Front. Psychiatry Frontiers in Psychiatry Front. Psychiatry 1664-0640 Frontiers Media S.A. 10.3389/fpsyt.2025.1664678 Psychiatry Original Research Population pharmacokinetics modelling to predict DDI from zopiclone on clozapine in schizophrenia patients Han Huan-Huan 1 † Zhang Yue 2 † Wang Jie 2 † Tian Xue 2 † Li Ye 2 He Su-Mei 3 * Zhang Cun 4 * Chen Xiao 5 * Wang Dong-Dong 2 * 1 Department of Pharmacy, The Affiliated Lianyungang Hospital of Xuzhou Medical University, Lianyungang, Jiangsu, China 2 Jiangsu Key Laboratory of New Drug Research and Clinical Pharmacy & School of Pharmacy, Xuzhou Medical University, Xuzhou, Jiangsu, China 3 Department of Pharmacy, Suzhou Research Center of Medical School, Suzhou Hospital, Affiliated Hospital of Medical School, Nanjing University, Suzhou, Jiangsu, China 4 Department of Pharmacy, Xuzhou Oriental Hospital Affiliated to Xuzhou Medical University, Xuzhou, Jiangsu, China 5 School of Nursing, Xuzhou Medical University, Xuzhou, Jiangsu, China

Edited by: Massimo Tusconi, University of Cagliari, Italy

Reviewed by: Marcin Siwek, Medical College, Krakow, Poland

Amelia Ramón López, Miguel Hernández University of Elche, Spain

*Correspondence: Su-Mei He, hehe8204@163.com; Cun Zhang, 17512656365@163.com; Xiao Chen, chenxiao112733@163.com; Dong-Dong Wang, 13852029591@163.com

†These authors have contributed equally to this work and share first authorship

 22 09 2025 2025 16 1664678 12 07 2025 01 09 2025 Copyright © 2025 Han, Zhang, Wang, Tian, Li, He, Zhang, Chen and Wang. 2025 Han, Zhang, Wang, Tian, Li, He, Zhang, Chen and Wang

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 Introduction

Clozapine, as a core drug for the treatment of schizophrenia, is widely used in the drug treatment of schizophrenia patients. However, when multiple drugs are used in combination, it is not clear whether there are drug-drug interactions (DDI) of clozapine in patients with schizophrenia. This study aims to use population pharmacokinetics (PPK) modelling to predict DDI and individualized therapy of clozapine in schizophrenia patients.

 Methods

We collected 81 patients with schizophrenia and included their physiological data, biochemical data, treatment plans and information on combined medication during the clinical treatment process. Next, PPK modelling was used to analyze drugs with potential DDI when clozapine was used in schizophrenia patients, and dosage adjustments were recommended.

 Results

Final analysis revealed that weight and coadministration of zopiclone affected clozapine clearance, and there was DDI with clozapine when zopiclone was used concurrently in schizophrenia patients. Further, for schizophrenia patients without zopiclone, 10 mg/kg/day, 9 mg/kg/day, 8 mg/kg/day and 7 mg/kg/day clozapine were recommended for 40–50 kg, 50–67 kg, 67–88 kg, and 88–120 kg patients, respectively. For schizophrenia patients with zopiclone, 6 mg/kg/day and 5 mg/kg/day clozapine were recommended for 40–70 kg and 70–120 kg patients, respectively. This study was the first to systematically analyze DDI when clozapine was used in schizophrenia patients and found DDI when zopiclone and clozapine were taken concurrently.

 Conclusion

When zopiclone was taken concurrently, clozapine dosage need to be reduced. Based on this, schizophrenia patients individualized dosage adjustment was recommended.

 population pharmacokinetics modelling drug-drug interactions individualized therapy clozapine schizophrenia section-in-acceptance Schizophrenia Introduction

Schizophrenia is a psychotic disorder characterized by a combination of positive and negative symptoms, along with cognitive impairment, affective symptoms, and behavioral disturbances (1). In terms of epidemiology, schizophrenia has a lifetime prevalence of 1% (2, 3), and the incidence is high among young and middle-aged people, the sex ratio is close, the genetic factors are significant, and the risk of developing schizophrenia is significantly increased when the immediate family member is schizophrenia.

The mainstay of schizophrenia treatment is the use of atypical antipsychotic medications, and non-pharmacological treatment serves as a complementary therapy, where clozapine, as the core drug for treating schizophrenia, occupies an important position in the drug treatment of schizophrenia patients, especially suitable for refractory cases (4, 5). Clozapine is not only particularly useful in treatment-resistant schizophrenia as monotherapy, but it is also used in combination therapy, such as combination with aripiprazole, lurasidone, or cariprazine (6–13).

It is important that latest research indicating that the use of clozapine at recommended doses does not guarantee achieving therapeutic concentrations of clozapine and women and nonsmokers were at the highest risk of having toxic levels of clozapine (14). Drug-drug interactions (DDI) can significantly affect drug concentrations, and the occurrence of DDI is often accompanied during the treatment of mental disorders (15, 16). For example, zopiclone is metabolized by the CYP3A4 enzyme (17, 18), which may compete CYP3A4 metabolic enzymes with clozapine, influence clozapine clearance in schizophrenia patients. In the routine clinical practice, the dosage information for both clozapine and zopiclone are mainly based on the instructions.

Population pharmacokinetics (PPK), by integrating sparse clinical data with covariate modelling, addresses the limitations of traditional pharmacokinetics in real-world complex populations. Its core value lies in quantifying sources of variation (such as differences in liver and kidney functions), providing feasible pharmacokinetic research methods for special populations (children, the elderly), and identifying key influencing factors (especially DDI), supporting precision medical decision-making (19). At present, PPK has been widely used in the analysis of potential DDI in clinical practice. For example, Fujita et al. reported PPK Analysis of DDI between perampanel and carbamazepine using enzyme induction model in epileptic patients (20). Cleary et al. reported estimation of FMO3 ontogeny by mechanistic PPK modelling of risdiplam and its impact on DDI in children (21). Li et al. reported PPK of ruxolitinib in children with hemophagocytic lymphohistiocytosis: focus on the DDI (22). Courlet et al. reported PPK modelling to quantify the magnitude of DDI between amlodipine and antiretroviral drugs (23). Barcelo et al. reported PPK of dolutegravir: influence of DDI in a real-life setting (24).

