Ensuring confidence in predictions: A scheme to assess the scientific validity of in silico models
The use of in silico tools within the drug development process to predict a wide range of properties including absorption, distribution, metabolism, elimination and toxicity has become increasingly important due to changes in legislation and both ethical and economic drivers to reduce animal testing...
Gespeichert in:
Veröffentlicht in: | Advanced drug delivery reviews 2015-06, Vol.86, p.101-111 |
---|---|
Hauptverfasser: | , , , , , , , |
Format: | Artikel |
Sprache: | eng |
Schlagworte: | |
Online-Zugang: | Volltext |
Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
container_end_page | 111 |
---|---|
container_issue | |
container_start_page | 101 |
container_title | Advanced drug delivery reviews |
container_volume | 86 |
creator | Hewitt, Mark Ellison, Claire M. Cronin, Mark T.D. Pastor, Manuel Steger-Hartmann, Thomas Munoz-Muriendas, Jordi Pognan, Francois Madden, Judith C. |
description | The use of in silico tools within the drug development process to predict a wide range of properties including absorption, distribution, metabolism, elimination and toxicity has become increasingly important due to changes in legislation and both ethical and economic drivers to reduce animal testing. Whilst in silico tools have been used for decades there remains reluctance to accept predictions based on these methods particularly in regulatory settings. This apprehension arises in part due to lack of confidence in the reliability, robustness and applicability of the models. To address this issue we propose a scheme for the verification of in silico models that enables end users and modellers to assess the scientific validity of models in accordance with the principles of good computer modelling practice. We report here the implementation of the scheme within the Innovative Medicines Initiative project “eTOX” (electronic toxicity) and its application to the in silico models developed within the frame of this project.
[Display omitted] |
doi_str_mv | 10.1016/j.addr.2015.03.005 |
format | Article |
fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_miscellaneous_1693706621</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><els_id>S0169409X1500040X</els_id><sourcerecordid>1693706621</sourcerecordid><originalsourceid>FETCH-LOGICAL-c470t-92a80fd43448ebd4e53edde36c14cded193f79c429004fd1903403f72526e1e23</originalsourceid><addsrcrecordid>eNp9kE1LxDAQhoMouq7-AQ-So5fWyUfbrXgR8QsWvCh4C91kqlnaZs10Bf-9WVc9ehoyPO_L5GHsREAuQJTny7xxLuYSRJGDygGKHTYRs0pmM1nrXTZJUJ1pqF8O2CHREkDIqoR9diCLqtZ6BhO2uBloHf3wym0YWu9wsMj9wFcRnbejDwNd8CtO9g175GPgDRES8fEN09LjMPrWW_7RdN758ZOHdpMm33kbeB8cdnTE9tqmIzz-mVP2fHvzdH2fzR_vHq6v5pnVFYxZLZsZtE6rdBgunMZCoXOoSiu0dehErdqqtlrWALpNT1Aa0koWskSBUk3Z2bZ3FcP7Gmk0vSeLXdcMGNZkkgxVQVlKkVC5RW0MRBFbs4q-b-KnEWA2bs3SbNyajVsDyiS3KXT6079e9Oj-Ir8yE3C5BdKn8cNjNN-GbDIZ0Y7GBf9f_xeICosF</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>1693706621</pqid></control><display><type>article</type><title>Ensuring confidence in predictions: A scheme to assess the scientific validity of in silico models</title><source>MEDLINE</source><source>Elsevier ScienceDirect Journals</source><creator>Hewitt, Mark ; Ellison, Claire M. ; Cronin, Mark T.D. ; Pastor, Manuel ; Steger-Hartmann, Thomas ; Munoz-Muriendas, Jordi ; Pognan, Francois ; Madden, Judith C.</creator><creatorcontrib>Hewitt, Mark ; Ellison, Claire M. ; Cronin, Mark T.D. ; Pastor, Manuel ; Steger-Hartmann, Thomas ; Munoz-Muriendas, Jordi ; Pognan, Francois ; Madden, Judith C.</creatorcontrib><description>The use of in silico tools within the drug development process to predict a wide range of properties including absorption, distribution, metabolism, elimination and toxicity has become increasingly important due to changes in legislation and both ethical and economic drivers to reduce animal testing. Whilst in silico tools have been used for decades there remains reluctance to accept predictions based on these methods particularly in regulatory settings. This apprehension arises in part due to lack of confidence in the reliability, robustness and applicability of the models. To address this issue we propose a scheme for the verification of in silico models that enables end users and modellers to assess the scientific validity of models in accordance with the principles of good computer modelling practice. We report here the implementation of the scheme within the Innovative Medicines Initiative project “eTOX” (electronic toxicity) and its application to the in silico models developed within the frame of this project.
