Sampling schedule design towards optimal drug monitoring for individualizing therapy

We study the individualization of therapy by simultaneously taking into account the design of sampling schedule and optimal therapeutic drug monitoring. The sampling schedule design in this work is to determine the number of samples, the sampling times, the switching time from the loading to the maintenance period, and the drug dosages. A closed-loop control policy is employed to determine the sampling schedule, and an advanced stochastic global optimization algorithm, which integrates the stochastic approximation and simulated annealing techniques, is implemented to search the optimal sampling schedule. A simulated one-compartment model of intravenous theophylline therapy is used to illustrate our method. This method can be readily extended to multiple compartment systems and allow incorporating other criteria of drug control. While currently the method is mainly of theoretical interest, it offers a starting point for practical applications and thus is hopefully of great value for the clinically individualizing therapy in the future.

[1]  G. Yin.  Convergence of a global stochastic optimization algorithm with partial step size restarting , 2000, Advances in Applied Probability.

[2]  J. Kiefer,et al.  Stochastic Estimation of the Maximum of a Regression Function , 1952 .

[3]  Discrete approximations to continuous density functions that are L1 optimal , 1983 .

[4]  Gang George Yin.  Rates of Convergence for a Class of Global Stochastic Optimization Algorithms , 1999, SIAM J. Optim..

[5]  F Mentré,et al.  Designing an optimal experiment for Bayesian estimation: application to the kinetics of iodine thyroid uptake. , 1994, Statistics in medicine.

[6]  Harold J. Kushner,et al.  Penalty Function Methods for Constrained Stochastic Approximation , 1974 .

[7]  D Z D'Argenio,et al.  Incorporating prior parameter uncertainty in the design of sampling schedules for pharmacokinetic parameter estimation experiments. , 1990, Mathematical biosciences.

[8]  F Mentré,et al.  Stochastic optimization algorithms of a Bayesian design criterion for Bayesian parameter estimation of nonlinear regression models: application in pharmacokinetics. , 1997, Mathematical biosciences.

[9]  H. Kushner,et al.  Stochastic Approximation and Recursive Algorithms and Applications , 2003 .

[10]  H. Kesten.  Accelerated Stochastic Approximation , 1958 .

[11]  France Mentré,et al.  Optimal Sampling Times for Bayesian Estimation of the Pharmacokinetic Parameters of Nortriptyline During Therapeutic Drug Monitoring , 1999, Journal of Pharmacokinetics and Biopharmaceutics.

[12]  David Z. D'Argenio,et al.  Discrete approximation of multivariate densities with application to Bayesian estimation , 1984 .

[13]  C Kulcsár,et al.  Optimal experimental design and therapeutic drug monitoring. , 1994, International journal of bio-medical computing.