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by Diane R. Mould, Honghui Zhou
Quantitative Pharmacology and Individualized Therapy Strategies in Development of Therapeutic Proteins for Immune-Mediated Inflammatory Diseases
Cover
About the Editors
Foreword
References
Preface
1 Disease Interception in Autoimmune Diseases: From a Conceptual Framework to Practical Implementation
1.1 Introduction to Disease Interception
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
Acknowledgments
References
2 The Role of Biomarkers in Treatment Algorithms for Ulcerative Colitis (UC)
2.1 Background
2.2 Histology
2.3 Treatment Algorithms
2.4 Assessing Response to Therapy
2.5 Predicting Relapse
2.6 Summary
References
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
3.3 Target Engagement of Biologics at Site of Action
3.4 Translational Application of Mechanistic PBPK Modeling
3.5 Conclusion
References
4 Application of Minimal Anticipated Biological Effect Level (MABEL) in Human Starting Dose Selection for 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
4.4 Incorporating Mabel Approach in FIH Starting Dose Selection for High‐Risk Immunomodulatory Protein Therapeutics
4.5 Case Studies of Mabel Calculation
4.6 Discussion and Conclusion
References
5 5Model‐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
5.5 MBMA Literature Search
5.6 Kinetic‐Pharmacodynamic Models
5.7 MBMA Implications for Inflammatory Bowel Disease
5.8 Example 2: MBMA in Rheumatoid Arthritis
5.9 Conclusion
References
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
6.4 Structural Correlation Modeling Between a Continuous Endpoint and a Categorical Endpoint
6.5 Conclusion
References
7 Modeling 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
7.6 Identifiability of Model Parameters
7.7 Summary
References
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
8.4 How to Select Correct Approximation
8.5 Numerical Implementation
8.6 Summary
References
Appendix Diagnostic Plots
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
9.4 Translational Considerations
9.5 Immunogenicity
9.6 Clinical Development of BsAbs
9.7 Conclusion
References
10 Application of Pharmacometrics and Systems Pharmacology to Current and Emerging Biologics in Inflammatory Bowel Diseases
10.1 Introduction
10.2 Pharmacological Approaches for the Treatment of IBD
10.3 Mathematical Models in IBD
10.4 Role of FDA in the Drug Development of Biologics in the Treatment of IBD
10.5 Summary
References
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
References
12 Pharmacokinetics‐Based Dosing Strategies for Therapeutic Proteins in Inflammatory Bowel Disease
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
12.7 Summary and Conclusions
References
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
References
14 Quantitative Pharmacology Assessment Strategy Therapeutic Proteins in Pediatric Subjects – Challenges and Opportunities
14.1 Introduction
14.2 Extrapolation of Efficacy
14.3 Initiation of Pediatric Trials
14.4 Trial Design Considerations
14.5 Challenges in Pediatric Trials for First‐in‐Class vs. Follow‐on Drug‐in‐Class
References
15 Case Examples of Using Quantitative Pharmacology in Developing Therapeutic Proteins for Plaque Psoriasis – Guselkumab
15.1 Introduction
15.2 Understanding of Exposure–Response (ER) Relationship of Guselkumab in Psoriasis
15.3 Dose Selection for Guselkumab in Psoriasis
15.4 Quantitative Pharmacology in Post‐submission Support
15.5 Conclusion
References
16 Vedolizumab—A Case Example of Using Quantitative Pharmacology in Developing Therapeutic Biologics in Inflammatory Bowel Disease
Abbreviations
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 vs. 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
Disclosure
References
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 The 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
References
18 Case Examples of Using Quantitative Pharmacology in Developing Therapeutic Proteins 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
18.3 Population PK/PD Analyses of Daclizumab Beta and Phase 3 Dose Selection
18.4 Model‐Based Approach for the Clinical Development of Subcutaneous Natalizumab
18.5 Summary
References
Index
End User License Agreement
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Title Page
Table of Contents
Cover
About the Editors
Foreword
References
Preface
1 Disease Interception in Autoimmune Diseases: From a Conceptual Framework to Practical Implementation
1.1 Introduction to Disease Interception
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
Acknowledgments
References
2 The Role of Biomarkers in Treatment Algorithms for Ulcerative Colitis (UC)
2.1 Background
2.2 Histology
2.3 Treatment Algorithms
2.4 Assessing Response to Therapy
2.5 Predicting Relapse
2.6 Summary
References
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
3.3 Target Engagement of Biologics at Site of Action
3.4 Translational Application of Mechanistic PBPK Modeling
3.5 Conclusion
