{"paper_id":"1e274b86-df71-48c6-af59-8ebc2e621e09","body_text":"Population Pharmacokinetics of Amisulpride in Chinese Schizophrenia Patients: Quantifying Drug-Drug Interactions and Renal Function-Guided Dosing Individualization | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Population Pharmacokinetics of Amisulpride in Chinese Schizophrenia Patients: Quantifying Drug-Drug Interactions and Renal Function-Guided Dosing Individualization Tongying Gan, Chang Liu, Bixuan Wu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7289163/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Objective Patients with schizophrenia often require antipsychotic combinations, among which amisulpride is widely used due to its predominant renal excretion and low dependence on CYP450 enzymes. However, the impact of concomitant medications on the pharmacokinetics of amisulpride remains poorly characterized. The objective of this study was to establish a population pharmacokinetic model to quantitatively evaluate the effects of concomitant medications on amisulpride clearance. Additionally, we aimed to use simulation to recommend effective dosing regimens based on the newly revised therapeutic reference range (100–600 ng/mL). Methods A total of 386 therapeutic drug monitoring samples of amisulpride from 194 hospitalized patients were utilized to develop a PPK model, for which a one-compartment model with first-order absorption was developed using nonlinear mixed-effects modeling. The model systematically evaluated demographics, creatinine clearance, and co-medications as covariates. After model evaluation using bootstrap, goodness-of-fit plots, and normalized prediction distribution error (NPDE), simulations were conducted to assess drug-drug interactions, and to optimize dosing across renal function strata. Results CLcr and metformin coadministration were ultimately identified as significant covariates influencing amisulpride apparent clearance (CL/F). CL/F decreased by 16 L/h per 10 mL/min reduction in CLcr, while metformin inhibited CL/F by 23%. Metformin coadministration increases amisulpride exposure by ≥ 45% across a wide range of renal function. The revised therapeutic range (100–600 ng/mL) was suitable for most clinical scenarios, with 58.3% of simulated concentrations within this window in patients with normal renal function (CLcr 90–120 mL/min). Renal function-based effective dosing recommendations were as follows: renal impairment (CLcr 30–60 mL/min): 200–600 mg/day; mild impairment (60–90 mL/min): 200–800 mg/day; normal function (90–120 mL/min): 300–800 mg/day; mild hyperfiltration (120–150 mL/min): 300–1000 mg/day; hyperfiltration (150–180 mL/min): 400–1000 mg/day. Conclusion This study provides the first clinical evidence of significant amisulpride-metformin interactions, warranting caution during concomitant use. The Cockcroft-Gault formula is optimal for renal function assessment to optimize amisulpride dosing. Individualized regimens stratified by CLcr and adherence to the 100–600 ng/mL therapeutic range can optimize treatment efficacy and safety in Chinese patients with schizophrenia. Amisulpride Therapeutic drug monitoring Population Pharmacokinetics Individualized drug therapy Renal clearance Drug-Drug Interactions Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Schizophrenia, a chronic and severe mental disorder, significantly impairs patients' quality of life. Epidemiological data indicate a global prevalence of approximately 24 million cases, positioning it among the top 20 contributors to global disability and a substantial component of the worldwide mental health burden (GBD 2019 Mental Disorders Collaborators 2022 ; Solmi et al. 2023 ). Owing to pathological heterogeneity in pathological mechanisms, involving intricate genetic, neurobiological, and psychosocial interactions(McCutcheon et al. 2023 ; Srivastava et al. 2021 ), combination pharmacotherapy is prevalent treatment strategy in clinical practice. Systematic analyses of prescription patterns reveal that approximately one-third of patients with schizophrenia worldwide receive antipsychotic polypharmacy, with meta-analytic data showing a global antipsychotic polypharmacy prevalence of 33.2% (95% CI 30.6–36.0%)(Højlund et al. 2024 ). Beyond traditional augmentation strategies for mental disorders (mood stabilizers, antidepressants, and benzodiazepines), hypoglycemic agents have emerged as important adjunctive therapies. An epidemiological study suggested that the prevalence of metabolic syndrome reaches 10.93% in first-episode, drug-naïve schizophrenia patients(Hu et al. 2025 ). Additionally, data from UK primary care show that 17.53% (4,652/26,537) of patients with severe mental illnesses received metformin specifically for managing antipsychotic-induced weight gain or pre-existing comorbidities (diabetes or PCOS) (Farache Trajano et al. 2025 ). Notably, current evidence-based clinical practice guidelines endorse metformin as a recommended intervention (Level 1a, Grade A) for preventing and managing antipsychotic-associated weight gain in patients with severe mental disorders(Wharton et al. 2020 ). In clinical practice, combination therapy with glucagon-like peptide-1 receptor agonists (GLP-1 RAs) may also be used for this indication in patients(Manta et al. 2025 ; Solmi et al. 2024 ). The comorbidity between schizophrenia and metabolic disorders manifests through interdependent mechanisms. Pharmacologically, antipsychotics frequently induce adverse metabolic effects, including weight gain and metabolic syndrome, which significantly exacerbate obesity in this patient population (Carli et al. 2021 ; Siskind et al. 2021 ). Pathophysiologically, the bidirectional association between schizophrenia and obesity may stem from shared pathological mechanisms, such as dopaminergic reward circuitry dysfunction(Edwin Thanarajah et al. 2023 ) and dysregulation of the hypothalamic-pituitary-adrenal (HPA) axis(Dionysopoulou et al. 2021 ). This dual-pathway synergy translates into an increase in comorbidities. The pooled prevalence of obesity among individuals with severe mental illness is 25.9% (95% CI = 23.3–29.1%), while the combined prevalence of overweight and obesity is 60.1% (95% CI = 55.8–63.1%)(Afzal et al. 2021 ). This is approximately twice the rate in the general population (obesity rate: 16.4%; overweight rate: 34.3%)(Pan et al. 2021 ). In order to mitigate weight gain, metabolic disorders, and their associated cardiovascular disease burden, lifestyle interventions remain foundational. However, pharmacologic approaches including metformin adjunctive therapy have demonstrated efficacy(Hegde et al. 2024 ) . Amisulpride is a widely used antipsychotic drug, exhibiting distinct pharmacokinetics characterized by renal excretion of approximately 70% of the unchanged drug and low dependence on cytochrome P450 (CYP450) enzymes for metabolism(Fox et al. 2019 ). Despite these characteristics, significant interpatient variability in plasma concentrations has consistently been observed in clinical practice(Ding et al. 2023 ). While existing studies have primarily focused on age, gender, and body mass index (BMI) as determinants of amisulpride pharmacokinetics(Li et al. 2020 ), the potential impact of concomitant medications has been largely overlooked. This oversight stems from the conventional assumption that drugs with low CYP450 metabolism dependence have minimal drug-drug interaction potential. However, emerging mechanistic studies suggest that amisulpride may serve as a potential substrate for organic cation transporters (OCTs), multidrug and toxic compound extrusion proteins (MATEs), and P-glycoprotein (P-gp)(Dos Santos Pereira et al. 2014 ; El Ela et al. 2004 ; Schmitt et al. 2006 ; Schmitt et al. 2012 ; Sekhar et al. 2019 ). Transporter-mediated drug-drug interactions (DDIs) could significantly alter amisulpride's pharmacokinetic profile, potentially impacting its clinical efficacy and adverse effect spectrum. Current research has insufficiently investigated how concomitant medications might influence amisulpride's absorption, distribution, metabolism, and excretion (ADME) processes, especially interactions mediated through renal clearance mechanisms. Given that renal excretion is the primary elimination pathway for amisulpride, such interactions could significantly impact its therapeutic efficacy and safety profile. Notably, data from China's 686 Program indicate that the clozapine-amisulpride combination ranks as the third most frequently prescribed antipsychotic polypharmacy regimen (Hu et al. 2024 ). This high-frequency prescribing pattern not only confirms the widespread use of amisulpride in combination therapies but also underscores an urgent need to investigate potential pharmacokinetic interactions in these complex therapeutic regimens. Furthermore, from a metabolic perspective, amisulpride demonstrates relative advantages among second-generation antipsychotics (SGAs). Comparative pharmacodynamic studies have shown that amisulpride is associated with a lower propensity for weight gain(Bak et al. 2021 ; Pillinger et al. 2020 ) and may have a less adverse impact on metabolic parameters(Pillinger, et al. 2020 ; Wu et al. 2022 ), compared with other SGAs. For patients at increased risk of metabolic complications, amisulpride appears to maintain therapeutic efficacy while showing superior weight preservation and glycemic stability of body weight and glycemic control(Asif et al. 2024 ), potentially making it a suitable treatment option for this population. However, the safety and potential pharmacokinetic interactions between amisulpride and commonly prescribed antidiabetic agents remain to be fully elucidated. ​ Population pharmacokinetics (PPK) modeling and simulation is a powerful tool that integrates multiple covariates to predict drug concentration profiles. It simulates ADME processes to provide quantitative predictions under various dosing scenarios. Renal function is a key determinant of drug elimination, especially for renally cleared compounds such as amisulpride. PPK can simulate drug exposure across the full renal function spectrum (normal, impaired, or hyperfiltration), thereby establishing an evidence-based foundation for individualized dosing regimens in patients with varying renal function. Additionally, regarding DDIs, PPK modeling serves as a complementary approach to preclinical assessments by quantitatively translating clinical study findings into optimized combination therapies that balance therapeutic efficacy with safety considerations. This guides clinicians in optimizing combination therapies to minimize adverse drug reactions while maximizing therapeutic efficacy, thus enhancing the overall risk-benefit profile of pharmacotherapy. To systematically investigate real-world DDI scenarios and to provide individualized effective dosing regimens for patients with varying renal function, this study employed a PPK modeling approach using retrospective clinical data. The PPK framework will integrated population-level analyses to: (1) identify key determinants of amisulpride exposure, including population-typical values and fixed-effect parameters; (2) quantify inter-individual and intra-individual variability; and (3) quantitatively assess the impact of intrinsic/extrinsic factors (particularly renal function and DDIs) on amisulpride's pharmacokinetic profile. Materials and Methods Study Population and Data Collection Retrospective therapeutic drug monitoring (TDM) data were collected from inpatients receiving amisulpride treatment at the Affiliated Brain Hospital of Guangzhou Medical University between July 2023 and June 2024.Steady-state trough blood concentrations for TDM were routinely measured from samples collected in the morning before daily drug administration. Demographic and clinical parameters—including age, sex, serum creatinine, concomitant medications, administered amisulpride doses, and plasma concentrations—were retrieved from the electronic medical record (EMR) system. The study was approved by the Institutional Review Board of the Affiliated Brain Hospital of Guangzhou Medical University (Approval Number: 2021027).​​ Inclusion and Exclusion Criteria Inclusion Criteria: (1) Hospitalized patients receiving oral amisulpride with routine therapeutic drug monitoring (TDM); (2) Patients diagnosed with schizophrenia according to ICD-10 criteria. Exclusion Criteria: (1) Lack of an established amisulpride treatment regimen;(2) TDM samples outside the assay quantification range. Statistical Analysis All statistical analyses were performed using SPSS software (version 26; International Business Machines Corporation, Armonk, NY, USA). Continuous variables, including age, serum creatinine, creatinine clearance (CLcr), daily dose, plasma concentration, and dose-corrected concentration (C/D ratios), were described as mean ± standard deviation ( \\(\\:\\stackrel{-}{\\text{x}}\\) ± s) and median (interquartile range) [M(P25, P75)] to ensure a comprehensive representation of their distributions. Categorical variables were dummy-coded for analysis. To meet normality assumptions, plasma concentrations and C/D ratios were appropriately transformed (e.g., square root transformation, logarithmic transformation). Analysis of covariance (ANCOVA) was conducted for each covariate. Multiple comparisons were adjusted using the Bonferroni correction. A two-tailed P < 0.05 was considered statistically significant. All figures were generated using Origin 2022 (version 2022; OriginLab Corporation, Northampton, MA, USA). Population Pharmacokinetic Model Development The PPK analysis of amisulpride was performed using nonlinear mixed-effects modeling (NONMEM® version 7.3; ICON Development Solutions, Hanover, MD, USA) in conjunction with Perl speaks NONMEM (PsN, version 4.2.0). Model development and visualization were conducted using Pirana (version 2.9.7) and R (version 4.2.2). A one-compartment pharmacokinetic model (ADVAN 2) with first-order conditional estimation with interaction (FOCEI) was employed to estimate amisulpride PPK parameters, including apparent clearance (CL/F), apparent volume of distribution (V/F), and absorption rate constant (Ka). Due to sparse sampling in the TDM data, with most concentration measurements obtained during the elimination phase, the Ka parameter was fixed at a stable value following establishing the structural model to facilitate subsequent model refinement. The interindividual variability (IIV) of pharmacokinetic parameters was estimated using an exponential random-effects model: P j = P TV × e ηp , where P j is the predicted parameter for individual j, P TV is the population typical value of the parameter, and ηp represents the random interindividual variability for the jth subject, which follows a normal distribution with mean 0 and variance ωp². A comprehensive covariate analysis was performed to identify potential factors influencing amisulpride pharmacokinetic parameters. Demographic characteristics (age, sex, body weight), renal function indicators (serum creatinine, creatinine clearance), and concomitant medications (metformin, fluoxetine, valproic acid) identified through preliminary screening were evaluated as potential covariates. The model-building process employed a stepwise approach incorporating both demographic and concomitant medication data as covariates. During forward selection, covariates demonstrating statistically significant effects (defined as a reduction in objective function value (OFV) > 6.63, χ² test, P < 0.01, df = 1) were incorporated into the base model. Subsequently, backward elimination was performed on the full model, with covariates retained if their removal resulted in an OFV increase > 10.83 (χ² test, P < 0.001, df = 1). The final model selection was based on the following comprehensive criteria: a reduction in OFV, improved goodness-of-fit plots, decreased IIV in structural model parameters, analysis of residual distributions, robustness of parameter estimates, and overall model stability. Population Pharmacokinetic Model Evaluation The final model and parameter estimates were rigorously evaluated through an integrated validation approach incorporating bootstrap analysis, goodness-of-fit (GOF) diagnostic plots, and normalized prediction distribution error (NPDE) assessment. Bootstrap validation with 1000 resampling iterations was employed to evaluate model robustness by comparing parameter estimates from bootstrapped datasets with those from the original model. GOF diagnostic evaluation included four key plots: observed versus individual predicted values (DV vs. IPRED), observed versus population predicted values (DV vs. PRED), conditional weighted residuals versus time (CWRES vs. TIME), and conditional weighted residuals versus population predictions (CWRES vs. PRED), which collectively assessed predictive performance. Model adequacy was further verified through NPDE analysis implemented using the R npde package (version 2.0), examining Q-Q plots against N(0,1) distribution, NPDE histograms overlaid with theoretical normal density curves, and trend analyses of NPDE versus time and population predictions, thereby providing a comprehensive evaluation of model validity. This multi-dimensional validation framework ensured thorough assessment of model robustness, predictive accuracy, and overall performance. Population Pharmacokinetic Model Simulation Based on the final population pharmacokinetic model incorporating two significant covariates (creatinine clearance and metformin coadministration), we performed comprehensive simulations of population predictions (PRED) and 1000 individual predictions (IPRED) for 144 distinct population scenarios receiving fixed-dose amisulpride therapy over 14 consecutive days. The simulation framework systematically evaluated: (1) 12 common amisulpride regimens in clinical practice (Table 1 ); (2) 6 creatinine clearance strata (30, 60, 90, 120, 150, and 180 mL/min) representing the full spectrum of renal function from severe impairment to hyperfiltration; and (3) binary metformin coadministration status (with or without). Three critical analyses were subsequently conducted: (i) Quantitative characterization of the metformin-amisulpride pharmacokinetic interaction: a comparative assessment of PRED values with versus without metformin coadministration was performed to quantify the drug-drug interaction effects on amisulpride clearance and steady-state plasma concentrations. (ii) Evidence-based validation of current therapeutic guidelines: The clinical applicability of the revised therapeutic reference range for amisulpride from the Expert Consensus on Clinical Application of Psychiatric Therapeutic Drug Monitoring in China (2022 edition) (Guo et al. 2022 ) was rigorously evaluated using 1000 IPRED simulations in the standard patient population (CLcr = 90–120 mL/min without metformin). (iii) Practical dosing optimization strategies tailored to patients' renal function status: We developed personalized dosing recommendations across renal function subgroups (without metformin) by integrating PRED simulations with the consensus guideline's therapeutic range. Table 1 12 Common Amisulpride Regimens in Clinical Practice Dosing regimen Daily dose(mg) Time of administration of daily dose (mg) 8:30 dose (mg) 19:00 dose (mg) 1 100 100 0 2 200 200 0 3 300 300 0 4 400 200 200 5 500 300 200 6 600 300 300 7 700 400 300 8 800 400 400 9 900 500 400 10 1000 500 500 