目的 主要以表征分子“反应活性”的结构参数为自变量,建立预测其经皮促渗活性的定量构效关系模型(QSAR)。方法 采用DFT中的B3/LYP方法,在6-311++G(2d,p)基组上,对41种萜类化合物进行结构优化,计算其结构参数和热力学参数,用逐步线性回归中的后退法建立预测萜类化合物促进药物经人体皮肤吸收的定量构效关系模型。结果 得到复相关系数(r2)为0.655,交互检验系数的平方RCV2为0.654。模型表明,表面静电势(VS,ta, VS,max, VS,min)、化学势(λ)、热容(Cp)、偶极矩(D)与萜类化合物的离体经皮促渗活性间具有良好的线性相关性。结论 由密度泛函量子化学理论获得的反应活性指标可更好地模拟促渗剂结构与其经皮促渗活性间的相关性,有利于定量构效关系模型的合理解释。
Abstract
OBJECTIVE To establish the quantitative structure-activity relationship (QSAR) for predicting the percutaneous permeation enhancing activity of terpenoids, using the indexes of reactivity in molecules as the important descriptors. METHODS The structural and thermodynamic parameters of 41 terpenoids were optimized using B3/LYP method of DFT at the level of 6-311++G (2d,p). The backward method in stepwise multiple regression was used to establish the QSAR model for predicting activities of terpenes that enhanced drug penetration through human skin. RESULTS The multiple correlation coefficient ( r2) was determined to be 0.655 and the square cross validated correlation coefficient (RCV2) was determined to be 0.654. The QSAR model indicated that the electrostatic potentials (VS,ta, VS,max,VS,min), chemical potential (λ), thermal capacity (Cp), and dipole moment (D) of individual molecules could be responsible for the in vitro permeation enhancing abilities of terpenoids. CONCLUSION The indexes of reactivity in molecules based on density functional theory can simulate more exactly the quantitative relationship between the structures of penetration enhancers and their percutaneous permeation enhancing abilities for drugs, which will facilitate reasonable explanation of the model.
关键词
结构参数 /
热力学参数 /
渗透活性 /
定量构效关系模型方程 /
密度泛函
{{custom_keyword}} /
Key words
structural parameter /
thermodynamic parameter /
permeability activity /
QSAR equation /
density functional theory
{{custom_keyword}} /
中图分类号：
R944
{{custom_clc.code}}
({{custom_clc.text}})
{{custom_sec.title}}
{{custom_sec.title}}
{{custom_sec.content}}
参考文献
[1] DRAKULI B J, JURANI I O, ERI S, et al. Role of complexes formation between drugs and penetration enhancers in transdermal delivery. Int J Pharm, 2008, 363(1-2): 40-49.
[2] GHOSH I, MICHNIAK-KOHNB.Design and characterization of submicron formulation for a poorly soluble drug: The effect of vitamin E TPGS and other solubilizers on skin permeability enhancement.Int J Pharm, 2012, 434(1-2):90-98.
[3] KANG L, YAP C W, LIM P F, et al. Formulation development of transdermal dosage forms: Quantitative structure-activity relationship model for predicting. J Controlled Release, 2007, 120(3): 211-219.
[4] YERRAMSETTY K M, NEELY B J, MADIHALLY S V, et al. A skin permeability model of insulin in the presence of chemical penetration enhancer. Int J Pharm, 2010, 388(1-2):13-23.
[5] NARISHETTY S T K, PANCHAGNULA R. Transdermal delivery of zidovudine: Effect of terpenes and their mechanism of action. J Controlled Release, 2004, 95(3):367-379.
[6] LIOU Y B, HO H O, YANG C J, et al. Construction of a quantitative structure-permeability relationship (QSPR) for the transdermal delivery of NSAIDs. J Controlled Release, 2009, 138(3): 260-267.
[7] WILLIAMS A C, BARRY B W. Terpenes and the lipid protein partitioning theory of skin penetration enhancement. Pharmacol Res, 1991, 8(1): 17-24.
[8] NARISHETTY S T K, PANCHAGNULA R. Transdermal delivery of zidovudine: Effect of terpenes and their mechanism of action. J Controlled Release, 2004, 95(3): 367-379.
[9] KALHAPURER S, SALUNKEC L, AKAMANCHI K G. QSAR Model for chemical penetration enhancers containing long hydrocarbon chain. Chemometr Intell Lab , 2012, 118: 267-270.
PUGH W J. Epidermal permeability-penetrant structure relationships: 4, QSAR of permeant diffusion across human stratum corneum in terms of molecular weight, H-bonding and electronic charge. Int J Pharm, 2000, 197(1-2): 203-211.
SUN Y, BROWN M B, PRAPOPOULOU M, et al. The application of stochastic machine learning methods in the prediction of skin penetration. Appl Soft Comput, 2011, 11(2): 2367-2375.
SONG J, WANG Z, FINDLATERA, et al. Terpenoid mosquito repellents: A combined DFT and QSAR study. Bioorg Med Chem Lett, 2013, 23(5):1245-1248.
FRISCH M J, POPLE J A, BINKLEY J S. Self-consistent molecular orbital methods (25): Supplementary functions for Gussian basis sets. J Chem Phys, 1984, 80(7):3265-3269.
DEEPHA V, PRAVEENA R, SADASIVAM K. DFT Studies on antioxidant mechanisms, electronic properties, spectroscopic (FT-IR and UV) and NBO analysis of C-glycosyl flavone, an isoorientin. J Mol Struct, 2015, 1082:131-142.
LU T, CHEN F. Multiwfn: A multifunctional wavefunction analyzer. J Comput Chem, 2012, 33(5):580-592.
ZOU J W, ZHAO W N, SHANG Z C, et al. A quantitative structure-property relationship analysis of logP for disubstituted benzenes. J Phys Chem, 2002, 106(47):11550-11557.
PAPA E, DEARDEN J C, GRAMATICA P. Linear QSAR regression models for the prediction of bio-concentration factors by physicochemical properties and structural theoretical molecular descriptors. Chemosphere, 2007, 67(2):351-358.
PODUNAVAC-KUZMANOVI S O, CVETKOVI D D, BARNA D J.QSAR Analysis of 2-amino or 2-methyl-1-substituted benzimidazoles against pseudomonas aeruginosa.Int J Mol Sci, 2009, 10(4):1670-1682.
AAKERYC B , WIJETHUNGA T K, DESPER J. Practical crystal engineering using halogen bonding: A hierarchy based on calculated molecular electrostatic potential surfaces. J Mol Struct, 2014, 1072(12):20-27.
{{custom_fnGroup.title_cn}}
脚注
{{custom_fn.content}}
基金
山西省自然科学基金资助项目(2012011007-5); 山西省科技攻关资助项目(20140313018-8)
{{custom_fund}}
PDF(1031 KB)
