Lipoxins and epoxyeicosatrienoic acids. Potential for inhibitors of soluble epoxide hydrolase in severe asthma?

The arachidonic acid metabolite 11,12-epoxyeicosatrienoic acid alleviates pulmonary fibrosis

Epoxyeicosatrienoic acids (EETs) are metabolites of arachidonic acid that are rapidly metabolized into diols by soluble epoxide hydrolase (sEH). sEH inhibition has been shown to increase the biological activity of EETs, which are known to have anti-inflammatory properties. However, the role of EETs in pulmonary fibrosis remains unexplored. Liquid chromatography with tandem mass spectrometry (LC-MS/MS) was used to analyze EETs in the lung tissues of patients with idiopathic pulmonary fibrosis (IPF, n = 29) and controls (n = 15), and the function of 11,12-EET was evaluated in in vitro and in vivo in pulmonary fibrosis models. EET levels in IPF lung tissues, including those of 8,9-EET, 11,12-EET, and 14,15-EET, were significantly lower than those in control tissues. The 11,12-EET/11,12-DHET ratio in human lung tissues also differentiated IPF from control tissues. 11,12-EET significantly decreased transforming growth factor (TGF)-β1-induced expression of α-smooth muscle actin (SMA) and collagen type-I in MRC-5 cells and primary fibroblasts from IPF patients. sEH-specific siRNA and 1-trifluoromethoxyphenyl-3-(1-propionylpiperidin-4-yl) urea (TPPU; sEH inhibitor) also decreased TGF-β1-induced expression of α-SMA and collagen type-I in fibroblasts. Moreover, 11,12-EET and TPPU decreased TGF-β1-induced p-Smad2/3 and extracellular-signal-regulated kinase (ERK) expression in primary fibroblasts from patients with IPF and fibronectin expression in Beas-2B cells. TPPU decreased the levels of hydroxyproline in the lungs of bleomycin-induced mice. 11,12-EET or sEH inhibitors could inhibit pulmonary fibrosis by regulating TGF-β1-induced profibrotic signaling, suggesting that 11,12-EET and the regulation of EETs could serve as potential therapeutic targets for IPF treatment.

Signaling molecules called eicosanoids, which are derived from fatty acids, can suppress lung damage in idiopathic pulmonary fibrosis (IPF), a chronic, progressive disease in which scar tissue builds up in the lungs, making it hard to breathe. The causes of IPF are unknown. Eicosanoids, which have anti-inflammatory properties, have been studied in various lung diseases. Jin Woo Song at the University of Ulsan College of Medicine in Seoul, South Korea, and co-workers investigated how they might affect IPF. They found that eicosanoid levels were lower in lung tissues from patients with IPF than in healthy tissues. Further investigation showed eicosanoid levels could be boosted by suppressing an enzyme called sEH that degrades them. Thus, suppression of sEH and boosting of eicosanoid levels show promise as therapeutic targets for IPF.

Asthma Biomarkers: Do They Bring Precision Medicine Closer to the Clinic?

Measurement of biomarkers has been incorporated within clinical research of asthma to characterize the population and to associate the disease with environmental and therapeutic effects. Regrettably, at present, there are no specific biomarkers, none is validated or qualified, and endotype-driven choices overlap. Biomarkers have not yet reached clinical practice and are not included in current asthma guidelines. Last but not least, the choice of the outcome upholding the value of the biomarkers is extremely difficult, since it has to reflect the mechanistic intervention while being relevant to both the disease and the particular person. On the verge of a new age of asthma healthcare standard, we must embrace and adapt to the key drivers of change. Disease endotypes, biomarkers, and precision medicine represent an emerging model of patient care building on large-scale biologic databases, omics and diverse cellular assays, health information technology, and computational tools for analyzing sizable sets of data. A profound transformation of clinical and research pattern from population to individual risk and from investigator-imposed subjective disease clustering (hypothesis driven) to unbiased, data-driven models is facilitated by the endotype/biomarker-driven approach.