11 1100 600 500 12 1200 600 600 Results Demographic Characteristics For model development, this study included 386 TDM-derived amisulpride plasma concentrations from 194 patients with schizophrenia. The demographic and clinical characteristics are presented in Table 2. No significant multicollinearity was observed among gender, concomitant medications, plasma concentrations, or C/D ratios (tolerance: 0.794–0.917 > 0.1; variance inflation factor [VIF]: 1.090–1.260 < 5). Covariate analysis indicated significantly higher plasma concentrations and C/D ratios in females versus males ( P < 0.05). Patients concomitantly administered either metformin or fluoxetine exhibited statistically significant elevations in both plasma concentrations and C/D ratios compared to those without these comedications ( P < 0.05; Table 3). Table 2 Demographic and Clinical Characteristics of the Study Cohort \\(\\:\\stackrel{-}{\\text{x}}\\)±s M(P25, P75) Number of patients (male/female) 194(95/99) Age (years) 30.62 ± 12.11 27.00(23.00, 35.00) Body Weight (kg) 65.17 ± 14.18 63.00(54.75, 75.00) Creatinine (µmol/L) 71.92 ± 16.41 72.00(58.00, 84.00) Creatinine clearance* (mL/min) 117.89 ± 36.90 112.93(91.90, 129.86) Number of samples 386 Plasma concentration (ng/mL) 323.90 ± 222.62 275.26(171.07, 439.94) Daily dose (mg) 532.51 ± 243.30 600(400, 800) C/D ratios[(ng/mL)/(mg/d)] 0.62 ± 0.33 0.55(0.39, 0.79) Note: *Creatinine clearance was calculated using the Cockcroft-Gault formula: (140-age)×Body Weight/(72×creatinine/88.4), (×0.85, if female), where serum creatinine is in µmol/L. Table 3 Gender and Concomitant Medication Effects on Amisulpride Plasma Concentrations and C/D Ratios Number of patients (n = 194) Number of samples (n = 386) Plasma concentration (ng/mL) C/D ratios [(ng/mL)/(mg/d)] Gender Male 95 214 241.41 ± 159.19** 0.53 ± 0.26** Female 99 172 426.53 ± 246.80 0.73 ± 0.37 Concomitant medications Metformin + 21 44 534.65 ± 313.32*** 0.83 ± 0.42*** - 173 342 394.78 ± 192.63 0.59 ± 0.30 Repaglinide + 1 1 303.05 0.38 - 193 385 323.95 ± 222.91 0.62 ± 0.33 Acarbose + 1 2 434.05 ± 17.72 0.54 ± 0.02 - 193 384 323.33 ± 223.06 0.62 ± 0.33 Sitagliptin + 2 2 296.83 ± 8.80 0.55 ± 0.25 - 192 384 324.04 ± 223.19 0.62 ± 0.33 Risperidone/Paliperidone + 32 51 337.11 ± 260.85 0.68 ± 0.39 - 162 335 321.89 ± 216.59 0.61 ± 0.32 Venlafaxine + 15 22 230.33 ± 150.90 0.66 ± 0.29 - 179 364 329.55 ± 225.13 0.62 ± 0.33 Duloxetine + 1 1 629.83 0.79 - 193 385 323.10 ± 222.36 0.62 ± 0.33 Olanzapine + 46 100 301.61 ± 226.26 0.59 ± 0.33 - 148 286 331.69 ± 221.21 0.63 ± 0.33 Clozapine + 52 102 404.74 ± 214.35 0.64 ± 0.29 - 142 284 294.86 ± 218.69 0.61 ± 0.34 Quetiapine + 7 9 282.74 ± 175.80 0.67 ± 0.36 - 187 377 324.89 ± 223.71 0.62 ± 0.33 Fluoxetine + 15 26 433.40 ± 294.69*** 0.89 ± 0.41*** - 179 360 315.99 ± 214.88 0.60 ± 0.31 Paroxetine + 2 2 193.45 ± 99.86 0.64 ± 0.03 - 192 384 324.58 ± 222.94 0.62 ± 0.33 Sertraline + 27 45 308.57 ± 188.47 0.63 ± 0.30 - 167 341 325.92 ± 226.91 0.62 ± 0.33 Fluvoxamine + 9 20 320.13 ± 196.96 0.63 ± 0.28 - 185 366 324.10 ± 224.18 0.62 ± 0.33 Citalopram + 1 1 176.48 0.88 - 193 385 324.28 ± 222.78 0.62 ± 0.33 Lithium + 17 28 251.05 ± 188.63 0.53 ± 0.29 - 177 358 329.60 ± 224.29 0.63 ± 0.33 Valproic acid + 31 59 356.90 ± 197.68* 0.65 ± 0.30* - 163 327 317.94 ± 226.59 0.61 ± 0.33 Note: * P < 0.05, ** P < 0.01, *** P < 0.001 Covariate Screening The population pharmacokinetics of amisulpride were best described by a one-compartment model with a mixed-error structure. Based on preliminary estimates, Ka was fixed at 1.1 h⁻¹. In addition to demographic variables, three clinically significant comedications (metformin, fluoxetine, and valproate) were systematically evaluated using stepwise covariate modeling, which significantly improved model fit as evidenced by reduced OFV. The final model identified CLcr and metformin coadministration as significant covariates affecting amisulpride CL/F. Specifically, CL/F decreased by 16 L/h for every 10 mL/min reduction in CLcr. Patients receiving metformin demonstrated a 23% lower CL/F compared to non-users. The covariate selection process is summarized in Table 4. The final population pharmacokinetic model was defined as: CL/F(L/h) = 39.8×(CLcr/113) 0.376 ×(MET) 0.38 V/F(L) = 671 Ka(h -1 ) =1.1(FIXED) where CLcr represents creatinine clearance calculated using the Cockcroft-Gault equation, and MET denotes metformin coadministration status (MET = 1 present, MET = 2 absent). Table 4 Covariate Screening Process Stepwise Covariate Screening Procedure Model Number Based on Model Variables OFV dOFV First run of inclusion 1 structural model 4060.018 2 CLcr-CL -16.206 3 AGE-CL -10.215 4 FLUO-CL -8.937 5 CLcr-V -8.665 6 SEX-CL -8.131 7 MET-CL -7.074 8 BW-V -5.696 9 FLUO-V -5.455 10 BW-CL -5.417 11 SEX-V -4.888 13 AGE-V -3.492 16 VPA-V -2.105 18 MET-V -1.061 23 VPA-CL -0.442 30 Cr-V -0.137 32 Cr-CL -0.085 Second run of inclusion 2 AMI-CLcr-CL 4043.812 40 MET-CL -16.27 41 SEX-CL -11.16 42 FLUO-CL -9.827 43 AGE-CL -4.706 44 CLcr-V -0.075 Third run of inclusion 40 AMI-CLcr-CL-MET-CL 4027.542 45 FLUO-CL -8.509 46 SEX-CL -6.607 Full variable model AMI-CLcr-CL-MET-CL-FLUO-CL First run of elimination 45 AMI-CLcr-CL-MET-CL-FLUO-CL 4019.033 47 CLcr-CL 25.998 48 MET-CL 14.952 49 FLUO-CL 8.509 Second run of elimination 40 AMI-CLcr-CL-MET-CL 4027.542 50 CLcr-CL 25.402 51 MET-CL 16.27 Final model AMI-CLcr-CL-MET-CL Note: AMI represent amisulpride; CLcr represent Creatinine clearance rate; Cr represent creatinine level; AGE represent age; FLUO represent fluoxetine; MET represent Metformin; BW represent body weight; VPA represent valproate. Model Evaluation Bootstrap Analysis The final parameter estimates and bootstrap validation results (1000 iterations) demonstrated good model stability (88.3% success rate). All parameters showed good precision (relative standard errors < 30%) and strong agreement with bootstrap medians, indicating excellent predictive accuracy (Table 5). Table 5 Estimation of final model parameters and bootstrap results Final model Bootstrap (1000 iterations) Parameters estimated value RSE (%) VII (%) median 95% CI CL/F(L/h) 39.8 8 24 40.1 33.50-47.48 θ CLcr−CL 0.376 16 / 0.372 0.24–0.51 θ MET−CL 0.38 25 / 0.377 0.14–0.58 V/F(L) 671 22 67 676 461.52-1044.15 Ka(h − 1 ) 1.1(FIXED) 1.1(FIXED) ADDITI 4.00E-5 16 / 4.00E-5 3.99E-5-4.01E-5 PROPOR 0.0980 12 / 0.0963 0.0757–0.1227 Normalized Prediction Distribution Error Normalized prediction distribution errors (NPDE) analysis indicated excellent model performance, with a mean of 0.075 (SE: 0.047) and variance of 0.868 (SE: 0.063). Non-significant results from the t-test ( P = 0.35), Fisher's variance test (P = 0.176), and Shapiro-Wilk test ( P = 0.176) collectively confirmed NPDE adherence to N(0,1) (Fig. 1). These findings demonstrate the final model's stability and absence of significant bias. Goodness of Fit PRED and IPRED concentrations showed symmetric distribution along the identity line when plotted against observed concentrations. CWRES were randomly scattered around zero, with 95% within ± 2 units. These diagnostics collectively demonstrate the final model's good predictive performance (Fig. 2). Model Simulation Interaction Between Metformin and Amisulpride Concomitant administration of metformin significantly altered amisulpride pharmacokinetics, reducing its clearance to 77% of that in its absence and resulting in consistently elevated systemic exposure. Population pharmacokinetic simulation results demonstrated that metformin coadministration increased amisulpride exposure by 45% or more across all dose ranges, with this pharmacokinetic interaction showing no dependence on renal function status (Fig. 3). Evidence-Based Validation of Current Therapeutic Guidelines We assessed the applicability of the therapeutic reference range proposed in the Expert Consensus on Clinical Application of Psychiatric Therapeutic Drug Monitoring in China (2022 edition)(Guo, et al. 2022), using IPRED data from all dosing regimens based on 24,000 simulations (1,000 concentrations per dosing regimen) in patients without metformin and with normal renal function (CLcr = 90–120 mL/min). The results demonstrated that 58.34% (28,002/48,000) of simulated amisulpride plasma concentrations fell within the 100–600 ng/mL range, whereas only 29.94% (14,369/48,000) fell within the narrower range (100–320 ng/mL) (Fig. 4). Individualized Medication Strategy Based on Renal Function There was an inverse relationship between creatinine clearance and amisulpride plasma concentration. In the absence of metformin, the PRED values for each dosing regimen under different renal function states are shown in Fig. 5. Based on the newly revised therapeutic reference range for amisulpride in the Expert Consensus on Clinical Application of Psychiatric Therapeutic Drug Monitoring in China(2022 edition) (Guo, et al. 2022), the effective doses for different renal function groups were as follows (Table 6). For patients with renal impairment (30–60 mL/min), the effective dose was 200–600 mg. For patients with mild renal impairment (60–90 mL/min), the effective dose was 200–800 mg. For patients with normal renal function (90–120 mL/min), the effective dose was 300–800 mg. For patients with mild hyperfiltration (120–150 mL/min), the effective dose was 300–1000 mg. For patients with hyperfiltration (150–180 mL/min), the effective dose was 400–1000 mg. Table 6 Recommended Amisulpride Dosing Ranges by Renal Function Status without metformin CLcr (mL/min) Lower Threshold of Therapeutic Range (100ng/mL) Upper Limit of Therapeutic Range (600ng/mL) Daily Dose (mg) 8:30 dose (mg) 19:00 dose (mg) Daily Dose (mg) 8:30 dose (mg) 19:00 dose (mg) Renal impairment ( Figs. 5A, B ) 30 200 200 0 500 300 200 60 200 200 0 600 300 300 Mild renal impairment ( Figs. 5B, C ) 60 200 200 0 600 300 300 90 300 300 0 800 400 400 Normal renal function ( Figs. 5C, D ) 90 300 300 0 800 400 400 120 300 300 0 800 400 400 Mild hyperfiltration ( Figs. 5D, E ) 120 300 300 0 800 400 400 150 400 200 200 1000 500 500 Hyperfiltration ( Figs. 5E, F ) 150 400 200 200 1000 500 500 180 400 200 200 1000 500 500 Discussion This study successfully developed and validated a PPK model of amisulpride incorporating 386 plasma concentrations obtained from 194 patients. Our analysis identified two clinically significant covariates affecting amisulpride's apparent CL/F: CLcr exhibited a positive correlation with drug elimination, while metformin coadministration demonstrated an inhibitory effect. The final model demonstrated excellent predictive performance for estimating amisulpride exposure in patients with varying renal function, establishing its utility for precision dosing in clinical practice. These findings provide a pharmacokinetic foundation for optimizing amisulpride therapy through renal function- and comedication-adjusted dosing strategies. Our estimated amisulpride CL/F (39.8 L/h) aligns with prior PPK studies (32.60–61.1 L/h) (Glatard et al. 2020 ; Huang et al. 2021 ; Li et al. 2023 ; Liu et al. 2023 ; Reeves et al. 2016 ; Yan et al. 2024 ), thereby supporting the reliability of our model. However, substantial inter-study variability was observed in the estimated Vd/F (391–1720 L) across these PPK studies(Glatard, et al. 2020 ; Huang, et al. 2021 ; Li, et al. 2023 ; Liu, et al. 2023 ; Reeves, et al. 2016 ; Yan, et al. 2024 ), likely attributable to differences in demographic and clinical characteristics among study populations. Specifically, the relatively low Vd/F estimate of 391 L reported by Liu et al. (Liu, et al. 2023 ) may be explained by the pronounced gender imbalance in their study cohort (78.41% male, n = 88). Given that males typically exhibit lower body fat percentages than females(Zucker and Prendergast 2020 ) and considering amisulpride's limited distribution in adipose tissue, which would be expected to yield smaller distribution volumes in male-dominated populations. In contrast, the markedly elevated Vd/F estimate of 1720 L reported by Huang et al. (Huang, et al. 2021 ) may reflect the impaired renal function evident in their study population (CLcr: 1.36 ± 0.48 mL/min), potentially leading to suboptimal estimation of drug distribution capacity. In developing our population pharmacokinetic model, we conducted a systematic comparison of three renal function estimation methods: the MDRD formula, the CKD-EPI formula, and the Cockcroft-Gault formula. Comparative analysis revealed that implementation of the Cockcroft-Gault formula alone yielded the most optimal model fit, as evidenced by producing the lowest OFV. Two others (Liu, et al. 2023 ; Yan, et al. 2024 ) like our study, found that the clearance of amisulpride decreases with decreasing creatinine clearance rate calculated using the Cockcroft-Gault formula. Although it also indicates that as renal function deteriorates, the clearance rate of amisulpride will decrease, but only one study incorporated the CKD-EPI equation for estimating glomerular filtration rate (GFR) in covariate analysis(Li, et al. 2023 ). While the CKD-EPI formula is less influenced by age, sex, and ethnicity and provides a more accurate GFR estimation, the Cockcroft-Gault formula offers greater simplicity and practicality, making it more advantageous for clinical dose adjustments. Our results suggest that the Cockcroft-Gault formula may provide superior performance for characterizing amisulpride pharmacokinetics in relation to renal function compared to alternative estimation methods. Our PPK model analysis identified CLcr as the most statistically significant covariate influencing amisulpride clearance, different from three previous studies that reported age as the primary determinant and demonstrate an inverse correlation between age and drug clearance.(Glatard, et al. 2020 ; Huang, et al. 2021 ; Reeves, et al. 2016 ). Importantly, our study provides a mechanistic explanation for this discrepancy by elucidating that the age-related decline in amisulpride clearance is mediated through progressive deterioration of renal function, as quantified by CLcr measurements. While two of these investigations additionally identified body weight as a positively correlated covariate for amisulpride clearance(Glatard, et al. 2020 ; Reeves, et al. 2016 ), the observed discrepancies likely reflect methodological differences in covariate assessment. Specifically, our use of the Cockcroft-Gault formulas, employed in our analysis, provides a comprehensive estimation of renal function by simultaneously accounting for the effects of age, sex, and body weight. This integrated approach likely explains our finding that although both age and sex demonstrated significant effects during the forward inclusion phase of our PPK modeling, their contributions became nonsignificant following CLcr incorporation into the final model, confirming renal function as the fundamental determinant of amisulpride clearance. The pharmacokinetic analysis in this study revealed a amisulpride total clearance (CL/F) of 39.8 L/h (663.33 mL/min), representing 5.63-fold the mean creatinine clearance (117.89 ± 36.90 mL/min) observed in our cohort. This marked discrepancy implies contributions from active transport mechanisms beyond glomerular filtration beyond passive glomerular filtration in amisulpride elimination. The transporter-mediated nature of amisulpride pharmacokinetics is further corroborated by documented DDIs with various psychotropic agents, including lithium(Bergemann et al. 2004 ; Bergemann et al. 2007 ; Müller et al. 2009 ), clozapine(Bergemann, et al. 2004 ; Qu et al. 2022 ), and valproate (Yang et al. 2023 ), all of which have been shown to significantly alter amisulpride plasma concentrations in TDM reports. Our systematic evaluation of co-medication effects, incorporating multiple renally excreted drugs such as paliperidone and metformin, demonstrated that valproate, fluoxetine, and metformin coadministration significantly increased amisulpride plasma concentrations and C/D ratios after controlling for age, sex, creatinine clearance, and other covariates. Notably, metformin coadministration resulted in ≥ 45% elevation in amisulpride exposure, warranting clinical caution when combining these medications. The mechanistic basis for this interaction appears to involve competition at renal transporter systems, as both drugs share several pharmacological characteristics: predominant renal excretion in unchanged form, share cationic properties (metformin pKa = 11.5(Elezović et al. 2021 ) vs. amisulpride pKa = 9.37(Younes et al. 2021 )), suggesting possible transporter competition and appear to compete for common renal transporter systems. Specifically, metformin is a well-established substrate of OCT2, MATE-1, and MATE-2K transporters (Paglialunga et al. 2024 ; Pernecker and Ciarimboli 2024 ), while preclinical studies suggest amisulpride may also be a potential substrate for OCTs and MATEs transporter systems (Dos Santos Pereira, et al. 2014 ; Sekhar, et al. 2019 ). This competitive inhibition at renal tubular secretion sites probably underlies the observed elevation in amisulpride concentrations. In clinical practice, metformin is widely used as a hypoglycemic agent. In psychiatric pharmacotherapy, it is frequently employed to mitigate metabolic syndrome or hyperprolactinemia induced by antipsychotics such as risperidone, olanzapine, amisulpride and all so(Bo et al. 2016 ; Yu et al. 2024 ; Zhu et al. 2022 ). The application of metformin in psychiatry has increased, whether for glycemic management in diabetic patients with comorbidities or for addressing adverse effects caused by psychiatric medications. Importantly, for renally excreted drugs like amisulpride and metformin that undergo minimal hepatic metabolism, transporter-mediated DDIs may lead to bidirectional alterations in drug clearance, potentially increasing adverse effect risks. Therefore, it is essential to evaluate the impact of amisulpride on metformin plasma concentrations and carefully consider potential drug interactions when combining these medications, optimizing treatment regimens to minimize the risk of adverse effects. Interestingly, our study found not significant impact of risperidone or paliperidone on amisulpride pharmacokinetics, likely reflecting distinct renal elimination pathways, as these drugs primarily interact with breast cancer resistance protein(BCRP)(Zhou et al. 2018 ), rather than substrates of OCT2, MATE-1, or MATE-2K (Wang et al. 2024 ), that appear more critical for amisulpride clearance. Our integrated covariance analysis and population pharmacokinetic (PPK) modeling revealed fluoxetine's significant, albeit secondary, influence on amisulpride pharmacokinetics. The observed interaction between fluoxetine and amisulpride likely involves multiple pharmacokinetic mechanisms. Fluoxetine's renal excretion profile, with approximately 70% of its metabolites are renally excreted(Bergstrom et al. 1993 ), combined with its established role as both a selective P-glycoprotein (P-gp) inhibitor (Pinto et al. 2020 ; Schrickx and Fink-Gremmels 2014 ; Xu et al. 2021 ; Zhang et al. 2013 ) and a substrate for OCTs and P-gp(Wang et al. 2022 ), creates a complex interplay that may reduce amisulpride clearance. This reduction potentially occurs through competitive inhibition at shared transporter sites and disruption of P-gp-mediated efflux pathways, mirroring the previously characterized fluoxetine-olanzapine interaction dynamic(Xu, et al. 2021 ). Notably, while both fluoxetine and