Long short-term memory recurrent neural network for pharmacokinetic-pharmacodynamic modeling
Recurrent neural network (RNN) has been demonstrated as a powerful tool for analyzing various types of time series data. There is limited knowledge about the application of the RNN model in the area of pharmacokinetic (PK) and pharmacodynamic (PD) analysis. In this paper, a specific variation of RNN...
Gespeichert in:
| Veröffentlicht in: | International journal of clinical pharmacology and therapeutics 2021-02, Vol.59 (2), p.138-146 |
|---|---|
| Hauptverfasser: | , , , , , , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 146 |
|---|---|
| container_issue | 2 |
| container_start_page | 138 |
| container_title | International journal of clinical pharmacology and therapeutics |
| container_volume | 59 |
| creator | Liu, Xiangyu Liu, Chao Huang, Ruihao Zhu, Hao Liu, Qi Mitra, Sunanda Wang, Yaning |
| description | Recurrent neural network (RNN) has been demonstrated as a powerful tool for analyzing various types of time series data. There is limited knowledge about the application of the RNN model in the area of pharmacokinetic (PK) and pharmacodynamic (PD) analysis. In this paper, a specific variation of RNN, long short-term memory (LSTM) network, is presented to analyze the simulated PK/PD data of a hypothetical drug.
The plasma concentration and effect level under one dosing regimen were used to train the LSTM model. The developed LSTM model was used to predict the individual PK/PD data under other dosing regimens.
The optimized LSTM model captured temporal dependencies and predicted PD profiles accurately for the simulated indirect PK-PD relationship.
The results demonstrated that the generic LSTM model can approximate the complex underlying mechanistic biological processes. |
| doi_str_mv | 10.5414/cp203800 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_miscellaneous_2462411899</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2480572972</sourcerecordid><originalsourceid>FETCH-LOGICAL-c377t-d90381698c02235c8b5636f28092436c43ff61735ee06380b03c10756a72665b3</originalsourceid><addsrcrecordid>eNpdkEtLw0AUhWeh2FoFf4EE3LiJzjuZpRRfUNCF7oQwmUzatJlMvJMg_feOtFVwdTiXj8O5B6ELgm8EJ_zW9BSzHOMjNMWKy5QoKSboNIQ1xlSITJ2gCWOUYKX4FH0sfLdMwsrDkA4WXOKs87BNwJoRwHZD0tkRdBtl-PKwSWoPSb_S4LTxmyZeG5MefLXttGtM4nxl26ZbnqHjWrfBnu91ht4f7t_mT-ni5fF5frdIDcuyIa1ULEykyg2mlAmTl0IyWdMcK8qZNJzVtSQZE9ZiGV8rMTMEZ0LqjEopSjZD17vcHvznaMNQuCYY27a6s34MBeWSckJypSJ69Q9d-xG62C5SORYZVRn9CzTgQwBbFz00TsO2ILj4WbmYv-5WjujlPnAsna1-wcPE7BvBMXge</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2480572972</pqid></control><display><type>article</type><title>Long short-term memory recurrent neural network for pharmacokinetic-pharmacodynamic modeling</title><source>MEDLINE</source><source>Alma/SFX Local Collection</source><creator>Liu, Xiangyu ; Liu, Chao ; Huang, Ruihao ; Zhu, Hao ; Liu, Qi ; Mitra, Sunanda ; Wang, Yaning</creator><creatorcontrib>Liu, Xiangyu ; Liu, Chao ; Huang, Ruihao ; Zhu, Hao ; Liu, Qi ; Mitra, Sunanda ; Wang, Yaning</creatorcontrib><description>Recurrent neural network (RNN) has been demonstrated as a powerful tool for analyzing various types of time series data. There is limited knowledge about the application of the RNN model in the area of pharmacokinetic (PK) and pharmacodynamic (PD) analysis. In this paper, a specific variation of RNN, long short-term memory (LSTM) network, is presented to analyze the simulated PK/PD data of a hypothetical drug.
The plasma concentration and effect level under one dosing regimen were used to train the LSTM model. The developed LSTM model was used to predict the individual PK/PD data under other dosing regimens.
The optimized LSTM model captured temporal dependencies and predicted PD profiles accurately for the simulated indirect PK-PD relationship.
