Abstract：
Ziprasidone is widely used in the treatment of psychiatric disorders. Despite its prevalence, there is a notable lack of population pharmacokinetics (PPK) studies on ziprasidone in serum, both domestically and internationally. This study aimed to comprehensively investigate the various factors influencing the PPK characteristics of Ziprasidone, thereby providing a scientific basis for personalized treatment strategies in clinical settings. This is a retrospective study. A non-linear mixed-effects modeling method was used for data analysis, with the ziprasidone PPK model established using the Phoenix NLME 8.1 software. Model evaluation employed goodness-of-fit plots, visual predictive checks, and Bootstrap methods to ensure reliability and accuracy. To further validate the model\'s applicability, data from an additional 30 patients meeting the same inclusion criteria but not included in the final model were collected for external validation. Simulations were performed to explore the personalized dosage regimens. This retrospective analysis collected 547 drug concentration data points from 185 psychiatric disorder patients, along with related medical records. The data included detailed demographic information (such as age, gender, weight), dosing regimens, laboratory test results, and concomitant medication details. In the final model, Ka was fixed at 0.5 h-1 based on literature, and the population typical values for ziprasidone clearance (CL) and volume of distribution (V) were 18.74 L/h and 110.24 L, respectively. Co-administration of lorazepam and valproic acid significantly influenced the clearance of ziprasidone. Moreover, the model evaluation indicated good stability and predictive accuracy. A simple to use dosage regimen table was derived based on the results of simulations. This study successfully established and validated a PPK model for ziprasidone in Chinese patients with psychiatric disorders. The model provides a scientific reference for individualized dosing of ziprasidone and holds the potential to optimize treatment strategies, thereby enhancing therapeutic efficacy and safety.
摘要：
齐拉西酮广泛用于治疗精神疾病。尽管流行,血清中齐拉西酮的群体药代动力学（PPK）研究明显缺乏，国内和国际。本研究旨在全面调查影响齐拉西酮PPK特性的各种因素,从而为临床上的个性化治疗策略提供科学依据。这是一项回顾性研究。采用非线性混合效应建模方法进行数据分析，使用PhoenixNLME8.1软件建立齐拉西酮PPK模型。模型评估采用拟合优度图，视觉预测检查，和Bootstrap方法，以确保可靠性和准确性。为了进一步验证模型的适用性，收集另外30例符合相同纳入标准但未纳入最终模型的患者的数据进行外部验证.进行模拟以探索个性化剂量方案。这项回顾性分析收集了185名精神疾病患者的547个药物浓度数据点，还有相关的医疗记录.数据包括详细的人口统计信息(如年龄、性别,weight),给药方案，实验室测试结果，以及伴随用药的细节。在最终模型中，根据文献，Ka固定为0.5h-1，齐拉西酮清除率（CL）和分布体积（V）的种群典型值分别为18.74L/h和110.24L，分别。劳拉西泮和丙戊酸的共同给药显着影响齐拉西酮的清除率。此外，模型评价具有良好的稳定性和预测准确性。基于模拟结果得出了一个简单易用的剂量方案表。本研究成功建立并验证了中国精神疾病患者齐拉西酮的PPK模型。该模型为齐拉西酮的个体化给药提供了科学参考，并具有优化治疗策略的潜力。从而提高疗效和安全性。
