The MM-USCPACK 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, A Bustad, M Khayat, and A Thomson.
Laboratory of Applied Pharmacokinetics, USC Keck School of Medicine, Los Angeles CA, USA.
The BigNPAG maximum likelihood nonparametric population adaptive grid modeling software runs in XP. The user runs the BOXES routine to make the PK/PD model. This is compiled and linked transparently. Routines for checking data and viewing results are provided. Likelihoods are exact. Behavior is statistically consistent - studying more subjects gives estimates progressively closer to 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 multiple model (MM) dosage regimens to hit desired targets with minimum expected weighted squared error, providing, for the first time, maximal precision in dosage regimens. Models for planning, monitoring, and adjusting therapy with aminoglycosides, vancomycin (including continuous IV vancomycin), digoxin, carbamazepine, and valproate are available. For maximum safety, hybrid MM Bayesian posteriors composed of MAP estimates plus added support points in that area now assure adequate support points to augment the population model for the new data it will receive, increasing safety of posteriors and maximal precision in the subsequent regimen. The interactive multiple model (IMM) Bayesian fitting option [3] allows parameter values to change if more likely during the period of data analysis, and provides most precise tracking of drugs in over 130 clinically unstable gentamicin and 130 vancomycin patients [4]. In all the software, creatinine clearance is estimated based on one stable or two unstable serum creatinines, age, gender, height, and weight [5].
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]. 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: 3200-324, 2002.