The results demonstrated that the generic LSTM model can approximate the complex underlying mechanistic biological processes.</description><identifier>ISSN: 0946-1965</identifier><identifier>DOI: 10.5414/cp203800</identifier><identifier>PMID: 33210994</identifier><language>eng</language><publisher>Germany: Dustri - Verlag Dr. Karl Feistle GmbH & Co. KG</publisher><subject>Algorithms ; Artificial intelligence ; Biomarkers ; Drug dosages ; Humans ; Machine learning ; Memory ; Memory, Short-Term ; Neural networks ; Neural Networks, Computer ; Pharmacodynamics ; Pharmacokinetics ; Pharmacology ; Physiology ; Time series</subject><ispartof>International journal of clinical pharmacology and therapeutics, 2021-02, Vol.59 (2), p.138-146</ispartof><rights>Copyright Dustri - Verlag Dr. Karl Feistle GmbH & Co. KG Feb 2021</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c377t-d90381698c02235c8b5636f28092436c43ff61735ee06380b03c10756a72665b3</citedby></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,27924,27925</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/33210994$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Liu, Xiangyu</creatorcontrib><creatorcontrib>Liu, Chao</creatorcontrib><creatorcontrib>Huang, Ruihao</creatorcontrib><creatorcontrib>Zhu, Hao</creatorcontrib><creatorcontrib>Liu, Qi</creatorcontrib><creatorcontrib>Mitra, Sunanda</creatorcontrib><creatorcontrib>Wang, Yaning</creatorcontrib><title>Long short-term memory recurrent neural network for pharmacokinetic-pharmacodynamic modeling</title><title>International journal of clinical pharmacology and therapeutics</title><addtitle>Int J Clin Pharmacol Ther</addtitle><description>Recurrent neural network (RNN) has been demonstrated as a powerful tool for analyzing various types of time series data. There is limited knowledge about the application of the RNN model in the area of pharmacokinetic (PK) and pharmacodynamic (PD) analysis. In this paper, a specific variation of RNN, long short-term memory (LSTM) network, is presented to analyze the simulated PK/PD data of a hypothetical drug.
The plasma concentration and effect level under one dosing regimen were used to train the LSTM model. The developed LSTM model was used to predict the individual PK/PD data under other dosing regimens.
The optimized LSTM model captured temporal dependencies and predicted PD profiles accurately for the simulated indirect PK-PD relationship.
The results demonstrated that the generic LSTM model can approximate the complex underlying mechanistic biological processes.</description><subject>Algorithms</subject><subject>Artificial intelligence</subject><subject>Biomarkers</subject><subject>Drug dosages</subject><subject>Humans</subject><subject>Machine learning</subject><subject>Memory</subject><subject>Memory, Short-Term</subject><subject>Neural networks</subject><subject>Neural Networks, Computer</subject><subject>Pharmacodynamics</subject><subject>Pharmacokinetics</subject><subject>Pharmacology</subject><subject>Physiology</subject><subject>Time series</subject><issn>0946-1965</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><recordid>eNpdkEtLw0AUhWeh2FoFf4EE3LiJzjuZpRRfUNCF7oQwmUzatJlMvJMg_feOtFVwdTiXj8O5B6ELgm8EJ_zW9BSzHOMjNMWKy5QoKSboNIQ1xlSITJ2gCWOUYKX4FH0sfLdMwsrDkA4WXOKs87BNwJoRwHZD0tkRdBtl-PKwSWoPSb_S4LTxmyZeG5MefLXttGtM4nxl26ZbnqHjWrfBnu91ht4f7t_mT-ni5fF5frdIDcuyIa1ULEykyg2mlAmTl0IyWdMcK8qZNJzVtSQZE9ZiGV8rMTMEZ0LqjEopSjZD17vcHvznaMNQuCYY27a6s34MBeWSckJypSJ69Q9d-xG62C5SORYZVRn9CzTgQwBbFz00TsO2ILj4WbmYv-5WjujlPnAsna1-wcPE7BvBMXge</recordid><startdate>202102</startdate><enddate>202102</enddate><creator>Liu, Xiangyu</creator><creator>Liu, Chao</creator><creator>Huang, Ruihao</creator><creator>Zhu, Hao</creator><creator>Liu, Qi</creator><creator>Mitra, Sunanda</creator><creator>Wang, Yaning</creator><general>Dustri - Verlag Dr. Karl Feistle GmbH & Co. KG</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>BENPR</scope><scope>CCPQU</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>K9.