The USC*PACK collection of BigWinPops software for nonparametric adaptive grid (NPAG) population PK/PD modeling, and the MM-USCPACK clinical software
R Jelliffe, A Schumitzky, D Bayard, R Leary, M Van Guilder, A Gandhi, M Neely, and A Bustad.
Laboratory of Applied Pharmacokinetics, USC Keck School of Medicine, Los Angeles CA, USA.
The BigWinPops maximum likelihood nonparametric population adaptive grid (NPAG) modeling software runs in XP. The user defines a PK/PD model using the BOXES program to make the structural model. This is compiled and linked transparently. The subject data files are entered, and instructions. Routines for checking data files and for viewing results are provided. Likelihoods are exact. Behavior is statistically consistent, so studying more subjects gives estimates progressively closer to the true values. Stochastic convergence is as good as theory predicts. Parameter estimates are precise [1]. The software is available by license from the University for a nominal donation.
The MM-USCPACK clinical software [2] uses NPAG population models, currently for a 3 compartment linear system, and computes the dosage regimen to hit desired targets with minimum expected weighted squared error, thus providing, for the first time, maximal precision in dosage regimen design, a feature not seen with other currently known clinical software. Models for planning, monitoring, and adjusting therapy with aminoglycosides, vancomycin (including continuous IV vancomycin), digoxin, carbamazepine, and valproate are available.
The interactive multiple model (IMM) Bayesian fitting option [3] now allows parameter values to change if needed during the period of data analysis, and provides more precise tracking of the changing behavior of drugs in clinically unstable patients
In all the software, creatinine clearance is estimated based on one or two either stable or unstable serum creatinines, age, gender, height, and weight [4].
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 (1): 75-107, 2004.
[4]. Jelliffe R: Estimation of Creatinine Clearance in Patients with Unstable Renal Function, without a Urine Specimen. Am. J. Nephrology, 22: 3200-324, 2002.
