The effect for hyperuricemia inpatient of uric acid overproduction type or in combination with topiroxostat on the pharmacokinetics, pharmacodynamics and safety of dotinurad, a selective urate reabsorption inhibitor

Optimizing gout treatment: A comprehensive review of current and emerging uricosurics

Gout is the most common inflammatory arthritis, affecting approximately 5.1% of adults in the United States (US) population. Gout is a metabolic and autoinflammatory disease. Elevated uric acid pools lead to the precipitation of monosodium urate (MSU) crystals in and around joints, as well as other tissues, and the subsequent autoinflammatory response. Since elevated serum urate (SU) levels (hyperuricemia) correspond with gout severity, urate-lowering therapies (ULTs) are the cornerstone of gout treatment. ULTs include xanthine oxidoreductase inhibitors, uricosurics, less commonly used in the US but widely used in Europe and Asia, including benzbromarone, dotinurad, and probenecid (the only US Food and Drug Administration (FDA) approved uricosuric in the US), and uricases, including rasburicase and pegloticase (available only in the US). Over 90% of the daily load of uric acid filtered by the kidneys is reabsorbed through renal transporters. These urate transporters include uric acid transporter 1 (URAT1), glucose transporter 9, and organic anion transporters 1, 3, and 4 (OAT1, OAT3, OAT4). They are the target of approved and in-the-pipeline uricosurics. Any drug that increases renal excretion of uric acid, independently of the mechanism through which it exerts its effect, may be considered a uricosuric drug. This review discusses drugs that increase renal excretion of uric acid, either approved or in development, as well as off-label drugs with uricosuric properties.

Machine learning-based prediction of sertraline concentration in patients with depression through therapeutic drug monitoring

Background: Sertraline is a commonly employed antidepressant in clinical practice. In order to control the plasma concentration of sertraline within the therapeutic window to achieve the best effect and avoid adverse reactions, a personalized model to predict sertraline concentration is necessary. Aims: This study aimed to establish a personalized medication model for patients with depression receiving sertraline based on machine learning to provide a reference for clinicians to formulate drug regimens. Methods: A total of 415 patients with 496 samples of sertraline concentration from December 2019 to July 2022 at the First Hospital of Hebei Medical University were collected as the dataset. Nine different algorithms, namely, XGBoost, LightGBM, CatBoost, random forest, GBDT, SVM, lasso regression, ANN, and TabNet, were used for modeling to compare the model abilities to predict sertraline concentration. Results: XGBoost was chosen to establish the personalized medication model with the best performance (R 2 = 0.63). Five important variables, namely, sertraline dose, alanine transaminase, aspartate transaminase, uric acid, and sex, were shown to be correlated with sertraline concentration. The model prediction accuracy of sertraline concentration in the therapeutic window was 62.5%. Conclusion: In conclusion, the personalized medication model of sertraline for patients with depression based on XGBoost had good predictive ability, which provides guidance for clinicians in proposing an optimal medication regimen.

Changes in Urinary Uric Acid Concentration after Dotinurad Administration to Patients with Hyperuricemia: A Post Hoc Analysis of Two Clinical Trials in Japan

Dotinurad has been approved in Japan as a selective urate reabsorption inhibitor for the treatment of gout and hyperuricemia. The relationship between uric acid crystallization and the use of uricosuric drugs is widely acknowledged; however, the relationship between changes in urinary uric acid concentration and urine pH or volume has not been sufficiently analyzed. Therefore, we investigated the changes in urinary uric acid concentration following dotinurad administration as well as the relationship between urine pH or volume and urinary uric acid concentration. This post hoc analysis used data from 2 clinical trials that included 12 and 26 patients with hyperuricemia who received dotinurad treatment (for 7 days on an inpatient basis and 14 weeks on an outpatient basis, respectively). The urinary uric acid concentration transiently increased in the early stages of dotinurad use and when its dose was increased, but decreased over time. No uric acid concentrations exceeded the soluble limit at any urine pH. An inverse correlation was observed between urine volume and urinary uric acid concentration. This study highlights the significance of adequately managing urinary uric acid concentrations by increasing urine volume and alkalinizing urine to prevent uric acid crystallization during dotinurad administration.

