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Preparation of controlled porosity osmotic pump tablets for salvianolic acid and optimization of the formulation using an artificial neural network method

机译:丹酚酸可控孔隙度渗透泵片的制备及人工神经网络法优化配方

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In this paper controlled porosity osmotic pump tablets (CPOPT) for salvianolic acid (SA) were prepared and optimized with experimental design methods including an artificial neutral network (ANN) method. Three causal factors, i.e., drug, osmotic pressure promoting agent rate, PEG400 content in coating solution and coating weight, were evaluated based on their effects on drug release rate. The linear correlation coefficient of the accumulative amount of drug release and the time of 12h, r(Y"1), and the sum of the absolute value between measured and projected values, Y"2, were used as outputs to optimize the formulation. The weight expression Y=(1-Y"1)^2+Y"2^2 was used in the calculation. Furthermore, the ANN and uniform design gave similar optimization results, but ANN projected the outputs better than the uniform design. This paper showed that the release rate of salvianolic acid B and that of the total salvianolic acid was consistent in the optimized formulation.
机译:本文制备并用实验设计方法(包括人工中性网络(ANN)方法)优化了丹参酚酸(SA)的控制孔隙渗透泵片(CPOPT)。基于它们对药物释放速率的影响,评估三个因果关系,即药物,渗透压促进剂速率,包衣溶液中的PEG400含量和包衣重量。药物释放累积量与时间12h的线性相关系数r(Y“ 1)以及测量值与投影值之间的绝对值之和Y” 2被用作优化配方的输出。在计算中使用权重表达式Y =(1-Y“ 1)^ 2 + Y” 2 ^ 2。此外,人工神经网络和统一设计给出了相似的优化结果,但人工神经网络比统一设计更好地预测了输出。本文表明,在优化配方中丹酚酸B和总丹酚酸的释放速率是一致的。

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