The coronavirus disease 2019 pandemic has exacerbated global mental health challenges, underscoring the urgent need for advancements in digital healthcare technologies. Digital therapeutics have demonstrated clinical utility across various domains; however, their development and implementation in the mental health remain constrained within the traditional clinical paradigm. This study conceptualizes and classifies digital interventions for mental health (DIMH), delineates their core technologies, and discusses their strengths and limitations.Current Concepts: DIMH interventions predominantly utilize cognitive behavioral therapy frameworks, targeting conditions such as anxiety, depression, and psychological distress. Despite their potential, low user engagement remains a persistent challenge, necessitating personalized approaches tailored to individual needs. Platforms bridge users and therapists, facilitating hybrid online-offline care; however, service delivery may falter due to shortages in provider availability. Emerging social chatbots, while not specifically designed for treatment, show promising outcomes but are limited by insufficient consideration of social contexts and potential interactional errors. Digital phenotyping, which leverages behavioral data such as location tracking and smartphone usage patterns, enhances the personalization of interventions by embedding social context. Additionally, virtual reality (VR) offers effective solutions for exposure therapy and social interaction training, serving as a viable alternative to traditional therapy.Discussion and Conclusion: This review underscores the complementary nature of DIMH technologies and advocates for an integrated approach. The convergence of group-based interventions, chatbots, digital phenotyping, and VR technology presents opportunities for more effective and personalized mental health care. Such advancements could redefine mental health services, addressing unmet needs and fostering innovation.

The coronavirus disease 2019 pandemic has exacerbated global mental health challenges, underscoring the urgent need for advancements in digital healthcare technologies. Digital therapeutics have demonstrated clinical utility across various domains; however, their development and implementation in the mental health remain constrained within the traditional clinical paradigm. This study conceptualizes and classifies digital interventions for mental health (DIMH), delineates their core technologies, and discusses their strengths and limitations.Current Concepts: DIMH interventions predominantly utilize cognitive behavioral therapy frameworks, targeting conditions such as anxiety, depression, and psychological distress. Despite their potential, low user engagement remains a persistent challenge, necessitating personalized approaches tailored to individual needs. Platforms bridge users and therapists, facilitating hybrid online-offline care; however, service delivery may falter due to shortages in provider availability. Emerging social chatbots, while not specifically designed for treatment, show promising outcomes but are limited by insufficient consideration of social contexts and potential interactional errors. Digital phenotyping, which leverages behavioral data such as location tracking and smartphone usage patterns, enhances the personalization of interventions by embedding social context. Additionally, virtual reality (VR) offers effective solutions for exposure therapy and social interaction training, serving as a viable alternative to traditional therapy.Discussion and Conclusion: This review underscores the complementary nature of DIMH technologies and advocates for an integrated approach. The convergence of group-based interventions, chatbots, digital phenotyping, and VR technology presents opportunities for more effective and personalized mental health care. Such advancements could redefine mental health services, addressing unmet needs and fostering innovation.

The coronavirus disease 2019 pandemic has exacerbated global mental health challenges, underscoring the urgent need for advancements in digital healthcare technologies. Digital therapeutics have demonstrated clinical utility across various domains; however, their development and implementation in the mental health remain constrained within the traditional clinical paradigm. This study conceptualizes and classifies digital interventions for mental health (DIMH), delineates their core technologies, and discusses their strengths and limitations.Current Concepts: DIMH interventions predominantly utilize cognitive behavioral therapy frameworks, targeting conditions such as anxiety, depression, and psychological distress. Despite their potential, low user engagement remains a persistent challenge, necessitating personalized approaches tailored to individual needs. Platforms bridge users and therapists, facilitating hybrid online-offline care; however, service delivery may falter due to shortages in provider availability. Emerging social chatbots, while not specifically designed for treatment, show promising outcomes but are limited by insufficient consideration of social contexts and potential interactional errors. Digital phenotyping, which leverages behavioral data such as location tracking and smartphone usage patterns, enhances the personalization of interventions by embedding social context. Additionally, virtual reality (VR) offers effective solutions for exposure therapy and social interaction training, serving as a viable alternative to traditional therapy.Discussion and Conclusion: This review underscores the complementary nature of DIMH technologies and advocates for an integrated approach. The convergence of group-based interventions, chatbots, digital phenotyping, and VR technology presents opportunities for more effective and personalized mental health care. Such advancements could redefine mental health services, addressing unmet needs and fostering innovation.

