群体药物动力学 基本知识

1、)(1 BWBWiVpopVpopiBWX, VivKXotKeVXoCijtKiExpViXoiCij)70(1 ijiBWtKiExpBWVpopXoiCijijijiKiViitExpKpopExpExpVpopXoCij)()(ijijiKiViBWitExpKpopExpExpBWVpopXoCij)()()70(1 )65BW( )65(1 (BWiPpopPpopiBW65/iBWBWPpopPpopi1 therwise , 1 0DDFoDSFMifDFFiPpopPpopiDFijPpopjPij)()()(ijExpPpopjPijijPpopjLnPijLnYpredY

2、obs)1 ( YpredYobs2) 1 1 ( YpredYobs2)Pr(iiiOLSedObsOFV)(/)Pr(2VariLnVariedObsOFViiiELS)()(P )Pred -(Obs )Pred -(Obs O1ii1TiiPpopPPpopFVjTjijijiiiijijijtKExpVXoYpredC)(ijijiKiViBWitExpKpopExpExpBWVpopXoCij)()()70(1 )()70(1 )(iViBWiiKiExpBWVpopVExpKpopK)()70(1 iViBWiExpBWVpopV群体群体亚群体亚群体个体个体群体群体亚群体亚群体个

3、体个体PLLAIC2)2( )(iExpCLpopCLiCL)()1 ()DF1 (iExpMNFCCLpopCLiCL)()70(1 (2222/2/iViVGNDRiVBWiExpGNDRBWpopVV)(222iViExppopVV以群体药物动力学方法建立以中国人群为对象的智能化环孢A数据库,指导临床合理化用药 对象: 成年肾移植病人 给药: 100550 mg/日 采样时间: C0, C2 模型化工具: NONMEM) i Exp( Ppop iP21 Cpred Cpred CobslBase ModellStatistical Model & Residual Random Eff

4、ect ModellFixed Effect (Covariate) Model-K t-Ka t0Ka XC (e-e)(V/F) (Ka-K)ijTV jijP PExp()OBSPRED12C C (1 ) TVTVCOVRMEANiP P f (COVR - COVR )iTVTVCOVRiP P f COVRiCovariatesOFVP-valueCovariatesOFVP-valueForward InclusionBackward EliminationPOD-366.320.001TBIL-71.9230.05HCT47.2390.001CBW-55.2260.05INHI

5、52.9550.001Age-48.850.05Age57.1550.001INHI-55.5510.05CBW78.8480.001HCT-45.8380.05TBIL62.5570.001ALT-10.810.05POD134.260.05HGHT-2.4370.05ALP-2.4340.05GGT-1.2610.05DRUG-1.060.05Population Pharmacokinetic Parameter Estimates ParameterEstimate% RSEInter-individual variabilityCL/FTV (L/h)28.53.2719.7 %V/

6、FTV (L)1334.03KaTV (1/h)1.28179 %fPOD 1.2416.9fTBIL0.25217Residual error fCBW0.18818.71-Proportional30.8 %fAge0.19117.5 2-Additive42.4 ng/mlfINHI2.4527.2fHCT0.21219.2i(Ka) =1.28 Exp( Kai) 1/hriiiiiii(CL/F)28.5 - 1.24 POD - 0.252 (TBIL - TBIL) 0.188 (CBW- CBW) - 0.191 (Age - Age) - 2.45 INHI - 0.212

7、(HCT - HCT) Exp( CLi) L/hri(V/F) =133 LPOD-Postoperative Days, TBIL-Total bilirubin CBW-Current BW, INHI-Inhibitor, HCT-HematocritObservation (OBS) versus population (PRED) & individual (IPRED) predictionsWeight residual (WRES) versus population prediction (PRED), postoperative hours, and patient ID

8、Mean, SE & 95% C.I. from 10 subsets & those from full data set NONMEM estimate Jackknife estimateParameterEstimateSE95% C.I.EstimateSE95% C.I.CL/FTV (L/h)28.50.93226.7 -30.328.50.84926.6-30.4V/FTV (L)1335.36122 - 1441342.848127-140fPOD 1.240.210.830 - 1.651.290.1590.926-1.644fTBIL0.2520.0430.168 - 0

9、.3360.3060.0350.226-0.386fCBW0.1880.0350.119 - 0.2570.2170.0410.124-0.309fAge0.1910.0330.126 - 0.2560.1580.0330.082-0.233fINHI2.450.6661.14 - 3.762.80.4111.87-3.73fHCT0.2120.0410.132 - 0.2920.220.0490.108-0.332 Prediction errors established in the validation groupBase modelFinal modelME (ng/mL) (95%

10、C.I.)-5.5-2.2(-21.8 10.8)(-17.4 12.84)MSE (ng/mL)22735923387 (95%C.I.)(22408 32310)(18820 27953)RMSE (ng/mL) (95%C.I.)115.1101.1(103.5 126.9)(89.7 112.4)SPE Value (95%C.I.)-0.05 (-0.04 - 0.14) S.D.-0.97Trial SimulationTrial SimulationDose IndividualizationDose IndividualizationThe Simulated Steady State Profile for Each Situation Dose Individualization by MS EXCELCsA Population PK Model MS EXCEL Module SOLVERInter-Indv. VariabilityDose &Patient Info