Individualized Drug Therapy for Patients

Basic Foundations, Relevant Software and Clinical Applications

1st Edition - November 8, 2016
Editors: Roger W Jelliffe, Michael Neely
Language: English
Paperback ISBN:
9 7 8 - 0 - 1 2 - 8 0 3 3 4 8 - 7
eBook ISBN:
9 7 8 - 0 - 1 2 - 8 0 3 3 4 9 - 4

Individualized Drug Therapy for Patients: Basic Foundations, Relevant Software and Clinical Applications focuses on quantitative approaches that maximize the precision with whic… Read more

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Individualized Drug Therapy for Patients: Basic Foundations, Relevant Software and Clinical Applications focuses on quantitative approaches that maximize the precision with which dosage regimens of potentially toxic drugs can hit a desired therapeutic goal. This book highlights the best methods that enable individualized drug therapy and provides specific examples on how to incorporate these approaches using software that has been developed for this purpose.

The book discusses where individualized therapy is currently and offers insights to the future. Edited by Roger Jelliffe, MD and Michael Neely, MD, renowned authorities in individualized drug therapy, and with chapters written by international experts, this book provides clinical pharmacologists, pharmacists, and physicians with a valuable and practical resource that takes drug therapy away from a memorized ritual to a thoughtful quantitative process aimed at optimizing therapy for each individual patient.

