盐酸坦洛新缓释制剂的制备及体外释放度考察

目的研究坦洛新缓释片的处方、工艺,对其释放度进行考察。方法选用羟丙甲基纤维素(HPMC)为亲水凝胶型骨架材料,湿法制粒制备盐酸坦洛新缓释片,并采用正交实验设计优化处方;小杯桨法测定其释放度。结果优化处方组成为羟丙甲基纤维素(15M)占处方量的40%,羟丙甲基纤维素/甲基纤维素为5∶1,淀粉/乳糖为2∶1,乙基纤维素的用量为2mg/pcs;优化处方的缓释片体外释放曲线符合一级动力学程,释放度符合设计要求。结论本研究所制得盐酸坦洛新骨架缓释片具有良好的缓释效果,制备工艺合理,简单易行,可以满足盐酸坦洛新的临床需要。     

人工神经网络预测HPMC缓释片中易溶性药物的释放

目的应用BP人工神经网络模型预测水溶性药物从HPMC缓释片中的释放。方法以6种不同溶解性的水溶性药物(对乙酰氨基酚、氧氟沙星、盐酸环丙沙星、乳酸左氧氟沙星、多索茶碱、氯苯那敏、维拉帕米)为模型药物,设计62个处方,其中前面55个处方作为训练处方,另外7个处方作为验证处方,压制HPMC缓释片,进行释放度检查。以溶解度、载药量、HPMC的量、HPMC的固有黏度、MCC的量、PVP的浓度和药物溶出仪的转速作为自变量,药物在各个取样时间点的累积释放量作为输出,建立BP人工神经网络模型,并与响应面法进行对照,通过线性回归法和相似因子法比较人工神经网络和响应面法的预测能力,借助三维图说明各个变量对药物释放的影响。结果线性回归和相似因子法表明人工神经网络较响应面法的预测值与实际测定值更吻合,更能充分地说明单因素对药物释放的影响规律。结论人工神经网络可以代替响应面法处理HPMC缓释片处方设计中的不同溶解度的水溶性药物的多因素多响应的非线性问题而且可以推广到别的制剂设计中。     

人工神经网络预测HPMC缓释片中易溶性药物的释放

目的应用BP人工神经网络模型预测水溶性药物从HPMC缓释片中的释放。方法以6种不同溶解性的水溶性药物(对乙酰氨基酚、氧氟沙星、盐酸环丙沙星、乳酸左氧氟沙星、多索茶碱、氯苯那敏、维拉帕米)为模型药物,设计62个处方,其中前面55个处方作为训练处方,另外7个处方作为验证处方,压制HPMC缓释片,进行释放度检查。以溶解度、载药量、HPMC的量、HPMC的固有黏度、MCC的量、PVP的浓度和药物溶出仪的转速作为自变量,药物在各个取样时间点的累积释放量作为输出,建立BP人工神经网络模型,并与响应面法进行对照,通过线性回归法和相似因子法比较人工神经网络和响应面法的预测能力,借助三维图说明各个变量对药物释放的影响。结果线性回归和相似因子法表明人工神经网络较响应面法的预测值与实际测定值更吻合,更能充分地说明单因素对药物释放的影响规律。结论人工神经网络可以代替响应面法处理HPMC缓释片处方设计中的不同溶解度的水溶性药物的多因素多响应的非线性问题而且可以推广到别的制剂设计中。     

辛伐他汀-烟酸双层缓释片的制备及其体外质量评价

目的 制备辛伐他汀(SVT) -烟酸(NA)双层缓释片并进行体外质量评价.方法 分别通过正交实验和单因素实验,筛选NA缓释层与SVT常释层处方,确定最佳处方组成并制备SVT-NA双层缓释片,测定缓释片中NA与SVT的含量.以国内已上市NA缓释片为参比制剂,测定自制双层缓释片中NA的释放度,计算释放度相似因子(f2),并进行方程拟合.按照中国药典2010版中SVT普通片溶出度测定条件,测定自制双层缓释片常释层中SVT的溶出度.结果 自制双层缓释片中NA的释放度与市售NA缓释片相似(f2＞75),其释放行为符合Higuchi方程;常释层中SVT 30 min内的累积溶出度＞98％,符合中国药典有关规定.结论 SVT-NA双层缓释片处方组成合理,制备工艺稳定,重现性良好,有望开发成为新的复方制剂.     

