4.3 Implications
Inflammation is a pathological stage of many diseases. Inflammation
manifests in a variety of disease forms, and the currently selected
therapeutic drugs are mainly non-steroidal anti-inflammatory drugs,
glucocorticoids and other drugs. However, the application of
anti-inflammatory effects of plant monomers is relatively limited. In
addition, animal experiments to study the anti-inflammatory effect of
monomers lack the results of effective clinical transformation, and
there is also a lack of corresponding preclinical data collection. This
article comprehensively utilizes the research on the treatment of
inflammation with icariin, conducts meta-analysis based on its outcome
index data to explore the curative effect, machine learning was then
used to mine for more effective doses and courses of Icariin and its
derivatives for the treatment of inflammation, and the types of
inflammatory diseases for which they have an efficacy advantage. The
results showed that icariin and its derivatives could improve TNF-α,
IL-1β, IL-6, IFN-γ, TGF-β1, IkB-α, NF-κB p65, NLRP3, PPARα, SOD, MDA,
BCL -2 and caspase-1 levels. And icariin and its derivatives have more
favorable effect of inhibiting inflammation in respiratory, urinary,
neurological and digestive diseases and at a dose of 27.52 mg/kg/day or
more and a course of treatment of 31.22 days or more.
Animal experiments have always been an integral part of basic medical
research, but laboratory animal research has always been subjected to
ethical tests(Meier & Stocker, 1989). With the increase in the number
of animal experiments and the expansion of the scale of a single
experiment, the ethical review of the use of animals in experiments has
become more stringent(Gruber & Hartung, 2004). How to efficiently
select appropriate experimental animal models, treatment regimens and
dosages of reagents, and make better use of animal experimental results,
has gradually become an important issue(Loeb, Hendee, Smith, &
Schwartz, 1989). Meta-analysis of animals can integrate published
experimental results, save experimental resources, and obtain
higher-level and more accurate preclinical evidence. At the same time,
machine learning can be used to screen out features that are more
important for drug treatment of diseases, so as to provide a reference
for the selection of sample features for more in-depth animal
experiments.
According to research, many drugs have shown good curative effects in
the laboratory for treating diseases, but their clinical transformation
rate is still low(van der Worp & Sandercock, 2012). Although there are
many common mechanisms discovered in the laboratory, there are still
differences in the organ structure between animals and humans, which
cannot be ignored. Therefore, it is necessary to use machine learning to
mine key features and select an appropriate animal model thereby
increasing the clinical translation rate.