The NCI Drug Information System. 2. DIS Pre-Registry

Tyrosine phosphatase SHP2 inhibitors in tumor-targeted therapies

Src homology containing protein tyrosine phosphatase 2 (SHP2) represents a noteworthy target for various diseases, serving as a well-known oncogenic phosphatase in cancers. As a result of the low cell permeability and poor bioavailability, the traditional inhibitors targeting the protein tyrosine phosphate catalytic sites are generally suffered from unsatisfactory applied efficacy. Recently, a particularly large number of allosteric inhibitors with striking inhibitory potency on SHP2 have been identified. In particular, few clinical trials conducted have made significant progress on solid tumors by using SHP2 allosteric inhibitors. This review summarizes the development and structure–activity relationship studies of the small-molecule SHP2 inhibitors for tumor therapies, with the purpose of assisting the future development of SHP2 inhibitors with improved selectivity, higher oral bioavailability and better physicochemical properties.

Inhibitors of Src homology-2 domain containing protein tyrosine phosphatase-2 (Shp2) based on oxindole scaffolds

Screening of the NCI diversity set of compounds has led to the identification of 5 (NSC-117199), which inhibits the protein tyrosine phosphatase (PTP) Shp2 with an IC50 of 47 microM. A focused library incorporating an isatin scaffold was designed and evaluated for inhibition of Shp2 and Shp1 PTP activities. Several compounds were identified that selectively inhibit Shp2 over Shp1 and PTP1B with low to submicromolar activity. A model for the binding of the active compounds is proposed.

Exploring the binding mode of semicarbazide-sensitive amine oxidase/VAP-1: identification of novel substrates with insulin-like activity

We previously reported that substrates of semicarbazide-sensitive amine oxidase in combination with low concentrations of vanadate exert potent insulin-like effects. Here we performed homology modeling of the catalytic domain of mouse SSAO/VAP-1 and searched through chemical databases to identify novel SSAO substrates. The modeling of the catalytic domain revealed that aromatic residues Tyr384, Phe389, and Tyr394 define a pocket of stable size that may participate in the binding of apolar substrates. We identified a number of amines as substrates of human, rat, and mouse SSAO. The compounds PD0119035, 2,3-dimethoxy-benzylamine, and C-naphthalen-1-yl-methylamine showed high affinity as substrates of rat SSAO. C-Naphthalen-1-yl-methylamine was the only substrate that showed high affinity for human SSAO. C-Naphthalen-1-yl-methylamine and 4-aminomethyl-benzenesulfonamide showed the highest capacity to stimulate glucose transport in isolated rat adipocytes. The impact of these findings on the development of new treatments for diabetes is discussed.

Shape signatures: a new approach to computer-aided ligand- and receptor-based drug design

A unifying principle of rational drug design is the use of either shape similarity or complementarity to identify compounds expected to be active against a given target. Shape similarity is the underlying foundation of ligand-based methods, which seek compounds with structure similar to known actives, while shape complementarity is the basis of most receptor-based design, where the goal is to identify compounds complementary in shape to a given receptor. These approaches can be extended to include molecular descriptors in addition to shape, such as lipophilicity or electrostatic potential. Here we introduce a new technique, which we call shape signatures, for describing the shape of ligand molecules and of receptor sites. The method uses a technique akin to ray-tracing to explore the volume enclosed by a ligand molecule, or the volume exterior to the active site of a protein. Probability distributions are derived from the ray-trace, and can be based solely on the geometry of the reflecting ray, or may include joint dependence on properties, such as the molecular electrostatic potential, computed over the surface. Our shape signatures are just these probability distributions, stored as histograms. They converge rapidly with the length of the ray-trace, are independent of molecular orientation, and can be compared quickly using simple metrics. Shape signatures can be used to test for both shape similarity between compounds and for shape complementarity between compounds and receptors and thus can be applied to problems in both ligand- and receptor-based molecular design. We present results for comparisons between small molecules of biological interest and the NCI Database using shape signatures under two different metrics. Our results show that the method can reliably extract compounds of shape (and polarity) similar to the query molecules. We also present initial results for a receptor-based strategy using shape signatures, with application to the design of new inhibitors predicted to be active against HIV protease.

PASS biological activity spectrum predictions in the enhanced open NCI database browser

The application of the program PASS (Prediction of Activity Spectra for Substances) to about 250 000 compounds of the NCI Open Database and the incorporation of over 64 million PASS predictions in the Enhanced NCI Database Browser are described. A total of 565 different types of activity are included, encompassing general pharmacological effects, specific mechanisms of action, known toxicities, and others. Application of this Web-based service to prediction of activities of the kinds "Angiogenesis inhibitor," "Antiviral (HIV)", and a set of activities that can be associated with antineoplastic action are reported. For this latter data set, a very substantial enrichment over random selection was found in the PASS predictions. It is shown how the user can conduct complex searches by combining ranges of PASS-predicted probabilities of compounds to be active or to be inactive, respectively, with, e.g., value ranges of physicochemical parameters, presence or absence of particular substructural fragment, and other search criteria.