盐酸他喷他多缓释片的研制及体外释放考察

目的 研制盐酸他喷他多12 h缓释片并对其体外释放机制进行初步研究.方法 以HPMC与CMC-Na为混合骨架材料制备缓释片,以单因素试验法对缓释片释放因素进行考察,通过f2相似因子与累积释放度综合评分进行评价,采用正交试验设计筛选最优处方,将自制缓释片与参比制剂的体外释放参数进行比较.结果 最优处方以HPMC K15M和CMC-Na作为混合骨架材料,质量分数各占总质量的40％和12％,乳糖作为致孔剂,质量分数占总质量的12％,微晶纤维素为填充剂,质量分数占总质量的11％.制备的盐酸他喷他多缓释片释放规律符合Ritger-Peppas方程曲线,释药机制为药物扩散和骨架溶蚀协同作用,自制缓释片与参比制剂体外释放行为相似度较高.结论 制得的盐酸他喷他多缓释片有良好的12 h体外释药行为,制备工艺简单、可行,与参比制剂体外释放行为接近,符合缓释制剂要求.     

盐酸氯米帕明缓释片的制备及其体外释放度考察

目的:制备盐酸氯米帕明缓释片并考察其体外释放度。方法:以辅料羟丙基甲基纤维素(HPMC)、乳糖、可压性淀粉在处方中的含量为因素,体外释放度为指标,用正交试验优化处方并制备制剂,同时考察其体外释放度。结果:筛选最优处方为HPMC45mg、乳糖35mg、可压性淀粉40mg。所制制剂可持续24h释药,释药行为符合零级释放模型。结论:所制缓释片的处方合理,具有良好的缓释效果。     

盐酸曲美他嗪缓释片的研制及其体外释药特性考察

目的:研制盐酸曲美他嗪(TMZ)缓释片,并考察其体外药物释放特性。方法:以羟丙基甲基纤维素(HPMC)为主要骨架材料,采用湿法制粒压片法,制备TMZ缓释片;以释放度为评价指标,采用正交试验设计对处方进行优化;根据药物不同时间释放度,拟合释放度方程,确定释药特性。结果:TMZ缓释片的处方组成为:HPMC 90 mg,羧甲基纤维素钠30 mg,微晶纤维素60 mg,10%聚乙烯吡咯烷酮-乙醇溶液(w/v)适量,硬脂酸镁2 mg;12 h释放度为(98.1±1.8)%,体外释放曲线符合一级动力学方程(r=0.994 2);压片力对药物释放有一定影响。结论:研制的TMZ缓释片制备工艺简单,重现性好,释药特性达到缓释要求。     

格列齐特缓释片的制备及其释药因素考察

目的以格列齐特为模型药物,考察缓释片的处方及工艺因素对其体外释放的影响。方法以羟丙基甲基纤维素(HPMC)为骨架材料,预胶化淀粉等为填充剂,以体外释放度为判断原则考察处方及工艺因素对药物溶出度的影响。结果获得了满足设计要求的缓释片处方,通过对体内生物利用度的初步研究,发现格列齐特缓释凝胶骨架片在体内的有效血药浓度可维持24h以上。结论该制剂工艺简单,所用各种辅料成本低,制得的格列齐特缓释片释放度符合规定。     

星点设计-效应面法优化复方氯雷伪麻缓释片的缓释片芯处方

目的:优化复方氯雷伪麻缓释片的缓释片芯处方。方法:以氯雷他定加入包衣层并包衣硫酸伪麻黄碱片芯制备复方氯雷伪麻缓释片。采用星点设计-效应面法优化硫酸伪麻黄碱片芯处方,以单硬脂酸甘油酯(GM)和羟丙甲纤维素(HPMC)K15M用量为考察因素,以硫酸伪麻黄碱1、6、12 h的累积释放度为指标,通过重叠等高线图确定优化处方,并进行处方验证。结果:氯雷他定与欧巴代包衣粉按1∶3比例混合包衣成为速释层;硫酸伪麻黄碱片芯作为缓释层,其处方为每100片(600 mg/片)含硫酸伪麻黄碱12 g、GM 16.72 g、HPMC K15M 20.95 g、微晶纤维素9.73 g、硬脂酸镁0.6 g。优化处方所制制剂中氯雷他定15 min的累积溶出度为87.2%,硫酸伪麻黄碱1、6、12 h的累积释放度分别为34.20%、74.32%、94.60%。结论:缓释片芯处方合理、可行,所制备的复方氯雷伪麻缓释片具有缓释作用。     

