R Jelliffe, A Schumitzky, D Bayard, R Leary, M Van Guilder, S Goutelle, A Bustad, A Botnen, J Bartroff, W Yamada, and M Neely.
Laboratory of Applied Pharmacokinetics, USC Keck School of Medicine, Los Angeles, CA, USA
The Pmetrics population modeling software is embedded in R, called by R, and output into R. It runs on PC's and Macs. Minimal experience with R is required, but the user has all the power of R for further analyses and displays, for example. Libraries of many models are available, and differential equations may also be used. Large models of multiple drugs, with interactions, and multiple outputs and effects may be made if desired. Analytic solutions may also be used if feasible. The model is compiled with GFortran. Runs are made with simple R commands. Routines for checking data and displaying results are provided. Likelihoods are exact. Behavior is statistically consistent – studying more subjects yields parameter estimates closer to the true ones. Stochastic convergence is as good as theory predicts. Parameter estimates are precise [1].The software is available freely for research uses. In addition, prototype new nonparametric Bayesian (NPB) software has been developed. Standard errors of parameter estimates and rigorous Bayesian credibility intervals are now available. This work, presented at this meeting, is progressing.
The RightDose clinical software [2] used Pmetrics population models, currently for a 3 compartment linear system, and develops multiple model (MM) dosage regimens to hit desired targets with minimum expected weighted squared error, thus providing maximally precise dosage regimens for patient care. If needed, hybrid MAP and NP Bayesian posteriors provide maximum safety with more support points and more precise dosage regimens. In addition, the interacting multiple model (IMM) sequential Bayesian analysis when model parameter distributions are changing during the period of data analysis [[3] has been upgraded by using the hybrid analysis in advance to provide more support points than were present in the original population model, again for more capable Bayesian parameter distributions and more informed dosage regimens than were available before. This work was also presented at this meeting. IMM has tracked drug behavior better than other methods in unstable post surgical cardiac patients [4]. In all the software, creatinine clearance is estimated in either stable or changing clinical situations, based on analyzing pairwise serum creatinine values, age, gender, height, weight, muscle mass, and dialysis status [5]. It also now runs on IPads and IPhones as virtual machines to access a PC. A new method for optimal experimental design for nonparametric population models has been developed and will soon be incorporated into the software. It avoids the circular reasoning flaw in D-optimal design [6].
References:
[1]. Bustad A, Terziivanov D, Leary R, Port R, Schumitzky A, and Jelliffe R: Parametric and Nonparametric Population Methods: Their Comparative Performance in Analysing a Clinical Data set and Two Monte Carlo Simulation Studies. Clin. Pharmacokinet. 45: 365-383, 2006.
[2]. Jelliffe R, Schumitzky A, Bayard D, Milman M, Van Guilder M, Wang X, Jiang F, Barbaut X, and Maire P: Model-Based, Goal-Oriented, Individualized Drug Therapy: Linkage of population Modeling, New "Multiple Model" Dosage Design, Bayesian Feedback, and Individualized Target Goals. Clin. Pharmacokinet. 34: 57-77, 1998.
[3]. Bayard D and Jelliffe R: A Bayesian Approach to Tracking Patients having Changing Pharmacokinetic Parameters. J. Pharmacokin. Pharmacodyn. 31: 75-107, 2004.
[4]. MacDonald I, Staatz C, Jelliffe R, and Thomson A: Evaluation and Comparison of Simple Multiple Model, Richer Data Multiple Model, and Sequential Interacting Multiple Model (IMM) Bayesian Analyses of Gentamicin and Vancomycin data collected from Patients undergoing Cardiothoracic Surgery. Ther. Drug Monit. 30: 67-74, 2008.
[5]. Jelliffe R: Estimation of Creatinine Clearance in patients with Unstable Renal Function, without a Urine Specimen. Am, J, Nephrology, 22: 320-324, 2002.
[6]. Tatarinova T, Neely M, Bartroff J, van Guilder M, Walter Yamada W, Bayard D, Jelliffe R, Leary R, Chubatiuk A, and Schumitzky A: Two General Methods for Population Pharmacokinetic Modeling: Non-Parametric Adaptive Grid and Non-Parametric Bayesian. J. Pharmacokin. Pharmacodyn, in press.
Reference: PAGE 22 () Abstr 2664 [www.page-meeting.org/?abstract=2664]
Poster: Software Demonstration