Fluoxetine-induced hepatic lipid accumulation is mediated by prostaglandin endoperoxide synthase 1 and is linked to elevated 15-deoxy-Δ12,14PGJ2

Major depressive disorder and other neuropsychiatric disorders are often managed with long-term use of antidepressant medication. Fluoxetine, an SSRI antidepressant, is widely used as a first-line treatment for neuropsychiatric disorders. However, fluoxetine has also been shown to increase the risk of metabolic diseases such as non-alcoholic fatty liver disease. Fluoxetine has been shown to increase hepatic lipid accumulation in vivo and in vitro. In addition, fluoxetine has been shown to alter the production of prostaglandins which have also been implicated in the development of non-alcoholic fatty liver disease. The goal of this study was to assess the effect of fluoxetine exposure on the prostaglandin biosynthetic pathway and lipid accumulation in a hepatic cell line (H4-II-E-C3 cells). Fluoxetine treatment increased mRNA expression of prostaglandin biosynthetic enzymes (Ptgs1, Ptgs2, and Ptgds), PPAR gamma (Pparg), and PPAR gamma downstream targets involved in fatty acid uptake (Cd36, Fatp2, and Fatp5) as well as production of 15-deoxy-Δ^12,14PGJ₂ a PPAR gamma ligand. The effects of fluoxetine to induce lipid accumulation were attenuated with a PTGS1 specific inhibitor (SC-560), whereas inhibition of PTGS2 had no effect. Moreover, SC-560 attenuated 15-deoxy-Δ^12,14PGJ₂ production and expression of PPAR gamma downstream target genes. Taken together these results suggest that fluoxetine-induced lipid abnormalities appear to be mediated via PTGS1 and its downstream product 15d-PGJ₂ and suggest a novel therapeutic target to prevent some of the adverse effects of fluoxetine treatment.     

Immune checkpoint inhibitors in advanced nasopharyngeal carcinoma: Beyond an era of chemoradiation?

Now is an exciting era of development in immunotherapy checkpoint inhibitors and their effect on the treatment of NPC. While the general prognosis of R/M disease is poor, immunotherapy offers some promise in a malignancy associated with EBV and characterized by a peritumoural immune infiltrate. Our study aims to review past and on-going clinical trials of monoclonal antibody therapies against the checkpoint inhibitors (e.g. PD1 and CTLA-4), in R/M NPC. All randomized and nonrandomized controlled trials involving immune checkpoint inhibitor interventions for treatment of NPC were included in the study. We utilized a validated “risk of bias” tool to assess study quality. Four separate Phase I–II trials report the potential of PD1 inhibitor treatment for patients with NPC. Within the observed groups, camrelizumab combined with chemotherapy achieved an objective response in 91% of patients as first-line treatment for metastatic NPC (PFS 68% at 1-year) but this was associated with a high rate of grade >3 adverse events (87%; CTCAE version 4.03). The remaining three studies focused on recurrent NPC disease in patients who had received at least one line of prior chemotherapy. Within this group, camrelizumab monotherapy achieved an objective response in 34% of patients (PFS 27% at 1-year; range across all three studies 20.5–34%). No NPC trial has yet reported on specific outcomes for non-PD1 checkpoint inhibitors but 11 on-going studies include alternative targets (e.g. PD-L1/CTLA-4) as combination or monotherapy treatments. In considering checkpoint immunotherapies for NPC, initial results show promise for anti-PD1 interventions. Further phase I–III trials are in progress to clarify clinical outcomes, fully determine safety profiles, and optimize drug combinations and administration schedules.     

Hepatic effects of repeated oral administration of diclofenac to hepatic cytochrome P450 reductase null (HRN™) and wild-type mice

Hepatic NADPH-cytochrome P450 oxidoreductase null (HRN?) mice exhibit normal hepatic and extrahepatic biotransformation enzyme activities when compared to wild-type (WT) mice, but express no functional hepatic cytochrome P450 activities. When incubated in vitro with [¹⁴C]-diclofenac, liver microsomes from WT mice exhibited extensive biotransformation to oxidative and glucuronide metabolites and covalent binding to proteins was also observed. In contrast, whereas glucuronide conjugates and a quinone-imine metabolite were formed when [¹⁴C]-diclofenac was incubated with HRN? mouse liver, only small quantities of P450-derived oxidative metabolites were produced in these samples and covalent binding to proteins was not observed. Livers from vehicle-treated HRN? mice exhibited enhanced lipid accumulation, bile duct proliferation, hepatocellular degeneration and necrosis and inflammatory cell infiltration, which were not present in livers from WT mice. Elevated liver-derived alanine aminotransferase, glutamate dehydrogenase and alkaline phosphatase activities were also observed in plasma from HRN? mice. When treated orally with diclofenac for 7 days, at 30 mg/kg/day, the severities of the abnormal liver histopathology and plasma liver enzyme findings in HRN? mice were reduced markedly. Oral diclofenac administration did not alter the liver histopathology or elevate plasma enzyme activities of WT mice. These findings indicate that HRN? mice are valuable for exploration of the role played by hepatic P450s in drug biotransformation, but poorly suited to investigations of drug-induced liver toxicity. Nevertheless, studies in HRN? mice could provide novel insights into the role played by inflammation in liver injury and may aid the evaluation of new strategies for its treatment.     

Automated classification of depression from structural brain measures across two independent community‐based cohorts

Major depressive disorder (MDD) has been the subject of many neuroimaging case–control classification studies. Although some studies report accuracies ≥80%, most have investigated relatively small samples of clinically‐ascertained, currently symptomatic cases, and did not attempt replication in larger samples. We here first aimed to replicate previously reported classification accuracies in a small, well‐phenotyped community‐based group of current MDD cases with clinical interview‐based diagnoses (from STratifying Resilience and Depression Longitudinally cohort, ‘STRADL’). We performed a set of exploratory predictive classification analyses with measures related to brain morphometry and white matter integrity. We applied three classifier types—SVM, penalised logistic regression or decision tree—either with or without optimisation, and with or without feature selection. We then determined whether similar accuracies could be replicated in a larger independent population‐based sample with self‐reported current depression (UK Biobank cohort). Additional analyses extended to lifetime MDD diagnoses—remitted MDD in STRADL, and lifetime‐experienced MDD in UK Biobank. The highest cross‐validation accuracy (75%) was achieved in the initial current MDD sample with a decision tree classifier and cortical surface area features. The most frequently selected decision tree split variables included surface areas of bilateral caudal anterior cingulate, left lingual gyrus, left superior frontal, right precentral and paracentral regions. High accuracy was not achieved in the larger samples with self‐reported current depression (53.73%), with remitted MDD (57.48%), or with lifetime‐experienced MDD (52.68–60.29%). Our results indicate that high predictive classification accuracies may not immediately translate to larger samples with broader criteria for depression, and may not be robust across different classification approaches.