Therefore, this study aims to using PPK modelling to predict DDI of clozapine in schizophrenia patients, and to recommend individualized dosage adjustments for these patients.

 Methods Data collection

This study was approved by the Research Ethics Committee of Xuzhou Oriental Hospital Affiliated to Xuzhou Medical University, which collected schizophrenia patients at Xuzhou Oriental Hospital Affiliated to Xuzhou Medical University from December 2023 to November 2024, including their physiological data, biochemical data, treatment plans and information on combined medication during the clinical treatment process, where the requirement for written informed consent could be waived since the data were collected retrospectively without patient identifiers. The dosage information for clozapine was mainly based on the instruction. The analytical technique used for the determination of clozapine was homogeneous enzyme immunoassay. The sample extraction times for plasma concentrations were before the next administration, which was the value of the trough concentration.

 Model building

PPK model of clozapine in schizophrenia patients was set up, where CL/F, V/F, and Ka [fixed at 1.3/h (25, 26)] were the main pharmacokinetic parameters. In terms of individual variation, we chose to express it using Equation 1:

Z i = TV ( Z ) × exp  ( η i )

The abbreviation Zi denoted individual parameter, TV(Z) denoted typical individual parameter and ηi denoted symmetrical distribution.

In terms of random residual variation, we chose to express it using Equation 2:

Y i =   X i +   X i ∗ ϵ 1

The abbreviation Yi denoted observed concentration, Xi denoted individual predicted concentration, and ϵ1 denoted symmetrical distribution.

In terms of relationship between parameter and weight, we chose to express it using Equation 3:

U i = U std × ( V i / V std ) W

The abbreviation Ui denoted individual parameter, Vi denoted individual weight, Vstd denoted standard weight of 70 kg, and Ustd denoted typical individual parameter. W denoted allometric coefficients: 0.75 and 1 for CL/F and V/F, respectively (27).

In terms of continuous or categorical covariate parameter, we chose to express it using Equation 4 or 5, respectively:

R i = TV ( R ) × ( S i / S m ) Q R i = TV ( R ) × ( 1 + Q × S i )

The abbreviation Ri denoted individual parameter, TV(R) denoted typical individual parameter, Q denoted the parameter needed to be fitted, Si denoted covariate of the i-th individual, and Sm denoted population median for the covariate. To construct covariate model, we used two-step method.

 Model evaluation

We used visual diagram and bootstrap methods to evaluate the final clozapine PPK model of schizophrenia patients.

 Dosage simulation

We used Monte Carlo simulation to simulate the clozapine concentrations of schizophrenia patients under different simulated clozapine dosages, including 1 mg/kg/day, 2 mg/kg/day, 3 mg/kg/day, 4 mg/kg/day, 5 mg/kg/day, 6 mg/kg/day, 7 mg/kg/day, 8 mg/kg/day, 9 mg/kg/day, 10 mg/kg/day. Additionally, the simulated patients were divided into two parts: (a) schizophrenia patients not taking zopiclone, and (b) schizophrenia patients taking zopiclone, where simulated weight groups contained 40 kg, 60 kg, 80 kg, 100 kg, 120 kg. We simulated each scenario 1000 times, and the therapeutic range was 350–800 ng/ml along with 1000 ng/ml toxicity threshold (28–31).

 Results Patient’s data

We collected 81 patients with schizophrenia. 37 schizophrenia patients were men and 44 schizophrenia patients were women. The age ranges were from 20.67 to 73.11 years old, and weight ranges were from 38.00 to 120.00 kg. Tables 1 and 2 denoted demographic data of schizophrenia patients and drug combination, respectively.

Schizophrenia patients (n = 81).

Characteristic Mean ± SD Median (range)
Gender (men/women) 37/44 /
Age (years) 49.46 ± 11.15 50.67 (20.67-73.11)
Weight (kg) 70.49 ± 13.53 71.00 (38.00-120.00)
Albumin (g/L) 39.46 ± 3.21 39.40 (27.90-47.90)
Globulin (g/L) 26.28 ± 3.23 26.30 (19.60-35.10)
Alanine transaminase (IU/L) 25.47 ± 21.61 20.00 (4.00-162.00)
Aspartate transaminase (IU/L) 21.46 ± 12.58 19.00 (9.00-119.00)
Creatinine (μmol/L) 61.14 ± 11.37 60.00 (32.00-96.00)
Urea (mmol/L) 4.54 ± 1.39 4.33 (1.82-11.71)
Total protein (g/L) 65.74 ± 4.71 66.30 (51.90-76.60)
Total cholesterol (mmol/L) 4.08 ± 0.85 4.04 (2.27-6.51)
Triglyceride (mmol/L) 1.58 ± 0.81 1.45 (0.44-5.11)
Direct bilirubin (μmol/L) 2.33 ± 1.18 2.00 (0.50-8.30)
Total bilibrubin (μmol/L) 7.35 ± 3.27 6.60 (2.70-21.30)
Hematocrit (%) 37.98 ± 3.52 37.40 (31.40-49.20)
Hemoglobin (g/L) 124.93 ± 14.53 124.00 (21.00-166.00)
Mean corpuscular hemoglobin (pg) 30.17 ± 1.45 30.40 (24.50-34.00)
Mean corpuscular hemoglobin concentration (g/L) 329.93 ± 7.89 330.00 (312.00-351.00)

Drug combination (n = 81).

Drug Category N Drug Category N
Acarbose capsules 0 75 Metoprolol succinate sustained-release tablets 0 78
1 6 1 3
Alprazolam tablets 0 76 Nifedipine sustained-release tablets 0 78
1 5 1 3
Amisulpride tablets 0 71 Paliperidone sustained-release tablets 0 77
1 10 1 4
Amlodipine besylate tablets 0 78 Perphenazine tablets 0 79
1 3 1 2
Aripiprazole tablets 0 60 Phenhyxol hydrochloride tablets 0 62
1 21 1 19
Atorvastatin calcium tablets 0 75 Propranolol hydrochloride tablets 0 63
1 6 1 18
Bezafibrate 0 79 Risperidone oral liquid 0 79
1 2 1 2
Clonazepam tablets 0 72 Risperidone tablets 0 53
1 9 1 28
Enteric-coated aspirin tablets 0 77 Sertraline hydrochloride tablets 0 77
1 4 1 4
Glimepiride 0 79 Sodium valproate sustained-release tablets 0 59
1 2 1 22
Lamotrigine tablets 0 79 Sulpiride tablets 0 80
1 2 1 1
Lithium carbonate extended-release tablets 0 72 Valsartan capsules 0 76
1 9 1 5
Lorazepam tablets 0 70 Ziprasidone hydrochloride capsules 0 75
1 11 1 6
Metformin hydrochloride tablets 0 68 Zopiclone tablets 0 73
1 13 1 8

Category, 0: without drug, 1: with drug; N: number of patients.

