喹啉酮类小分子p53-MDM2结合抑制剂3D-QSAR研究

目的 设计、合成高活性的小分子p53-MDM2结合抑制剂,建立具有预测能力的3D-QSAR模型。方法 采用分子模拟软件Sybyl,利用比较分子场方法(CoMFA)、比较分子相似性指数法(CoMSIA),选择已报道的具有p53-MDM2结合抑制活性的一类有相同母核的21个异喹啉酮衍生物作为训练集,7个作为预测集进行3D-QSAR模型的建立和验证。结果 模型具有较高q²(q²_CoMFA=0.545,q²_CoMSIA=0.528)和r2(r²_CoMFA=0.984,r²_CoMSIA=0.972)值,表明2组模型具有较高的拟和能力及预测能力。结论 该模型具有较高的预测能力,为设计、合成高活性的小分子p53-MDM2结合抑制剂提供了理论依据。     

噻唑类衍生物二氢乳清酸脱氢酶抑制剂的三维定量构效关系研究

目的 应用三维定量构效关系（3D-QSAR）研究噻唑类衍生物结构的二氢乳清酸脱氢酶抑制活性,为该类药物的设计和筛选提供可靠的理论依据。方法 针对38个以噻唑为基本骨架的二氢乳清酸脱氢酶抑制剂,分别应用分子力场分析（CoMFA）和比较分子相似性指数分析（CoMSIA）2种经典的方法进行了三维定量构效关系（3D-QSAR）研究,建立相关模型,验证模型的预测能力,三维等势图分析噻唑类衍生物结构与活性的关系。结果 CoMFA模型的交叉验证系数q²为0.796,相关系数r²为0.978;CoMSIA模型的q²以及r²分别为0.721和0.976;2种模型对化合物的活性预测与实际值接近;三维等势图可以全面直观的分析化合物结构对其活性的影响。结论 该3D-QSAR模型三维等势图揭示了结构特征与抑制活性的关系,模型具有较好的预测能力和较强的稳定性,为进一步开发研究打下了较好的基础。     

噻唑-吡唑啉衍生物作为EGFR抑制剂的3D-QSAR和分子对接模型研究

目的 建立EGFR抑制剂结构和活性之间的关系模型,基于对分子活性产生影响的重要结构性因素的信息,设计新的抑制剂分子并预测其活性,为抑制剂分子的设计提供依据。方法 使用Discovery Studio 2019软件进行3D-QSAR的研究以及偏最小二乘的计算;利用Autodock进行分子对接;使用LigPlot研究二维相互作用。结果 模型具有较高的q²(0.521),和r²(r²_training=0.993,r²_test=0.916,r²_blind=0.940),表明模型具有较高的预测能力和拟合能力。结论 预测结果表明,新设计的化合物活性较高,为EGFR抑制剂分子的设计提供了参考。     

噻嗪类11β-羟类固醇脱氢酶抑制药三维定量构效关系研究

摘 要 目的：通过构建噻嗪类11β-羟类固醇脱氢酶（11β-HSD）抑制药的三维定量构效关系(3D QSAR)模型,用于其结构改造及发现具有更高生物活性的11β-HSD抑制药。方法: 基于骨架叠合的模式,采用比较分子力场分析（CoMFA）的方法,构建噻嗪类衍生物定量构效关系模型,并用分子对接的方法对已构建的3D QSAR模型进行验证。结果: 得到了高精度的11β-HSD抑制药的3D-QSAR模型（CoMFA:q²=0.346,r²=0.850;其中q²为交叉验证系数,r²为非交叉验证系数）。结论:本文构建的3D QSAR模型可为11β-HSD抑制药骨架各个位点的化学修饰,实施定向合理设计,为开发新型抗2型糖尿病药物提供理论基础。     

阿片类药物透过血脑屏障的三维构效研究

目的:建立药物透过血脑屏障的三维构效模型,为药物分子设计提供理论依据。方法与结果:利用比较分子力场分析方法建立了阿片类药物透过血脑屏障的三维定量构效模型,该模型有较高的预测能力,交叉验证系数r²_cv=0.718,相关系数r²=0.978,F₃,7=67.902,标准偏差SE=0.209。结论:根据CoMFA模型系数等势图,解释了该类药物透过血脑屏障的构效关系。     

