盐酸川芎嗪缓释片剂人工神经网络计算机辅助设计及其评价
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摘要
实验设计与优化是药剂学研究中的有机组成部分，精确、高效的优化方法及其合理选用在药物制剂的研究中起着重要的作用。人工神经网络是一种基于仿生学原理模拟人脑处理信息能力的自适应学习系统，通过非线性并行神经运算方法处理数据，拟和预测精度高。本文以盐酸川芎嗪为模型药物，对计算机辅助人工神经网络技术和多种统计学优化方法在缓释剂型设计中的应用进行了研究。
建立了紫外分光光度法用于盐酸川芎嗪的基本理化性质及片剂的释放度、含量和小肠吸收试验的测定；在文献报道的基础上，采用紫外高效液相色谱法检测体内血药浓度，方法灵敏度高、专属性强，检测限为4ng，绝对回收率为87.78±3.42％。上述方法准确、可靠、方便，满足了本研究中各项分析的要求。
在处方前研究中，对与口服固体缓释制剂设计密切相关的药物基本理化性质进行了考察。测定得盐酸川芎嗪在水、0.1mol／L盐酸液、pH7.4磷酸缓冲液中的平衡溶解度(37℃)分别为5.956，11.275，6.681mg／ml，特性溶出速率分别为18.717，41.332，33.215mg／(min·cm~2)。不同pH下氯仿／水系统中的油水分配系数P_(app)测定结果表明，盐酸川芎嗪在中性、碱性环境下P_(app)较大，在接近生理条件的pH7.4时的P_(app)为46.93；原料药的稳定性影响因素试验表明盐酸川芎嗪在试验条件下对光、热、湿度、空气等稳定。
采用大鼠在体回流实验方法研究了盐酸川芎嗪的小肠吸收情况及肠壁通透系数。发现其在十二指肠、回肠、空肠、结肠均有良好吸收，每10cm的4hr吸收百分率分别为24.48％，21.73％，21.27％，15.17％，小肠内无特定吸收部位。通过不同浓度和不同pH介质对药物吸收影响的研究，推断盐酸川芎嗪在大鼠体内主要以被动扩散方式吸收。在体小肠吸收实验结果表明，盐酸川芎嗪适合设计成以通过控制药物溶
硕士研究生论文 盐酸川等呼缓释片剂人工神经网络计算机辅助设计及其评价 中文摘要
解和扩散而延长吸收时间的骨架型缓释制剂。
采用湿法制粒压片，制备以羟两基甲基纤维素和乙基纤维素为骨
架材料的缓释片剂。以缓释片在 0～12hr内的药物释放度为指标，考
察了处方因素、工艺因素对释药的影响。药物的释放动力学特征显示
盐酸川菜咦缓释片的释药机理是以药物扩散为主导的Fickian扩散过
程。
在盐酸川等嗓缓释片剂的设计中，以总评归一值为指标，采用正
交设计、均匀设计。析因设计、单纯形设计等统计学优化方法及近年
来国外新发展的CCD法进行了处方优化。对各种优化方法的应用范
围、数据处理、优化方法及结果进行了比较与评价，提出了各统计学
优化方法的选用规律。
利用计算机辅助人工神经网络技术，采用响应曲面法对盐酸川等
嗓缓释片剂进行了处方优化，预测了药物的体内血药浓度，最终得到
了优化处方。与统计学优化方法进行的比较表明：人工神经网络方法
对处方各因素间非线性关系的拟合和预测能力更强，是药物制剂处方
优化的有力工具。
犬体内药动学研究结果表明，盐酸川穹埃缓释片剂在测定时间范
围内血药浓度曲线较平稳，可维持体内血药浓度达 12hr左右，达到了
设计要求。药物动力学特征符合单隔室一级吸收过程，统计分析结果
显示，与普通制剂相比，缓释制剂的生物利用度为105．36％。缓释片
剂的体外释放与体内吸收具有良好的相关性c
Experimental design and formulation optimazation are organically parts in pharmaceutical research. Precisive and high performence optimazation method and its reasonable application is very important in the development of new pharmaceutical preparations. Artificial neural network is a biologically inspired mathmatical method designed to simulate the way in which the human brain process information. High precision simulation,learning ability are the result of the non-linear parall neuralcomputation incorprated in it. Using Tetra-methy pyrazine hydrochloride (TMPH) as model drug, computer aided Artificial neural network technology and different statistical optimazation methods were investigated during the design of TMPH sustained release tablet.
