Antibacterial activity of N-quaternary chitosan derivatives: Synthesis, characterization and structure activity relationship (SAR) investigations

Quaternary N-(2-(N,N,N-tri-alkyl ammoniumyl and 2-pyridiniumyl) acetyl) derivatives of chitosan polymer, chitooligomer, and glucosamine (monomer) were synthesized for the purpose of investigating the structure activity relationship (SAR) for the antibacterial effect. Novel methods were used in the synthesis. The final chitosan and chitooligomer derivatives could thus be obtained in two steps without prior protection of the hydroxyl groups. However, in order to obtain chitosan derivatives with the bulky N,N-dimethyl-N-dodecyl- and N,N-dimethyl-N-butyl side chains three steps were needed, starting from 3,6-O-di-tert-butyldimethylsilyl chitosan (3,6-O-di-TBDMS chitosan) as the key intermediate. The quaternary ammoniumyl acetyl derivatives of glucosamine were synthesized from glucosamine or tetra-O-acetylglucosamine. N,N,N-trimethyl chitosan (TMC) was used as reference compound for investigation of antibacterial activity. Clinical Laboratory Standard Institute (CLSI) protocols were used to determine MIC and MLC for activity against clinically important Gram-positive strains Staphylococcus aureus (ATCC 25923), and S. aureus (MRSA) (ATCC 43300), and Gram-negative strains of Escherichia coli (ATCC 25922), P. aeriginosa (ATCC 27853) and Enterococcus facialis (ATCC 29212). The MIC values for the compounds ranged from 8 to ?8192 mg/L. In general the N-(2-(N,N-dimethyl-N-dodecyl ammoniumyl) acetyl) derivatives of chitooligomer and glucosamine monomer were more active against bacteria than derivatives with shorter alkyl chains. In contrast the N-(2-(N,N-dimethyl-N-dodecyl ammoniumyl) acetyl) derivatives of chitosan were less active than derivatives with N-(2-N,N,N-trimetylammoniumyl) acetyl or N-(2-(N-pyridiniumyl) acetyl) quaternary moiety. N,N,N-trimethyl chitosan (TMC) was the most active compound in this study.     

Structural identification of SAR-943 metabolites generated by human liver microsomes in vitro using mass spectrometry in combination with analysis of fragmentation patterns

SAR-943 (32-deoxo rapamycin) is a proliferation signal inhibitor via interaction with the mammalian target of rapamycin (mTOR). Most importantly, SAR-943 has improved chemical stability compared to rapamycin (sirolimus) and is currently under investigation as a drug coated on coronary stents. It was the goal of this study to identify the SAR-943 metabolites generated after incubation with human liver microsomes using high-resolution mass spectrometry (MS) and MS/iontrap (MS(n)) and comparison of fragmentation patterns of the metabolites with those of SAR-943 and other known rapamycin derivatives. Our study showed that SAR-943 is mainly hydroxylated and/or demethylated by human liver microsomes. The structures of the following metabolites were identified: O-demethylated metabolites: 39-O-desmethyl, 16-O-desmethyl and 27-O-desmethyl SAR-943; hydroxylated metabolites: hydroxy piperidine SAR-943, 11-hydroxy, 12-hydroxy, 14-hydroxy, 23-hydroxy, 24-hydroxy, 25-hydroxy, 46-hydroxy and 49-hydroxy SAR-943; didemethylated metabolites: 16,39-O-didesmethyl and 27,39-O-didesmethyl SAR-943; demethylated-hydroxylated metabolites: 39-O-desmethyl, 23- or 24-hydroxy and 39-O-desmethyl, hydroxy piperidine SAR-943 and dihydroxylated metabolites: 12-,23- or 24-dihydroxy SAR-943. In addition, several other demethylated-hydroxylated and dihydroxylated metabolites were detected. However, their exact structures could not be identified.     

