Quantitative Pharmacology and Individualized Therapy Strategies in Development of Therapeutic Proteins for Immune-Mediated Inflammatory Diseases

List of Contributors

About the editors

Foreword

Preface

Table of Contents

Chapter 1. Disease Interception in Autoimmune Disease: From a Conceptual Framework to Practical Implementation

1.1. Introduction to Disease Interception

1.1.1. What is disease interception and how does this impact our prospective thinking towards novel solutions for patients suffering from autoimmune diseases?

1.2. Disease Interception in Autoimmune Diseases

1.3. Progress in Modulation of The Adaptive Immune Response in Autoimmune Inflammatory Diseases

1.4. The Complex Interplay between the Specificity of the Pathogenic Immune Repertoire and Its Sculpting by the Environment – Implications for Disease Interception

1.5. Clinical Application and Concluding Remarks

Chapter 2: The Role of Biomarkers in Treatment Algorithms for Ulcerative Colitis (UC)

2.1. Background

2.1.1. Serum Biomarkers

2.1.2. Serum Hematologic Markers

2.1.3. Fecal Markers

2.1.3.1. Fecal Calprotectin

2.1.3.2. Additional Fecal Biomarkers

2.1.4. Urine Biomarkers

2.1.5. Endoscopic Outcomes

2.2. Histology

2.2.1. Tissue Markers

2.3. Treatment Algorithms

2.3.1. Differentiating Inflammatory and Non-Inflammatory Disease

2.4. Assessing Response to Therapy

2.5. Predicting Relapse

2.6. Summary

Chapter 3: Mechanism and Physiologically-based PK/PD Model in Assisting Translation from Preclinical to Clinical: Understanding PK/PD of Therapeutic Proteins at Site-of-Action

3.1. Introduction

3.2. Biologic Distribution to Tissue Site of Action

3.2.1. Overview

3.2.2. Bioanalytical Methods for Biologics at Tissue Sites

3.2.3. Full PBPK Model and Minimal PBPK (mPBPK) Model

3.2.4. Application of PBPK and mPBPK Models to Facilitate Understanding of Biologic Tissue Distribution

3.3. Target Engagement of Biologics at Site of Action

3.3.1. Overview

3.3.2. Bioanalytical Methods to Understand Target Engagement by Biologics

3.3.3. Mechanistic PBPK/PD Modeling to Facilitate Understanding of Target Engagement at Site of Action

3.4. Translational Application of Mechanistic PBPK Modeling

3.5. Conclusion

Chapter 4: Application of Minimal Anticipated Biological Effect Level (MABEL) in Human Starting Dose Selection from Immunomodulatory Protein Therapeutics – Principles and Case Studies

4.1. Introduction

4.2. Safety and Immune-Related Toxicities of Immunomodulatory Protein Therapeutics

4.3. Uncertainties of Toxicology Approach in FIH Safe Starting Dose Selection for Immunomodulatory Protein Therapeutics

4.3.1. HED Calculation for Immunomodulatory Protein Therapeutics

4.3.2. Determination of Safety Factor for Immunomodulatory Protein Therapeutics

4.3.3. TGN1412 Incident and Minimal Anticipated Biological Effect Level

4.4. Incorporating MABEL Approach in FIH Starting Dose Selection for High-Risk Immunomodulatory Protein Therapeutics

4.4.1. In Vitro Cytokine Release Assay and Other In Vitro Assays As Toxicity Assessment for Immunomodulatory Protein Therapeutics

4.4.2. Integrate In Vitro Pharmacology Data to Estimate MABEL for High-risk Immunomodulatory Protein Therapeutics

4.5. Case Studies of MABEL Calculation

4.5.1. Case Study I: MABEL Determination for Anti-CD28 Antagonist Domain Antibody BMS-931699

4.5.2. Case Study II: MABEL Determination for Anti-CD40L Receptor Antagonist BMS-986004

4.5.3. Case Study III: MABEL Determination for MOXR0916, an Agonistic Antibody Targeting OX40

4.5.4. Case Study IV: MABEL Determination for Bispecific Immunomodulatory P-Cadherin LP-DART (PF-06671008) in Immune-oncology

