1. 1 Develop a Tool for Therapeutic Drug Monitoring in R Using OpenBUGS Speaker: Miao-ting Chen 1, M.S. Mentor: Yung-jin Lee 2 1 Department of Hospital Pharmacy, Kaohsiung Veteran General Hospital 2 College of Pharmacy, Kaohsiung Medical University Kaohsiung, Taiwan

2. 2 Therapeutic Drug Monitoring (TDM) To optimize individual patient’s drug therapy through monitoring its serum concentrations of the target drugs, as well as the observed clinical response Observation  estimate PK/PD parameters  dosage adjustment

3. 3 BUGS posterior distribution prior distribution likelihood The BUGS (Bayesian inference Using Gibbs Sampling): Bayesian analysis of complex statistical models using Markov chain Monte Carlo (MCMC) methods prior likelihood posterior

4. 4 model { for (i in 1:N) { INR[i]~dnorm(mu[i],1.0E+6) mu[i]<-pow(a[i],(1/0.383)) a[i]<-((1/((m[i]*(cl_F[i]/v_F[i]))/(pow(kc[i],2))*(1- (kc[i]*tau[i]/(1-exp(kc[i]*tau[i]))))m[i]/kc[i]*log ((D[i]/v_F[i])/(Cpmax[i]*(1-exp(cl_F[i]/v_F[i]*tau[i] )))))+3.36)/4.368) m[i]~dgamma(0.1,0.1) Cpmax[i]~dgamma(0.1,0.1) kc[i]~dgamma(0.1,0.1) cl_F[i]~dgamma(0.1,0.1) v_F[i]~dnorm(7.5,100) }} Bayesian PK Hierarchical Model (using warfarin as the example) likelihood Prior distribution

5. 5 OpenBUGS Required programs or R packages BRugs

6. 6 Setting steps PK model modelcheck(“modelname.txt”)modeldata(“dataname.txt”)modelCompile(numChains=1)modelGenInits( )modelUpdate(10000) samplesSet(c("ka","cl_F")) modelUpdate(10000) show(samplesStats("*")) samplesHistory("*",mfrow=c(3,1), ask = FALSE) samplesDensity("*", mfrow = c(3, 2), ask = FALSE) samplesAutoC("*",1, mfrow = c(3, 2), ask = FALSE bugsData( …….),fileName=file.path(getwd(),“modelname.txt"),digits=5)

7. 7 The ability of the tdm package estimate PE (Prediction Error, %) = ( Eq.1) P pr= predicted value P true= true values Convergence of MCMC chain (history, density and autocorrelation plots) Validation

8. 8 tdm Menu driven UI 16 PK & 1 PD models most steady-state (ss) Four data types single subject & one conc. single subject & multiple conc. many subjects & one conc. many subjects & multiple different conc. Convergence plots Dose adjustment Menu Aminoglycoside Carbamazepine Digoxin Lithium Lithium carbonate Lithium citrate Theophylline salt Aminophylline anhydrous Aminophylline dihydrous Oxtriphylline Theophylline Phenytoin Valproate Vancomycin Anti-HIV Enfuvirtide Indinavir Ritonavir Immunosuppressant Cyclosporine-A Everolimus Tacrolimus Enoxaparin Imatinib mesylate Warfarin History plot Auto-correlation plot Density plot

9. 9 Comparison Between tdm and JPKD Prediction error (%) of PK parameters were similar to those using nonlinear regression (empirical Bayesian) obtained from JPKD (Java PK For Desktop).

10. 10 Convergence Low PE(%) is not necessarily imply that Markov chains converge successfully. Also, successful convergence of Markov chains do not necessarily result in low PE(%). In setting of tdm, we did not increase updating for convergence.

11. 11 Limitation of tdm Currently tdm is only available for Windows platform computer (BRugs and OpenBUGS are now only available for Windows). ODE equation can not currently be used to define model in tdm.

12. 12 tdm website: http://pkpd.kmu.edu.tw/tdm

13. Acknowledge Chun-ying Lee (Changhua Christian Hospital, Changhua, Taiwan): package building and environment setting Uwe Ligges (Fakultät Statistik, Technische Universität Dortmund, Dortmund, Germany): coding and compiling (by e- mail) Kurt Hornik (Department of Statistics and Mathematics of the Wirtschaftsuniversität Wien, Austria): coding and compiling (by e-mail) Kaohsiung Veteran General Hospital and Dr. Cheng DL Medical Research Foundation, Kaohsiung, Taiwan: sponsoring this trip

14. 14 References Yamaoka K, et al., A nonlinear multiple regression program, MULTI2 (BAYES), based on Bayesian algorithm for microcomputers. Journal of Pharmacobio- Dynamic 1985;8: 246-56. Application of Bayesian Estimation to a Two compartment Model in PK/PD OpenBUGS website: http://mathstat.helsinki.fi/openbugs/Home.html R website: www.r-project.org

15. 15 Thanks for your attention