 PPK modelling

Equations 6 and 7 were the final clozapine PPK model of schizophrenia patients:

CL / F = 29.6 × ( weight / 70 ) 0.75 × ( 1 − 0.254 × ZOP ) V / F = 308 × ( weight / 70 )

ZOP denoted zopiclone and when schizophrenia patients took ZOP, ZOP denoted 1, otherwise ZOP denoted 0.

 Model evaluation

Figures 1 , 2 and Table 3 denoted visual diagram, individual plots and bootstrap, showing clozapine PPK model of schizophrenia patients was credible. When schizophrenia patients taking zopiclone, the clozapine clearance of schizophrenia patients was reduced by 25.4%, which was shown in Figure 3 .

Model evaluation. (A) Observations vs. population predictions. (B) Observations vs. individual predictions. (C) Conditional weighted residuals (WRES) vs. population predictions. (D) Conditional WRES vs. time. (E) Observations/Predictions vs. time. (F) Density vs. weighted residuals. (G) Quantilies of weighted residuals vs. quantilies of normal. (H) Visual predictive check (VPC) of model.

Panel of graphs illustrating model predictions and residuals analysis. Graph A compares population predictions with observations. Graph B shows individual predictions versus observations. Graph C plots conditional weighted residuals (WRES) against population predictions. Graph D charts WRES over time. Panel E depicts observations, individual, and population predictions over time. Graph F presents a histogram of weighted residuals density. Graph G is a quantile-quantile plot of weighted residuals. Graph H shows observations over time with confidence intervals. These visualizations assess the model fit and residual distribution.

Individual plots.

Grid of graphs showing observations versus predictions over time for multiple IDs. Each plot compares the observed values (DV) with independent predictions (IPRED) and actual predictions (PRED). Data patterns vary across IDs. Overall, the graphs assess prediction accuracy over time.

Parameter estimates of clozapine final model and bootstrap validation in schizophrenia patients.

Parameter Estimate SE (%) Bootstrap Bias (%)
Median 95% Confidence interval SE(%)
CL/F (L/h) 29.6 6.5 29.4 [26.1, 33.9] 6.5 -0.68
V/F (L) 308 14.4 309 [230, 421] 14.4 0.32
Ka (h-1) 1.3 (fixed) – – – – –
θZOP -0.254 30.8 -0.241 [-0.408, -0.009] 30.4 -5.12
ωCL/F 0.348 11.3 0.342 [0.264, 0.422] 11.0 -1.72
σ1 0.257 6.6 0.254 [0.220, 0.289] 6.6 -1.17

95% confidential interval was displayed as the 2.5th, 97.5th percentile of bootstrap estimates. CL/F, apparent oral clearance (L/h); V/F, apparent volume of distribution (L); Ka, absorption rate constant (h-1); θZOP was the coefficient of zopiclone; ωCL/F, inter-individual variability of CL/F; σ1, residual variability, proportional error; Bias, prediction error, Bias = (Median-Estimate)/Estimate×100%.

Clozapine clearance rates of schizophrenia patients. (A) Schizophrenia patients not taking zopiclone. (B) Schizophrenia patients taking zopiclone.

Line graph showing CL/F (L/h/kg) against weight (kg). Two curves represent variables “a” in blue and “b” in red. Dashed lines indicate weights of forty kilograms and one hundred twenty kilograms. Dosage recommendation and safety evaluation

Figures 4 and 5 denoted simulated clozapine concentrations of schizophrenia patients not taking zopiclone and simulated clozapine concentrations of schizophrenia patients taking zopiclone, respectively. Figure 6 denoted the probabilities to attain the target clozapine concentrations of schizophrenia patients. For schizophrenia patients without zopiclone, 10 mg/kg/day, 9 mg/kg/day, 8 mg/kg/day and 7 mg/kg/day clozapine were recommended for 40–50 kg, 50–67 kg, 67–88 kg, and 88–120 kg patients, respectively. For schizophrenia patients with zopiclone, 6 mg/kg/day and 5 mg/kg/day clozapine were recommended for 40–70 kg and 70–120 kg patients, respectively. Figure 7 denoted the probabilities to exceed the upper limit of safe concentrations of schizophrenia patients. The detailed dosage recommendation and safety evaluation were shown in Table 4 .

Simulated clozapine concentrations of schizophrenia patients not taking zopiclone. (A) 40 kg schizophrenia patients. (B) 60 kg schizophrenia patients. (C) 80 kg schizophrenia patients. (D) 100 kg schizophrenia patients. (E) 120 kg schizophrenia patients.

Scatter plots labeled A to E showing concentrations (ng/ml) on the vertical axis and dosage groups (1 to 10 mg/kg) using different colors. Horizontal dashed lines at 500 and 1000 ng/ml indicate threshold levels. Data points vary across dosage groups, showing a trend of increasing concentration with higher dosages.

Simulated clozapine concentrations of schizophrenia patients taking zopiclone. (A) 40 kg schizophrenia patients. (B) 60 kg schizophrenia patients. (C) 80 kg schizophrenia patients. (D) 100 kg schizophrenia patients. (E) 120 kg schizophrenia patients.

Dot plots labeled A to E display concentration levels in nanograms per milliliter, ranging from zero to three thousand. Each graph shows distinct colored clusters representing different dosages, ranging from one to ten milligrams per kilogram. Horizontal lines indicate benchmark levels at five hundred and one thousand nanograms per milliliter.

The probabilities to attain the target clozapine concentrations of schizophrenia patients. (A) Schizophrenia patients not taking zopiclone. (B) Schizophrenia patients taking zopiclone.

