Massei Ambra, Falco Nunzia, Fissore Davide
Dipartimento di Scienza Applicata e Tecnologia, Politecnico di Torino, corso Duca degli Abruzzi 24, 10129 Torino, Italy; Global Pharmaceutical Development Department, Merck Serono SpA, via Luigi Einaudi 11, 00012 Guidonia Montecelio (Roma), Italy.
Global Pharmaceutical Development Department, Merck Serono SpA, via Luigi Einaudi 11, 00012 Guidonia Montecelio (Roma), Italy.
Spectrochim Acta A Mol Biomol Spectrosc. 2023 May 15;293:122485. doi: 10.1016/j.saa.2023.122485. Epub 2023 Feb 14.
Residual Moisture (RM) in freeze-dried products is one of the most important critical quality attributes (CQAs) to monitor, since it affects the stability of the active pharmaceutical ingredient (API). The standard experimental method adopted for the measurements of RM is the Karl-Fischer (KF) titration, that is a destructive and time-consuming technique. Therefore, Near-Infrared (NIR) spectroscopy was widely investigated in the last decades as an alternative tool to quantify the RM. In the present paper, a novel method was developed based on NIR spectroscopy combined with machine learning tools for the prediction of RM in freeze-dried products. Two different types of models were used: a linear regression model and a neural network based one. The architecture of the neural network was chosen so as to optimize the prediction of the residual moisture, by minimizing the root mean square error with the dataset used in the learning step. Moreover, the parity plots and the absolute error plots were reported, allowing a visual evaluation of the results. Different factors were considered when developing the model, namely the range of wavelengths considered, the shape of the spectra and the type of model. The possibility of developing the model using a smaller dataset, obtained with just one product, that could be then applied to a wider range of products was investigated, as well as the performance of a model developed for a dataset encompassing several products. Different formulations were analyzed: the main part of the dataset was characterized by a different percentage of sucrose in solution (3%, 6% and 9% specifically); a smaller part was made up of sucrose-arginine mixtures at different percentages and only one formulation was characterized by another excipient, the trehalose. The product-specific model for the 6% sucrose mixture was found consistent for the prediction of RM in other sucrose containing mixtures and in the one containing trehalose, while failed for the dataset with higher percentage of arginine. Therefore, a global model was developed by including a certain percentage of all the available dataset in the calibration phase. Results presented and discussed in this paper demonstrate the higher accuracy and robustness of the machine learning based model with respect to the linear models.
冻干产品中的残留水分（RM）是需要监测的最重要的关键质量属性（CQA）之一，因为它会影响活性药物成分（API）的稳定性。测量RM所采用的标准实验方法是卡尔费休（KF）滴定法，这是一种具有破坏性且耗时的技术。因此，近红外（NIR）光谱在过去几十年中被广泛研究，作为量化RM的替代工具。在本文中，基于近红外光谱结合机器学习工具开发了一种新方法，用于预测冻干产品中的RM。使用了两种不同类型的模型：线性回归模型和基于神经网络的模型。选择神经网络的架构是为了通过最小化学习步骤中使用的数据集的均方根误差来优化残留水分的预测。此外，还报告了奇偶图和绝对误差图，以便对结果进行直观评估。在开发模型时考虑了不同因素，即所考虑的波长范围、光谱形状和模型类型。研究了使用仅一种产品获得的较小数据集开发模型并将其应用于更广泛产品范围的可能性，以及为包含多种产品的数据集开发的模型的性能。分析了不同的配方：数据集的主要部分的特征是溶液中蔗糖的百分比不同（具体为3%、6%和9%）；较小的一部分由不同百分比的蔗糖-精氨酸混合物组成，只有一种配方的特征是另一种赋形剂海藻糖。发现6%蔗糖混合物的产品特定模型在预测其他含蔗糖混合物和含海藻糖混合物中的RM时是一致的，但对于精氨酸百分比更高的数据集则失败。因此，通过在校准阶段纳入一定比例的所有可用数据集开发了一个全局模型。本文给出并讨论的结果表明，基于机器学习的模型相对于线性模型具有更高的准确性和鲁棒性。
