Abstract
BackgroundJinqi 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.MethodsThis 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).ResultsOptimized 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 (R2 < 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.ConclusionsThis proposed artificial intelligence approach is desirable for quick and easy identification of Q-markers with bioactivity from JQJT preparation.
Highlights
Jinqi Jiangtang (JQJT) has been widely used in clinical practice to prevent and treat type 2 diabetes
Limited information about its Q-markers associated with anti-diabetes bioactivity has been gathered from these research far. With consideration of this situation, we proposed a data-driven approach combining mass spectrometry-based untargeted metabolomics with backpropagation artificial neural network (BP-ANN)-based machine learning approach to screening Q-markers from JQJT
Untargeted metabolomic profiling of different JQJT combinations Different batches of the three herbal medicines collected from diverse origins were profiled respectively by using the developed UPLC-LTQ-Orbitrap method
Summary
Jinqi Jiangtang (JQJT) has been widely used in clinical practice to prevent and treat type 2 diabetes. 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. The identification of chemical markers for quality control, those contributing most to their therapeutic efficacy, is the key to understand the scientific basis for the therapeutic application of herbal medicines [5]. Due to the high complexity of the MS datasets obtained, researchers commonly focus on the analysis of a restricted number of identified compounds to screen Q-markers of interest. The welltrained ANN models have been employed to identify complex patterns from datasets, make real-time predictions and offer adaptive solutions in multiple fields, such as natural products, nanotechnology, and bioresource technology [14, 15]
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