The BigWinPops and MM-USCPACK Programs, USC Laboratory of Applied Pharmacokinetics ( The BigWinPops and MM-USCPACK Programs, USC Laboratory of Applied Pharmacokinetics ( Roger Jelliffe, MD, Alan Schumitzky, PhD, David Bayard, PhD, Michael Van Guilder, PhD, Andreas Botnen, M.S., Michael Neely, MD, Alison Thomson, Ph.D, Maurice Khayat, B.S., and Aida Bustad, B. S., Laboratory of Applied Pharmacokinetics, USC Keck School of Medicine, Los Angeles CA NONPARAMETRIC POPULATION MODELS Get the entire ML distribution, a Discrete Joint Density: one parameter set per subject, + its probability. Shape of distribution not determined by some equation, only by the data itself. Multiple individual models, up to one model set per subject. Can discover, locate, unsuspected subpopulations. Behavior is statistically consistent. Study more subjects, guaranteed better results. The multiple models permit multiple predictions. Can optimize precision of goal achievement by a MM dosage regimen. Use IIV +/or assay SD, stated ranges. Computes environmental noise. Bootstrap, for confidence limits, significance tests. Multiple Model (MM) Dosage Design 1)Use a prior with discrete multiple models - an NPEM or NPAG model. 2)Give a candidate regimen to each model. 3)Predict results with each model. 4)Compute weighted squared error of failure to hit target goal at target time. 5)Find the regimen hitting target with minimal weighted squared error. 6)This is multiple model (MM) dosage design – the IMPORTANT clinical reason for using nonparametric population PK models. Lidocaine stepwise infusion regimen based on Parameter MEANS: Predicted response of full 81 point lidocaine population model. Target = 3ug/ml MM maximally precise stepwise lido infusion regimen: Predicted response of full 81 point lidocaine population model. Most precise regimen. Target = 3ug/ml EFFICIENCY AND RELATIVE ERROR Estimator Relative Efficiency % Relative Error Direct Observation PEM NPAG NONMEM FOCE IT2B FOCE NONMEM FO Approximate likelihoods can destroy precision of estimation ABSTRACT The BIGWINPOPS modeling software runs in XP. The user defines a structural PK/PD model using the BOXES program. This is compiled and linked transparently. The data files are entered. along with the instructions. Routines for checking data files and 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]. The software is 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. It computes 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 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 the 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 or two either stable or changing serum creatinines, age, gender, height, and weight [5]. 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: , [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, [3]. Bayard D, and Jelliffe R: A Bayesian Approach to Tracking Patients having Changing Pharmacokinetic Parameters. J. Pharmacokin. Pharmacodyn. 31 (1): , [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, [5]. Jelliffe R: Estimation of Creatinine Clearance in Patients with Unstable Renal Function, without a Urine Specimen. Am. J. Nephrology, 22: , c c SMM: only the first serum creatinine – MM Bayesian updating – poor tracking RMM: all serum creatinines – changing renal function richer data MM Bayesian updating – better tracking IMM: interacting sequential MM Bayesian updating – best tracking MULTIPLE MODEL (MM) BAYESIAN POSTERIOR UPDATING. Support point values don’t change. Use Bayes’ theorem to compute the Bayesian posterior probability of each support point, given the patient’s data. Problem: will not reach out beyond pop parameter ranges. May miss unusual patient. Start with MAP Bayesian. It reaches out, but pop prior holds it back. Add new support points nearby, inside and outside, to precondition the population model for the patient data it will receive. Then do MM Bayesian on ALL the support points. We are implementing this now. Out soon. HYBRID BAYESIAN POSTERIOR UPDATING BAYESIAN FOR VERY UNSTABLE PATIENTS: INTERACTING MULTIPLE MODEL (IMM) UPDATING Limitation of all other current Bayesian methods - find only the 1 set of fixed parameter values which fit the data. Sequential MAP or MM Bayesian = same as fitting all at once. IMM - Relax this assumption. Let the “true patient” change during data analysis if more likely to do so. Box and whisker plots of estimation errors from SMM, RMM, and IMM analyses of gentamicin data from cardiothoracic surgery patients at various initial % probabilities of change. Plots of measured versus estimated gentamicin data from a typical patient with unstable renal function, using (a) SMM, (b) RMM and (c) IMM analysis. IMM tracks drug behavior best.