[Display omitted]</description><identifier>ISSN: 0169-409X</identifier><identifier>EISSN: 1872-8294</identifier><identifier>DOI: 10.1016/j.addr.2015.03.005</identifier><identifier>PMID: 25794480</identifier><language>eng</language><publisher>Netherlands: Elsevier B.V</publisher><subject>Computer Simulation ; Good computer modelling practice ; Humans ; Model reliability ; Models, Theoretical ; Peer-verification ; Pilot Projects ; QSAR ; Reproducibility of Results ; Toxicity prediction ; Validation</subject><ispartof>Advanced drug delivery reviews, 2015-06, Vol.86, p.101-111</ispartof><rights>2015 Elsevier B.V.</rights><rights>Copyright © 2015 Elsevier B.V. All rights reserved.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c470t-92a80fd43448ebd4e53edde36c14cded193f79c429004fd1903403f72526e1e23</citedby><cites>FETCH-LOGICAL-c470t-92a80fd43448ebd4e53edde36c14cded193f79c429004fd1903403f72526e1e23</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://dx.doi.org/10.1016/j.addr.2015.03.005$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>314,777,781,3537,27905,27906,45976</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/25794480$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Hewitt, Mark</creatorcontrib><creatorcontrib>Ellison, Claire M.</creatorcontrib><creatorcontrib>Cronin, Mark T.D.</creatorcontrib><creatorcontrib>Pastor, Manuel</creatorcontrib><creatorcontrib>Steger-Hartmann, Thomas</creatorcontrib><creatorcontrib>Munoz-Muriendas, Jordi</creatorcontrib><creatorcontrib>Pognan, Francois</creatorcontrib><creatorcontrib>Madden, Judith C.</creatorcontrib><title>Ensuring confidence in predictions: A scheme to assess the scientific validity of in silico models</title><title>Advanced drug delivery reviews</title><addtitle>Adv Drug Deliv Rev</addtitle><description>The use of in silico tools within the drug development process to predict a wide range of properties including absorption, distribution, metabolism, elimination and toxicity has become increasingly important due to changes in legislation and both ethical and economic drivers to reduce animal testing. Whilst in silico tools have been used for decades there remains reluctance to accept predictions based on these methods particularly in regulatory settings. This apprehension arises in part due to lack of confidence in the reliability, robustness and applicability of the models. To address this issue we propose a scheme for the verification of in silico models that enables end users and modellers to assess the scientific validity of models in accordance with the principles of good computer modelling practice. We report here the implementation of the scheme within the Innovative Medicines Initiative project “eTOX” (electronic toxicity) and its application to the in silico models developed within the frame of this project.
[Display omitted]</description><subject>Computer Simulation</subject><subject>Good computer modelling practice</subject><subject>Humans</subject><subject>Model reliability</subject><subject>Models, Theoretical</subject><subject>Peer-verification</subject><subject>Pilot Projects</subject><subject>QSAR</subject><subject>Reproducibility of Results</subject><subject>Toxicity prediction</subject><subject>Validation</subject><issn>0169-409X</issn><issn>1872-8294</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2015</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNp9kE1LxDAQhoMouq7-AQ-So5fWyUfbrXgR8QsWvCh4C91kqlnaZs10Bf-9WVc9ehoyPO_L5GHsREAuQJTny7xxLuYSRJGDygGKHTYRs0pmM1nrXTZJUJ1pqF8O2CHREkDIqoR9diCLqtZ6BhO2uBloHf3wym0YWu9wsMj9wFcRnbejDwNd8CtO9g175GPgDRES8fEN09LjMPrWW_7RdN758ZOHdpMm33kbeB8cdnTE9tqmIzz-mVP2fHvzdH2fzR_vHq6v5pnVFYxZLZsZtE6rdBgunMZCoXOoSiu0dehErdqqtlrWALpNT1Aa0koWskSBUk3Z2bZ3FcP7Gmk0vSeLXdcMGNZkkgxVQVlKkVC5RW0MRBFbs4q-b-KnEWA2bs3SbNyajVsDyiS3KXT6079e9Oj-Ir8yE3C5BdKn8cNjNN-GbDIZ0Y7GBf9f_xeICosF</recordid><startdate>20150623</startdate><enddate>20150623</enddate><creator>Hewitt, Mark</creator><creator>Ellison, Claire M.</creator><creator>Cronin, Mark T.D.</creator><creator>Pastor, Manuel</creator><creator>Steger-Hartmann, Thomas</creator><creator>Munoz-Muriendas, Jordi</creator><creator>Pognan, Francois</creator><creator>Madden, Judith C.</creator><general>Elsevier B.V</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>7X8</scope></search><sort><creationdate>20150623</creationdate><title>Ensuring confidence in predictions: A scheme to assess the scientific validity of in silico models</title><author>Hewitt, Mark ; Ellison, Claire M. ; Cronin, Mark T.D. ; Pastor, Manuel ; Steger-Hartmann, Thomas ; Munoz-Muriendas, Jordi ; Pognan, Francois ; Madden, Judith C.