盐酸氨溴索缓释片体内外相关性研究

目的考察盐酸氨溴索缓释片体外释放度与体内吸收的相关性。方法应用释放度测定法研究盐酸氨溴索缓释片体外释药行为 ,采用HPLC法测定盐酸氨溴索缓释制剂在家犬体内的血药浓度 ,按照Wagner Nelson公式计算药物的吸收分数。 结果 3种自制盐酸氨溴索缓释片与参比制剂生物等效 ,以药物累积吸收百分数 f(t)与相应时刻的体外累积释放百分数F(t)建立的一元线性回归方程 ,参比制剂与 3种自制制剂的体内外相关系数分别为 0 969、0 979、0 970和 0 983。结论盐酸氨溴索缓释片的体外释放度与体内吸收具有显著的相关性。     

Comparison of four artificial neural network software programs used to predict the in vitro dissolution of controlled-release tablets

The purpose of this study was to evaluate four commercially available artificial neural network (ANN) software programs: NeuroShell2 v3.0, BrainMaker v3.7, CAD/Chem v5.0, and NeuralWorks Professional II/Plus for prediction of in vitro dissolution-time profiles of controlled-release tablets containing a model sympathomimetic drug. Seven independent formulation variables and three other tablet variables (moisture content of granules, granule particle size, and tablet hardness), for 22 tablet formulations, were used as the ANN model input. In vitro dissolution time-profiles at 10 different sampling times were used as the output. The models' optimum architectures were determined for each ANN software by varying the number of hidden layers and number of nodes in hidden layer(s). The ANN developed from the four software programs were validated by predicting the in vitro dissolution time-profiles of each of the 19 formulations, which were excluded from the training process. Although the same data set was used, the optimum ANN architectures generated from the four software programs were different. Using the four optimum ANN models, the plots of predicted vs. observed percentage of drug dissolved gave slopes ranging from 0.95 to 1.01 and r2 values ranging from 0.95 to 0.99 for all 190 dissolution data points for the 19 training formulations. The difference factors (f1) and similarity factors (f2) between the ANN predicted and the observed in vitro dissolution profiles were also used to compare the predictions for the four software programs. It was concluded that the four programs provided reasonable predictions of in vitro dissolution profiles for the data set employed in this study, with NeuralShell2 showing the best overall prediction.     

Application of Artificial Neural Networks in the Design and Optimization of a Nanoparticulate Fingolimod Delivery System Based on Biodegradable Poly(3-Hydroxybutyrate-Co-3-Hydroxyvalerate)

Formulation of a nanoparticulate Fingolimod delivery system based on biodegradable poly(3-hydroxybutyrate-co-3-hydroxyvalerate) was optimized according to artificial neural networks (ANNs). Concentration of poly(3-hydroxybutyrate-co-3-hydroxyvalerate), PVA and amount of Fingolimod is considered as the input value, and the particle size, polydispersity index, loading capacity, and entrapment efficacy as output data in experimental design study. In vitro release study was carried out for best formulation according to statistical analysis. ANNs are employed to generate the best model to determine the relationships between various values. In order to specify the model with the best accuracy and proficiency for the in vitro release, a multilayer percepteron with different training algorithm has been examined. Three training model formulations including Levenberg-Marquardt (LM), gradient descent, and Bayesian regularization were employed for training the ANN models. It is demonstrated that the predictive ability of each training algorithm is in the order of LM > gradient descent > Bayesian regularization. Also, optimum formulation was achieved by LM training function with 15 hidden layers and 20 neurons. The transfer function of the hidden layer for this formulation and the output layer were tansig and purlin, respectively. Also, the optimization process was developed by minimizing the error among the predicted and observed values of training algorithm (about 0.0341).     