References
4 Application of Minimal Anticipated Biological Effect Level (MABEL) in Human Starting Dose Selection for 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
4.4 Incorporating Mabel Approach in FIH Starting Dose Selection for High‐Risk Immunomodulatory Protein Therapeutics
4.5 Case Studies of Mabel Calculation
4.6 Discussion and Conclusion
References
5 5Model‐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
5.5 MBMA Literature Search
5.6 Kinetic‐Pharmacodynamic Models
5.7 MBMA Implications for Inflammatory Bowel Disease
5.8 Example 2: MBMA in Rheumatoid Arthritis
5.9 Conclusion
References
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
6.4 Structural Correlation Modeling Between a Continuous Endpoint and a Categorical Endpoint
6.5 Conclusion
References
7 Modeling 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
7.6 Identifiability of Model Parameters
7.7 Summary
References
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
8.4 How to Select Correct Approximation
8.5 Numerical Implementation
8.6 Summary
References
Appendix Diagnostic Plots
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
9.4 Translational Considerations
9.5 Immunogenicity
9.6 Clinical Development of BsAbs
9.7 Conclusion
References
10 Application of Pharmacometrics and Systems Pharmacology to Current and Emerging Biologics in Inflammatory Bowel Diseases
10.1 Introduction
10.2 Pharmacological Approaches for the Treatment of IBD
10.3 Mathematical Models in IBD
10.4 Role of FDA in the Drug Development of Biologics in the Treatment of IBD
10.5 Summary
References
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
References
12 Pharmacokinetics‐Based Dosing Strategies for Therapeutic Proteins in Inflammatory Bowel Disease
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
12.7 Summary and Conclusions
References
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
References
14 Quantitative Pharmacology Assessment Strategy Therapeutic Proteins in Pediatric Subjects – Challenges and Opportunities
14.1 Introduction
14.2 Extrapolation of Efficacy
14.3 Initiation of Pediatric Trials
14.4 Trial Design Considerations
14.5 Challenges in Pediatric Trials for First‐in‐Class vs. Follow‐on Drug‐in‐Class
References
15 Case Examples of Using Quantitative Pharmacology in Developing Therapeutic Proteins for Plaque Psoriasis – Guselkumab
15.1 Introduction
15.2 Understanding of Exposure–Response (ER) Relationship of Guselkumab in Psoriasis
15.3 Dose Selection for Guselkumab in Psoriasis
15.4 Quantitative Pharmacology in Post‐submission Support
15.5 Conclusion
References
16 Vedolizumab—A Case Example of Using Quantitative Pharmacology in Developing Therapeutic Biologics in Inflammatory Bowel Disease
Abbreviations
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 vs. 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
Disclosure
References
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 The 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
References
18 Case Examples of Using Quantitative Pharmacology in Developing Therapeutic Proteins 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
18.3 Population PK/PD Analyses of Daclizumab Beta and Phase 3 Dose Selection
18.4 Model‐Based Approach for the Clinical Development of Subcutaneous Natalizumab
18.5 Summary
References
Index
End User License Agreement
List of Tables
Chapter 4
Table 4.1 BMS‐931699 FIH starting dose selection using MABEL approach.
Table 4.2 BMS‐986004 FIH starting dose selection using MABEL approach.
Table 4.3 P‐cadherin LP‐DART FIH starting dose selection using MABEL approach.
Chapter 5
Table 5.1 Data sources used to develop the CDAI, CR100, CRP, and immunogenicity ...
Table 5.2 Final model parameters.
Chapter 6
Table 6.1 Number of subjects and observations in study PSUMMIT I.
Table 6.2 Initial PASI‐ACR exposure–response model parameter estimates.
Table 6.3 DAS28 exposure–response model main effect parameter estimates.
Table 6.4 DAS28 exposure–response model between‐subject random effect parameter ...
Table 6.5 ACR exposure–response model parameter estimates.
Chapter 7
Table 7.1 Typical ranges of parameters for therapeutic monoclonal antibodies.
Chapter 10
Table 10.1 Summary of risk factors for the development of IBD.
Table 10.2 Survey of FDA‐approved biologics in IBD and their targets (biomarkers...
Table 10.3 Summary of the clinical trials for biologics in inflammatory bowel di...
Chapter 12
Table 12.1 Varying clearance of therapeutic monoclonal antibodies across differe...
Table 12.2 Characteristics of study designs.
Chapter 13
Table 13.1 List of approved mAbs in Europe (European Medicine Agency, EMA) and U...
Table 13.2 Examples of pharmacokinetic parameter values and dosing regimens in a...
Table 13.3 Examples of the clearance (CL) and/or volume of distribution (
V
) valu...
Table 13.4 List of mPBPK model parameters based on the level of evidence (LOE).
Table 13.5 List of mPBPK model parameters and the allometric setting used for si...