基于幅度统计多分辨率分析的SAR图像灰度压缩

基于幅度统计多分辨率分析的原理，针对SAR图像特殊的显示要求，提出了一种新的灰度压缩方法，解决了常规灰度级压缩方法不能很好保持原始图像数据直方图分布的问题，实验结果表明该方法能有效保证灰度压缩前后图像数据直方图分布形状的一致性，较好地保持原始图像的视觉特征。     

New chemical constituents from the Piper betle Linn. (Piperaceae)

The phytochemical investigation of chloroform extract from Piper betle var. haldia, Piperaceae, leaves has resulted in the isolation of two new chemical constituents which were identified as 1-n-dodecanyloxy resorcinol (H1) and desmethylenesqualenyl deoxy-cepharadione-A (H4), on the basis of spectroscopic data 1D NMR (¹H and ¹³C) and 2D NMR (¹H-¹H COSY and HMBC) as well as ESI-MS, FT-IR and HR-ESI-MS analyses. Compounds H1 and H4 showed excellent antioxidant DPPH free radical scavenging activity with IC₅₀ values of 7.14 μg/mL and 8.08 μg/mL compared to ascorbic acid as a standard antioxidant drug with IC₅₀ value of 2.52 μg/mL, respectively. Evaluation of cytotoxic activity against human hepatoma cell line (PLC-PRF-5) showed moderate effect with the GI₅₀ values of 35.12 μg/mL for H1, 31.01 μg/mL for H4, compared to Doxorubicin^® as a standard cytotoxic drug with GI₅₀ value of 18.80 μg/mL.     

Calculating physical properties of organic compounds for environmental modeling from molecular structure

Mathematical models for predicting the transport and fate of pollutants in the environment require reactivity parameter values – that is the value of the physical and chemical constants that govern reactivity. Although empirical structure–activity relationships have been developed that allow estimation of some constants, such relationships are generally valid only within limited families of chemicals. The computer program, SPARC, uses computational algorithms based on fundamental chemical structure theory to estimate a large number of chemical reactivity parameters and physical properties for a wide range of organic molecules strictly from molecular structure. Resonance models were developed and calibrated using measured light absorption spectra, whereas electrostatic interaction models were developed using measured ionization pK_as in water. Solvation models (i.e., dispersion, induction, H-bonding, etc.) have been developed using various measured physical properties data. At the present time, SPARC’s physical property models can predict vapor pressure and heat of vaporization (as a function of temperature), boiling point (as a function of pressure), diffusion coefficient (as a function of pressure and temperature), activity coefficient, solubility, partition coefficient and chromatographic retention time as a function of solvent and temperature. This prediction capability crosses chemical family boundaries to cover a broad range of organic compounds.     

Fragment-based QSAR: perspectives in drug design

Drug design is a process driven by innovation and technological breakthroughs involving a combination of advanced experimental and computational methods. A broad variety of medicinal chemistry approaches can be used for the identification of hits, generation of leads, as well as to accelerate the optimization of leads into drug candidates. Quantitative structure–activity relationship (QSAR) methods are among the most important strategies that can be applied for the successful design of small molecule modulators having clinical utility. Hologram QSAR (HQSAR) is a modern 2D fragment-based QSAR method that employs specialized molecular fingerprints. HQSAR can be applied to large data sets of compounds, as well as traditional-size sets, being a versatile tool in drug design. The HQSAR approach has evolved from a classical use in the generation of standard QSAR models for data correlation and prediction into advanced drug design tools for virtual screening and pharmacokinetic property prediction. This paper provides a brief perspective on the evolution and current status of HQSAR, highlighting present challenges and new opportunities in drug design.     

基于Contourlet变换的自适应SAR图像相干斑噪声抑制算法

为了保持高岛分辨率合成孔径雷达(SAR)图像中的纹理结构,提出了一种基于BivaShrink模型的Contourlet 域SAR图像相干斑噪声抑制算法.联合当前层和父层的Contourlet系数,通过计算局部方差一致性范数和区域能量比,自适应地确定方差估计区域的形状和大小.从而对原始图像方差进行最优估计.实验结果表明,算法在噪声的去除和结构信息等细节的保持上均不同程度的优于小波BivaShrink去噪算法和Contourlet 阈值去噪算法,主观效果和数值指标都有较好改进.