药物透过血脑屏障的3D-QSPR:H2受体拮抗剂的CoMFA和EVA分析

目的：药物透过血脑屏障是药代动力学的重要过程,H₂受体拮抗剂是作用于神经外周的抗溃疡药物,为避免该类药物透过血脑屏障损伤中枢神经,产生毒副作用,指导该类药物的设计与合成。方法和结果：选择了不依赖于实验参数的比较分子力场分析(CoMFA)方法和最近发展的本征值(EVA)方法,建立了有关的三维药代动力学性质(3D-QSPR)模型。CoMFA模型的统计参数为：交叉验证系数r²_cv=0.625,相关系数r2=0.893,F_3,17=47.270,标准偏差SE=0.254;EVA模型的统计参数为：交叉验证系数r²_cv=0.697,相关系数r²=0.922,F_3,17=67.766,标准偏差SE=0.203。结论：两种方法都能建立三维定量构效模型,EVA模型有更高的预测能力。     

CO2超临界流体色谱法同时测定莪术油中3个倍半萜类成分的含量

目的 建立CO₂超临界流体色谱法测定莪术油中呋喃二烯、牻牛儿酮和莪术二酮含量的方法。方法 采用ACQUITY UPC² HSS C₁₈ SB色谱柱（3.0 mm×150 mm,1.8 μm）,以CO₂-乙腈为流动相,梯度洗脱;流速为1.0 mL·min^-1;检测波长为216 nm,柱温为55℃,背压为2 000 psi。结果 呋喃二烯在2.67~1 337.26μg·mL^-1内线性关系良好（r=1.000）,加样回收率为97.94%（n=6,RSD=1.50%）。牻牛儿酮在2.77~1 386.00 μg·mL^-1内线性关系良好（r=1.000）,加样回收率为96.07%（n=6,RSD=1.68%）;莪术二酮在6.99~3 493.00 μg·mL^-1内线性关系良好（r=1.000）,加样回收率为99.33%（n=6,RSD=1.88%）。结论 本方法快捷准确、稳定且绿色环保,可用于莪术油中上述3个倍半萜类成分的质量控制。     

三维全息原子场作用矢量用于四氢-咪唑-苯二氮酮类抗艾滋病药物定量构效关系研究

目的对23个四氢-咪唑-苯二氮酮(TIBO)类抗艾滋病药物分子进行定量构效关系(QSAR)研究。方法采用本实验室新近提出的三维全息原子场作用矢量(3D-HoVAIF)表征TIBO类抗艾滋病药物分子结构。然后运用偏最小二乘回归(partial least square regression，PLS)建立3D-HoVAIF描述符与TIBO类抗艾滋病药物活性之间的QSAR模型。结果用此方法建模的复相关系数(r^2cum)、交互校验复相关系数(q^{2cum)和模型的标准偏差(SD)分别为r2cum=0.824，q2_cum=0.778与SD=0.56，均优于文献值。结论3D-HoVAIF能较好表征TIBO类抗艾滋病药物分子结构信息，因而能建立具有良好稳定性和预测能力的QSAR模型。}     

UHPLC测定芎菊上清丸中9种成分含量及化学模式分析

目的 建立UHPLC波长切换法同时测定芎菊上清丸中9种成分的含量方法。方法 采用Agilent Ecilipse C₁₈（2.1 mm×100 mm,1.6 μm）色谱柱,流动相：甲醇-0.05%磷酸水溶液,梯度洗脱;流速为0.3 mL·min^-1;检测波长：327,237,320,345,278,254 nm;柱温30℃;进样量2 μL;并采用SPSS 22.0统计软件对含量测定结果进行主成分分析与聚类分析。结果 绿原酸、3,5-二咖啡酰奎宁酸、栀子苷、甘草苷、阿魏酸、盐酸小檗碱、黄芩苷、升麻素苷、5-O-甲基维斯阿米醇苷线性范围分别为4.30~68.80 μg·mL^-1（r=0.999 0）、6.66~106.56 μg·mL^-1（r=0.999 2）、7.67~122.72 μg·mL^-1（r=0.999 4）、4.88~78.08 μg·mL^-1（r=0.999 1）、2.37~37.92 μg·mL^-1（r=0.999 1）、6.50~103.92 μg·mL^-1（r=0.999 2）、8.85~141.60 μg·mL^-1（r=0.999 4）、0.88~14.08 μg·mL^-1（r=0.999 7）、0.74~11.92 μg·mL^-1（r=0.999 3）;平均加样回收率（n=9）均在99.42%~103.10%,RSD均<2.0%。主成分分析与聚类分析均可将不同生产厂家的芎菊上清丸很好地分类,且分类结果一致。结论 所建立的多成分方法快捷、准确、重复性好,可用于芎菊上清丸的质量控制。     