格列吡嗪双层渗透泵控释片的制备及体外释放度考察

目的对格列吡嗪双层渗透泵的处方与工艺进行研究。方法以自制片与进口片体外释药数据 ( 2～ 1 6h)的相似因子作为评价指标 ,采用正交设计优化出格列吡嗪双层渗透泵控释片的处方。结果所得优化处方为 :氯化钠 ,1 8mg;助推层聚氧化乙烯 ,4 9mg;羟丙甲基纤维素 ,2 5mg;半透膜重量 ,2 8mg。优化处方的三批样品在 2～ 1 6h内均呈现零级释放特征 (r =0 9989) ,平均释药量为0 2 8mg/h ,与进口片相比具有良好的相似性 (f2 =70 2 )。结论自制片与进口片体外释药行为相似 ,可进一步进行体内释药行为的考察。     

人工神经网络预测盐酸帕罗西汀缓释微丸的药物释放

目的利用人工神经网络对盐酸帕罗西汀缓释微丸的释药行为进行预测。方法设计20个处方,其中16个处方作为训练处方,其余4个处方作为测试处方,制备盐酸帕罗西汀膜控释微丸,进行释放度检查。以致孔剂PVPK30的用量、包衣增重作为自变量,考察药物在各个取样点的累积释放量作为输出,建立盐酸帕罗西汀缓释微丸释药行为的人工神经网络预测模型。通过线性回归法、相似因子法、AIC法评价人工神经网络的预测能力。结果通过实测数据和BP神经网络预测结果比较,验证了人工神经网络的预测精度达0.989 9。结论用人工神经网络对盐酸帕罗西汀缓释微丸的释药行为进行预测,拟合度较高,从而为盐酸帕罗西汀缓释微丸的处方优化和释药行为预测提供了可行的依据。     

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

目的　将多目标同步优化技术应用于药物剂型的处方筛选中。方法　通过硫酸沙丁胺醇渗透泵型控释片的处方设计,将两种同步优化技术：反应曲面法(response surface method, RSM)与人工神经网络(artificial neural network, ANN)应用于药物剂型的优化筛选过程中,并将两种方法进行比较。结果　两种方法筛选的最优处方结果较为接近,但ANN的预测结果误差较小。结论　在处理多目标同步优化问题上,人工神经网络技术是值得推广应用的一种新型的处方优化筛选技术。     

人工神经网络辅助设计褪黑素缓释片处方

应用人工神经网络模型辅助设计褪黑素缓释片处方,将HPMC粘度、HPMC、MCC 和乳糖的量作为输入变量,累积释放百分率作为输出变量,选择反向传播网络,隐含层为1层,隐含层神经元个数为6,建立人工神经网络模型,预测和评价褪黑素缓释片体外释放度,研究缓释片的释放机理.结果显示,该人工神经网络模型能很好地预测褪黑素缓释片的释放量,成功优化褪黑素缓释片处方,其释放机理为溶蚀与扩散的结合,辅料的种类和量会对药物的释放机理产生不同影响.     

定制设计法在盐酸丁咯地尔缓释片处方设计中的应用

目的应用定制设计法优化盐酸丁咯地尔缓释片的处方。方法以片芯中乙基纤维素和氯化钠比例的变化、包衣增重为考察因素,以不同时间点的累积释放量为优化指标,应用定制设计法筛选最佳处方,并对优化处方进行验证。结果最优处方组成为,盐酸丁咯地尔600 mg、乙基纤维素90 mg、氯化钠10 mg、包衣增量为3%,12 h累积释放量为80%以上,体外释放曲线平稳。结论采用定制设计法得到了盐酸丁咯地尔缓释片的处方优化模型,应用优化处方制备缓释片所得的实测值与预测值无明显差异,实现了处方优化。     

Performance comparison of neural network training algorithms in modeling of bimodal drug delivery

