维甲类化合物构效关系研究:抗致癌活性的三维构效关系

使用比较分子场分析法(comparativemolecularfieldanalysis,CoMFA)[1]建立了一个维甲类化合物抗致癌活性的三维构效关系(3D-QSAR)模型,用交叉验证法(cros-validation)、和非交叉验证方法(non-cros-validation)分别验证和建立的分子场模型,高的交叉验证回归系数(R2CV=0.905)说明系列化合物分子周围空间场和静电场分布的差异与其生物活性差异间存在良好的相关性。用这个模型预测在建立模型时没有包括进去的三个化合物的活性,预测值接近实验值,提示该模型有较好的活性预测能力,可用来指导设计新的高活性维甲分子。化合物分子的最低能量构象未必是其活性构象。用得到的化合物最低能量构象进行CoMFA研究,其交叉验证系数较低(R²_CV=0.420),不具有统计学意义。但将部分分子的单键微弱旋转,构象能变化控制在2kcal·mol^-1之内,其它分子采取最低能量构象,则得到高的交叉验证系数。叠合的配体分子场模拟了配体分子周围的作用环境,反映受体结合部位与配体之间存在相互作用的基团和(或)原子的空间和静电性质,分子场模型在作为预测活性模板的同时也在一定程度上映射出受体结合部位的三维拓扑形状和理化特性。     

噻唑烷二酮和芳酮酸类PPARγ激动剂三维定量构效关系研究

目的建立PPARγ激动剂-噻唑烷二酮和芳酮酸类化合物的三维定量构效关系,为设计高活性PPARγ激动剂提供结构信息。方法与结果用比较分子力场分析方法得到噻唑烷二酮和芳酮酸类化合物CoMFA模型,其交叉验证相关系数R²=0.656,非交叉验证相关系数R²=0.982,F_10,37=201.1,绝对误差SE=0.115。结论从CoMFA系数等势图中揭示芳酮酸类化合物较噻唑烷二酮类化合物活性更高的原因,提示芳酮酸类化合物与PPARγ结合时形成了不同于BRL-PPARγ复合物晶体的结合腔。     

3-甲基芬太尼衍生物构效关系及受体结合特征研究

对3-甲基芬太尼的1-苯乙基、4-N-丙酰基进行了结构改造、测定了所合成化合物的镇痛活性及部分化合物的镇痛作用持续时间、阿片受体亲和力和对阿片受体亚型的选择性。结果表明,大部分化合物均有较强的吗啡样镇痛活性,强度约为吗啡的2～180倍。所试化合物的镇痛作用持续时间比芬太尼延长6～10倍。化合物1～4的受体亲和力(IC₅₀)约为10^-7～10^-3mol。化合物13对阿片μ-受体有较高的选择性,对大鼠脑膜的λ/δ比值大于700,对小鼠脑膜的μ/δ比值为1000。     

安定在家兔体内的肠胃循环药代动力学分析

6只家兔iv安定5 mg·kg^-1后,浓度—时间数据呈现双峰形。本文提出肠胃循环动力学模型,用于分析实测数据,得到了一般动力学参数:T_1/2(α)=0.21±=0.15 h,T_1/2(β)=2.2+0.6 h,K_e=1.5±0.6 h^-1,K₁₂=2.0±1.0 h^-1,K₂₁=1.0±0.4 h^-1,V₁=3.1±1.6 L·kg^-1,AUC=1.7±0.5μg·h^-1·ml^-1。此外,还求得有关肠胃循环的参数,即:重吸收滞后时间T′=0.25±0.24h,重吸收速率常数K_A=3.5±1.4 h^-1,重吸收率R_A=24±7%。     

