Population pharmacokinetic approach to guide personalized sertraline treatment in Chinese patients.

Object: Sertraline is a first-line SSRI for the treatment of depression and has the same effectiveness along with a superior safety profile compared to other medications. There are few population pharmacokinetic (PPK) studies of sertraline and a lack of studies in the Chinese population. Therefore, we performed a PPK analysis of Chinese patients treated with sertraline to identify factors that can influence drug exposure. In addition, the dosing and discontinuation regimen of sertraline when applied to adolescents was explored. Methods: Sertraline serum drug concentration data were collected from 140 hospitalized patients to generate a sertraline PPK dataset, and data evaluation and examination of the effects of covariates on drug exposure in the final model were performed using nonlinear mixed-effects models (NONMEM) and first-order conditional estimation with interaction (FOCE-I). Examining rational medication administration and rational withdrawal of sertraline based on significant covariates and final modeling. Results: A one-compartment model with first-order absorption and elimination of sertraline was developed for Chinese patients with psychiatric disorders. Analysis of covariates revealed that age was a covariate that significantly affected sertraline CL/F (P < 0.01) and that sertraline clearance decreased progressively with aging, whereas other factors had no effect on CL/F and V/F of sertraline. In the age range of 11-79, there were 54 adolescent patients (about 1/3) aged 13-18 years, and the safe and effective optimal daily dose for adolescent patients based on the final model simulations was 50-250 mg/d. For adolescent patients, serum concentration fluctuations were moderate for OD doses of 50 mg and 100 mg, using a fixed dose-descent regimen. For patients with OD doses of 150-200 mg and BID doses of 100-200 mg, a more gradual decrease in serum concentration was achieved with a fixed dose interval of 7 or 14 days for 25 mg as the regimen of descent. Conclusions: To our knowledge, this may be the first PPK study of sertraline in Chinese patients. We found that age was an important factor affecting clearance in Chinese patients taking sertraline. Patients taking sertraline may be exposed to increased amounts of sertraline due to decreased clearance with increasing age. The rational dosing and safe discontinuation of sertraline in adolescent patients can be appropriately referenced to the results of the model simulation, thus providing assistance for individualized dosing in adolescents.

A Bibliometric and Visual Analysis of Single Nucleotide Polymorphism Studies in Depression.

BACKGROUND: Genetic polymorphism has been proven to have an important association with depression, which can influence the risk of developing depression, the efficacy of medications, and adverse effects via metabolic and neurological pathways. Nonetheless, aspects of the association between single nucleotide polymorphisms and depression have not been systematically investigated by bibliometric analysis. OBJECTIVE: The aim of this study was to analyze the current status and trends of single nucleotide polymorphism research on depression through bibliometric and visual analysis. METHODS: The Web of Science Core Collection was used to retrieve 10,043 articles that were published between 1998 and 2021. CiteSpace (6.1 R4) was used to perform collaborative network analysis, co-citation analysis, co-occurrence analysis, and citation burst detection. RESULTS: The most productive and co-cited journals were the Journal of Affective Disorders and Biological Psychiatry, respectively, and an analysis of the references showed that the most recent research focused on the largest thematic cluster, "5-HT", reflecting the important research base in this area. "CYP2D6" has been in the spotlight since its emergence in 2009 and has become a research hotspot since its outbreak in 2019. However, "BDNF ", "COMT ", "older adults", "loci", and "DNA methylation" are also the new frontier of research, and some of them are currently in the process of exploration. CONCLUSION: These findings offer a useful perspective on existing research and potential future approaches in the study of the association between single nucleotide polymorphisms and depression, which may assist researchers in selecting appropriate collaborators or journals.

Factors influencing concentrations of risperidone and 9-hydroxyrisperidone in psychiatric outpatients taking immediate-release formulations of risperidone.

