2(3H)-Dihydrofranolactone metabolites from Pleosporales sp. NUH322 as anti-amyotrophic lateral sclerosis drugs.

Amyotrophic lateral sclerosis (ALS) is a devastating motor disease with limited treatment options. A domestic fungal extract library was screened using three assays related to the pathophysiology of ALS with the aim of developing a novel ALS drug. 2(3H)-dihydrofuranolactones 1 and 2, and five known compounds 3-7 were isolated from Pleosporales sp. NUH322 culture media, and their protective activity against the excitotoxicity of ß-N-oxalyl-L-α,ß-diaminopropionic acid (ODAP), an α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid (AMPA)-type glutamatergic agonist, was evaluated under low mitochondrial glutathione levels induced by ethacrynic acid (EA) and low sulfur amino acids using our developed ODAP-EA assay. Additional assays evaluated the recovery from cytotoxicity caused by transfected SOD1-G93A, an ALS-causal gene, and the inhibitory effect against reactive oxygen species (ROS) elevation. The structures of 1 and 2 were elucidated using various spectroscopic methods. We synthesized 1 from D-ribose, and confirmed the absolute structure. Isolated and synthesized 1 displayed higher ODAP-EA activities than the extract and represented its activity. Furthermore, 1 exhibited protective activity against SOD1-G93A-induced toxicity. An ALS mouse model, SOD1-G93A, of both sexes, was treated orally with 1 at pre- and post-symptomatic stages. The latter treatment significantly extended their lifespan (p = 0.03) and delayed motor deterioration (p = 0.001-0.01). Our result suggests that 1 is a promising lead compound for the development of ALS drugs with a new spectrum of action targeting both SOD1-G93A proteopathy and excitotoxicity through its action on the AMPA-type glutamatergic receptor.

Detection of potential drug-drug interactions for risk of acute kidney injury: a population-based case-control study using interpretable machine-learning models.

Background: Acute kidney injury (AKI), with an increase in serum creatinine, is a common adverse drug event. Although various clinical studies have investigated whether a combination of two nephrotoxic drugs has an increased risk of AKI using traditional statistical models such as multivariable logistic regression (MLR), the evaluation metrics have not been evaluated despite the fact that traditional statistical models may over-fit the data. The aim of the present study was to detect drug-drug interactions with an increased risk of AKI by interpreting machine-learning models to avoid overfitting. Methods: We developed six machine-learning models trained using electronic medical records: MLR, logistic least absolute shrinkage and selection operator regression (LLR), random forest, extreme gradient boosting (XGB) tree, and two support vector machine models (kernel = linear function and radial basis function). In order to detect drug-drug interactions, the XGB and LLR models that showed good predictive performance were interpreted by SHapley Additive exPlanations (SHAP) and relative excess risk due to interaction (RERI), respectively. Results: Among approximately 2.5 million patients, 65,667 patients were extracted from the electronic medical records, and assigned to case (N = 5,319) and control (N = 60,348) groups. In the XGB model, a combination of loop diuretic and histamine H2 blocker [mean (|SHAP|) = 0.011] was identified as a relatively important risk factor for AKI. The combination of loop diuretic and H2 blocker showed a significant synergistic interaction on an additive scale (RERI 1.289, 95% confidence interval 0.226-5.591) also in the LLR model. Conclusion: The present population-based case-control study using interpretable machine-learning models suggested that although the relative importance of the individual and combined effects of loop diuretics and H2 blockers is lower than that of well-known risk factors such as older age and sex, concomitant use of a loop diuretic and histamine H2 blocker is associated with increased risk of AKI.

Benzodiazepine-related dementia risks and protopathic biases revealed by multiple-kernel learning with electronic medical records.

Objectives: To simultaneously estimate how the risk of incident dementia nonlinearly varies with the administration period and cumulative dose of benzodiazepines, the duration of disorders with an indication for benzodiazepines, and other potential confounders, with the goal of settling the controversy over the role of benzodiazepines in the development of dementia. Methods: The classical hazard model was extended using the techniques of multiple-kernel learning. Regularised maximum-likelihood estimation, including determination of hyperparameter values with 10-fold cross-validation, bootstrap goodness-of-fit test, and bootstrap estimation of confidence intervals, was applied to cohorts retrospectively extracted from electronic medical records of our university hospitals between 1 November 2004 and 31 July 2020. The analysis was mainly focused on 8160 patients aged 40 or older with new onset of insomnia, affective disorders, or anxiety disorders, who were followed up for 4.10±3.47 years. Results: Besides previously reported risk associations, we detected significant nonlinear risk variations over 2-4 years attributable to the duration of insomnia and anxiety disorders, and to the administration period of short-acting benzodiazepines. After nonlinear adjustment for potential confounders, we observed no significant risk associations with long-term use of benzodiazepines. Conclusions: The pattern of the detected nonlinear risk variations suggested reverse causation and confounding. Their putative bias effects over 2-4 years suggested similar biases in previously reported results. These results, together with the lack of significant risk associations with long-term use of benzodiazepines, suggested the need to reconsider previous results and methods for future analysis.