强效镇痛剂研究——Ⅱ.3-甲基芬太尼类衍生物的合成及镇痛活性

本文报道了N-[1-(β-苯乙基)-3-甲基-4-哌啶基]-N-丙酰苯胺(7209)和N-[1-(β-羟基-β-苯乙基)-3-甲基-4-哌啶基]-N-丙酰苯胺(7302)等一系列3-甲基芬太尼类衍生物的合成及镇痛活性。绝大部分该类衍生物均具有典型的吗啡样镇痛活性,是一类结构较简单、易于合成、镇痛作用极强的麻醉镇痛剂。化合物7302的ED₅₀为0.0022mg/kg(ip,小鼠,热板法),比芬太尼强28倍,竟达吗啡的6318倍,为我们至今合成该类衍生物中作用最强者。     

羟甲芬太尼立体异构体的晶体结构

对强效镇痛剂羟甲芬太尼的两个光学异构体体cis-(3R,4S,2′R)-羟甲芬太尼(I)和trans-(3R,4R,2′S)-羟甲芬太尼(Ⅱ)进行了X-射线衍射晶体结构分析。两个异构体均有一个sp³N(l)原子和一个sp²N(7)原子。哌啶环呈椅式构象,顺式异构体I的3-甲基处于直立键,4-N-苯基丙酰胺基处于平伏键;反式异构体II的3-甲基与4-N-苯基丙酰胺基均处于平伏键。在I分子中,C(4)原子与4-丙酰胺基组成的平面与N-苯环平面近似相互垂直,而在II中,两平面的二面角近似为100℃。两异构体分子中均存在分子内氢键O(1)一H…N(1),反式异构体II还存在分子间氢键O(1)一H(A)…O(2)(B)。     

白杨素衍生物的合成和晶体结构及与DNA的作用

以白杨素为先导化合物经羟甲基化反应合成中间体5,7-二羟基-6,8-二羟甲基黄酮(1)，进而合成了5,7-二羟基-6,8-二(甲氧基甲基)黄酮(2)，5,7-二羟基-6,8-二(乙氧基甲基)黄酮(3)，5,7-二羟基-6,8-二(丁氧基甲基)黄酮(4)，5,7-二羟基-6,8-二(戊氧基甲基)黄酮(5)和5-羟基-7-甲氧基-6,8-二(乙氧基甲基)黄酮(6)；采用IR，¹H NMR，¹³C NMR和元素分析对1～6进行了表征，同时用X-射线单晶衍射法对6进行了晶体结构测定。利用荧光法分别对1～4与CT-DNA的作用进行了研究，根据Stern-Volmer方程，它们对EB-DNA体系的荧光猝灭常数分别为k_q1=9.71×10³ L·mol^-1，k_q2=2.25×10⁴L·mol^-1，k_q3=1.03×10⁴L·mol^-1和k_q4=7.96×10³ L·mol^-1。1～4与白杨素相比，对DNA更具亲和力，为开发更有效的黄酮类化合物提供了实验依据。     

新型二芳基三嗪类抗锥体虫病化合物的三维定量构效关系研究

目的 建立具有预测能力的新型二芳基三嗪类抗锥体虫病化合物三维定量构效关系（3D-QSAR）模型。方法 通过对具有抗锥体虫活性的二芳基三嗪类化合物库进行结构分析,利用比较分子场分析法（CoMFA）和比较分子相似性指数分析法（CoMSIA）,建立3D-QSAR模型。结果 模型具有较高q²（q_CoMFA²=0.697,q_CoMSIA²=0.561）和r²（r_CoMFA²=0.998,r_CoMSIA²=0.966）值,表明2组模型具有较高的拟和能力及预测能力。结论 建立的CoMFA和CoMSIA模型均具有良好的预测能力,为设计更高活性的新型二芳基三嗪类抗锥体虫病化合物提供了理论依据和研究方向。     