HPLC波长切换法同时测定心神安胶囊中9种成分的含量

目的 建立HPLC波长切换法同时测定心神安胶囊中9种成分的含量。方法 采用Agilent Eclipse XDB-C₁₈色谱柱,流动相乙腈（A）-0.1%甲酸溶液（B）,梯度洗脱;流速0.9 mL·min^-1;检测波长分别为320 nm[检测远志（口山）酮Ⅲ、3,6''-二芥子酰基蔗糖]、203 nm （检测人参皂苷Rb₁、绞股蓝皂苷XLIX、绞股蓝皂苷XVⅡ）和254 nm （检测毛蕊异黄酮葡萄糖苷、芒柄花苷、毛蕊异黄酮、芒柄花素）;柱温25℃。结果 远志（口山）酮Ⅲ、3,6''-二芥子酰基蔗糖、人参皂苷Rb₁、绞股蓝皂苷XLIX、绞股蓝皂苷XVⅡ、毛蕊异黄酮葡萄糖苷、芒柄花苷、毛蕊异黄酮、芒柄花素分别在2.070~41.40 μg·mL^-1（r=0.999 2）、3.860~77.20 μg·mL^-1（r=0.999 6）、11.29~225.8 μg·mL^-1（r=0.999 8）、5.070~101.4 μg·mL^-1（r=0.999 9）、19.86~397.2 μg·mL^-1（r=0.999 5）、1.280~25.60 μg·mL^-1（r=0.999 1）、0.960 0~19.20 μg·mL^-1（r=0.999 3）、0.670 0~13.40 μg·mL^-1（r=0.999 7）、2.580~51.60 μg·mL^-1（r=0.999 1）内线性关系良好,平均回收率分别为98.04%,99.26%,99.05%,97.42%,100.0%,98.27%,97.81%,96.84%和99.86%,RSD分别为1.28%,0.82%,1.43%,1.43%,0.86%,1.26%,1.38%,1.16%和0.69%。结论 本方法操作简便、准确、重复性好,能够对心神安胶囊中9种成分进行同时含量测定,为提高和完善心神安胶囊的质量标准提供了有效方法。     

3D-QSAR study of pyrrolidine derivatives as matrix metalloproteinase-2 inhibitors

In order to develop potent inhibitors of matrix metalloproteinase-2(MMP-2) as anticancer agents, a three-dimensional quantitative structure–activity relationship (3D-QSAR) model was established by using comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) methods. This study correlates the MMP-2 inhibitory activities of 67 pyrrolidine derivatives to steric, electrostatic, hydrophobic, and hydrogen-bond donor and acceptor fields. After using two different molecular alignments, both CoMFA and CoMSIA models resulted in good statistical predictions, a case in point being their high q ² values of between 0.757 and 0.843. The CoMFA and CoMSIA models established herein will be helpful in understanding the structure–activity relationship of pyrrolidine derivatives as well as in the design of novel derivatives with enhanced MMP-2 inhibitory activity.     

Pharmacophore-based 3D-QSAR of HIF-1 inhibitors

(Aryloxyamino)benzoic acids and nicotinic/isonicotinic acids represent an important new class of small molecules that inhibit the activation of Hypoxia-Inducible Factor (HIF)-1. In order to understand the factors affecting inhibitory potency of HIF-1 inhibitors, 3 dimensional-quantitative structure activity relationship (3D-QSAR) studies were performed. Since no receptor structure are available, the pharmacophore-based alignment was used for comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA). The CoMFA and CoMSIA models gave reasonable statistics (CoMFA: q² = 0.564, r²=0.945; CoMSIA: q² = 0.575, r²=0.929). Both CoMFA and CoMSIA results indicate that the steric interaction is a major factor, while CoMSIA suggests importance of hydrogen bonding. These findings about steric and H-bonding effects can be useful to design new inhibitors. Equally contributed in this work.     

