ObjectivesTo characterize the population pharmacokinetics (PPK) of lurasidone in Chinese psychiatric inpatients and to quantify the sources of inter-individual variability, with a specific focus on the impact of valproate (VPA) co-medication and age on drug exposure.MethodsRoutine therapeutic drug monitoring (TDM) data were collected from 156 patients (providing 212 serum concentrations). A PPK model was developed utilizing a nonlinear mixed-effects modeling approach (NONMEM). Model performance was assessed via goodness-of-fit plots, normalized prediction distribution errors (NPDE), and bootstrap analysis. Additionally, model-based simulations were performed to estimate concentration profiles stratified by age and VPA co-medication.ResultsA one-compartment model with first-order absorption and elimination best described the data. The typical estimate for apparent clearance (CL/F) was 339 L·h-1. Age and concomitant VPA were identified as significant covariates. VPA co-administration increased CL/F by 47.7%, leading to significantly reduced systemic exposure. Notably, the majority of observed steady-state concentrations (3–11 ng/mL) fell below the lower limit of the AGNP reference range.ConclusionThe pharmacokinetics of lurasidone in Chinese inpatients were effectively characterized by the established PPK model. Lurasidone exposure in this population is generally lower than the AGNP reference range, particularly in patients receiving VPA. Age and VPA co-medication are critical determinants of CL/F. These findings suggest that TDM interpretation should be adjusted for these factors, and optimal concentration targets for Chinese patients may need to be revisited.

BACKGROUND:The aim of this study was to determine the therapeutic reference range of lurasidone, and to analyze the factors influencing the dose-corrected concentration of lurasidone in Chinese psychiatric patients, thereby providing a basis for the development of individualized dosing of lurasidone. METHODS:A retrospective analysis was conducted for hospitalized patients who had received lurasidone and undergone blood concentration monitoring from May 2022 to September 2023 at the Affiliated Brain Hospital of Guangzhou Medical University. Analyses were based on patient demographic data, treatment regimens, and administered drug concentrations. RESULTS:Data for a total of 123 lurasidone steady-state trough concentrations were collected from 120 hospitalized patients. It was found that 85.56% of lurasidone steady-state trough concentrations were below the lower limit of the lurasidone therapeutic reference range (15 ng·mL -1 ), and that the median steady-state trough concentration was 7.09 ng·mL -1 (IQ1-IQ3 = 4.12-11.82 ng·mL -1 ). Gender, age, and co-medication with valproic acid were found to be significant factors influencing lurasidone steady-state trough concentration/daily dose (C/D) values. C/D values for females were 14% higher than those obtained for males. Among patients who did not receive concomitant administration of valproic acid, the C/D values were 55% higher than those who had received co-administered valproic acid. Furthermore, C/D values obtained for elderly patients (≥60 years) were 140% higher than those recorded for adolescents (<18 years) and 157% higher than those in younger adults (18-60 years). CONCLUSIONS:The findings of this study indicated that the guideline-recommended therapeutic reference range (15-40 ng·mL -1 ) for lurasidone may not be appropriate, at least for the Chinese population. More extensive therapeutic drug monitoring is recommended for elderly female patients and those receiving co-medication with lurasidone and valproic acid.

目的:评价高效液相色谱串联质谱法(HPLC-MS/MS法)测定人血浆中舒巴坦和氨苄西林浓度的不确定度.方法:用HPLC-MS/MS法测定人血浆中舒巴坦和氨苄西林浓度,分析称量、溶液配制、测量重复性及标准曲线拟合等因素对结果的影响,计算各因素的不确定度及合成不确定度,并评定扩展不确定度.结果:当置信概率P=95％(k=2)时,人血浆中低(120 ng·mL-1),中(1 600 ng·mL-1)和高(16 000 ng·mL-1)浓度舒巴坦的拓展不确定度分别为8.06,81.60,765.79 ng·mL-1.人血浆中低(180 ng·mL-1),中(2 400 ng·mL-1)和高(24 000 ng·mL-1)质量浓度氨苄西林的拓展不确定度分别为9.16,113.24,1 069.69 ng·mL-1.结论:本方法灵敏度高,分析处理时间短,能快速准确测定人血浆中舒巴坦和氨苄西林浓度,在不确定度分析中发现低浓度时标曲拟合引入不确定度值最大,中浓度时回收率引入不确定度值最大,高浓度时溶液配制引入不确定度值最大,通过改良样本前处理方法、改善仪器测定条件及提高操作人员规范性等可降低不确定度值.