OBJECTIVES: To analyze the factors affecting the concentrations of the active moiety of risperidone (RIS) and its active metabolite 9-hydroxyrisperidone (9-OH-RIS) in psychiatric outpatients taking immediate-release formulations. METHODS: This is a retrospective study on the therapeutic drug monitoring (TDM) data regarding RIS and 9-OH-RIS in adult psychiatric outpatients. TDM data with simultaneous RIS and 9-OH-RIS monitoring from March 2018 to February 2020 and relevant medical records (including dosage, dosage form, sex, age, diagnosis, combined medication, and comorbid disease) from 399 adult psychiatric outpatients (223 males and 176 females) were included in this study. RESULTS: The daily dose of RIS was 5.56 ± 2.05 mg, the concentration of total active moiety was 42.35 ± 25.46 ng/mL, and the dose-adjusted plasma concentration (C/D) of active moiety was 7.83 ± 3.87 (ng/ml)/(mg/day). Dose, sex, and age were identified as important factors influencing concentrations of RIS and 9-OH-RIS in adult psychiatric outpatients. CONCLUSIONS: Individualized medication adjustments should be made according to the specific conditions of psychiatric outpatients. The findings strongly support the use of TDM to guide dosing decisions in psychiatric outpatients taking RIS.

The pharmacological actions of Danzhi-xiaoyao-San on depression involve lysophosphatidic acid and microbiota-gut-brain axis: novel insights from a systems pharmacology analysis of a double-blind, randomized, placebo-controlled clinical trial.

Danzhi-xiaoyao-San (DZXYS), a Traditional Chinese Medicine, plays an essential role in the clinical treatment of depression, but its mechanisms in humans remain unclear. To investigate its pharmacological effects and mechanisms as an add-on therapy for depression, we conducted a double-blind, placebo-controlled trial with depressed patients receiving selective serotonin reuptake inhibitors (SSRIs). Serum and fecal samples were collected for metabolomic and microbiome analysis using UHPLC-QTRAP-MS/MS and 16S rRNA gene sequencing technologies, respectively. Depression symptoms were assessed using the 24-item Hamilton Depression Scale. We employed network pharmacology, metabolomics, and molecular docking to identify potential targets associated with DZXYS. We also examined the correlation between gut microbes and metabolites to understand how DZXYS affects the microbiota-gut-brain axis. The results showed that DZXYS combined with SSRIs was more effective than SSRIs alone in improving depression. We identified 39 differential metabolites associated with DZXYS treatment and found seven upregulated metabolic pathways. The active ingredients quercetin and luteolin were docked to targets (AVPR2, EGFR, F2, and CDK6) associated with the enriched pathways 'pancreatic cancer' and 'phospholipase D signaling pathway', which included the metabolite lysophosphatidic acid [LPA(0:0/16:0)]. Additionally, we identified 32 differential gut microbiota species related to DZXYS treatment, with Bacteroides coprophilus and Ruminococcus gnavus showing negative correlations with specific metabolites such as L-2-aminobutyric acid and LPA(0:0/16:0). Our findings indicate that DZXYS's antidepressant mechanisms involve multiple targets, pathways, and the regulation of LPA and the microbiota-gut-brain axis. These insights from our systems pharmacology analysis contribute to a better understanding of DZXYS's potential pharmacological mechanisms in depression treatment.Communicated by Ramaswamy H. Sarma.

HIGHLIGHTSThis study presents a double-blind, randomized, placebo-controlled clinical trial comparing the clinical effects of Danzhi-xiaoyao-San (DZXYS) plus selective serotonin reuptake inhibitors (SSRIs) and SSRIs alone.This study is the first system pharmacology approach to integrate multi-omics and network pharmacology and examine the clinical pharmacological mechanisms of DZXYS as an add-on therapy for depression.This study highlights that regulation of lysophosphatidic acid (LPA) and the microbiota-gut-brain axis by DZXYS plays an essential role in its antidepressant mechanisms.

Bibliometric analysis of global research on the role of apolipoprotein E in Alzheimer's disease.

Alzheimer's disease (AD) has attracted considerable attention from the public and scientific researchers, leading to a rapid growth in relevant research on this disorder in the last 10 years. The present study aimed to conduct a bibliometric analysis to elucidate the trends of global research on the role of apolipoprotein E in AD in the past decade. Three bibliometric software (CiteSpace, VOSviewer, and R Bibliometrix) were used to analyze the active journals, countries/regions, institutes, authors, co-cited references, and keywords in this field. The USA was the most influential country, and the University of California was the most productive institute. Zetterberg H contributed the highest number of publications, and Petersen RC was the most cited author in this field. On the basis of the co-cited reference analysis, knowledge base on biomarkers, risk factors, and mechanisms were updated in the past decade. Current research hotspots are shifting to tau-related mechanisms and identification of genetic risk factors. Our study provides insights into the developing knowledge base and trends related to research on apolipoprotein E in AD, which may provide new directions for further research in this field.