metformin initially showed significant effects on amisulpride clearance (P < 0.01), only metformin's impact remained statistically significant under more stringent criteria (P < 0.001), suggesting metformin's broader spectrum of transporter interactions may dominate the overall clearance pathway more than fluoxetine. Moreover, this study revealed a distinct interaction pattern for valproate, which significantly increased amisulpride plasma concentrations without affecting its clearance. This observation is mechanistically consistent with competitive plasma protein binding displacement, given the substantial difference in protein binding affinity between valproate (90–95% bound) (Zhang et al. 2023 ) and amisulpride (17% bound) (Rosenzweig et al. 2002 ).. The marked binding affinity disparity suggests that valproate effectively competes for albumin binding sites, displacing amisulpride and consequently increasing its pharmacologically active free fraction in systemic circulation. The conventional therapeutic reference range for amisulpride (100–320 ng/mL), established by the 2017 Arbeitsgemeinschaft für Neuropsychopharmakologie und Pharmakopsychiatrie (AGNP) consensus guidelines(Huang, et al. 2021 ), requires critical re-evaluation due to several evidence-based limitations. First, the upper threshold of 320 ng/mL was derived from ROC analysis of extrapyramidal symptoms with suboptimal predictive accuracy (AUC = 0.62, 95% CI: 0.56–0.67) (Müller et al. 2007 ). Second, the recommended 24-hour dosing interval for blood collection contradicts standard clinical practice of divided daily dosing and results in lower plasma concentration, as it overlooks the fact that divided dosing helps stabilize and reduce significant fluctuations in blood concentration (Ding, et al. 2023 ; Huang, et al. 2021 ). Thirly, accumulating evidence suggests this range may substantially underestimate effective therapeutic concentrations(Ding, et al. 2023 ; Jönsson et al. 2019 ; Sparshatt et al. 2009 ; Wang and Li 2017 ). These limitations have prompted the 2022 Chinese Expert Consensus to revise the therapeutic range to 100–600 ng/mL(Guo, et al. 2022 ), a modification supported by a real-world clinical data correlated with clinical global impression(CGI) scores(Gan et al. 2025 ) and confirmed by population pharmacokinetic simulations (n = 1000) in the current study. Building upon this revised therapeutic range, we developed personalized dosing algorithms stratified by creatinine clearance (CLcr), identifying three subpopulations requiring initial doses below the standard 400 mg/day recommendation: renal impairment (CLcr 30–60 mL/min), mild impairment (CLcr 60–90 mL/min), and normal renal function (CLcr 90–120 mL/min). Clinical practitioners are advised to assess CLcr and implement a dose-titration strategy, with continuous monitoring of both therapeutic response and adverse effects to facilitate individualized dosage optimization. Our PPK analysis further demonstrated significant concentration variability influenced by dosing frequency and timing, with divided sub-400 mg doses producing higher plasma levels than single-dose regimens and evening administration yielding elevated concentrations, underscoring the importance of comprehensive TDM interpretation that integrates clinical presentation (including symptom improvement and adverse effects), laboratory parameters (hepatic/renal function), and dosing regimen specifics for accurate pharmacokinetic assessment and optimal individualized dosing within the revised therapeutic range. Our study has several limitations that should be acknowledged. First, as a PPK model-based investigation, this study inherently relies more heavily on prior knowledge and modeling assumptions than would real-world studies. Given the prevalence of polypharmacy in clinical practice, this methodological approach may introduce extrapolation uncertainty when generalizing findings to broader clinical settings. Therefore, results should be carefully interpreted within real-world clinical practice. Furthermore, in the future validation of the revised therapeutic range through concentration-response analyses and larger-scale prospective studies with fixed-dose designs is essential to confirm generalizability across complex medication regimens. Second, while the observed drug interactions were hypothesized to arise from transporter-mediated mechanisms based on pharmacokinetic data, the study did not quantify dose-dependent effects of concomitant medications on amisulpride disposition. Molecular-level validation (e.g., in vitro OCT/MATE inhibition assays) remains essential to confirm transporter involvement and elucidate definitive interaction mechanisms. Lastly, in antipsychotic pharmacology, drug transporters critically modulate not only pharmacokinetics but also pharmacodynamics. Specifically, blood-brain barrier transport governs cerebral drug distribution, directly influencing therapeutic efficacy and treatment resistance. Future studies should incorporate pharmacodynamic endpoints to dissect the mechanistic role of transporters in antipsychotic action. Conclusion In Conclusion, our study presents several novel and clinically significant findings regarding amisulpride pharmacokinetics. Our findings represent the first empirical evidence that comedications significantly alter amisulpride pharmacokinetics, among which metformin produces the most pronounced reduction in systemic clearance. This observation strongly suggests a potential transporter-mediated drug-drug interaction between metformin and amisulpride. Furthermore, our results provide robust validation for the newly revised therapeutic reference range for amisulpride (100–600 ng/mL) in Chinese schizophrenia patients, confirming its clinical appropriateness for this population. From a practical perspective, it is strongly recommended to evaluate creatinine clearance using the Cockcroft-Gault formula prior to amisulpride initiation to enable optimal renal function-based dose individualization. These findings collectively advance our understanding of amisulpride pharmacokinetics and provide actionable guidance for clinical practice. Declarations Funding Partial financial support was received from the clinical characteristic technology project of Guangzhou Research-oriented Hospital (2021-2025), Guangzhou Municipal Key Discipline in Medicine (2025-2027). Competing Interests The authors have no relevant financial or non-financial interests to disclose. Author Contributions All authors contributed to the study conception. Tongying Gan responsible for the study design, data analysis, data interpretation, drafting and critical revision of the manuscript. Methodology and Visualization were performed by Tongying Gan and Chang Liu. Data collection and analysis were performed by Chang Liu and Bixuan Wu. The authors declare that all data were generated in-house and that no paper mill was used. Ethical approval The study protocol was approved by the Institutional Review Board of the Affiliated Brain Hospital of Guangzhou Medical University (approval number: 2021027). As a retrospective analysis, the IRB granted a waiver of informed consent. All procedures adhered to the Declaration of Helsinki, with participant data de-identified to protect confidentiality. Data availability statement The datasets generated and/or analyzed during the current study are not publicly available due to ethical restrictions protecting participant privacy and confidentiality. 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CMAJ : Canadian Medical Association journal = journal de l'Association medicale canadienne 192:E875-e891. https://doi.org/10.1503/cmaj.191707 Wu H, Siafis S, Hamza T, Schneider-Thoma J, Davis JM, Salanti G and Leucht S (2022) Antipsychotic-Induced Weight Gain: Dose-Response Meta-Analysis of Randomized Controlled Trials. Schizophrenia bulletin 48:643-654. https://doi.org/10.1093/schbul/sbac001 Xu Y, Lu J, Yao B, Zhang Y, Huang S, Liu J, Zhang Y, Guo Y and Wang X (2021) P-glycoprotein mediates the pharmacokinetic interaction of olanzapine with fluoxetine in rats. Toxicology and applied pharmacology 431:115735. https://doi.org/10.1016/j.taap.2021.115735 Yan D, Ju G, Liu X, Shao Q, Zhang Y, Wang N and Yan K (2024) External Validation of the Population Pharmacokinetic Models of Amisulpride and Remedial Strategies for Delayed or Missed Doses. Drug design, development and therapy 18:6345-6358. https://doi.org/10.2147/dddt.S469149 Yang S, Wang H, Zheng GF and Wang Y (2023) Age, Sex, and Comedication Effects on the Steady-State Plasma Concentrations of Amisulpride in Chinese Patients with Schizophrenia. Therapeutic drug monitoring 45:676-682. https://doi.org/10.1097/ftd.0000000000001089 Younes NF, El Assasy AEI and Makhlouf AIA (2021) Microenvironmental pH-modified Amisulpride-Labrasol matrix tablets: development, optimization and in vivo pharmacokinetic study. Drug delivery and translational research 11:103-117. https://doi.org/10.1007/s13346-019-00706-2 Yu O, Lu M, Lai TKY, Hahn M, Agarwal SM, O'Donoghue B, Ebdrup BH and Siskind D (2024) Metformin co-commencement at time of antipsychotic initiation for attenuation of weight gain: a systematic review and meta-analysis. Therapeutic advances in psychopharmacology 14:20451253241255476. https://doi.org/10.1177/20451253241255476 Zhang L, Liu M, Qin W, Shi D, Mao J and Li Z (2023) Modeling the protein binding non-linearity in population pharmacokinetic model of valproic acid in children with epilepsy: a systematic evaluation study. Frontiers in pharmacology 14:1228641. https://doi.org/10.3389/fphar.2023.1228641 Zhang Y, Zhou T, Duan J, Xiao Z, Li G and Xu F (2013) Inhibition of P-glycoprotein and glutathione S-transferase-pi mediated resistance by fluoxetine in MCF-7/ADM cells. Biomedicine & pharmacotherapy = Biomedecine & pharmacotherapie 67:757-762. https://doi.org/10.1016/j.biopha.2013.04.012 Zhou Y, Li H, Xu P, Sun L, Wang Q, Lu Q, Yuan H and Liu Y (2018) Paliperidone, a relatively novel atypical antipsychotic drug, is a substrate for breast cancer resistance protein. Experimental and therapeutic medicine 16:5410-5416. https://doi.org/10.3892/etm.2018.6847 Zhu C, Li R, Ju M, Xiao X, Yuan TF, Jin Z and Zhao J (2022) Metformin in the Treatment of Amisulpride-Induced Hyperprolactinemia: A Clinical Trial. Frontiers in molecular neuroscience 15:892477. https://doi.org/10.3389/fnmol.2022.892477 Zucker I and Prendergast BJ (2020) Sex differences in pharmacokinetics predict adverse drug reactions in women. Biology of sex differences 11:32. https://doi.org/10.1186/s13293-020-00308-5 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {\"props\":{\"pageProps\":{\"initialData\":{\"identity\":\"rs-7289163\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":true,\"archivedVersions\":[],\"articleType\":\"Research Article\",\"associatedPublications\":[],\"authors\":[{\"id\":506092092,\"identity\":\"6bf61dea-079c-4bf6-9368-bf159ef8df50\",\"order_by\":0,\"name\":\"Tongying Gan\",\"email\":\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAArElEQVRIiWNgGAWjYPACGx5+/gZSNBxgSJORnHGANC2HbQwaEohULe9+9gDzx7bzPAYMBxg/fMwhQovhmbwEhoNtt3nMmRuYJWduI0ZLQ44BWItlwwE2Zl6itPS/AWk5x2NwIIFILfISYFsOkKDFQAJoy5lzyTySMw42E+cX+X6gLRVldvb8/M0HP3wkypYDDOw/GNlATMYGItSDbAGr+0Oc4lEwCkbBKBihAAAcKzZyjEGJSAAAAABJRU5ErkJggg==\",\"orcid\":\"\",\"institution\":\"The Affiliated Brain Hospital, Guangzhou Medical University\",\"correspondingAuthor\":true,\"prefix\":\"\",\"firstName\":\"Tongying\",\"middleName\":\"\",\"lastName\":\"Gan\",\"suffix\":\"\"},{\"id\":506092093,\"identity\":\"cbf65df3-dce8-4258-9531-d5334620e8b4\",\"order_by\":1,\"name\":\"Chang Liu\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"The Affiliated Brain Hospital, Guangzhou Medical University\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Chang\",\"middleName\":\"\",\"lastName\":\"Liu\",\"suffix\":\"\"},{\"id\":506092094,\"identity\":\"8952410a-191b-4484-8905-98651fadaa2c\",\"order_by\":2,\"name\":\"Bixuan Wu\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"The Affiliated Brain Hospital, Guangzhou Medical University\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Bixuan\",\"middleName\":\"\",\"lastName\":\"Wu\",\"suffix\":\"\"}],\"badges\":[],\"createdAt\":\"2025-08-04 09:08:34\",\"currentVersionCode\":1,\"declarations\":{\"humanSubjects\":false,\"vertebrateSubjects\":false,\"conflictsOfInterestStatement\":false,\"humanSubjectEthicalGuidelines\":false,\"humanSubjectConsent\":false,\"humanSubjectClinicalTrial\":false,\"humanSubjectCaseReport\":false,\"vertebrateSubjectEthicalGuidelines\":false},\"doi\":\"10.21203/rs.3.rs-7289163/v1\",\"doiUrl\":\"https://doi.org/10.21203/rs.3.rs-7289163/v1\",\"draftVersion\":[],\"editorialEvents\":[],\"editorialNote\":\"\",\"failedWorkflow\":false,\"files\":[{\"id\":89997029,\"identity\":\"e68b9479-a611-40d8-924b-19a0d66c68b9\",\"added_by\":\"auto\",\"created_at\":\"2025-08-27 08:09:36\",\"extension\":\"png\",\"order_by\":1,\"title\":\"Figure 1\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":219596,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eNPDE plots for the final model of amisulpride. (A) Histogram of NPDE superimposed on density plot of standard normal distribution; (B) Q-Q plot of NPDE-standard normal distribution; (C) Scatterplot of NPDE vs. time of administration; (D) Scatterplot of NPDE vs. predicted value of population\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"floatimage1.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7289163/v1/78affe9276407fa1f99bc550.png\"},{\"id\":89997031,\"identity\":\"e86313b1-3275-4b50-bcc2-70c4ffffbde5\",\"added_by\":\"auto\",\"created_at\":\"2025-08-27 08:09:36\",\"extension\":\"png\",\"order_by\":2,\"title\":\"Figure 2\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":1705649,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eGoodness-of-fit plots for the final model of amisulpride. (A) DV-PRED scatterplot; (B) DV-IPRED scatterplot; (C) PRED-CWRES scatterplot; (D) TIME-CWRES scatterplot\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"floatimage2.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7289163/v1/557e347953dfaa7a1b5b1aae.png\"},{\"id\":89997039,\"identity\":\"fb04add8-ee74-41df-9073-6907ba92f708\",\"added_by\":\"auto\",\"created_at\":\"2025-08-27 08:09:36\",\"extension\":\"png\",\"order_by\":3,\"title\":\"Figure 3\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":151147,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eImpact of metformin concomitant medication on amisulpride pharmacokinetics. (A) Relative clearance reduction and (B) Predicted plasma concentration (PRED) profiles with versus without metformin therapy\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"floatimage3.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7289163/v1/871cbd479302ce2446ec30c2.png\"},{\"id\":89998417,\"identity\":\"7a181bc4-9322-46c9-839c-a3439219bfbe\",\"added_by\":\"auto\",\"created_at\":\"2025-08-27 08:25:36\",\"extension\":\"png\",\"order_by\":4,\"title\":\"Figure 4\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":741693,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eIndividual predicted (IPRED) amisulpride plasma concentration ranges across full dosing regimens in metformin-free patients with normal renal function (CLcr 90-120 mL/min).\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"floatimage4.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7289163/v1/cd99fafe6b1b34a4d1a1aac7.png\"},{\"id\":89997339,\"identity\":\"8d36663d-7c6d-44c3-908a-9043eb02afb7\",\"added_by\":\"auto\",\"created_at\":\"2025-08-27 08:17:36\",\"extension\":\"png\",\"order_by\":5,\"title\":\"Figure 5\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":470968,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003ePredicted amisulpride plasma concentration (PRED) profiles across renal function strata. (A) CLcr=30 mL/min; (B) CLcr=60 mL/min; (C) CLcr=90 mL/min; (D) CLcr=120 mL/min; (E) CLcr=150 mL/min; (F) CLcr=180 mL/min\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"floatimage5.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7289163/v1/472d8fef67f11e75e8ea72af.png\"},{\"id\":92983254,\"identity\":\"9d7fcd32-9a25-4cd0-907a-22f02344968e\",\"added_by\":\"auto\",\"created_at\":\"2025-10-07 20:31:44\",\"extension\":\"pdf\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":4907921,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"manuscript.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7289163/v1/4ffbf9dc-4b03-4726-9056-b4f81b7d4172.pdf\"}],\"financialInterests\":\"No competing interests reported.\",\"formattedTitle\":\"Population Pharmacokinetics of Amisulpride in Chinese Schizophrenia Patients: Quantifying Drug-Drug Interactions and Renal Function-Guided Dosing Individualization\",\"fulltext\":[{\"header\":\"Introduction\",\"content\":\"\\u003cp\\u003eSchizophrenia, a chronic and severe mental disorder, significantly impairs patients' quality of life. Epidemiological data indicate a global prevalence of approximately 24\\u0026nbsp;million cases, positioning it among the top 20 contributors to global disability and a substantial component of the worldwide mental health burden (GBD 2019 Mental Disorders Collaborators \\u003cspan citationid=\\\"CR18\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e; Solmi et al. \\u003cspan citationid=\\\"CR47\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e). Owing to pathological heterogeneity in pathological mechanisms, involving intricate genetic, neurobiological, and psychosocial interactions(McCutcheon et al. \\u003cspan citationid=\\\"CR31\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e; Srivastava et al. \\u003cspan citationid=\\\"CR50\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e), combination pharmacotherapy is prevalent treatment strategy in clinical practice. Systematic analyses of prescription patterns reveal that approximately one-third of patients with schizophrenia worldwide receive antipsychotic polypharmacy, with meta-analytic data showing a global antipsychotic polypharmacy prevalence of 33.2% (95% CI 30.6\\u0026ndash;36.0%)(H\\u0026oslash;jlund et al. \\u003cspan citationid=\\\"CR22\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e). Beyond traditional augmentation strategies for mental disorders (mood stabilizers, antidepressants, and benzodiazepines), hypoglycemic agents have emerged as important adjunctive therapies. An epidemiological study suggested that the prevalence of metabolic syndrome reaches 10.93% in first-episode, drug-na\\u0026iuml;ve schizophrenia patients(Hu et al. \\u003cspan citationid=\\\"CR23\\\" class=\\\"CitationRef\\\"\\u003e2025\\u003c/span\\u003e). Additionally, data from UK primary care show that 17.53% (4,652/26,537) of patients with severe mental illnesses received metformin specifically for managing antipsychotic-induced weight gain or pre-existing comorbidities (diabetes or PCOS) (Farache Trajano et al. \\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e2025\\u003c/span\\u003e). Notably, current evidence-based clinical practice guidelines endorse metformin as a recommended intervention (Level 1a, Grade A) for preventing and managing antipsychotic-associated weight gain in patients with severe mental disorders(Wharton et al. \\u003cspan citationid=\\\"CR54\\\" class=\\\"CitationRef\\\"\\u003e2020\\u003c/span\\u003e). In clinical practice, combination therapy with glucagon-like peptide-1 receptor agonists (GLP-1 RAs) may also be used for this indication in patients(Manta et al. \\u003cspan citationid=\\\"CR30\\\" class=\\\"CitationRef\\\"\\u003e2025\\u003c/span\\u003e; Solmi et al. \\u003cspan citationid=\\\"CR48\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e). The comorbidity between schizophrenia and metabolic disorders manifests through interdependent mechanisms. Pharmacologically, antipsychotics frequently induce adverse metabolic effects, including weight gain and metabolic syndrome, which significantly exacerbate obesity in this patient population (Carli et al. \\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e; Siskind et al. \\u003cspan citationid=\\\"CR46\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e). Pathophysiologically, the bidirectional association between schizophrenia and obesity may stem from shared pathological mechanisms, such as dopaminergic reward circuitry dysfunction(Edwin Thanarajah et al. \\u003cspan citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e) and dysregulation of the hypothalamic-pituitary-adrenal (HPA) axis(Dionysopoulou et al. \\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e). This dual-pathway synergy translates into an increase in comorbidities. The pooled prevalence of obesity among individuals with severe mental illness is 25.9% (95% CI\\u0026thinsp;=\\u0026thinsp;23.3\\u0026ndash;29.1%), while the combined prevalence of overweight and obesity is 60.1% (95% CI\\u0026thinsp;=\\u0026thinsp;55.8\\u0026ndash;63.1%)(Afzal et al. \\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e). This is approximately twice the rate in the general population (obesity rate: 16.4%; overweight rate: 34.3%)(Pan et al. \\u003cspan citationid=\\\"CR35\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e). In order to mitigate weight gain, metabolic disorders, and their associated cardiovascular disease burden, lifestyle interventions remain foundational. However, pharmacologic approaches including metformin adjunctive therapy have demonstrated efficacy(Hegde et al. \\u003cspan citationid=\\\"CR21\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e) .