Design, synthesis and biological evaluation of novel carbapenems

[[abstract]]Racemic trans-3-hydroxycarbonyl-6-(phenylacetamido)carbapenem 13 and trans-3-phosphono-6-(phenylacetamido)carbapenem 17 were synthesized. Formation of the carbapenem nuclei in 13 involved an internal Wittig reaction of the corresponding monocyclic β-lactam 10 using NaH in THF. The key step in the transformation of carbapenam 15 to 3-phosphonocarbapenem 17 involved either a decarboxylative-bromination reaction and subsequent elimination or a bromination reaction followed by decarboxylative-elimination. Carbapenem 13 was found to possess antibacterial activity, comparable with imipenem (+)-3, against Staphylococcus aureus FDA 209P, S. aureus 95, Escherichia coli ATCC 39188, Klebsiella pneumoniae NCTC 418, Pseudomonas aeruginosa 1101-75, P. aeruginosa 18S-H, and Xanthomonas maltophilia GN 12873. Like imipenem ((+)-3), carbapenem 13 was not stable to X. maltophilia oxyiminocephalosporinase type II. Its phosphonate analog 17, however, was neither a significant antibacterial agent nor a good β-lactamase inhibitor

Carbapenern-based prodrugs. Design, synthesis, and biological evaluation of carbapenems

[[abstract]]Syntheses of racemic trans-3-hydroxycarbonyl-6-(phenylacetamido)carbapenem (13), trans-3-phosphono-6-(phenylacetamlido)carbapenem (17), and P-lactam based prodrugs 19 and 22 were accomplished. Carbapenern 13 was found to possess antibacterial activity, comparable with imipenem (+)-3, against Staphylococcus aureus FDA 209P, S. aureus 95, Escherichia coli ATCC 39188, Klebsiella pneumoniae NCTC 418, Pseudomonas aeruginosa 1101-75, P. acruginosa 18S-H, and Xanthomonas inaltophilia GN 12873. Like imipenern ((+)-3), carbapenem 13 was not stable to X. inaltophilia oxyiminocephalospormase type II. Its phosphonate analog 17, however, was neither a significant antibacterial agent nor a good P-lactamase inhibitor. Chemical combinations of trans carbapenem 13 with cis carbapenem 6 (compound 19) as well as clavulanic acid (20) with cis carbapenem 6 (compound 22) via a tetrachloroethane linker exhibited remarkable activity against P-lactamase producing microorganisms in vitro. (c) 2005 Elsevier SAS. All rights reserved

A novel approach towards studying non-genotoxic enediynes as potential anticancer therapeutics

[[abstract]]A novel uracil-containing enediyne 7 was synthesized by the fusion at N-1 and N-3 of uracil with an 11-membered cyclic enediyne. Compound 7 was found to be stable against cycloaromatization at 80degreesC. Thus. it did not cause DNA-damage. Unlike other alkylated uracil derivatives 2-6, highly strained uracil-containing enediyne 7 was reacted with methyl thioglycolate at 25 degreesC to produce uracil (1) and linear enediyne 8. This reactivity toward a sulfhydryl group may play a significant role in the mechanism by which compound 7 directed its cytotoxicity toward tumor cell lines. Tumor cells were found to be more susceptible to enediyne 7 than normal human embryonic lung cells. A combination of 7 with adriamycin or 1-(beta-D-arabinofuranosyl)cytosine resulted in synergistic anticancer activity against murine L1210 and P388 leukemias, Sarcoma 180, and human CCRF-CEM lymphoblastic leukemia. After treatment of Molt-4 cells with uracil-containing enediyne 7. light microscope examination demonstrated the presence of cell shrinkage and nuclear segmentation. Treatment of cultured Molt-4 human leukemia cells with enediyne 7 resulted in a time-dependent depletion of glutathione (GSH) whereas the exposure of the cells to the GSH precursor N-acetylcysteine (NAC) resulted in a substantial suppression of this effect. As such, involvement of GSH depletion in the process of apoptosis may explain the mechanism of action of non-genotoxic enediyne 7 against malignant tumor cell lines.[[fileno]]2010332010006[[department]]化學

In-silico predictive mutagenicity model generation using supervised learning approaches

<p>Abstract</p> <p>Background</p> <p>Experimental screening of chemical compounds for biological activity is a time consuming and expensive practice. <it>In silico</it> predictive models permit inexpensive, rapid “virtual screening” to prioritize selection of compounds for experimental testing. Both experimental and <it>in silico</it> screening can be used to test compounds for desirable or undesirable properties. Prior work on prediction of mutagenicity has primarily involved identification of toxicophores rather than whole-molecule predictive models. In this work, we examined a range of <it>in silico</it> predictive classification models for prediction of mutagenic properties of compounds, including methods such as J48 and SMO which have not previously been widely applied in cheminformatics.</p> <p>Results</p> <p>The Bursi mutagenicity data set containing 4337 compounds (Set 1) and a Benchmark data set of 6512 compounds (Set 2) were taken as input data set in this work. A third data set (Set 3) was prepared by joining up the previous two sets. Classification algorithms including Naïve Bayes, Random Forest, J48 and SMO with 10 fold cross-validation and default parameters were used for model generation on these data sets. Models built using the combined performed better than those developed from the Benchmark data set. Significantly, Random Forest outperformed other classifiers for all the data sets, especially for Set 3 with 89.27% accuracy, 89% precision and ROC of 95.3%. To validate the developed models two external data sets, AID1189 and AID1194, with mutagenicity data were tested showing 62% accuracy with 67% precision and 65% ROC area and 91% accuracy, 91% precision with 96.3% ROC area respectively. A Random Forest model was used on approved drugs from DrugBank and metabolites from the Zinc Database with True Positives rate almost 85% showing the robustness of the model.</p> <p>Conclusion</p> <p>We have created a new mutagenicity benchmark data set with around 8,000 compounds. Our work shows that highly accurate predictive mutagenicity models can be built using machine learning methods based on chemical descriptors and trained using this set, and these models provide a complement to toxicophores based methods. Further, our work supports other recent literature in showing that Random Forest models generally outperform other comparable machine learning methods for this kind of application.</p