基于药物临床试验计算机模拟技术(CTS)的二房室模型分析研究
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摘要
药物临床研究的计算机模拟(CTS)是将计算机模拟技术和药物的临床研究相结合,通过对生物系统的病理生理学和治疗药理学建立数学模型,以仿真的方法模拟试验设计、人体行为、疾病进程和药物行为,来模拟虚拟研究对象的反应。作为药物研发的一部分,药物临床研究的计算机模拟(CTS)有效地解决了新药研发周期长、研制成本高等诸多制约新药研发的现实问题。
     在CTS的多种模型中,作为基本的输入/输出模型,药代动力学和药效动力学模型对分析药物和人体的相互作用具有重要意义。本文基于CTS技术,通过仿真分析二房室药代动力学模型和Sigmoid型Emax药效动力学模型中参数对中央室血药浓度和药效的影响,并以模糊集合和模糊逻辑理论为工具,考察模型参数的不确定性对血药浓度的影响。具体工作主要从以下几个方面展开:1、通过仿真考察右美沙芬口服给药二房室药代动力学模型中,中央室血药浓度随吸收速率常数和消除速率常数的变化规律;2、通过仿真分析右美沙芬Sigmoid型Emax药效动力学模型中药效随剂量和各参数的变化规律;3、考虑模型参数的不确定性,应用模糊集合理论以模糊数表示参数,预测血药浓度,通过计算模糊贴近度,分析口服给药二房室模型中各参数对中央室血药浓度的影响;4、以期望血药浓度为参考,设计两变量模糊调节器,通过调整参数来调整血药浓度,考察两变量模糊调节器在调整模型参数时对血药浓度的影响。
     通过仿真可以清楚的观察血药浓度和药效与各参数的关系,仿真结果表明不同个体具有不同的药代动力学和药效动力学特征,与临床试验结果具有较好的一致性,对于不同个体的临床给药方案具有重要参考作用,模糊理论作为分析参数不确定性的有力工具,为预测和调节血药浓度提供了科学操作依据。
Clinical trial simulation combines the simulation and clinical trials to generate a response for a virtual subject. It can simulate the trial design, human behavior, disease progess and drug behavior by modeling physiopathology and therapy pharmacology of the biosystem. As one part of the modern drug development, CTS effectively solved many practical problems that restrict drug development such as the long term and high cost.
     Among various models of CTS, as the basic input/output model, pharmacokinetics and pharmacodynamics models are of great importance to analyze the interaction between drug and body.
     Based on CTS, the influence of parameters on the plasma drug concentration of central compartment and drug effect was analyzed in the two-comparment pharmacokinetic model and Sigmoid Emax pharmacodynamic model by simulating in this thesis. The influence of uncertainty of parameters on the plasma drug concentration was investigated using fuzzy sets and fuzzy logic theory. The main parts of the thesis are as follow:1. The realationship among absorption rate constant, elimination rate constant and the plasma drug concentration characteristics of central compartment in the Dextromethorphan two-comparment pharmacokinetic model with oral administration was investigated by simulating.2. The relationship among dose, parameters and the drug effect in the Dextromethorphan Sigmoid.Emax pharmacodynamic model was investigated by simulating.3. Considering the uncertainty of model parameters, the parameters were presented as fuzzy number and the plasma drug concentration was predicted based on the fuzzy sets theory. The influence of parameters on central compartment plasma drug concentration in the two-compartment model with oral administration was analyzed by calculating fuzzy neartude.4. The two-variable fuzzy adjuster was designed using expected plasma drug concentration as reference to adjust the central compartment plasma drug concentration by altering parameters. The influence of the two-variable fuzzy adjuster on the plasma drug concentration while adjusting model parameters was investigated.
     The relationship between drug effect and dose and parameters can be clearly observed via simulation.The results which were accordance with clinical trial demonstrated that every individual is of different PK and PD characteristics. It is of great importance to individualized dosage regimen. Fuzzy theory as a powerful tool analyzing the uncertainty of parameters can offer scientific grounds to predict and adjust plasma drug concentration.
引文
[1]郑丹,朱玲,施心陵,等.药物临床试验计算机仿真简介[J].中国临床药理学与治疗学,2007,12(6):705-709.
    [2]Heilman R D. Drug development history, "overview," and what are GCPs [J]. Quality Assur,1995,4:75-9.
    [3]Watling K J, Milius R A, Williams M. Recent advances in drug discovery [J]. RBI neurotransmissions. Newsletter for the Neuroscientist,1997,11:1-5.
    [4]Sheiner LB, Rubin DB. Intention-to-treat analysis and the goals of clinical trials [J]. Clinical Pharmacology & Therapeutics,1995,57:6-15.