Dedication
List of Contributors
Preface
- Reference
Acknowledgments
Introduction: Don’t Just Dose—Choose a Specific Target Goal, Suited to the Patient’s Need, and Dose to Hit it Most Precisely
- 1 Ways of Thinking—Qualitative and Quantitative
- 2 Graphical Plots and Optical Illusions
- 3 Other Illusions Sharing This Feature of Perception
- 4 General Remarks About Dosing
- References
Section I: Basic Techniques for Individualized Therapy
- Chapter 1. Basic Pharmacokinetics and Dynamics for Clinicians
  - Abstract
  - 1.1 Excretion is Usually Proportional to Amount or Concentration
  - 1.2 Accumulation Takes Place by the Mirror Image of Elimination
  - 1.3 Suiting Loading and Maintenance Doses to Each Other
  - 1.4 The Basic Idea—Dose and Half-Time—They Let You Control the Total Amount of Drug You Permit the Patient to Have in the Body at Any Time
  - 1.5 Events Following a Change in Daily Maintenance Dose
  - 1.6 Events Following a Change in Excretion Rate
  - 1.7 Separating Elimination Into Renal and Nonrenal Components
  - 1.8 Adding More Compartments for a More Realistic Pharmacokinetic Model
  - 1.9 Output Equations: Describing the Observations
  - 1.10 Parameterizing the Model: Volume and Clearance or Volume and Rate Constant?
  - 1.11 The Clearance Community in PK
  - 1.12 A Current Clinical Issue: “Augmented Renal Clearance” in the ICU
  - 1.13 Properties of Systems: Observability, Identifiability, and Controllability
  - 1.14 Nonlinear Drug Systems
  - 1.15 Conclusions
  - References
- Chapter 2. Describing Drug Behavior in Groups of Patients
  - Abstract
  - 2.1 Early Approaches to Modeling
  - 2.2 True Population Modeling Approaches
  - References
- Chapter 3. Developing Maximally Precise Dosage Regimens for Patients—Multiple Model (MM) Dosage Design
  - Abstract
  - 3.1 Again, Select a Specific Target, Not a Range
  - 3.2 The Separation Principle
  - 3.3 The Way Around the Separation Principle: Multiple Model Dosage Design
  - References
- Chapter 4. Optimizing Laboratory Assay Methods for Individualized Therapy
  - Abstract
  - 4.1 Introduction: Wrong Weighting of Data, Wrong PK Models, Wrong Doses
  - 4.2 Percent Coefficient of Variation is Not the Correct Measure
  - 4.3 Methods
  - 4.4 Results: Application to Real Assay Data
  - 4.5 Discussion: LLOQ is an Illusion
  - 4.6 Conclusion
  - Acknowledgments
  - References
- Chapter 5. Evaluation of Renal Function
  - Abstract
  - 5.1 Classical Estimation of Creatinine Clearance (CCr), Based on Urinary Excretion
  - 5.2 Problems With Estimates of CCr Using Only a Single Serum Creatinine (SCr) Sample
  - 5.3 Estimating CCr from a Pair of SCr Samples at Known Times
  - 5.4 The Final Overall Formula
  - 5.5 When Did the Patient’s Renal Function Change?
  - 5.6 Uncertainties in the Gold Standard Measurement of Creatinine Clearance
  - 5.7 Comparison of Estimated Versus Measured Creatinine Clearance
  - 5.8 Comparison With Cockcroft–Gault Estimation When SCr is Stable
  - 5.9 Should Ideal Body Weight Be Used Instead of Total Body Weight?
  - 5.10 Changing SCr—The Direct Clinical Link Between the Patient’s Changing Renal Function and Drug Behavior
  - 5.11 Summary
  - References
Section II: The Clinical Software
- Chapter 6. Using the BestDose Clinical Software—Examples With Aminoglycosides
  - Abstract
  - 6.1 Introduction—The BestDose Clinical Software
  - 6.2 Two Representative Drugs—Amikacin and Gentamicin
  - 6.3 Planning the Initial Regimen
  - 6.4 Analyzing a Gentamicin Patient’s Existing Data, and Developing the Adjusted Regimen
  - 6.5 The Effect Model
  - 6.6 Planning the New Adjusted Dosage Regimen
  - 6.7 Summary
  - References
- Chapter 7. Monitoring the Patient: Four Different Bayesian Methods to Make Individual Patient Drug Models
  - Abstract
  - 7.1 Introduction
  - 7.2 But First, Weighted Nonlinear Least Squares Regression
  - 7.3 Using Bayes’ Theorem in Analyzing Data, Using Parametric PK Models
  - 7.4 Bayesian Analysis for Nonparametric (NP) Models
  - 7.5 Hybrid Bayesian Analysis
  - 7.6 The Interacting Multiple Model (IMM) Bayesian Approach to Unstable ICU Patients
  - 7.7 Using the Augmented Population Model from the Hybrid as the Bayesian Prior for Subsequent IMM Analysis
  - 7.8 Conclusion
  - References
  - Appendix: More Detail on Nonparametric Bayesian Analysis
- Chapter 8. Monitoring Each Patient Optimally: When to Obtain the Best Samples for Therapeutic Drug Monitoring
  - Abstract
  - 8.1 Introduction
  - 8.2 Optimizing Therapeutic Drug Monitoring (TDM) Protocols and Policies
  - 8.3 D-Optimal Design and Its Variants
  - 8.4 D-Optimal Times Also Depend Upon the Dosage Format
  - 8.5 Multiple Model Optimal (MMopt) design
  - 8.6 New Specific Clinical Tasks that Can Also Be Optimized with WEIGHTED MMopt (wMMopt)
  - 8.7 Conclusion
  - References
- Chapter 9. Optimizing Individualized Drug Therapy in the ICU
  - Abstract
  - 9.1 Introduction
  - 9.2 Renal Function
  - 9.3 Apparent Volume of Distribution, Drug Elimination, and Clearance
  - 9.4 Increased and Changing V and “Augmented Renal Clearance” in ICU Patients
  - 9.5 Tracking Drug Behavior Optimally in Unstable Patients
  - 9.6 An Illustrative Chronic Dialysis Patient With Sepsis
  - 9.7 IMM Analysis of the Patient’s Data
  - 9.8 Another Patient, Highly Unstable, With High Intraindividual Variability