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c470t-92a80fd43448ebd4e53edde36c14cded193f79c429004fd1903403f72526e1e23</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2015</creationdate><topic>Computer Simulation</topic><topic>Good computer modelling practice</topic><topic>Humans</topic><topic>Model reliability</topic><topic>Models, Theoretical</topic><topic>Peer-verification</topic><topic>Pilot Projects</topic><topic>QSAR</topic><topic>Reproducibility of Results</topic><topic>Toxicity prediction</topic><topic>Validation</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Hewitt, Mark</creatorcontrib><creatorcontrib>Ellison, Claire M.</creatorcontrib><creatorcontrib>Cronin, Mark T.D.</creatorcontrib><creatorcontrib>Pastor, Manuel</creatorcontrib><creatorcontrib>Steger-Hartmann, Thomas</creatorcontrib><creatorcontrib>Munoz-Muriendas, Jordi</creatorcontrib><creatorcontrib>Pognan, Francois</creatorcontrib><creatorcontrib>Madden, Judith C.</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><jtitle>Advanced drug delivery reviews</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Hewitt, Mark</au><au>Ellison, Claire M.</au><au>Cronin, Mark T.D.</au><au>Pastor, Manuel</au><au>Steger-Hartmann, Thomas</au><au>Munoz-Muriendas, Jordi</au><au>Pognan, Francois</au><au>Madden, Judith C.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Ensuring confidence in predictions: A scheme to assess the scientific validity of in silico models</atitle><jtitle>Advanced drug delivery reviews</jtitle><addtitle>Adv Drug Deliv Rev</addtitle><date>2015-06-23</date><risdate>2015</risdate><volume>86</volume><spage>101</spage><epage>111</epage><pages>101-111</pages><issn>0169-409X</issn><eissn>1872-8294</eissn><abstract>The use of in silico tools within the drug development process to predict a wide range of properties including absorption, distribution, metabolism, elimination and toxicity has become increasingly important due to changes in legislation and both ethical and economic drivers to reduce animal testing. Whilst in silico tools have been used for decades there remains reluctance to accept predictions based on these methods particularly in regulatory settings. This apprehension arises in part due to lack of confidence in the reliability, robustness and applicability of the models. To address this issue we propose a scheme for the verification of in silico models that enables end users and modellers to assess the scientific validity of models in accordance with the principles of good computer modelling practice. We report here the implementation of the scheme within the Innovative Medicines Initiative project “eTOX” (electronic toxicity) and its application to the in silico models developed within the frame of this project.
[Display omitted]</abstract><cop>Netherlands</cop><pub>Elsevier B.V</pub><pmid>25794480</pmid><doi>10.1016/j.addr.2015.03.005</doi><tpages>11</tpages><oa>free_for_read</oa></addata></record> |
fulltext | fulltext |
identifier | ISSN: 0169-409X |
ispartof | Advanced drug delivery reviews, 2015-06, Vol.86, p.101-111 |
issn | 0169-409X 1872-8294 |
language | eng |
recordid | cdi_proquest_miscellaneous_1693706621 |
source | MEDLINE; Elsevier ScienceDirect Journals |
subjects | Computer Simulation Good computer modelling practice Humans Model reliability Models, Theoretical Peer-verification Pilot Projects QSAR Reproducibility of Results Toxicity prediction Validation |
title | Ensuring confidence in predictions: A scheme to assess the scientific validity of in silico models |
url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-17T17%3A34%3A53IST&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=Ensuring%20confidence%20in%20predictions:%20A%20scheme%20to%20assess%20the%20scientific%20validity%20of%20in%20silico%20models&rft.jtitle=Advanced%20drug%20delivery%20reviews&rft.au=Hewitt,%20Mark&rft.date=2015-06-23&rft.volume=86&rft.spage=101&rft.epage=111&rft.pages=101-111&rft.issn=0169-409X&rft.eissn=1872-8294&rft_id=info:doi/10.1016/j.addr.2015.03.005&rft_dat=%3Cproquest_cross%3E1693706621%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=1693706621&rft_id=info:pmid/25794480&rft_els_id=S0169409X1500040X&rfr_iscdi=true |