MS-DDF: an intelligent mass spectrometry framework tailored for overcoming the in vivo analytical bottleneck in animal-derived medicines

In vivo analysis of animal-derived medicines remains challenging due to poor chromatographic behavior and low mass spectrometry response. Chemical derivatization is commonly used to enhance the detection but produces complex datasets requiring advanced processing. Herein, we developed Mass Spectrometry Deep-refine Derivatization Filter (MS-DDF), an instrument-agnostic, intelligent post-processing workflow providing an advanced approach for in vivo analysis of animal-derived medicines. MS-DDF includes three functional modules: system noise subtraction to eliminate instrumental background, endogenous interference subtraction to remove interferences from endogenous components, and combinatorial peak matching enables efficient extraction of derivatized target components. As a proof of concept, we adopted MS-DDF to investigate Eupolyphaga sinensis Walker, to validate its suitability for in vivo analysis of components in animal-derived medicines, with simple operation and broad applicability. Additionally, MS-DDF can serve as an analytical platform for metabolomics studies across different functional groups. MS-DDF is an intelligent post-processing framework that eliminates system noise and endogenous interferences for enhanced in vivo analysis sensitivity towards animal-derived medicines.