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首页> 外文期刊>Drug development and industrial pharmacy >Latent structure modeling underlying theophylline tablet formulations using a Bayesian network based on a self-organizing map clustering
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Latent structure modeling underlying theophylline tablet formulations using a Bayesian network based on a self-organizing map clustering

机译：基于自组织图聚类的贝叶斯网络潜在的茶碱片配方的潜在结构建模

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摘要

The "quality by design" concept in pharmaceutical formulation development requires the establishment of a science-based rationale and design space. In this article, we integrate thin-plate spline (TPS) interpolation, Kohonen's self-organizing map (SOM) and a Bayesian network (BN) to visualize the latent structure underlying causal factors and pharmaceutical responses. As a model pharmaceutical product, theophylline tablets were prepared using a standard formulation. We measured the tensile strength and disintegration time as response variables and the compressibility, cohesion and dispersibility of the pretableting blend as latent variables. We predicted these variables quantitatively using nonlinear TPS, generated a large amount of data on pretableting blends and tablets and clustered these data into several clusters using a SOM. Our results show that we are able to predict the experimental values of the latent and response variables with a high degree of accuracy and are able to classify the tablet data into several distinct clusters. In addition, to visualize the latent structure between the causal and latent factors and the response variables, we applied a BN method to the SOM clustering results. We found that despite having inserted latent variables between the causal factors and response variables, their relation is equivalent to the results for the SOM clustering, and thus we are able to explain the underlying latent structure. Consequently, this technique provides a better understanding of the relationships between causal factors and pharmaceutical responses in theophylline tablet formulation.
机译：药物制剂开发中的“按设计质量”概念要求建立基于科学的原理和设计空间。在本文中，我们整合了薄板样条（TPS）插值，Kohonen的自组织图（SOM）和贝叶斯网络（BN），以可视化潜在因果关系和药物反应的潜在结构。作为标准药品，使用标准制剂制备茶碱片。我们测量了拉伸强度和崩解时间作为响应变量，并测量了预调制混合物的可压缩性，内聚性和分散性作为潜在变量。我们使用非线性TPS定量预测了这些变量，在预装混合物和片剂上生成了大量数据，并使用SOM将这些数据聚集成几个聚类。我们的结果表明，我们能够高度准确地预测潜伏和响应变量的实验值，并且能够将数位板数据分类为几个不同的簇。此外，为了可视化因果和潜在因素与响应变量之间的潜在结构，我们将BN方法应用于SOM聚类结果。我们发现，尽管在因果因子和响应变量之间插入了潜在变量，但是它们之间的关系等同于SOM聚类的结果，因此我们能够解释潜在的潜在结构。因此，该技术可以更好地理解茶碱片剂中因果关系与药物反应之间的关系。

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