Three line graphs show the probability percentages versus weight in kg, with different doses from 1 mg/kg to 10 mg/kg. Vertical dashed lines mark weight thresholds at 50, 67, 70, and 88 kg. Graph A highlights weights at 50, 67, and 88 kg, while Graph B focuses on 70 kg. Different colored lines and markers distinguish the doses and weight markers, showing varying probability trends for each dose across weights.

The probabilities to exceed the upper limit of safe concentrations of schizophrenia patients. (A) Schizophrenia patients not taking zopiclone. (B) Schizophrenia patients taking zopiclone.

Line graphs A and B display the probability percentage versus weight in kilograms. Different colored lines represent various dosages from 1 mg/kg to 10 mg/kg. Dashed lines indicate weight thresholds (50 kg, 67 kg, 88 kg, 120 kg) and corresponding probability percentages (13%, 12.5%, 11.2%, 9.8%). Graph A peaks at 35%, while graph B peaks at 70%.

Initial dosage recommendation of clozapine in schizophrenia patients.

Without zopiclone With zopiclone
Body weight (kg) Dosage (mg/kg/day) Probability to exceed the upper limit of the safe concentration (%) Body weight (kg) Dosage (mg/kg/day) Probability to exceed the upper limit of the safe concentration (%)
[40-50) 10 < 13 [40-70) 6 < 11.1
[50-67) 9 < 12.5 [70-120] 5 < 9
[67-88) 8 < 11.2
[88-120] 7 < 9.8
Discussion

Clozapine is a classic old drug that was synthesized over 65 years ago, which is the only psychotherapeutic drug been used to treat treatment-resistant schizophrenia. up to now, the actual mechanism why clozapine has a remarkable therapeutic effect in the treatment of treatment-resistant schizophrenia (32). Regarding when to initiate clozapine therapy for patients with schizophrenia remains contentious. According to current guidelines, clozapine is recommended for initiation when patients show no response after two adequate courses of treatment with standard antipsychotic drugs (32). However, recent research indicates that in patients with first-episode schizophrenia experiencing their first psychotic relapse, neither continuing the same non-clozapine oral antipsychotic nor switching to another non-clozapine oral antipsychotic demonstrated beneficial effects for relapse prevention (33). These findings, combined with existing knowledge regarding clozapine’s association with reduced mortality, challenge current treatment guidelines recommending clozapine as a third-line therapy (33). That is to say, the earlier clozapine is used among patients with schizophrenia, the more benefits they may gain.

However, patients with schizophrenia often have more concomitant medications. In the clinical application process, combined medication often needs to be viewed dialectically. It is a double-edged sword. Reasonable application can significantly enhance the therapeutic effect, but unreasonable use may cause serious risks. Especially when combined medication causes bad DDI, it may pose a serious threat to the efficacy of the drug and even the health of the patient. When the harmful drug significantly increases or decreases the clearance rate of the victimized drug, and thereby significantly reduces or increases the blood concentration of the target drug, it is necessary to focus on pharmaceutical care, and even a new administration plan for the target drug needs to be reformulated.

These DDI clinical cases include the use of immunosuppressants after organ transplantation (34–38), clinical use of hypoglycemic drugs (39–41), individualized administration of anti-hepatitis virus drugs (42, 43), clinical use of anticoagulant drugs (44–46), individualized drug administration for patients with dementia (47), clinical medication for patients with asthma (48), individualized drug administration for patients with liver and kidney damage (49, 50), rational drug use for patients with heart failure (51), individualized drug administration for patients with atrial fibrillation (52), clinical medication for patients with HIV (53–56), precise medication for special pediatric populations (57, 58), individualized drug administration for the special population of pregnant women (59, 60), use of antibiotics for critically infected patients in the ICU (61), clinical medication for COVID-19 patients (62), clinical use of anti-tumor drugs in cancer patients (63–71), clinical use of pain treatment drugs for cancer patients (72), which have been widely carried out.

Based on this, in the current research, we used PPK modelling to predict DDI of clozapine in schizophrenia patients. This study included the drugs used in combination in the schizophrenia population, including acarbose capsules, alprazolam tablets, amisulpride tablets, amlodipine besylate tablets, aripiprazole tablets, atorvastatin calcium tablets, bezafibrate, clonazepam tablets, enteric-coated aspirin tablets, glimepiride, lamotrigine tablets, lithium carbonate extended-release tablets, lorazepam tablets, metformin hydrochloride tablets, metoprolol succinate sustained-release tablets, nifedipine sustained-release tablets, paliperidone sustained-release tablets, perphenazine tablets, phenhyxol hydrochloride tablets, propranolol hydrochloride tablets, risperidone oral liquid, risperidone tablets, sertraline hydrochloride tablets, sodium valproate sustained-release tablets, sulpiride tablets, valsartan capsules, ziprasidone hydrochloride capsules, zopiclone tablets.

Finally, the PPK model found that weight and coadministration of zopiclone affected the clearance rate of clozapine, and there was DDI with clozapine when zopiclone was used concurrently in schizophrenia patients. When schizophrenia patients took zopiclone simultaneously, the clozapine clearance rate of the patients decreased by 25.4%. This is mainly because clozapine is mainly metabolized in the liver through CYP3A4 and CYP1A2 (4, 73, 74), and zopiclone is also metabolized by the CYP3A4 enzyme (17, 18), which may compete CYP3A4 metabolic enzymes with clozapine, influence clozapine clearance in schizophrenia patients. Furthermore, we optimized the optimal dosage adjustment of clozapine in schizophrenia patients with or without zopiclone through Monte Carlo simulation. For schizophrenia patients without zopiclone, 10 mg/kg/day, 9 mg/kg/day, 8 mg/kg/day and 7 mg/kg/day clozapine were recommended for 40–50 kg, 50–67 kg, 67–88 kg, and 88–120 kg patients, respectively. For schizophrenia patients with zopiclone, 6 mg/kg/day and 5 mg/kg/day clozapine were recommended for 40–70 kg and 70–120 kg patients, respectively.

 Conclusion

This study was the first to systematically analyze DDI when clozapine was used in schizophrenia patients and found DDI when zopiclone and clozapine were taken concurrently. Furthermore, when zopiclone was taken concurrently, the requirement of clozapine dosage needed to reduce. Based on this, schizophrenia patients individualized dosage adjustment was recommended.