4.6. Discussion and Conclusion

Chapter 5: Model-Based Meta-Analysis Use in the Development of Therapeutic Proteins

5.1. Introduction

5.2. Types of MBMA and Database Considerations

5.3. Data Analytic Models Useful for MBMA

5.4. Example 1: MBMA in Inflammatory Bowel Disease

5.4.1. Overview of Inflammatory Bowel Disease and Clinical Endpoints

5.4.2. MBMA for Inflammatory Bowel Disease Treated with Biologics

5.5. MBMA Literature Search

5.6. Kinetic-Pharmacodynamic Models

5.6.1. K-PD Models Results

5.6.1.1. CDAI K-PD Model Results

5.6.1.2. CR100 K-PD Model

5.6.1.3. C-Reactive Protein K-PD Model

5.6.1.4. Immunogenicity K-PD Model

5.7. MBMA Implications for Inflammatory Bowel Disease

5.8. Example 2: MBMA in Rheumatoid Arthritis

5.9. Conclusion

Chapter 6: Utility of Joint Population Exposure-response Modeling Approach to Assess Multiple Continuous and Categorical Endpoints in Immunology Drug Development

6.1. Introduction

6.2. Latent Variable Indirect Response Models

6.3. Residual Correlation Modeling Between A Continuous and A Categorical Endpoint 

6.3.1. Application Example: Ustekinumab in Psoriatic Arthritis (PsA)

6.3.1.1. Population PK Modeling of Ustekinumab in PsA

6.3.1.2. E-R Modeling of Ustekinumab in PsA

6.3.1.3. Application Example Summary of Ustekinumab in PsA

6.4. Structural Correlation Modeling Between a Continuous Endpoint and a Categorical Endpoint

6.4.1. Application Example: Rheumatoid Arthritis

6.4.1.1. Population PK Modeling of mAb X in Rheumatoid Arthritis

6.4.1.2. E-R Modeling of mAb X in Rheumatoid Arthritis

6.4.1.2.1. DAS28 Model Component

6.4.1.2.2. Initial DAS28 Model of mAb X in Rheumatoid Arthritis

6.4.1.2.3. Initial ACR Model of mAb X in Rheumatoid Arthritis

6.4.1.2.4. Joint DASC28-ACR E-R Model of mAb X in Rheumatoid Arthritis

6.4.1.3. Application Example Summary

6.5. Conclusion

Chapter 7: Modelling Approaches to Characterize Target-Mediated Pharmacokinetics and Pharmacodynamics for Therapeutic Proteins

7.1. Introduction

7.2. Target-Mediated Drug Disposition Model

7.3. Data and Practical Considerations

7.4. What to Expect from the Concentration-Time Course

7.5. Approximations of the TMDD Model

7.5.1.  Quasi-Steady-State and Rapid Binding Approximations

7.5.2.  Michaelis-Menten Approximation

7.5.3. Wagner Equation

7.5.4.  Irreversible Binding Approximation

7.5.5.  Hierarchy of TMDD Model Approximations

7.5.6.  Relationship between the QSS Approximation and the Indirect Response Models

7.5.7.  Two-Target TMDD Model and Approximations: Soluble and Membrane Targets

7.5.8.  Latest Developments

7.6. Identifiability of Model Parameters

7.7. Summary

Chapter 8: Tutorial: Numerical (NONMEM) Implementation of the Target-Mediated Drug Disposition Model

8.1. Introduction

8.2. Notations and Data

8.3. NONMEM Code for TMDD Model and Approximations

8.3.1. Full TMDD Model

8.3.2. Quasi-Steady-State and Rapid Binding Approximations

8.3.3. Michaelis-Menten Approximation

8.4. How to Select Correct Approximation

8.4.1. Bottom Up Approach

8.4.2. Approach Based on Biological Considerations

8.5. Numerical Implementation

8.6. Summary

Appendix to Chapter 8

Chapter 9: Translational Considerations in Developing Bispecific Antibodies: What Can We Learn from Quantitative Pharmacology?

