Contrast-enhanced MRI T1 mapping of the brain suggested that sleep was associated with greater brain clearance of gadolinium-based contrast agent, potentially due to greater glymphatic clearance. Background Evaluation of the glymphatic system with intrathecal contrast material injection has limited clinical use. Purpose To investigate the feasibility of using serial intravenous contrast-enh...

Objectives: Digital therapeutics (DTx) are software-based therapeutic interventions based on clinical evidence. Randomized clinical trials (RCTs) are often the source of clinical evidence, similar to conventional drugs or medical devices. However, novel approaches such as the use of real-world data or digital biomarkers are also utilized. This article aimed to review how DTx products have been clinically evaluated. Methods: DTx products approved by the US Food and Drug Administration as of 2020 were reviewed and products with sufficient published information were selected. Pivotal clinical trials were analyzed according to the elements of the Consolidated Standards of Reporting Trials (CONSORT) guideline. Case reviews were presented for other clinical evaluation strategies, considering the small number of publications. Results: Most approved DTx products used RCTs for clinical evaluations. Similar to conventional RCTs, parallel-group designs with statistical hypothesis testing were adopted. However, DTx trials were often not blinded due to practical issues and involved various comparator groups. In addition, DTx products could be readily evaluated in home-based settings and delivered through the internet. Other evaluation approaches included retrospective analyses using insurance claims data or usage data, which enabled long-term evaluations of effectiveness. Digital biomarkers obtained from real-time and continuous log data were also used to improve the objectiveness of endpoints. Conclusions: RCTs accounted for the majority of DTx evaluations. The designs of DTx trials were comparable to those of drug or device trials, but blinding and comparator elements were often different. Furthermore, the use of real-world data and digital biomarkers are also being tried.

Park J, et al. Transl Clin Pharmacol. 2024 Mar;32(1):41-51. https://doi.org/10.12793/tcp.2024.32.e2

Several adult omics studies have been conducted to understand the pathophysiology of nonalcoholic fatty liver disease (NAFLD). However, the histological features of children are different from those of adults, and the onset and progression of pediatric NAFLD are not fully understood. In this study, we aimed to evaluate the metabolome profile and metabolic pathway changes associated with pediatric NAFLD to elucidate its pathophysiology and to develop machine learning-based NAFLD diagnostic models. We analyzed the metabolic profiles of healthy control, lean NAFLD, overweight control, and overweight NAFLD groups of children and adolescent participants (N = 165) by assessing plasma samples. Additionally, we constructed diagnostic models by applying three machine learning methods (ElasticNet, random forest, and XGBoost) and multiple logistic regression by using NAFLD-specific metabolic features, genetic variants, and clinical data. We identified 18 NAFLD-specific metabolic features and metabolic changes in lipid, glutathione-related amino acid, and branched-chain amino acid metabolism by comparing the control and NAFLD groups in the overweight pediatric population. Additionally, we successfully developed and cross-validated diagnostic models that showed excellent diagnostic performance (ElasticNet and random forest model: area under the receiver operating characteristic curve, 0.95). Metabolome changes in the plasma of pediatric patients with NAFLD are associated with the pathophysiology of the disease and can be utilized as a less-invasive approach to diagnosing the disease.

Early detection and proper management of chronic kidney disease (CKD) can delay progression to end-stage kidney disease. We applied metabolomics to discover novel biomarkers to predict the risk of deterioration in patients with different causes of CKD. We enrolled non-dialytic diabetic nephropathy (DMN, n = 124), hypertensive nephropathy (HTN, n = 118), and polycystic kidney disease (PKD, n = 124) patients from the KNOW-CKD cohort. Within each disease subgroup, subjects were categorized as progressors (P) or non-progressors (NP) based on the median eGFR slope. P and NP pairs were randomly selected after matching for age, sex, and baseline eGFR. Targeted metabolomics was performed to quantify 188 metabolites in the baseline serum samples. We selected ten progression-related biomarkers for DMN and nine biomarkers each for HTN and PKD. Clinical parameters showed good ability to predict DMN (AUC 0.734); however, this tendency was not evident for HTN (AUC 0.659) or PKD (AUC 0.560). Models constructed with selected metabolites and clinical parameters had better ability to predict CKD progression than clinical parameters only. When selected metabolites were used in combination with clinical indicators, random forest prediction models for CKD progression were constructed with AUCs of 0.826, 0.872, and 0.834 for DMN, HTN, and PKD, respectively. Select novel metabolites identified in this study can help identify high-risk CKD patients who may benefit from more aggressive medical treatment.

Sorafenib, an oral multikinase inhibitor, exhibits a highly variable absorption profile due to enterohepatic reabsorption and poor solubility. SYO-1644 improved the solubility of sorafenib by nanoparticulation technology leading to enhanced bioavailability. To evaluate the pharmacokinetically equivalent dose of SYO-1644 to the reference Nexavar^® 200 mg, a randomized, open-label, replicated two-period study was conducted in healthy volunteers. A total of 32 subjects orally received a single dose of the following assigned treatment under a fasted state in the first period and repeated once more in the second period with a two-week washout: SYO-1644 100, 150 and 200 mg and Nexavar^® 200 mg. Pharmacokinetic (PK) samples were collected up to 168 h post-dose. The PK profile was evaluated by both non-compartmental analysis and population PK method. With the final model, 2 × 2 crossover trial scenarios with Nexavar^® 200 mg and each dose of SYO-1644 ranging from 100 to 150 mg were repeated 500 times by Monte Carlo simulation, and the proportion of bioequivalence achievement was assessed. Transit absorption compartments, followed by a one-compartment model with first-order elimination and enterohepatic reabsorption components were selected as the final model. The simulation results demonstrated that the SYO-1644 dose between 120 and 125 mg could yielded the highest proportion of bioequivalence.