 Data availability statement

The original contributions presented in the study are included in the article/supplementary material. Further inquiries can be directed to the corresponding author/s.

 Ethics statement

The studies involving humans were approved by the Research Ethics Committee of Xuzhou Oriental Hospital Affiliated to Xuzhou Medical University. The studies were conducted in accordance with the local legislation and institutional requirements. The ethics committee/institutional review board waived the requirement of written informed consent for participation from the participants or the participants’ legal guardians/next of kin because the data were collected retrospectively without patient identifiers.

 Author contributions

H-HH: Data curation, Formal Analysis, Methodology, Project administration, Software, Supervision, Visualization, Writing – original draft. YZ: Data curation, Formal Analysis, Investigation, Methodology, Software, Writing – original draft. JW: Investigation, Methodology, Software, Writing – original draft. XT: Data curation, Formal Analysis, Methodology, Software, Writing – original draft. YL: Data curation, Formal Analysis, Investigation, Writing – original draft. S-MH: Conceptualization, Data curation, Formal Analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing – review & editing. CZ: Conceptualization, Data curation, Formal Analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing – review & editing. XC: Conceptualization, Data curation, Formal Analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing – review & editing. D-DW: Conceptualization, Data curation, Formal Analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing.

 Funding

The author(s) declare financial support was received for the research and/or publication of this article. This study was supported by the grant from Jiangsu Key Laboratory of New Drug Research and Clinical Pharmacy, The Xuzhou Special Fund for Promoting Scientific and Technological Innovation (No. KC23217, No. KC23254), The Medical Research Project of Jiangsu Provincial Health Commission (No. Z2023010), Jiangsu Province Education Science Planning Project (No. C/2022/01/36), Xuzhou Medical University Labor Education Special Project (No. X1d202209), Jiangsu Province Higher Education Informatization Research Topic (No. 2023JSETKT136), Xuzhou Medical University Research Topic of Higher Education Teaching Reform (No. Xjyzrd202304). Xuzhou Medical University Research Project on Reform of Postgraduate Education and Teaching (No. XYJGKT202506). Xuzhou Medical University Teaching Academic Research Topics (No. 2024ZDKT02-Y03). Suzhou Applied Basic Research Science and Technology Innovation Project (No. SYWD2024258).

 Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

 Generative AI statement

The author(s) declare that no Generative AI was used in the creation of this manuscript.

