Inflammatory responses in the kidney lead to tubulointerstitial fibrosis, a common feature of chronic kidney diseases. Here we examined the role of prostaglandin E(2) (PGE(2)) in the development of tubulointerstitial fibrosis. In the kidneys of wild-type mice, unilateral ureteral obstruction leads to progressive tubulointerstitial fibrosis with macrophage infiltration and myofibroblast proliferation. This was accompanied by an upregulation of COX-2 and PGE(2) receptor subtype EP(4) mRNAs. In the kidneys of EP(4) gene knockout mice, however, obstruction-induced histological alterations were significantly augmented. In contrast, an EP(4)-specific agonist significantly attenuated these alterations in the kidneys of wild-type mice. The mRNAs for macrophage chemokines and profibrotic growth factors were upregulated in the kidneys of wild-type mice after ureteral obstruction. This was significantly augmented in the kidneys of EP(4)-knockout mice and suppressed by the EP(4) agonist but only in the kidneys of wild-type mice. Notably, COX-2 and MCP-1 proteins, as well as EP(4) mRNA, were localized in renal tubular epithelial cells after ureteral obstruction. In cultured renal fibroblasts, another EP(4)-specific agonist significantly inhibited PDGF-induced proliferation and profibrotic connective tissue growth factor production. Hence, an endogenous PGE(2)-EP(4) system in the tubular epithelium limits the development of tubulointerstitial fibrosis by suppressing inflammatory responses.

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Artificial neural networks are the main tools for data mining and were inspired by the human brain and nervous system. Studies have demonstrated their usefulness in medicine. However, no studies have used artificial neural networks for the prediction of adverse drug reactions. We aimed to validate the usefulness of artificial neural networks for the prediction of adverse drug reactions and focused on vancomycin -induced nephrotoxicity. For constructing an artificial neural network, a multilayer perceptron algorithm was employed. A 10-fold cross validation method was adopted for evaluating the resultant artificial neural network. In total, 1141 patients who received vancomycin at Hokkaido University Hospital from November 2011 to February 2019 were enrolled. Among these patients, 179 (15.7%) developed vancomycin -induced nephrotoxicity. The top three risk factors of vancomycin -induced nephrotoxicity which are relatively important in the artificial neural networks were average vancomycin trough concentration ≥ 13.0 mg/L and concomitant use of piperacillin–tazobactam and vasopressor drugs. The predictive accuracy of the artificial neural network was 86.3% and that of the multiple logistic regression model (conventional statistical method) was 85.1%. Moreover, area under the receiver operating characteristic curve (AUROC) of the artificial neural network was 0.83. In the 10-fold cross-validation, the accuracy obtained was 86.0% and AUROC was 0.82. The artificial neural network model predicting the vancomycin -induced nephrotoxicity showed good predictive performance. This appears to be the first report of the usefulness of artificial neural networks for an adverse drug reactions risk prediction model.

The effect of selective activation of platelet prostaglandin (PG) E2 receptor subtype EP2 or EP4 on platelet aggregation remains to be determined. In platelets prepared from wild-type mice (WT platelets), high concentrations of PGE2 inhibited platelet aggregation induced by U-46619, a thromboxane receptor agonist. However, there was no significant change in the inhibitory effect of PGE2 on platelets lacking EP2 (EP2-/- platelets) and EP4 (EP4-/- platelets) compared with the inhibitory effect on WT platelets. On the other hand, AE1-259 and AE1-329, agonists for EP2 and EP4, respectively, potently inhibited U-46619 -induced aggregation with respective IC50 values of 590 ± 14 and 100 ± 4.9 nM in WT platelets, while the inhibition was significantly blunted in EP2-/- and EP4-/- platelets. In human platelets, AE1-259 and AE1-329 inhibited U-46619-induced aggregation with respective IC50 values of 640 ± 16 and 2.3 ± 0.3 nM. Notably, the inhibitory potency of AE1-329 in human platelets was much higher than that in murine platelets, while such a difference was not observed in the inhibitory potency of AE1-259. AE1-329 also inhibited adenosine diphosphate-induced platelet aggregation, and the inhibition was almost completely blocked by AE3-208, an EP4 antagonist. In addition, AE1-329 increased intracellular cAMP concentrations in a concentration- and EP4-dependent manner in human platelets. These results indicate that selective activation of EP2 or EP4 can inhibit platelet aggregation and that EP4 agonists are particularly promising as novel anti-platelet agents.