Mining the NCI anticancer drug discovery databases: genetic function approximation for the QSAR study of anticancer ellipticine analogues

Application of BCUT metrics and genetic algorithm in binary QSAR analysis

The application of three-dimensional H-suppressed BCUT metrics (BCUTs) in binary QSAR analysis was investigated using carbonic anhydrase II inhibitors and estrogen receptor ligands as test cases. Variable selection was accomplished with a genetic algorithm (GA). Highly predictive binary QSAR models were obtained for both sets of compounds within 200 GA generations. The derived binary QSAR models were validated with two sets of compounds not included in the training sets. The results indicate that BCUTs are very useful molecular descriptors, and the genetic algorithm is a very efficient variable selection tool in binary QSAR analysis.

A multivariate insight into the in vitro antitumour screen database of the National Cancer Institute: classification of compounds, similarities among cell lines and the influence of molecular targets

A multivariate insight into the in vitro antitumour screen database of the NCI by means of the SIMCA package allows to propose hypotheses on the mechanism of action of novel anticancer compounds. As an example, the application of multivariate analysis to the NCI standard database provided clues to the classification of drugs whose mechanism is either unknown or controversial. Moreover, the influence of intrinsic biochemical cell line properties (molecular targets) on the sensitivity to drug treatment could be evaluated simultaneously for classes of compounds which act by the same mechanism. Interestingly, the present approach can also provide a correlation between the molecular targets and the therapeutical fingerprint of novel active compounds thus suggesting specific biochemical studies for the investigation of new mechanisms of drug action and resistance. The statistical approach reported here represents a valuable tool for handling theenormous data sets deriving from recent genome-wide investigations of gene expression in the NCI cell lines.

LeadScope: software for exploring large sets of screening data

Modern approaches to drug discovery have dramatically increased the speed and quantity of compounds that are made and tested for potential potency. The task of collecting, organizing, and assimilating this information is a major bottleneck in the discovery of new drugs. We have developed LeadScope a novel, interactive computer program for visualizing, browsing, and interpreting chemical and biological screening data that can assist pharmaceutical scientists in finding promising drug candidates. The software organizes the chemical data by structural features familiar to medicinal chemists. Graphs are used to summarize the data, and structural classes are highlighted that are statistically correlated with biological activity.

Understanding the antifungal activity of terbinafine analogues using quantitative structure-activity relationship (QSAR) models

Terbinafine and its analogues, which are a major class of non-azole antifungal agents, are known to act by inhibition of squalene epoxidase enzyme in fungal cells. We have performed a quantitative structure-activity relationship (QSAR) study on a series of 92 molecules using different types of physicochemical descriptors. Inhibitors were divided into five classes depending upon chemical structure. QSAR models were generated for correlation between antifungal activity against Candida albicans using genetic function approximation (GFA) technique. Equations were evaluated using internal as well as external test set predictions. Models generated for all these classes show that steric properties and conformational rigidity of side chains play an important role for the activity. The present QSAR analysis agrees with the results of the previously reported CoMFA study.

A gene expression database for the molecular pharmacology of cancer

We used cDNA microarrays to assess gene expression profiles in 60 human cancer cell lines used in a drug discovery screen by the National Cancer Institute. Using these data, we linked bioinformatics and chemoinformatics by correlating gene expression and drug activity patterns in the NCI60 lines. Clustering the cell lines on the basis of gene expression yielded relationships very different from those obtained by clustering the cell lines on the basis of their response to drugs. Gene-drug relationships for the clinical agents 5-fluorouracil and L-asparaginase exemplify how variations in the transcript levels of particular genes relate to mechanisms of drug sensitivity and resistance. This is the first study to integrate large databases on gene expression and molecular pharmacology.

Mining and visualizing large anticancer drug discovery databases

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.

Mining the National Cancer Institute Anticancer Drug Discovery Database: cluster analysis of ellipticine analogs with p53-inverse and central nervous system-selective patterns of activity

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.