Kynurenine metabolite changes in individuals with alcohol use disorder: A systematic review and meta-analysis.

OBJECTIVE: Growing evidence suggests an abnormal metabolism of kynurenine in individuals with alcohol use disorder (AUD). This systematic review and meta-analysis was aimed at assessing the possible differences in kynurenine metabolites between individuals with AUD and controls. METHODS: We searched PubMed, Embase, and Web of Science databases and included any clinical studies comparing the peripheral blood levels of at least one metabolite, between individuals with AUD and controls without AUD. Random-effects meta-analyses were conducted to generate pooled standardized mean differences (SMD). Subgroup analyses and meta-regression analyses were conducted. RESULTS: A total of seven eligible studies with 572 participants were included. The peripheral blood levels of kynurenine (SMD = 0.58; p = 0.004) along with the ratio of kynurenine and tryptophan (SMD = 0.73; p = 0.002) were higher in individuals with AUD, while kynurenic acid levels (SMD = -0.81; p = 0.003) were reduced in individuals with AUD compared to controls. The peripheral blood levels of tryptophan along with the ratio of kynurenic acid and kynurenine were unaltered. Subgroup analyses confirmed these results. CONCLUSION: Our results suggested a shift in the tryptophan metabolism to the kynurenine pathway and a down-regulation of the potentially neuroprotective kynurenic acid in individuals with AUD.

Development and validation of an LC-MS/MS method by one-step precipitation for cinacalcet in human plasma.

A sensitive, convenient, rapid and economic liquid chromatography-tandem mass spectrometry (LC-MS/MS) method was developed to determine cinacalcet concentration in human plasma. A stable isotope cinacalcet (cinacalcet-D3) was selected as internal standard and the analytes were extracted from plasma samples by a one-step precipitation procedure. Chromatography separation was conducted on an Eclipse Plus C18 column by gradient elution with mobile phase of methanol-water-ammonium formate system at a constant flow rate of 0.6 mL/min. Mass spectrometric detection was conducted by multiple reaction monitoring using positive electrospray ionization. Cinacalcet concentrations in human plasma were determined over the concentration range of 0.1-50 ng/mL. The accuracies of lower limit of quantification (LLOQ) and quality control samples were all within the range of 85-115%, and the inter- and intra-batch precisions (CV%) were all within 15%. The average extraction recovery rates were 95.67-102.88%, and the quantification was not interfered by the matrix components. The validated method was successfully applied to determined cinacalcet concentrations in human plasma from secondary hyperparathyroidism patients.

Joint population pharmacokinetic modeling of venlafaxine and O-desmethyl venlafaxine in healthy volunteers and patients to evaluate the impact of morbidity and concomitant medication.

Introduction: Venlafaxine (VEN) is a widely used dual selective serotonin/noradrenaline reuptake inhibitor indicated for depression and anxiety. It undergoes first-pass metabolism to its active metabolite, O-desmethyl venlafaxine (ODV). The aim of the present study was to develop a joint population pharmacokinetic (PPK) model to characterize their pharmacokinetic characters simultaneously. Methods: Plasma concentrations with demographic and clinical data were derived from a bioequivalence study in 24 healthy subjects and a naturalistic TDM setting containing 127 psychiatric patients. A parent-metabolite PPK modeling was performed with NONMEM software using a non-linear mixed effect modeling approach. Goodness of fit plots and normalized prediction distribution error method were used for model validation. Results and conclusion: Concentrations of VEN and ODV were well described with a one-compartment model incorporating first-pass metabolism. The first-pass metabolism was modeled as a first-order conversion. The morbid state and concomitant amisulpride were identified as two significant covariates affecting the clearance of VEN and ODV, which may account for some of the variations in exposure. This model may contribute to the precision medication in clinical practice and may inspire other drugs with pre-system metabolism.

Machine learning advances the integration of covariates in population pharmacokinetic models: Valproic acid as an example.