Detection of Synergistic Interaction on an Additive Scale Between Two Drugs on Abnormal Elevation of Serum Alanine Aminotransferase Using Machine-Learning Algorithms.

Drug-induced liver injury (DILI) is a common adverse drug reaction, with abnormal elevation of serum alanine aminotransferase (ALT). Several clinical studies have investigated whether a combination of two drugs alters the reporting frequency of DILI using traditional statistical methods such as multiple logistic regression (MLR), but this model may over-fit the data. This study aimed to detect a synergistic interaction between two drugs on the risk of abnormal elevation of serum ALT in Japanese adult patients using three machine-learning algorithms: MLR, logistic least absolute shrinkage and selection operator (LASSO) regression, and extreme gradient boosting (XGBoost) algorithms. A total of 58,413 patients were extracted from Nihon University School of Medicine's Clinical Data Warehouse and assigned to case (N = 4,152) and control (N = 54,261) groups. The MLR model over-fitted a training set. In the logistic LASSO regression model, three combinations showed relative excess risk due to interaction (RERI) for abnormal elevation of serum ALT: diclofenac and famotidine (RERI 2.427, 95% bootstrap confidence interval 1.226-11.003), acetaminophen and ambroxol (0.540, 0.087-4.625), and aspirin and cilostazol (0.188, 0.135-3.010). Moreover, diclofenac (adjusted odds ratio 1.319, 95% bootstrap confidence interval 1.189-2.821) and famotidine (1.643, 1.332-2.071) individually affected the risk of abnormal elevation of serum ALT. In the XGBoost model, not only the individual effects of diclofenac (feature importance 0.004) and famotidine (0.016), but also the interaction term (0.004) was included in important predictors. Although further study is needed, the combination of diclofenac and famotidine appears to increase the risk of abnormal elevation of serum ALT in the real world.

Identifying Antidepressants Less Likely to Cause Hyponatremia: Triangulation of Retrospective Cohort, Disproportionality, and Pharmacodynamic Studies.

Antidepressants are known to cause hyponatremia, but conflicting evidence exists regarding specific antidepressants. To identify antidepressants less likely to cause hyponatremia, we conducted a triangulation study integrating retrospective cohort, disproportionality, and pharmacodynamic studies. In the retrospective cohort study of patients (≥ 60 years) in Nihon University School of Medicine's Clinical Data Warehouse (2004-2020), a significant decrease in serum sodium levels was observed within 30 days after initiation of a selective serotonin reuptake inhibitor (SSRI; mean change -1.00 ± 0.23 mmol/L, P < 0.001) or serotonin-noradrenaline reuptake inhibitor (SNRI; -1.01 ± 0.31 mmol/L, P = 0.0013), whereas no decrease was found for a noradrenergic and specific serotonergic antidepressant (mirtazapine; +0.55 ± 0.47 mmol/L, P = 0.24). Within-class comparison revealed no decrease in serum sodium levels for fluvoxamine (+0.74 ± 0.75 mmol/L, P = 0.33) among SSRIs and milnacipran (+0.08 ± 0.87 mmol/L, P = 0.93) among SNRIs. In the disproportionality analysis of patients (≥ 60 years) in the Japanese Adverse Drug Event Report database (2004-2020), a significant increase in hyponatremia reports was observed for SSRIs (reporting odds ratio 4.41, 95% confidence interval 3.58-5.45) and SNRIs (5.66, 4.38-7.31), but not for mirtazapine (1.08, 0.74-1.58), fluvoxamine (1.48, 0.94-2.32), and milnacipran (0.85, 0.45-1.62). Finally, pharmacoepidemiological-pharmacodynamic analysis revealed a significant correlation between the decrease in serum sodium levels and binding affinity for serotonin transporter (SERT; r = -0.84, P = 0.02), suggesting that lower binding affinity of mirtazapine, fluvoxamine, and milnacipran against SERT is responsible for the above difference. Although further research is needed, our data suggest that mirtazapine, fluvoxamine, and milnacipran are less likely to cause hyponatremia.

Signal Detection of Potential Hepatotoxic Drugs: Case-Control Study Using Both a Spontaneous Reporting System and Electronic Medical Records.

Drug-induced liver injury (DILI) is a common adverse drug event. Spontaneous reporting systems such as the Japanese Adverse Event Report Database (JADER) have been used to evaluate the association between drugs and adverse drug events. However, the association of drugs with adverse drug events may be overestimated due to reporting biases. Therefore, it is important to objectively evaluate the association using liver function test values. The aim of the present study was to predict potential hepatotoxic drugs using real-world data including electronic medical records and the JADER database. A total of 70009 (2779 with DILI and 67230 without DILI) and 438515 (10235 with DILI and 428280 without DILI) Japanese adult patients were extracted from electronic medical records and the JADER database, respectively. Drugs with ≥100 DILI patients in both of the two databases were regarded as suspected drugs for DILI. We used multivariate logistic regression to evaluate the association between the suspected drugs and increased risk of DILI. Among the suspected drugs, broad-spectrum antibiotics such as meropenem, tazobactam/piperacillin and ceftriaxone were significantly associated with an increased risk of DILI, and meropenem had a greater risk of DILI in both of the two databases. Additionally, there were significant associations of mosapride and L-carbocisteine with increased risk of DILI. In addition to well-known associations between antibiotic drugs and DILI, mosapride and L-carbocisteine were found to be new potential signals of drugs causing hepatotoxicity. This study indicates potential hepatotoxic drugs that require further causality assessment.