目的 探讨精神分裂症患者服用帕利哌酮后剂量校正浓度(C/D)的影响因素,为帕利哌酮的临床个体化用药提供参考.方法 选取2019年1月至2021年1月在本院进行过帕利哌酮治疗药物监测的126例精神分裂症患者的138例次血药浓度检测数据.帕利哌酮给药方案为3、6、9或12 mg,qd或bid.收集患者性别、年龄、给药频次、给药剂量、联合用药等资料.用SPSS 25.0软件对数据进行统计分析.用独立样本t检验比较性别、给药频次、联合用药对帕利哌酮剂量校正谷浓度的影响;用One-Way ANOVA方差分析比较年龄对帕利哌酮谷浓度的影响;用多重线性回归分析帕利哌酮C/D的影响因素.结果 性别显著影响帕利哌酮的C/D(P<0.01),女性患者帕利哌酮的C/D(4.86±2.78)ng·mL-1·mg-1·d显著高于男性(3.56±2.36)ng·mL-1·mg-1·d(P<0.01).进行多重线性回归分析,发现性别、联合应用阿立哌唑2个自变量影响帕利哌酮C/D(F=7.03,P<0.001,R2=0.136).结论 本研究结果表明,性别与联合应用阿立哌唑为影响帕利哌酮C/D的2个差异较显著的因素.

目的:研究奥氮平与舍曲林联合应用是否会对奥氮平的体内血药浓度产生影响.方法:回顾性查阅2020—2021年某院住院患者的奥氮平治疗药物监测结果,按照是否联合应用舍曲林分为联合用药组(68例)与对照组(117例),比较两组间的奥氮平血药浓度.结果:联合用药组、对照组患者的奥氮平血药浓度分别为(31.68±18.58)、(49.86±25.63)ng/mL,剂量校正血药浓度分别为(2.08±0.88)、(2.69±1.21)ng·d/(mL·mg),联合用药组显著低于对照组,差异有统计学意义(P<0.05).联合用药组、对照组患者低于治疗窗下限的病例数占比分别为32.35％(22/68)和7.69％(9/117),联合用药组显著高于对照组,差异有统计学意义(P<0.05).结论:奥氮平与舍曲林联合应用可使奥氮平的血药浓度显著降低,当两药合用时,建议关注患者奥氮平血药浓度的变化,必要时可能需要视临床情况适当增加剂量.

目的 建立一种简便快捷的高效液相色谱串联质谱(HPLC-MS/MS)测定人血清中鲁拉西酮浓度的方法.方法 内标选用鲁拉西酮-d8,血清样品处理采用乙腈蛋白沉淀法,色谱柱为Agilent XDB-C18(4.6 mm×50.0 mm,1.8 μm),流动相为甲醇(含5 mmol·L-1甲酸铵和0.1％甲酸)-水-乙腈=66.67 ∶16.67∶16.67,等度洗脱,流速:0.5 mL·min-1,柱温:40℃,进样量:2μL.应用电喷雾离子化,正离子模式下进行多反应监测:对照品鲁拉西酮(m/z 493.10→166.00)和内标鲁拉西酮-d8(m/z 501.10→166.00).结果 鲁拉西酮在1～200 ng·mL-1范围内线性关系良好,准确度在96.57％～99.34％,提取回收率在96.02％～98.39％,日内及日间RSD均小于15％,稳定性考察结果良好.结论 本方法简便快捷,灵敏度高,专属性强,适用于测定临床精神障碍患者中鲁拉西酮的血药浓度监测.

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.

目的 研究服用舒必利患者的剂量校正浓度(C/D)的影响因素,为患者实现舒必利个体化治疗及用药提供依据.方法 收集分析2018年3月至2022年3月在广州医科大学附属脑科医院住院治疗且服用舒必利进行治疗药物浓度监测的患者血药浓度监测数据212份,并收集患者的性别、年龄、体质量指数及合并用药等资料,通过SPSS 25.0软件进行数据统计并对结果进行分析.结果 患者舒必利的给药日剂量为(597.17±17.79)mg·d-1,平均血药浓度为(919.19±41.81)ng·mL-1,其中有43.9％例次超出治疗窗参考范围.性别、年龄、体质量指数对舒必利的C/D产生的影响有统计学意义(均P<0.05).通过多重线性回归分析发现,年龄与体质量指数对舒心利的C/D具有影响(P<0.05).结论 舒必利的血药浓度与剂量相关.临床上使用舒必利时,需根据患者性别、年龄和体质量指数进行给药剂量的调整.患者体质量指数小于18 kg·m-2时,可以考虑适当降低患者服用舒必利的剂量,同时加强治疗药物浓度监测以防药物不良反应的发生.

