Day Two

• Leveraging clinical pharmacology and quantitative modelling to translate evidence across therapeutic areas when applying established mechanisms to new patient populations
• Building models that account for differences in disease biology, treatment goals, and benefit-risk expectations across oncology and autoimmune indications
• Using MIDD to support first-in-indication dose selection when clinical data are available in one disease area but limited in another
• Understanding how therapeutic windows, safety tolerability, and dosing regimens must evolve when moving from oncology to chronic disease populations

• Moving beyond the use of MIDD solely for regulatory submissions to support critical development and lifecycle management decisions
• Using quantitative evidence to inform dosing, special populations, benefit-risk assessments, and broader clinical development strategies
• Case studies demonstrating how MIDD has influenced regulatory discussions, development plans, and cross-functional decision-making

• Quantifying ROI from modelling activities
• Demonstrating programme acceleration and risk reduction
• Building organisational support for increased MIDD investment

• Case examples illustrating how the same organisation has used different modelling approaches at candidate selection, dose optimisation, and regulatory submission for the same programme, and how each approach was chosen based on the data available and the decision being made
• How a MIDD team that supports the full development pipeline structures the feedback loop between clinical learnings and earlier-stage modelling assumptions, and how this changes the way programmes are designed from the outset

Disease progression modelling has matured into a powerful tool for understanding disease biology, predicting treatment impact, and supporting precision medicine strategies. Yet despite its potential, many organisations struggle to translate modelling outputs into decisions because key stakeholders outside modelling functions lack a common framework for evaluating and applying the insights.

This session explores recommendations developed through the Clinical Trials Transformation Initiative (CTTI), bringing together lessons from regulators, industry leaders, and modelling experts on how organisations can better communicate, evaluate, and implement disease progression modelling throughout medical product development.

Key Discussion Points

• Understanding where disease progression modelling delivers unique value compared with other modelling approaches such as PK/PD and PBPK
• Communicating model assumptions, limitations, and outputs across clinical, regulatory, commercial, and executive stakeholders
• Building organisational “multi-fluency” to ensure modelling insights influence decision making rather than remain siloed within specialist teams
• Applying CTTI’s framework and recommendations to increase confidence, consistency, and adoption of disease progression modelling across the development lifecycle
• Case examples illustrating how improved cross-functional engagement enabled more effective development decisions

• The practical challenge of communicating model outputs, assumptions, and uncertainty to cross-functional colleagues and senior leaders who need to make decisions based on those outputs without a pharmacometrics background
• Frameworks for presenting model credibility to internal stakeholders, including how to communicate what the model can and cannot tell you without undermining confidence in the work
• Case examples where poor internal communication of MIDD results delayed a development decision, and what changed when the communication approach was redesigned

• Successful examples across our industry where MIDD approaches have been successfully implemented
• Identifying scientific and organizational limitations and barriers to success
• Assess and discuss future MIDD opportunities

Building on a format introduced in 2025, this session splits the room into small groups each focused on a specific therapy area or modality.

• Small molecules: off-target effects, phase one design, and covariate analysis
• ADCs and bispecifics: payload optimisation, linker stability modelling, and dose selection
• CNS: blood-brain barrier penetration modelling, delayed PK/PD effects, and long follow-up challenges
• Novel modalities (RNAi, gene therapy, oligonucleotides): where existing MIDD frameworks apply and where they break down

• Exploring how PBPK models supported early candidate and dose selection ahead of first-in-human studies
• Demonstrating how the same PBPK framework was refined and re-validated as clinical data accumulated

A moderated panel drawing on speakers from across both conference days to share honest retrospective experience of MIDD implementation.

• Where has MIDD genuinely changed a development decision, and where has it been used to validate a decision that had already been made?
• What has been the hardest internal sell for MIDD, and what argument finally worked?
• Where have you seen the field overclaim what modelling can do, and what are the consequences of that for regulatory trust?