  - 9.9 Two New Moves to Further Improve the IMM Approach
  - 9.10 Summary
  - References
- Chapter 10. Quantitative Modeling of Diffusion Into Endocardial Vegetations, the Postantibiotic Effect, and Bacterial Growth and Kill
  - Abstract
  - 10.1 Introduction
  - 10.2 Diffusion Into Endocardial Vegetations
  - 10.3 Simulating a Small Microorganism
  - 10.4 Modeling Bacterial Growth and Kill
  - 10.5 An Illustrative Case
  - Acknowledgements
  - References
- Chapter 11. Individualizing Digoxin Therapy
  - Abstract
  - 11.1 Introduction
  - 11.2 The Population Model of Digoxin
  - 11.3 Implications for Dosage
  - 11.4 Adjusting Initial Dosage to Body Weight and Renal Function
  - 11.5 Variability in Response: The Need for Monitoring Serum Concentrations and Dosage Adjustment
  - 11.6 The Very Wide Spectrum of Serum Digoxin Concentrations and Patient Responses
  - 11.7 Management of Patients with Atrial Fibrillation and Flutter
  - 11.8 An Illustrative Patient
  - 11.9 Another Patient Who Converted Three Times but Relapsed
  - 11.10 Another Case—A Very Large, Heavy Patient Who Did Not Convert
  - 11.11 Ratios Between Central and Peripheral Compartments
  - 11.12 The Effect of Serum Potassium
  - 11.13 A Very Relevant Patient
  - References
Section III: Clinical Applications of Individualized Therapy
- Chapter 12. Optimizing Single-Drug Antibacterial and Antifungal Therapy
  - Abstract
  - 12.1 Introduction
  - 12.2 Minimum Inhibitory Concentration
  - 12.3 Breakpoints
  - 12.4 The Approach
  - 12.5 Antifungal Agents
  - 12.6 Use of Therapeutic Drug Management and Multiple Model Bayesian Adaptive Control of Dosage Regimens
  - 12.7 Problems with Trough-Only Sampling
  - 12.8 An Illustrative Patient
  - 12.9 Issues in Fitting Data
  - 12.10 The Approach to the Patient
  - 12.11 Voriconazole
  - 12.12 An Illustrative Patient
  - 12.13 Evaluation of Dosage Guidelines
  - 12.14 Another Illustrative Patient
  - 12.15 Conclusion
  - References
- Chapter 13. Combination Chemotherapy With Anti-Infective Agents
  - Abstract
  - 13.1 Why Employ Combination Therapy?
  - 13.2 Increased Spectrum of Empirical Coverage
  - 13.3 Increased Bacterial Kill with Additive or Synergistic Interaction
  - 13.4 What are Synergy, Additivity, and Antagonism?
  - 13.5 Suppression of Amplification of Less-Susceptible Subpopulations
  - 13.6 Suppression of Protein Expression (If One Agent is a Protein Synthesis Inhibitor)
  - 13.7 Summary
  - References
- Chapter 14. Controlling Antiretroviral Therapy in Children and Adolescents with HIV Infection
  - Abstract
  - 14.1 Introduction
  - 14.2 Pharmacokinetics (PK)
  - 14.3 Pharmacodynamics (PD)
  - 14.4 Pharmacogenomics (PG)
  - 14.5 ARV Therapeutic Drug Monitoring/Management (TDM)
  - 14.6 Multiple-Model Bayesian Adaptive Control: Case Examples
  - 14.7 Patients
  - 14.8 Patient 1. General Techniques and the Need for Nonstandard Dosage Schedules
  - 14.9 Patient 2. Unsuspected Impaired Clearance: Patients Needing Smaller Doses Than Usual
  - 14.10 Patient 3. Low Concentrations: Underdosing or Poor Adherence?
  - 14.11 Patient 4. Adolescents: Should They Get Adult Doses?
  - 14.12 Patient 5. Extrapolating from Adults to Children
  - 14.13 Moving Forward
  - Acknowledgments
  - References
- Chapter 15. Individualizing Tuberculosis Therapy
  - Abstract
  - 15.1 Introduction: The WHO and Public Health Approach to Anti-TB Drug Dosing: One-Size-Fits-All
  - 15.2 The Rationale for Dose Individualization of Anti-TB Drugs
  - 15.3 How to Individualize Anti-TB Drug Regimens
  - 15.4 Conclusions
  - References
- Chapter 16. Individualizing Transplant Therapy
  - Abstract
  - 16.1 Introduction
  - 16.2 Calcineurin Inhibitors (CNI)
  - 16.3 Overall Summary
  - References
- Chapter 17. Individualizing Dosage Regimens of Antineoplastic Agents
  - Abstract
  - 17.1 History and Current Status
  - 17.2 Conclusions
  - References
- Chapter 18. Controlling Busulfan Therapy in Children
  - Abstract
  - 18.1 Introduction
  - 18.2 Discussion
  - 18.3 Conclusion
  - References
- Chapter 19. Individualizing Antiepileptic Therapy for Patients
  - Abstract
  - 19.1 Introduction
  - 19.2 Population Modeling: Results
  - 19.3 External Validation
  - 19.4 More Complex Nonlinear PK Models
  - 19.5 Indications for TDM and Individualizing AED Dosage
  - 19.6 Conclusion
  - Acknowledgments
  - References
- Chapter 20. Individualizing Drug Therapy in the Elderly
  - Abstract
  - 20.1 Introduction
  - 20.2 Highlights of Some Biological Aspects of Aging
  - 20.3 Pharmacodynamic Changes in the Elderly and Their Therapeutic Implications
  - 20.4 The Renal Aging Process and its Pharmacokinetic Consequences
  - 20.5 A Special Case: Intraindividual Variability in the Elderly
  - 20.6 Conclusions and Perspectives
  - Acknowledgments
  - References
- Chapter 21. The Present and Future State of Individualized Therapy
  - Abstract
  - 21.1 Models of Large, Nonlinear Systems of Multiple Interacting Drugs
  - 21.2 Equations Without Constant Coefficients
  - 21.3 The Pharmaceutical Industry, Doses, Patients, and Missed Opportunities
  - 21.4 The Pharmaceutical Industry and Clinical Trials
  - 21.5 Bayes’ Theorem and Medical Decisions
  - 21.6 The Two-Armed Bandit
  - 21.7 Conclusion—Monitor Each Patient Optimally and Control the System Optimally
  - References
Index