9.1. Introduction

9.2. Quantitative Pharmacokinetic Considerations of BsAbs

9.3. Preclinical Considerations

9.3.1. Antibody Properties

9.3.2. Selection of a BsAb Format

9.3.3. In Vitro Models

9.3.4. In Vivo Models

9.3.5. Catumaxomab

9.3.6. Emicizumab

9.3.7. Blinatumomab

9.3.8. Anti TfR/BACE1

9.4. Translational Considerations

9.5. Immunogenicity

9.6. Clinical Development of BsAbs

9.6.1. Catumaxomab

9.6.2. Emicizumab

9.6.3. Blinatumomab

9.7. Conclusion

Chapter 10: Application of Pharmacometrics and Systems Pharmacology to Current and Emerging Biologics in Inflammatory Bowel Diseases

10.1. Introduction

10.1.1. Pathophysiology of IBD

10.1.2. Current Advances in Biomarkers for IBD

10.1.2.1. C-Reactive Protein

10.1.2.2. Fecal Calprotectin

10.1.2.3. Atypical Perinuclear Antineutrophil Cytoplasmic Antibodies (pANCA)

10.1.2.4. Anti-Outer Membrane Porin C

10.1.2.5. Other Mediators of Inflammation

10.2. Pharmacological Approaches for the Treatment of IBD

10.2.1.  Biologics for the Treatment of IBD

10.2.1.1. Tumor Necrosis Factor alpha (TNF-α) Inhibition

10.2.1.2. Side-effects of Anti-TNFα Agents

10.2.2. Emerging Therapeutic Options for Inflammatory Bowel Disease

10.2.2.1. Anti-Adhesion (Anti-Integrin) Molecules

10.2.2.2. Anti-ICAM-1 Therapy

10.2.2.3. Anti-IL-6R Antibodies

10.2.2.4. Immunostimulators

10.2.2.5. T-cell Directed Therapies

10.2.2.6. Fontolizumab

10.2.2.7. Ustekinumab

10.2.2.8. Inhibitors of T-cell Proliferation

10.3. Mathematical Models in IBD

10.3.1. Infliximab

10.3.2. Adalimumab

10.3.3. Certolizumab Pegol

10.3.4 Vedolizumab

10.3.5. Challenges in Systems PK/PD Modeling of mAbs in IBD

10.4. Role of FDA in the Drug Development of Biologics in the Treatment of IBD

10.5. Summary

Chapter 11: Pharmacokinetics-based Dosing for Therapeutic Monoclonal Antibodies in Inflammatory Bowel Disease

11.1. Inflammatory Bowel Disease

11.2. Population Pharmacokinetics

11.3. Exposure-Response

11.4. Exposure-Based Dosing Strategies

11.5. Discussion

Chapter 12: Bayesian adaptive dosing strategies to manage patient variability

12.1. Introduction

12.2. The Need for Understanding and Controlling Variability in Exposure

12.3. History of Dose Individualization

12.4. Bayesian Methods for Dose Individualization

12.5. Clinical Need for Improved Dosing with mAbs

12.6. Expectations for Bayesian Adaptive Dosing

12.6.1. What Bayesian Systems Can Achieve

12.6.2. Limitations of Adaptive Dosing and Bayesian Systems

12.7. Summary and Conclusions

Chapter 13: Quantitative Pharmacology Approach to Select Optimal Dose and Study the Important Factors in Determining Disposition of Therapeutic Monoclonal Antibody in Pediatric Subjects – Some Considerations

13.1. Introduction

13.2. Pharmacokinetics of Therapeutic Monoclonal Antibody in Pediatric Population

13.3. Quantitative Pharmacology Considerations to Select Optimal Pediatric Dose of mAbs Based on Adult PK Studies

13.4. Using mPBPK Model to Study the Effects of FcRn Developmental Pharmacology on the PK of mAbs in Pediatric Subjects

Chapter 14: Quantitative Pharmacology Assessment Strategy Therapeutic Proteins in Pediatric Subjects – Challenges and Opportunities

