The USC*PACK software for nonparametric adaptive grid (NPAG) population PK/PD modeling, and the MM-USCPACK clinical software for individualized drug regimens.
R Jelliffe, A Schumitzky, D Bayard, R Leary, M Van Guilder, M Neely, S Goutelle, and A Bustad.
Laboratory of Applied Pharmacokinetics, USC Keck School of Medicine
The BigWinPops maximum likelihood nonparametric population adaptive grid (NPAG) modeling software runs in XP. The user runs the BOXES routine to make the PK/PD 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 . The software is available by license from the University for a nominal donation.
The MM-USCPACK clinical software  uses NPAG population models, currently for a 3 compartment linear system, and computes dosage regimens to hit desired targets with minimum expected weighted squared error, thus providing, for the first time, maximal precision in dosage regimens, a feature not seen with other 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  now allows parameter values to change if needed during the period of data analysis, and provides the most precise tracking of drugs in over 130 clinically unstable gentamicin and 130 vancomycin 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 .
 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.
 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.
 Bayard D, and Jelliffe R: A Bayesian Approach to Tracking Patients having Changing Pharmacokinetic Parameters. J. Pharmacokin. Pharmacodyn. 31 (1): 75-107, 2004.
 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.
 Jelliffe R: Estimation of Creatinine Clearance in Patients with Unstable Renal Function, without a Urine Specimen. Am. J. Nephrology, 22: 3200-324, 2002.