辛伐他汀药物动力学及生物利用度

目的研究辛伐他汀(SV)的药物动力学,评价SV片剂和胶囊剂的生物等效性.方法采用反相高效液相色谱法测定10名志愿受试者单剂量口服40mgSV胶囊供试品与40mg标准参比制剂后血药浓度的变化.结果SV胶囊剂和片剂的AUC分别为(150.79±34.17)与(150.05±26.78)h*ng/ml,tmax分别为(2.35±0.41)与(2.45±0.28)h,cmax分别是(25.63±5.09)与(28.14±8.31)ng/ml.结论以SV胶囊供试品与标准参比制剂AUC、cmax和tmax为指标,经双单侧t检验,两者为生物等效制剂.     

氟康唑的药物动力学和生物利用度的研究

采用高效液相色谱柱切换法，研究了单剂口服国产氟康唑胶囊剂２００、１５０ｍｇ与进口氟康唑１５０ｍｇ胶囊剂的志愿受试者体内药物动力学过程。结果表明：单剂口服不同剂量的两种胶囊血药浓度－时间曲线符合二至模型。国产氟康唑胶囊２００、１５０ｍｇ及进口氟康唑胶囊剂１５０ｍｇ的药物动力学参数依序如后：消除半衰期（Ｔ１／２β）分别为３４．５５±５．９７、３４．７６±２．６８及３１．０７±２．４２ｈ。达峰时间（Ｔｍａｘ）分别为１．５５±０．２８、１．６１±０．８９及１．９７±０．４４ｈ。峰浓度（Ｃｍａｘ）分别为４．６３±０．３３、３。７７±０．５３及３．９２±０．５２ｍｇ／Ｌ。曲线下面积（ＡＵＣ）分别为１９９．５４±２３．５２、１６８．２４±１３．８２及１６４．３１±１４。９１ｍｇ·ｈ／Ｌ。Ｖ／Ｆ（ｃ）值分别为３２．９７±４．３６、３１．３２±７．６７及３０．１２±２．４６Ｌ。清除率（Ｃｌ（ｓ））分别为１．０１±０．１１、０．９０±０．０７及０．９２±０．０９Ｌ／ｈ。国产胶囊剂对美国进口胶囊剂的平均相对生物利用度为１０２．８７％。     

药物化学和生物利用度控制

目的：就２１世纪药物化学所关注的４个问题,着重综述药物化学对生物利用度控制的意义。方法：以实例说明。结果：药物化学方法有助于防止药物的首过代谢,提高其生物利用度;获得部位传输药物;获得部位专一生物活化药物;肽类的前药衍生物,以克服肽类的传输障碍;用大分子载体作为药物寻靶的工具。结论：是推出新药的简易途径。     

尼莫地平片的相对生物利用度及药物动力学研究

目的　对尼莫地平片相对生物利用度及药物动力学进行研究。方法　采用反相高效液相色谱法 (RP HPLC)测定 10名志愿受试者单剂量口服 12 0mg尼莫地平片供试品与标准参比制剂后 ,尼莫地平血药浓度的变化。用 3p87药动学程序处理实验数据 ,并对实验结果进行配对t检验和双单侧t检验。结果　药时曲线下面积分别为 82 .5 1± 2 8.73ng·ml-1·h与 82 .92± 30 .93ng·ml-1·h ,达峰时间分别为 1.75± 0 .2 6h与 1.70± 0 .2 6h ,峰浓度分别为 2 8.84± 9.0 5ng·ml-1与 2 9.0 5± 8.6 9ng·ml-1。结果表明二者AUC、峰浓度及达峰时间无显著性差异 ,尼莫地平片供试品的相对生物利用度为 10 0 .70 %± 7.4 4 %。结论　尼莫地平片供试品与标准参比制剂为生物等效制剂。     

拉马宁碱兔体内药物动力学和生物利用度研究

剂量均统一为40mg/kg 家兔静注拉马宁碱的生理盐水溶液,体内动力学符合二室开放模型,主要参数为:C_0=28.34μg/ml,α=0.077min~(-1),β=0.016 min~(-1),V=2040.73ml/kg,Cl=32.65ml/[min·kg),AUC=1225.1μg·min/(ml·kg)。口服和肌注时其动力学均符合单室模型,生物利用度分别为43.12%、104.43%。     

