Analysis of hemorrhagic drug-drug interactions between P-gp inhibitors and direct oral anticoagulants from the FDA Adverse Event Reporting System.

BACKGROUND: Limited understanding exists regarding the hemorrhagic risk resulting from potential interactions between P-glycoprotein (P-gp) inhibitors and direct oral anticoagulants (DOACs). Utilizing the Food and Drug Administration Adverse Event Reporting System (FAERS) data, we analyzed hemorrhagic adverse events (AEs) linked with the co-administration of P-gp inhibitors and DOACs, aiming to offer guidance for their safe and rational use. METHODS: Hemorrhagic events associated with P-gp inhibitors in combination with DOACs were scrutinized from the FAERS database. Hemorrhagic signals mining was performed by estimating the reported odds ratios (RORs), corroborated by additive and multiplicative models and a combination risk ratio (PRR) model. RESULTS: Our analysis covered 4,417,195 cases, revealing 11,967 bleeding events associated with P-gp inhibitors. We observed a significantly higher risk of bleeding with the combination of apixaban and felodipine (ROR 118.84, 95% CI 78.12-180.79, additive model 0.545, multiplicative model 1.253, PRR 22.896 (2450.141)). Moreover, consistent associations were found in the co-administration analyzes of rivaroxaban with dronedarone and diltiazem, and apixaban with losartan, telmisartan, and simvastatin. CONCLUSION: Our FAERS data analysis unveils varying degrees of bleeding risk associated with the co-administration of P-gp inhibitors and DOACs, underscoring the importance of vigilance about them in clinical practice.

Post-Marketing Safety Concerns with Upadacitinib: A Disproportionality Analysis of the FDA Adverse Event Reporting system.

BACKGROUND: Upadacitinib was approved to treat rheumatoid arthritis, psoriasis, ulcerative colitis, ankylosing spondylitis, and atopic dermatitis. This study assessed the adverse events (AEs) associated with upadacitinib by mining data from the US Food and Drug Administration Adverse Event Reporting System (FAERS). METHODS: Disproportionality analyses, including the reporting odds ratio (ROR), the proportional reporting ratio (PRR), the Bayesian confidence propagation neural network (BCPNN), and the multi-item gamma Poisson shrinker (MGPS) algorithms, were employed to quantify the signals of upadacitinib-associated AEs. RESULTS: A total of 3,837,420 reports of AEs were collected from the FAERS database, of which 4494 reports were identified with upadacitinib as the "primary suspect (PS)". Upadacitinib-induced AEs occurrence targeted 27 system organ clases (SOCs). A total of 200 significant disproportionality PTs conforming to the four algorithms were simultaneously retained. Unexpected significant AEs, such as arthralgia, musculoskeletal stiffness, diverticulitis, and cataract might also occur. The median onset time of upadacitinib-associated AEs was 65 days (interquartile range [IQR] 21-182 days), and most of the onsets occurred within the first 1, 2, 3, and 4 months after initiation of upadacitinib. CONCLUSION: This study found potential new AEs signals and might provide important support for clinical monitoring and risk identification of upadacitinib.

Selective Action Mechanism of Fenclorim on Rice and Echinochloa crusgalli Is Associated with the Inducibility of Detoxifying Enzyme Activities and Antioxidative Defense.

Fenclorim (Fen) is a safener developed for pretilachlor (Pre) that can protect rice from injury caused by Pre but does not lower the weed control effects of Pre. Unfortunately, the mechanism of selective action of Fen between rice and weeds, such as Echinochloa crusgalli (barnyard grass), has not been clarified. In this study, the differences in physiology, biochemistry, and gene transcription between rice and E. crusgalli response to Fen were compared. Comparing the protection effects of Fen on plant growth, it was found that Fen significantly protected rice from Pre, but did not protect E. crusgalli. The detection of malondialdehyde (MDA) content and activities of antioxidant enzymes showed that Pre induced significant oxidative damage both in rice and E. crusgalli; however, Fen reduced oxidative damage in rice but not in E. crusgalli. Transcriptome analysis revealed that Fen induced more genes related to herbicide metabolism in rice than in E. crusgalli, especially the glutathione-S-transferase (GST) genes, with six upregulated in rice but no genes upregulated in E. crusgalli. Accordingly, the GST activity analysis showed that Fen increased the activity of rice instead of E. crusgalli. These results indicate that the elevation of detoxifying enzyme activities and antioxidative defense may be the mechanism of selective action of Fen in rice but not in E. crusgalli.

Effects of fenclorim on rice physiology, gene transcription and pretilachlor detoxification ability.

BACKGROUND: Fenclorim (Fen) can effectively protect rice from pretilachlor (Pre) injury, but its effects on rice have not been formally evaluated; thus, the Fen mode of action for alleviating the phytotoxicity caused by Pre in rice is not clear. This study aimed to examine the biochemical and physiological effects of Fen on rice and to determine the changes induced by Fen at the transcriptome level. RESULT: The chlorophyll content of rice plants was significantly affected by Pre but not by Fen. The activity of oxidative stress enzymes showed that Fen did not elicit any changes in oxidative stress; however, it reduced lipid peroxidation and oxidative damage induced by Pre. Fen did not affect the uptake of Pre but did affect its persistence in rice. In a transcriptome experiment, Fen upregulated genes in a detoxification pathway. Overall, 25 genes related to detoxification were identified, including P450, GST, and GT. Moreover, qRT-PCR analysis showed that four P450 genes, CYP71Y83, CYP71K14, CYP734A2 and CYP71D55, and two GST genes, GSTU16 and GSTF5, were upregulated by Fen and/or Pre. CONCLUSION: Our work indicates that Fen acts in antioxidative defense in addition to enhancing the metabolism of herbicides in rice.