Population Pharmacokinetic Modeling and Simulation for Dose Optimization of GB-5001, a Long-Acting Intramuscular Injection of Donepezil, in Healthy Participants.

INTRODUCTION: GB-5001 is an intramuscular (IM) formulation of donepezil under development for the treatment of Alzheimer's disease. The objective of this study was to develop a population pharmacokinetic (PK) model for donepezil in both IM and oral formulations, and to optimize the IM dosage of GB-5001 using bioequivalence (BE) simulation. METHODS: A population PK model of donepezil was developed using NONMEM. It was based on plasma concentration data from a Phase 1 dose escalation study, which involved a single administration of donepezil IM formulation at doses of 70, 140, and 280 mg, and the oral formulation at 10 mg. The model was evaluated based on goodness-of-fit plots, conditional weighted residuals, visual predictive checks, and bootstrapping. BE simulations were conducted using a parallel design between various doses of the IM formulation and the 10-mg dose of oral formulation. RESULTS: The PKs of donepezil were best described by a two-compartment model, which incorporated distinct absorption compartments for the IM (dual first-order absorption and simultaneous zero-order absorption with lag time) and oral (first-order absorption with lag time) formulations. Based on the simulation results, an IM dosage range of 210-215 mg in a sample size of over 92 was estimated to achieve a success rate of approximately 80% for BE. CONCLUSION: The population PK model well explained the PKs of donepezil following administration of both the IM and oral formulations. This model could be applied for the design and dose selection of future BE trials. TRIAL REGISTRATION: ClinicalTrials.gov identifier, NCT05525780.

Real-World Application of Artificial Intelligence for Detecting Pathologic Gastric Atypia and Neoplastic Lesions.

PURPOSE: Results of initial endoscopic biopsy of gastric lesions often differ from those of the final pathological diagnosis. We evaluated whether an artificial intelligence-based gastric lesion detection and diagnostic system, ENdoscopy as AI-powered Device Computer Aided Diagnosis for Gastroscopy (ENAD CAD-G), could reduce this discrepancy. MATERIALS AND METHODS: We retrospectively collected 24,948 endoscopic images of early gastric cancers (EGCs), dysplasia, and benign lesions from 9,892 patients who underwent esophagogastroduodenoscopy between 2011 and 2021. The diagnostic performance of ENAD CAD-G was evaluated using the following real-world datasets: patients referred from community clinics with initial biopsy results of atypia (n=154), participants who underwent endoscopic resection for neoplasms (Internal video set, n=140), and participants who underwent endoscopy for screening or suspicion of gastric neoplasm referred from community clinics (External video set, n=296). RESULTS: ENAD CAD-G classified the referred gastric lesions of atypia into EGC (accuracy, 82.47%; 95% confidence interval [CI], 76.46%-88.47%), dysplasia (88.31%; 83.24%-93.39%), and benign lesions (83.12%; 77.20%-89.03%). In the Internal video set, ENAD CAD-G identified dysplasia and EGC with diagnostic accuracies of 88.57% (95% CI, 83.30%-93.84%) and 91.43% (86.79%-96.07%), respectively, compared with an accuracy of 60.71% (52.62%-68.80%) for the initial biopsy results (P<0.001). In the External video set, ENAD CAD-G classified EGC, dysplasia, and benign lesions with diagnostic accuracies of 87.50% (83.73%-91.27%), 90.54% (87.21%-93.87%), and 88.85% (85.27%-92.44%), respectively. CONCLUSIONS: ENAD CAD-G is superior to initial biopsy for the detection and diagnosis of gastric lesions that require endoscopic resection. ENAD CAD-G can assist community endoscopists in identifying gastric lesions that require endoscopic resection.

Comprehensive Evaluation of OATP- and BCRP-Mediated Drug-Drug Interactions of Methotrexate Using Physiologically-Based Pharmacokinetic Modeling.

Methotrexate (MTX) is an antifolate agent widely used for treating conditions such as rheumatoid arthritis and hematologic cancer. This study aimed to quantitatively interpret the drug-drug interactions (DDIs) of MTX mediated by drug transporters using physiologically-based pharmacokinetic (PBPK) modeling. An open-label, randomized, 4-treatment, 6-sequence, 4-period crossover study was conducted to investigate the effects of rifampicin (RFP), an inhibitor of organic anionic transporting peptides (OATP) 1B1/3, and febuxostat (FBX), an inhibitor of breast cancer resistance protein (BCRP), on the pharmacokinetics of MTX in healthy volunteers. PBPK models of MTX, RFP, and FBX were developed based on in vitro and in vivo data, and the performance of the simulation results for final PBPK models was validated in a clinical study. In the clinical study, when MTX was co-administered with RFP or FBX, systemic exposure of MTX increased by 33% and 17%, respectively, compared with that when MTX was administered alone. When MTX was co-administered with RFP and FBX, systemic exposure increased by 52% compared with that when MTX was administered alone. The final PBPK model showed a good prediction performance for the observed clinical data. The PBPK model of MTX was well developed in this study and can be used as a potential mechanistic model to predict and evaluate drug transporter-mediated DDIs of MTX with other drugs.

Pharmacokinetic interactions between fexuprazan, a potassium-competitive acid blocker, and nonsteroidal anti-inflammatory drugs in healthy males.

