Department of Chemistry and Biochemistry, University of Notre Dame, Notre Dame, Indiana 46556, United States.
Precise Software Solutions Inc, Rockville, Maryland 20850, United States.
Anal Chem. 2022 Sep 20;94(37):12586-12594. doi: 10.1021/acs.analchem.2c00998. Epub 2022 Sep 6.
Near-infrared (NIR) spectroscopy is a promising technique for field identification of substandard and falsified drugs because it is portable, rapid, nondestructive, and can differentiate many formulated pharmaceutical products. Portable NIR spectrometers rely heavily on chemometric analyses based on libraries of NIR spectra from authentic pharmaceutical samples. However, it is difficult to build comprehensive product libraries in many low- and middle-income countries due to the large numbers of manufacturers who supply these markets, frequent unreported changes in materials sourcing and product formulation by the manufacturers, and general lack of cooperation in providing authentic samples. In this work, we show that a simple library of lab-formulated binary mixtures of an active pharmaceutical ingredient (API) with two diluents gave good performance on field screening tasks, such as discriminating substandard and falsified formulations of the API. Six data analysis models, including principal component analysis and support-vector machine classification and regression methods and convolutional neural networks, were trained on binary mixtures of acetaminophen with either lactose or ascorbic acid. While the models all performed strongly in cross-validation (on formulations similar to their training set), they individually showed poor robustness for formulations outside the training set. However, a predictive algorithm based on the six models, trained only on binary samples, accurately predicts whether the correct amount of acetaminophen is present in ternary mixtures, genuine acetaminophen formulations, adulterated acetaminophen formulations, and falsified formulations containing substitute APIs. This data analytics approach may extend the utility of NIR spectrometers for analysis of pharmaceuticals in low-resource settings.
近红外（NIR）光谱学是一种很有前途的现场鉴定劣药和假药的技术，因为它便携、快速、无损，并且可以区分许多已配制的药物产品。便携式 NIR 光谱仪严重依赖于基于真实药物样本 NIR 光谱库的化学计量学分析。然而，由于为数众多的制造商供应这些市场，以及制造商在材料来源和产品配方方面频繁未经报告的变更，以及在提供真实样本方面缺乏普遍合作，许多低收入和中等收入国家难以建立全面的产品库。在这项工作中，我们表明，用两种稀释剂对活性药物成分（API）进行实验室配方的二元混合物的简单库，在现场筛选任务中表现良好，例如区分 API 的劣药和假药配方。六种数据分析模型，包括主成分分析和支持向量机分类和回归方法以及卷积神经网络，都在对乙酰氨基酚与乳糖或抗坏血酸的二元混合物上进行了训练。虽然这些模型在交叉验证中表现都很强（在与训练集相似的配方上），但它们各自在训练集之外的配方上表现出较差的稳健性。然而，一种基于仅在二元样本上训练的六个模型的预测算法，可以准确预测正确量的乙酰氨基酚是否存在于三元混合物、真实的乙酰氨基酚配方、掺假的乙酰氨基酚配方和含有替代 API 的伪造配方中。这种数据分析方法可能会扩展 NIR 光谱仪在资源匮乏环境下分析药物的实用性。
