1 Develop a Tool for Therapeutic Drug Monitoring in R Using OpenBUGS Speaker: Miao-ting Chen 1, M.S....

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Develop a Tool for Therapeutic Drug Monitoring

in R Using OpenBUGS

Speaker: Miao-ting Chen1, M.S.Mentor: Yung-jin Lee2

1 Department of Hospital Pharmacy, Kaohsiung Veteran General Hospital2 College of Pharmacy, Kaohsiung Medical UniversityKaohsiung, Taiwan

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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 responseObservation estimate PK/PD parameters

dosage adjustment

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BUGS

posterior distribution

prior distributionlikelihood

The BUGS (Bayesian inference Using Gibbs Sampling): Bayesian analysis of complex statistical models using Markov chain Monte Carlo (MCMC) methods

priorlikelihood

posterior

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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

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OpenBUGS

Required programs or R packages

BRugs

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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)

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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

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tdm Menu driven UI

16 PK & 1 PD modelsmost steady-state (ss)

Four data typessingle 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

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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).

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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.

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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.

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

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

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ReferencesYamaoka 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/PDOpenBUGS website: http://mathstat.helsinki.fi/openbugs/Home.htmlR website: www.r-project.org

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Thanks for your attention