Module 4: Foundations of Model-Informed Drug Development (MIDD)

Introduction

• Focus on the foundation of MIDD and Modelled Interconnectedness
• Developed by Adekimi Taylor, Rajash Krishna, Amy Chung at Cetara, in collaboration with Critical Path Institute

Drivers of Uncertainty in Drug Development

• Confidence in targets, drug, chosen endpoints, and regulatory decisions
• MIDD as a rational approach to accelerate drug development
• Five key objectives: pathway, target, drug, risk-benefit, payer perspectives

Phases of Drug Development

• Discovery: Target knowledge, metabolism, molecule design, synthetic pathway, off-target effects
• Preclinical: Choosing compounds, first human dose, drug interactions, cardiac safety, biomarkers
• Early Clinical: Proof of concept, best dose, risk-benefit profile, subpopulations, combination therapy
• Late Clinical: Development continuation, therapeutic window, study design, pricing, market access
• Commercial: Benefit-risk assessment, real-world evidence, market access, additional indications

MIDD Strategic Plan

• Identify key R&D questions for MIDD impact
• Seven themes: Medical need, efficacy, safety, pharmacokinetics, benefit-risk, clinical viability, study design
• Modeling approach selection, assumption setting, evaluation strategy

Model Types

• Empirical Models: Simple drug effect description (linear, hyperbolic)
• Semi-Mechanistic PK/PD Models: Biological and pharmacological mechanisms
• Model-Based Meta-Analysis: Comparative risk-benefit analysis using trial-level data
• Quantitative Systems Pharmacology (QSP) and PBPK Models: Biological, physiological processes
• Epidemiology Models: Disease spread and status transitions
• Health Economics and Outcomes Research (HEOR) Models: Economic analysis of healthcare interventions

Considerations for Modeling

• Choose based on purpose, questions, data, and knowledge
• Time and resources impact model complexity
• Importance of a fit-for-purpose model

Data in Modeling

• Individual-level and aggregate-level data
• Real-world data from outside clinical trials
• System and drug property data

Extrapolation and Strategy

• Minimize unnecessary clinical studies
• Supports regulatory guidance development

Translational Aspects

• Bridging clinical trial findings to real-world applications
• Health economics and value assessment

Conclusions

• MIDD is streamlined and essential in drug development
• Enhances safety, efficacy, and decision-making
• Financially sustainable by reducing uncertainty

Acknowledgments

• Contributions from Dr. Craig Rayner, Jeff Barrett, Mark Selich
• Presentation by Critical Path Institute and Sitara