Chapter 14
Table 14.1 Trials for regulatory approval in adult and pediatric ulcerative coli...
Table 14.2 Adalimumab pediatric and adult CD studies.
Chapter 15
Table 15.1 Serum guselkumab concentrations (µg ml
−1
) at Week 40 by treatme...
Table 15.2 Serum guselkumab concentrations (µg ml
−1
) observed at Week 28 (...
Table 15.3 Parameter estimates from final landmark ER models.
Chapter 16
Table 16.1 95% Confidence intervals from the nonparametric bootstrap for the fin...
Table 16.2 Summary statistics of the simulated trough concentrations at steady s...
Table 16.3 Summary statistics of the simulated trough concentrations at steady s...
Table 16.4 Summary of baseline body weight reported by subjects in the GEMINI 1,...
Table 16.5 Median trough concentrations for vedolizumab at the end of induction ...
Table 16.6 Median trough concentrations for vedolizumab at steady state (Week 46...
Table 16.7 Remission rates at Week 6 in the GEMINI 1 trial stratified by body we...
Table 16.8 Remission rates at Week 6 in the GEMINI 2 and GEMINI 3 trials stratif...
Chapter 18
Table 18.1 Peginterferon beta‐1a population PK parameter estimates based on nonp...
Table 18.2 Parameter estimates of the AUC‐Gd + lesion count mixture negative bin...
Table 18.3 Parameter estimates of the AUC‐T2 lesion count mixture negative binom...
Table 18.4 Summary of AUC‐relapse model parameter estimates.
Table 18.5 PD model parameter estimate summary for daclizumab HYP.
Table 18.6 Parameter estimates of the final natalizumab population pharmacokinet...
Table 18.7 Parameter estimates of the final natalizumab pharmacodynamic model ba...
List of Illustrations
Chapter 1
Figure 1.1 Graphical progression of an individual from a normal state toward a...
Figure 1.2 Window for disease interception in type 1 diabetes. Progression of a...
Figure 1.3 Utilizing the pathogenic immune repertoire for identifying earliest ...
Chapter 3
Figure 3.1 First‐generation mPBPK model with additional target/elimina...
Figure 3.2 Second‐generation PBPK model with an additional target tissu...
Figure 3.3 Second‐generation mPBPK model with TMDD implemented. Model s...
Figure 3.4 Model scheme for a biologic translation application. Model s...
Figure 3.5 Translational projection of free IL‐23 suppression by usteki...
Figure 3.6 Simulated guselkumab serum concentration profiles (a) and fr...
Figure 3.7 Simulated free IL‐23 concentration profiles in serum (a) and...
Chapter 4
Figure 4.1 Approaches for FIH safe starting dose selection for novel i...
Chapter 5
Figure 5.1 Model diagram.
K
e
, elimination rate constant.
Figure 5.2 Crohn's Disease Activity Index (CDAI).
Figure 5.3 Predicted median Crohn's Disease Activity Index (CDAI). IV - intrave...
Figure 5.4 Range of CDAI response following labeled infliximab dosing regimens.
Figure 5.5 Median CDAI following a labeled infliximab dosing regimens. Label – ...
Figure 5.6 Median CDAI following a labeled infliximab dosing regimen and a prop...
Figure 5.7 CR100 and infliximab with labeled dosing regimens.
Figure 5.8 C‐reactive protein response and visual predictive check plot. mg, mi...
Figure 5.9 C‐reactive protein and infliximab proposed dosing regimens.
Figure 5.10 Median C‐reactive protein concentrations following infliximab treat...
Figure 5.11 Visual predicative check of immunogenicity vs. time; The center sol...
Figure 5.12 Percent of subjects with anti‐drug antibodies (ADAs) vs. dose.
Chapter 6
Figure 6.1 Visual predictive check of
American College Rheumatology
(ACR) respo...
Figure 6.2 Visual predictive check of Psoriasis Area and Severity Index (PASI) ...
Figure 6.3 A random sample of observed 28‐joint disease activity (DAS28) scores...
Figure 6.4 Visual predictive check of the 28‐joint disease activity (DAS28) sco...
Figure 6.5 Median model predictions at planned observation times and 90% predic...
Figure 6.6 Median model predictions at planned observation times and 90% predic...
Chapter 7
Figure 7.1 General pharmacokinetic model of target‐mediated drug disposition. S...
Figure 7.2 Schematic representation of the ELISA. Triangles, immobilized coatin...
Figure 7.3 Characteristic concentration–time course following IV bolus doses fo...
Figure 7.4 Characteristic concentration–time course following IV bolus doses: c...
Figure 7.5 Hierarchy of TMDD model approximations.