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 C 18 column (4.6 × 100 mm, 3.5  μ m) 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-d 8 ). 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.

目的 基于前期已建立的老年精神障碍患者奥氮平群体药动学模型,研究漏服场景下补服剂量,为临床提供补服方案.方法 以临床常用方案奥氮平10 mg,口服,每晚9点1次进行仿真,设计1次漏服、连续2次和3次漏服3个场景.应用NONMEM软件分析数据,评估不同漏服场景对奥氮平血药浓度的影响,并设计相应的补救方案.补服策略分为2次补服和1次补服,2次补服指患者立即服用10 mg,在下次计划时间服用补服剂量,之后按原剂量方案服药.1次补服指跳过漏服剂量,在下次计划时间通过1次补服剂量,之后按常规服用.以血药浓度迅速达到稳态浓度水平为标准,考察相应的最佳补服剂量,补服剂量可最小精确到2.5 mg.结果 漏服2、3次低于治疗窗的风险比漏服1次高,漏服3次所需要的补服剂量更大.在2次补服策略下,随着延迟时间的增加,第2次补服剂量逐渐减少.采取1次补服策略,其剂量并非常规经验的剂量加倍,若剂量加倍补服,可能会增加不良反应的风险.结论 本研究通过群体药动学研究,制定了老年患者漏服奥氮平后的补救方案,可为临床药物治疗决策提供参考.

目的 探讨O-去甲基文拉法辛与文拉法辛血药浓度比值(ratio of 0-desmethylvenlafaxine to venlafaxine concentration,CODV/CVEN)的影响因素.方法 回顾性收集146例次文拉法辛和O-去甲基文拉法辛血药浓度监测的住院患者信息,包括年龄、细胞色素P450 2D6(cytochrome P450 2D6,CYP2D6)基因型、CYP2C19基因型、日剂量、文拉法辛及O-去甲基文拉法辛浓度等信息,用SPSS 16.0软件进行统计分析.结果 ＜60岁和≥60岁患者的CODV/CVEN分别为2.09±1.75和3.15±1.99,差异有统计学意义(P＜0.05).CYP2D6慢代谢者(poor metabolizers,PM)、中间代谢者(intermediate metabolizers,IM)和广泛代谢者(extensive metabolizers,EM)的 CODV/CVEN分别为 1.48±1.43,2.70±1.71 和3.74±2.43,差异均有统计学意义(均P＜0.05).CYP2C19 PM、IM和EM的CODV/CVEN分别为1.41±1.59,2.47±1.76 和 2.59±2.33,PM 的 CODV/CVEN 和IM、EM比较,差异均有统计学意义(均P＜0.05).结论 ≥60岁、CYP2D6 EM的患者可能更适合使用文拉法辛进行治疗.

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.

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.

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.

Background: Pulmonary fibrosis (PF) is a fatal lung disease and affects over 5 million patients worldwide. Precise and early detection of PF is of pivotal importance to slow the disease progression. However, there are currently no effective tools to detect PF directly. Purpose: This study aimed to develop an imaging modality to detect PF directly. Excessive collagen deposition is the hallmark of PF. Herein, we developed a novel PF diagnostic agent, namely PVD (platelets-derived nanovesicles labeled with dye), by utilizing near-infrared (NIR)-responsive biomimetic platelets that specifically recognize collagen. Methods: In brief, platelets membrane was extracted from purified platelets by freeze/thaw and formed to PVD nanovesicles via sonication and extrusion, when loaded with DiR dye. Red blood cells membrane loaded with DiR was prepared in the same way as PVD to form RVD as control. Collagen self-assembled on microplates was used as an in vitro collagen fibrils model and monocrotaline-induced rats were used as an in vivo PF model. Results: We demonstrated that PVD, but not RVD nor other controls, could bind collagen both in vitro and in vivo, and directly detect pulmonary fibrosis in vivo and ex vivo at the early PF stage. Conclusion: Collectively, PVD is a versatile NIR-responsive probe for the direct visualization of collagen, and can be particularly helpful in direct detecting PF. To the best of our knowledge, PVD is the first report of a NIR probe for the direct detection of pulmonary fibrosis.

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.

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.

