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| 001 | 126658 | ||
| 005 | 20240228140839.0 | ||
| 024 | 7 | _ | |a 10.1016/j.tiv.2015.01.011 |2 doi |
| 024 | 7 | _ | |a pmid:25678044 |2 pmid |
| 024 | 7 | _ | |a 0887-2333 |2 ISSN |
| 024 | 7 | _ | |a 1879-3177 |2 ISSN |
| 024 | 7 | _ | |a altmetric:17981752 |2 altmetric |
| 037 | _ | _ | |a DKFZ-2017-02686 |
| 041 | _ | _ | |a eng |
| 082 | _ | _ | |a 610 |
| 100 | 1 | _ | |a Hamon, Jérémy |b 0 |
| 245 | _ | _ | |a Quantitative in vitro to in vivo extrapolation of tissues toxicity. |
| 260 | _ | _ | |a Amsterdam [u.a.] |c 2015 |b Elsevier Science |
| 336 | 7 | _ | |a article |2 DRIVER |
| 336 | 7 | _ | |a Output Types/Journal article |2 DataCite |
| 336 | 7 | _ | |a Journal Article |b journal |m journal |0 PUB:(DE-HGF)16 |s 1521207539_30036 |2 PUB:(DE-HGF) |
| 336 | 7 | _ | |a ARTICLE |2 BibTeX |
| 336 | 7 | _ | |a JOURNAL_ARTICLE |2 ORCID |
| 336 | 7 | _ | |a Journal Article |0 0 |2 EndNote |
| 520 | _ | _ | |a Predicting repeated-dosing in vivo drug toxicity from in vitro testing and omics data gathering requires significant support in bioinformatics, mathematical modeling and statistics. We present here the major aspects of the work devoted within the framework of the European integrated Predict-IV to pharmacokinetic modeling of in vitro experiments, physiologically based pharmacokinetic (PBPK) modeling, mechanistic models of toxicity for the kidney and brain, large scale dose-response analyses methods and biomarker discovery tools. All of those methods have been applied to various extent to the drug datasets developed by the project's partners. Our approach is rather generic and could be adapted to other drugs or drug candidates. It marks a successful integration of the work of the different teams toward a common goal of predictive quantitative in vitro to in vivo extrapolation. |
| 536 | _ | _ | |a 313 - Cancer risk factors and prevention (POF3-313) |0 G:(DE-HGF)POF3-313 |c POF3-313 |f POF III |x 0 |
| 588 | _ | _ | |a Dataset connected to CrossRef, PubMed, |
| 700 | 1 | _ | |a Renner, Maria |b 1 |
| 700 | 1 | _ | |a Jamei, Masoud |b 2 |
| 700 | 1 | _ | |a Lukas, Arno |b 3 |
| 700 | 1 | _ | |a Kopp-Schneider, Annette |0 P:(DE-He78)bb6a7a70f976eb8df1769944bf913596 |b 4 |u dkfz |
| 700 | 1 | _ | |a Bois, Frédéric Y |b 5 |
| 773 | _ | _ | |a 10.1016/j.tiv.2015.01.011 |g Vol. 30, no. 1 Pt A, p. 203 - 216 |0 PERI:(DE-600)1501079-x |n 1 Pt A |p 203 - 216 |t Toxicology in vitro |v 30 |y 2015 |x 0887-2333 |
| 909 | C | O | |o oai:inrepo02.dkfz.de:126658 |p VDB |
| 910 | 1 | _ | |a Deutsches Krebsforschungszentrum |0 I:(DE-588b)2036810-0 |k DKFZ |b 4 |6 P:(DE-He78)bb6a7a70f976eb8df1769944bf913596 |
| 913 | 1 | _ | |a DE-HGF |l Krebsforschung |1 G:(DE-HGF)POF3-310 |0 G:(DE-HGF)POF3-313 |2 G:(DE-HGF)POF3-300 |v Cancer risk factors and prevention |x 0 |4 G:(DE-HGF)POF |3 G:(DE-HGF)POF3 |b Gesundheit |
| 914 | 1 | _ | |y 2015 |
| 915 | _ | _ | |a Nationallizenz |0 StatID:(DE-HGF)0420 |2 StatID |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0300 |2 StatID |b Medline |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0310 |2 StatID |b NCBI Molecular Biology Database |
| 915 | _ | _ | |a JCR |0 StatID:(DE-HGF)0100 |2 StatID |b TOXICOL IN VITRO : 2015 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0200 |2 StatID |b SCOPUS |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0600 |2 StatID |b Ebsco Academic Search |
| 915 | _ | _ | |a Peer Review |0 StatID:(DE-HGF)0030 |2 StatID |b ASC |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0199 |2 StatID |b Thomson Reuters Master Journal List |
| 915 | _ | _ | |a WoS |0 StatID:(DE-HGF)0110 |2 StatID |b Science Citation Index |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0150 |2 StatID |b Web of Science Core Collection |
| 915 | _ | _ | |a WoS |0 StatID:(DE-HGF)0111 |2 StatID |b Science Citation Index Expanded |
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| 915 | _ | _ | |a IF < 5 |0 StatID:(DE-HGF)9900 |2 StatID |
| 920 | 1 | _ | |0 I:(DE-He78)C060-20160331 |k C060 |l Biostatistik |x 0 |
| 980 | _ | _ | |a journal |
| 980 | _ | _ | |a VDB |
| 980 | _ | _ | |a I:(DE-He78)C060-20160331 |
| 980 | _ | _ | |a UNRESTRICTED |
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