Chapter 9
Figure 9.1 A selection of different BsAbs scaffolds approved or under developm...
Chapter 10
Figure 10.1 Schematic representation of the multiple factors involved in the pa...
Figure 10.2 General workflow for the integration of multi‐scale systems biology...
Chapter 11
Figure 11.1 Personalized induction dosing of infliximab based on a population ...
Chapter 12
Figure 12.1 Likelihoods contributing to the Bayes estimation objective function...
Figure 12.2 Example of the increasing contribution of individual data. The plot...
Figure 12.3 Time‐course of trough infliximab concentrations for simulation stud...
Figure 12.4 Time‐course of average trough, median and maximum infliximab concen...
Figure 12.5 Implications of sampling and assay differences on Bayesian adaptive...
Chapter 13
Figure 13.1 Pharmacokinetic–pharmacodynamic–disease relationship of pharmacothe...
Figure 13.2 The comparison of AUC variability after body size and fixed dosing ...
Figure 13.3 (a) AUC
0–∞
vs. body weight of the pediatric subjects af...
Figure 13.4 The two‐pore mPBPK model for TmAbs. The antibodies are administered...
Figure 13.5 Observed vs. mPBPK prediction of palivizumab and endogenous/exogeno...
Figure 13.6 (a) Weight normalized FcRn (FcRn
expression
) vs. age, (b) CL vs. bod...
Chapter 15
Figure 15.1 Percentage of patients achieving PASI 100 response through Week 52...
Figure 15.2 Percent of patients achieving (a) PGA 0/1 and (b) PGA 0 at Week 40 ...
Figure 15.3 Percent of patients achieving (a) IGA 0/1 or (b) IGA 0 at Week 28 b...
Figure 15.4 VPC plot of modeling IGA 0/1, IGA 0, and PASI 75/90/100 responses a...
Figure 15.5 Model predicted IGA 0/1, IGA 0, and PASI 75/90/100 responses at Wee...
Figure 15.6 Model predicted IGA 0/1, IGA 0, and PASI 75/90/100 responses at Wee...
Chapter 16
Figure 16.1 Mean serum concentration–time profiles by dose group for vedolizum...
Figure 16.2 Mean serum concentration–time profiles by dose cohort for vedolizum...
Figure 16.3 Relationship between linear clearance, nonlinear clearance, and tot...
Figure 16.4 Individual vedolizumab linear clearance estimates by Mayo endoscopi...
Figure 16.5 Diagrammatic representation of the population pharmacokinetic model...
Figure 16.6 Effects of covariates on linear clearance in the population pharmac...
Figure 16.7 Simulated vedolizumab trough concentrations at Week 6 for the propo...
Figure 16.8 Percentage of patients with clinical remission by vedolizumab troug...
Figure 16.9 Fraction of patients with clinical remission at Week 6 vs. individu...
Figure 16.10 Model‐simulated average exposure–efficacy relationship for clinica...
Figure 16.11 Model‐simulated average exposure–efficacy relationship for clinica...
Chapter 17
Figure 17.1 Comparison of average serum concentrations (
C
avg
) between IV (a) a...
Figure 17.2 Comparison of PK profiles and steady‐state PK parameters for belimu...
Figure 17.3 Baseline IgG concentration vs. half‐life (post hoc parameter estima...
Figure 17.4 Baseline proteinuria vs. central clearance (CL, post hoc parameter ...
Chapter 18
Figure 18.1 Final pharmacokinetic model visual predictive check. Solid line re...
Figure 18.2 Visual predictive check for marginal probability of different lesio...
Figure 18.3 Observed marginal mean Gd+ lesion count by AUC subgroup, overlaid w...
Figure 18.4 Observed marginal mean new or newly enlarged T2 lesion count by mon...
Figure 18.5 Comparison of observed and simulated data for the AUC‐ARR model. Pe...
Figure 18.6 Visual predictive check of the PK model (solid line, median; dotted...
Figure 18.7Figure 18.7 Visual predictive check for CD25 occupancy model (solid ...
Figure 18.8Figure 18.8 Visual predictive check for CD56
bright
NK cell expansion...
Figure 18.9 Visual predictive check for
T
reg
down‐regulation model (solid line,...
Figure 18.10 Simulated (a) CD25 occupancy profile, (b) CD56
bright
NK cell perce...
Figure 18.11 Structural model describing natalizumab pharmacokinetics. Note: CL...
Figure 18.12 Prediction‐corrected visual predictive check for the concentration...
Figure 18.13 Simulated natalizumab PK and PD profiles at steady state.
Figure 18.14 Distribution of simulated natalizumab steady‐state PK–PD parameter...
Guide
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