Objective: We aimed to evaluate the efficacy of EmboTrap II in terms of first-pass recanalization and to determine whether it could yield favorable outcomes. Materials and Methods: In this multicenter, prospective study, we consecutively enrolled patients who underwent mechanical thrombectomy using EmboTrap II as a front-line device. The primary outcome was the first pass effect (FPE) rate defined by modified Thrombolysis In Cerebral Infarction (mTICI) grade 2c or 3 by the first pass of EmboTrap II. In addition, modified FPE (mFPE; mTICI grade 2b–3 by the first pass of EmboTrap II), successful recanalization (final mTICI grade 2b–3), and clinical outcomes were assessed. We also analyzed the effect of FPE on a modified Rankin Scale (mRS) score of 0–2 at 3 months. Results: Two hundred-ten patients (mean age ± standard deviation, 73.3 ± 11.4 years; male, 55.7%) were included. Ninetynine patients (47.1%) had FPE, and mFPE was achieved in 150 (71.4%) patients. Successful recanalization was achieved in 191 (91.0%) patients. Among them, 164 (85.9%) patients underwent successful recanalization by exclusively using EmboTrap II. The time from groin puncture to FPE was 25.0 minutes (interquartile range, 17.0–35.0 minutes). Procedure-related complications were observed in seven (3.3%) patients. Symptomatic intracranial hemorrhage developed in 14 (6.7%) patients. One hundred twenty-three (58.9% of 209 completely followed) patients had an mRS score of 0–2. Sixteen (7.7% of 209) patients died during the follow-up period. Patients who had successful recanalization with FPE were four times more likely to have an mRS score of 0–2 than those who had successful recanalization without FPE (adjusted odds ratio, 4.13;95% confidence interval, 1.59–10.8; p = 0.004). Conclusion Mechanical thrombectomy using the front-line EmboTrap II is effective and safe. In particular, FPE rates were high. Achieving FPE was important for an mRS score of 0–2, even in patients with successful recanalization.

Fixed-dose combinations development requires pharmacokinetic drugdrug interaction (DDI) studies between active ingredients. For some drugs, pharmacokinetic properties such as long half-life or delayed distribution, make it difficult to conduct such clinical trials and to estimate the exact magnitude of DDI. In this study, the conventional (non-compartmental analysis and bioequivalence [BE]) and modelbased analyses were compared for their performance to evaluate DDI using amlodipine as an example. Raw data without DDI or simulated data using pharmacokinetic models were compared to the data obtained after concomitant administration.Regardless of the methodology, all the results fell within the classical BE limit. It was shown that the model-based approach may be valid as the conventional approach and reduce the possibility of DDI overestimation. Several advantages (i.e., quantitative changes in parameters and precision of confidence interval) of the model-based approach were demonstrated, and possible application methods were proposed. Therefore, it is expected that the model-based analysis is appropriately utilized according to the situation and purpose.

Ischemic stroke results from arterial occlusion and can cause irreversible brain injury. A non-human primate (NHP) model of ischemic stroke was previously developed to investigate its pathophysiology and for efficacy testing of therapeutic candidates; however, fine motor impairment remains to be well-characterized. We evaluated hand motor function in a cynomolgus monkey model of ischemic stroke. Endovascular transient middle cerebral artery occlusion (MCAO) with an angiographic microcatheter induced cerebral infarction. In vivo magnetic resonance imaging mapped and measured the ischemia-induced infarct lesion. In vivo diffusion tensor imaging (DTI) of the stroke lesion to assess the neuroplastic changes and fiber tractography demonstrated three-dimensional patterns in the corticospinal tract 12 weeks after MCAO. The hand dexterity task (HDT) was used to evaluate fine motor movement of upper extremity digits. The HDT was modified for a home cage-based training system, instead of conventional chair restraint training. The lesion was localized in the middle cerebral artery territory, including the sensorimotor cortex. Maximum infarct volume was exhibited over the first week after MCAO, which progressively inhibited ischemic core expansion, manifested by enhanced functional recovery of the affected hand over 12 weeks after MCAO. The total performance time decreased with increasing success rate for both hands on the HDT. Compensatory strategies and retrieval failure improved in the chronic phase after stroke. Our findings demonstrate the recovery of fine motor skill after stroke, and outline the behavioral characteristics and features of functional disorder of NHP stroke model, providing a basis for assessing hand motor function after stroke.

ymptoms of Parkinson’s disease (PD) caused by loss of dopaminergic neurons are accompanied by movement disorders, including tremors, rigidity, bradykinesia, and akinesia. Non-human primate (NHP) models with PD play an essential role in the analysis of PD pathophysiology and behavior symptoms. As impairments of hand dexterity function can affect activities of daily living in patients with PD, research on hand dexterity function in NHP models with chronic PD is essential. Traditional rating scales previously used in the evaluation of animal spontaneous behavior were insufficient due to factors related to subjectivity and passivity. Thus, experimentally designed applications for an appropriate apparatus are necessary. In this study, we aimed to longitudinally assess hand dexterity function using hand dexterity task (HDT) in NHP-PD models. To validate this assessment, we analyzed the alteration in Parkinsonian tremor signs and the functionality of presynaptic dopaminergic neuron using positron emission tomography imaging of dopamine transporters in these models. In addition, a significant inverse correlation between HDT and DAT level was identified, but no local bias was found. The correlation with intention tremor signs was lower than the resting tremor. In conclusion, the evaluation of HDT may reflect behavioral symptoms of NHP-PD models. Furthermore, HDT was effectively used to experimentally distinguish intention tremors from other tremors.

