Mining and Visualizing Large Anticancer Drug Discovery Databases

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(GI 50 )], values. GI 50 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.

Accelerated structure-based design of chemically diverse allosteric modulators of a muscarinic G protein-coupled receptor

“…Based on the hypothesis that incorporation of receptor flexibility is key to effective GPCR drug design (34,35), we used aMD simulations to construct structural ensembles for molecular docking in the extracellular vestibule of the receptor. Ensemble docking of chemical compounds obtained from the National Cancer Institute (NCI) compound library (36) was performed to identify new potential allosteric modulators. The computationally selected lead compounds were then tested experimentally to investigate their binding and functional properties.…”

Transcriptional Profiling of Sorghum Induced by Methyl Jasmonate, Salicylic Acid, and Aminocyclopropane Carboxylic Acid Reveals Cooperative Regulation and Novel Gene Responses

“…3). The tree was built using an Average-Linkage algorithm (Shi et al, 2000), and standard correlation on log-transformed ratios (parametric test, assuming equal variances). Many clusters corresponding to specific compound 3 tissue 3 time regulation patterns were visible.…”

A strategy for predicting the chemosensitivity of human cancers and its application to drug discovery

“…The NCI-60 panel has been used by the Developmental Therapeutics Program (DTP) of the U.S. National Cancer Institute (NCI) to screen Ͼ100,000 chemically defined compounds plus a large number of natural product extracts for anticancer activity since 1990 (9)(10)(11). It has been controversial, however, whether tumor cell activities in such in vitro assays can predict human patient chemotherapeutic responses (12).…”