Process Research & Development, MRL, Merck & Co., Inc., West Point, Pennsylvania 19486, United States.
Mol Pharm. 2024 Nov 4;21(11):5565-5576. doi: 10.1021/acs.molpharmaceut.4c00540. Epub 2024 Sep 17.
Biopharmaceutical resins are pivotal inert matrices used across industry and academia, playing crucial roles in a myriad of applications. For biopharmaceutical process research and development applications, a deep understanding of the physical and chemical properties of the resin itself is frequently required, including for drug purification, drug delivery, and immobilized biocatalysis. Nevertheless, the prevailing methodologies currently employed for elucidating these important aspects of biopharmaceutical resins are often lacking, frequently require significant sample alteration, are destructive or ionizing in nature, and may not adequately provide representative information. In this work, we propose the use of unsupervised machine learning technologies, in the form of both non-negative matrix factorization (NMF) and -means segmentation, in conjugation with Raman hyperspectral imaging to rapidly elucidate the molecular and spatial properties of biopharmaceutical resins. Leveraging our proposed technology, we offer a new approach to comprehensively understanding important resin-based systems for application across biopharmaceuticals and beyond. Specifically, focusing herein on a representative resin widely utilized across the industry (i.e., Immobead 150P), our findings showcase the ability of our machine learning-based technology to molecularly identify and spatially resolve all chemical species present. Further, we offer a comprehensive evaluation of optimal excitation for hyperspectral imaging data collection, demonstrating results across 532, 638, and 785 nm excitation. In all cases, our proposed technology deconvoluted, both spatially and spectrally, resin and glass substrates via NMF. After NMF deconvolution, image segmentation was also successfully accomplished in all data sets via -means clustering. To the best of our knowledge, this is the report utilizing the combination of two unsupervised machine learning methodologies, combining NMF and -means, for the rapid deconvolution and segmentation of biopharmaceutical resins. As such, we offer a powerful new data-rich experimentation tool for application across multidisciplinary fields for a deeper understanding of resins.
生物制药树脂是在工业和学术界广泛应用的关键惰性基质，在众多应用中发挥着重要作用。对于生物制药工艺研发应用，经常需要深入了解树脂本身的物理和化学性质，包括药物纯化、药物输送和固定化生物催化。然而，目前用于阐明生物制药树脂这些重要方面的主流方法往往存在不足，通常需要对样品进行重大改变，具有破坏性或电离性，并且可能无法充分提供代表性信息。在这项工作中，我们提出使用无监督机器学习技术，包括非负矩阵分解（NMF）和 -means 分割，以及拉曼高光谱成像，快速阐明生物制药树脂的分子和空间特性。利用我们提出的技术，我们提供了一种新的方法来全面理解生物制药和其他领域应用的重要树脂基系统。具体来说，本文重点关注一种在行业中广泛使用的代表性树脂（即 Immobead 150P），我们的研究结果展示了我们基于机器学习的技术在分子上识别和空间上分辨所有存在化学物质的能力。此外，我们还对最佳激发进行了全面评估用于高光谱成像数据采集，在 532、638 和 785nm 激发下展示了结果。在所有情况下，我们提出的技术通过 NMF 对树脂和玻璃基质进行了空间和光谱上的反卷积。在 NMF 反卷积之后，还通过 -means 聚类成功地完成了所有数据集的图像分割。据我们所知，这是首次利用两种无监督机器学习方法（NMF 和 -means）的组合，快速反卷积和分割生物制药树脂的报告。因此，我们为跨多个领域的应用提供了一种强大的新数据丰富实验工具，以更深入地了解树脂。