A new bioinformatics approach identifies overexpression of GRB2 as a poor prognostic biomarker for prostate cancer.

A subset of prostate cancer displays a poor clinical outcome. Therefore, identifying this poor prognostic subset within clinically aggressive groups (defined as a Gleason score (GS) ≧8) and developing effective treatments are essential if we are to improve prostate cancer survival. Here, we performed a bioinformatics analysis of a TCGA dataset (GS ≧8) to identify pathways upregulated in a prostate cancer cohort with short survival. When conducting bioinformatics analyses, the definition of factors such as "overexpression" and "shorter survival" is vital, as poor definition may lead to mis-estimations. To eliminate this possibility, we defined an expression cutoff value using an algorithm calculated by a Cox regression model, and the hazard ratio for each gene was set so as to identify genes whose expression levels were associated with shorter survival. Next, genes associated with shorter survival were entered into pathway analysis to identify pathways that were altered in a shorter survival cohort. We identified pathways involving upregulation of GRB2. Overexpression of GRB2 was linked to shorter survival in the TCGA dataset, a finding validated by histological examination of biopsy samples taken from the patients for diagnostic purposes. Thus, GRB2 is a novel biomarker that predicts shorter survival of patients with aggressive prostate cancer (GS ≧8).

NF-kB signaling in cardiomyocytes is inhibited by sevoflurane and promoted by propofol.

Both inhalational and intravenous anesthetics affect myocardial remodeling, but the precise effect of each anesthetic on molecular signaling in myocardial remodeling is unknown. Here, we performed in silico analysis to investigate signaling alterations in cardiomyocytes induced by inhalational [sevoflurane (Sevo)] and intravenous [propofol (Prop)] anesthetics. Bioinformatics analysis revealed that nuclear factor-kappa B (NF-kB) signaling was inhibited by Sevo and promoted by Prop. Moreover, nuclear accumulation of p65 and transcription of NF-kB-regulated genes were suppressed in Sevo-administered mice, suggesting that Sevo inhibits the NF-kB signaling pathway. Our data demonstrate that NF-kB signaling is inhibited by Sevo and promoted by Prop. As NF-kB signaling plays an important role in myocardial remodeling, our results suggest that anesthetics may affect myocardial remodeling through NF-kB.

Stress-related over-enhancement of the hypothalamic-pituitary-adrenal axis causes experimental neurolathyrism in rats.

Neurolathyrism is a motor neuron disease that is caused by the overconsumption of grass peas (Lathyrus sativus L.) under stressful conditions. The neuro-excitatory ß-N-oxalyl-L-α,ß-diaminopropionic acid present in grass peas was proposed the causative agent of spastic paraparesis of the legs. Historical reports of neurolathyrism epidemics, studies of neurolathyrism animal models, and in vitro studies on the mechanism of ß-N-oxalyl-L-α,ß-diaminopropionic acid toxicity support the hypothesis that stress increases susceptibility to neurolathyrism. To elucidate the role of stress in neurolathyrism-induced motor dysfunction, we focused on the hypothalamic-pituitary-adrenal axis in a rodent model of neurolathyrism. Our results implicated increased glucocorticoid and neuroinflammation in the motor dysfunction (paraparesis) exhibited by the stress loaded rat models of neurolathyrism.

A bioinformatics-to-clinic sequential approach to analysis of prostate cancer biomarkers using TCGA datasets and clinical samples: a new method for precision oncology?

Biomarker-driven cancer therapy has met with significant clinical success. Identification of a biomarker implicated in a malignant phenotype and linked to poor clinical outcome is required if we are to develop these types of therapies. A subset of prostate adenocarcinoma (PACa) cases are treatment-resistant, making them an attractive target for such an approach. To identify target molecules implicated in shorter survival of patients with PACa, we established a bioinformatics-to-clinic sequential analysis approach, beginning with 2-step in silico analysis of a TCGA dataset for localized PACa. The effect of candidate genes identified by in silico analysis on survival was then assessed using biopsy specimens taken at the time of initial diagnosis of localized and metastatic PACa. We identified PEG10 as a candidate biomarker. Data from clinical samples suggested that increased expression of PEG10 at the time of initial diagnosis was linked to shorter survival time. Interestingly, PEG10 overexpression also correlated with expression of chromogranin A and synaptophysin, markers for neuroendocrine prostate cancer, a type of treatment-resistant prostate cancer. These results indicate that PEG10 is a novel biomarker for shorter survival of patients with PACa. Also, PEG10 expression at the time of initial diagnosis may predict focal neuroendocrine differentiation of PACa. Thus, PEG10 may be an attractive target for biomarker-driven cancer therapy. Thus, bioinformatics-to-clinic sequential analysis is a valid tool for identifying targets for precision oncology.