Abstract
Background: Due to the special nature of Chinese Herbal medicine and the complexity of its clinical use, it is difficult to identify and evaluate its toxicity and resulting herb induced liver injury (HILI).
Methods: First, the database would provide full profile of HILI from the basic ingredients to clinical outcomes by the most advanced algorithms of artificial intelligence, and it is also possible that we can predict possibilities of HILI after patients taking Chinese herbs by individual patient evaluation and prediction. Second, the database would solve the chaos and lack of the relevant data faced by the current basic research and clinical practice of Chinese Herbal Medicine. Third, we can also screen the susceptible patients from the database and thus prevent the accidents of HILI from the very beginning.
Results: The Roussel Uclaf Causality Assessment Method (RUCAM) is the most accepted method to evaluate DILI, but at present before using the RUCAM evaluation method, data resource collection and analysis are yet to be perfected. Based on existing research on drug-metabolizing enzymes mediating reactive metabolites (RMs), the aim of this study is to explore the possibilities and methods of building multidimensional hierarchical database composing of RMs evidence library, Chinese herbal evidence library, and individualized reports evidence library of herb induced liver injury HILI.
Conclusion: The potential benefits lie in its ability to organize, use vast amounts of evidence and use big data mining techniques at the center for Chinese herbal medicine liver toxicity research, which is the most difficult key point of scientific research to be investigated in the next few years.
Keywords: Drug-induced Liver Injury (DILI), Herb-induced Liver Injury (HILI), Evidence-based Medicine, Reactive Metabolites (RMs), evidence library, hepatotoxicity individualized, clinical study, Chinese herbal recognition evidence library, RUCAM.
Graphical Abstract
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