UV spectrophotometry were developed for in vitro assay during the studies of physicochemical properties, content, release as well as in situ absorption. According to literatures, High-performance liquid chromatography with UV detection was applied to quatitation of plasma concentration in dogs. The extraction recovery of TMPH was 78.44 + 8.41% and the detective limit is 4.0ng.
In the preformulation research the physico-chemical properties of TMPH were investigated,which were connected closely with pharmaceutical formulation designs. The studies on solubility showed that the equilibrium solubility in distilled water, 0.1M HC1 aqueous solution, pH7.4 phosphate buffer were 5. 956,11. 275and 6.681mg -ml-1, respectively; The intrinsic dissolution rates were 18.717, 41.332,33.2154 mg -cm-2 -min-1, respectively. Apparant oil/water partition coefficient in basic environment were as high as 46.93 at physicological pH7.4. In addition ,the chemical stability of solid TMPH was observed, the result of which indicated that temperature, moisture, oxygen and illumination had little effect on it.
To clarify the absorption of TMPH from intestine, the absorption and intestinal wall permeability were measured by utilizing the rat intestestinal recirculating method in situ. After perfusing at same concentration for 4hr, no significant difference of absorption percentage per 10cm was observed among duodenum, jejunum, ileum and colon, which were 24.48%, 21.73%, 21.27%, 15.17%, respectively. It provided evidence that there is no site specific absorption in the intestines. The mechanism of intestinal absorption was studied by investigating absorption in lumen solutions at different pH and concentrations, which suggested that the intestinal absorption of TMPH was via passive transport mechanism .
The sustained release tablets were prepared with HPMC and EC by wetting granulation procedure. The studies of the influence of formulation and manufacture on the release were carried out using the accumulative release during 12hr. It suggested that the way that TMPH release from gel layer could be described as Fickian diffusion.
Optimal formulations of TMPH sustained release tablet were developed by othogonal design, uniform design, full factorial design, simplex lattice design and central compsite design which is a new method applied in pharmceutical research overseas in recent years, on the base of the evaluation between different designs, a simple rule on the selecting of experimetal designs were recmmended .
Artificial neural network is a biologically inspired mathmetical method designed to simulate the way in which the human brain process information. A optimal formulation of TMPH sustained release tablet were development based on its' outstanding prediction abilities. The comparasion to statistical methods using response surface methodology showed that artificial neural network is a powerful tool in the formulation optimazation of pharmaceutical research.
The studies of pharmacokinetics in dogs verified that desired formulation was achieved. The plasma concentration maintained for about
12hr. The pharmacokinetic characteristics of TMPH sustained release tablet conformed to one compartment open model. The result showed that the relative bioavailability of TMPH sustaine
建立了紫外分光光度法用于盐酸川芎嗪的基本理化性质及片剂的释放度、含量和小肠吸收试验的测定；在文献报道的基础上，采用紫外高效液相色谱法检测体内血药浓度，方法灵敏度高、专属性强，检测限为4ng，绝对回收率为87.78±3.42％。上述方法准确、可靠、方便，满足了本研究中各项分析的要求。