Enhanced CACTVS browser of the Open NCI Database

A Web-based, graphical user interface has been developed to conduct rapid searches by numerous criteria in the more than 250,000 structures of the Open NCI Database. It is based on the chemistry information toolkit CACTVS. Nearly all structures and anticancer and anti-HIV screening data provided by NCI's Developmental Therapeutics Program have been included. This data set has been augmented by a large amount of additional, mostly computed, data, such as calculated log P values, predicted biological activities, systematically determined names, and others. Complex boolean searches are possible. Flexible substructure searches have been implemented. The user can conduct 3D pharmacophore queries in up to 25 conformations precalculated for each compound. Numerous output formats as well as 2D and 3D visualization options are provided. It is possible to export search results in various forms and with choices for data contents in the exported files, for structure sets ranging in size from a single compound to the entire database. Only a Web browser is needed to use this service, with a few plug-ins being useful but optional.

Comparison of the NCI open database with seven large chemical structural databases

Eight large chemical databases have been analyzed and compared to each other. Central to this comparison is the open National Cancer Institute (NCI) database, consisting of approximately 250 000 structures. The other databases analyzed are the Available Chemicals Directory ("ACD," from MDL, release 1.99, 3D-version); the ChemACX ("ACX," from CamSoft, Version 4.5); the Maybridge Catalog and the Asinex database (both as distributed by CamSoft as part of ChemInfo 4.5); the Sigma-Aldrich Catalog (CD-ROM, 1999 Version); the World Drug Index ("WDI," Derwent, version 1999.03); and the organic part of the Cambridge Crystallographic Database ("CSD," from Cambridge Crystallographic Data Center, 1999 Version 5.18). The database properties analyzed are internal duplication rates; compounds unique to each database; cumulative occurrence of compounds in an increasing number of databases; overlap of identical compounds between two databases; similarity overlap; diversity; and others. The crystallographic database CSD and the WDI show somewhat less overlap with the other databases than those with each other. In particular the collections of commercial compounds and compilations of vendor catalogs have a substantial degree of overlap among each other. Still, no database is completely a subset of any other, and each appears to have its own niche and thus "raison d'être". The NCI database has by far the highest number of compounds that are unique to it. Approximately 200 000 of the NCI structures were not found in any of the other analyzed databases.

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

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.

HIV-1 integrase pharmacophore: discovery of inhibitors through three-dimensional database searching

Starting from a known inhibitor of human immunodeficiency virus type 1 (HIV-1) integrase (IN); caffeic acid phenethyl ester (CAPE), a putative three-point pharmacophore for binding of inhibitors to IN was derived. This pharmacophore was used to search the National Cancer Institute three-dimensional (3D) structural database. Out of the open, nonproprietary part of this database, comprising approximately 200000 compounds, 267 structures were found to match the pharmacophore in at least one conformation, and 60 of those were tested in an in vitro assay against HIV-1 IN. Out of these, 19 were found to inhibit both the 3'-processing and strand transfer of IN at micromolar concentrations. In order to test the validity of this pharmacophore, a small 3D database of 152 published IN inhibitors was built. A search in this database yielded a statistically significant correlation of the presence of this pharmacophore and the potency of the compounds. An automated pharmacophore identification procedure performed on this set of compounds provided additional support for the importance of this pharmacophore for binding of inhibitors to IN and hinted at a possible second pharmacophore. The role of aromatic moieties in the binding of ligands to HIV-1 IN through interactions with divalent metal cations, which are known to be necessary for activity of the enzyme, was explored in ab initio calculations.

Depsides and depsidones as inhibitors of HIV-1 integrase: discovery of novel inhibitors through 3D database searching

Seventeen lichen acids comprising despides, depsidones, and their synthetic derivatives have been examined for their inhibitory activity against HIV-1 integrase, and two pharmacophores associated with inhibition of this enzyme have been identified. A search of the NCI 3D database of approximately 200,000 structures yielded some 800 compounds which contain one or the other pharmacophore. Forty-two of these compounds were assayed for HIV-1 integrase inhibition, and of these, 27 had inhibitory IC50 values of less than 100 microM; 15 were below 50 microM. Several of these compounds were also examined for their activity against HIV-2 integrase and mammalian topoisomerase I.

Molecular modeling in the discovery of drug leads

The National Cancer Institute of the U.S.A. maintains a repository of about 500,000 chemicals which it has tested at some time for anticancer activity. As new chemotherapeutic targets present themselves, methods have been developed by which this large database can be re-examined without resorting to expensive high-volume biological screening. Electronic screening, the method described in this paper, begins with the identification of a target enzyme. The pharmacophore used by the enzyme in binding to substrates is identified, and then all compounds in the database that contain the pharmacophore are found. These compounds are then further filtered, for example, by physical properties such as solubility, and the relatively small number of compounds that survive are submitted for biological testing. This use of a primary electronic screen in the search for ligands of protein kinase C is described. The screen is very fast, and the method is quite generally applicable to different enzymes.