    [5]DiMasi J A. A new look at United States drug development and approval times [J]. American Journal of Therapeutics,1996,3(9):647-657.
    [6]Peck C C. Drug development: improving the process [J]. Food & Drug LJ,1997, 52(2):163-167.
    [7]Leahy D E. Drug discovery information integration:virtual humans for pharmaco-kinetics [J]. Drug Discovery Today:Biosilico,2004,2(2):78-84.
    [8]Crawford L M. Speech before R&D Leaders'Forum [EB/OL]. http://www.fda. gov/oc/speeches/2004/leaders1005.html,2005-1-14.
    [9]Holford N H G, Kimko H C, Monteleone J P R, et al. Simulation of clinical trials [J]. Annual review of pharmacology and toxicology,2000,40(1):209-234.
    [10]Holford N H G, Hale M, Ko H C, et al. Simulation in drug development:good practices [J]. Draft Publication of the Center for Drug Development Science (CDDS). Draft version,1999,1:23.
    [11]Kimko H C, Dufful S B. Simulation for designing clinical trials.1st ed [M]. New York, USA:Marcel Dekker Inc,2003.
    [12]Sun H, Fadiran E O, Jones C D, et al. Population pharmacokinetics. A regulatory perspective [J]. Clinical pharmacokinetics,1999,37(1):41-58.
    [13]柳晓泉,陈渊成,郝琨,等.药动学一药效学结合模型的研究进展及在新药研发中的应用[J].中国药科大学学报,2007,38(6):481.
    [14]Reigner B G, Williams P E, Patel I H, et al. An evaluation of the integration of pharmacokinetic and pharmacodynamic principles in clinical drug development. Experience within Hoffmann La Roche [J]. Clinical pharmacokinetics,1997,33(2): 142.
    [15]Sun H, Fadiran E O, Jones C D, et al. Population pharmacokinetics:a regulatory perspective [J]. Clinical pharmacokinetics,1999,37(1):41-58.
    [16]Department of Health and Human Services, US Food and Drug Administration. Challenge and Opportunity on the Critical Path to New Products [S]. Rockville, MD,2004.
    [17]焦正,蒋新国,钟明康,等.药物临床研究的计算机模拟[J].中国新药与临床杂志,2005,24(6).
    [18]Holford N, Ma S C, Ploeger B A. Clinical trial simulation:a review [J]. Clinical Pharmacology & Therapeutics,2010,88(2):166-182.
    [19]Sheiner L B, Beal S L. Evaluation of methods for estimating population pharmacokinetic parameters. III. Monoexponential model:routine clinical pharmacokinetic data [J]. Journal of Pharmacokinetics and Pharmacodynamics, 1983,11(3):303-319.
    [20]Peck C C, Beal S L, Sheiner L B, et al. Extended least squares nonlinear regression:a possible solution to the "choice of weights" problem in analysis of individual pharmacokinetic data [J]. Journal of Pharmacokinetics and Pharmacodynamics,1984,12(5):545-558.
    [21]D'Argenio D Z. Optimal sampling times for pharmacokinetic experiments [J]. Journal of Pharmacokinetics and Pharmacodynamics,1981,9(6):739-756.
    [22]Sanathanan L P, Peck C C. The randomized concentration-controlled trial:an evaluation of its sample size efficiency [J]. Controlled clinical trials,1991,12(6): 780-794.
    [23]Sheiner L B, Beal S L, Sambol N C. Study designs for dose-ranging [J]. Clinical Pharmacology & Therapeutics,1989,46(1):63-77.
    [24]Sheiner L B, Hashimoto Y, Beal S L. A simulation study comparing designs for dose ranging [J]. Statistics in Medicine,2006,10(3):303-321.
    [25]Holford N H G. Simulation based evaluation of an enrichment trial design for Alzheimer's disease [J]. Clinical Pharmacology & Therapeutics,1998,63(2):200.
    [26]Holford N H G. Modelling therapeutic effects and disease progress. Modeling and Simulation of Clinical Trials in Drug Development and Regulation [C]. Washington, DC:Cent.Drug Dev. Sci., Georgetown Univ. Med. Cent.,1997: 61-62.
    [27]El-Tahtawy A A, Jackson A J, Ludden T M. Evaluation of bioequivalence of highly variable drugs using Monte Carlo simulations. I. Estimation of rate of absorption for single and multiple dose trials using Cmax [J]. Pharmaceutical research,1995,12(11):1634-1641.
    [28]Hale MD. Using population pharmacokinetics for planning a randomized concen-tration-controlled trial with a binary response. European Cooperation in the Field of Scientific and Technical Research [C]. Geneva, Switzerland: Eur. Comm.,1997: 227-35.