在处方前研究中，对与口服固体缓释制剂设计密切相关的药物基本理化性质进行了考察。测定得盐酸川芎嗪在水、0.1mol／L盐酸液、pH7.4磷酸缓冲液中的平衡溶解度(37℃)分别为5.956，11.275，6.681mg／ml，特性溶出速率分别为18.717，41.332，33.215mg／(min·cm~2)。不同pH下氯仿／水系统中的油水分配系数P_(app)测定结果表明，盐酸川芎嗪在中性、碱性环境下P_(app)较大，在接近生理条件的pH7.4时的P_(app)为46.93；原料药的稳定性影响因素试验表明盐酸川芎嗪在试验条件下对光、热、湿度、空气等稳定。
采用大鼠在体回流实验方法研究了盐酸川芎嗪的小肠吸收情况及肠壁通透系数。发现其在十二指肠、回肠、空肠、结肠均有良好吸收，每10cm的4hr吸收百分率分别为24.48％，21.73％，21.27％，15.17％，小肠内无特定吸收部位。通过不同浓度和不同pH介质对药物吸收影响的研究，推断盐酸川芎嗪在大鼠体内主要以被动扩散方式吸收。在体小肠吸收实验结果表明，盐酸川芎嗪适合设计成以通过控制药物溶
硕士研究生论文 盐酸川等呼缓释片剂人工神经网络计算机辅助设计及其评价 中文摘要
解和扩散而延长吸收时间的骨架型缓释制剂。
采用湿法制粒压片，制备以羟两基甲基纤维素和乙基纤维素为骨
架材料的缓释片剂。以缓释片在 0～12hr内的药物释放度为指标，考
察了处方因素、工艺因素对释药的影响。药物的释放动力学特征显示
盐酸川菜咦缓释片的释药机理是以药物扩散为主导的Fickian扩散过
程。
在盐酸川等嗓缓释片剂的设计中，以总评归一值为指标，采用正
交设计、均匀设计。析因设计、单纯形设计等统计学优化方法及近年
来国外新发展的CCD法进行了处方优化。对各种优化方法的应用范
围、数据处理、优化方法及结果进行了比较与评价，提出了各统计学
优化方法的选用规律。
利用计算机辅助人工神经网络技术，采用响应曲面法对盐酸川等
嗓缓释片剂进行了处方优化，预测了药物的体内血药浓度，最终得到
了优化处方。与统计学优化方法进行的比较表明：人工神经网络方法
对处方各因素间非线性关系的拟合和预测能力更强，是药物制剂处方
优化的有力工具。
犬体内药动学研究结果表明，盐酸川穹埃缓释片剂在测定时间范
围内血药浓度曲线较平稳，可维持体内血药浓度达 12hr左右，达到了
设计要求。药物动力学特征符合单隔室一级吸收过程，统计分析结果
显示，与普通制剂相比，缓释制剂的生物利用度为105．36％。缓释片
剂的体外释放与体内吸收具有良好的相关性c
Experimental design and formulation optimazation are organically parts in pharmaceutical research. Precisive and high performence optimazation method and its reasonable application is very important in the development of new pharmaceutical preparations. Artificial neural network is a biologically inspired mathmatical method designed to simulate the way in which the human brain process information. High precision simulation,learning ability are the result of the non-linear parall neuralcomputation incorprated in it. Using Tetra-methy pyrazine hydrochloride (TMPH) as model drug, computer aided Artificial neural network technology and different statistical optimazation methods were investigated during the design of TMPH sustained release tablet.
UV spectrophotometry were developed for in vitro assay during the studies of physicochemical properties, content, release as well as in situ absorption. According to literatures, High-performance liquid chromatography with UV detection was applied to quatitation of plasma concentration in dogs. The extraction recovery of TMPH was 78.44 + 8.41% and the detective limit is 4.0ng.
In the preformulation research the physico-chemical properties of TMPH were investigated,which were connected closely with pharmaceutical formulation designs. The studies on solubility showed that the equilibrium solubility in distilled water, 0.1M HC1 aqueous solution, pH7.4 phosphate buffer were 5. 956,11. 275and 6.681mg -ml-1, respectively; The intrinsic dissolution rates were 18.717, 41.332,33.2154 mg -cm-2 -min-1, respectively. Apparant oil/water partition coefficient in basic environment were as high as 46.93 at physicological pH7.4. In addition ,the chemical stability of solid TMPH was observed, the result of which indicated that temperature, moisture, oxygen and illumination had little effect on it.
To clarify the absorption of TMPH from intestine, the absorption and intestinal wall permeability were measured by utilizing the rat intestestinal recirculating method in situ. After perfusing at same concentration for 4hr, no significant difference of absorption percentage per 10cm was observed among duodenum, jejunum, ileum and colon, which were 24.48%, 21.73%, 21.27%, 15.17%, respectively. It provided evidence that there is no site specific absorption in the intestines. The mechanism of intestinal absorption was studied by investigating absorption in lumen solutions at different pH and concentrations, which suggested that the intestinal absorption of TMPH was via passive transport mechanism .
