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Transcript of 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
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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