The effect of small dose of topiroxostat on serum uric acid in patients receiving hemodialysis

Introduction : Topiroxostat, a recently developed xanthine oxidase inhibitor, is expected to have fewer adverse effects than allopurinol because it has different mechanism of action from alloprinol. However, its dosage, usage and safety have not been established in patients with impaired renal function or those undergoing dialysis at the development since no studies was conducted in these patients. Methods : Cross over clinical trial using 3 months of allopurinol and topiroxostat on 27 maintain Japanese HD patients were carried out. The effects on oxidative stress status of both drugs were also evaluated by measuring oxidation reduction potential. Findings : Twenty‐five of twenty‐seven patients completed study. The mean serum uric acid levels in the topiroxostat‐treated arm was significantly lower than it in the allopurinol‐treated arm time‐dependently (P < 0.0001). Corrected oxidative stress ratio defined as biological antioxidant potential/diacron reactive oxygen metabolites was significantly increased in topiroxostat‐arm (*P = 0.0035), but not in allopurinol‐arm (P = 0.1429). No significant difference was seen in diacron reactive oxygen metabolites, biological antioxidant potential, static oxidation‐reduction potential, and capacity oxidation‐reduction potential between pre and post treatment of both drugs. Discussion : It is suggested that a low dose of topiroxostat decreased serum uric acid sufficiently to maintain it below 7.0 mg/dL in patients receiving hemodialysis.     

Modified Rule Ensemble Method for Binary Data and Its Applications

Ensemble learning, which combines multiple base learners to improve statistical prediction accuracy, is frequently used in statistical science and data mining. However, because of their “black box” nature, ensemble learning models are difficult to interpret. A recently proposed rule ensemble method known as RuleFit presents the base learner as a production rule and also generates a measure that influences the response variable. The RuleFit method for binary response applies a squared-error ramp loss function, and base learners are weighted by shrinkage regression using the lasso method. Thus, RuleFit is not constructed by a logistic regression model. Moreover, highly correlated pairs of base learners may be excessively pruned by the lasso method. In this study, we solved the excess pruning problem by constructing RuleFit within a logistic regression framework, weighting the base learners by elastic net. The effectiveness ofour proposed RuleFit model is illustrated through a real data set. In small-scale simulations, this method demonstrated higher predictive performance than the original RuleFit model.