Fexuprazan, a novel potassium-competitive acid blocker, is expected to be used for the prevention of nonsteroidal anti-inflammatory drugs (NSAIDs) induced ulcer. This study aimed to evaluate pharmacokinetic (PK) interactions between fexuprazan and NSAIDs in healthy subjects. A randomized, open-label, multicenter, six-sequence, one-way crossover study was conducted in healthy male subjects. Subjects randomly received one of the study drugs (fexuprazan 40 mg BID, celecoxib 200 mg BID, naproxen 500 mg BID, or meloxicam 15 mg QD) for 5 or 7 days in the first period followed by the combination of fexuprazan and one of NSAIDs for the same days and the perpetrator additionally administered for 1-2 days in the second period. Serial blood samples for PK analysis were collected until 48- or 72-h post-dose at steady state. PK parameters including maximum plasma concentration at steady state (Cmax,ss) and area under plasma concentration-time curve over dosing interval at steady state (AUCτ,ss) were compared between monotherapy and combination therapy. The PKs of NSAIDs were not significantly altered by fexuprazan. For fexuprazan, differences in PK parameters (22% in Cmax, 19% in AUCτ,ss) were observed when co-administered with naproxen, but not clinically significant. The geometric mean ratio (90% confidence interval) of combination therapy to monotherapy for Cmax,ss and AUCτ,ss was 1.22 (1.02-1.46) and 1.19 (1.00-1.43), respectively. There were no significant changes in the systemic exposure of fexuprazan by celecoxib and meloxicam. Fexuprazan and NSAIDs did not show clinically meaningful PK interactions.

The effect of renal function on the pharmacokinetics and pharmacodynamics of enavogliflozin, a potent and selective sodium-glucose cotransporter-2 inhibitor, in type 2 diabetes.

AIMS: To explore the effect of renal function on the pharmacokinetic (PK) and pharmacodynamic (PD) profile and safety of enavogliflozin, a selective sodium-glucose cotransporter 2 (SGLT2) inhibitor, in patients with type 2 diabetes mellitus (T2DM). METHODS: An open-label, two-part clinical trial was conducted in T2DM patients, stratified by renal function: Group 1, normal renal function; Group 2, mild renal impairment (RI); Group 3, moderate RI; and Group 4, severe RI. In Part A, Groups 2 and 4 received enavogliflozin 0.5 mg once. In Part B, Groups 1 and 3 received enavogliflozin 0.5 mg once daily for 7 days. Serial blood and timed urine samples were collected to analyse the PK and PD characteristics of enavogliflozin. Pearson's correlation coefficients were calculated to assess the correlations between PK or PD parameters and creatinine clearance (CrCL). RESULTS: A total of 21 patients completed the study as planned. The area under the curve (AUC) for enavogliflozin was not significantly correlated with CrCL, although the maximum concentration slightly decreased as renal function decreased. By contrast, daily urinary glucose excretion (UGE) was positively correlated with CrCL after both single- (r = 0.7866, p < 0.0001) and multiple-dose administration (r = 0.6606, p = 0.0438). CONCLUSIONS: Systemic exposure to oral enavogliflozin 0.5 mg was similar among the patients with T2DM regardless of their renal function levels. However, the glucosuric effect of enavogliflozin decreased with RI. Considering the UGE observed and approved therapeutic use of other SGLT2 inhibitors, the efficacy of enavogliflozin with regard to glycaemic control could be explored in patients with mild and moderate RI (estimated glomerular filtration rate ≥30 or ≥45 mL/min/1.73 m2) in a subsequent larger study.

Estimation of the benefit from pre-emptive genotyping based on the nationwide cohort data in South Korea.

Genetic variants affect drug responses, making pre-emptive genotyping crucial for averting serious adverse events (SAEs) and treatment failure. However, assessing the benefits of pre-emptive genotyping based on genetic distribution, drug exposure, and demographics is challenging. This study aimed to estimate the population-level benefits of pre-emptive genotyping in the Korean population using nationwide cohort data. We reviewed actionable gene-drug combinations recommended by both the Clinical Pharmacogenomics Implementation Consortium (CPIC) and the Dutch Pharmacogenetics Working Group (DPWG) as of February 2022, identifying high-risk phenotypes. We collected reported risk reduction from genotyping and standardized it into population attributable risks. Healthcare reimbursement costs for SAEs and treatment failures were obtained from the Health Insurance Review and Assessment Service Statistics in 2021. The benefits of pre-emptive genotyping for a specific group were determined by multiplying drug exposure from nationwide cohort data by individual genotyping benefits. We identified 31 gene-drug-event pairs, with CYP2D6 and CYP2C19 demonstrating the greatest benefits for both male and female patients. Individuals aged 65-70 years had the highest individual benefit from pre-emptive genotyping, with $84.40 for men and $100.90 for women. Pre-emptive genotyping, particularly for CYP2D6 and CYP2C19, can provide substantial benefits.

Adaptive design clinical trials: current status by disease and trial phase in various perspectives.

An adaptive design is a clinical trial design that allows for modification of a structured plan in a clinical trial based on data accumulated during pre-planned interim analyses. This flexible approach to clinical trial design improves the success rate of clinical trials while reducing time, cost, and sample size compared to conventional methods. The purpose of this study is to identify the current status of adaptive design and present key considerations for planning an appropriate adaptive design based on specific circumstances. We searched for clinical trials conducted between January 2006 to July 2021 in the Clinical Trials Registry (ClinicalTrials.gov) using keywords specified in the Food and Drug Administration Adaptive Design Clinical Trial Guidelines. In order to analyze the adaptive designs used in selected cases, we classified the results according to the phase of the clinical trial, type of indication, and the specific adaptation method employed. A total of 267 clinical trials were identified on ClinicalTrials.gov. Among them, 236 clinical trials actually applied adaptive designs and were classified according to phase, indication types, and adaptation methods. Adaptive designs were most frequently used in phase 2 clinical trials and oncology research. The most commonly used adaptation method was the adaptive treatment selection design. In the case of coronavirus disease 2019, the most frequently used designs were adaptive platform design and seamless design. Through this study, we expect to provide valuable insights and considerations for the implementation of adaptive design clinical trials in different diseases and stages.