Predicting plasma concentration of quetiapine in patients with depression using machine learning techniques based on real-world evidence

OBJECTIVES

We develop a model for predicting quetiapine levels in patients with depression, using machine learning to support decisions on clinical regimens.

METHODS

Inpatients diagnosed with depression at the First Hospital of Hebei Medical University from 1 November 2019, to 31 August were enrolled. The ratio of training cohort to testing cohort was fixed at 80%:20% for the whole dataset. Univariate analysis was executed on all information to screen the important variables influencing quetiapine TDM. The prediction abilities of nine machine learning and deep learning algorithms were compared. The prediction model was created using an algorithm with better model performance, and the model's interpretation was done using the SHapley Additive exPlanation.

RESULTS

There were 333 individuals and 412 cases of quetiapine TDM included in the study. Six significant variables were selected to establish the individualized medication model. A quetiapine concentration prediction model was created through CatBoost. In the testing cohort, the projected TDM's accuracy was 61.45%. The prediction accuracy of quetiapine concentration within the effective range (200-750 ng/mL) was 75.47%.

CONCLUSIONS

This study predicts the plasma concentration of quetiapine in depression patients by machine learning, which is meaningful for the clinical medication guidance.

Eurycomanol alleviates hyperuricemia by promoting uric acid excretion and reducing purine synthesis

BACKGROUND

An elevated level of blood uric acid (UA) leads to serious damages to human health. In clinic, xanthine oxidase inhibitor is commonly used to reduce uric acid production. However, UA excretion promotion drug is rare. Our previous study demonstrated that the 70% ethanolic extract of stem of Eurycoma longifolia could effectively increase UA excretion and decrease blood level of UA in hyperuricemia animal model. In this paper, we tried to find active substance on UA regulation from E. longifolia.

METHODS

The constituents of stem from E. longifolia were isolated and analyzed by chemical and spectral methods. Ultra Performance Liquid Chromatography was applied to measure the concentrations of UA in serum and urine. H&E staining was used to characterize renal histopathological changes. The protein and mRNA expressions of UA transporters were measured by western blot and quantitative real-time PCR analysis.

RESULTS

Ten kinds of quassinoids were isolated from stem of E. longifolia, and the structures were identified. Pharmacological research revealed the major component, eurycomanol (5-20 mg/kg, p.o.) significantly decreased serum UA level and increased 24 h clearance of uric acid in potassium oxonate and adenine induced hyperuricemic mice. Eurycomanol ameliorated UA induced kidney histological injury, inhibited hepatic purine synthesis through decreasing phosphoribosyl pyrophosphate synthetase, promoted UA excretion by modulation of renal and intestinal urate transporters, such as GLUT9, ABCG2, OAT1, and NPT1.

CONCLUSION

The results showed eurycomanol from E. longifolia can promote UA excretion through kidney and intestine, decrease hepatic purine synthesis and further keep UA homeostasis, suggesting that eurycomanol has the potential to be developed into a novel drug for the treatment of under-excretion type hyperuricemia.

Efficacy and Safety of Dotinurad in Hyperuricemic Patients With or Without Gout: A Systematic Review and Meta-Analysis of Randomized Controlled Trials

