Department of Pharmaceutics, Hoshi University, Ebara, Shinagawa-ku, Tokyo, Japan.
Drug Dev Ind Pharm. 2011 Nov;37(11):1290-7. doi: 10.3109/03639045.2011.569935. Epub 2011 Jun 27.
When designing pharmaceutical products, the relationships between causal factors and pharmaceutical responses are intricate. A Bayesian network (BN) was used to clarify the latent structure underlying the causal factors and pharmaceutical responses of a tablet containing solid dispersion (SD) of indomethacin (IMC).
IMC, a poorly water-soluble drug, was tested with polyvinylpyrrolidone as the carrier polymer. Tablets containing a SD or a physical mixture of IMC, different quantities of magnesium stearate, microcrystalline cellulose, and low-substituted hydroxypropyl cellulose, and subjected to different compression force were selected as the causal factors. The pharmaceutical responses were the dissolution properties and tensile strength before and after the accelerated test and a similarity factor, which was used as an index of the storage stability.
BN models were constructed based on three measurement criteria for the appropriateness of the graph structure. Of these, the BN model based on Akaike's information criterion was similar to the results for the analysis of variance. To quantitatively estimate the causal relationships underlying the latent structure in this system, conditional probability distributions were inferred from the BN model. The responses were accurately predicted using the BN model, as reflected in the high correlation coefficients in a leave-one-out cross-validation procedure.
The BN technique provides a better understanding of the latent structure underlying causal factors and responses.
在设计药物产品时，因果因素与药物反应之间的关系错综复杂。贝叶斯网络（BN）被用于阐明包含吲哚美辛（IMC）固体分散体（SD）的片剂中因果因素和药物反应的潜在结构。
IMC 是一种水溶性较差的药物，使用聚乙烯吡咯烷酮作为载体聚合物进行测试。选择含有 SD 或 IMC 物理混合物、不同量的硬脂酸镁、微晶纤维素和低取代羟丙基纤维素以及不同压缩力的片剂作为因果因素。药物反应是加速试验前后的溶解性能和拉伸强度以及相似因子，用作储存稳定性的指标。
基于图形结构适当性的三个测量标准构建了 BN 模型。其中，基于赤池信息量准则的 BN 模型与方差分析的结果相似。为了定量估计该系统中潜在结构下的因果关系，从 BN 模型中推断出条件概率分布。BN 模型能够准确预测响应，这反映在留一交叉验证过程中的高相关系数上。
BN 技术提供了对因果因素和响应潜在结构的更好理解。