3D-QSAR study using CoMFA and CoMSIA methods for a series of histone H3 phosphorylation inhibitors

Three-dimensional quantitative structure–activity relationship (3D-QSAR) analysis of inhibitory activities for a series of pyrrolotriazine derivatives against histone H3 phosphorylation (pHH3) was performed using comparative of molecular field analysis (CoMFA) and comparative of molecular similarities indices analysis (CoMSIA) techniques. 62 derivatives were used to establish and validate two models by considering a high deviation in biological activities and structural variations. Optimum CoMFA and CoMSIA models obtained from the training set were statistically significant with cross-validated correlation coefficients q ² of 0.551 and 0.621, and conventional correlation coefficients (r ²) of 0.999 and 0.995, respectively. The predicted correlation coefficients of test set (R ²) for CoMFA and CoMSIA were 0.835 and 0.918, respectively. Two models obtained provide guidelines to trace the features that really matter chiefly with respect to the design of novel pyrrolotriazine derivatives.     

3D-QSAR studies of chromone derivatives as iron-chelating agent

The metal-chelating activity of a series of 48 chromone compounds, evaluated by ferrous (Fe²⁺) chelating test, were subjected to 3D-QSAR studies using comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA). The best CoMFA model obtained from HF/6-31G* geometry optimization and field fit alignment gave cross-validated r ² (q ²) = 0.582, non-cross-validated r ² = 0.975. The best CoMSIA model gave q ² = 0.617, non-cross-validated r ² = 0.917. The resulted CoMFA and CoMSIA contour maps proposed the Fe²⁺-chelating sites of chromone compounds compared with those of quercetin.     

Molecular docking and 3D-QSAR studies on checkpoint kinase 1 inhibitors

Checkpoint kinase 1(Chk1) is a promising target for cancer treatment. Here three-dimensional quantitative structure–activity relationship (3D-QSAR) studies were performed on 174 1,4-dihydroindeno[1,2-c]pyrazole inhibitors of Chk1 using comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA). Two satisfactory ligand-based QSAR models were built (CoMFA model: q ² = 0.541, r ² = 0.880, CoMSIA model: q ² = 0.590, r ² = 0.902). The docking-based studies presented a detailed understanding of interaction between the inhibitors and Chk1. The obtained QSAR models are highly predictable (CoMFA model: q ² = 0.567, r ² = 0.891, CoMSIA model: q ² = 0.596, r ² = 0.917). The models were further validated by an external testing set obtaining $ r_{\text{pred}}^{2} $ r pred 2 values 0.896 and 0.923 for CoMFA and CoMSIA, respectively. So our models might be helpful for further modification of 1,4-dihydroindeno[1,2-c]pyrazole derivatives.     

3D-QSAR and docking studies on pyridopyrazinones as BRAF inhibitors

BRAF has become an important and exciting therapeutic target toward human cancer. 3D-QSAR and docking studies were performed to explore the interaction of the BRAF with a series of pyridopyrazinones. The comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) methods were carried out in terms of their potential for predictability. The CoMFA and CoMSIA models using 71 compounds in the training set gave r _cv² values of 0.567 and 0.662, r ² values of 0.900 and 0.907, respectively. The 3D contour maps generated by the CoMFA and CoMSIA models were used to identify the key structural requirements responsible for the biological activity. Molecular docking was applied to explore the binding mode between the ligands and the receptor. The information obtained by 3D-QSAR models may be useful to design novel potential BRAF inhibitors.     

A combined 3D QSAR and pharmacophore-based virtual screening for the identification of potent p38 MAP kinase inhibitors: an in silico approach

p38 kinase plays a vital role in inflammation mediated by tumor necrosis factor-α and interleukin-1β pathways. Inhibition of p38 kinase provides an effective way to treat inflammatory diseases. 3D-QSAR study was performed to obtain reliable comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) models for a series of p38 inhibitors with three different alignment methods (Receptor based, atom by atom matching, and pharmacophore based). Among the different alignment methods, better statistics were obtained with receptor-based alignment (CoMFA: q ² = 0.777, r ² = 0.958; CoMSIA: q ² = 0.782, r ² = 0.927). Superposing CoMFA/CoMSIA contour maps on the p38 active site gave a valuable insight to understand physical factors which are important for binding. In addition, this pharmacophore model was used as a 3D query for virtual screening against NCI database. The hit compounds were further filtered by docking and scoring, and their biological activities were predicted by CoMFA and CoMSIA models.