Advances in Intelligent Mass Spectrometry Data Processing Technology for In vivo Analysis of Natural Medicines

Natural medicines (NMs) are crucial for treating human diseases. Efficiently characterizing their bioactive components in vivo has been a key focus and challenge in NM research. High-performance liquid chromatography-high-resolution mass spectrometry system offer high sensitivity, resolution, and precision for conducting in vivo analysis of NMs. However, because of the complexity of NMs, conventional data-acquisition, -mining, and -processing techniques often fail to meet the practical needs of in vivo NM analysis. Over the past two decades, intelligent spectral data-processing techniques based on various principles and algorithms have been developed and applied for in vivo NM analysis. Thus, improvements have been achieved in the overall analytical performance by relying on these techniques without the need to change the instrument hardware, and these improvements include instrument analysis sensitivity, compound analysis coverage, intelligent identification, and characterization of nontargeted in vivo compounds, providing powerful technical means for studying the in vivo metabolism of NMs and screening for pharmacologically active components. This review summarizes the research progress on in vivo analysis strategies for NMs via the use of intelligent mass spectrometry data-processing techniques reported over the past two decades. Covering differences in compound structures, variances between biological samples, and the use of artificial intelligence neural network algorithms. This review also summarizes and outlook on the applications of in vivo process tracking for NMs, including the screening of bioactive components and the exploration of pharmacokinetic markers. The review aims to serve as a reference for the integration and development of new technologies and strategies for future in vivo analysis of NMs.