We Used Poped in R 1 2 to Perform the Optimal Design, Optimizing Method

1Purpose

According to the research proposal, the clinical trial aims to test the pharmacokinetic (PK) profile of dexmedetomidine after intranasal administration on healthy infant and pediatric subjects. The subjects age between 3months to 36 months (or PMA 53-196 weeks). Totally 50 (or less, for example, 20) subjects will be recruited.

2Method

Optimal design is aimed to produce maximal information in population pharmacokinetic or pharmacodynamic analysis studies through providing a strategic sampling schedule with reduced number of subjects and sampling points [2][6].

Previous study of intranasal administration of dexmedetomidine on healthy adult volunteers showed that the PK profile was best described by a two-compartment model with first-order absorption while no lag time. Therefore there will be five PK parameters to be estimated afterwards: central clearance Cl, central distribution volume V1, inter-compartment clearance Q, peripheral distribution volume V2, and absorption rate Ka. Hence, for each subject, 5 blood samples will be taken at the optimized time points up to 6 hours after dosing.

We used PopED in R [1][2] to perform the optimal design, optimizing method:

D-optimality

Method for calculating FIM: full FIM.

Approximation method: FO

Use Exchange algorithm: True.

Number of random search iterations : 1000

Number of stochastic gradient search iterations: 1000

Number of grid points in the line search: 50

The PK model used is obtained from previous study on intranasal administration of dexmedetomidine on healthy adult subjects. The model is a two-compartment model with first order absorption while no lag time, and the clearance is described by the following equation [4]. The parameter estimates of this model are also obtained from our previous study of intranasal dexmedetomidine on adult subjects.

Where the population parameter estimates are:

CL01=35.5,V01=24.8,Q01=84.7,V02=45.2,Favail=0.465,Ka=0.652;

between subject variabilityfor each parameters are:

CL01=0.0939,V01=0.234,Q01=0.321,V02=0.125,Ka=0.0705

and TDEV50 equals to 46.5, which means the clearance matured with a half-life of 46.5 weeks [4], and total number of subject, value range of WT, body weight, and PMA are set as the follows in the optimal design. We tried

The results were diagnosed using the following parameters [5]:

1. OFV(FIM), or the determinant of FIM, standard option.
2. Normalized efficiency: where p=number of unfixed parameters in the model

1. CV of parameters: RSE% of unfixed parameters in the model

Sampling windows ±0.1hr (6min), ±0.25hr(15min), and ±0.5(30min) around each

sampling points were evaluated by the result of1000 times simulations. The sampling windows with efficiency loss lower than 20% was considered acceptable. The

following two pictures represent the example of the efficiency higher and lower than 80%, respectively.

3Results

The optimal sampling schedules for group with totally 50 and 20 subjects are as the follows. We used two algorithms, RS+SG+LS (random search+stochastic gradient+line search) and MFEA (modified Fedrov exchange algorithm) to optimize the objective function. The results showed that RS+SG+LS provided more precise

time points and MFEA provided integer time points, which may be more

convenient in practical.

The following chart shows the estimated RSE% of each parameters that will obtained if using the optimized sampling schedule. We can see that with more subjects (50 subjects), we can have lower RSE% and thus better estimation of the parameters. Also, RS+SG+LS is slightly better than MFEA.

The following four charts show the plots of model predictions for the typical value (initial value) and where the optimized sampling time points locate.

4Reference

1. Nyberg J, Ueckert S, Stroemberg EA, Hennig S, Karlsson MO and Hooker AC (2012). “PopED: An extended, parallelized, nonlinear mixed effects models optimal design tool.”Computer Methods and Programs in Biomedicine,108.
2. Foracchia M, Hooker AC, Vicini P and Ruggeri A (2004). “POPED, a software for optimal experiment design in population kinetics.”Computer Methods and Programs in Biomedicine,74.
3. Potts, Amanda L., Guy R. Warman, and Brian J. Anderson. "Dexmedetomidine disposition in children: a population analysis."Pediatric Anesthesia18.8 (2008): 722-730.
4. “Diagnostics in PopED v.2 – Course in Optimal Experimental Design”. Division of Pharmacokinetics and Drug Therapy, Department of Pharmaceutical Biosciences, Uppsala University, Sweden
5. Tod, Michel, et al. "Robust optimal design for the estimation of hyperparameters in population pharmacokinetics."Journal of pharmacokinetics and biopharmaceutics26.6 (1998): 689-716.

Appendix

If 50 subjects are divided into three groups according to their age: 0-1 years old, 1-2 years old, and 2-3 years old.

Number of groups=3

Number of subject per group=16, minimal 15, maximal 20.