Background and Aim: Many studies associated with the combination of machine learning (ML) and pharmacometrics have appeared in recent years. ML can be used as an initial step for fast screening of covariates in population pharmacokinetic (popPK) models. The present study aimed to integrate covariates derived from different popPK models using ML. Methods: Two published popPK models of valproic acid (VPA) in Chinese epileptic patients were used, where the population parameters were influenced by some covariates. Based on the covariates and a one-compartment model that describes the pharmacokinetics of VPA, a dataset was constructed using Monte Carlo simulation, to develop an XGBoost model to estimate the steady-state concentrations ( C s s ) of VPA. We utilized SHapley Additive exPlanation (SHAP) values to interpret the prediction model, and calculated estimates of VPA exposure in four assumed scenarios involving different combinations of CYP2C19 genotypes and co-administered antiepileptic drugs. To develop an easy-to-use model in the clinic, we built a simplified model by using CYP2C19 genotypes and some noninvasive clinical parameters, and omitting several features that were infrequently measured or whose clinically available values were inaccurate, and verified it on our independent external dataset. Results: After data preprocessing, the finally generated combined dataset was divided into a derivation cohort and a validation cohort (8:2). The XGBoost model was developed in the derivation cohort and yielded excellent performance in the validation cohort with a mean absolute error of 2.4 mg/L, root-mean-squared error of 3.3 mg/L, mean relative error of 0%, and percentages within ± 20% of actual values of 98.85%. The SHAP analysis revealed that daily dose, time, CYP2C19*2 and/or *3 variants, albumin, body weight, single dose, and CYP2C19*1*1 genotype were the top seven confounding factors influencing the C s s of VPA. Under the simulated dosage regimen of 500 mg/bid, the VPA exposure in patients who had CYP2C19*2 and/or *3 variants and no carbamazepine, phenytoin, or phenobarbital treatment, was approximately 1.74-fold compared to those with CYP2C19*1/*1 genotype and co-administered carbamazepine + phenytoin + phenobarbital. The feasibility of the simplified model was fully illustrated by its performance in our external dataset. Conclusion: This study highlighted the bridging role of ML in big data and pharmacometrics, by integrating covariates derived from different popPK models.

An interpretable stacking ensemble learning framework based on multi-dimensional data for real-time prediction of drug concentration: The example of olanzapine.

Background and Aim: Therapeutic drug monitoring (TDM) has evolved over the years as an important tool for personalized medicine. Nevertheless, some limitations are associated with traditional TDM. Emerging data-driven model forecasting [e.g., through machine learning (ML)-based approaches] has been used for individualized therapy. This study proposes an interpretable stacking-based ML framework to predict concentrations in real time after olanzapine (OLZ) treatment. Methods: The TDM-OLZ dataset, consisting of 2,142 OLZ measurements and 472 features, was formed by collecting electronic health records during the TDM of 927 patients who had received OLZ treatment. We compared the performance of ML algorithms by using 10-fold cross-validation and the mean absolute error (MAE). The optimal subset of features was analyzed by a random forest-based sequential forward feature selection method in the context of the top five heterogeneous regressors as base models to develop a stacked ensemble regressor, which was then optimized via the grid search method. Its predictions were explained by using local interpretable model-agnostic explanations (LIME) and partial dependence plots (PDPs). Results: A state-of-the-art stacking ensemble learning framework that integrates optimized extra trees, XGBoost, random forest, bagging, and gradient-boosting regressors was developed for nine selected features [i.e., daily dose (OLZ), gender_male, age, valproic acid_yes, ALT, K, BW, MONO#, and time of blood sampling after first administration]. It outperformed other base regressors that were considered, with an MAE of 0.064, R-square value of 0.5355, mean squared error of 0.0089, mean relative error of 13%, and ideal rate (the percentages of predicted TDM within ± 30% of actual TDM) of 63.40%. Predictions at the individual level were illustrated by LIME plots, whereas the global interpretation of associations between features and outcomes was illustrated by PDPs. Conclusion: This study highlights the feasibility of the real-time estimation of drug concentrations by using stacking-based ML strategies without losing interpretability, thus facilitating model-informed precision dosing.

Integrating machine learning with electronic health record data to facilitate detection of prolactin level and pharmacovigilance signals in olanzapine-treated patients.

