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
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% |