2-(3,4-二甲氧基苯基)-4-氧代-4H-色烯-3-基乙酸酯的晶体结构及旋光性

目的 探讨具有降血脂作用的黄酮醇衍生物2-（3,4-二甲氧基苯基）-4-氧代-4H-色烯-3-基乙酸酯（4）晶体结构及旋光性。方法 采用X射线单晶衍射技术获得绝对构型及SGW^®-1自动旋光仪测定化合物旋光性。结果 X射线单晶衍射表明化合物4属于正交晶系,P2₁2₁2₁空间群,晶胞参数a=7.763（2）Å,b=13.930（4）Å,c=14.906（4）Å,α=β=γ=90.00°,V=1 611.91（8）ų,Z=8;该晶态下分子间不存在氢键联系,分子以范德华力维系其在空间的稳定排列。从单晶数据可以看出,化合物4中二甲氧基取代的苯环相对于4H-色烯骨架有33.9（2）°扭转,推测可能存在旋光性;进一步的旋光实验结果证明该化合物具有左旋光性,比旋光度为[α]_D^19.1=-5.077°。结论 从单晶结果可知,由于化合物4中,二甲氧基取代的苯环相对于4H-色烯骨架有33.9（2）°扭转,形成手性,化合物具有旋光性。这类非黄酮苷类黄酮具有旋光性,未曾见报道。     

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。     

Cytotoxicity and Structure-activity Relationships of Naphthyridine Derivatives in Human Cervical Cancer,Leukemia, and Prostate Cancer

Naphthyridine compounds are important, because they exhibit various biological activities including anticancer, antimicrobial, and anti-inflammatory activity. Some naphthyridines have antimitotic effects or demonstrate anticancer activity by inhibiting topoisomerase II. These compounds have been investigated as potential anticancer agents, and several compounds are now part of clinical trials. A series of naphthyridine derivatives were evaluated for their in vitro cytotoxic activities against human cervical cancer (HeLa), leukemia (HL-60), and prostate cancer (PC-3) cell lines using an MTT assay. Some compounds (14, 15, and 16) were more potent than colchicine against all three human cancer cell lines and compound (16) demonstrated potency with IC₅₀ values of 0.7, 0.1, and 5.1 µM, respectively. Comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) were used for quantitative structure-activity relationship (QSAR) molecular modeling of these compounds. We obtained accurate and predictive three-dimensional QSAR (3D-QSAR) models as indicated by the high PLS parameters of the HeLa (q², 0.857; r², 0.984; r²_pred, 0.966), HL-60 (q², 0.777; r², 0.937; r²_pred, 0.913), and PC-3 (q², 0.702; r², 0.983; r²_pred, 0.974) cell lines. The 3D-QSAR contour maps suggested that the C-1 NH and C-4 carbonyl group of the naphthyridine ring and the C-2 naphthyl ring were important for cytotoxicity in all three human cancer cell lines.     

3D-QSAR and docking studies of arylmethylamine-based DPP IV inhibitors

The present work was focused on the study of the three-dimensional (3D) structural requirements for the highly potent bioactivity of dipeptidyl peptidase (DPP) IV's inhibitor. At first, molecular dynamic and mechanic (MD/MM) simulations were performed to research the conformations of the potent DPP IV's inhibitor 5-(aminomethyl)-6-(2,4-dichlorophenyl)-2-(3,5-dimethoxy-phenyl)pyrimidin-4-amine. Using the MD/MM-determined molecular conformers as templates, the 3D quantitative structure activity relationship (QSAR) studies were carried out based on a set of arylmethylamine DPP IV inhibitors with the comparative molecular field analysis (CoMFA) approach. The best 3D-QSAR model was constructed with good statistic values of r_cv² and R² using PLS analyses (CoMFA: r_cv²=0.660, R²=0.953). The generated 3D-QSAR model was proved to be reliable by internal and external validations. Docking studies were further performed to analyze the interaction mode between the highly potent or low potent arylmethylamine derivatives and DPP IV. Our flexible docking results also confirmed the possible bioactive conformation obtained from the 3D-QSAR model, of arylmethylamine-based DPP IV inhibitors. The 3D-QSAR model may provide information of pharmacophoric features for further design and optimization of new scaffold compounds with high inhibitory activity to DPP IV.     