布洛芬混悬液人体生物利用度和药物动力学研究

用反相高效液相色谱法，血样甲醇沉淀蛋白后直接进样测定。１０名受试者随机分组自身交叉对照试验，单剂量口服布洛芬片或混悬液３００ｍｇ，布洛芬药时数据经３Ｐ８７程序拟合为一房室模型，其主要药动学参数，片剂和混悬液分别为：Ｋａ０．７２±０．１８ｈ－１，２．５０±０．８ｈ－１；Ｔ１／２ｋｅ２．０４±０．３２ｈ，２．２±０．５ｈ；ＡＵＣ０－∞１１１±２５ｍｇ·ｌ·ｈ－１，１１９±２８ｍｇ·ｌ·ｈ；Ｔｍａｘ２．８±０．６ｈ，０．９８±０．２３ｈ；Ｃｍａｘ１９．９±３．４μｇ·ｍｌ－１，２７．３±３．３μｇ·ｍｌ；相对生物利用度１０７．１６±８．１２％。     

神经网络反传算法改进及其在药物分析中的应用

目的对神经网络反传算法(BP)加以改进并用于复方药物的测定.方法采用改进算法(MBPN),并用XOR问题考察改进前后的收敛速度.结果将MBPN算法用于复方扑热息痛中三组分的同时测定,相对误差为-6.0～ 9.0%.结论 MBPN法经优化选择传播参数γ加快了神经网络NN的收敛速度,增强了反传算法BP的可用性和实用性.用于复方药物的同时测定结果良好、操作简便.     

人工神经网络技术预测癫痫患儿服用丙戊酸后体内药物浓度

目的利用人工神经网络技术预测癫痫患儿服用丙戊酸后体内药物浓度。方法收集200例癫痫患儿服用丙戊酸后血药浓度监测结果、身高、体重及监测当日肝肾功能等15项相关指标,根据神经网络和遗传优化反向传播算法的基本原理,构建丙戊酸血药浓度预测模型,并用该浓度预测模型进行样本预测分析。结果 50个病例样本的预测结果表明,与实际测定浓度相比,误差小于10%的有29个浓度,误差在10%～15%的有10个浓度,误差在15%～20%的有7个浓度,误差大于20%的有4个浓度。误差小于15%的比率是78%。人工神经网络预测的血药浓度和实际测定浓度之间的相关系数为0.9476。结论用人工神经网络技术预测癫痫患儿服用丙戊酸后的血药浓度是可行的;有待将其广泛应用于个体化给药设计。     

Prediction of enzyme activity with neural network models based on electronic and geometrical features of substrates

BackgroundArtificial Neural Networks (ANNs) are introduced as robust and versatile tools in quantitative structure-activity relationship (QSAR) modeling. Their application to the modeling of enzyme reactivity is discussed, along with methodological issues. Methods of input variable selection, optimization of network internal structure, data set division and model validation are discussed. The application of ANNs in the modeling of enzyme activity over the last 20 years is briefly recounted.MethodsThe discussed methodology is exemplified by the case of ethylbenzene dehydrogenase (EBDH). Intelligent Problem Solver and genetic algorithms are applied for input vector selection, whereas k-means clustering is used to partition the data into training and test cases.ResultsThe obtained models exhibit high correlation between the predicted and experimental values (R² > 0.9). Sensitivity analyses and study of the response curves are used as tools for the physicochemical interpretation of the models in terms of the EBDH reaction mechanism.ConclusionsNeural networks are shown to be a versatile tool for the construction of robust QSAR models that can be applied to a range of aspects important in drug design and the prediction of biological activity.     

BP神经网络结合遗传算法用于丹参提取工艺的多目标优化

目的:使用BP神经网络结合遗传算法用于丹参提取工艺的多目标优化。方法:通过已知文献的丹参提取工艺优化实例,采用均匀设计法优化BP神经网络模型参数,并建立网络模型,再利用遗传算法对网络进行多目标寻优,获得丹参最佳提取工艺。结果:BP神经网络结合遗传算法用于丹参提取工艺的多目标优化,模型拟合度和预测性均高于文献采用的多元回归法。结论:BP神经网络结合遗传算法可用于丹参提取工艺的多目标优化。     

人工神经网络在药物控释系统研究中的应用

目的介绍人工神经网络在药物控释系统研究中的应用.方法查阅相关文献,总结、归纳国内外人工神经网络在药物控释系统中的应用.结果人工神经网络能优化处方组成和工艺过程,使其在控释片剂、控释微粒以及透皮吸收中得到应用.结论人工神经网络在设计和开发药物控释系统中具有广阔的前景.     

