A Scalable, Traceable Computational-to-Experimental Pipeline for Activity-Guided Identification of Bioactive Constituents: A Case Study in Salvia miltiorrhiza Bunge.
- 1College of Pharmacy, Gansu University of Chinese Medicine, Lanzhou, China.
- 2CAS Key Laboratory of Chemistry of Northwestern Plant Resources and Key Laboratory of Natural Medicine of Gansu Province, Lanzhou Institute of Chemical Physics, Chinese Academy of Sciences (CAS), Lanzhou, China.
- 3Institute of Traditional Chinese Medicine Pharmacology and Toxicology, Sichuan Academy of Chinese Medicine Sciences, Chengdu, China.
Abstract
Systematic identification of bioactive constituents from complex natural products remains challenging. Here, a scalable and traceable integrated workflow was developed and validated in Salvia miltiorrhiza Bunge (SMB) for anti-Parkinson's disease (PD) constituent discovery. Network pharmacology first identified four candidate compounds, which were quantified and dynamically tracked during X-5 macroporous resin enrichment and fractionation using a validated HPLC method. Transfer rates exceeded 80% for all candidates, confirming efficient enrichment and chemical traceability. Bioactivity was then evaluated in MPP⁺-induced PC12 cells and a rotenone-induced PD mouse model. SMB-2 showed the strongest neuroprotective activity and was subjected to spectrum-effect relationship analysis, which identified five activity-associated compounds. Integration of pharmacological validation, molecular docking, and molecular dynamics simulations progressively narrowed the candidate space and identified cryptotanshinone as a major bioactive constituent. Cryptotanshinone exhibited favorable predicted binding affinity and stable interactions with key residues in the monoamine oxidase B binding pocket, supporting its potential MAO-B inhibitory activity. It also restored intracellular ATP levels and superoxide dismutase activity, indicating improved energy metabolism and antioxidant defense. Overall, this workflow enables stepwise convergence from complex natural matrices to experimentally supported bioactive constituents and provides a broadly applicable strategy for natural-product-based drug discovery.
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BibTeX
@article{wang2026scalable,
title = {{A Scalable, Traceable Computational-to-Experimental Pipeline for Activity-Guided Identification of Bioactive Constituents: A Case Study in Salvia miltiorrhiza Bunge.}},
author = {Xin Wang and Zhenghuai Tan and Xiaojie Jin and Jun Hai and Dong Pei and Duo-Long Di},
journal = {Chemistry \& biodiversity},
year = {2026},
month = nov,
doi = {10.1002/cbdv.71898},
url = {https://europepmc.org/article/MED/42967599},
note = {CC BY-NC-ND},
}
RIS
TY - JOUR
TI - A Scalable, Traceable Computational-to-Experimental Pipeline for Activity-Guided Identification of Bioactive Constituents: A Case Study in Salvia miltiorrhiza Bunge.
AU - Xin Wang
AU - Zhenghuai Tan
AU - Xiaojie Jin
AU - Jun Hai
AU - Dong Pei
AU - Duo-Long Di
PY - 2026
DA - 2026/11/01
JO - Chemistry & biodiversity
DO - 10.1002/cbdv.71898
UR - https://europepmc.org/article/MED/42967599
AB - Systematic identification of bioactive constituents from complex natural products remains challenging. Here, a scalable and traceable integrated workflow was developed and validated in Salvia miltiorrhiza Bunge (SMB) for anti-Parkinson's disease (PD) constituent discovery. Network pharmacology first identified four candidate compounds, which were quantified and dynamically tracked during X-5 macroporous resin enrichment and fractionation using a validated HPLC method. Transfer rates exceeded 80% for all candidates, confirming efficient enrichment and chemical traceability. Bioactivity was then evaluated in MPP⁺-induced PC12 cells and a rotenone-induced PD mouse model. SMB-2 showed the strongest neuroprotective activity and was subjected to spectrum-effect relationship analysis, which identified five activity-associated compounds. Integration of pharmacological validation, molecular docking, and molecular dynamics simulations progressively narrowed the candidate space and identified cryptotanshinone as a major bioactive constituent. Cryptotanshinone exhibited favorable predicted binding affinity and stable interactions with key residues in the monoamine oxidase B binding pocket, supporting its potential MAO-B inhibitory activity. It also restored intracellular ATP levels and superoxide dismutase activity, indicating improved energy metabolism and antioxidant defense. Overall, this workflow enables stepwise convergence from complex natural matrices to experimentally supported bioactive constituents and provides a broadly applicable strategy for natural-product-based drug discovery.
SN - 1612-1872
SN - 1612-1880
ER -