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.