Introduction A systematic review and meta-analysis of the available randomized controlled trials (RCTs) were conducted to investigate the efficacy and safety of dotinurad in hyperuricemic patients with or without gout. Dotinurad is a novel selective urate reabsorption inhibitor (SURI) that increases uric acid excretion by selectively inhibiting urate transporter 1 (URAT1). To the best of our knowledge, this is the first meta-analysis conducted to gauge the efficacy and safety of dotinurad.  Methods Electronic databases (PubMed, the Cochrane Library, and ClinicalTrials.gov) were searched from inception till March 2, 2021, according to the Preferred Reporting Items for Systematic Review and Meta-Analysis statement. Randomized controlled trials comparing the efficacy and safety of dotinurad with placebo- or active (febuxostat or benzbromarone) control were included. The eligible studies were analyzed with RevMan 5.3 Software (The Nordic Cochrane Centre, Cochrane Collaboration, Copenhagen). Results Four eligible studies, consisting of 684 hyperuricemic patients were included. The number of patients who achieved serum uric acid (sUA) levels ≤ 6.0 mg/dl favoured dotinurad 1 mg group as compared to placebo group (risk ratio {RR} = 39.27, 95% onfidence interval {CI}, 5.59 to 275.65; p = 0.0002), dotinurad 2 mg group compared with placebo group (RR = 45.36, 95% CI, 6.48 to 317.38; p= 0.0001), and dotinurad 4 mg group compared with placebo group (RR = 54.16, 95% CI, 7.76 to 377.77; p < 0.0001). Conversely, there was no significant difference in the number of patients who achieved the target sUA levels between dotinurad 2 mg and active control (RR = 1.00, 95% CI, 0.92 to 1.08; p = 0.91). Moreover, the percentage change in sUA levels from baseline to final visit favoured dotinurad 1 mg vs. placebo ((RR = 36.51, 95% CI, 33.00 to 40.02; p < 0.00001), dotinurad 2 mg vs. placebo (RR = 46.70, 95% CI, 42.53 to 50.87; p < 0.00001), and dotinurad 4 mg vs. placebo (RR = 63.84, 95% CI, 60.51 to 67.16; p < 0.00001), while no significant difference was seen in dotinurad 2 mg vs. active control (RR = -0.08, 95% CI, -4.27 to 4.11; p= 0.97). Compared with active or placebo control, dotinurad 2 mg showed no significant difference in the number of events of gouty arthritis (RR= 1.31, 95% CI, 0.47 to 3.71; p = 0.60), the number patients with adverse events (RR = 1.09, 95% CI, 0.91 to 1.30; p = 0.36), and the number of patients who experienced adverse drug reactions (RR = 1.00, 95% CI, 0.68 to 1.47; p = 0.99). Conclusion Dotinurad shows significant improvement in serum uric acid levels in hyperuricemic individuals with or without gout. Its urate-lowering effect is comparable to the commonly available anti-hyperuricemic agents. Moreover, it is effective at doses 1 mg, 2 mg, and 4 mg and well-tolerated at a dose of 2 mg.

Diagnostic Accuracy of Single Spot Urine for Detecting Renal Uric Acid Underexcretion in Men

Background

The uric acid (UA) clearance test to evaluate the hyperuricemia phenotype requires a great deal of time. However, the utility of single spot urine is scarce. The study aimed to determine the diagnostic accuracy of single spot urine for predicting renal UA underexcretion (the decreased UA excretion) in men.

Methods

A total of 73 male participants aged 20 - 74 years with a UA level of 6.0 - 7.9 mg/dL were enrolled in the study. Renal UA underexcretion was defined as < 7.3 mL/min using the 60-min method. Urinary UA to creatinine ratio (UACR), fractional clearance of urate (FCU), and the Simkin index were calculated. A receiver operating characteristic (ROC) analysis was performed to compare the diagnostic utility of these parameters for predicting UA underexcretion.

Results

In the ROC analysis, the area under the curve values of the UACR, FCU, and the Simkin index for predicting UA underexcretion were 0.903 (95% confidence interval (CI): 0.830 - 0.976), 0.841 (95% CI: 0.749 - 0.933), and 0.779 (95% CI: 0.673 - 0.885), respectively. An optimal UACR cutoff of 0.460 (sensitivity 89.2%, specificity 80.6%, overall diagnostic accuracy 84.9%, positive predictive value 82.5%, and negative predictive value 87.9%) was identified.

Conclusions

These results suggest that the UACR is a simple and efficient test with high sensitivity and specificity levels for predicting renal UA underexcretion in men.