</scope><scope>M0S</scope><scope>M1P</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>7X8</scope></search><sort><creationdate>202102</creationdate><title>Long short-term memory recurrent neural network for pharmacokinetic-pharmacodynamic modeling</title><author>Liu, Xiangyu ; Liu, Chao ; Huang, Ruihao ; Zhu, Hao ; Liu, Qi ; Mitra, Sunanda ; Wang, Yaning</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c377t-d90381698c02235c8b5636f28092436c43ff61735ee06380b03c10756a72665b3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Algorithms</topic><topic>Artificial intelligence</topic><topic>Biomarkers</topic><topic>Drug dosages</topic><topic>Humans</topic><topic>Machine learning</topic><topic>Memory</topic><topic>Memory, Short-Term</topic><topic>Neural networks</topic><topic>Neural Networks, Computer</topic><topic>Pharmacodynamics</topic><topic>Pharmacokinetics</topic><topic>Pharmacology</topic><topic>Physiology</topic><topic>Time series</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Liu, Xiangyu</creatorcontrib><creatorcontrib>Liu, Chao</creatorcontrib><creatorcontrib>Huang, Ruihao</creatorcontrib><creatorcontrib>Zhu, Hao</creatorcontrib><creatorcontrib>Liu, Qi</creatorcontrib><creatorcontrib>Mitra, Sunanda</creatorcontrib><creatorcontrib>Wang, Yaning</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical Database (Alumni Edition)</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central</collection><collection>ProQuest One Community College</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Medical Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>MEDLINE - Academic</collection><jtitle>International journal of clinical pharmacology and therapeutics</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Liu, Xiangyu</au><au>Liu, Chao</au><au>Huang, Ruihao</au><au>Zhu, Hao</au><au>Liu, Qi</au><au>Mitra, Sunanda</au><au>Wang, Yaning</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Long short-term memory recurrent neural network for pharmacokinetic-pharmacodynamic modeling</atitle><jtitle>International journal of clinical pharmacology and therapeutics</jtitle><addtitle>Int J Clin Pharmacol Ther</addtitle><date>2021-02</date><risdate>2021</risdate><volume>59</volume><issue>2</issue><spage>138</spage><epage>146</epage><pages>138-146</pages><issn>0946-1965</issn><abstract>Recurrent neural network (RNN) has been demonstrated as a powerful tool for analyzing various types of time series data. There is limited knowledge about the application of the RNN model in the area of pharmacokinetic (PK) and pharmacodynamic (PD) analysis. In this paper, a specific variation of RNN, long short-term memory (LSTM) network, is presented to analyze the simulated PK/PD data of a hypothetical drug.
The plasma concentration and effect level under one dosing regimen were used to train the LSTM model. The developed LSTM model was used to predict the individual PK/PD data under other dosing regimens.
The optimized LSTM model captured temporal dependencies and predicted PD profiles accurately for the simulated indirect PK-PD relationship.
The results demonstrated that the generic LSTM model can approximate the complex underlying mechanistic biological processes.</abstract><cop>Germany</cop><pub>Dustri - Verlag Dr. Karl Feistle GmbH & Co. KG</pub><pmid>33210994</pmid><doi>10.5414/cp203800</doi><tpages>9</tpages></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 0946-1965 |
| ispartof | International journal of clinical pharmacology and therapeutics, 2021-02, Vol.59 (2), p.138-146 |
| issn | 0946-1965 |
| language | eng |
| recordid | cdi_proquest_miscellaneous_2462411899 |
| source | MEDLINE; Alma/SFX Local Collection |
| subjects | Algorithms Artificial intelligence Biomarkers Drug dosages Humans Machine learning Memory Memory, Short-Term Neural networks Neural Networks, Computer Pharmacodynamics Pharmacokinetics Pharmacology Physiology Time series |
| title | Long short-term memory recurrent neural network for pharmacokinetic-pharmacodynamic modeling |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-28T21%3A11%3A05IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Long%20short-term%20memory%20recurrent%20neural%20network%20for%20pharmacokinetic-pharmacodynamic%20modeling&rft.jtitle=International%20journal%20of%20clinical%20pharmacology%20and%20therapeutics&rft.au=Liu,%20Xiangyu&rft.date=2021-02&rft.volume=59&rft.issue=2&rft.spage=138&rft.epage=146&rft.pages=138-146&rft.issn=0946-1965&rft_id=info:doi/10.5414/cp203800&rft_dat=%3Cproquest_cross%3E2480572972%3C/proquest_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2480572972&rft_id=info:pmid/33210994&rfr_iscdi=true |