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| USC Laboratory of Applied Pharmacokinetics |  | |
Workshop: November 3-4, 2000
The USC Schools of Medicine and Pharmacy had a hands-on workshop on:
"Model-Based, Goal-Oriented Optimal Individualized Drug Therapy: Relationship
to Parametric and Nonparametric Population PK/PD Modeling, Therapeutic Drug
Monitoring, and Multiple Model Dosage Design"
Location:
USC School of Pharmacy Computer Classroom
1985 Zonal Avenue, Los Angeles, CA, 90033
This course was intended for physicians, pharmacists and biomedical scientists with an interest in population pharmacokinetic/pharmacodynamic modelling and in therapeutic drug monitoring and optimal individualization of drug therapy for patient care. Participants were introduced to the USC*PACK software which can be used both for therapeutic drug monitoring as well as for parametric and nonparametric population PK/PD and physiological modelling.
[Faculty] [Participants] [Teaching topics]
Faculty
Roger Jelliffe, MD Course Co-Coordinator, USC School of Medicine Paul Beringer, PharmD Course Co-Coordinator, USC School of Pharmacy David Bayard, PhD Jet Propulsion Laboratory, Pasadena CA Darrell Clardy, Consultant Analytical and Forensic Toxicology, Brea, CA Robert Leary, PhD San Diego Supercomputer Center, San Diego, CA Mark Milman, PhD Jet Propulsion Laboratory, Pasadena, CA Tim Synold, PharmD City of Hope Medical Center, Duarte CA Paul Williams, PharmD University of the Pacific, Stockton CA
Participants
Dr. Mohammed Abdel-Hamid Kuwait University
Faculty of Pharmacy
Safat Kuwait
Dr. Abdulla Al-Khars Kuwait University
Faculty of Pharmacy
Safat Kuwait
Dr. Heath Branscum Sparks Regional Medical Center
Fort Smith, AR
Dr. Richa Chandra Pfizer, Inc.
Global Research and Development
Clinical Pharmacology
Groton, CT
Dr. Robert DiCenzo University at Buffalo
University of Rochester
Medical Center
Rochester, NY
Dr. Marty Frankel Sonoma State University
Center for Distributed Learning
Rohnert Park, CA
Dr. George Jaresko USC School of Pharmacy
Los Angeles, CA
Dr. Nelson Jumbe Albany Medical College
Clinical Pharmacology
Albany, NY
Dr. Gesche Jurgens Rigshospitalet
Department of Clinical Pharmacology
Copenhagen, Denmark
Dr. Nobuyuki Kimura Kitasato University
School of Pharmaceutical Sciences
Tokyo, Japan
Dr. Andreas Kirschbaum Krankenhaus Nordwest
Department of Surgical Critical Care
Frankfurt, Germany
Dr. Michelle LaMar Sonoma State University
Center for Distributed Learning
Rohnert Park, CA
Dr. Sukhyang Lee Sookmyung Women's University
Seoul, Korea
Dr. Soo-youn Lee Samsung Medical Center
Dept. of Clinical Pathology
Seoul, Korea
Dr. Sergei Leonov SmithKline Beecham
Philadelphia, PA
Dr. Constance Mazurek USC School of Medicine
Institute of Genetic Medicine
Los Angeles, CA
Dr. Alice McAfee Community Health Foundation
Pharmacy of East Los Angeles
Los Angeles, CA
Dr. Jung Mi Oh Sookmyung Women's University
Seoul, Korea
Dr. Megan Montgomery USC School of Pharmacy
Los Angeles, CA
Dr. Aimn Noureldin University of Manitoba
Clinical Microbiology
Winnipeg, Canada
Dr. Beatrice Perotti Amgen
Thousand Oaks, CA
Dr. Dotun Phillips Kuwait University
Faculty of Pharmacy
Safat, Kuwait
Dr. Ruedi Port German Cancer Research Center
Heidelberg, German
Dr. Jun Shi Aventis Pharmaceutics, Inc.
Bridgewater, NJ
Dr. William Shoemaker USC School of Medicine
Los Angeles, CA
Dr. Charles Wo USC School of Medicine
Los Angeles, CA
Dr. Waverly Woo National Cancer Center
Bethesda, MD
Dr. Victoria Zavotsky USC School of Pharmacy
Los Angeles, CA
Teaching topics
Day 1: Introduction and Review of Basic
pharmacokinetics, related responses, and Clinical Applications
1. Review of Basic Pharmacokinetics
Ref.:
· Introduction to basic pharmacokinetics, and individualization of drug therapy (.ppt)
· Goal-oriented, model-based drug regimens: Setting individualized
goals for each patient (.doc)
2. Evaluating Renal Function
Ref.:
· Estimation of creatinine clearance in patients with unstable renal function,
without a urine specimen. (.doc)
· Related slides (.ppt)
3. Bayes' Theorem and the MAP Bayesian Scenario of Planning,
Monitoring,and Adjusting drug dosage for patients
Ref.:
· Achieving concentration goals using parametric pharmacokinetic models -
a clinical review of the current unimodal gaussian bayesian approach (.doc)
· Bayes' Theorem (.ppt)
4. Modeling diffusion into endocardial vegetations, and the
postantibiotic effect
Ref.:
· Linked pharmacodynamic models: Diffusion into endocardial vegetations,
postantibiotic effect, and bacterial growth and kill (.doc)
· Related slides (.ppt)
5. Modeling bacterial growth and kill
6. Demo Vancomycin - Setting the initial goals. Planning the initial
regimen. - Dr. Paul Beringer
Ref.:
· Clinical Applications: Vancomycin (.ppt)
7. Introduction to Population Modelling
Why model? For description? For action? For what purpose?
Linear regression, Weighted Nonlinear Least Squares
Optimal procedures for population modelling
First, determine the assay error pattern, 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. Why?
Ref.:
· Population pharmacokinetic models: Parametric and nonparametric approaches (.doc)
· Related slides (.ppt)
8. Optimal Strategies for PK/PD Studies and for Patient Monitoring
Ref.:
· Related slides (.ppt)
9. Multiple Model Dosage Design for maximally precise goal oriented,
model based drug dosage regimens
Ref.:
· Multiple Model (mm) Dosage design: Achieving target goals with maximum precision (.ppt)
· Related slides (.ppt)
· Interacting Multiple Model Sequential Bayesian posterior joint parameter densities
to detect changing parameter values during the period of data analysis (.ppt)
10. Maximum Entropy Methods for Creating Discrete Joint Densities for
Multiple Model Dosage Design - Dr. Mark Milman
Ref.:
· Related slides (.ppt)
Day 2 Intermediate and Advanced Population Modeling
11. Development of a Pharmacokinetic Model of Ciprofloxacin
in Adult CF Patients - Dr. Paul Beringer
Ref.:
· PK/PD of Ciprofloxacin in Patients with Cystic Fibrosis (.ppt)
12. Determining the Assay Error Pattern: the First Step in Population
Modeling and TDM
Ref.:
· Fitting drug concentration data according to its credibility:
Determining the assay error pattern (.doc)
· Related slides (.ppt)