14.1. Introduction

14.2. Extrapolation of Efficacy

14.2.1. Disease and response similarity between adults and children with UC and CD

14.3. Initiation of Pediatric Trials

14.4. Trial Design Considerations

14.4.1. Dose Selection

14.4.2. Therapeutic Drug Monitoring

14.4.3. Adaptive Design

14.4.4. Advantages and Disadvantages of Using External/Historical Controls

14.4.5. Real World Evidence

14.4.6. Quantitative Systems Pharmacology

14.4.7. Clinical Trial Simulation

14.5. Challenges in pediatric trials for first-in-class vs. follow-on drugs in-class

Chapter 15: Case Examples of Using Quantitative Pharmacology in Developing Therapeutic Proteins in Plaque Psoriasis – Guselkumab

15.1. Introduction

15.1.1. Pathogenesis of Plaque Psoriasis

15.1.2. Current Treatment Paradigms for Psoriasis

15.2. Understanding of Exposure-Response (ER) Relationship of Guselkumab in Psoriasis

15.2.1. Phase 1 Study (PSO1001)

15.2.2. Phase 2 Study (X-PLORE)

15.2.3. Phase 3 Studies (VOYAGE 1 and 2)

15.2.4. Methodologies Used in Dose-Response and Exposure-Response Analyses

15.2.4.1. Dose-Response Analyses

15.2.4.2. Correlational Quantitative Analyses

15.2.4.3. Landmark Modeling Analyses

15.2.4.4. Longitudinal Modeling Analyses

15.2.4.5. Model-Based Simulations

15.3. DOSE SELECTION FOR GUSELKUMAB IN PSORIASIS

15.3.1. Step 1: Exposure-Response Analyses Using Data from Phase 1 (PSO1001) to Design Phase 2b (X-PLORE)

15.3.1.1. Dose-Response Analyses in Phase 1 (PSO1001, Part 2)

15.3.1.2. Exposure-Response Modeling Analyses in Phase 1 (PSO1001, Part 2)

15.3.2. Step 2: Exposure-Response Analyses Using Data from Phase 2 (X-PLORE) to Design Phase 3 (VOYAGE 1 and 2)

15.3.2.1. Dose-Response Analysis in Phase 2 (X-PLORE)

15. 3.2.2. Correlational Quantitative Analysis in Phase 2 (X-PLORE)

15.3.2.3. Model-Based Exposure-Response Analyses in Phase 2 (X-PLORE)

15.3.3. Step 3. Exposure-Response Analyses Using Data from Phase 3 (VOYAGE 1 and 2) to Confirm the ER Relationship Established from Phase 2 and Provide Dose Recommendations

15.3.3.1. Correlational Quantitative Analysis in Phase 3 (VOYAGE 1 and 2)

15.3.3.2. Landmark Modeling Analysis in Phase 3 (VOYAGE 1 and 2)

15.3.3.2.1. Population PK and Model Predicted Exposure Parameters

15.3.3.2.2. Logistic Regression

15.3.3.2.3. Covariate Analysis in ER Modeling

15.3.3.2.4. Landmark ER Modeling Analysis

15.3.3.3. Longitudinal Modeling Analysis in Phase 3 (VOYAGE 1 and 2)

15.3.4. Step 4. Model-Based Simulations to Support Dose Recommendations

15.3.4.1. Simulation of Alternative Doses to Support To-be Marketed Dose

15.3.4.2. Simulation of Covariate Effect to Evaluate Dose Adjustment Needs in Subgroups

15.3.4.3. Guselkumab Dose Recommendations

15.4. Quantitative Pharmacology in Post-Submission Support

15.5. Conclusion

Chapter 16: Case Examples of Using Quantitative Pharmacology in Developing Therapeutic Proteins in Inflammatory Bowel Disease – Vedolizumab

16.1. Introduction

16.2. Dose Selection for Adult Patients in Phase 3 Trials

16.3. Pharmacokinetic Profile of Vedolizumab

16.4. Population Pharmacokinetics in Phase 1 and 2 Trials

16.5. Comparison of Simulated Versus Measured Vedolizumab Trough Concentrations

16.6. Population Pharmacokinetics in Phase 3 Trials

16.7. Dose Selection for Pediatric Populations

16.8. Exposure-Response Analysis

16.9. Logistic Regression Analyses

16.10. Exposure-Response: Causal Inferences

16.11. Conclusion

Chapter 17: Case Examples of Using Quantitative Pharmacology in Developing Therapeutic Proteins in Systemic Lupus Erythematosus – Belimumab