Background: There is a crucial link between the gut microbiota and the host central nervous system, and the communication between them occurs via a bidirectional pathway termed the “microbiota-gut-brain axis.” The gut microbiome in the modern environment has markedly changed in response to environmental factors. These changes may affect a broad range of host psychiatric disorders, such as depression, by interacting with the host through metabolic, immune, neural, and endocrine pathways. Nevertheless, the general aspects of the links between the gut microbiota and depression have not been systematically investigated through bibliometric analysis. Aim: This study aimed to analyze the current status and developing trends in gut microbiota research in the depression field through bibliometric and visual analysis. Methods: A total of 1,962 publications published between 1999 and 2019 were retrieved from the Web of Science Core Collection. CiteSpace (5.6 R5) was used to perform collaboration network analysis, co-citation analysis, co-occurrence analysis, and citation burst detection. Results: The number of publications has been rapidly growing since 2010. The collaboration network analysis revealed that the USA, University College Cork, and John F. Cryan were the most influential country, institute, and scholar, respectively. The most productive and co-cited journals were Brain Behavior and Immunity and Proceedings of the National Academy of Sciences of the United States of America, respectively. The co-citation analysis of references revealed that the most recent research focus was in the largest theme cluster, “cytokines,” thus reflecting the important research foundation in this field. The co-occurrence analysis of keywords revealed that “fecal microbiota” and “microbiome” have become the top two research hotspots since 2013. The citation burst detection for keywords identified several keywords, including “Parkinson's disease,” “microbiota-gut-brain axis,” “microbiome,” “dysbiosis,” “bipolar disorder,” “impact,” “C reactive protein,” and “immune system,” as new research frontiers, which have currently ongoing bursts. Conclusions: These results provide an instructive perspective on the current research and future directions in the study of the links between the gut microbiota and depression, which may help researchers choose suitable cooperators or journals, and promote their research illustrating the underlying molecular mechanisms of depression, including its etiology, prevention, and treatment.

Targeted metabolomics analysis based on triple quadrupole (QQQ) MS coupled with multiple reaction monitoring mode (MRM) is the gold standard for metabolite quantification and it is widely applied in metabolomics. However, standard compounds for each metabolite and the corresponding analogs are necessary for quantitative measurements. To identify the differentially present metabolites in various groups, determining the relative concentration of metabolites would be more efficient than accurate quantification. In this study, a relatively quantitative targeted method was established for metabonomics research, on the basis of hydrophilic interaction liquid chromatography (HILIC)/QQQ MS operated in MRM mode. The quality control-base random forest signal correction algorithm (QC-RFSC algorithm) was applied for quality control instead of the internal standard method. High quality relative quantification was achieved without internal standards, and integrated peak areas were successfully used for statistical and pathway analyses. Amino acids and neurotransmitters (dopamine, kynurenic acid, urocanic acid, tryptophan, kynurenine, tyrosine, valine, threonine, serine, alanine, glycine, glutamine, citrulline, GABA, glutamate, aspartate, arginine, ornithine and histidine) in serum samples were simultaneously determined with the newly developed method. To demonstrate the applicability of this method in large-scale analyses, we analyzed the above metabolites in serum from patients with major depression. The serum levels of glutamate, aspartate, threonine, glycine and alanine were significantly higher, and those of citrulline, kynurenic acid and urocanic acid were significantly lower, in patients with major depression than in controls. This is the first report of the difference in urocanic acid, a compound reported to improve glutamate biosynthesis and release in the central nervous system, between healthy controls and patients with major depression.

目的 建立同时测定人血清中氨磺必利和阿立哌唑质量浓度的方法.方法 以乙腈为沉淀剂,通过蛋白沉淀法处理血清样本.色谱柱:Agilent XDB-C18(4.6 mm×50.0 mm,1.8μm),流动相:甲醇-水(含2 mmol·L-1甲酸铵),等度洗脱,柱温:35℃,流速:0.7 mL·min-1,进样量:1μL.电喷雾离子源,正离子化,多反应离子监测.考察该方法的专属性、标准曲线与定量下限、精密度与回收率、基质效应和稳定性.结果 氨磺必利和阿立哌唑均在20～2000 ng·mL-1线性关系良好,定量下限均为20 ng·mL-1,批内和批间精密度RSD在1.43％ ～13.62％,提取回收率为92.95％ ～103.84％,稳定性良好.结论 本方法的样品处理简便,专属性强,稳定性好,适用于单独或联合使用氨磺必利和阿立哌唑药物的患者进行治疗药物监测.