The sustained release tablets were prepared with HPMC and EC by wetting granulation procedure. The studies of the influence of formulation and manufacture on the release were carried out using the accumulative release during 12hr. It suggested that the way that TMPH release from gel layer could be described as Fickian diffusion.
Optimal formulations of TMPH sustained release tablet were developed by othogonal design, uniform design, full factorial design, simplex lattice design and central compsite design which is a new method applied in pharmceutical research overseas in recent years, on the base of the evaluation between different designs, a simple rule on the selecting of experimetal designs were recmmended .
Artificial neural network is a biologically inspired mathmetical method designed to simulate the way in which the human brain process information. A optimal formulation of TMPH sustained release tablet were development based on its' outstanding prediction abilities. The comparasion to statistical methods using response surface methodology showed that artificial neural network is a powerful tool in the formulation optimazation of pharmaceutical research.
The studies of pharmacokinetics in dogs verified that desired formulation was achieved. The plasma concentration maintained for about
12hr. The pharmacokinetic characteristics of TMPH sustained release tablet conformed to one compartment open model. The result showed that the relative bioavailability of TMPH sustaine
引文
[1] 平其能等编著，现代药剂学，北京：中国医药科技出版社，1998：156．
[2] 郑俊民主编，经皮给药新剂型，北京：人民卫生出版社，1997：264．
[3] 庄越，曹宝成，萧端祥，实用药物制剂技术，人民卫生出版社，1999：1．
[4] A.S. Achanta, J.G. Kowalski, Artifical Neural Networks: implications for pharmcentianl sciences. Drug Dev Ind Pharm, 1995, 21(1): 119～155.
[5] Tomao Aoyama, Hiroshi Ichikawa, Neural Networks appllied to pharmaceutical problems Ⅰ: Method and Application the decision making. Chem Pharm Bull, 1988, 37(9):2558～2560.
[6] S.Agatonovic-Kustrin R.Beresford, Basic concepts of artificral neural network (ANN) modeling and its application in pharmaceutical research, J Pharm Biomed Anal, 2000, 22(11):717～727.
[7] Junichi Takahara, kozo Takayama, Tsuneji Nagai, Introduction to backpropagati- on neural network computation, Pharm Res, 1993, 10(2)165～170.
[8] James R, long Vasilis G, Gregorious, Spectroscopic calibration and quantitation using artificial neural network, Anal chem. 1990, 62(5):1791～1797.
[9] 潘忠孝，王拴虎，陈玮，人工神经网络-紫外光谱定量多组分体系的研究分析化学，1994，22(9)：939～941．
[10] 屈凌波，王晓微，相秉仁，改进BP网络在联邦镇咳露复方制剂分析中的应用中国药科大学学报，1997，28(6)：342～344．
[11] 张亮，蓝妥武，安登魁，人工神经网络用于中药材需公藤和昆明山海棠的分类识别研究，药学学报，1995，30(2)：127～132．
[12] 乔延江，王玺，毕开顺，人工神经网络在中药蟾酥化学模式识别特征提取中的应用，药学学报，1995，30(9)：698～701．
[13] 屈凌波，相秉仁，安登魁，人工神经网络技术及其在药物复方制剂和中药分析中的应用，药物分析杂志，1996，16(3)：201～203．
[14] 蔡煜东，宫家文，程兆年中药质量的人工神经网络评价方法，中草药，1994，11(1)：57～60．
[15] Peter Veng-Pedersen, Nishit B.Modi, Application of neural network to pharmacodynamics, J pharm Sci, 1993, 82(9): 918-926.
[16] James A Dowell, Ajaz Hussain, Artificial neural networks applied to the in
vitro-In vivo correlation of an extended-release formulation: intial trials and Experence, J Pharm Sci, 1999, 188(1):154～160.
[17] 徐芬，孙立贤，刘焕文，人工神经元网络用于复方氯丙嗪的含量测定，药学学报，1994，29(10)：767～772．
[18] Beom-Jin Lee, Seung-Goo Ryu, Controlled release of dual drug-loaded hydroxypropyl methylcellulose matrix tablet using drug-containing polymeric coatings, Int J Pharm, 1999, 188(1):71～80.