    [29]Hale M D, Nicholls A J, Bullingham R E S, et al. The pharmacokinetic-pharmacodynamic relationship for mycophenolate mofetil in renal transplantation [J]. Clinical Pharmacology & Therapeutics,1998,64(6):672-683.
    [30]Krall R L, Engleman K H, Ko H C, et al. Clinical trial modeling and simu-lation-work in progress [J]. Drug information journal,1998,32:971-976.
    [31]Gobburu J V S, Holford N H G, Ko H C, et al. Model optimization, via "lateral validation" for purposes of clinical trial simulation. Clinical Pharmacology & Therapeutics,1999,65(2):164.
    [32]黄继汉,黄晓晖,李禄金,等.新药临床试验的计算机模拟[J].中国临床药理学与治疗学,2010,15(6):691-699.
    [33]Veyrat-Follet C, Bruno R, Olivares R, et al. Clinical trial simulation of docetaxel in patients with cancer as a tool for dosage optimization [J]. Clinical Pharmacology & Therapeutics,2000,68(6):677-687.
    [34]Nestorov I, Graham G, Duffull S, et al. Modeling and simulation for clinical trial design involving a categorical response:a phase II case study with naratriptan [J]. Pharmaceutical research,2001,18(8):1210-1219.
    [35]Chabaud S, Girard P, Nony P, et al. Clinical trial simulation using therapeutic effect modeling:application to ivabradine efficacy in patients with angina pectoris [J]. Journal of pharmacokinetics and pharmacodynamics,2002,29(4):339-363.
    [36]Lockwood P A, Cook J A, Ewy W E, et al. The use of clinical trial simulation to support dose selection:application to development of a new treatment for chronic neuropathic pain [J]. Pharmaceutical research,2003,20(11):1752-1759.
    [37]Yim D S, Zhou H, Buckwalter M, et al. Population pharmacokinetic analysis and simulation of the time-concentration profile of etanercept in pediatric patients with juvenile rheumatoid arthritis [J]. Journal of clinical pharmacology,2005,45(3): 246-256.
    [38]Laer S, Elshoff J P, Meibohm B, et al. Development of a safe and effective pediatric dosing regimen for sotalol based on population pharmacokinetics and pharmacodynamics in children with supraventricular tachycardia[J]. Journal of the American College of Cardiology,2005,46(7):1322-1330.
    [39]Gruwez B, Dauphin A, Tod M. A mathematical model for paroxetine anti-depressant effect time course and its interaction with pindolol [J]. Journal of pharmacokinetics and pharmacodynamics,2005,32(5):663-683.
    [40]Lockwood P, Ewy W, Hermann D, et al. Application of clinical trial simulation to compare proof-of-concept study designs for drugs with a slow onset of effect; an example in Alzheimer's disease [J]. Pharmaceutical research,2006,23(9): 2050-2059.
    [41]Chan P L S, Nutt J G, Holford N H G. Levodopa slows progression of Parkinson's disease. External validation by clinical trial simulation [J]. Pharmaceutical research,2007,24(4):791-802.
    [42]Dickinson G L, Rezaee S, Proctor N J, et al. Incorporating in vitro information on drug metabolism into clinical trial simulations to assess the effect of CYP2D6 polymorphism on pharmacokinetics and pharmacodynamics:dextromethorphan as a model application [J]. The Journal of Clinical Pharmacology,2007,47(2): 175-186.
    [43]Gruwez B, Poirier M F, Dauphin A, et al. A kinetic-pharmacodynamic model for clinical trial simulation of antidepressant action:Application to clomipramine-lithium interaction [J]. Contemporary clinical trials,2007,28(3):276-287.
    [44]Kowalski K G, Olson S, Remmers A E, et al. Modeling and simulation to support dose selection and clinical development of SC-75416, a selective COX-2 inhibitor for the treatment of acute and chronic pain [J]. Clinical Pharmacology & Therapeutics,2007,83(6):857-866.
    [45]Putnam W S, Li J, Haggstrom J, et al. Use of quantitative pharmacology in the development of HAE1, a high-affinity anti-IgE monoclonal antibody [J]. The AAPS journal,2008,10(2):425-430.
    [46]Mouksassi M S, Marier J F, Cyran J, et al. Clinical trial simulations in pediatric patients using realistic covariates:application to teduglutide, a glucagon-like peptide-2 analog in neonates and infants with short-bowel syndrome [J]. Clinical Pharmacology & Therapeutics,2009,86(6):667-671.
    [47]De Ridder F. Predicting the outcome of phase III trials using phase II data:a case study of clinical trial simulation in late stage drug development [J]. Basic & clinical pharmacology & toxicology,2005,96(3):235-241.
    [48]Ozawa K, Minami H, Sato H. Clinical trial simulations for dosage optimization of docetaxel in patients with liver dysfunction, based on a log-binominal regression for febrile neutropenia [J]. Yakugaku Zasshi,2009,129(6):749-757.