Background and aim: Available evidence suggests elevated serum prolactin (PRL) levels in olanzapine (OLZ)-treated patients with schizophrenia. However, machine learning (ML)-based comprehensive evaluations of the influence of pathophysiological and pharmacological factors on PRL levels in OLZ-treated patients are rare. We aimed to forecast the PRL level in OLZ-treated patients and mine pharmacovigilance information on PRL-related adverse events by integrating ML and electronic health record (EHR) data. Methods: Data were extracted from an EHR system to construct an ML dataset in 672×384 matrix format after preprocessing, which was subsequently randomly divided into a derivation cohort for model development and a validation cohort for model validation (8:2). The eXtreme gradient boosting (XGBoost) algorithm was used to build the ML models, the importance of the features and predictive behaviors of which were illustrated by SHapley Additive exPlanations (SHAP)-based analyses. The sequential forward feature selection approach was used to generate the optimal feature subset. The co-administered drugs that might have influenced PRL levels during OLZ treatment as identified by SHAP analyses were then compared with evidence from disproportionality analyses by using OpenVigil FDA. Results: The 15 features that made the greatest contributions, as ranked by the mean (|SHAP value|), were identified as the optimal feature subset. The features were gender_male, co-administration of risperidone, age, co-administration of aripiprazole, concentration of aripiprazole, concentration of OLZ, progesterone, co-administration of sulpiride, creatine kinase, serum sodium, serum phosphorus, testosterone, platelet distribution width, α-L-fucosidase, and lipoprotein (a). The XGBoost model after feature selection delivered good performance on the validation cohort with a mean absolute error of 0.046, mean squared error of 0.0036, root-mean-squared error of 0.060, and mean relative error of 11%. Risperidone and aripiprazole exhibited the strongest associations with hyperprolactinemia and decreased blood PRL according to the disproportionality analyses, and both were identified as co-administered drugs that influenced PRL levels during OLZ treatment by SHAP analyses. Conclusions: Multiple pathophysiological and pharmacological confounders influence PRL levels associated with effective treatment and PRL-related side-effects in OLZ-treated patients. Our study highlights the feasibility of integration of ML and EHR data to facilitate the detection of PRL levels and pharmacovigilance signals in OLZ-treated patients.

Population pharmacokinetics and dosing optimization of olanzapine in Chinese paediatric patients: Based on the impact of sex and concomitant valproate on clearance.

WHAT IS KNOWN AND OBJECTIVE: Olanzapine is an atypical antipsychotic drug used for mental disorders. There are limited studies providing sufficient pharmacokinetic data, thus the variability of concentrations of olanzapine used in Chinese paediatric patients aged 10 to 17 years remains to be evaluated. METHODS: Therapeutic drug monitoring data were collected from 151 paediatric patients aged 10 to 17 years who received olanzapine. The model was developed with a NONMEM software program. The final model validation and evaluation were assessed by bootstrap, diagnostic scatter plots, and normalized prediction distribution error (NPDE). Regimens of different dosages were simulated to reach the target concentration levels of 20 ng/ml, by using the final model with typical parameters. RESULTS: The one-compartment model was considered the best fit for the data. Typical estimates of the absorption rate constant (Ka), apparent clearance (CL/F), and apparent distribution volume (V/F) in the final model were 0.142 h-1 , 15.4 L/h, and 322 L, respectively. Sex and concomitant valproate (VPA) were included as significant predictors of olanzapine clearance, which was described by the following equation: CL/F = 15.4 × (1 + 0.546 × SEX) × (1 + 0.264 × VPA). Results of Monte-Carlo simulation suggested that male paediatric patients with concomitant VPA were advised to take no less than 15 mg per day of olanzapine orally, and in female paediatric patients with concomitant VPA, a dosing regimen of 10 mg may be sufficient to achieve the therapeutic range of olanzapine. WHAT IS NEW AND CONCLUSION: Our results identified concomitant valproate and sex as significant covariates in olanzapine population pharmacokinetics. Our model may be a useful tool for recommending dosage adjustments for physicians. The pharmacokinetics of olanzapine in patients aged 10 to 17 years was generally similar to that of adults and the elderly.

Pharmacokinetics of immediate and sustained-release formulations of paroxetine: Population pharmacokinetic approach to guide paroxetine personalized therapy in chinese psychotic patients.