17.1. Introduction

17.2. Overview of Supporting Data and Methods

17.3. Body Size Characterizations and Impact on Switching from Weight Proportional to Fixed Dosing

17.4. Then Yin and Yang of FcRn – Opposing Effect of Albumin and IgG on mAb Clearance

17.5. Lost in Filtration – Renal Contributions to mAb Clearance

17.6. Conclusion

Chapter 18: Case Examples of Using Quantitative Pharmacology in Developing Therapeutic Proteins in in Multiple Sclerosis – Peginterferon Beta-1a, Daclizumab Beta, Natalizumab

18.1. Introduction

18.2. Application of Quantitative Clinical Pharmacology for Dosing Regimen Recommendation of Peginterferon Beta 1a

18.2.1. Background of Peginterferon Beta-1a

18.2.2. Peginterferon Beta-1a Population PK Model

18.2.3. AUC-Gd+ Lesion Count Model for Peginterferon Beta-1a

18.2.4. AUC-T2 Lesion Count Model for Peginterferon Beta-1a

18.2.5. AUC-ARR Model for Peginterferon Beta-1a

18.2.6. Label Recommendation

18.3. Population PK/PD Analyses of Daclizumab Beta and Phase 3 Dose Selection

18.3.1. Daclizumab Beta Population PK Model

18.3.2. PK/PD Model

18.3.3. Simulation in Support of Phase 3 Dose Selection

18.4. Model-Based Approach for the Clinical Development of Subcutaneous Natalizumab

18.4.1. Pharmacokinetic Model of Natalizumab

18.4.2. Natalizumab Pharmacodynamic Model

18.4.3. Simulation for Natalizumab SC dose selection

18.5. Summary

Index

Thorough Overview Identifies and Addresses Critical Gaps in the Treatment of Several Chronic Diseases.

With increasing numbers of patients suffering from Immune-Mediated Inflammatory Diseases (IMIDs), and with the increasing reliance on biopharmaceuticals to treat them, it is imperative that researchers and medical practitioners have a thorough understanding of the absorption, distribution, metabolism and excretion (ADME) of therapeutic proteins as well as translational pharmacokinetic/pharmacodynamic (PK/PD) modeling for them. This comprehensive volume answers that need to be addressed.

Featuring eighteen chapters from world-renowned experts and opinion leaders in pharmacology, translational medicine and immunology, editors Honghui Zhou and Diane Mould have curated a much-needed collection of research on the advanced applications of pharmacometrics and systems pharmacology to the development of biotherapeutics and individualized treatment strategies for the treatment of IMIDs. Authors discuss the pathophysiology of autoimmune diseases in addition to both theoretical and practical aspects of quantitative pharmacology for therapeutic proteins, current translational medicine research methodologies and novel thinking in treatment paradigm strategies for IMIDs. Other notable features include:

• Contributions from well-known authors representing leading academic research centers, specialized contract research organizations and pharmaceutical industries whose pipelines include therapeutic proteins
• Chapters on a wide range of topics (e.g., pathophysiology of autoimmune diseases, biomarkers in ulcerative colitis, model-based meta-analysis use in the development of therapeutic proteins)
• Case studies of applying quantitative pharmacology approaches to guiding therapeutic protein drug development in IMIDs such as psoriasis, inflammatory bowel disease, multiple sclerosis and lupus

Zhou and Mould’s timely contribution to the critical study of biopharmaceuticals is a valuable resource for any academic and industry researcher working in pharmacokinetics, pharmacology, biochemistry, or biotechnology as well as the many clinicians seeking the safest and most effective treatments for patients dealing with chronic immune disorders.

About the Author

HONGHUI ZHOU, PHD, FCP, FAAPS, is a Senior Director and Janssen Fellow as well as US Head of Pharmacometrics at Janssen Research & Development, LLC.

DIANE R. MOULD, PHD, FCP, FAAPS, is President of Projections Research Inc., a consulting company offering pharmacokinetic and pharmacometric services, and the founder of Baysient LLC, a company that develops systems to individualize doses of drugs that are difficult to manage.