| Literature DB >> 24050133 |
Monica Simeoni, Giuseppe De Nicolao, Paolo Magni, Maurizio Rocchetti, Italo Poggesi.
Abstract
Xenograft models are commonly used in oncology drug development. Although there are discussions about their ability to generate meaningful data for the translation from animal to humans, it appears that better data quality and better design of the preclinical experiments, together with appropriate data analysis approaches could make these data more informative for clinical development. An approach based on mathematical modeling is necessary to derive experiment-independent parameters which can be linked with clinically relevant endpoints. Moreover, the inclusion of biomarkers as predictors of efficacy is a key step towards a more general mechanism-based strategy.Entities:
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Year: 2013 PMID: 24050133 DOI: 10.1016/j.ddtec.2012.07.004
Source DB: PubMed Journal: Drug Discov Today Technol ISSN: 1740-6749