Paroxetine is one of the most potent selective serotonin reuptake inhibitors (SSRIs) approved for treating depression, panic disorder, and obsessive-compulsive disorder. There is evidence linking genetic polymorphisms and nonlinear metabolism to the Paroxetine's pharmacokinetic (PK) variability. The purpose of the present study was to develop a population PK (PPK) model of paroxetine in Chinese patients, which was used to define the paroxetine's PK parameters and quantify the effect of clinical and baseline demographic factors on these PK characteristics. The study included 184 inpatients with psychosis (103 females and 81 males), with a total of 372 serum concentrations of paroxetine for PPK analyses. The total daily dosage ranged from 20 to 75 mg. One compartment model could fit the PKs characterize of paroxetine. Covariate analysis revealed that dose, formulation, and sex had a significant effect on the PK parameters of paroxetine; however, there was no evident genetic influence of CYP2D6 enzymes on paroxetine concentrations in Chinese patients. The study determined that the population's apparent distribution volume (V/F) and apparent clearance (CL/F), respectively, were 8850 and 21.2 L/h. The CL/F decreased 1-2-fold for each 10 mg dose increase, whereas the different formulations caused a decrease in V/F of 66.6%. Sex was found to affect bioavailability (F), which decreased F by 47.5%. Females had higher F values than males. This PPK model described data from patients with psychosis who received paroxetine immediate-release tablets (IR-T) and/or sustained-release tablets (SR-T). Paroxetine trough concentrations and relative bioavailability were different between formulations and sex. The altered serum concentrations of paroxetine resulting from individual variants and additive effects need to be considered, to optimize the dosage regimen for individual patients.

Knowledge atlas of the involvement of glutamate and GABA in alcohol use disorder: A bibliometric and scientometric analysis.

Introduction: Abnormal neurotransmission of glutamate and γ-aminobutyric acid (GABA) is a key characteristic of alcohol-related disorders. To track research output, we conducted a bibliometric analysis to explore the current status and trends in this field over the past decades. Methods: Studies related to neurotransmitters and alcohol use disorder published in English from 2005-2021 were retrieved from the Web of Science Core Collection and Scopus databases. The R-bibliometrix package was used for a descriptive analysis of the publications. Citespace, WOSviewer, and R-bibliometrix were used to construct networks of countries/institutions/authors based on co-authorship, co-citation analysis of cited references and co-occurrence as well as burst detection of keywords. Results: A total of 4,250 unique articles and reviews were included in the final analysis. The annual growth rate of publications was 5.4%. The USA was the most productive country in this field, contributing nearly half of the total documents. The top ten most productive institutions were all located in the USA. The most frequent worldwide collaboration was between the USA and Italy. The most productive and influential institution was the University of California. The author contributing the most productions to this field was Marisa Roberto from the Scripps Research Institute. The top co-cited reference was a review titled "Neurocircuitry of addiction." The top journal in terms of the number of records and citations was Alcoholism: Clinical and Experimental Research. Comprehensive analyses have been conducted over past decades based on co-cited reference analysis, including modulators, transporters, receptor subtypes, and animal models. In recent years, the research frontiers have been shifting to the identification of risk factors/biomarkers, drug development for alcohol use disorder, and mechanisms related to alcoholic and non-alcoholic fatty liver. Conclusion: Our bibliometric analysis shows that glutamate and GABA continue to be of interest in alcohol use disorder. The focus has evolved from mechanisms and medications related to glutamate and GABA in alcohol use disorder, to novel drug development, risk factor/biomarker identification targeting neurotransmitters, and the mechanisms of related diseases.

Population pharmacokinetics model for escitalopram in Chinese psychiatric patients: effect of CYP2C19 and age.

Objective: To establish a population pharmacokinetic model in Chinese psychiatric patients to characterize escitalopram pharmacokinetic profile to identify factors influencing drug exposure, and through simulation to compare the results with the established therapeutic reference range. Methods: Demographic information, dosing regimen, CYP2C19 genotype, concomitant medications, and liver and kidney function indicators were retrospectively collected for inpatients taking escitalopram with therapeutic drug monitoring from 2018 to 2021. Nonlinear mixed-effects modeling was used to model the pharmacokinetic characteristics of escitalopram. Goodness-of-fit plots, bootstrapping, and normalized prediction distribution errors were used to evaluate the model. Simulation for different dosing regimens was based on the final estimations. Results: The study comprised 106 patients and 337 measurements of serum sample. A structural model with one compartment with first-order absorption and elimination described the data adequately. The population-estimated apparent volume of distribution and apparent clearance were 815 and 16.3 L/h, respectively. Age and CYP2C19 phenotype had a significant effect on the apparent clearance (CL/F). CL/F of escitalopram decreased with increased age, and CL/F of poor metabolizer patients was significantly lower than in extensive and immediate metabolizer patients. The final model-based simulation showed that the daily dose of adolescents with poor metabolizer might be as high as 15 mg or 20 mg and referring to the therapeutic range for adults may result in overdose and a high risk of adverse effects in older patients. Conclusion: A population pharmacokinetics model of escitalopram was successfully created for the Chinese population. Depending on the age of the patients, CYP2C19 genotype and serum drug concentrations throughout treatment are required for adequate individualization of dosing regimens. When developing a regimen for older patients, especially those who are poor metabolizers, vigilance is required.

