Hattori Yusuke, Naganuma Miki, Otsuka Makoto
Research Institute of Pharmaceutical Sciences, Faculty of Pharmacy, Musashino University, 1-20 Shin-machi, Nishi-Tokyo city, Tokyo 202-8585, Japan.
Pharmaceutics. 2020 Jan 20;12(1):85. doi: 10.3390/pharmaceutics12010085.
In this study, we established a robust feed-forward control model for the tableting process by partial least squares regression using the near-infrared (NIR) spectra and physical attributes of the granules to be compressed. The NIR spectra of granules are rich in information about chemical attributes, such as the compositions of any ingredients and moisture content. Polymorphism and pseudo-polymorphism can also be quantitatively evaluated by NIR spectra. We used the particle size distribution, flowability, and loose and tapped density as the physical attributes of the granules. The tableting process was controlled by the lower punch fill depth and the minimum distance between the upper and lower punches at compression, which were specifically related to the tablet weight and thickness, respectively. The feed-forward control of the process would be expected to provide some advantages for automated and semi-automated continuous pharmaceutical manufacturing. As a result, our model, using a combination of NIR spectra and the physical attributes of granules to control the distance between punches, resulted in respectable agreement between the predicted process parameters and actual settings to produce tablets of the desired thickness.
在本研究中，我们通过偏最小二乘回归建立了一个稳健的压片过程前馈控制模型，该模型使用待压缩颗粒的近红外（NIR）光谱和物理属性。颗粒的近红外光谱富含化学属性信息，例如任何成分的组成和水分含量。多晶型和假多晶型也可以通过近红外光谱进行定量评估。我们使用粒度分布、流动性以及松装密度和振实密度作为颗粒的物理属性。压片过程由下冲头填充深度和压缩时上下冲头之间的最小距离控制，这分别与片剂重量和厚度具体相关。预计该过程的前馈控制将为自动化和半自动化连续制药生产提供一些优势。因此，我们的模型结合近红外光谱和颗粒的物理属性来控制冲头之间的距离，使得预测的过程参数与生产所需厚度片剂的实际设置之间达成了可观的一致性。