Three-dimensional QSAR and pharmacophore mapping of biphenyl benzoic acid derivatives as selective human β3-adrenergic receptor agonists

Molecular modeling studies were performed to develop a predictive common pharmacophore hypothesis (CPH) and use it for alignment in three-dimensional (3D) quantitative structure-activity relationship (QSAR) studies using CoMFA and CoMSIA, with a diverse set of 80 β₃-adrenergic receptor (β₃-AR) agonists. Using PHASE (Pharmacophore Alignment and Scoring Engine) six-point CPH with one acceptor, one negative charge, one positive charge, and three rings, features were derived for pharmacophore-based alignment of molecules. CPH was selected by correlating the observed and estimated activity for the training set and test set of molecules using partial least squares analysis. The validated pharmacophore hypothesis was used for alignment of molecules in CoMFA and CoMSIA model development. The models so generated showed a good “predictive” r ² value of 0.6635 and 0.8665 for CoMFA and CoMSIA, respectively. The 3D contour CoMFA/CoMSIA maps provided an interpretable explanation of SAR for the compounds and also permitted interesting conclusions about the substituent effects at different positions of the biphenyl benzoic acid derivatives. CPH can also provide a powerful template for virtual screening and design of new β₃-AR agonists.     

Computational Study and Modified Design of Selective Dopamine D3 Receptor Agonists

Dopamine D₃ receptor (D₃R) is considered as a potential target for the treatment of nervous system disorders, such as Parkinson's disease. Current research interests primarily focus on the discovery and design of potent D₃ agonists. In this work, we selected 40 D₃R agonists as the research system. Comparative molecular field analysis (CoMFA) of three‐dimensional quantitative structure–activity relationship (3D‐QSAR), structure–selectivity relationship (3D‐QSSR), and molecular docking was performed on D₃ receptor agonists to obtain the details at atomic level. The results indicated that both the CoMFA model (r² = 0.982, q² = 0.503, = 0.893, SEE = 0.057, F = 166.308) for structure–activity and (r² = 0.876, q² = 0.436, = 0.828, F = 52.645) for structure–selectivity have good predictive capabilities. Furthermore, docking studies on three compounds binding to D₃ receptor were performed to analyze the binding modes and interactions. The results elucidate that agonists formed hydrogen bond and hydrophobic interactions with key residues. Finally, we designed six molecules under the guidance of 3D‐QSAR/QSSR models. The activity and selectivity of designed molecules have been improved, and ADMET properties demonstrate they have low probability of hepatotoxicity (<0.5). These results from 3D‐QSAR/QSSR and docking studies have great significance for designing novel dopamine D₃ selective agonists in the future.     

烯丙胺类抗真菌药物比较分子力场分析(CoMFA)的研究

本实验采用“活性类似物法”确定了烯丙胺类抗真菌化合物的药效构象,采用比较分子力场分析法(CoMFA)拟合了48个烯丙胺类抗真菌化合物对6种常见致病真菌的3D-QSAR方程,并比较了2种不同叠合规则对模型的影响,最后,用5个新合成化合物对所拟合的抗石膏样毛癣菌、抗烟曲霉菌的CoMFA模型的预测能力进行了检验,得到比较满意的结果。     

CoMFA AND CoMSIA STUDIES ON A SET OF BENZYL PIPERAZINES, PIPERADINES, PYRAZINOPYRIDOINDOLES, PYRAZINOISOQUINOLINES AND SEMI RIGID ANALOGS OF DIPHENHYDRAMINE

