Unravelling pharmacological mechanisms and effects of Tianma Siwu Decoction-derived compounds on ischemic stroke by multidimensional network pharmacological analysis

Ischemic stroke (IS) a complex pathological event emerging as one of the most serious threats with huge economic impact in the 21st century. Following IS, multiple cascades and pathways are stimulated, culminating in long term consequences. One of Chinese Traditional Medicine, Tianma Siwu Decoction (TSD), is known to have sedative-hypnotic, anticonvulsant and anti-inflammatory effects, which is usually used to treat migraine and ischemic stroke, but its potential pharmacological mechanism remains unclear.

This study is aimed to identify the active principles from TSD that has strong pharmacological effect on the treatment of IS.

Based on liquid chromatography-triple quadrupole mass spectrometry (LC-Q-MS/MS) technology, a new three-step-based approach integrating concentration parameters and Quality marker (Q-marker) with network pharmacology and bioactivity evaluation to explore the therapeutic effects and mechanisms of TSD on ischemic stroke. Ultimately, as the main herb of the TSD, high-concentration compounds from Gastrodia elata Blume (GEB) were identified and collected by LC-Q-MS/MS, and an optimized analytical model in multidimensional network pharmacology was introduced to more accurately explore the potential mechanisms by which TSD affects IS.

The results showed that 280 overlapping targets of TSD were obtained after the introduction of compound concentration parameters into the multidimensional network pharmacology analysis. Additionally, TSD might regulate IS through the AGE-RAGE and Rap1 signaling pathways. Through an in vitro hypoxia-reoxygenation injury cell model, it was discovered that as the Q-markers of GEB, gastrodin and parishin could effectively reduce neuronal hypoxic injury by modulating the expression levels of p-JNK and p-p38 proteins. According to the results of molecular docking, gastrodin and baicalin exhibits strong binding affinity to GAPDH and MAPK3, respectively (≦-7 kcal/mol).

We discovered that compound concentration is a key factor that influence the activity of substances, affects the accuracy and reliability of predictive outcomes. Consequently, the study enhances the network pharmacology model by incorporating concentration factors, aiming for a more accurate understanding of the potential mechanisms behind TSD anti-ischemic stroke actions.