (SARS-CoV), and Epstein−Barr virus (EBV).9 In light of the recent COVID-  \n19 pandemic, a publication in Science by Tummino et al.10 presented findings that highlight the pivotal role of PLD in the context of drugs with anti-SARS-CoV-2 activity, revealing that most of the molecules return antivi","cbCaivtlv7gyP6TE","https://ap.wps.com/l/cbCaivtlv7gyP6TE","pdf",3091610,1,9,"English","en",105,"# Abstract\n# Introduction\n## Background on phospholipidosis and lysosomal accumulation\n## PLD relevance to antiviral drug repurposing\n## Need for early prediction and limitations of assays\n# Supporting model approach and dataset\n# AMALPHI platform for medicinal chemistry screening","[{\"question\":\"What causes drug-induced phospholipidosis and where does it accumulate?\",\"answer\":\"Drug-induced phospholipidosis is associated with prolonged exposure to druglike compounds, predominantly cationic amphiphilic drugs, and involves phospholipid accumulation in multiple tissues, particularly within lysosomes.\"},{\"question\":\"Why is predicting PLD important in drug repurposing for SARS-CoV-2?\",\"answer\":\"PLD can confound antiviral data because many molecules regain antiviral activity status during repurposing depending on whether they induce PLD. This can lead to false in vitro antiviral activity conclusions.\"},{\"question\":\"How does AMALPHI predict PLD-inducing potential and how accurate is it?\",\"answer\":\"The platform uses machine learning classifiers trained on a curated chemical collection of 545 small molecules extracted from ChEMBL v30. The best balanced random forest model achieved validation performance up to AUC 0.90 and is provided through a user-friendly web interface.\"}]","AMALPHI - A Machine Learning Platform for Predicting Drug-Induced Phospholipidosis | PDF",1785894284,23,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"amalphi-a-machine-learning-platform-for-predicting-drug-induced-phospholipidosis","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/healthcare/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/amalphi-a-machine-learning-platform-for-predicting-drug-induced-phospholipidosis/124751/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What causes drug-induced phospholipidosis and where does it accumulate?","Question",{"text":75,"@type":76},"Drug-induced phospholipidosis is associated with prolonged exposure to druglike compounds, predominantly cationic amphiphilic drugs, and involves phospholipid accumulation in multiple tissues, particularly within lysosomes.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Why is predicting PLD important in drug repurposing for SARS-CoV-2?",{"text":80,"@type":76},"PLD can confound antiviral data because many molecules regain antiviral activity status during repurposing depending on whether they induce PLD. This can lead to false in vitro antiviral activity conclusions.",{"name":82,"@type":73,"acceptedAnswer":83},"How does AMALPHI predict PLD-inducing potential and how accurate is it?",{"text":84,"@type":76},"The platform uses machine learning classifiers trained on a curated chemical collection of 545 small molecules extracted from ChEMBL v30. 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