In-line monitoring of granule water content during fluid bed granulation is important to control drug product qualities. In this study, a practical scale-free soft sensor to predict water content was proposed to cope with the manufacturing scale changes in drug product development. The proposed method exploits two key ideas to construct a scale-free soft sensor. First, to accommodate the changes in the manufacturing scale, the process parameters (PPs) that are critical to water content at different manufacturing scales were selected as input variables. Second, to construct an accurate statistical model, locally weighted partial least squares regression (LW-PLSR), which can cope with collinearity and nonlinearity, was utilized. The soft sensor was developed using both laboratory (approx. 4 kg) data and pilot (approx. 25 kg) scale data, and the prediction accuracy in the commercial (approx. 100 kg) scale was evaluated based on the assumption that the process was scaled-up from the pilot scale to the commercial scale. The developed soft sensor exhibited a high prediction accuracy, which was equivalent to the commonly used near-infrared (NIR) spectra-based method. The proposed method requires only standard instruments; therefore, it is expected to be a cost-effective alternative to the NIR spectra-based method.

在线监测流化床制粒过程中的颗粒水含量对于控制药品质量非常重要。在这项研究中，提出了一种实用的无标度软传感器来预测水含量，以应对药品开发中制造规模的变化。所提出的方法利用两个关键思想来构建无标度的软传感器。首先，为了适应制造规模的变化，选择对于不同制造规模的水含量至关重要的工艺参数（PP）作为输入变量。其次，利用局部加权偏最小二乘回归（LW-PLSR）来构建准确的统计模型，该模型可以处理共线性和非线性。使用实验室（约4 kg）数据和飞行员（约25 kg）秤数据开发了软传感器，并基于将过程从中试规模扩大到商业规模的假设，评估了商业规模（约100千克）的预测准确性。开发的软传感器显示出很高的预测精度，这相当于常用的基于近红外（NIR）光谱的方法。所提出的方法仅需要标准仪器。因此，它有望成为基于NIR光谱的方法的一种经济高效的替代方法。