\\u003c/p\\u003e\\u003cp\\u003eAmisulpride is a widely used antipsychotic drug, exhibiting distinct pharmacokinetics characterized by renal excretion of approximately 70% of the unchanged drug and low dependence on cytochrome P450 (CYP450) enzymes for metabolism(Fox et al. \\u003cspan citationid=\\\"CR16\\\" class=\\\"CitationRef\\\"\\u003e2019\\u003c/span\\u003e). Despite these characteristics, significant interpatient variability in plasma concentrations has consistently been observed in clinical practice(Ding et al. \\u003cspan citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e). While existing studies have primarily focused on age, gender, and body mass index (BMI) as determinants of amisulpride pharmacokinetics(Li et al. \\u003cspan citationid=\\\"CR28\\\" class=\\\"CitationRef\\\"\\u003e2020\\u003c/span\\u003e), the potential impact of concomitant medications has been largely overlooked. This oversight stems from the conventional assumption that drugs with low CYP450 metabolism dependence have minimal drug-drug interaction potential. However, emerging mechanistic studies suggest that amisulpride may serve as a potential substrate for organic cation transporters (OCTs), multidrug and toxic compound extrusion proteins (MATEs), and P-glycoprotein (P-gp)(Dos Santos Pereira et al. \\u003cspan citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e2014\\u003c/span\\u003e; El Ela et al. \\u003cspan citationid=\\\"CR13\\\" class=\\\"CitationRef\\\"\\u003e2004\\u003c/span\\u003e; Schmitt et al. \\u003cspan citationid=\\\"CR42\\\" class=\\\"CitationRef\\\"\\u003e2006\\u003c/span\\u003e; Schmitt et al. \\u003cspan citationid=\\\"CR43\\\" class=\\\"CitationRef\\\"\\u003e2012\\u003c/span\\u003e; Sekhar et al. \\u003cspan citationid=\\\"CR45\\\" class=\\\"CitationRef\\\"\\u003e2019\\u003c/span\\u003e). Transporter-mediated drug-drug interactions (DDIs) could significantly alter amisulpride's pharmacokinetic profile, potentially impacting its clinical efficacy and adverse effect spectrum. Current research has insufficiently investigated how concomitant medications might influence amisulpride's absorption, distribution, metabolism, and excretion (ADME) processes, especially interactions mediated through renal clearance mechanisms. Given that renal excretion is the primary elimination pathway for amisulpride, such interactions could significantly impact its therapeutic efficacy and safety profile. Notably, data from China's 686 Program indicate that the clozapine-amisulpride combination ranks as the third most frequently prescribed antipsychotic polypharmacy regimen (Hu et al. \\u003cspan citationid=\\\"CR24\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e). This high-frequency prescribing pattern not only confirms the widespread use of amisulpride in combination therapies but also underscores an urgent need to investigate potential pharmacokinetic interactions in these complex therapeutic regimens. Furthermore, from a metabolic perspective, amisulpride demonstrates relative advantages among second-generation antipsychotics (SGAs). Comparative pharmacodynamic studies have shown that amisulpride is associated with a lower propensity for weight gain(Bak et al. \\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e; Pillinger et al. \\u003cspan citationid=\\\"CR37\\\" class=\\\"CitationRef\\\"\\u003e2020\\u003c/span\\u003e) and may have a less adverse impact on metabolic parameters(Pillinger, et al. \\u003cspan citationid=\\\"CR37\\\" class=\\\"CitationRef\\\"\\u003e2020\\u003c/span\\u003e; Wu et al. \\u003cspan citationid=\\\"CR55\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e), compared with other SGAs. For patients at increased risk of metabolic complications, amisulpride appears to maintain therapeutic efficacy while showing superior weight preservation and glycemic stability of body weight and glycemic control(Asif et al. \\u003cspan citationid=\\\"CR2\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e), potentially making it a suitable treatment option for this population. However, the safety and potential pharmacokinetic interactions between amisulpride and commonly prescribed antidiabetic agents remain to be fully elucidated.\\u003c/p\\u003e\\u003cp\\u003e\\u003cb\\u003e​\\u003c/b\\u003ePopulation pharmacokinetics (PPK) modeling and simulation is a powerful tool that integrates multiple covariates to predict drug concentration profiles. It simulates ADME processes to provide quantitative predictions under various dosing scenarios. Renal function is a key determinant of drug elimination, especially for renally cleared compounds such as amisulpride. PPK can simulate drug exposure across the full renal function spectrum (normal, impaired, or hyperfiltration), thereby establishing an evidence-based foundation for individualized dosing regimens in patients with varying renal function. Additionally, regarding DDIs, PPK modeling serves as a complementary approach to preclinical assessments by quantitatively translating clinical study findings into optimized combination therapies that balance therapeutic efficacy with safety considerations. This guides clinicians in optimizing combination therapies to minimize adverse drug reactions while maximizing therapeutic efficacy, thus enhancing the overall risk-benefit profile of pharmacotherapy. To systematically investigate real-world DDI scenarios and to provide individualized effective dosing regimens for patients with varying renal function, this study employed a PPK modeling approach using retrospective clinical data. The PPK framework will integrated population-level analyses to: (1) identify key determinants of amisulpride exposure, including population-typical values and fixed-effect parameters; (2) quantify inter-individual and intra-individual variability; and (3) quantitatively assess the impact of intrinsic/extrinsic factors (particularly renal function and DDIs) on amisulpride's pharmacokinetic profile.\\u003c/p\\u003e\"},{\"header\":\"Materials and Methods\",\"content\":\"\\u003cp\\u003e\\u003cb\\u003eStudy Population and Data Collection\\u003c/b\\u003e\\u003c/p\\u003e\\u003cp\\u003eRetrospective therapeutic drug monitoring (TDM) data were collected from inpatients receiving amisulpride treatment at the Affiliated Brain Hospital of Guangzhou Medical University between July 2023 and June 2024.Steady-state trough blood concentrations for TDM were routinely measured from samples collected in the morning before daily drug administration. Demographic and clinical parameters\\u0026mdash;including age, sex, serum creatinine, concomitant medications, administered amisulpride doses, and plasma concentrations\\u0026mdash;were retrieved from the electronic medical record (EMR) system. The study was approved by the Institutional Review Board of the Affiliated Brain Hospital of Guangzhou Medical University (Approval Number: 2021027).​​\\u003c/p\\u003e\\u003cp\\u003e\\u003cb\\u003eInclusion and Exclusion Criteria\\u003c/b\\u003e\\u003c/p\\u003e\\u003cp\\u003eInclusion Criteria: (1) Hospitalized patients receiving oral amisulpride with routine therapeutic drug monitoring (TDM); (2) Patients diagnosed with schizophrenia according to ICD-10 criteria. Exclusion Criteria: (1) Lack of an established amisulpride treatment regimen;(2) TDM samples outside the assay quantification range.\\u003c/p\\u003e\\u003cdiv id=\\\"Sec3\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003eStatistical Analysis\\u003c/h2\\u003e\\u003cp\\u003eAll statistical analyses were performed using SPSS software (version 26; International Business Machines Corporation, Armonk, NY, USA). Continuous variables, including age, serum creatinine, creatinine clearance (CLcr), daily dose, plasma concentration, and dose-corrected concentration (C/D ratios), were described as mean\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;standard deviation (\\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:\\\\stackrel{-}{\\\\text{x}}\\\\)\\u003c/span\\u003e\\u003c/span\\u003e \\u0026plusmn; s) and median (interquartile range) [M(P25, P75)] to ensure a comprehensive representation of their distributions. Categorical variables were dummy-coded for analysis. To meet normality assumptions, plasma concentrations and C/D ratios were appropriately transformed (e.g., square root transformation, logarithmic transformation). Analysis of covariance (ANCOVA) was conducted for each covariate. Multiple comparisons were adjusted using the Bonferroni correction. A two-tailed \\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05 was considered statistically significant. All figures were generated using Origin 2022 (version 2022; OriginLab Corporation, Northampton, MA, USA).\\u003c/p\\u003e\\u003cp\\u003e\\u003cb\\u003ePopulation Pharmacokinetic Model Development\\u003c/b\\u003e\\u003c/p\\u003e\\u003cp\\u003eThe PPK analysis of amisulpride was performed using nonlinear mixed-effects modeling (NONMEM\\u0026reg; version 7.3; ICON Development Solutions, Hanover, MD, USA) in conjunction with Perl speaks NONMEM (PsN, version 4.2.0). Model development and visualization were conducted using Pirana (version 2.9.7) and R (version 4.2.2). A one-compartment pharmacokinetic model (ADVAN 2) with first-order conditional estimation with interaction (FOCEI) was employed to estimate amisulpride PPK parameters, including apparent clearance (CL/F), apparent volume of distribution (V/F), and absorption rate constant (Ka). Due to sparse sampling in the TDM data, with most concentration measurements obtained during the elimination phase, the Ka parameter was fixed at a stable value following establishing the structural model to facilitate subsequent model refinement. The interindividual variability (IIV) of pharmacokinetic parameters was estimated using an exponential random-effects model: P\\u003csub\\u003ej\\u003c/sub\\u003e = P\\u003csub\\u003eTV\\u003c/sub\\u003e \\u0026times; e\\u003csup\\u003eηp\\u003c/sup\\u003e, where P\\u003csub\\u003ej\\u003c/sub\\u003e is the predicted parameter for individual j, P\\u003csub\\u003eTV\\u003c/sub\\u003e is the population typical value of the parameter, and ηp represents the random interindividual variability for the jth subject, which follows a normal distribution with mean 0 and variance ωp\\u0026sup2;.\\u003c/p\\u003e\\u003cp\\u003eA comprehensive covariate analysis was performed to identify potential factors influencing amisulpride pharmacokinetic parameters. Demographic characteristics (age, sex, body weight), renal function indicators (serum creatinine, creatinine clearance), and concomitant medications (metformin, fluoxetine, valproic acid) identified through preliminary screening were evaluated as potential covariates. The model-building process employed a stepwise approach incorporating both demographic and concomitant medication data as covariates. During forward selection, covariates demonstrating statistically significant effects (defined as a reduction in objective function value (OFV)\\u0026thinsp;\\u0026gt;\\u0026thinsp;6.63, χ\\u0026sup2; test, \\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.01, df\\u0026thinsp;=\\u0026thinsp;1) were incorporated into the base model. Subsequently, backward elimination was performed on the full model, with covariates retained if their removal resulted in an OFV increase\\u0026thinsp;\\u0026gt;\\u0026thinsp;10.83 (χ\\u0026sup2; test, \\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001, df\\u0026thinsp;=\\u0026thinsp;1). The final model selection was based on the following comprehensive criteria: a reduction in OFV, improved goodness-of-fit plots, decreased IIV in structural model parameters, analysis of residual distributions, robustness of parameter estimates, and overall model stability.\\u003c/p\\u003e\\u003cp\\u003e\\u003cb\\u003ePopulation Pharmacokinetic Model Evaluation\\u003c/b\\u003e\\u003c/p\\u003e\\u003cp\\u003eThe final model and parameter estimates were rigorously evaluated through an integrated validation approach incorporating bootstrap analysis, goodness-of-fit (GOF) diagnostic plots, and normalized prediction distribution error (NPDE) assessment. Bootstrap validation with 1000 resampling iterations was employed to evaluate model robustness by comparing parameter estimates from bootstrapped datasets with those from the original model. GOF diagnostic evaluation included four key plots: observed versus individual predicted values (DV vs. IPRED), observed versus population predicted values (DV vs. PRED), conditional weighted residuals versus time (CWRES vs. TIME), and conditional weighted residuals versus population predictions (CWRES vs. PRED), which collectively assessed predictive performance. Model adequacy was further verified through NPDE analysis implemented using the R npde package (version 2.0), examining Q-Q plots against N(0,1) distribution, NPDE histograms overlaid with theoretical normal density curves, and trend analyses of NPDE versus time and population predictions, thereby providing a comprehensive evaluation of model validity. This multi-dimensional validation framework ensured thorough assessment of model robustness, predictive accuracy, and overall performance.\\u003c/p\\u003e\\u003cp\\u003e\\u003cb\\u003ePopulation Pharmacokinetic Model Simulation\\u003c/b\\u003e\\u003c/p\\u003e\\u003cp\\u003eBased on the final population pharmacokinetic model incorporating two significant covariates (creatinine clearance and metformin coadministration), we performed comprehensive simulations of population predictions (PRED) and 1000 individual predictions (IPRED) for 144 distinct population scenarios receiving fixed-dose amisulpride therapy over 14 consecutive days. The simulation framework systematically evaluated: (1) 12 common amisulpride regimens in clinical practice (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e); (2) 6 creatinine clearance strata (30, 60, 90, 120, 150, and 180 mL/min) representing the full spectrum of renal function from severe impairment to hyperfiltration; and (3) binary metformin coadministration status (with or without).\\u003c/p\\u003e\\u003cp\\u003eThree critical analyses were subsequently conducted: (i) Quantitative characterization of the metformin-amisulpride pharmacokinetic interaction: a comparative assessment of PRED values with versus without metformin coadministration was performed to quantify the drug-drug interaction effects on amisulpride clearance and steady-state plasma concentrations. (ii) Evidence-based validation of current therapeutic guidelines: The clinical applicability of the revised therapeutic reference range for amisulpride from the Expert Consensus on Clinical Application of Psychiatric Therapeutic Drug Monitoring in China (2022 edition) (Guo et al. \\u003cspan citationid=\\\"CR20\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e) was rigorously evaluated using 1000 IPRED simulations in the standard patient population (CLcr\\u0026thinsp;=\\u0026thinsp;90\\u0026ndash;120 mL/min without metformin). (iii) Practical dosing optimization strategies tailored to patients' renal function status: We developed personalized dosing recommendations across renal function subgroups (without metformin) by integrating PRED simulations with the consensus guideline's therapeutic range.