Search

Topoisomerase 1 (top1) inhibitors are proving useful against a range of refractory tumors, and there is considerable interest in the development of additional top1 agents. Despite crystallographic studies, the binding site and ligand properties that lead to activity are poorly understood. Here we report a unique approach to quantitative structure-activity relationship (QSAR) analysis based on the National Cancer Institute's (NCI) drug databases. In 1990, the NCI established a drug discovery program in which compounds are tested for their ability to inhibit the growth of 60 different human cancer cell lines in culture. More than 70 000 compounds have been screened, and patterns of activity against the 60 cell lines have been found to encode rich information on mechanisms of drug action and drug resistance. Here, we use hierarchical clustering to define antitumor activity patterns in a data set of 167 tested camptothecins (CPTs) in the NCI drug database. The average pairwise Pearson correlation coefficient between activity patterns for the CPT set was 0.70. Coherence between chemical structures and their activity patterns was observed. QSAR studies were carried out using the mean 50% growth inhibitory concentrations (GI(50)) for 60 cell lines as the dependent variables. Different statistical methods, including stepwise linear regression, principal component regression (PCR), partial least-squares regression (PLS), and fully cross-validated genetic function approximation (GFA) were applied to construct quantitative structure-antitumor relationship models. For our data set, the GFA method performed better in terms of correlation coefficients and cross-validation analysis. A number of molecular descriptors were identified as being correlated with antitumor activity. Included were partial atomic charges and three interatomic distances that define the relative spatial dispositions of three significant atoms (the hydroxyl hydrogen of the E-ring, the lactone carbonyl oxygen of the E-ring, and the carbonyl oxygen of the D-ring). The cross-validated r(2) for the final GFA model was 0.783, indicating a predictive QSAR model.

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.

The U.S. National Cancer Institute (NCI) conducts a drug discovery program in which approximately 10,000 compounds are screened every year in vitro against a panel of 60 human cancer cell lines from different organs of origin. Since 1990, approximately 63,000 compounds have been tested, and their patterns of activity profiled. Recently, we analyzed the antitumor activity patterns of 112 ellipticine analogues using a hierarchical clustering algorithm. Dramatic coherence between molecular structures and activity patterns was observed qualitatively from the cluster tree. In the present study, we further investigate the quantitative structure-activity relationships (QSAR) of these compounds, in particular with respect to the influence of p53-status and the CNS cell selectivity of the activity patterns. Independent variables (i.e., chemical structural descriptors of the ellipticine analogues) were calculated from the Cerius2 molecular modeling package. Important structural descriptors, including partial atomic charges on the ellipticine ring-forming atoms, were identified by the recently developed genetic function approximation (GFA) method. For our data set, the GFA method gave better correlation and cross-validation results (R2 and CVR2 were usually approximately 0.3 higher) than did classical stepwise linear regression. A procedure for improving the performance of GFA is proposed, and the relative advantages and disadvantages of using GFA for QSAR studies are discussed.

The United States National Cancer Institute conducts an anticancer drug discovery program in which approximately 10,000 compounds are screened every year in vitro against a panel of 60 human cancer cell lines from different organs. To date, approximately 62,000 compounds have been tested in the program, and a large amount of information on their activity patterns has been accumulated. For the current study, anticancer activity patterns of 112 ellipticine analogs were analyzed with the use of a hierarchical clustering algorithm. A dramatic coherence between molecular structures and their activity patterns could be seen from the cluster tree: the first subgroup (compounds 1-66) consisted principally of normal ellipticines, whereas the second subgroup (compounds 67-112) consisted principally of N2-alkyl-substituted ellipticiniums. Almost all apparent discrepancies in this clustering were explainable on the basis of chemical transformation to active forms under cell culture conditions. Correlations of activity with p53 status and selective activity against cells of central nervous system origin made this data set of special interest to us. The ellipticiniums, but not the ellipticines, were more potent on average against p53 mutant cells than against p53 wild-type ones (i.e., they seemed to be "p53-inverse") in this short term assay. This study strongly supports the hypothesis that "fingerprint" patterns of activity in the National Cancer Institute in vitro cell screening program encode incisive information on the mechanisms of action and other biological behaviors of tested compounds. Insights gained by mining the activity patterns could contribute to our understanding of anticancer drugs and the molecular pharmacology of cancer.