A Simple and Sensitive HPLC-MS/MS Assay for the Quantitation of Blonanserin and N-Desethyl Blonanserin in Rat Plasma and Its Application to Pharmacokinetic Study.

A high-performance liquid chromatographic method coupled with triple quadrupole mass spectrometry (LC-MS/MS) for the analysis of blonanserin and its active metabolite, N-desethyl blonanserin, in rat plasma has been developed and validated. The biological samples were treated by simple direct protein precipitation before separation on an Agilent Eclipse Plus C18 column (4.6 × 100 mm, 3.5 µm) with a column temperature of 35°C at a flow rate of 0.5 mL/min. The mobile phase A is a mixture of methanol and water (75 : 25, v/v, 5 mM ammonium formate), and the mobile phase B is acetonitrile containing 0.1% formic acid. The ratio of mobile phase A to mobile phase B is 15 : 85. Electrospray ionization (ESI) multiple reaction monitoring modes are used for detection, which are m/z 368.10 ⟶ 296.90 (blonanserin), m/z 340.15 ⟶ 297.05(N-desethyl blonanserin), and m/z 348.15⟶ 302.05 (N-desethyl blonanserin-d8). The linear response range was 0.1-100.0 ng/mL for blonanserin and N-desethyl blonanserin. The lower limit of quantification (LLOQ), calibration curves, carryover, and matrix effects were sufficiently accurate and precise according to the National Medical Products Administration (NMPA) guidelines for bioanalytical method validation. This analytical method was successfully applied in a blonanserin-poloxamer thermosensitive gel pharmacokinetic study in rats.

Physicians' perceptions of generic drugs in China.

Background: In recent years, there has been a significant worldwide increase in the use of generic drugs. China has committed to a consistency evaluation of generic drugs, with the aim to improve the rate of substitution. However, there is little research on physicians' perceptions of generic drugs in China. Objective: The study aimed to explore the perceptions of physicians in China toward generic drugs. Methods: Perceptions of Chinese physicians towards generic drugs were evaluated by a cross-sectional study from June to July 2020. The online survey tool Sojump was adopted to distribute the questionnaires using convenience sampling. A total of 598 questionnaires were analyzed. Results: Perceptions of Chinese physicians towards generic drugs are generally positive. However, not all physicians appear to have sufficient knowledge about generic drugs and some of them expressed negative perceptions of generic drugs, such as perceiving generic drugs as less effective and more likely to cause side effects compared to brand-name drugs. There were significant differences in physicians' opinions about generic drugs according to age group, years in practice, educational background, clinical specialty and residential area. Conclusion: It is imperative to provide physicians with more extensive education about the consistency evaluation of generic drugs to meet the policy goal of reducing overall national medical healthcare costs.

Proteomics and weighted gene correlated network analysis reveal glutamatergic synapse signaling in diazepam treatment of alcohol withdrawal.

Background: Alcohol use disorder (AUD) is characterized by chronic excessive alcohol consumption, often alternating with periods of abstinence known as alcohol withdrawal syndrome (AWS). Diazepam is the preferred benzodiazepine for treatment of alcohol withdrawal syndrome under most circumstances, but the specific mechanism underlying the treatment needs further research. Methods: We constructed an animal model of two-bottle choices and chronic intermittent ethanol exposure. LC-MS/MS proteomic analysis based on the label-free and intensity-based quantification approach was used to detect the protein profile of the whole brain. Weighted gene correlated network analysis was applied for scale-free network topology analysis. We established a protein-protein interaction network based on the Search Tool for the Retrieval of Interacting Genes (STRING) database and Cytoscape software and identified hub proteins by CytoHubba and MCODE plugins of Cytoscape. The online tool Targetscan identified miRNA-mRNA pair interactions. Results: Seven hub proteins (Dlg3, Dlg4, Shank3, Grin2b, Camk2b, Camk2a and Syngap1) were implicated in alcohol withdrawal syndrome or diazepam treatment. In enrichment analysis, glutamatergic synapses were considered the most important pathway related to alcohol use disorder. Decreased glutamatergic synapses were observed in the late stage of withdrawal, as a protective mechanism that attenuated withdrawal-induced excitotoxicity. Diazepam treatment during withdrawal increased glutamatergic synapses, alleviating withdrawal-induced synapse inhibition. Conclusion: Glutamatergic synapses are considered the most important pathway related to alcohol use disorder that may be a potential molecular target for new interventional strategies.

