Model-Based Optimal Design and Execution of the First-Inpatient Trial of the Anti-IL-6, Olokizumab

Area of Science:

• Pharmacometrics
• Clinical Trial Design
• Immunology

Background:

• Olokizumab (OKZ) is a novel therapeutic agent.
• Understanding the pharmacokinetic/pharmacodynamic (PK/PD) relationship of OKZ is crucial for optimal dosing.
• Existing models for similar drugs (e.g., tocilizumab) provide a basis for PK/PD analysis.

Purpose of the Study:

• To define the concentration-response relationship for C-reactive protein (CRP) suppression by OKZ.
• To employ model-based adaptive design for efficient clinical trial execution.
• To optimize study design and sample size dynamically based on emerging data.

Main Methods:

• Reverse-engineering of tocilizumab PK/PD models.
• Adaptation of models to OKZ using preclinical data and a priori knowledge.
• Application of a multidimensional Desirability Index for optimal study design.
• Adaptive sample size reestimation and interim analyses for study part optimization using Bayesian methods.

Main Results:

• The initial design suggested 72 patients were sufficient to explore the dose window (0.1-3 mg/kg) for CRP suppression.
• Adaptive execution led to a 50% reduction in the required sample size.
• Interim analyses confirmed the attainment of PK/PD objectives, demonstrating study efficiency.

Conclusions:

• Model-based adaptive design is an efficient and optimal strategy for early-phase clinical trials.
• This approach allows for dynamic adjustments to study design, leading to resource optimization.
• The study successfully defined the OKZ concentration-CRP suppression response, paving the way for further development.