G. R. Revankar

Bio: G. R. Revankar is an academic researcher from Brigham Young University. The author has contributed to research in topics: Nucleoside & Glycosylation. The author has an hindex of 14, co-authored 42 publications receiving 748 citations.

Synergistic inhibition of growth of breast and colon human cancer cell lines by site-selective cyclic AMP analogues.

Abstract: Our past studies on the mechanism of cyclic AMP (cAMP)-mediated control of tumor growth, using the experimental rat mammary tumor models as well as human breast cancer cell lines, indicated that the action of cAMP is mediated by the RII cAMP receptor protein, the regulatory subunit of cAMP-dependent protein kinase type II (Y. S. Cho-Chung, J. Cyclic Nucleotide Res., 6: 163, 1980). We now shown that the site-selective cAMP analogues, which are manyfold more active in binding to the cAMP receptor protein than previously studied analogues, demonstrate a potent growth inhibition of seven breast and three colon human cancer cell lines. The cAMP receptor protein has two different cAMP binding sites, and cAMP analogues that selectively bind to either one of the two binding sites are known as either site 1 selective (C-8 analogues) or site 2 selective (C-6 analogues). Nineteen site-selective analogues, C-6 and C-8 monosubstituted and C-6,-8 disubstituted, were tested for their growth regulatory effect. The majority of these analogues demonstrated an appreciable growth inhibition, with no sign of toxicity in all 10 cancer lines at micromolar concentrations. The three most potent inhibitors were 8-Cl-, N6-benzyl-, and N6-phenyl-8-thio-p-chlorophenyl-cAMP, demonstrating 50% growth inhibition at 5-25 microM concentrations (IC50). Furthermore, N6-analogues, in combination with halogen or thio derivatives of C-8 analogues, demonstrated synergistic enhancement of growth inhibition. The growth inhibition paralleled a change in cell morphology, an augmentation of the RII cAMP receptor protein, and a reduction in p21 ras protein. The growth inhibition by 8-Cl-cAMP was not due to its metabolite, 8-Cl-adenosine, since: (a) the growth inhibition by 8-Cl-cAMP was released upon cessation of treatment, whereas that by 8-Cl-adenosine was not released; (b) 8-Cl-cAMP treatment did not affect cell cycle progression, whereas 8-Cl-adenosine brought about G1 synchronization; (c) 8-Cl-cAMP treatment caused reduction of p21 ras protein, whereas 8-Cl-adenosine did not affect p21 levels; and (d) 8-Cl-adenosine was not detected in either cell extracts or medium from the cells treated with 8-Cl-cAMP for 48-72 h. Site-selective cAMP analogues thus provide a new physiological means to control the growth of breast and colon human cancer cells.

Novel heterocyclic nitrofurfural hydrazones. In vivo antitrypanosomal activity

Abstract: Hydrazine derivatives of several pyrazolo[1,5-alpha]pyrimidines (A), pyrazolo[1,5-alpha]-1,3,5-triazines (B), s-triazolo[1,5-alpha]pyrimidines (C), and imidazo[1,2-alpha]pyrimidines (D) were synthesized and condensed with 5-nitrofurfural in order to obtain the corresponding nitrofurfural hydrazones of each heterocycle (1d-14d). Each compound was screened for in vitro activity against Trypanosoma cruzi. The compounds were then tested in vivo against experimental infections of T. cruzi in laboratory (C3H/He strain) mice. An interesting structure-activity relationship was uncovered, revealing that 5-methyl-7-(5-nitrofurfurylidenehydrazino)pyrazolo[1,5-alpha]pyrimidine (2d) greatly increased the mean survival time (IMST) of mice with terminal infections. Subtle alterations in the structure of 2d, such as the substitution of a 5-hydrogen for the 5-methyl group (1d) or the substitution of the 3-hydrogen by the water-soluble 3-sulfonic acid (3d) or 3-sodium sulfonate (4d), resulted in a drastic loss of in vivo and in vitro activity.

A knowledge-based approach in designing combinatorial or medicinal chemistry libraries for drug discovery. 1. A qualitative and quantitative characterization of known drug databases.

Abstract: The discovery of various protein/receptor targets from genomic research is expanding rapidly. Along with the automation of organic synthesis and biochemical screening, this is bringing a major change in the whole field of drug discovery research. In the traditional drug discovery process, the industry tests compounds in the thousands. With automated synthesis, the number of compounds to be tested could be in the millions. This two-dimensional expansion will lead to a major demand for resources, unless the chemical libraries are made wisely. The objective of this work is to provide both quantitative and qualitative characterization of known drugs which will help to generate “drug-like” libraries. In this work we analyzed the Comprehensive Medicinal Chemistry (CMC) database and seven different subsets belonging to different classes of drug molecules. These include some central nervous system active drugs and cardiovascular, cancer, inflammation, and infection disease states. A quantitative characterization ...

Prediction of Hydrophobic (Lipophilic) Properties of Small Organic Molecules Using Fragmental Methods: An Analysis of ALOGP and CLOGP Methods

Abstract: Molecular hydrophobicity (lipophilicity), usually quantified as log P (the logarithm of 1-octanol/water partition coefficient), is an important molecular characteristic in drug discovery. ALOGP and CLOGP are two of the most widely used methods for the estimation of log P. This work describes an extensive reparametrization of the atomic log P values and a detailed comparison of the performance of ALOGP and CLOGP methods on the Pomona Medchem database. Only the “star list” compounds having precisely measured log P values were used in this analysis. While the overall results with both methods are similar, analysis shows that the CLOGP method is better for very small molecules in the range of 1−20 atoms. The two methods are almost comparable in the range of 21−45 atoms, while the ALOGP method has better accuracy for molecules with more than 45 atoms. Although the rms deviation and the correlation coefficient for CLOGP predictions were marginally better than those for corresponding ALOGP predictions, the latte...

Novel variable selection quantitative structure--property relationship approach based on the k-nearest-neighbor principle

Abstract: A novel automated variable selection quantitative structure−activity relationship (QSAR) method, based on the K-nearest neighbor principle (kNN-QSAR) has been developed. The kNN-QSAR method explore...