Roger W Jelliffe

Roger Jelliffe MD, FCP, FAAPS, an adult cardiologist, developed the first software, for individualizing digitalis dosage, in 1967. He founded the USC Laboratory of Applied Pharmacokinetics and directed it until he retired and had recruited Dr. Michael Neely, who became director in 2013. The laboratory has developed what is now the USC Pmetrics software for population modeling and the Bestdose clinical software for individualizing drug dosage regimens specifically with maximum precision, including several different Bayesian methods for managing individual patients in various clinical situations. The laboratory has contributed significantly to optimize therapy for cardiovascular, bacterial and fungal diseases, transplants, and for acutely ill and unstable patients in the ICU.

Affiliations and expertise

Professor of Medicine Emeritus, University of Southern California School of Medicine, Los Angeles, CA; Founder and Director Emeritus, Laboratory of Applied Pharmacokinetics and Bioinformatics, University of Southern California, Los Angeles, CA; Consultant in Infectious Diseases, Children's Hospital of Los Angeles, Los Angeles, CA, USA

Michael Neely

Dr. Neely is an Associate Professor of Pediatrics and a Clinical Scholar at the University of Southern California (USC) in Los Angeles, CA. He is a board-certified pediatric infectious diseases physician with an active clinical practice at the Children’s Hospital of Los Angeles (CHLA). His research interests are in pediatric clinical pharmacometrics, including population pharmacokinetic and pharmacodynamic modeling, pharmacogenomics, simulation, and most importantly, use of models to optimize therapy for individual patients. He is currently the Director of the Laboratory of Applied Pharmacokinetics and Bioinformatics at the CHLA Saban Research Institute. LAPKB is a multidisciplinary team of physicians, mathematicians, statisticians, engineers, and information technologists, who are leading experts in non-parametric population modeling and multiple-model Bayesian adaptive control of therapeutic drug regimens in individual patients. LAPKB maintains the freely available Pmetrics pharmacometric package for R, and the BestDose software for individualized dosing. LAPKB has numerous local, national and international collaborators.

Dr. Neely has a Master’s of Science Degree in Clinical and Biomedical Investigations at USC, is a Fellow and Regent in the American College of Clinical Pharmacology, a member of the Society for Pediatric Research, and he consults on the United States Food and Drug Administration Anti-infective Drug Advisory Committee. He is currently the principle investigator on two NIH-sponsored clinical projects involving the developmental aspects of voriconazole pharmacokinetics, as well as creating software tools to optimally dose voriconazole, vancomycin, and other therapeutic drugs. He mentors numerous trainees and visiting scholars, is an invited speaker worldwide, and is the author of over 80 peer-reviewed papers.

Affiliations and expertise

Associate Professor of Pediatrics and Clinical Scholar, University of Southern California, Los Angeles, CA; Director, Laboratory of Applied Pharmacokinetics and Bioinformatics, Children's Hospital Los Angeles Saban Research Institute, Los Angeles, CA, USA

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