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PKfit - A Pharmacokinetic Data Analysis Tool in R

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Presentation on theme: "PKfit - A Pharmacokinetic Data Analysis Tool in R"— Presentation transcript:

1 PKfit - A Pharmacokinetic Data Analysis Tool in R
Speaker: Chun-ying Lee1 Advisor: Yung-jin Lee2 1Pharmacy Department, Changhua Christian Hospital Changhua, Taiwan 2College of Pharmacy, Kaohsiung Medical University, Kaohsiung, Taiwan

2 Motivation Using available packages in R to create the program
Selecting menu-driven mode as the user interface Studying the application of genetic algorithm to PK data analysis Comparing with WinNonlin and Boomer WinNonlin Boomer 2017/4/14

3 User Interface Menu-driven mode
Users need not require to be familiar with programming of R Analyses data step by step 2017/4/14

4 PK models included Fourteen PK models are currently available (all single-dosed) Intravenous drug administrations with i.v. bolus or i.v. infusion Extravascular drug administrations, linear PK with 1st-order absorption/elimination or with nonlinear (Michaelis-Menten) elimination models 2017/4/14

5 Model fitting

6 Packages required odesolve (by R. Woodrow Setzer)
lsoda function: solving differential equations rgenoud (by Walter R. Mebane and Jasjeet S. Sekhon ) genoud function: Genetic algorithm stats nls function: Gauss-Newton algorithm (by Douglas M. Bates and Saikat DebRoy) optim function: Nelder-Mead simplex method logLik function: Log-Likelihood (by Jose Pinheiro and Douglas Bates) AIC function: Akaike’s Information Criterion (by Jose Pinheiro and Douglas Bates) stats4 BIC function: Bayesian information criterion 2017/4/14

7 Coding process 7 2017/4/14

8 Output information Summary table
Time, observed and calculated concentrations Weighted residuals Area under plasma concentration curves (AUC) Area under the first moment curves (AUMC) Final values of PK parameters Model selection criteria Log-Likelihood (LL) Akaike’s Information Criterion (AIC) Schwarz’s Bayesian Criterion (SBC) Diagnostic plots Linear plots Semi-log plots Residual plots 2017/4/14

9 Validation All the conditions are set the same in these three software
Data sets Selected models Initial values for parameters Fitting algorithms and numerical integration tools WinNonlin: Nelder-Mead Simplex method / RKF5 Boomer: Nelder-Mead Simplex  Gauss-Newton / RKF5 Criteria Prediction (absolute) error (PE) Percentage of prediction (absolute) error (%PE) 2017/4/14

10 Comparison of software
WinNonlin Boomer PKfit equal 1/Cp 1/Cp2 Model 1 Model 2 Model 3 Model 4 Model 1: One-Compartment PK Model I.V. Bolus Single-Dose with Linear Elimination Model 2: One-Compartment PK Model I.V. Bolus Single-Dose with Nonlinear Elimination Model 3: One-Compartment PK Model I.V. Infusion Single-Dose with Linear Elimination Model 4: One-Compartment PK Model I.V. Infusion Single-Dose with Nonlinear Elimination : Running O.K. (AE%  5%) 2017/4/14

11 Comparison of software
WinNonlin Boomer PKfit equal 1/Cp 1/Cp2 Model 5 Model 6 X Model 7 Model 8 Model 5: One-Compartment PK Model Extravascular Single-Dose with First-Ordered Absorption and Linear Elimination without Lag Time Model 6: One-Compartment PK Model Extravascular Single-Dose with First-Ordered Absorption and Nonlinear Elimination without Lag Time Model 7: One-Compartment PK Model Extravascular Single-Dose with Zero-Ordered Model 8: One-Compartment PK Model Extravascular Single-Dose with Zero-Ordered : Running O.K. (AE%  5%) X: Not acceptable for final PK parameters (AE% > 5%) 2017/4/14

12 Comparison of software
WinNonlin Boomer PKfit equal 1/Cp 1/Cp2 Model 9 Model 10 Model 11 X Model 9: Two-Compartment PK Model I.V. Bolus Single-Dose with Linear Elimination Model 10: Two-Compartment PK Model I.V. Infusion Single-Dose with Linear Elimination Model 11: Two-Compartment PK Model Extravascular Single-Dose with First-Ordered Absorption and Linear Elimination without Lag Time : Running O.K. (AE%  5%) X: Not acceptable for final PK parameters (AE% > 5%) 2017/4/14

13 Comparison of software
WinNonlin Boomer PKfit equal 1/Cp 1/Cp2 Model 12 Model 13 Model 14 X Model 12: One-Exponential Term Model 13: Two-Exponential Term Model 14: Three-Exponential Term : Running O.K. (AE%  5%) X: Not acceptable for final PK parameters (AE% > 5%) X: Crashed 2017/4/14

14 Simulation

15 Packages required stats
runif function: random uniform distribution derivates rnorm function: random normal distribution derivates 2017/4/14

16 Coding process 16 2017/4/14

17 Output information Time and concentration Linear plot Semi-log plot
2017/4/14

18 Validation All the simulation results in these three software are very similar with “Error type = No error” with four significant digits WinNonlin Boomer PKfit Time Conc. 0.5 12.39 1 22.29 2 36.15 3 44.14 4 48.08 6 48.64 8 44.33 10 38.37 12 32.27 16 21.82 20 14.34 24 9.314 A one-compartment PK model with extravascular, single-dose with first-ordered absorption without lag time and linear elimination Dose= 500 (mg) ka= 0.32 (1/hr) kel= 0.11 (1/hr) Vd= 5.8 (L) 2017/4/14

19 Thanks to… Dr. Woodrow Setzer for odesolve
Dr. Jasjeet Sekhon for rgenoud Dr. Anthony Rossini for scripting 2017/4/14

20 Reference R Installation and Administration, Version 2.0.1, 2001.
(http://cran.r-project.org/doc/manuals/R-admin.html) R Language Definition, Version 1.9.1, 2004. (http://cran.r-project.org/doc/manuals/R-lang.html) Sekhon, J.S. and Mebane, W.R.Jr., Genetic Optimization Using Derivatives: Theory and Application to Nonlinear Models. Political Analysis, 7: , 1998. Setzer, R.W., The odeslove Package, 2004. The R Project for Statistical Computing. (http://www.r-project.org) Writing R Extensions, Version 1.9.1, 2004. (http://cran.r-project.org/doc/manuals/R-exts.html) 2017/4/14

21 Thanks for your attention!


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