[19] Mirjana Gasperlin, Linja Tusar, Viscosity prediction of lipophilic semisolid emulsion systems by neural network modeling, Int J pharm, 2000, 196(2):37～50.
[20] Ere Murtoniemi. Jouko Yliruusi, The advantages by the use of neural networks in modelmg the fluidized bed granulation process, Int J pharm, 1994, 108(3): 155～164.
[21] Brajesh K, Sanjav S, Tambe, Estimating diffusion coefficients of a micellar system using an artificral neural network, J Colli Interface, 1995, 170(7): 392-398.
[22] 徐培庆，川芎有效成份的研究，中华医学杂志，1977，32(7)：420～421．
[23] 刘纪云，王文彤，朱连全，川芎的研究进展，天津药学，1991，3(2)：29～33．
[24] 宫伟星，川芎嗪心血管药理的研究进展，中国医院药学杂志，1990，10(11)：511～512．
[25] 陆彬主编，药剂学实验，北京：人民卫生出版社，1994：131～135．
[26] 张立坤，陈新旺，邹安庆，高效液相色谱法测定复方制剂中阿魏酸和川芎嗪，中草药，1996，27(4)：213～214．
[27] 曾苏，生物药物分析方法的认证、质量控制及其标准操作规程，中国医药工业杂志，1995，26(3)：136～139．
[28] 徐淑云，卞如濂，陈修主编，药理实验方法学．北京：人民卫生出版社，1982：11
[29] 胡一桥，Josef T, Ramipril在大鼠小肠内转运机制的研究，中国药科大学学报，1993，24(6)：338～344．
[30] Laurence XY, Lipka E, Crison JR, Transport approaches to the biopharmaceutical design of oral drug delivery system: predition of intestinal absorption, Adv Drug Del Rev, 1996, 19(7): 359～369
[31] Schurger N, Bijdendijk J, Tukker JJ, Comparison of four experimental techniques for studying drug absorption kinetics in the anesthetized rat in situ.
J Pharm Sci, 1986, 75(2):117～128.
[32] Ochsenfahrt H, Winne D, Naunyn-schmiedeberg, Contribution of solvent drag to the intestinal absorption of tritiated water and urea form the jejunum of the rat, Arch Pharmacol, 1973, 279(2): 133～152.
[33] 杨正管，朱家壁，雷艳云，盐酸地尔硫卓大鼠肠吸收动力学及在口服缓释制剂设计的意义，中国药科大学学报，1998，29(3)：179～182．
[34] Ford JL, Rubinstein, MH, Mcaul. F, Importance of drug type, tablet shape and added diluents on drug release kinetics from hydroxypropyl methylcellulose matrix tablets, Int J Pharm, 1989, 40(8): 223～234.
[35] Higuchi T, Mechanism of sustained-action medication theoretical analysis of rate of release of solid drugs dispered in solid matrices, J Pharm Sci, 1963, 52(10): 1145～1149.
[36] Korsmeyer, R.W. Gurny R, Doelker E, et al, Mechanisms of solute release from porous hydrphlic polymers, Int J Pharm, 1983, 15(1): 25～35.
[37] Sinclair GW, Peppas NA, Analysis of non-Fickian transport in polymers using simplified exponential expression, J Membr Sci, 1984, 17(2):329～331.
[38] Ritger RL, Peppas NA, A simple equation for description of solute release Ⅱ: Fickian and anomalous release from swellable devices, J Control Rel, 1987, 5(1): 37～42.
[39] Xu G, Sunada H, Influence of formulation Change on drug release kinetics from hydroxypropylmethylcellulose matrix tablet, Chem Pharm Bull, 1995, 43(4):483～487.
[40] Sarisuta N, Mahapaunt D, Effects of compression force and type of diclofenae sodium from matrix tablets, Drug Dev Ind Pharm, 1994, 20(9): 1049～1061.
[41] 徐晖，丁平田，郑俊民聚合物系统中药物的非扩散控制释放，药学学报，2000，35(9)：95～102．
[42] Lapids H, Lordi NG, Some factors affecting the release of water-soluble drug from a compressed hydrophilic matrix, J Pharm Sci, 1966, 55(7):840～843.
