INDIVIDUALIZED DRUG THERAPY FOR PATIENTS

DESCRIPTION

• Uses pharmacokinetic approaches as the tools with which therapy is individualized

• Provides examples using specific software that illustrate how best to apply these approaches and to make sense of the more sophisticated mathematical foundations upon which this book is based

• Incorporates clinical cases throughout to illustrate the real-world benefits of using these approaches

• Focuses on quantitative approaches that maximize the precision with which dosage regimens of potentially toxic drugs can hit a desired therapeutic goal

TABLE OF CONTENTS

• Dedication

• List of Contributors

• Preface
  • Reference

• Acknowledgments

• Introduction: Dont Just DoseChoose a Specific Target Goal, Suited to the Patients Need, and Dose to Hit it Most Precisely
  • 1 Ways of ThinkingQualitative 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 IdeaDose and Half-TimeThey 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 PatientsMultiple 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 Patients 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 CockcroftGault Estimation When SCr is Stable

    • 5.9 Should Ideal Body Weight Be Used Instead of Total Body Weight?

    • 5.10 Changing SCrThe Direct Clinical Link Between the Patients Changing Renal Function and Drug Behavior

    • 5.11 Summary

    • References

• Section II: The Clinical Software
  • Chapter 6. Using the BestDose Clinical SoftwareExamples With Aminoglycosides
    • Abstract

    • 6.1 IntroductionThe BestDose Clinical Software

    • 6.2 Two Representative DrugsAmikacin and Gentamicin

    • 6.3 Planning the Initial Regimen

    • 6.4 Analyzing a Gentamicin Patients 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 Patients 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 CaseA 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 ConclusionMonitor Each Patient Optimally and Control the System Optimally

    • References

• Index