The major aim of this study was to model the effect of two causal factors, i.e. coating weight gain and amount of pectin-chitosan in the coating solution on the in vitro release profile of theophylline for bimodal drug delivery. Artificial neural network (ANN) as a multilayer perceptron feedforward network was incorporated for developing a predictive model of the formulations. Five different training algorithms belonging to three classes: gradient descent, quasi-Newton (Levenberg-Marquardt, LM) and genetic algorithm (GA) were used to train ANN containing a single hidden layer of four nodes. The next objective of the current study was to compare the performance of aforementioned algorithms with regard to predicting ability. The ANNs were trained with those algorithms using the available experimental data as the training set. The divergence of the RMSE between the output and target values of test set was monitored and used as a criterion to stop training. Two versions of gradient descent backpropagation algorithms, i.e. incremental backpropagation (IBP) and batch backpropagation (BBP) outperformed the others. No significant differences were found between the predictive abilities of IBP and BBP, although, the convergence speed of BBP is three- to four-fold higher than IBP. Although, both gradient descent backpropagation and LM methodologies gave comparable results for the data modeling, training of ANNs with genetic algorithm was erratic. The precision of predictive ability was measured for each training algorithm and their performances were in the order of: IBP, BBP>LM>QP (quick propagation)>GA. According to BBP-ANN implementation, an increase in coating levels and a decrease in the amount of pectin-chitosan generally retarded the drug release. Moreover, the latter causal factor namely the amount of pectin-chitosan played slightly more dominant role in determination of the dissolution profiles.     

左卡尼汀胃漂浮缓释片的制备及体外评价

目的:应用Box-behnken效应面法优化左卡尼汀胃漂浮缓释片处方,并评价其体外漂浮和释放特性.方法:以粉末直接压片法制备片剂.采用单因素法筛选出主要影响因素,即硬度、HPMC用量及碳酸氢钠用量,以漂浮性能和不同时间点释药性能为评价指标,通过Boxbehnken设计实验优化处方,对体外释药数据进行方程拟合,并结合扫描电子显微镜对溶出前后片剂表面形态的观察,探讨其释药机理.结果:优选处方为每片含HPMCK100M 29.7％,碳酸氢钠5.0％,十八烷醇15.0％,硬度为4 kg· mm-2.体外释药符合Makoid-Banakar模型,药物的释药机制为骨架溶蚀与药物扩散双重作用.结论:Box-behnken效应面法可用于左卡尼汀胃漂浮缓释片处方优化,且制备工艺简单,优化处方具漂浮缓释作用.     

Prediction of dissolution profiles of acetaminophen beads using artificial neural networks

Immediate release acetaminophen (APAP) beads with 40% drug loading were prepared using the extrusion-spheronization process. Eighteen batches of beads were prepared based on a full factorial design by varying process variables such as extruder type, extruder screw speed, spheronization speed, and spheronization time. An in vitro dissolution test was carried out using the USP 27 Apparatus II (paddle) method. Artificial Neural Network (ANN) models were developed based on the aforementioned process variables and dissolution data. The trained ANN models were used to predict the dissolution profiles of APAP from the beads, which were prepared with various processing conditions. For training the ANN models, process variables were used as inputs, and percent drug released from APAP beads was used as the output. The dissolution data from one out of 18 batches of APAP beads was selected as the validation data set. The dissolution data of other 17 batches were used to train the ANN models using the ANN software (AI Trilogy) with two different training strategies, namely, neural and genetic. The validation results showed that the ANN model trained with the genetic strategy had better predictability than the one trained with the neural strategy. The ANN model trained with the genetic strategy was then used to predict the drug release profiles of two new batches of APAP beads, which were prepared with process variables that were not used during the ANN model training process. However, the process variables used to prepare the two new batches of APAP beads were within the confines of the process variables used to prepare the 18 batches. The actual drug release profile of these two batches of APAP beads was similar to the ones predicted by the trained and validated ANN model, as indicated by the high f2 values. Furthermore, the ANN model trained with genetic strategy was also used to optimize process variables to achieve the desired dissolution profiles. These batches of APAP beads were then actually prepared using the process variables predicted by the trained and validated ANN model. The dissolution results showed that the actual dissolution profiles of the APAP beads prepared from the predicted process variables were similar to the desired dissolution profiles.