Develop a Tool for Therapeutic Drug Monitoring in R Using OpenBUGS

1 Speaker: Miao-ting Chen 1 , M.S.

Mentor: Yung-jin Lee 2 Department of Hospital Pharmacy, Kaohsiung Veteran General Hospital 2 College of Pharmacy, Kaohsiung Medical University Kaohsiung, Taiwan 1

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 2

BUGS

The BUGS (

B

ayesian inference

U

sing

G

ibbs

S

ampling): Bayesian analysis of complex statistical models using Markov chain Monte Carlo (MCMC) methods

likelihood prior distribution

posterior likelihood prior

posterior distribution

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Bayesian PK Hierarchical Model (using warfarin as the example) model { for (i in 1:N) { INR[i]~dnorm(mu[i],1.0E+6) mu[i]<-pow(a[i],(1/0.383)) )))))+3.36)/4.368)

likelihood

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] }} 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)

Prior distribution

4

Required programs or R packages

BRugs

OpenBUGS

5

Setting steps

bugsData( …….) ,fileName=file.path(getwd(),“modelname.txt") ,digits=5) PK model show(samplesStats("*")) modeldata(“dataname.txt”) samplesSet(c("ka","cl_F")) samplesAutoC("*",1, mfrow = c(3, 2), ask = FALSE 6

Validation

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) 7

tdm

Menu driven UI 16 PK & 1 PD models most steady-state (ss) Four data types single subject & one conc.

single subject & multiple conc.

Menu Aminoglycoside Carbamazepine Digoxin History plot Lithium Lithium carbonate Lithium citrate Theophylline salt Aminophylline anhydrous Aminophylline dihydrous Oxtriphylline many subjects & one conc.

many subjects & multiple different conc. Convergence plots Phenytoin Valproate Vancomycin Anti-HIV Enfuvirtide Indinavir Ritonavir Dose adjustment Everolimus Tacrolimus Enoxaparin Imatinib mesylate

Warfarin

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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. 10

Limitation of

tdm

Currently

tdm

is only available for Windows platform computer (

BRugs

and

OpenBUGS

now only available for Windows) .

are ODE equation can not currently be used to define model in

tdm

.

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tdm

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

Acknowledge

• Chun-ying Lee (Changhua Christian Hospital, Changhua, Taiwan): package building and environment setting • Uwe Ligges ( mail) Fakultät Statistik, Technische Universität Dortmund, Dortmund, Germany): coding and compiling (by e • 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

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

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Thanks for your attention

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