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 References McCutcheon RA Reis Marques T Howes OD . Schizophrenia-an overview. JAMA Psychiatry. (2020) 77:201–10. doi: 10.1001/jamapsychiatry.2019.3360, PMID: 31664453 Saha S Chant D Welham J McGrath J . A systematic review of the prevalence of schizophrenia. PloS Med. (2005) 2:e141. doi: 10.1371/journal.pmed.0020141, PMID: 15916472 Yoo S Montazeri A McNulty H Potvin Kent M Bennett D Little J . Global evaluation of the impact of food fortification with folic acid on rates of schizophrenia. Schizophr Res. (2025) 280:39–47. doi: 10.1016/j.schres.2025.04.007, PMID: 40220606 Wysokinski A Dreczka J . Clozapine Toxicity Predictor: Deep neural network model predicting clozapine toxicity and its therapeutic dose range. Psychiatry Res. (2024) 342:116256. doi: 10.1016/j.psychres.2024.116256, PMID: 39522262 Tiihonen J Tanskanen A Mittendorfer-Rutz E Howes OD Correll CU Siskind D . Effectiveness of clozapine augmentation with specific doses of other antipsychotics in schizophrenia: a meta-analysis from two nationwide cohorts. World Psychiatry. (2025) 24:250–9. doi: 10.1002/wps.21316, PMID: 40371869 Sepede G Di Iorio G Spano MC Lorusso M Sarchione F Santacroce R . A case of resistant schizophrenia successfully treated with clozapine/long-acting injectable aripiprazole combination. Clin Neuropharmacol. (2016) 39:322–4. doi: 10.1097/WNF.0000000000000191, PMID: 27764052 Huh L Lee BJ . Efficacy of low-dose aripiprazole to treat clozapine-associated tardive dystonia in a patient with schizophrenia. Turk Psikiyatri Derg. (2017) 28:208–11., PMID: 28936820 Balcioglu YH Gokcay H Yesilkaya UH . One plus one sometimes equals more than two: long-acting injectable aripiprazole adjunction in clozapine-resistant schizophrenia. Clin Neuropharmacol. (2020) 43:166–8. doi: 10.1097/WNF.0000000000000404, PMID: 32947430 Olivola M Arienti V Bassetti N Giovanna G Brondino N . Lurasidone augmentation of clozapine in refractory schizophrenia: A case series. J Clin Psychopharmacol. (2023) 43:157–60. doi: 10.1097/JCP.0000000000001662, PMID: 36825872 Siwek M Chrobak AA Gorostowicz A Krol P Dudek D . Lurasidone augmentation of clozapine in schizophrenia-retrospective chart review. Brain Sci. (2023) 13:445. doi: 10.3390/brainsci13030445, PMID: 36979255 Wang YH Liu CY Her YN Wu HH Yao CY Chen TY . Lurasidone successfully reversed clozapine-induced type 2 diabetes mellitus and hypertriglyceridemia in a patient with schizophrenia. Am J Ther. (2023) 30:e490–1. doi: 10.1097/MJT.0000000000001566, PMID: 37713707 Dmuhovskis A Taube M . Cariprazine and clozapine combination for the treatment of psychosis in a young, female patient with schizophrenia: a case report. Front Psychiatry. (2024) 15:1452980. doi: 10.3389/fpsyt.2024.1452980, PMID: 39234624 Pappa S Csehi R Caldwell-Dunn E Dombi ZB Hjorth S . Cariprazine & Clozapine: A systematic review of a promising antipsychotic combination for treatment-resistant schizophrenia. Int J Neuropsychopharmacol. (2025) 28:pyaf053. doi: 10.1093/ijnp/pyaf053, PMID: 40679420 Mach A Wnorowska A Siwek M Wojnar M Radziwon-Zaleska M . Clinical and pharmacological factors influencing serum clozapine and norclozapine levels. Front Pharmacol. (2024) 15:1356813. doi: 10.3389/fphar.2024.1356813, PMID: 38601469 Vermeulen A Piotrovsky V Ludwig EA . Population pharmacokinetics of risperidone and 9-hydroxyrisperidone in patients with acute episodes associated with bipolar I disorder. J Pharmacokinet Pharmacodyn. (2007) 34:183–206. doi: 10.1007/s10928-006-9040-2, PMID: 17136449 Zhang C Jiang L Hu K Chen L Zhang YJ Shi HZ . Effects of aripiprazole on olanzapine population pharmacokinetics and initial dosage optimization in schizophrenia patients. Neuropsychiatr Dis Treat. (2024) 20:479–90. doi: 10.2147/NDT.S455183, PMID: 38469209 Alderman CP Gebauer MG Gilbert AL Condon JT . Possible interaction of zopiclone and nefazodone. Ann Pharmacother. (2001) 35:1378–80. doi: 10.1345/aph.1A074, PMID: 11724087 Tornio A Neuvonen PJ Backman JT . The CYP2C8 inhibitor gemfibrozil does not increase the plasma concentrations of zopiclone. Eur J Clin Pharmacol. (2006) 62:645–51. doi: 10.1007/s00228-006-0155-6, PMID: 16832679 Hu K Fu M Huang X He S Jiao Z Wang D . Editorial: Model-informed drug development and precision dosing in clinical pharmacology practice. Front Pharmacol. (2023) 14:1224980. doi: 10.3389/fphar.2023.1224980, PMID: 37456757 Fujita Y Murai M Muraki S Suetsugu K Tsuchiya Y Hirota T . Population pharmacokinetic analysis of drug-drug interactions between perampanel and carbamazepine using enzyme induction model in epileptic patients. Ther Drug Monit. (2023) 45:653–9. doi: 10.1097/FTD.0000000000001055, PMID: 36645709 Cleary Y Kletzl H Grimsey P Heinig K Ogungbenro K Silber Baumann HE . Estimation of FMO3 ontogeny by mechanistic population pharmacokinetic modelling of risdiplam and its impact on drug-drug interactions in children. Clin Pharmacokinet. (2023) 62:891–904. doi: 10.1007/s40262-023-01241-7, PMID: 37148485 Li Z Zhang Q He H Sun N Zhang R Yang CQ . Population pharmacokinetics of ruxolitinib in children with hemophagocytic lymphohistiocytosis: focus on the drug-drug interactions. Cancer Chemother Pharmacol. (2023) 91:121–32. doi: 10.1007/s00280-022-04494-4, PMID: 36510033 Courlet P Guidi M Alves Saldanha S Cavassini M Stoeckle M Buclin T . Population pharmacokinetic modelling to quantify the magnitude of drug-drug interactions between amlodipine and antiretroviral drugs. Eur J Clin Pharmacol. (2021) 77:979–87. doi: 10.1007/s00228-020-03060-2, PMID: 33452585 Barcelo C Aouri M Courlet P Guidi M Braun DL Gunthard HF . Population pharmacokinetics of dolutegravir: influence of drug-drug interactions in a real-life setting. J Antimicrob Chemother. (2019) 74:2690–7. doi: 10.1093/jac/dkz217, PMID: 31119275 Li LJ Shang DW Li WB Guo W Wang XP Ren YP . Population pharmacokinetics of clozapine and its primary metabolite norclozapine in Chinese patients with schizophrenia. Acta Pharmacol Sin. (2012) 33:1409–16. doi: 10.1038/aps.2012.71, PMID: 22820910 Shang DW Li LJ Wang XP Wen YG Ren YP Guo W . Population pharmacokinetic/pharmacodynamic model of clozapine for characterizing the relationship between accumulated exposure and PANSS scores in patients with schizophrenia. Ther Drug Monit. (2014) 36:378–86. doi: 10.1097/FTD.0000000000000014, PMID: 