[43] Lipidus H, Lordi NG, Drug release from compressed hydrophlic matrices, J Pharm Sci, 1968, 157(10): 1292～1301.
[44] 陆彬，吴伟，中心多点等距设计法优化硝酸地塞米松聚丙交酯微球的制备工艺，药学学报，1999，34(5)：387～391．
[45] L·拉赫曼，H·A·利伯曼，J·L，卡凡希主编，北京医学院药学系译．工业药剂学理论与实践，北京：化学工业出版社，1984：72～73．
[46] 吴伟，崔光华，陆彬，实验设计中多指标的优化：星点设计和总评“归一值”
的应用，中国药学杂志，2000，35(8)：530～533．
[47] Jesus Mapeceres Manuel Guzman, Application of central composite designs to the preparation of polycaprolactone nanoparticles by solvent displacement, J Pharm Sci, 1996, 185(2):206～213.
[48] 张霖泽，王兰勤，Navnitits，口服控释制剂的质量评价，中国药学杂志，1995，30(3)：366～371．
[49] 吴涛，潘卫三，多目标同步优化法优化硫酸沙丁胺醇渗透泵控释片的制备工艺，药学学报，2000，35(8)：617～621．
[50] Kozo Takayama, Tsuneji Nagai, Multi-objective simultaneous optimization technique based on an artificial neural network in sustained release formulations, J Contrl Release, 1997, 49(1): 11～20.
[51] Kozo Takayama, Mikito Fujikawa, Tsuneji Nagai, Artificial neural networks as a novel method to optimize pharmaceutical formulations, Pharm Res, 1999, 116(1): 1～6.
[52] 蔡伟，董善年，楼雅卿，正常人口服磷酸川芎嗪的药代动力学研究，药学学报，1989，33(12)：881～886．
[53] K.Rocksloh, F.R.Rapp, Optimization of crushing strength and disintegration time of a high-dose plant extract tablet by neural networks, Drug Dev Ind Pharm, 1999, 25(9): 1015～1025.
[54] Jacques Bourqin, Heinz schmidli, Advantage of Artificial Neural Networks (ANNs) as alternative modeling technique for data sets showing non-linear relationships using data from a galenical study on a solid dosage form, European J Pharm Sci, 1998, 7(1):5～16.
[55] 蒋知俭主编，医学统计学．北京：人民卫生出版社，1997：239．
[56] 魏树礼主编，生物药剂学与药物动力学，北京：北京医科大学，中国协和医科大学联合出版社，1997：376．
[57] 刘昌孝，缓释制剂的药物动力学原理及其评价，天津药学，1995，11(1)：1-3．
[58] M．吉伯尔迪，D，佩里尔著，朱家壁译，药物动力学(第二版)．北京：科学出版社，1987：5．
[2] 郑俊民主编，经皮给药新剂型，北京：人民卫生出版社，1997：264．
[3] 庄越，曹宝成，萧端祥，实用药物制剂技术，人民卫生出版社，1999：1．
[4] A.S. Achanta, J.G. Kowalski, Artifical Neural Networks: implications for pharmcentianl sciences. Drug Dev Ind Pharm, 1995, 21(1): 119～155.
[5] Tomao Aoyama, Hiroshi Ichikawa, Neural Networks appllied to pharmaceutical problems Ⅰ: Method and Application the decision making. Chem Pharm Bull, 1988, 37(9):2558～2560.
[6] S.Agatonovic-Kustrin R.Beresford, Basic concepts of artificral neural network (ANN) modeling and its application in pharmaceutical research, J Pharm Biomed Anal, 2000, 22(11):717～727.
[7] Junichi Takahara, kozo Takayama, Tsuneji Nagai, Introduction to backpropagati- on neural network computation, Pharm Res, 1993, 10(2)165～170.
[8] James R, long Vasilis G, Gregorious, Spectroscopic calibration and quantitation using artificial neural network, Anal chem. 1990, 62(5):1791～1797.
