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New Approaches to Drug Discovery

Overview of attention for book
Attention for Chapter 29: Predictive In Vivo Models for Oncology
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Chapter title
Predictive In Vivo Models for Oncology
Chapter number 29
Book title
New Approaches to Drug Discovery
Published in
Handbook of experimental pharmacology, January 2015
DOI 10.1007/164_2015_29
Pubmed ID
Book ISBNs
978-3-31-928912-0, 978-3-31-928914-4
Authors

Diana Behrens, Jana Rolff, Jens Hoffmann, Behrens, Diana, Rolff, Jana, Hoffmann, Jens

Abstract

Experimental oncology research and preclinical drug development both substantially require specific, clinically relevant in vitro and in vivo tumor models. The increasing knowledge about the heterogeneity of cancer requested a substantial restructuring of the test systems for the different stages of development. To be able to cope with the complexity of the disease, larger panels of patient-derived tumor models have to be implemented and extensively characterized. Together with individual genetically engineered tumor models and supported by core functions for expression profiling and data analysis, an integrated discovery process has been generated for predictive and personalized drug development.Improved "humanized" mouse models should help to overcome current limitations given by xenogeneic barrier between humans and mice. Establishment of a functional human immune system and a corresponding human microenvironment in laboratory animals will strongly support further research.Drug discovery, systems biology, and translational research are moving closer together to address all the new hallmarks of cancer, increase the success rate of drug development, and increase the predictive value of preclinical models.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 25 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 25 100%

Demographic breakdown

Readers by professional status Count As %
Other 3 12%
Student > Master 2 8%
Professor > Associate Professor 2 8%
Researcher 2 8%
Student > Ph. D. Student 2 8%
Other 4 16%
Unknown 10 40%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 6 24%
Agricultural and Biological Sciences 3 12%
Medicine and Dentistry 2 8%
Computer Science 1 4%
Nursing and Health Professions 1 4%
Other 2 8%
Unknown 10 40%