    [49]http://www.page-meeting.org/
    [50]http://www.ecpag.org/
    [51]http://www.paganz.org/
    [52]http://holford.fmhs.auckland.ac.nz/
    [53]Maxwell C, Domenet J G, Joyce C R B. Instant experience in clinical trials:a novel aid to teaching by simulation [J]. The Journal of Clinical Pharmacology, 1971,11(5):323-331.
    [54]Tiefenbrunn A J, Graor R A, Robison A K, et al. Pharmacodynamics of tissue-type plasminogen activator characterized by computer-assisted simulation [J]. Circulation,1986,73(6):1291-1299.
    [55]Hale M D, Nicholls A J, Bullingham R E S, et al. The pharmacokinetic-pharmacodynamic relationship for mycophenolate mofetil in renal transplantation [J]. Clinical Pharmacology & Therapeutics,1998,64(6):672-683.
    [56]魏树礼.生物药剂学与药物动力学[M].北京:北京医科大学中国协和医科大学联合出版社,2001.
    [57]郭涛.新编药物动力学[M].北京:中国科学技术出版社,2005.
    [58]王广基.药物代谢动力学[M].北京:化学工业出版社,2005.
    [59]罗建平,张银娣.药代动力学药效学结合模型的研究进展[J].中国临床药理学杂志,2000,16(4):309-312.
    [60]Sheiner L B, Stanski D R, Vozeh S, et al. Simultaneous modeling of pharmaco-kinetics and pharmacodynamics:application to d-tubocurarine [J]. Clinical pharmacology and therapeutics,1979,25(3):358-371.
    [61]吴士力.通俗模糊数学与程序设计[M].北京:中国水利水电出版社,2008.
    [62]Zadeh L A. Fuzzy sets [J]. Information and Control,1965,8:338-353.
    [63]宋晓秋.模糊数学原理与方法[M].江苏:中国矿业大学出版社,2004.
    [64]曹炳元.应用模糊数学与系统[M].北京:科学出版社,2005.
    [65]李士勇.工程模糊数学及应用[M].黑龙江:哈尔滨工业大学出版社,2004.
    [66]汪培庄.模糊数学简介(Ⅰ)[J].数学的实践与认识,1980,2:45-59.
    [67]蔡自兴.智能控制[M].北京:电子工业出版社,2004.
    [68]Moghadamnia A A, Rostami-Hodjegan A, Abdul-Manap R, et al. Physiologically based modelling of inhibition of metabolism and assessment of the relative potency of drug and metabolite:dextromethorphan vs. dextrorphan using quinidine inhibition [J]. British journal of clinical pharmacology,2003,56(1): 57-67.
    [69]Nestorov I. Modelling and simulation of variability and uncertainty in toxico-kinetics and pharmacokinetics [J]. Toxicology letters,2001,120(1):411-420.
    [70]Nestorov I, Gueorguieva I, Jones H M, et al. Incorporating measures of variability and uncertainty into the prediction of in vivo hepatic clearance from in vitro data [J]. Drug metabolism and disposition,2002,30(3):276-282.
    [71]Gueorguieva Ⅱ, Nestorov I A, Rowland M. Fuzzy simulation of pharmacokinetic models:Case study of whole body physiologically based model of diazepam [J]. Journal of pharmacokinetics and pharmacodynamics,2004,31(3):185-213.
    [72]Gueorguieva I, Nestorov I A, Aarons L, et al. Uncertainty analysis in pharmaco-kinetics and pharmacodynamics:application to naratriptan [J]. Pharmaceutical research,2005,22(10):1614-1626.
    [73]Seng K Y, Vicini P, Nestorov I A. A fuzzy physiologically based pharmacokinetic modeling framework to predict drug disposition in humans [C]. 28th Annual International Conference of the IEEE Engineering in Medicine and Biology Society,2006. EMBS'06. IEEE,2006:5037-5040.
    [74]Seng K Y, Nestorov I, Vicini P. Physiologically based pharmacokinetic modeling of drug disposition in rat and human:a fuzzy arithmetic approach [J]. Pharmaceutical research,2008,25(8):1771-1781.
    [75]Seng K Y, Nestorov I, Vicini P. Simulating pharmacokinetic and pharmaco-dynamic fuzzy-parameterized models:a comparison of numerical methods [J]. Journal of Pharmacokinetics and Pharmacodynamics,2007,34(5):595-621.
    [76]Seng K Y, Nestorov I, Vicini P. Fuzzy Least Squares for Identification of Individual Pharmacokinetic Parameters [J]. IEEE Transactions on Biomedical Engineering,2009,56(12):2796-2805.