24342896 Anderson BJ Holford NH . Mechanism-based concepts of size and maturity in pharmacokinetics. Annu Rev Pharmacol Toxicol. (2008) 48:303–32. doi: 10.1146/annurev.pharmtox.48.113006.094708, PMID: 17914927 Liu HC Chang WH Wei FC Lin SK Lin SK Jann MW . Monitoring of plasma clozapine levels and its metabolites in refractory schizophrenic patients. Ther Drug Monit. (1996) 18:200–7. doi: 10.1097/00007691-199604000-00015, PMID: 8721285 Schulte P . What is an adequate trial with clozapine?: therapeutic drug monitoring and time to response in treatment-refractory schizophrenia. Clin Pharmacokinet. (2003) 42:607–18. doi: 10.2165/00003088-200342070-00001, PMID: 12844323 Stark A Scott J . A review of the use of clozapine levels to guide treatment and determine cause of death. Aust N Z J Psychiatry. (2012) 46:816–25. doi: 10.1177/0004867412438871, PMID: 22327098 Wills KH Behan SJ Nance MJ Dawson JL Polasek TM Hopkins AM . Combining therapeutic drug monitoring and pharmacokinetic modelling deconvolutes physiological and environmental sources of variability in clozapine exposure. Pharmaceutics. (2021) 14:47. doi: 10.3390/pharmaceutics14010047, PMID: 35056943 Correll CU . Clozapine: past, present and future. World Psychiatry. (2025) 24:153–4. doi: 10.1002/wps.21335, PMID: 40371784 Taipale H Tanskanen A Howes O Correll CU Kane JM Tiihonen J . Comparative effectiveness of antipsychotic treatment strategies for relapse prevention in first-episode schizophrenia in Finland: a population-based cohort study. Lancet Psychiatry. (2025) 12:122–30. doi: 10.1016/S2215-0366(24)00366-3, PMID: 39848730 Maruyama T Kasai H Fukaya Y Shiokawa M Kimura T Hamada Y . Drug-drug interactions between letermovir and tacrolimus in Japanese renal transplant recipients simulated using a physiologically based pharmacokinetic model. Front Microbiol. (2024) 15:1480874. doi: 10.3389/fmicb.2024.1480874, PMID: 39444685 Coppinger C Anderson PL . Considerations for drug-drug interactions between long-acting antiretrovirals and immunosuppressants for solid organ transplantation. Expert Opin Drug Metab Toxicol. (2025) 21:343–6. doi: 10.1080/17425255.2024.2448970, PMID: 39757464 Pagan Santini RA Nair V Berlinrut I Nair G Bhaskaran M . Drug-drug interactions leading to tacrolimus toxicity in a renal transplant patient with COVID-19: the role of paxlovid and the mitigating use of phenytoin. Cureus. (2025) 17:e80902. doi: 10.7759/cureus.80902, PMID: 40255720 Wang P Lu J Yang J . Physiologically based pharmacokinetic modeling supports investigation of potential drug-drug interactions in the pre- and early post-hematopoietic stem cell transplantation stages. Front Pharmacol. (2025) 16:1578643. doi: 10.3389/fphar.2025.1578643, PMID: 40385481 Zhao YC Zhang YK Gao W Liu HY Xiao CL Hou JJ . A preliminary exploration of liver microsomes and PBPK to uncover the impact of CYP3A4/5 and CYP2C19 on tacrolimus and voriconazole drug-drug interactions. Sci Rep. (2025) 15:6389. doi: 10.1038/s41598-025-91356-7, PMID: 39984708 Kha QH Nguyen NTK Le NQK Kang JH . Development and validation of a machine learning model for predicting drug-drug interactions with oral diabetes medications. Methods. (2024) 232:81–8. doi: 10.1016/j.ymeth.2024.10.012, PMID: 39489198 Dwivedi J Kaushal S Wal P Darshan JC Sharma A Nathiya D . A data mining approach on polypharmacy and drug-drug interactions of common diabetes medications. Curr Drug Metab. (2025). doi: 10.2174/0113892002358291250401190533, PMID: 40248924 Min JS Jo SJ Lee S Kim DY Kim DH Lee CB . A comprehensive review on the pharmacokinetics and drug-drug interactions of approved GLP-1 receptor agonists and a dual GLP-1/GIP receptor agonist. Drug Des Devel Ther. (2025) 19:3509–37. doi: 10.2147/DDDT.S506957, PMID: 40330819 Billi M Soloperto S Bonora S D’Avolio A De Nicolo A . Clinical pharmacology of bulevirtide: focus on known and potential drug-drug interactions. Pharmaceutics. (2025) 17:250. doi: 10.3390/pharmaceutics17020250, PMID: 40006617 Nakamoto D Piao Y Mizutani H Wakabayashi R Otokita S Stead A . Prescription patterns of comedications associated with drug-drug interactions risk in HCV-infected patients undergoing direct-acting antiviral treatment: an analysis of an administrative claims database in Japan. J Pharm Health Care Sci. (2025) 11:33. doi: 10.1186/s40780-025-00442-5, PMID: 40251686 Li M Xiao J Yu T Huang L Cai R Yu H . Analysis of hemorrhagic drug-drug interactions between P-gp inhibitors and direct oral anticoagulants from the FDA Adverse Event Reporting System. Expert Opin Drug Saf. (2024) 23:1453–61. doi: 10.1080/14740338.2024.2376693, PMID: 38962834 Grymonprez M Capiau A Steurbaut S Boussery K Mehuys E Somers A . Pharmacodynamic drug-drug interactions and bleeding outcomes in patients with atrial fibrillation using non-vitamin K antagonist oral anticoagulants: a nationwide cohort study. Cardiovasc Drugs Ther. (2025) 39:133–43. doi: 10.1007/s10557-023-07521-5, PMID: 37930588 Tombolini E Squizzato A Podda GM Aghemo A Ferri N Segato S . Drug-drug interactions between DAAs and anticoagulants or antiplatelets: A position paper of the Italian anticoagulation clinics. Liver Int. (2025) 45:e16177. doi: 10.1111/liv.16177, PMID: 39736105 Munoz-Contreras MC Cerda B Lopez-Roman FJ Segarra I . Patients with dementia: prevalence and type of drug-drug interactions. Front Pharmacol. (2024) 15:1472932. doi: 10.3389/fphar.2024.1472932, PMID: 39529888 Momcilovic M Turcic P Butkovic F Grle SP . A retrospective study on potential drug–drug interactions in patients with severe asthma receiving biological therapy: a single-center experience. BMC Pulm Med. (2025) 25:23. doi: 10.1186/s12890-025-03495-2, PMID: 39819388 Chen S Shen C Tian Y Peng Y Hu J Xie H . Physiologically based pharmacokinetic modeling and simulation of topiramate in populations with renal and hepatic impairment and considerations for drug-drug interactions. CPT Pharmacometrics Syst Pharmacol. (2025) 14:510–22. doi: 10.1002/psp4.13292, PMID: 39676284 Ren J Shan D Yue G He Q . Optimizing zanubrutinib dosing in patients: A PBPK-BO model approach to drug-drug interactions and patients with hepatic impairment. J Clin Pharmacol. (2025). doi: 10.1002/jcph.70042, PMID: 40304686 Hvarchanova N Radeva-Ilieva M Georgiev KD . Using physiologically based pharmacokinetic models for assessing pharmacokinetic drug-drug interactions