¡Oferta! INDIVIDUALIZED DRUG THERAPY FOR PATIENTS

INDIVIDUALIZED DRUG THERAPY FOR PATIENTS

Autor(es): JELLIFFE
Editorial: ACADEMIC PRESS
Fecha de publicación: noviembre 2016
Nº de edición:
Nº de páginas: 434
Medidas: 19x24x3 cms

9780128033487

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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

 

 

 

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INDIVIDUALIZED DRUG THERAPY FOR PATIENTS

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INDIVIDUALIZED DRUG THERAPY FOR PATIENTS

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