\\u003c/p\\u003e\\u003cp\\u003e\\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab1\\\" border=\\\"1\\\"\\u003e\\u003ccaption language=\\\"En\\\"\\u003e\\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 1\\u003c/div\\u003e\\u003cdiv class=\\\"CaptionContent\\\"\\u003e\\u003cp\\u003e12 Common Amisulpride Regimens in Clinical Practice\\u003c/p\\u003e\\u003c/div\\u003e\\u003c/caption\\u003e\\u003ccolgroup cols=\\\"4\\\"\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e\\u003cthead\\u003e\\u003ctr\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eDosing regimen\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eDaily dose(mg)\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c4\\\" namest=\\\"c3\\\"\\u003e\\u003cp\\u003eTime of administration of daily dose (mg)\\u003c/p\\u003e\\u003c/th\\u003e\\u003c/tr\\u003e\\u003c/thead\\u003e\\u003ctbody\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e8:30 dose (mg)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e19:00 dose (mg)\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e1\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e100\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e100\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e0\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e2\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e200\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e200\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e0\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e3\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e300\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e300\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e0\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e4\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e400\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e200\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e200\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e5\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e500\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e300\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e200\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e6\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e600\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e300\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e300\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e7\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e700\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e400\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e300\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e8\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e800\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e400\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e400\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e9\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e900\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e500\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e400\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e10\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e1000\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e500\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e500\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e11\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e1100\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e600\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e500\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e12\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e1200\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e600\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e600\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003c/tbody\\u003e\\u003c/colgroup\\u003e\\u003c/table\\u003e\\u003c/div\\u003e\\u003c/p\\u003e\\u003c/div\\u003e\"},{\"header\":\"Results\",\"content\":\"\\u003cp\\u003e\\u003cstrong\\u003eDemographic Characteristics\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eFor model development, this study included 386 TDM-derived amisulpride plasma concentrations from 194 patients with schizophrenia. The demographic and clinical characteristics are presented in Table\\u0026nbsp;2. No significant multicollinearity was observed among gender, concomitant medications, plasma concentrations, or C/D ratios (tolerance: 0.794–0.917 \\u0026gt; 0.1; variance inflation factor [VIF]: 1.090–1.260 \\u0026lt; 5). Covariate analysis indicated significantly higher plasma concentrations and C/D ratios in females versus males (\\u003cem\\u003eP\\u003c/em\\u003e \\u0026lt; 0.05). Patients concomitantly administered either metformin or fluoxetine exhibited statistically significant elevations in both plasma concentrations and C/D ratios compared to those without these comedications (\\u003cem\\u003eP\\u003c/em\\u003e \\u0026lt; 0.05; Table\\u0026nbsp;3).\\u003c/p\\u003e\\n\\u003cdiv\\u003e\\n \\u003ctable id=\\\"Tab2\\\" border=\\\"1\\\"\\u003e\\n \\u003ccaption language=\\\"En\\\"\\u003e\\n \\u003cdiv\\u003eTable 2\\u003c/div\\u003e\\n \\u003cdiv\\u003e\\n \\u003cp\\u003eDemographic and Clinical Characteristics of the Study Cohort\\u003c/p\\u003e\\n \\u003c/div\\u003e\\n \\u003c/caption\\u003e\\n \\u003cthead\\u003e\\n \\u003ctr\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\\\(\\\\:\\\\stackrel{-}{\\\\text{x}}\\\\)±s\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eM(P25, P75)\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/thead\\u003e\\n \\u003ctbody\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eNumber of patients (male/female)\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e194(95/99)\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eAge (years)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e30.62 ± 12.11\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e27.00(23.00, 35.00)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eBody Weight (kg)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e65.17 ± 14.18\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e63.00(54.75, 75.00)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eCreatinine (µmol/L)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e71.92 ± 16.41\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e72.00(58.00, 84.00)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eCreatinine clearance* (mL/min)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e117.89 ± 36.90\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e112.93(91.90, 129.86)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eNumber of samples\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e386\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003ePlasma concentration (ng/mL)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e323.90 ± 222.62\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e275.26(171.07, 439.94)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eDaily dose (mg)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e532.51 ± 243.30\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e600(400, 800)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eC/D ratios[(ng/mL)/(mg/d)]\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.62 ± 0.33\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.55(0.39, 0.79)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tbody\\u003e\\n \\u003ctfoot\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd colspan=\\\"3\\\"\\u003eNote: *Creatinine clearance was calculated using the Cockcroft-Gault formula: (140-age)×Body Weight/(72×creatinine/88.4), (×0.85, if female), where serum creatinine is in µmol/L.\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tfoot\\u003e\\n \\u003c/table\\u003e\\n\\u003c/div\\u003e\\n\\u003cdiv\\u003e\\n \\u003ctable id=\\\"Tab3\\\" border=\\\"1\\\"\\u003e\\n \\u003ccaption language=\\\"En\\\"\\u003e\\n \\u003cdiv\\u003eTable 3\\u003c/div\\u003e\\n \\u003cdiv\\u003e\\n \\u003cp\\u003eGender and Concomitant Medication Effects on Amisulpride Plasma Concentrations and C/D Ratios\\u003c/p\\u003e\\n \\u003c/div\\u003e\\n \\u003c/caption\\u003e\\n \\u003cthead\\u003e\\n \\u003ctr\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eNumber of patients (n = 194)\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eNumber of samples (n = 386)\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003ePlasma concentration (ng/mL)\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eC/D ratios\\u003c/p\\u003e\\n \\u003cp\\u003e[(ng/mL)/(mg/d)]\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/thead\\u003e\\n \\u003ctbody\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eGender\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eMale\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e95\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e214\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e241.41 ± 159.19**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.53 ± 0.26**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eFemale\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e99\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e172\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e426.53 ± 246.80\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.73 ± 0.37\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"6\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eConcomitant medications\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eMetformin\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e+\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e21\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e44\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e534.65 ± 313.32***\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e0.83 ± 0.42***\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e-\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e173\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e342\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e394.78 ± 192.63\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.59 ± 0.30\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eRepaglinide\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e+\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e303.05\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.38\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e-\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e193\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e385\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e323.95 ± 222.91\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.62 ± 0.33\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eAcarbose\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e+\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e2\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e434.05 ± 17.72\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.54 ± 0.02\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e-\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e193\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e384\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e323.33 ± 223.06\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.62 ± 0.33\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eSitagliptin\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e+\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e2\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e2\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e296.83 ± 8.80\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.55 ± 0.25\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e-\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e192\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e384\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e324.04 ± 223.19\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.62 ± 0.33\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eRisperidone/Paliperidone\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e+\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e32\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e51\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e337.11 ± 260.85\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.68 ± 0.39\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e-\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e162\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e335\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e321.89 ± 216.59\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.61 ± 0.32\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eVenlafaxine\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e+\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e15\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e22\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e230.33 ± 150.90\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.66 ± 0.29\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e-\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e179\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e364\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e329.55 ± 225.13\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.62 ± 0.33\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eDuloxetine\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e+\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e629.83\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.79\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e-\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e193\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e385\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e323.10 ± 222.36\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.62 ± 0.33\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eOlanzapine\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e+\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e46\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e100\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e301.61 ± 226.26\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.59 ± 0.33\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e-\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e148\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e286\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e331.69 ± 221.21\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.63 ± 0.33\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eClozapine\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e+\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e52\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e102\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e404.74 ± 214.35\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.64 ± 0.29\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e-\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e142\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e284\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e294.86 ± 218.69\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.61 ± 0.34\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eQuetiapine\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e+\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e7\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e9\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e282.74 ± 175.80\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.67 ± 0.36\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e-\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e187\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e377\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e324.89 ± 223.71\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.62 ± 0.33\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eFluoxetine\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e+\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e15\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e26\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e433.40 ± 294.69***\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e0.89 ± 0.41***\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e-\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e179\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e360\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e315.99 ± 214.88\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.60 ± 0.31\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eParoxetine\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e+\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e2\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e2\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e193.45 ± 99.86\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.64 ± 0.03\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e-\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e192\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e384\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e324.58 ± 222.94\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.62 ± 0.33\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eSertraline\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e+\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e27\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e45\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e308.57 ± 188.47\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.63 ± 0.30\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e-\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e167\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e341\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e325.92 ± 226.91\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.62 ± 0.33\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eFluvoxamine\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e+\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e9\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e20\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e320.13 ± 196.96\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.63 ± 0.28\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e-\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e185\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e366\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e324.10 ± 224.18\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.62 ± 0.33\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eCitalopram\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e+\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e176.48\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.88\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e-\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e193\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e385\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e324.28 ± 222.78\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.62 ± 0.33\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eLithium\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e+\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e17\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e28\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e251.05 ± 188.63\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.53 ± 0.29\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e-\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e177\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e358\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e329.60 ± 224.29\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.63 ± 0.33\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eValproic acid\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e+\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e31\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e59\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e356.90 ± 197.68*\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e0.65 ± 0.30*\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e-\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e163\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e327\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e317.94 ± 226.59\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.61 ± 0.33\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tbody\\u003e\\n \\u003ctfoot\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd colspan=\\\"6\\\"\\u003eNote: *\\u003cem\\u003eP\\u003c/em\\u003e \\u0026lt; 0.05, ** \\u003cem\\u003eP\\u003c/em\\u003e \\u0026lt; 0.01, *** \\u003cem\\u003eP\\u003c/em\\u003e \\u0026lt; 0.001\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tfoot\\u003e\\n \\u003c/table\\u003e\\n\\u003c/div\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003e\\u003cem\\u003eCovariate Screening\\u003c/em\\u003e\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe population pharmacokinetics of amisulpride were best described by a one-compartment model with a mixed-error structure. Based on preliminary estimates, Ka was fixed at 1.1 h⁻¹. In addition to demographic variables, three clinically significant comedications (metformin, fluoxetine, and valproate) were systematically evaluated using stepwise covariate modeling, which significantly improved model fit as evidenced by reduced OFV. The final model identified CLcr and metformin coadministration as significant covariates affecting amisulpride CL/F. Specifically, CL/F decreased by 16 L/h for every 10 mL/min reduction in CLcr. Patients receiving metformin demonstrated a 23% lower CL/F compared to non-users. The covariate selection process is summarized in Table 4. The final population pharmacokinetic model was defined as:\\u003c/p\\u003e\\n\\u003cp\\u003eCL/F(L/h) = 39.8×(CLcr/113)\\u003csup\\u003e0.376\\u003c/sup\\u003e×(MET)\\u003csup\\u003e0.38\\u003c/sup\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eV/F(L) = 671\\u003c/p\\u003e\\n\\u003cp\\u003eKa(h\\u003csup\\u003e-1\\u003c/sup\\u003e) =1.1(FIXED)\\u003c/p\\u003e\\n\\u003cp\\u003ewhere CLcr represents creatinine clearance calculated using the Cockcroft-Gault equation, and MET denotes metformin coadministration status (MET = 1 present, MET = 2 absent).