[9] 潘忠孝，王拴虎，陈玮，人工神经网络-紫外光谱定量多组分体系的研究分析化学，1994，22(9)：939～941．
[10] 屈凌波，王晓微，相秉仁，改进BP网络在联邦镇咳露复方制剂分析中的应用中国药科大学学报，1997，28(6)：342～344．
[11] 张亮，蓝妥武，安登魁，人工神经网络用于中药材需公藤和昆明山海棠的分类识别研究，药学学报，1995，30(2)：127～132．
[12] 乔延江，王玺，毕开顺，人工神经网络在中药蟾酥化学模式识别特征提取中的应用，药学学报，1995，30(9)：698～701．
[13] 屈凌波，相秉仁，安登魁，人工神经网络技术及其在药物复方制剂和中药分析中的应用，药物分析杂志，1996，16(3)：201～203．
[14] 蔡煜东，宫家文，程兆年中药质量的人工神经网络评价方法，中草药，1994，11(1)：57～60．
[15] Peter Veng-Pedersen, Nishit B.Modi, Application of neural network to pharmacodynamics, J pharm Sci, 1993, 82(9): 918-926.
[16] James A Dowell, Ajaz Hussain, Artificial neural networks applied to the in
vitro-In vivo correlation of an extended-release formulation: intial trials and Experence, J Pharm Sci, 1999, 188(1):154～160.
[17] 徐芬，孙立贤，刘焕文，人工神经元网络用于复方氯丙嗪的含量测定，药学学报，1994，29(10)：767～772．
[18] Beom-Jin Lee, Seung-Goo Ryu, Controlled release of dual drug-loaded hydroxypropyl methylcellulose matrix tablet using drug-containing polymeric coatings, Int J Pharm, 1999, 188(1):71～80.
[19] Mirjana Gasperlin, Linja Tusar, Viscosity prediction of lipophilic semisolid emulsion systems by neural network modeling, Int J pharm, 2000, 196(2):37～50.
[20] Ere Murtoniemi. Jouko Yliruusi, The advantages by the use of neural networks in modelmg the fluidized bed granulation process, Int J pharm, 1994, 108(3): 155～164.
[21] Brajesh K, Sanjav S, Tambe, Estimating diffusion coefficients of a micellar system using an artificral neural network, J Colli Interface, 1995, 170(7): 392-398.
[22] 徐培庆，川芎有效成份的研究，中华医学杂志，1977，32(7)：420～421．
[23] 刘纪云，王文彤，朱连全，川芎的研究进展，天津药学，1991，3(2)：29～33．
[24] 宫伟星，川芎嗪心血管药理的研究进展，中国医院药学杂志，1990，10(11)：511～512．
[25] 陆彬主编，药剂学实验，北京：人民卫生出版社，1994：131～135．
[26] 张立坤，陈新旺，邹安庆，高效液相色谱法测定复方制剂中阿魏酸和川芎嗪，中草药，1996，27(4)：213～214．
[27] 曾苏，生物药物分析方法的认证、质量控制及其标准操作规程，中国医药工业杂志，1995，26(3)：136～139．
[28] 徐淑云，卞如濂，陈修主编，药理实验方法学．北京：人民卫生出版社，1982：11
[29] 胡一桥，Josef T, Ramipril在大鼠小肠内转运机制的研究，中国药科大学学报，1993，24(6)：338～344．
[30] Laurence XY, Lipka E, Crison JR, Transport approaches to the biopharmaceutical design of oral drug delivery system: predition of intestinal absorption, Adv Drug Del Rev, 1996, 19(7): 359～369
[31] Schurger N, Bijdendijk J, Tukker JJ, Comparison of four experimental techniques for studying drug absorption kinetics in the anesthetized rat in situ.
J Pharm Sci, 1986, 75(2):117～128.
[32] Ochsenfahrt H, Winne D, Naunyn-schmiedeberg, Contribution of solvent drag to the intestinal absorption of tritiated water and urea form the jejunum of the rat, Arch Pharmacol, 1973, 279(2): 133～152.
[33] 杨正管，朱家壁，雷艳云，盐酸地尔硫卓大鼠肠吸收动力学及在口服缓释制剂设计的意义，中国药科大学学报，1998，29(3)：179～182．
[34] Ford JL, Rubinstein, MH, Mcaul. F, Importance of drug type, tablet shape and added diluents on drug release kinetics from hydroxypropyl methylcellulose matrix tablets, Int J Pharm, 1989, 40(8): 223～234.