in patients with chronic heart failure taking narrow therapeutic window drugs. Pharm (Basel). (2025) 18:477. doi: 10.3390/ph18040477, PMID: 40283914 Bischof T Nagele F Kalkofen MM Blechschmidt MEO Domanovits H Zeitlinger M . Drug-drug-interactions in patients with atrial fibrillation admitted to the emergency department. Front Pharmacol. (2024) 15:1432713. doi: 10.3389/fphar.2024.1432713, PMID: 39508037 De Bellis E Donnarumma D Zarrella A Mazzeo SM Pagano A Manzo V . Drug-drug interactions between HIV antivirals and concomitant drugs in HIV patients: what we know and what we need to know. Pharmaceutics. (2024) 17:31. doi: 10.3390/pharmaceutics17010031, PMID: 39861680 Senneker T . Drug-drug interactions between gender-affirming hormone therapy and antiretrovirals for treatment/prevention of HIV. Br J Clin Pharmacol. (2024) 90:2366–82. doi: 10.1111/bcp.16097, PMID: 38866600 Branch C Parson-Martinez J Cory TJ . Drug-drug interactions in HIV-infected patients receiving chemotherapy. Expert Opin Drug Metab Toxicol. (2025) 21:15–27. doi: 10.1080/17425255.2024.2408004, PMID: 39305240 Calza L Giglia M Zuppiroli A Cretella S Vitale S Appolloni L . Decrease in drug-drug interactions between antiretroviral drugs and concomitant therapies in older people living with HIV-1 switching to bictegravir/emtricitabine/tenofovir alafenamide. New Microbiol. (2025) 48:70–7., PMID: 40314683 Adamiszak A Drobinska J Niewiadomska-Wojnalowicz I Derwich K Grzeskowiak E Bienert A . Potential drug-drug interactions analysis in Polish pediatric hemato-oncologic unit, including acute lymphoblastic leukemia patients. Pharmacol Rep. (2025) 77:751–60. doi: 10.1007/s43440-025-00719-4, PMID: 40167877 Soliman A Rodriguez-Vera L Alarcia-Lacalle A Pippa LF Subhani S Lukacova V . Leveraging omeprazole PBPK/PD modeling to inform drug-drug interactions and specific recommendations for pediatric labeling. Pharmaceutics. (2025) 17:373. doi: 10.3390/pharmaceutics17030373, PMID: 40143036 Atoyebi S Montanha MC Nakijoba R Orrell C Mugerwa H Siccardi M . Physiologically based pharmacokinetic modeling of drug-drug interactions between ritonavir-boosted atazanavir and rifampicin in pregnancy. CPT Pharmacometrics Syst Pharmacol. (2024) 13:1967–77. doi: 10.1002/psp4.13268, PMID: 39517110 Kiiza D Rostami-Hochaghan D Alhassan Y Seden K Reynolds H Kaboggoza JP . Clinical, pharmacological, and qualitative characterization of drug-drug interactions in pregnant women initiating HIV therapy in Sub-Saharan Africa. J Antimicrob Chemother. (2024) 79:2334–42. doi: 10.1093/jac/dkae232, PMID: 38997229 Xu S Song Z Bai J Wang J . Prevalence and clinical significance of potential drug-drug interactions of antimicrobials in Intensive Care Unit patients: a retrospective study. BMC Pharmacol Toxicol. (2025) 26:104. doi: 10.1186/s40360-025-00925-z, PMID: 40369697 Alfehaid LS Farah S Omer A Weber BN Alkhezi O Tawfik YMK . Drug-drug interactions and the clinical tolerability of colchicine among patients with COVID-19: A secondary analysis of the COLCORONA randomized clinical trial. JAMA Netw Open. (2024) 7:e2431309. doi: 10.1001/jamanetworkopen.2024.31309, PMID: 39240567 Bolek H Yazgan SC Yekeduz E Kaymakcalan MD McKay RR Gillessen S . Androgen receptor pathway inhibitors and drug-drug interactions in prostate cancer. ESMO Open. (2024) 9:103736. doi: 10.1016/j.esmoop.2024.103736, PMID: 39426080 Bu F Cho YS He Q Wang X Howlader S Kim DH . Prediction of pharmacokinetic drug-drug interactions involving anlotinib as a victim by using physiologically based pharmacokinetic modeling. Drug Des Devel Ther. (2024) 18:4585–600. doi: 10.2147/DDDT.S480402, PMID: 39429896 Kapagan T Bulut N Erdem GU . Polypharmacy and drug-drug interactions in metastatic breast cancer patients receiving cyclin-dependent kinase (CDK) 4/6 inhibitors. J Oncol Pharm Pract. (2024) 30:1403–10. doi: 10.1177/10781552231218959, PMID: 38073115 Zarrabi S Hosseini E Sadeghi K Vaezi M Shahrami B . Assessment of drug-drug interactions among patients with hematologic Malignancy: A clinical pharmacist-led study. J Oncol Pharm Pract. (2024), 10781552241281664. doi: 10.1177/10781552241281664, PMID: 39223926 Agostinetto E Pfeiler G Hlauschek D Mayer EL Lambertini M de Azambuja E . Drug-drug interactions between palbociclib and proton pump inhibitors in early breast cancer: an exploratory analysis of PALLAS (ABCSG-42/AFT-05/BIG-14-13/PrE0109). ESMO Open. (2025) 10:104096. doi: 10.1016/j.esmoop.2024.104096, PMID: 39754986 Alkathiri MA Bamogaddam RF Alhabshi HA AlAjmi MN Alashgaai AA Assiri GA . Potential drug-drug interactions among geriatric oncology patients: a retrospective study in Saudi Arabia. BMC Geriatr. (2025) 25:300. doi: 10.1186/s12877-025-05965-y, PMID: 40312689 Patil PH Desai MP Baburaj G Rao M Kunhikatta V Udupa K . A single-centre retrospective evaluation of potential drug-drug interactions in breast cancer patients undergoing CDK 4/6 inhibitors chemotherapy. J Oncol Pharm Pract. (2025) 10781552251314811. doi: 10.1177/10781552251314811, PMID: 39846287 Ratan C Rajeev M Krishnan K Jayamohanan H Kartha N Vijayan M . Assessment of potential drug-drug interactions in hospitalized cancer patients. J Oncol Pharm Pract. (2025) 31:256–65. doi: 10.1177/10781552241235573, PMID: 38404003 Sahin TK Kavgaci G Guven DC Aksoy S . Drug-Drug interactions and special considerations in breast cancer patients treated with CDK4/6 inhibitors: A comprehensive review. Cancer Treat Rev. (2025) 137:102956. doi: 10.1016/j.ctrv.2025.102956, PMID: 40367730 Kotlinska-Lemieszek A Klepstad P Haugen DF . Clinically significant drug-drug interactions involving opioid analgesics used for pain treatment in patients with cancer: update of a systematic review. Expert Opin Drug Metab Toxicol. (2025) 21:703–15. doi: 10.1080/17425255.2025.2491743, PMID: 40227246 Kuhn AK Determan ML Wright JA Matey E Leung JG . The potential influence of estrogen-containing oral contraception on clozapine metabolism in a patient with known pharmacogenomic status. Ment Health Clin. (2024) 14:220–3. doi: 10.9740/mhc.2024.06.220, PMID: 38835816 Rafizadeh R Sooch A Risi A Bihelek N Kanegawa K Barr AM . Impact of patient-specific factors on clozapine metabolism in individuals with treatment-resistant schizophrenia or schizoaffective disorder. J Psychopharmacol. (2024) 38:526–31. doi: 10.1177/02698811241241394, PMID: 38520287