3D QSAR studies using CoMFA and CoMSIA have been carried out using 40 antihistamines H1 belonging to substituted benzyl piperazines/piperidines, pyrazinopyridoindoles, pyrazinoisoquinolines and semirigid analogs of diphenhydramine. Chemical feature based pharmacophoric alignments were used to develop models using different fields viz. tripos standard, hydrogen bonding, parabolic indicator in the case of CoMFA and steric, electrostatic, donor, acceptor, hydrophobic, donor and acceptor, steric and electrostatic in the case of CoMSIA. All the eleven derived models were further optimized using the PLS region focusing tool to increase the model resolution which led to eleven more models. The removal of two outliers led to statistically significant models. The best CoMFA models was the one developed using the (region focused) CoMFA parabolic fields (r²=0.988,q² _LOO = 0.596, q² _{Leave 10% out} =0.592, q² _{Leave 30% out} = 0.606) and the best CoMSIA model was the one with (region focused) CoMSIA steric fields (r2=0.989, q² _LOO =0.74, q² _{Leave 10% out} = 0.726, q² _{Leave 30% out} =0.74).     

Molecular docking and 3D QSAR studies of substituted 4-amino-1H-pyrazolo[3,4-d]pyrimidines as insulin-like growth factor-1 receptor (IGF1R) inhibitors

Insulin-like growth factor-1 receptor (IGF1R) is one of receptor tyrosine kinase that plays a significant role in cancer and has been pursued as drug target for cancer therapy. Molecular docking and comparative molecular field analysis (CoMFA) studies were carried out on the series of substituted 4-amino-1H-pyrazolo[3,4-d]pyrimidines IGF1R inhibitors. The conformations of these inhibitors from docking results provided a reliable conformational alignment scheme for 3D QSAR model. Based on these conformations, significant CoMFA model was performed with a leave-one-out cross-validated q ² of 0.590. The noncross-validated analysis revealed r ² value of 0.941, F?=?34.882, and an estimated standard error of 0.178 with four optimum components. The predictive ability of our model was validated by the testing set with a conventional r ² value of 0.925. The effective model may be used to design more potent IGF1R inhibitors and predict their activities before synthesis or experimental test.     

Binding site analysis of CCR2 through in silico methodologies: docking, CoMFA, and CoMSIA

Chemokine receptor (CCR2) is a G protein‐coupled receptor that contains seven transmembrane domains. CCR2 is targeted for diseases like arthritis, multiple sclerosis, vascular disease, obesity and type 2 diabetes. Herein, we report on a binding site analysis of CCR2 through docking and three‐dimensional quantitative structure–activity relationship (3D‐QSAR). The docking study was performed with modeled receptor (CCR2) using β2‐andrenergic receptor structure as a template. Comparative molecular field analysis (CoMFA)‐ and comparative molecular similarity indices analysis (CoMSIA)‐based 3D‐QSAR models were developed using two different schemes: ligand‐based (CoMFA; q² = 0.820, r² = 0.966, = 0.854 and CoMSIA; q² = 0.762, r² = 0.846, = 0.684) and receptor‐guided (CoMFA; q² = 0.753, r² = 0.962, = 0.786, CoMSIA; q² = 0.750, r² = 0.800, = 0.797) methods. 3D‐QSAR analysis revealed the contribution of electrostatic and hydrogen bond donor parameters to the inhibitory activity. Contour maps suggested that bulky substitutions on the para position of R¹ substituted phenyl ring, electronegative and donor substitutions on meta (5′) and ortho (2′) position of R² substituted phenyl ring were favorable for activity. The results correlate well with previous results and newly report additional residues that may be crucial in CCR2 antagonism.     

Three-dimensional quantitative structure activity relationship analysis of anilinoquinazolines for c-Src kinase inhibition

Three-dimensional quantitative structure activity relationship (3D-QSAR) models was developed using molecular field analysis (MFA) for 36 anilinoquinazoline derivatives, inhibiting c-Src kinase. The QSAR model was developed using 29 compounds and its predictive ability was assessed using a test set of seven compounds. The predictive 3D-QSAR model has conventional r ² values of 0.961 while the cross-validated coefficient q ² and bootstrap correlation coefficient r _BS² values of 0.910 and 0.957, respectively. The developed model provides a powerful tool to design potent c-Src inhibitors as novel antitumor agents. Six new inhibitors were designed and their pIC₅₀ were predicted.