Yang Lele, Xue Yan, Wei Jinchao, Dai Qi, Li Peng
State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences, University of Macau, Macau, China.
Chengdu Institute for Food and Drug Control, Chengdu, China.
Chin Med. 2021 Mar 19;16(1):30. doi: 10.1186/s13020-021-00438-x.
Jinqi Jiangtang (JQJT) has been widely used in clinical practice to prevent and treat type 2 diabetes. However, little research has been done to identify and classify its quality markers (Q-markers) associated with anti-diabetes bioactivity. In this study, a strategy combining mass spectrometry-based untargeted metabolomics with backpropagation artificial neural network (BP-ANN)-based machine learning approach was proposed to screen Q-markers from JQJT preparation.
This strategy mainly involved chemical profiling of herbal medicines, statistic processing of metabolomic datasets, detection of different anti-diabetes activities and establishment of BP-ANN model. The chemical features of seventy-eight batches of JQJT extracts were first profiled by using the untargeted UPLC-LTQ-Orbitrap metabolomic approach. The chemical features obtained which were associated with different anti-diabetes activities based on three modes of action were normalized, ranked, and then pre-selected by using ReliefF feature selection. BP-ANN model was then established and optimized to screen Q-markers based on mean impact value (MIV).
Optimized BP-ANN architecture was established with high accuracy of R > 0.9983 and relative low error of MSE < 0.0014, which showed better performance than that of partial least square (PLS) model (R < 0.5). Meanwhile, the BP-ANN model was subsequently applied to further screen potential bioactive components from the pre-selected chemical features by calculating their MIVs. With this machine learning model, 10 potential Q-markers with bioactivity were discovered from JQJT. The tested anti-diabetes bioactivities of 78 batches of JQJT could be accurately predicted.
This proposed artificial intelligence approach is desirable for quick and easy identification of Q-markers with bioactivity from JQJT preparation.
金芪降糖方（JQJT）已在临床实践中广泛用于预防和治疗2型糖尿病。然而，关于鉴定和分类其与抗糖尿病生物活性相关的质量标志物（Q-标志物）的研究较少。在本研究中，提出了一种基于质谱的非靶向代谢组学与基于反向传播人工神经网络（BP-ANN）的机器学习方法相结合的策略，以从JQJT制剂中筛选Q-标志物。
该策略主要包括中药化学表征、代谢组学数据集的统计处理、不同抗糖尿病活性的检测以及BP-ANN模型的建立。首先采用非靶向超高效液相色谱-线性离子阱-轨道阱代谢组学方法对78批次JQJT提取物的化学特征进行表征。基于三种作用模式，对获得的与不同抗糖尿病活性相关的化学特征进行归一化、排序，然后采用ReliefF特征选择法进行预选。然后建立并优化BP-ANN模型，基于平均影响值（MIV）筛选Q-标志物。
建立了优化的BP-ANN结构，R>0.9983，准确率高，均方误差（MSE）<0.0014，相对误差低，表现优于偏最小二乘（PLS）模型（R<0.5）。同时，随后应用BP-ANN模型通过计算预选化学特征的MIV值进一步筛选潜在的生物活性成分。通过该机器学习模型，从JQJT中发现了10种具有生物活性的潜在Q-标志物。可以准确预测78批次JQJT的抗糖尿病生物活性。
所提出的人工智能方法有助于快速、简便地从JQJT制剂中鉴定出具有生物活性的Q-标志物。
