This course is intended for physicians, pharmacists and biomedical scientists with an interest in population pharmacokinetic / pharmacodynamic modeling, and also for those interested in therapeutic drug monitoring and optimally precise individualization of drug therapy for patient care.

Prior experience in clinical pharmacokinetics will be an advantage. Participants will be introduced to the USC*PACK software, which can be used both for therapeutic drug monitoring and optimal individualization of drug dosage regimens, as well as for parametric and nonparametric population PK/PD and physiological modeling.

This course will also introduce the new Win*USC*PACK software for “Multiple Model” design of dosage regimens to hit specific selected therapeutic target goals with maximal precision. This method is based first on nonparametric population models. It also obtains a patient’s Bayesian posterior nonparametric individual model based on serum concentrations measured. If needed, it can also detect and quantify unsuspected changes in parameter values such as take place with the volume of distribution (and other parameters), in aminoglycoside antibiotics, for example, with changes in the patient’s clinical status. This sequential Bayesian “Interacting Multiple Model” Bayesian approach comes from the aerospace community, where it is used to track evasive targets. It is new, to our knowledge, in the pharmacokinetic community. It is designed to track the behavior of drugs, especially in unstable patients, with maximum precision, to detect unsuspected changes in a patient’s parameter values during the period of the data analysis, and to permit achievement of target therapeutic goals with maximum precision.

Day 1 Basic Pharmacokinetics, Introduction to Population Modeling and Clinical Applications.

08:30 AM - Registration
09:00 AM - Welcome Dr. Sergienko V.I.
09:15 AM - Introduction to basic concepts in pharmacokinetics, including Review of Basic Pharmacokinetic Behavior.
	Drug Elimination and Renal Function - Dr. Bondareva
09:45 AM - Evaluating Renal Function  Dr. Jelliffe
10:00 AM - Bayes' Theorem and the MAP Bayesian Scenario of Planning, Monitoring, and Adjusting Drug Dosage for patients - Dr. Bondareva
10:15 AM - Introduction to Population Modeling - Dr. Jelliffe
	Why model? For description? For action? 
	Types of PK models: Linear regression, Nonlinear Least Squares, Bayesian

10:45 AM BREAK

11:00 AM - Parametric Population Models - Dr. Jelliffe
		 Iterative 2 stage Bayesian, NONMEM
11:30 AM - Nonparametric Population models - Dr. Jelliffe
		NPML, NPEM, NPAG

12:15 PM - LUNCH

01:15 PM - Comparing Parametric and Nonparametric Approaches - IT2B, NPEM, and NPAG - Dr. Jelliffe.
01:45 PM - Multiple Model (MM) Dosage Design for maximum precision regimens - Dr. Jelliffe
02:15 PM - Getting MM Bayesian Posterior Individual Parameter Distributions. The Interacting MM (IMM) Approach - Dr. Jelliffe.
02:45 PM - Introduction to the new Windows USC*PACK MM and IMM Clinical Program to Achieve Target Goals with Maximum Precision - Dr. Jelliffe 
	Demo - 1 compartment model  Planning the Initial regimen:  Gentamicin: CCr = 100, 50, 5.

03:00 PM - BREAK

03:15 PM - Entering past doses and levels, analysing the data.
		A patient on Gentamicin
		An interesting patient on Tobramycin.
03:45 PM - Demo - 2 compartment model  Digoxin - Dr. Jelliffe
		Setting the initial goals, planning the initial regimen
		A simple patient with atrial fibrillation
		Another interesting patient with atrial fib
04:15 PM - Demo  Vancomycin - Setting the initial goals, planning the 
		initial regimen. - Dr. Jelliffe
04:45 PM - Group discussion. Dr. Bondareva, moderator.
05:15 PM - Adjourn

Day 2 Intermediate Population Modelling.

09:00 AM - Modeling Antibiotic effects. Concentration - dependent and 	time - dependent drugs. Dr. Jelliffe.
09:30 AM - A Unifying Concept for these drugs. 
		Clinical Examples: Aminoglycosides, Vancomycin. - Dr. Jelliffe
10:00 AM - Optimal procedures for population modeling - Dr. Jelliffe
		First, determine the assay error pattern polynomial, to weight each data point properly
		Second, use a parametric population model, get gamma, ranges
		Third, use an NP population model, use gamma, ranges, get the entire parameter distribution.
10:15 AM - Demo - getting the assay error polynomial - Dr. Jelliffe

10:30 AM - BREAK

10:45 AM - Demo - The IT2B program.  Modelling Amikacin - Dr. Jelliffe
		A typical patient data file
		Running the program. Getting gamma, ranges and evaluating the results
11:15 AM - Demo - NPAG: Modeling Amikacin further. Using gamma, ranges results - Dr. Jelliffe
         	Evaluating the results - The log-likelihood function
		The 2 and 3-D plots of the marginal and joint marginal PDF's
		Linking Nonparametric Models to the Multiple Model Adaptive Control Software
		Deriving individual Bayesian posterior patient parameter joint densities
		Evaluating relationships between parameters and covariates.

12:00 Noon - LUNCH

01:00 PM - Optimal Times to Monitor Serum Concentrations and other Patient Responses - Dr. Jelliffe.
01:45 PM - Modeling of Antiepileptic Drugs - Dr. Bondareva 

02:30 PM - BREAK

02:45 PM - Making large and nonlinear population models.
	Demo - Using BOXES - making a Michaelis-Menten model of Phenytoin - Dr. Bondareva.
03:30 PM - Group Review and Discussion. Dr. Bondareva, moderator.