\\u003c/p\\u003e\\n\\u003cdiv\\u003e\\n \\u003ctable id=\\\"Tab4\\\" border=\\\"1\\\"\\u003e\\n \\u003ccaption language=\\\"En\\\"\\u003e\\n \\u003cdiv\\u003eTable 4\\u003c/div\\u003e\\n \\u003cdiv\\u003e\\n \\u003cp\\u003eCovariate Screening Process Stepwise Covariate Screening Procedure\\u003c/p\\u003e\\n \\u003c/div\\u003e\\n \\u003c/caption\\u003e\\n \\u003cthead\\u003e\\n \\u003ctr\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eModel Number\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eBased on Model\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eVariables\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eOFV\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003edOFV\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/thead\\u003e\\n \\u003ctbody\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eFirst run of inclusion\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003estructural model\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e4060.018\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e2\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eCLcr-CL\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e-16.206\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e3\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eAGE-CL\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e-10.215\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e4\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eFLUO-CL\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e-8.937\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e5\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eCLcr-V\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e-8.665\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e6\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eSEX-CL\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e-8.131\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e7\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eMET-CL\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e-7.074\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e8\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eBW-V\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e-5.696\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e9\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eFLUO-V\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e-5.455\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e10\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eBW-CL\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e-5.417\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e11\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eSEX-V\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e-4.888\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e13\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eAGE-V\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e-3.492\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e16\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eVPA-V\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e-2.105\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e18\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eMET-V\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e-1.061\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e23\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eVPA-CL\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e-0.442\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e30\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eCr-V\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e-0.137\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e32\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eCr-CL\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e-0.085\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eSecond run of inclusion\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e2\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003eAMI-CLcr-CL\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e4043.812\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e40\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eMET-CL\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e-16.27\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e41\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eSEX-CL\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e-11.16\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e42\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eFLUO-CL\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e-9.827\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e43\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eAGE-CL\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e-4.706\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e44\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eCLcr-V\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e-0.075\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eThird run of inclusion\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e40\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003eAMI-CLcr-CL-MET-CL\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e4027.542\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e45\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eFLUO-CL\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e-8.509\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e46\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eSEX-CL\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e-6.607\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eFull variable model\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"4\\\"\\u003e\\n \\u003cp\\u003eAMI-CLcr-CL-MET-CL-FLUO-CL\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eFirst run of elimination\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e45\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003eAMI-CLcr-CL-MET-CL-FLUO-CL\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e4019.033\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e47\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eCLcr-CL\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e25.998\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e48\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eMET-CL\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e14.952\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e49\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eFLUO-CL\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e8.509\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eSecond run of elimination\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e40\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003eAMI-CLcr-CL-MET-CL\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e4027.542\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e50\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eCLcr-CL\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e25.402\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e51\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eMET-CL\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e16.27\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eFinal model\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"3\\\"\\u003e\\n \\u003cp\\u003eAMI-CLcr-CL-MET-CL\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tbody\\u003e\\n \\u003ctfoot\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd colspan=\\\"5\\\"\\u003eNote: AMI represent amisulpride; CLcr represent Creatinine clearance rate; Cr represent creatinine level; AGE represent age; FLUO represent fluoxetine; MET represent Metformin; BW represent body weight; VPA represent valproate.\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tfoot\\u003e\\n \\u003c/table\\u003e\\n\\u003c/div\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003e\\u003cem\\u003eModel Evaluation\\u003c/em\\u003e\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003e\\u003cem\\u003eBootstrap Analysis\\u003c/em\\u003e\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe final parameter estimates and bootstrap validation results (1000 iterations) demonstrated good model stability (88.3% success rate). All parameters showed good precision (relative standard errors \\u0026lt; 30%) and strong agreement with bootstrap medians, indicating excellent predictive accuracy (Table\\u0026nbsp;5).\\u003c/p\\u003e\\n\\u003cdiv\\u003e\\n \\u003ctable id=\\\"Tab5\\\" border=\\\"1\\\"\\u003e\\n \\u003ccaption language=\\\"En\\\"\\u003e\\n \\u003cdiv\\u003eTable 5\\u003c/div\\u003e\\n \\u003cdiv\\u003e\\n \\u003cp\\u003eEstimation of final model parameters and bootstrap results\\u003c/p\\u003e\\n \\u003c/div\\u003e\\n \\u003c/caption\\u003e\\n \\u003cthead\\u003e\\n \\u003ctr\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\" colspan=\\\"3\\\"\\u003e\\n \\u003cp\\u003eFinal model\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\" colspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003eBootstrap (1000 iterations)\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/thead\\u003e\\n \\u003ctbody\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eParameters\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eestimated value\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eRSE (%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eVII (%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003emedian\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e95% CI\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eCL/F(L/h)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e39.8\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e8\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e24\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e40.1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e33.50-47.48\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eθ\\u003csub\\u003eCLcr−CL\\u003c/sub\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.376\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e16\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e/\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.372\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.24–0.51\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eθ\\u003csub\\u003eMET−CL\\u003c/sub\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.38\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e25\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e/\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.377\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.14–0.58\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eV/F(L)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e671\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e22\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e67\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e676\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e461.52-1044.15\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eKa(h\\u003csup\\u003e− 1\\u003c/sup\\u003e)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"3\\\"\\u003e\\n \\u003cp\\u003e1.1(FIXED)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003e1.1(FIXED)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eADDITI\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e4.00E-5\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e16\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e/\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e4.00E-5\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e3.99E-5-4.01E-5\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003ePROPOR\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.0980\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e12\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e/\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.0963\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.0757–0.1227\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tbody\\u003e\\n \\u003c/table\\u003e\\n\\u003c/div\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003e\\u003cem\\u003eNormalized Prediction Distribution Error\\u003c/em\\u003e\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eNormalized prediction distribution errors (NPDE) analysis indicated excellent model performance, with a mean of 0.075 (SE: 0.047) and variance of 0.868 (SE: 0.063). Non-significant results from the t-test (\\u003cem\\u003eP\\u003c/em\\u003e = 0.35), Fisher's variance test (P = 0.176), and Shapiro-Wilk test (\\u003cem\\u003eP\\u003c/em\\u003e = 0.176) collectively confirmed NPDE adherence to N(0,1) (Fig.\\u0026nbsp;1). These findings demonstrate the final model's stability and absence of significant bias.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eGoodness of Fit\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003ePRED and IPRED concentrations showed symmetric distribution along the identity line when plotted against observed concentrations. CWRES were randomly scattered around zero, with 95% within ± 2 units. These diagnostics collectively demonstrate the final model's good predictive performance (Fig.\\u0026nbsp;2).\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eModel Simulation\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eInteraction Between Metformin and Amisulpride\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eConcomitant administration of metformin significantly altered amisulpride pharmacokinetics, reducing its clearance to 77% of that in its absence and resulting in consistently elevated systemic exposure. Population pharmacokinetic simulation results demonstrated that metformin coadministration increased amisulpride exposure by 45% or more across all dose ranges, with this pharmacokinetic interaction showing no dependence on renal function status (Fig.\\u0026nbsp;3).\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eEvidence-Based Validation of Current Therapeutic Guidelines\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eWe assessed the applicability of the therapeutic reference range proposed in the Expert Consensus on Clinical Application of Psychiatric Therapeutic Drug Monitoring in China (2022 edition)(Guo, et al. 2022), using IPRED data from all dosing regimens based on 24,000 simulations (1,000 concentrations per dosing regimen) in patients without metformin and with normal renal function (CLcr = 90–120 mL/min). The results demonstrated that 58.34% (28,002/48,000) of simulated amisulpride plasma concentrations fell within the 100–600 ng/mL range, whereas only 29.94% (14,369/48,000) fell within the narrower range (100–320 ng/mL) (Fig.\\u0026nbsp;4).\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eIndividualized Medication Strategy Based on Renal Function\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThere was an inverse relationship between creatinine clearance and amisulpride plasma concentration. In the absence of metformin, the PRED values for each dosing regimen under different renal function states are shown in Fig.\\u0026nbsp;5. Based on the newly revised therapeutic reference range for amisulpride in the Expert Consensus on Clinical Application of Psychiatric Therapeutic Drug Monitoring in China(2022 edition) (Guo, et al. 2022), the effective doses for different renal function groups were as follows (Table\\u0026nbsp;6). For patients with renal impairment (30–60 mL/min), the effective dose was 200–600 mg. For patients with mild renal impairment (60–90 mL/min), the effective dose was 200–800 mg. For patients with normal renal function (90–120 mL/min), the effective dose was 300–800 mg. For patients with mild hyperfiltration (120–150 mL/min), the effective dose was 300–1000 mg. For patients with hyperfiltration (150–180 mL/min), the effective dose was 400–1000 mg.\\u003c/p\\u003e\\n\\u003cdiv\\u003e\\n \\u003ctable id=\\\"Tab6\\\" border=\\\"1\\\"\\u003e\\n \\u003ccaption language=\\\"En\\\"\\u003e\\n \\u003cdiv\\u003eTable 6\\u003c/div\\u003e\\n \\u003cdiv\\u003e\\n \\u003cp\\u003eRecommended Amisulpride Dosing Ranges by Renal Function Status without metformin\\u003c/p\\u003e\\n \\u003c/div\\u003e\\n \\u003c/caption\\u003e\\n \\u003cthead\\u003e\\n \\u003ctr\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eCLcr\\u003c/p\\u003e\\n \\u003cp\\u003e(mL/min)\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\" colspan=\\\"3\\\"\\u003e\\n \\u003cp\\u003eLower Threshold of Therapeutic Range (100ng/mL)\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\" colspan=\\\"3\\\"\\u003e\\n \\u003cp\\u003eUpper Limit of Therapeutic Range (600ng/mL)\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/thead\\u003e\\n \\u003ctbody\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eDaily Dose\\u003c/p\\u003e\\n \\u003cp\\u003e(mg)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e8:30 dose\\u003c/p\\u003e\\n \\u003cp\\u003e(mg)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e19:00 dose\\u003c/p\\u003e\\n \\u003cp\\u003e(mg)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eDaily Dose\\u003c/p\\u003e\\n \\u003cp\\u003e(mg)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e8:30 dose\\u003c/p\\u003e\\n \\u003cp\\u003e(mg)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e19:00 dose\\u003c/p\\u003e\\n \\u003cp\\u003e(mg)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"8\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eRenal impairment (\\u003c/strong\\u003eFigs.\\u0026nbsp;5A, B\\u003cstrong\\u003e)\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e30\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e200\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e200\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e500\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e300\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e200\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e60\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e200\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e200\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e600\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e300\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e300\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"8\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eMild renal impairment (\\u003c/strong\\u003eFigs.