Population PKPD Mode.. - Pharsight Corporation

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What to use in diagnostics ? ● ● ● Residuals • RES = DV-PRED • IRES = DV-IPRED • IWRES=IRES/SD(residual variance) • WRES are based on FO approximation and fails when FO fails ( high non linearity, sparse data) • CWRES are based on FOCE approximation and have better properties Simulation Based Residuals or Prediction Discrepancies • Prediction discrepancies (PD) which are the quantile of each observation within its simulated distribution • Normalized Prediction distribution errors (NPDE) ( decorrelated, then normalized) Empirical Bayesian Estimates (EBE) • IWRES=IRES/SD(residual variance), residual variability diagnostics • IPRED are based on individual parameters, structural model diagnostics • Are used in the estimation process (FOCE) • Are used as initial support points for a non-parametric engine (Leary’s Method) • May suffer from shrinkage ( It is advisable to report shrinkage in plots and reports) slide 46 © Pharsight Corporation All Rights Reserved
What to use in diagnostics ? ● ⎡Ω11 Ω= ⎢ ⎢ Ω Ω ⎢⎣ 0 0 Simulation Based Diagnostics • Posterior Predictive Checks 1 Θ= ⎢ θ ⎥ ⎢ 2 ⎥ ⎢θ ⎥ 3 12 22 ⎡σ ♦ ♦ ♦ ♦ ♦ Ω 33 ⎤ ⎥ ⎥ ⎥⎦ ⎤ 11 Σ= ⎢ 0 σ ⎥ 22 ⎣ ⎡θ ⎤ ⎣ ⎦ ⎦ Select a statistic of interest that a good model should not miss (e.g. AUC, Cmax, etc) and that can be derived from the raw data Simulate many data sets from the model (with uncertainty, TS2.2) Calculate the statistic from the N simulated data and J Replicates Calculate the statistic from the observed data Compare the statistic from raw and simulated data ⎡Var ⎤ ⎢ Cov Var ⎥ VARCOV = ⎢ ⎥ ⎢ Cov Cov Var ⎥ ⎢ ⎥ ⎣Cov Cov Cov Var⎦ Draw N Parameters form the final estimates and Variance Covariance Matrix Simulate Data according to the design of the study and compute the statistic Compare the statistic from raw and simulated data If we ignore parameter uncertainty this reduces to predictive check slide 47 © Pharsight Corporation All Rights Reserved
Page 1 and 2: Population PK/PD Modeling - The Pha
Page 3 and 4: Agenda Pharsight Company Overview R
Page 5 and 6: Who Is Pharsight? A company that he
Page 7 and 8: Who Are Pharsight Customers? This i
Page 9 and 10: Reporting and Analysis Services (RA
Page 11 and 12: RAS Value: High Quality Clinical Ph
Page 13 and 14: RAS Offers Three Analytic Competenc
Page 15 and 16: PK/PD Expertise in Early Drug Devel
Page 17 and 18: Communication Model: Single-Point C
Page 19 and 20: RAS Regulatory PK-PD Modeling RAS s
Page 21 and 22: Workflow: Population PK Analysis Pa
Page 23 and 24: QC/QA of the Analysis Dataset SAS D
Page 25 and 26: Regulatory and Timely POP PK/PD Pha
Page 27 and 28: PK Data PK from PHASE I, II and III
Page 29 and 30: Typical POP PK Model Development Wo
Page 31 and 32: Examples of GOF Plots Covariance ne
Page 33 and 34: Model Building : Adding Covariates
Page 35 and 36: Summary of Model building Absorptio
Page 37 and 38: Typical POP PK Model Development Wo
Page 39 and 40: Examples of Graphs to Aid Covariate
Page 41 and 42: Model Discrimination, Evaluation an
Page 43 and 44: What Can We Evaluate ? • The diff
Page 45: When to use diagnostics ? ● ●
Page 49 and 50: Example of predictive check Drug RO
Page 51: Proprietary Notice ● All contents

Population PKPD Mode.. - Pharsight Corporation

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