The USC*PACK BigWinPops and MM-USCPACK Software
Jelliffe, R., A Schumitzky, D Bayard, R Leary, M Van Guilder, A Gandhi, M Neely, and A Bustad.
Laboratory of Applied Pharmacokinetics, USC School of Medicine, Los Angeles CA, USA
The USC*PACK collection of BigWinPops software for nonparametric adaptive grid (NPAG) population PK/PD modeling, and the MM-USCPACK clinical software for "multiple model" Bayesian adaptive individualization of drug dosage regimens. R Jelliffe, A Schumitzky, D Bayard, R Leary, M Van Guilder, A Gandhi, M Neely, and A Bustad. Laboratory of Applied Pharmacokinetics, USC School of Medicine, Los Angeles CA, USA.
The BigWinPops 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 data files are entered. along with the instructions. Routines for checking data files and for viewing results are provided, similar to the older DOS version, but now in XP. Likelihoods are exact, behavior is statistically consistent, and parameter estimates are precise [1]. They are available by license from the first author for a nominal donation.
The MM-USCPACK clinical software [2] uses NPAG population models, currently for a 3 compartment linear system, and computed the dosage regimen to hit desired targets with minimum expected weighted squared error, thus providing maximal precision in dosage regimen design, a feature not seen with other currently known software. Models for planning, monitoring, and adjusting therapy with aminoglycosides, vancomycin (including continuous IV vancomycin), digoxin, carbamazepine, and valproate are available.
For both programs, creatinine clearance is estimated based on one or two either stable or unstable serum creatinines, age, gender, height, and weight [3].
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. Jelliffe R: Estimation of Creatinine Clearance in Patients with Unstable Renal Function, without a Urine Specimen. Am. J. Nephrology, 22: 3200-324, 2002.