Department of Pharmaceutical Technology, Graduate School of Medicine and Pharmaceutical Science for Research, University of Toyama, 2630 Sugitani, Toyama-shi, Toyama, 930-0194, Japan.
Department of Pharmaceutical Technology, Graduate School of Medicine and Pharmaceutical Science for Research, University of Toyama, 2630 Sugitani, Toyama-shi, Toyama, 930-0194, Japan.
Int J Pharm. 2017 Oct 30;532(1):82-89. doi: 10.1016/j.ijpharm.2017.08.111. Epub 2017 Aug 30.
In this study, we evaluated the correlation between the response surfaces for the tablet characteristics of placebo and active pharmaceutical ingredient (API)-containing tablets. The quantities of lactose, cornstarch, and microcrystalline cellulose were chosen as the formulation factors. Ten tablet formulations were prepared. The tensile strength (TS) and disintegration time (DT) of tablets were measured as tablet characteristics. The response surfaces for TS and DT were estimated using a nonlinear response surface method incorporating multivariate spline interpolation, and were then compared with those of placebo tablets. A correlation was clearly observed for TS and DT of all APIs, although the value of the response surfaces for TS and DT was highly dependent on the type of API used. Based on this knowledge, the response surfaces for TS and DT of API-containing tablets were predicted from only two and four formulations using regression expression and placebo tablet data, respectively. The results from the evaluation of prediction accuracy showed that this method accurately predicted TS and DT, suggesting that it could construct a reliable response surface for TS and DT with a small number of samples. This technique assists in the effective estimation of the relationships between design variables and pharmaceutical responses during pharmaceutical development.
在这项研究中,我们评估了安慰剂和含活性药物成分(API)片剂的片剂特性响应面之间的相关性。选择乳糖、玉米淀粉和微晶纤维素的量作为配方因素。制备了十种片剂配方。片剂的拉伸强度(TS)和崩解时间(DT)作为片剂特性进行测量。使用包含多元样条插值的非线性响应面方法估计了 TS 和 DT 的响应面,并将其与安慰剂片剂的响应面进行了比较。尽管 TS 和 DT 的响应面的值高度依赖于所用 API 的类型,但所有 API 的 TS 和 DT 均表现出明显的相关性。基于这一知识,使用回归表达式和安慰剂片剂数据,分别从仅两个和四个配方中预测了含 API 片剂的 TS 和 DT 的响应面。评估预测准确性的结果表明,该方法可以准确预测 TS 和 DT,表明它可以使用少量样本构建可靠的 TS 和 DT 响应面。该技术有助于在药物开发过程中有效估计设计变量与药物反应之间的关系。