人工神经网络预测7种药物的毛细管电泳迁移时间

目的建立毛细管电泳迁移时间的人工神经网络的预测方法。方法运用人工神经网络 ,通过毛细管区带电泳 (CZE)的实验电压 (V)和缓冲溶液的离子强度 (I)对维生素B1等 7种药物的迁移时间 (tmig)进行预测 ,网络采用三层结构即输入层 隐含层 输出层 ( 2 4 1型 ) ,权值修正采用误差反向传播算法 ,每种药物的样本数为 5 0 ,以“留一法”预测其迁移时间 ,网络学习次数为 1 0 0 0 0。结果当电压在 6～ 2 6kV以及硼砂溶液的离子强度为 1 0～ 1 0 0mmol/L时 ,网络预测的相对误差绝对值小于 1 2 %的概率占 82 3 %。结论人工神经网络对药物的CZE的迁移时间可准确预测     

人工神经网络法测定安钠咖注射液两组分含量

运用误差反向传递（BP）人工神经网络法同时测定安钠咖注射液两组分含量，测得苯甲酸钠与咖啡国的平均回收率和相对标准差分别为99.7％，0．83％，100．9％，0．59％，而偏最小二乘法（PLS）所测得苯甲酸钠与咖啡因的平均回收率和相对标准差分别为99．1％，0．98％，99．4％，1．2％。文中输入层、隐蔽层、输出层分别取34个、5个、2个节点，学习速率和动量项分别选定0．50，0．70。     

Modeling drug solubility in water-cosolvent mixtures using an artificial neural network

Application of the artificial neural network (ANN) to calculate the solubility of drugs in water-cosolvent mixtures was shown using 35 experimental data sets. The networks employed were feedforward backpropagation errors with one hidden layer. The topology of neural network was optimized and the optimum topology achieved was a 6-5-1 architecture. All data points in each set were used to train the ANN and the solubilities were back-calculated employing the trained networks. The differences between calculated solubilities and experimental values was used as an accuracy criterion and defined as mean percentage deviation (MPD). The overall MPD (OMPD) and its S.D. obtained for 35 data sets was 0.90 +/- 0.65%. To assess the prediction capability of the method, five data points in each set were used as training set and the solubility at other solvent compositions were predicted using trained ANNs whereby the OMPD (+/-S.D.) for this analysis was 9.04 +/- 3.84%. All 496 data points from 35 data sets were used to train a general ANN model, then the solubilities were back-calculated using the trained network and MPD (+/-S.D.) was 24.76 +/- 14.76%. To test the prediction capability of the general ANN model, all data points with odd set numbers from 35 data sets were employed to train the ANN model, the solubility for the even data set numbers were predicted and the OMPD (+/-S.D.) was 55.97 +/- 57.88%. To provide a general ANN model for a given cosolvent, the experimental data points from each binary solvent were used to train ANN and back-calculated solubilities were used to calculate MPD values. The OMPD (+/-S.D.) for five cosolvent systems studied was 2.02 +/- 1.05%. A similar numerical analysis was used to calculate the solubility of structurally related drugs in a given binary solvent and the OMPD (+/-S.D.) was 4.70 +/- 2.02%. ANN model also trained using solubility data from a given drug in different cosolvent mixtures and the OMPD (+/-S.D.) obtained was 3.36 +/- 1.66%. The results for different numerical analyses using ANN were compared with those obtained from the most accurate multiple linear regression model, namely the combined nearly ideal binary solvent/Redlich-Kister equation, and the ANN model showed excellent superiority to the regression model.     

应用人工神经网络法预测癫痫患者血浆中拉莫三嗪浓度

目的：探讨应用人工神经网络（artificial neural network，ANN）预测癫痫患者血浆中拉莫三嗪（lamotrigine，LTG）浓度的可行性和效果。方法：回顾性收集我院2015年11月—2018年5月300例长期服用LTG治疗癫痫患者信息，将患者性别，年龄，BMI，血红蛋白计数（HGB），红细胞计数（RBC），丙氨酸氨基转移酶（ALT），天冬氨酸氨基转移酶（AST），血尿素氮（BUN），肌酐清除率（CR），服药剂量，合并用药数据作为输入层，LTG血药浓度数据作为输出层，采用METLAB R2016a构建反向传播人工神经网络（back-propagation artificial neural network，BPANN）法预测LTG血药浓度，计算影响血药浓度各因素的平均影响值（mean impact value，MIV），并对2018年6月10例患者进行体内血药浓度预测。结果：本研究建立了预测LTG血药浓度的BPANN模型，性能验证结果MSE为0.004 28，梯度幅度为0.000 506 9，验证检查数为0，相关系数为0.957。对MIV进行分析，服药剂量、合并用药和肌酐清除率是影响LTG血药浓度的重要因素，将BPANN模型应用到患者血药浓度预测中，预测值与测量值相关系数为0.972，预测偏差在-13.26%~25.41%之间，预测准确度为90%。结论：本研究建立的LTG血药浓度预测模型能较为准确的预测血药浓度，为癫痫患者个体化给药提供了一种有效思路。