[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-128062-en":3,"doc-seo-128062-105":31,"detail-sidebar-cat-0-en-105":92},{"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},128062,5909887256941,"Levi","https://ap-avatar.wpscdn.com/davatar_9964176cb1d06d4a9deccf72a44ae3dc",8,"Research & Report","Machine learning and artificial intelligence in physiologically based pharmacokinetic modeling - Review","Physiologically based pharmacokinetic (PBPK) models support drug development and environmental risk assessment but require extensive species- and chemical-specific ADME parameter collection, which is costly and time-consuming. A review proposes an emerging workflow integrating PBPK modeling with machine learning (ML) and artificial intelligence (AI): obtain PK/ADME inputs from public databases, train ML/AI methods to predict ADME parameters, and embed these predictors into PBPK models to estimate PK summary statistics. It also discusses Neural-ODE architectures for improved time-series prediction, while highlighting needs for broader training diversity, model interpretability, and further exploration for limited-ADME scenarios.","UC Riverside  \nUC Riverside Previously Published Works  \nTitle  \nMachine learning and artificial intelligence in physiologically based pharmacokinetic modeling.  \nPermalink  \n[https://escholarship.org/uc/item/4h0785db](https://escholarship.org/uc/item/4h0785db)  \nJournal  \nToxicological Sciences, 191(1)  \nAuthors  \nChou, Wei-Chun  \nLin, Zhoumeng  \nPublication Date  \n2023-01-31  \nDOI  \n10.1093/toxsci/kfac101  \nPeer reviewed  \n[eScholarship.org](eScholarship.org) Powered by the California Digital Library  \nUniversity of California  \nToxicological Sciences, 2023, 191(1), 1–14  \n[https://doi.org/10.1093/toxsci/kfac101](https://doi.org/10.1093/toxsci/kfac101)  \nAdvance Access Publication Date: 26 September 2022 Contemporary Review  \nMachine learning and artificial intelligence in physiologically based pharmacokinetic modeling  \nWei-Chun Chou 1,2, Zhoumeng Lin 1,2,*  \n1Department of Environmental and Global Health, College of Public Health and Health Professions, University of Florida, Gainesville, FL 32610, USA 2Center for Environmental and Human Toxicology, University of Florida, Gainesville, FL 32608, USA  \n*To whom correspondence should be addressed at Department of Environmental and Global Health, College of Public Health and Health Professions, University of Florida, 1225 Center Drive, Gainesville, FL 32610, USA. E-mail: linzhoumeng@ufl .edu.  \nAbstract  \nPhysiologically based pharmacokinetic (PBPK) models are useful tools in drug development and risk assessment of environmental chemicals. PBPK model development requires the collection of species-specific physiological, and chemical-specific absorption, distribution, metabolism, and excretion (ADME) parameters, which can be a time-consuming and expensive process. This raises a need to create computational models capable of predicting input parameter values for PBPK models, especially for new compounds. In this review, we summarize an emerging paradigm for integrating PBPK modeling with machine learning (ML) or artificial intelligence (AI)-based computational methods. This paradigm includes 3 steps (1) obtain time-concentration PK data and/or ADME parameters from publicly available databases,(2) develop ML/AI-based approaches to predict ADME parameters, and (3) incorporate the ML/AI models into PBPK models to predict PK summary statistics (eg, area under the curve and maximum plasma concentration) . We also discuss a neural network architecture “neural ordinary differential equation (Neural-ODE)” that is capable of providing better predictive capabilities than other ML methods when used to directly predict time-series PK profiles. In order to support applications of ML/AI methods for PBPK model development, several challenges should be addressed (1) as more data become available, it is important to expand the training set by including the structural diversity of compounds to improve the prediction accuracy of ML/AI models; (2) due to the black box nature of many ML models, lack of sufficient interpretability is a limitation; (3) Neural-ODE has great potential to be used to generate time-series PK profiles for new compounds with limited ADME information, but its application remains to be explored. Despite existing challenges, ML/AI approaches will continue to facilitate the efficient development of robust PBPK models for a large number of chemicals.  \nKeywords: artificial intelligence; machine learning; physiologically based pharmacokinetic (PBPK) modeling; risk assessment; in vitro to in vivo extrapolation (IVIVE); pharmacometrics  \nPhysiologically based pharmacokinetic (PBPK) modeling is a valuable computational tool that is capable of characterizing the pharmacokinetics (PK) or toxicokinetics (TK) by describing the processes of absorption, distribution, metabolism, and excretion (ADME) of a chemical and/or its metabolites in animals and humans (Fisher et al. , 2020) . PBPK models have been widely applied to support dosing recommendations and optimize the design of clinical trials in d","cbCaifeVqvE9GOdZ","https://ap.wps.com/l/cbCaifeVqvE9GOdZ","pdf",710860,2,1,15,"English","en",105,"# Abstract\n## Proposed ML/AI-PBPK integration workflow\n## Neural-ODE for time-series PK prediction\n## Challenges and future directions","[{\"question\":\"Why are ML/AI approaches needed in PBPK model development?\",\"answer\":\"PBPK modeling typically requires many species- and chemical-specific ADME parameters, and obtaining them is expensive and time-consuming. ML/AI methods can predict input parameters for new compounds, speeding development and reducing reliance on new in vivo studies.\"},{\"question\":\"What three-step paradigm does the review describe for integrating ML/AI with PBPK models?\",\"answer\":\"First, obtain time-concentration PK data and/or ADME parameters from publicly available databases. Second, train ML/AI approaches to predict ADME parameters. Third, incorporate the ML/AI models into PBPK models to predict PK summary statistics such as AUC and maximum plasma concentration.\"},{\"question\":\"What is Neural-ODE and how is it used in this context?\",\"answer\":\"The review discusses a Neural-ODE neural network architecture that can directly predict time-series PK profiles. It is presented as having the potential for better predictive capabilities than other ML methods, especially when generating profiles for new compounds.\"}]","Machine learning and artificial intelligence in physiologically based pharmacokinetic modeling - Review | PDF",1785944558,38,{"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":87,"head_meta":89,"extra_data":91,"updated_unix":29},"machine-learning-and-artificial-intelligence-in-physiologically-based-pharmacokinetic-modeling-review","",{"@graph":37,"@context":86},[38,54,69],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,48,51],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":20},"https://docshare.wps.com/document/","Document",{"item":49,"name":12,"@type":44,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":44,"position":53},"https://docshare.wps.com/document/machine-learning-and-artificial-intelligence-in-physiologically-based-pharmacokinetic-modeling-review/128062/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":24,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":42,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-28","2026-08-05",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"Why are ML/AI approaches needed in PBPK model development?","Question",{"text":76,"@type":77},"PBPK modeling typically requires many species- and chemical-specific ADME parameters, and obtaining them is expensive and time-consuming. ML/AI methods can predict input parameters for new compounds, speeding development and reducing reliance on new in vivo studies.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"What three-step paradigm does the review describe for integrating ML/AI with PBPK models?",{"text":81,"@type":77},"First, obtain time-concentration PK data and/or ADME parameters from publicly available databases. Second, train ML/AI approaches to predict ADME parameters. Third, incorporate the ML/AI models into PBPK models to predict PK summary statistics such as AUC and maximum plasma concentration.",{"name":83,"@type":74,"acceptedAnswer":84},"What is Neural-ODE and how is it used in this context?",{"text":85,"@type":77},"The review discusses a Neural-ODE neural network architecture that can directly predict time-series PK profiles. It is presented as having the potential for better predictive capabilities than other ML methods, especially when generating profiles for new compounds.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,121,124,129,132,136],{"id":21,"doc_module":4,"doc_module_name":47,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":20,"doc_module":4,"doc_module_name":47,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":47,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":47,"category_name":108,"show_sort_weight":109,"slug":110},5,"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":107,"slug":139},19,"General","general"]