1 Introduction
As a paramount fundamental pathological mechanism, inflammation exerts a pivotal influence on the pathogenesis of numerous diseases, and its timely attenuation is imperative to prevent potential deleterious consequences on the organism.(Kong et al., 2022). The mechanism of the inflammatory response is intricate, involving a range of cytokines like TNF-α and IL-1β, as well as significant signaling pathways like NF-κB and P38 MAPK. Inhibiting these inflammatory processes presents a promising avenue for effectively treating associated diseases.
Icariin (PubChem CID: 5318997), as a flavonol glycoside compound purified from the traditional Chinese medicine Epimedium, which is also known as ”Yin Yang Huo”, has variety of bioactive components(M. Wang, Gao, Li, & Wu, 2020). It has been studied extensively and being considered as a potential therapeutic medicine for many diseases, and demonstrated to exert anti-inflammation(G. Wang et al., 2022), anti-oxidative(J. Jin et al., 2019), anti-apoptotic effect(L. Hu et al., 2019) and anti-tumor(Gao, Wang, & Gao, 2022). Increasing evidence has shown that icariin plays a key role in prevention and treatment of many diseases by mitigating inflammatory response. Therefore, it is of great significance to study the effect of icariin on anti-inflammation and to explore the underlying mechanism. The research roadmap is as follow (Figure1).
Accumulating evidence from a large number experiments is essential to draw definitive conclusions about potential therapies. However, currentin vivo studies often report inconsistent results and excessive animal testing raises ethical concerns(Ioannidis et al., 2014; Locker, 2004). Statistical synthesis of data from multiple in vivo studies will help address these issues by providing a more precise and objective assessment with fewer experiments. In particular, systematic reviews of data have the potential to accelerate drug translation. In recent years, this approach has been applied to several interventions, such as melatonin for myocardial ischemia-reperfusion injury and quercetin for liver fibrosis(X. Guo et al., 2022; Mao, Lin, Xiao, Huang, & Chen, 2020). Careful consideration of drug dosage and regimen is crucial, as they can impact animal model results. Advancements in machine learning now allow tapping into drug efficacy for different disease categories, guiding preclinical and early clinical study design, including ideal dose intervals and durations(Greener, Kandathil, Moffat, & Jones, 2022). In conclusion, systematic evaluation combined with machine learning enhances the effectiveness and translational value of preclinical studies. It also provides valuable reference for subsequent animal experiments, improving experimental efficacy.