[35] Higuchi T, Mechanism of sustained-action medication theoretical analysis of rate of release of solid drugs dispered in solid matrices, J Pharm Sci, 1963, 52(10): 1145～1149.
[36] Korsmeyer, R.W. Gurny R, Doelker E, et al, Mechanisms of solute release from porous hydrphlic polymers, Int J Pharm, 1983, 15(1): 25～35.
[37] Sinclair GW, Peppas NA, Analysis of non-Fickian transport in polymers using simplified exponential expression, J Membr Sci, 1984, 17(2):329～331.
[38] Ritger RL, Peppas NA, A simple equation for description of solute release Ⅱ: Fickian and anomalous release from swellable devices, J Control Rel, 1987, 5(1): 37～42.
[39] Xu G, Sunada H, Influence of formulation Change on drug release kinetics from hydroxypropylmethylcellulose matrix tablet, Chem Pharm Bull, 1995, 43(4):483～487.
[40] Sarisuta N, Mahapaunt D, Effects of compression force and type of diclofenae sodium from matrix tablets, Drug Dev Ind Pharm, 1994, 20(9): 1049～1061.
[41] 徐晖，丁平田，郑俊民聚合物系统中药物的非扩散控制释放，药学学报，2000，35(9)：95～102．
[42] Lapids H, Lordi NG, Some factors affecting the release of water-soluble drug from a compressed hydrophilic matrix, J Pharm Sci, 1966, 55(7):840～843.
[43] Lipidus H, Lordi NG, Drug release from compressed hydrophlic matrices, J Pharm Sci, 1968, 157(10): 1292～1301.
[44] 陆彬，吴伟，中心多点等距设计法优化硝酸地塞米松聚丙交酯微球的制备工艺，药学学报，1999，34(5)：387～391．
[45] L·拉赫曼，H·A·利伯曼，J·L，卡凡希主编，北京医学院药学系译．工业药剂学理论与实践，北京：化学工业出版社，1984：72～73．
[46] 吴伟，崔光华，陆彬，实验设计中多指标的优化：星点设计和总评“归一值”
的应用，中国药学杂志，2000，35(8)：530～533．
[47] Jesus Mapeceres Manuel Guzman, Application of central composite designs to the preparation of polycaprolactone nanoparticles by solvent displacement, J Pharm Sci, 1996, 185(2):206～213.
[48] 张霖泽，王兰勤，Navnitits，口服控释制剂的质量评价，中国药学杂志，1995，30(3)：366～371．
[49] 吴涛，潘卫三，多目标同步优化法优化硫酸沙丁胺醇渗透泵控释片的制备工艺，药学学报，2000，35(8)：617～621．
[50] Kozo Takayama, Tsuneji Nagai, Multi-objective simultaneous optimization technique based on an artificial neural network in sustained release formulations, J Contrl Release, 1997, 49(1): 11～20.
[51] Kozo Takayama, Mikito Fujikawa, Tsuneji Nagai, Artificial neural networks as a novel method to optimize pharmaceutical formulations, Pharm Res, 1999, 116(1): 1～6.
[52] 蔡伟，董善年，楼雅卿，正常人口服磷酸川芎嗪的药代动力学研究，药学学报，1989，33(12)：881～886．
[53] K.Rocksloh, F.R.Rapp, Optimization of crushing strength and disintegration time of a high-dose plant extract tablet by neural networks, Drug Dev Ind Pharm, 1999, 25(9): 1015～1025.
[54] Jacques Bourqin, Heinz schmidli, Advantage of Artificial Neural Networks (ANNs) as alternative modeling technique for data sets showing non-linear relationships using data from a galenical study on a solid dosage form, European J Pharm Sci, 1998, 7(1):5～16.
[55] 蒋知俭主编，医学统计学．北京：人民卫生出版社，1997：239．
[56] 魏树礼主编，生物药剂学与药物动力学，北京：北京医科大学，中国协和医科大学联合出版社，1997：376．
[57] 刘昌孝，缓释制剂的药物动力学原理及其评价，天津药学，1995，11(1)：1-3．
[58] M．吉伯尔迪，D，佩里尔著，朱家壁译，药物动力学(第二版)．北京：科学出版社，1987：5．