Discrimination of Substandard and Falsified Formulations from Genuine Pharmaceuticals Using NIR Spectra and Machine Learning
Discrimination of Substandard and Falsified Formulations from Genuine Pharmaceuticals Using NIR Spectra and Machine Learning
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DOI：
10.1021/acs.analchem.2c00998
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发表时间：
2022-09-06
影响因子：
7.4
通讯作者：
Lieberman, Marya
中科院分区：
文献类型：
--
作者：
Awotunde, Olatunde;Roseboom, Nicholas;Lieberman, Marya
近红外（NIR）光谱是一种用于现场鉴定不合格和伪造药物的有前途的技术，因为它便携、快速、无损，并且可以区分许多配制的药物产品。便携式近红外光谱仪严重依赖于基于真实药物样品的近红外光谱库的化学计量分析。然而，在许多低收入和中等收入国家很难建立全面的产品库，原因是供应这些市场的制造商数量众多，制造商在材料来源和产品配方方面经常发生未报告的变化，以及在提供真实样品方面普遍缺乏合作。在这项工作中，我们表明，一个简单的图书馆的实验室配制的二元混合物的活性药物成分（API）与两种稀释剂在现场筛选任务，如区分不合标准和伪造的配方的API提供了良好的性能。六个数据分析模型，包括主成分分析和支持向量机分类和回归方法和卷积神经网络，对乙酰氨基酚与乳糖或抗坏血酸的二元混合物进行了训练。虽然这些模型在交叉验证（与其训练集相似的制剂）中表现强劲，但它们各自对训练集以外的制剂表现出较差的稳健性。然而，基于六个模型的预测算法，仅在二元样本上训练，准确地预测三元混合物、真正的对乙酰氨基酚制剂、掺假的对乙酰氨基酚制剂和含有替代API的伪造制剂中是否存在正确量的对乙酰氨基酚。这种数据分析方法可以扩展近红外光谱仪在低资源环境中分析药物的实用性。
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 supportvector 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.