\\u0026nbsp;5B, C\\u003cstrong\\u003e)\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e60\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e200\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e200\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e600\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e300\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e300\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e90\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e300\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e300\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e800\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e400\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e400\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"8\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eNormal renal function (\\u003c/strong\\u003eFigs.\\u0026nbsp;5C, D\\u003cstrong\\u003e)\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e90\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e300\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e300\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e800\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e400\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e400\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e120\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e300\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e300\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e800\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e400\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e400\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"8\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eMild hyperfiltration (\\u003c/strong\\u003eFigs.\\u0026nbsp;5D, E\\u003cstrong\\u003e)\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e120\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e300\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e300\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e800\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e400\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e400\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e150\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e400\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e200\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e200\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1000\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e500\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e500\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"8\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eHyperfiltration (\\u003c/strong\\u003eFigs.\\u0026nbsp;5E, F\\u003cstrong\\u003e)\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e150\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e400\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e200\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e200\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1000\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e500\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e500\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e180\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e400\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e200\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e200\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1000\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e500\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e500\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tbody\\u003e\\n \\u003c/table\\u003e\\n\\u003c/div\\u003e\"},{\"header\":\"Discussion\",\"content\":\"\\u003cp\\u003e This study successfully developed and validated a PPK model of amisulpride incorporating 386 plasma concentrations obtained from 194 patients. Our analysis identified two clinically significant covariates affecting amisulpride's apparent CL/F: CLcr exhibited a positive correlation with drug elimination, while metformin coadministration demonstrated an inhibitory effect. The final model demonstrated excellent predictive performance for estimating amisulpride exposure in patients with varying renal function, establishing its utility for precision dosing in clinical practice. These findings provide a pharmacokinetic foundation for optimizing amisulpride therapy through renal function- and comedication-adjusted dosing strategies.\\u003c/p\\u003e\\u003cp\\u003eOur estimated amisulpride CL/F (39.8 L/h) aligns with prior PPK studies (32.60\\u0026ndash;61.1 L/h) (Glatard et al. \\u003cspan citationid=\\\"CR19\\\" class=\\\"CitationRef\\\"\\u003e2020\\u003c/span\\u003e; Huang et al. \\u003cspan citationid=\\\"CR25\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e; Li et al. \\u003cspan citationid=\\\"CR27\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e; Liu et al. \\u003cspan citationid=\\\"CR29\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e; Reeves et al. \\u003cspan citationid=\\\"CR40\\\" class=\\\"CitationRef\\\"\\u003e2016\\u003c/span\\u003e; Yan et al. \\u003cspan citationid=\\\"CR57\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e), thereby supporting the reliability of our model. However, substantial inter-study variability was observed in the estimated Vd/F (391\\u0026ndash;1720 L) across these PPK studies(Glatard, et al. \\u003cspan citationid=\\\"CR19\\\" class=\\\"CitationRef\\\"\\u003e2020\\u003c/span\\u003e; Huang, et al. \\u003cspan citationid=\\\"CR25\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e; Li, et al. \\u003cspan citationid=\\\"CR27\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e; Liu, et al. \\u003cspan citationid=\\\"CR29\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e; Reeves, et al. \\u003cspan citationid=\\\"CR40\\\" class=\\\"CitationRef\\\"\\u003e2016\\u003c/span\\u003e; Yan, et al. \\u003cspan citationid=\\\"CR57\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e), likely attributable to differences in demographic and clinical characteristics among study populations. Specifically, the relatively low Vd/F estimate of 391 L reported by Liu et al. (Liu, et al. \\u003cspan citationid=\\\"CR29\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e) may be explained by the pronounced gender imbalance in their study cohort (78.41% male, n\\u0026thinsp;=\\u0026thinsp;88). Given that males typically exhibit lower body fat percentages than females(Zucker and Prendergast \\u003cspan citationid=\\\"CR65\\\" class=\\\"CitationRef\\\"\\u003e2020\\u003c/span\\u003e) and considering amisulpride's limited distribution in adipose tissue, which would be expected to yield smaller distribution volumes in male-dominated populations. In contrast, the markedly elevated Vd/F estimate of 1720 L reported by Huang et al. (Huang, et al. \\u003cspan citationid=\\\"CR25\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e) may reflect the impaired renal function evident in their study population (CLcr: 1.36\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.48 mL/min), potentially leading to suboptimal estimation of drug distribution capacity. In developing our population pharmacokinetic model, we conducted a systematic comparison of three renal function estimation methods: the MDRD formula, the CKD-EPI formula, and the Cockcroft-Gault formula. Comparative analysis revealed that implementation of the Cockcroft-Gault formula alone yielded the most optimal model fit, as evidenced by producing the lowest OFV. Two others (Liu, et al. \\u003cspan citationid=\\\"CR29\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e; Yan, et al. \\u003cspan citationid=\\\"CR57\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e) like our study, found that the clearance of amisulpride decreases with decreasing creatinine clearance rate calculated using the Cockcroft-Gault formula. Although it also indicates that as renal function deteriorates, the clearance rate of amisulpride will decrease, but only one study incorporated the CKD-EPI equation for estimating glomerular filtration rate (GFR) in covariate analysis(Li, et al. \\u003cspan citationid=\\\"CR27\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e). While the CKD-EPI formula is less influenced by age, sex, and ethnicity and provides a more accurate GFR estimation, the Cockcroft-Gault formula offers greater simplicity and practicality, making it more advantageous for clinical dose adjustments. Our results suggest that the Cockcroft-Gault formula may provide superior performance for characterizing amisulpride pharmacokinetics in relation to renal function compared to alternative estimation methods. Our PPK model analysis identified CLcr as the most statistically significant covariate influencing amisulpride clearance, different from three previous studies that reported age as the primary determinant and demonstrate an inverse correlation between age and drug clearance.(Glatard, et al. \\u003cspan citationid=\\\"CR19\\\" class=\\\"CitationRef\\\"\\u003e2020\\u003c/span\\u003e; Huang, et al. \\u003cspan citationid=\\\"CR25\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e; Reeves, et al. \\u003cspan citationid=\\\"CR40\\\" class=\\\"CitationRef\\\"\\u003e2016\\u003c/span\\u003e). Importantly, our study provides a mechanistic explanation for this discrepancy by elucidating that the age-related decline in amisulpride clearance is mediated through progressive deterioration of renal function, as quantified by CLcr measurements. While two of these investigations additionally identified body weight as a positively correlated covariate for amisulpride clearance(Glatard, et al. \\u003cspan citationid=\\\"CR19\\\" class=\\\"CitationRef\\\"\\u003e2020\\u003c/span\\u003e; Reeves, et al. \\u003cspan citationid=\\\"CR40\\\" class=\\\"CitationRef\\\"\\u003e2016\\u003c/span\\u003e), the observed discrepancies likely reflect methodological differences in covariate assessment. Specifically, our use of the Cockcroft-Gault formulas, employed in our analysis, provides a comprehensive estimation of renal function by simultaneously accounting for the effects of age, sex, and body weight. This integrated approach likely explains our finding that although both age and sex demonstrated significant effects during the forward inclusion phase of our PPK modeling, their contributions became nonsignificant following CLcr incorporation into the final model, confirming renal function as the fundamental determinant of amisulpride clearance.\\u003c/p\\u003e\\u003cp\\u003eThe pharmacokinetic analysis in this study revealed a amisulpride total clearance (CL/F) of 39.8 L/h (663.33 mL/min), representing 5.63-fold the mean creatinine clearance (117.89\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;36.90 mL/min) observed in our cohort. This marked discrepancy implies contributions from active transport mechanisms beyond glomerular filtration beyond passive glomerular filtration in amisulpride elimination. The transporter-mediated nature of amisulpride pharmacokinetics is further corroborated by documented DDIs with various psychotropic agents, including lithium(Bergemann et al. \\u003cspan citationid=\\\"CR4\\\" class=\\\"CitationRef\\\"\\u003e2004\\u003c/span\\u003e; Bergemann et al. \\u003cspan citationid=\\\"CR5\\\" class=\\\"CitationRef\\\"\\u003e2007\\u003c/span\\u003e; M\\u0026uuml;ller et al. \\u003cspan citationid=\\\"CR33\\\" class=\\\"CitationRef\\\"\\u003e2009\\u003c/span\\u003e), clozapine(Bergemann, et al. \\u003cspan citationid=\\\"CR4\\\" class=\\\"CitationRef\\\"\\u003e2004\\u003c/span\\u003e; Qu et al. \\u003cspan citationid=\\\"CR39\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e), and valproate (Yang et al. \\u003cspan citationid=\\\"CR58\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e), all of which have been shown to significantly alter amisulpride plasma concentrations in TDM reports. Our systematic evaluation of co-medication effects, incorporating multiple renally excreted drugs such as paliperidone and metformin, demonstrated that valproate, fluoxetine, and metformin coadministration significantly increased amisulpride plasma concentrations and C/D ratios after controlling for age, sex, creatinine clearance, and other covariates. Notably, metformin coadministration resulted in \\u0026ge;\\u0026thinsp;45% elevation in amisulpride exposure, warranting clinical caution when combining these medications. The mechanistic basis for this interaction appears to involve competition at renal transporter systems, as both drugs share several pharmacological characteristics: predominant renal excretion in unchanged form, share cationic properties (metformin pKa\\u0026thinsp;=\\u0026thinsp;11.5(Elezović et al. \\u003cspan citationid=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e) vs. amisulpride pKa\\u0026thinsp;=\\u0026thinsp;9.37(Younes et al. \\u003cspan citationid=\\\"CR59\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e)), suggesting possible transporter competition and appear to compete for common renal transporter systems. Specifically, metformin is a well-established substrate of OCT2, MATE-1, and MATE-2K transporters (Paglialunga et al. \\u003cspan citationid=\\\"CR34\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e; Pernecker and Ciarimboli \\u003cspan citationid=\\\"CR36\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e), while preclinical studies suggest amisulpride may also be a potential substrate for OCTs and MATEs transporter systems (Dos Santos Pereira, et al. \\u003cspan citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e2014\\u003c/span\\u003e; Sekhar, et al. \\u003cspan citationid=\\\"CR45\\\" class=\\\"CitationRef\\\"\\u003e2019\\u003c/span\\u003e). This competitive inhibition at renal tubular secretion sites probably underlies the observed elevation in amisulpride concentrations. In clinical practice, metformin is widely used as a hypoglycemic agent. In psychiatric pharmacotherapy, it is frequently employed to mitigate metabolic syndrome or hyperprolactinemia induced by antipsychotics such as risperidone, olanzapine, amisulpride and all so(Bo et al. \\u003cspan citationid=\\\"CR7\\\" class=\\\"CitationRef\\\"\\u003e2016\\u003c/span\\u003e; Yu et al. \\u003cspan citationid=\\\"CR60\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e; Zhu et al. \\u003cspan citationid=\\\"CR64\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e). The application of metformin in psychiatry has increased, whether for glycemic management in diabetic patients with comorbidities or for addressing adverse effects caused by psychiatric medications. Importantly, for renally excreted drugs like amisulpride and metformin that undergo minimal hepatic metabolism, transporter-mediated DDIs may lead to bidirectional alterations in drug clearance, potentially increasing adverse effect risks. Therefore, it is essential to evaluate the impact of amisulpride on metformin plasma concentrations and carefully consider potential drug interactions when combining these medications, optimizing treatment regimens to minimize the risk of adverse effects. Interestingly, our study found not significant impact of risperidone or paliperidone on amisulpride pharmacokinetics, likely reflecting distinct renal elimination pathways, as these drugs primarily interact with breast cancer resistance protein(BCRP)(Zhou et al. \\u003cspan citationid=\\\"CR63\\\" class=\\\"CitationRef\\\"\\u003e2018\\u003c/span\\u003e), rather than substrates of OCT2, MATE-1, or MATE-2K (Wang et al. \\u003cspan citationid=\\\"CR51\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e), that appear more critical for amisulpride clearance.\\u003c/p\\u003e\\u003cp\\u003eOur integrated covariance analysis and population pharmacokinetic (PPK) modeling revealed fluoxetine's significant, albeit secondary, influence on amisulpride pharmacokinetics. The observed interaction between fluoxetine and amisulpride likely involves multiple pharmacokinetic mechanisms. Fluoxetine's renal excretion profile, with approximately 70% of its metabolites are renally excreted(Bergstrom et al. \\u003cspan citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e1993\\u003c/span\\u003e), combined with its established role as both a selective P-glycoprotein (P-gp) inhibitor (Pinto et al. \\u003cspan citationid=\\\"CR38\\\" class=\\\"CitationRef\\\"\\u003e2020\\u003c/span\\u003e; Schrickx and Fink-Gremmels \\u003cspan citationid=\\\"CR44\\\" class=\\\"CitationRef\\\"\\u003e2014\\u003c/span\\u003e; Xu et al. \\u003cspan citationid=\\\"CR56\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e; Zhang et al. \\u003cspan citationid=\\\"CR62\\\" class=\\\"CitationRef\\\"\\u003e2013\\u003c/span\\u003e) and a substrate for OCTs and P-gp(Wang et al. \\u003cspan citationid=\\\"CR52\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e), creates a complex interplay that may reduce amisulpride clearance. This reduction potentially occurs through competitive inhibition at shared transporter sites and disruption of P-gp-mediated efflux pathways, mirroring the previously characterized fluoxetine-olanzapine interaction dynamic(Xu, et al. \\u003cspan citationid=\\\"CR56\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e). Notably, while both fluoxetine and metformin initially showed significant effects on amisulpride clearance (P\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.01), only metformin's impact remained statistically significant under more stringent criteria (P\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001), suggesting metformin's broader spectrum of transporter interactions may dominate the overall clearance pathway more than fluoxetine. Moreover, this study revealed a distinct interaction pattern for valproate, which significantly increased amisulpride plasma concentrations without affecting its clearance. This observation is mechanistically consistent with competitive plasma protein binding displacement, given the substantial difference in protein binding affinity between valproate (90\\u0026ndash;95% bound) (Zhang et al. \\u003cspan citationid=\\\"CR61\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e) and amisulpride (17% bound) (Rosenzweig et al. \\u003cspan citationid=\\\"CR41\\\" class=\\\"CitationRef\\\"\\u003e2002\\u003c/span\\u003e).. The marked binding affinity disparity suggests that valproate effectively competes for albumin binding sites, displacing amisulpride and consequently increasing its pharmacologically active free fraction in systemic circulation.