Association between CYP19A1 rs6493497 and rs936306 polymorphisms and depression susceptibility in the Chinese population.

Aim: The CYP19A1 gene encodes the key aromatase for estrogen biosynthesis, and this study aimed to explore the relationship between CYP19A1 rs6493497 and rs936306 polymorphisms and depression risk. Methods: CYP19A1 rs6493497 and rs936306 genotyping was performed on 502 depression patients and 504 healthy controls. Results: In the general population, no significant association was observed between the CYP19A1 rs6493497 variant and depression, whereas that CYP19A1 rs936306 variant significantly reduced depression risk in the recessive model. In subgroup analysis, a significant association of the CYP19A1 rs6493497 variant with reduced depression risk was found in males aged 46-65 in the genotype, dominant and additive models. Conclusion: The CYP19A1 rs936306 variant may reduce depression risk, and the rs6493497 variant is associated with decreased depression risk in males aged 46-65.

The cause of depression is complex and not fully elucidated; the research evidence suggests that changes in estrogen levels may partly account for the risk of the onset of depression. The CYP19A1 gene encodes the key enzyme for estrogen biosynthesis, and this study aimed to explore whether the two loci (rs6493497 and rs936306) variants of the CYP19A1 gene are associated with the risk of occurrence of depression. Five hundred two patients with depression and 504 healthy controls were enrolled. The results of this study indicate that the CYP19A1 gene rs936306 variant may reduce the risk of occurrence of depression, and the rs6493497 variant is associated with decreased depression risk in men aged 46­65.

Combining Metabolomics and Interpretable Machine Learning to Reveal Plasma Metabolic Profiling and Biological Correlates of Alcohol-Dependent Inpatients: What About Tryptophan Metabolism Regulation?

Alcohol dependence (AD) is a condition of alcohol use disorder in which the drinkers frequently develop emotional symptoms associated with a continuous alcohol intake. AD characterized by metabolic disturbances can be quantitatively analyzed by metabolomics to identify the alterations in metabolic pathways. This study aimed to: i) compare the plasma metabolic profiling between healthy and AD-diagnosed individuals to reveal the altered metabolic profiles in AD, and ii) identify potential biological correlates of alcohol-dependent inpatients based on metabolomics and interpretable machine learning. Plasma samples were obtained from healthy (n = 42) and AD-diagnosed individuals (n = 43). The plasma metabolic differences between them were investigated using liquid chromatography-tandem mass spectrometry (AB SCIEX® QTRAP 4500 system) in different electrospray ionization modes with scheduled multiple reaction monitoring scans. In total, 59 and 52 compounds were semi-quantitatively measured in positive and negative ionization modes, respectively. In addition, 39 metabolites were identified as important variables to contribute to the classifications using an orthogonal partial least squares-discriminant analysis (OPLS-DA) (VIP > 1) and also significantly different between healthy and AD-diagnosed individuals using univariate analysis (p-value < 0.05 and false discovery rate < 0.05). Among the identified metabolites, indole-3-carboxylic acid, quinolinic acid, hydroxy-tryptophan, and serotonin were involved in the tryptophan metabolism along the indole, kynurenine, and serotonin pathways. Metabolic pathway analysis revealed significant changes or imbalances in alanine, aspartate, glutamate metabolism, which was possibly the main altered pathway related to AD. Tryptophan metabolism interactively influenced other metabolic pathways, such as nicotinate and nicotinamide metabolism. Furthermore, among the OPLS-DA-identified metabolites, normetanephrine and ascorbic acid were demonstrated as suitable biological correlates of AD inpatients from our model using an interpretable, supervised decision tree classifier algorithm. These findings indicate that the discriminatory metabolic profiles between healthy and AD-diagnosed individuals may benefit researchers in illustrating the underlying molecular mechanisms of AD. This study also highlights the approach of combining metabolomics and interpretable machine learning as a valuable tool to uncover potential biological correlates. Future studies should focus on the global analysis of the possible roles of these differential metabolites and disordered metabolic pathways in the pathophysiology of AD.