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50 Years of Preclinical Anticancer Drug Screening: Empirical to Target-Driven Approaches
Open accessAbstract
The number of anticancer agents that fail in the clinic far outweighs those considered effective, suggesting that the selection procedure for progression of molecules into the clinic requires improvement. The value of any preclinical model will ultimately depend on its ability to accurately predict clinical response. This review focuses on the major contributions of preclinical screening models to anticancer drug development over the past 50 years. Over time, a general transition has been observed from the empirical drug screening of cytotoxic agents against uncharacterized tumor models to the target-orientated drug screening of agents with defined mechanisms of action. New approaches to anticancer drug development involve the molecular characterization of models along with an appreciation of the pharmacodynamic and pharmacokinetic properties of compounds [e.g., the US National Cancer Institute (NCI) in vitro 60-cell line panel, hollow fiber assay, and s.c. xenograft]. Contributions of other potentially more clinically relevant in vivo tumor models including orthotopic, metastatic, and genetically engineered mouse models are also reviewed. Although this review concentrates on the preclinical screening efforts of the NCI, European efforts are not overlooked. Europe has played a key role in the development of new anticancer agents. The two largest academic drug development groups, the European Organisation for Research and Treatment of Cancer and Cancer Research UK, have been collaborating with the NCI in the acquisition and screening of compounds since the 1970s. As with the drug development process internationally, rational pharmacodynamic approaches have more recently been adopted by these two groups.
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Mice deficient for p53 are developmentally normal but susceptible to spontaneous tumours
Donehower, L. A., Harvey, M., Slagle, B. L., McArthur, M. J., Montgomery, C. A., Butel, J. S., & Bradley, A. (1992) · Nature · doi:10.1038/356215a0 · Cited by 4,649
Matrix metalloproteinase-9 triggers the angiogenic switch during carcinogenesis
Bergers, G., Brekken, R., McMahon, G., Vu, T. H., Itoh, T., Tamaki, K., Tanzawa, K., Thorpe, P., Itohara, S., Werb, Z., et al. (2000) · Nature Cell Biology · doi:10.1038/35036374 · Cited by 2,797
Effects of Angiogenesis Inhibitors on Multistage Carcinogenesis in Mice
Bergers, G., Javaherian, K., Lo, K.-M., Folkman, J., & Hanahan, D. (1999) · Science · doi:10.1126/science.284.5415.808 · Cited by 988
Activation of the mouse cellular Harvey-ras gene in chemically induced benign skin papillomas
Balmain, A., Ramsden, M., Bowden, G. T., & Smith, J. (1984) · Nature · doi:10.1038/307658a0 · Cited by 529
Kelland, L. R. (2004) · European Journal of Cancer · doi:10.1016/j.ejca.2003.11.028 · Cited by 352
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Biomaterials and emerging anticancer therapeutics: engineering the microenvironment
Gu, L., & Mooney, D. J. (2016) · Nature Reviews Cancer · doi:10.1038/nrc.2015.3 · Cited by 407
Cancer Cell Lines Are Useful Model Systems for Medical Research
Mirabelli, P., Coppola, L., & Salvatore, M. (2019) · Cancers · doi:10.3390/cancers11081098 · Cited by 336
Preclinical mouse solid tumour models: status quo, challenges and perspectives
Gengenbacher, N., Singhal, M., & Augustin, H. G. (2017) · Nature Reviews Cancer · doi:10.1038/nrc.2017.92 · Cited by 332
Woodhead, A. J., Angove, H., Carr, M. G., Chessari, G., Congreve, M., Coyle, J. E., Cosme, J., Graham, B., Day, P. J., Downham, R., et al. (2010) · Journal of Medicinal Chemistry · doi:10.1021/jm100060b · Cited by 264
Metadata
- Authors
- Suggitt, M., & Bibby, M. C.
- Venue
- Clinical Cancer Research
- Published
- 2005-02-01
- Volume
- 11
- Issue
- 3
- Pages
- 971-981
- ISSN
- 1078-0432 (ISSN-L)
- 1557-3265
- OpenAlex
- W1891860149
- Cited by
- 298
- References
- 5
- FWCI
- 8.20
- Type
- journal article
- Language
- en
- Open access
- bronze
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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