\\u003c/p\\u003e\\u003cp\\u003e The conventional therapeutic reference range for amisulpride (100\\u0026ndash;320 ng/mL), established by the 2017 Arbeitsgemeinschaft f\\u0026uuml;r Neuropsychopharmakologie und Pharmakopsychiatrie (AGNP) consensus guidelines(Huang, et al. \\u003cspan citationid=\\\"CR25\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e), requires critical re-evaluation due to several evidence-based limitations. First, the upper threshold of 320 ng/mL was derived from ROC analysis of extrapyramidal symptoms with suboptimal predictive accuracy (AUC\\u0026thinsp;=\\u0026thinsp;0.62, 95% CI: 0.56\\u0026ndash;0.67) (M\\u0026uuml;ller et al. \\u003cspan citationid=\\\"CR32\\\" class=\\\"CitationRef\\\"\\u003e2007\\u003c/span\\u003e). Second, the recommended 24-hour dosing interval for blood collection contradicts standard clinical practice of divided daily dosing and results in lower plasma concentration, as it overlooks the fact that divided dosing helps stabilize and reduce significant fluctuations in blood concentration (Ding, et al. \\u003cspan citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e; Huang, et al. \\u003cspan citationid=\\\"CR25\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e). Thirly, accumulating evidence suggests this range may substantially underestimate effective therapeutic concentrations(Ding, et al. \\u003cspan citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e; J\\u0026ouml;nsson et al. \\u003cspan citationid=\\\"CR26\\\" class=\\\"CitationRef\\\"\\u003e2019\\u003c/span\\u003e; Sparshatt et al. \\u003cspan citationid=\\\"CR49\\\" class=\\\"CitationRef\\\"\\u003e2009\\u003c/span\\u003e; Wang and Li \\u003cspan citationid=\\\"CR53\\\" class=\\\"CitationRef\\\"\\u003e2017\\u003c/span\\u003e). These limitations have prompted the 2022 Chinese Expert Consensus to revise the therapeutic range to 100\\u0026ndash;600 ng/mL(Guo, et al. \\u003cspan citationid=\\\"CR20\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e), a modification supported by a real-world clinical data correlated with clinical global impression(CGI) scores(Gan et al. \\u003cspan citationid=\\\"CR17\\\" class=\\\"CitationRef\\\"\\u003e2025\\u003c/span\\u003e) and confirmed by population pharmacokinetic simulations (n\\u0026thinsp;=\\u0026thinsp;1000) in the current study. Building upon this revised therapeutic range, we developed personalized dosing algorithms stratified by creatinine clearance (CLcr), identifying three subpopulations requiring initial doses below the standard 400 mg/day recommendation: renal impairment (CLcr 30\\u0026ndash;60 mL/min), mild impairment (CLcr 60\\u0026ndash;90 mL/min), and normal renal function (CLcr 90\\u0026ndash;120 mL/min). Clinical practitioners are advised to assess CLcr and implement a dose-titration strategy, with continuous monitoring of both therapeutic response and adverse effects to facilitate individualized dosage optimization. Our PPK analysis further demonstrated significant concentration variability influenced by dosing frequency and timing, with divided sub-400 mg doses producing higher plasma levels than single-dose regimens and evening administration yielding elevated concentrations, underscoring the importance of comprehensive TDM interpretation that integrates clinical presentation (including symptom improvement and adverse effects), laboratory parameters (hepatic/renal function), and dosing regimen specifics for accurate pharmacokinetic assessment and optimal individualized dosing within the revised therapeutic range.\\u003c/p\\u003e\\u003cp\\u003eOur study has several limitations that should be acknowledged. First, as a PPK model-based investigation, this study inherently relies more heavily on prior knowledge and modeling assumptions than would real-world studies. Given the prevalence of polypharmacy in clinical practice, this methodological approach may introduce extrapolation uncertainty when generalizing findings to broader clinical settings. Therefore, results should be carefully interpreted within real-world clinical practice. Furthermore, in the future validation of the revised therapeutic range through concentration-response analyses and larger-scale prospective studies with fixed-dose designs is essential to confirm generalizability across complex medication regimens. Second, while the observed drug interactions were hypothesized to arise from transporter-mediated mechanisms based on pharmacokinetic data, the study did not quantify dose-dependent effects of concomitant medications on amisulpride disposition. Molecular-level validation (e.g., in vitro OCT/MATE inhibition assays) remains essential to confirm transporter involvement and elucidate definitive interaction mechanisms. Lastly, in antipsychotic pharmacology, drug transporters critically modulate not only pharmacokinetics but also pharmacodynamics. Specifically, blood-brain barrier transport governs cerebral drug distribution, directly influencing therapeutic efficacy and treatment resistance. Future studies should incorporate pharmacodynamic endpoints to dissect the mechanistic role of transporters in antipsychotic action.\\u003c/p\\u003e\"},{\"header\":\"Conclusion\",\"content\":\"\\u003cp\\u003eIn Conclusion, our study presents several novel and clinically significant findings regarding amisulpride pharmacokinetics. Our findings represent the first empirical evidence that comedications significantly alter amisulpride pharmacokinetics, among which metformin produces the most pronounced reduction in systemic clearance. This observation strongly suggests a potential transporter-mediated drug-drug interaction between metformin and amisulpride. Furthermore, our results provide robust validation for the newly revised therapeutic reference range for amisulpride (100\\u0026ndash;600 ng/mL) in Chinese schizophrenia patients, confirming its clinical appropriateness for this population. From a practical perspective, it is strongly recommended to evaluate creatinine clearance using the Cockcroft-Gault formula prior to amisulpride initiation to enable optimal renal function-based dose individualization. These findings collectively advance our understanding of amisulpride pharmacokinetics and provide actionable guidance for clinical practice.\\u003c/p\\u003e\"},{\"header\":\"Declarations\",\"content\":\"\\u003cp\\u003e\\u003cstrong\\u003eFunding\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003ePartial financial support was received from the clinical characteristic technology project of Guangzhou Research-oriented Hospital (2021-2025), Guangzhou Municipal Key Discipline in Medicine (2025-2027).\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eCompeting Interests\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe authors have no relevant financial or non-financial interests to disclose.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAuthor Contributions\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eAll authors contributed to the study conception. Tongying Gan responsible for the study design, data analysis, data interpretation, drafting and critical revision of the manuscript. Methodology and Visualization were performed by Tongying Gan and Chang Liu. Data collection and analysis were performed by Chang Liu and Bixuan Wu. The authors declare that all data were generated in-house and that no paper mill was used.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eEthical approval\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe study protocol was approved by the Institutional Review Board of the Affiliated Brain Hospital of Guangzhou Medical University (approval number: 2021027). As a retrospective analysis, the IRB granted a waiver of informed consent. All procedures adhered to the Declaration of Helsinki, with participant data de-identified to protect confidentiality.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eData availability statement\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe datasets generated and/or analyzed during the current study are not publicly available due to ethical restrictions protecting participant privacy and confidentiality.\\u0026nbsp;\\u003c/p\\u003e\"},{\"header\":\"References\",\"content\":\"\\u003col\\u003e\\n\\u003cli\\u003eAfzal M, Siddiqi N, Ahmad B, Afsheen N, Aslam F, Ali A, Ayesha R, Bryant M, Holt R, Khalid H, Ishaq K, Koly KN, Rajan S, Saba J, Tirbhowan N and Zavala GA (2021) Prevalence of Overweight and Obesity in People With Severe Mental Illness: Systematic Review and Meta-Analysis. Frontiers in endocrinology 12:769309. https://doi.org/10.3389/fendo.2021.769309\\u003c/li\\u003e\\n\\u003cli\\u003eAsif S, Pattnaik JI, Ahmed SS and Ravan JR (2024) Amisulpride as the antipsychotic of choice in severe psychotic disorder with comorbid impaired glucose tolerance. Industrial psychiatry journal 33:168-171. https://doi.org/10.4103/ipj.ipj_133_23\\u003c/li\\u003e\\n\\u003cli\\u003eBak M, Drukker M, Cortenraad S, Vandenberk E and Guloksuz S (2021) Antipsychotics result in more weight gain in antipsychotic naive patients than in patients after antipsychotic switch and weight gain is irrespective of psychiatric diagnosis: A meta-analysis. PloS one 16:e0244944. https://doi.org/10.1371/journal.pone.0244944\\u003c/li\\u003e\\n\\u003cli\\u003eBergemann N, Kopitz J, Kress KR and Frick A (2004) Plasma amisulpride levels in schizophrenia or schizoaffective disorder. 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International clinical psychopharmacology 37:255-262. https://doi.org/10.1097/yic.0000000000000420\\u003c/li\\u003e\\n\\u003cli\\u003eReeves S, Bertrand J, D\\u0026apos;Antonio F, McLachlan E, Nair A, Brownings S, Greaves S, Smith A, Taylor D and Howard R (2016) A population approach to characterise amisulpride pharmacokinetics in older people and Alzheimer\\u0026apos;s disease. Psychopharmacology 233:3371-3381. https://doi.org/10.1007/s00213-016-4379-6\\u003c/li\\u003e\\n\\u003cli\\u003eRosenzweig P, Canal M, Patat A, Bergougnan L, Zieleniuk I and Bianchetti G (2002) A review of the pharmacokinetics, tolerability and pharmacodynamics of amisulpride in healthy volunteers. 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Schizophrenia bulletin 47:948-958. https://doi.org/10.1093/schbul/sbaa191\\u003c/li\\u003e\\n\\u003cli\\u003eSolmi M, Seitidis G, Mavridis D, Correll CU, Dragioti E, Guimond S, Tuominen L, Darg\\u0026eacute;l A, Carvalho AF, Fornaro M, Maes M, Monaco F, Song M, Il Shin J and Cortese S (2023) Incidence, prevalence, and global burden of schizophrenia - data, with critical appraisal, from the Global Burden of Disease (GBD) 2019. 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Acta psychiatrica Scandinavica 120:416-428. https://doi.org/10.1111/j.1600-0447.2009.01429.x\\u003c/li\\u003e\\n\\u003cli\\u003eSrivastava A, Dada O, Qian J, Al-Chalabi N, Fatemi AB, Gerretsen P, Graff A and De Luca V (2021) Epigenetics of Schizophrenia. Psychiatry research 305:114218. https://doi.org/10.1016/j.psychres.2021.114218\\u003c/li\\u003e\\n\\u003cli\\u003eWang G, Liu X, Huo Q, Lin S, Qiu Y, Wang F and Wang W (2024) Inflammation affects the pharmacokinetics of risperidone: Does the dose need to be adjusted during the acute-phase reaction? Schizophrenia research 264:122-129. https://doi.org/10.1016/j.schres.2023.12.006\\u003c/li\\u003e\\n\\u003cli\\u003eWang M, Sun Y, Hu B, He Z, Chen S, Qi D, An H and Wei Y (2022) Organic Cation Transporters are Involved in Fluoxetine Transport Across the Blood-Brain Barrier In Vivo and In Vitro. 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CMAJ : Canadian Medical Association journal = journal de l\\u0026apos;Association medicale canadienne 192:E875-e891. https://doi.org/10.1503/cmaj.191707\\u003c/li\\u003e\\n\\u003cli\\u003eWu H, Siafis S, Hamza T, Schneider-Thoma J, Davis JM, Salanti G and Leucht S (2022) Antipsychotic-Induced Weight Gain: Dose-Response Meta-Analysis of Randomized Controlled Trials. Schizophrenia bulletin 48:643-654. https://doi.org/10.1093/schbul/sbac001\\u003c/li\\u003e\\n\\u003cli\\u003eXu Y, Lu J, Yao B, Zhang Y, Huang S, Liu J, Zhang Y, Guo Y and Wang X (2021) P-glycoprotein mediates the pharmacokinetic interaction of olanzapine with fluoxetine in rats. Toxicology and applied pharmacology 431:115735. https://doi.org/10.1016/j.taap.2021.115735\\u003c/li\\u003e\\n\\u003cli\\u003eYan D, Ju G, Liu X, Shao Q, Zhang Y, Wang N and Yan K (2024) External Validation of the Population Pharmacokinetic Models of Amisulpride and Remedial Strategies for Delayed or Missed Doses. Drug design, development and therapy 18:6345-6358. https://doi.org/10.2147/dddt.S469149\\u003c/li\\u003e\\n\\u003cli\\u003eYang S, Wang H, Zheng GF and Wang Y (2023) Age, Sex, and Comedication Effects on the Steady-State Plasma Concentrations of Amisulpride in Chinese Patients with Schizophrenia. Therapeutic drug monitoring 45:676-682. https://doi.org/10.1097/ftd.0000000000001089\\u003c/li\\u003e\\n\\u003cli\\u003eYounes NF, El Assasy AEI and Makhlouf AIA (2021) Microenvironmental pH-modified Amisulpride-Labrasol matrix tablets: development, optimization and in vivo pharmacokinetic study. Drug delivery and translational research 11:103-117. https://doi.org/10.1007/s13346-019-00706-2\\u003c/li\\u003e\\n\\u003cli\\u003eYu O, Lu M, Lai TKY, Hahn M, Agarwal SM, O\\u0026apos;Donoghue B, Ebdrup BH and Siskind D (2024) Metformin co-commencement at time of antipsychotic initiation for attenuation of weight gain: a systematic review and meta-analysis. Therapeutic advances in psychopharmacology 14:20451253241255476. https://doi.org/10.1177/20451253241255476\\u003c/li\\u003e\\n\\u003cli\\u003eZhang L, Liu M, Qin W, Shi D, Mao J and Li Z (2023) Modeling the protein binding non-linearity in population pharmacokinetic model of valproic acid in children with epilepsy: a systematic evaluation study. Frontiers in pharmacology 14:1228641. https://doi.org/10.3389/fphar.2023.1228641\\u003c/li\\u003e\\n\\u003cli\\u003eZhang Y, Zhou T, Duan J, Xiao Z, Li G and Xu F (2013) Inhibition of P-glycoprotein and glutathione S-transferase-pi mediated resistance by fluoxetine in MCF-7/ADM cells. Biomedicine \\u0026amp; pharmacotherapy = Biomedecine \\u0026amp; pharmacotherapie 67:757-762. https://doi.org/10.1016/j.biopha.2013.04.012\\u003c/li\\u003e\\n\\u003cli\\u003eZhou Y, Li H, Xu P, Sun L, Wang Q, Lu Q, Yuan H and Liu Y (2018) Paliperidone, a relatively novel atypical antipsychotic drug, is a substrate for breast cancer resistance protein. Experimental and therapeutic medicine 16:5410-5416. https://doi.org/10.3892/etm.2018.6847\\u003c/li\\u003e\\n\\u003cli\\u003eZhu C, Li R, Ju M, Xiao X, Yuan TF, Jin Z and Zhao J (2022) Metformin in the Treatment of Amisulpride-Induced Hyperprolactinemia: A Clinical Trial. Frontiers in molecular neuroscience 15:892477. https://doi.org/10.3389/fnmol.2022.892477\\u003c/li\\u003e\\n\\u003cli\\u003eZucker I and Prendergast BJ (2020) Sex differences in pharmacokinetics predict adverse drug reactions in women. Biology of sex differences 11:32. https://doi.org/10.1186/s13293-020-00308-5\\u003c/li\\u003e\\n\\u003c/ol\\u003e\"}],\"fulltextSource\":\"\",\"fullText\":\"\",\"funders\":[],\"hasAdminPriorityOnWorkflow\":false,\"hasManuscriptDocX\":true,\"hasOptedInToPreprint\":true,\"hasPassedJournalQc\":\"\",\"hasAnyPriority\":true,\"hideJournal\":true,\"highlight\":\"\",\"institution\":\"\",\"isAcceptedByJournal\":false,\"isAuthorSuppliedPdf\":false,\"isDeskRejected\":\"\",\"isHiddenFromSearch\":false,\"isInQc\":false,\"isInWorkflow\":true,\"isPdf\":false,\"isPdfUpToDate\":true,\"isWithdrawnOrRetracted\":false,\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"researchsquare\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":true,\"externalIdentity\":\"\",\"sideBox\":\"\",\"snPcode\":\"\",\"submissionUrl\":\"/submission\",\"title\":\"Research Square\",\"twitterHandle\":\"researchsquare\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"\",\"reportingPortfolio\":\"\",\"inReviewEnabled\":false,\"inReviewRevisionsEnabled\":true},\"keywords\":\"Amisulpride, Therapeutic drug monitoring, Population Pharmacokinetics, Individualized drug therapy, Renal clearance, Drug-Drug Interactions\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-7289163/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-7289163/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003ch2\\u003eObjective\\u003c/h2\\u003e\\u003cp\\u003ePatients with schizophrenia often require antipsychotic combinations, among which amisulpride is widely used due to its predominant renal excretion and low dependence on CYP450 enzymes. However, the impact of concomitant medications on the pharmacokinetics of amisulpride remains poorly characterized. The objective of this study was to establish a population pharmacokinetic model to quantitatively evaluate the effects of concomitant medications on amisulpride clearance. Additionally, we aimed to use simulation to recommend effective dosing regimens based on the newly revised therapeutic reference range (100\\u0026ndash;600 ng/mL).\\u003c/p\\u003e\\u003ch2\\u003eMethods\\u003c/h2\\u003e\\u003cp\\u003eA total of 386 therapeutic drug monitoring samples of amisulpride from 194 hospitalized patients were utilized to develop a PPK model, for which a one-compartment model with first-order absorption was developed using nonlinear mixed-effects modeling. The model systematically evaluated demographics, creatinine clearance, and co-medications as covariates. After model evaluation using bootstrap, goodness-of-fit plots, and normalized prediction distribution error (NPDE), simulations were conducted to assess drug-drug interactions, and to optimize dosing across renal function strata.\\u003c/p\\u003e\\u003ch2\\u003eResults\\u003c/h2\\u003e\\u003cp\\u003eCLcr and metformin coadministration were ultimately identified as significant covariates influencing amisulpride apparent clearance (CL/F). CL/F decreased by 16 L/h per 10 mL/min reduction in CLcr, while metformin inhibited CL/F by 23%. Metformin coadministration increases amisulpride exposure by \\u0026ge;\\u0026thinsp;45% across a wide range of renal function. The revised therapeutic range (100\\u0026ndash;600 ng/mL) was suitable for most clinical scenarios, with 58.3% of simulated concentrations within this window in patients with normal renal function (CLcr 90\\u0026ndash;120 mL/min). Renal function-based effective dosing recommendations were as follows: renal impairment (CLcr 30\\u0026ndash;60 mL/min): 200\\u0026ndash;600 mg/day; mild impairment (60\\u0026ndash;90 mL/min): 200\\u0026ndash;800 mg/day; normal function (90\\u0026ndash;120 mL/min): 300\\u0026ndash;800 mg/day; mild hyperfiltration (120\\u0026ndash;150 mL/min): 300\\u0026ndash;1000 mg/day; hyperfiltration (150\\u0026ndash;180 mL/min): 400\\u0026ndash;1000 mg/day.\\u003c/p\\u003e\\u003ch2\\u003eConclusion\\u003c/h2\\u003e\\u003cp\\u003eThis study provides the first clinical evidence of significant amisulpride-metformin interactions, warranting caution during concomitant use. The Cockcroft-Gault formula is optimal for renal function assessment to optimize amisulpride dosing. Individualized regimens stratified by CLcr and adherence to the 100\\u0026ndash;600 ng/mL therapeutic range can optimize treatment efficacy and safety in Chinese patients with schizophrenia.\\u003c/p\\u003e\",\"manuscriptTitle\":\"Population Pharmacokinetics of Amisulpride in Chinese Schizophrenia Patients: Quantifying Drug-Drug Interactions and Renal Function-Guided Dosing Individualization\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2025-08-27 08:09:31\",\"doi\":\"10.21203/rs.3.rs-7289163/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"researchsquare\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":true,\"externalIdentity\":\"\",\"sideBox\":\"\",\"snPcode\":\"\",\"submissionUrl\":\"/submission\",\"title\":\"Research Square\",\"twitterHandle\":\"researchsquare\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"\",\"reportingPortfolio\":\"\",\"inReviewEnabled\":false,\"inReviewRevisionsEnabled\":true}}],\"origin\":\"\",\"ownerIdentity\":\"9d367bb2-b474-4e96-b210-54e2fa2a0d8e\",\"owner\":[],\"postedDate\":\"August 27th, 2025\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"posted\",\"subjectAreas\":[],\"tags\":[],\"updatedAt\":\"2025-10-07T20:23:35+00:00\",\"versionOfRecord\":[],\"versionCreatedAt\":\"2025-08-27 08:09:31\",\"video\":\"\",\"vorDoi\":\"\",\"vorDoiUrl\":\"\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-7289163\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-7289163\",\"identity\":\"rs-7289163\",\"version\":[\"v1\"]},\"buildId\":\"8U1c8b4HqxoKbykW_rLl7\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}