Microorganisms play important roles in obesity; however, the role of the gut microbiomes in obesity is controversial because of the inconsistent findings. This study investigated the gut microbiome communities in obese and lean groups of captive healthy cynomolgus monkeys reared under strict identical environmental conditions, including their diet. No significant differences in the relative abundance of Firmicutes, Bacteroidetes and Prevotella were observed between the obese and lean groups, but a significant difference in Spirochetes (p < 0.05) was noted. Microbial diversity and richness were similar, but highly variable results in microbial composition, diversity, and richness were observed in individuals, irrespective of their state of obesity. Distinct clustering between the groups was not observed by principal coordinate analysis using an unweighted pair group method. Higher sharedness values (95.81% ± 2.28% at the genus level, and 79.54% ± 5.88% at the species level) were identified among individual monkeys. This paper reports the association between the gut microbiome and obesity in captive non-human primate models reared under controlled environments. The relative proportion of Firmicutes and Bacteroidetes as well as the microbial diversity known to affect obesity were similar in the obese and lean groups of monkeys reared under identical conditions. Therefore, obesity-associated microbial changes reported previously appear to be associated directly with environmental factors, particularly diet, rather than obesity.
Bacteroidetes ; Diet ; Environment, Controlled ; Firmicutes ; Gastrointestinal Microbiome ; Haplorhini ; Macaca fascicularis ; Methods ; Microbiota ; Obesity ; Prevotella ; Primates ; Spirochaetales

As the pharmaceutical industry in Korea is reaching the golden era of drug discovery due to increased investments in research and development and government funds, the need for a more efficient tool for the quantitative analysis has emerged. Therefore, the demand for pharmacometrics (PMx) consultancy services increased. Higher quality service suitable for regulatory submission and out-licensing deals were desired. In this analysis, we compiled and summarized 3 years of experiences of Q-fitter, the first PMx consultancy service company providing PMx analysis to the pharmaceutical industry in Korea. The projects were organized by companies, company types, indications, therapeutic areas, drug development stages, purposes, and scope of services. Within each category, we subcategorized the sections and assessed proportions and a year-over-year trend. As a result, we observed an increase in the number of projects in an average of ~170% per year, with the most frequent types of companies collaborated being the domestic pharmaceutical companies. Among the projects, ~72% involved modeling and simulation using population pharmacokinetic (PK) models, and the other included non-compartmental analysis (NCA), drug-drug interaction (DDI) prediction, and interpretation of the modeling results. The most sought-after purpose in PMx analysis was first-in-human (FIH) dose prediction followed by PK analysis, next clinical trial prediction, and scenario-based simulation. Oncology has been the top therapeutic area of interest every year consisting of ~38% of total projects, followed by Neurology (~13%). From this review, we were able to characterize the PMx service needs and spot the trend of current PMx practices in Korea.
Drug Discovery ; Drug Industry ; Financial Management ; Investments ; Korea ; Neurology

Metformin, an oral antihyperglycemic agent, is widely used as the first-line pharmacotherapy for type 2 diabetes mellitus (T2DM). It has been in use for several decades as numerous different formulations. However, despite its use, population pharmacokinetic (PK) modeling of metformin is not well developed. The aim of the present study was to evaluate the effect of formulation on PK parameters by developing a population PK model of metformin in Koreans and using this model to assess bioequivalence. We used a comparative PK study of a single agent and a fixed-dose combination of metformin in 36 healthy volunteers. The population PK model of metformin was developed using NONMEM (version 7.3). Visual predictive checks and bootstrap methods were performed to determine the adequacy of the model. The plasma concentration-time profile was best described by a two-compartment, first-order elimination model with first-order absorption followed by zeroorder absorption with lag time. From the covariate analysis, formulation had significant effect (p < 0.01) on relative bioavailability (F = 0.94) and first-order absorption constant (Ka = 0.83), but the difference was within the range of bioequivalence criteria. No other covariate was shown to have significant effect on PK parameters. The PK profile of the disposition phase was consistent with the published literature. However, in the present study, the multiple peaks found during the absorption phase implied the possible diversity of absorption PK profile depending on formulation or population. Unlike traditional bioequivalence analysis, the population PK model reflects formulation differences on specific parameters and reflected simulation can be performed.
Absorption ; Adult ; Biological Availability ; Diabetes Mellitus, Type 2 ; Drug Therapy ; Healthy Volunteers ; Humans ; Metformin ; Pharmacokinetics ; Plasma ; Therapeutic Equivalency