摘要
目的 模拟分析10种抗精神病药物氟哌啶醇(haloperidol)、舒必利(sulpiride)、美哌隆(melperone)、氯氮平(clozapine)、喹硫平(quetiapine)、利培酮(risperidone)、齐拉西酮(ziprasidone)、瑞莫必利(remoxipride)、氨磺必利(amisulpride)、雷氯必利(raclopride)口服给药后在人体内对多巴胺D;受体(DRD;)占有的时间过程。方法 通过对10种抗精神病药物的口服给药和静脉给药的药动学数据模拟计算获取建模的药动学(PK)参数;通过已发表的文献数据计算获取10种抗精神病药物的结合动力学(BK)参数和细胞内DRD;受体合成动力学(TK)参数;基于获取的PK、BK、TK参数建立10种抗精神病药物的DRD;受体占有率数学计算模型(PK-BK-TK模型)。结果 已上市的9种(不包含雷氯必利)抗精神病药物在临床推荐剂量下对DRD;的最大受体占有率均在65%以上,预测的DRD;受体占有率曲线与其临床药效持续时间有良好的一致性;雷氯必利的合理给药剂量为2 mg。结论 利用PK-BK-TK数学模型能准确预测抗精神病药物口服后在人体内对DRD;受体的占有过程。该模型可为评估化合物在体内拮抗DRD;受体的活性与潜在毒性提供一种新的研究思路和方法。
Objective To simulate the time course of occupancy rates of 10 antipsychotic drugs including haloperidol, sulpiride,melperone, clozapine, quetiapine, risperidone, ziprasidone, remoxipride, amisulpride and raclopride on the dopamine D;receptors(DRD;) in vivo. Methods The modeled pharmacokinetic parameters(PK parameters) of 10 antipsychotic drugs were obtained by simulating and calculating the pharmacokinetic data of the oral and intravenous administration;The binding kinetic parameters(BK parameters) and the intracellular DRD;receptor synthesising kinetic parameters(TK parameters) of 10 antipsychotic drugs were obtained based on published literature data. Based on the acquired PK, BK and TK parameters we can establish a mathematical calculation model(PK-BK-TK model) for receptor occupancy of 10 antipsychotic drugs. Results The maximum receptor occupancy rates of all the nine antipsychotic drugs at the clinically recommended doses were above 65%, and the predicted curves of receptor occupancy rate in vivo were consistent with their duration of clinical efficacy. The reasonable dose of raclopride was 2 mg.Conclusion The PK-BK-TK mathematical model can effectively evaluate the receptor occupation process in vivo after oral antipsychotic drugs, which provides a new research idea and method for assessing the activity and potential toxicity of compounds antagonize DRD;receptors in vivo.
作者
森慕黎
刘洋
刘憬曈
潘福璐
江晓泉
杨文宁
李雪岩
陈洪娇
刘伟
祁东盈
汪国鹏
潘艳丽
SEN Muli;LIU Yang;LIU Jingtong;PAN Fulu;JIANG Xiaoquan;YANG Wenning;LI Xueyan;CHEN Hongjiao;LIU Wei;QI Dongying;WANG Guopeng;PAN Yanli(School of Chinese Materia Medica,Beijing University of Chinese Medicine,Beijing 102488,China;Zhongcai Health(Beijing)Biological Technology Development Co.,Ltd.,Beijing 101503,China;Institute of Information on Traditional Chinese Medicine,China Academy of Chinese Medical Sciences,Beijing 100700,China)
出处
《药物评价研究》
CAS
2022年第4期633-641,共9页
Drug Evaluation Research
基金
国家自然科学基金资助项目(81973295)。