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Mining and visualizing large anticancer drug discovery databases
1Laboratory of Molecular Pharmacology, National Cancer Institute, National Institutes of Health, Bethesda, Maryland 20892-4255, USA. shil@pt.cyanamid.com
Abstract:
In order to find more effective anticancer drugs, the U.S. National Cancer Institute (NCI) screens a large number of compounds in vitro against 60 human cancer cell lines from different organs of origin. About 70,000 compounds have been tested in the program since 1990, and each tested compound can be characterized by a vector (i.e., "fingerprint") of 60 anticancer activity, or -[log(GI50)], values. GI50 is the concentration required to inhibit cell growth by 50% compared with untreated controls. Although cell growth inhibitory activity for a single cell line is not very informative, activity patterns across the 60 cell lines can provide incisive information on the mechanisms of action of screened compounds and also on molecular targets and modulators of activity within the cancer cells. Various statistical and artificial intelligence methods, including principal component analysis, hierarchical cluster analysis, stepwise linear regression, multidimensional scaling, neural network modeling, and genetic function approximation, among others, can be used to analyze this large activity database. Mining the database can provide useful information: (a) for the development of anticancer drugs; (b) for a better understanding of the molecular pharmacology of cancer; and (c) for improvement of the drug discovery process.
Insights
The U.S. National Cancer Institute (NCI) analyzes anticancer drug activity across 60 cell lines to identify new drug candidates. This large-scale screening provides insights into cancer drug mechanisms and targets.
Area of Science:
- Pharmacology
- Oncology
- Computational Biology
Background:
- The U.S. National Cancer Institute (NCI) screens numerous compounds for anticancer properties.
- Over 70,000 compounds have been evaluated since 1990 using a panel of 60 human cancer cell lines.
- Each compound is characterized by a 60-dimensional activity vector, representing -[log(GI50)] values against each cell line.
Purpose of the Study:
- To identify effective anticancer drugs through large-scale compound screening.
- To gain insights into the mechanisms of action of anticancer compounds.
- To understand molecular targets and modulators involved in cancer cell activity.
Main Methods:
- In vitro screening of compounds against 60 human cancer cell lines.
- Analysis of compound activity patterns across diverse cell lines.
- Application of statistical and artificial intelligence methods (e.g., PCA, neural networks) to a large activity database.
Main Results:
- Activity patterns across 60 cell lines provide more information than single-cell line data.
- The analysis facilitates the understanding of compound mechanisms and potential drug targets.
- Mining the database aids in anticancer drug development and understanding cancer pharmacology.
Conclusions:
- Large-scale compound screening and activity pattern analysis are crucial for anticancer drug discovery.
- This approach enhances the understanding of cancer molecular pharmacology.
- The NCI's drug screening program provides valuable data for improving the drug discovery process.