Beyond Approval: Using Model-Informed Strategies to Maximize the Value of ADCs

ADC development doesn’t follow a straight path. The key questions evolve from identifying a first-in-human dose, optimizing regimens, or expanding into new populations and indications.

What’s becoming clear is that modeling is not a one-time activity; it’s a continuous decision-making framework.

As Amy Cheung noted: “ADC value is not defined as approval alone… it’s how we optimize, extend, and sustain that value across the full development and post-approval journey.”

For ADCs, labeling decisions require more than a single dataset. They rely on the integration of multiple modeling approaches and considerations, including:

• Population pharmacokinetics (PopPK)
• Exposure–response (E-R) analysis
• Physiologically based pharmacokinetic (PBPK) modeling
• Quantitative System Pharmacology (QSP) modelling
• Biomarker-driven insights

These approaches help translate complex data into clinically meaningful decisions, particularly in special populations where traditional data may be limited.

As Amy explained:

“Model-informed approaches allow us to move from exclusions to a quantitative inclusion.”

Drug–drug interaction (DDI) assessment for ADCs is inherently complex due to their multi-component structure.

Isabelle Deprez highlighted a critical consideration: “The DDI risk… will be determined not only by the in vitro potency of the payload, but also by circulating plasma concentrations.”

This reinforces the importance of mechanistic modeling approaches, such as PBPK, to evaluate risk in context. In many cases, these approaches can reduce the need for dedicated clinical studies while still supporting regulatory decision-making.

Pediatric oncology development is shifting from a delayed, empirical approach to a more predictive and strategic one.

Rather than replicating adult studies, teams are now using modeling to:

• Bridge exposure between adults and children
• Evaluate multiple analytes (intact ADC and payload)
• Simulate dosing strategies before first-in-child studies

As Amy noted: “Pediatric extrapolation… is shifting from an empirical approach to a more predictive, model-informed and strategic one.”

Even after approval, ADCs face a major hurdle: payer reimbursement.

Ananth Kadambi highlighted a growing challenge: “There’s a major evidence gap between the expectations of regulatory bodies and payers.”

While regulators focus on safety and efficacy, payers require additional evidence, including:

• Real-world effectiveness
• Comparative value vs. existing therapies
• Quality-of-life impact
• Budget and healthcare system considerations

Failing to address these questions early increases the risk of reimbursement and patient access delays due to the high evidentiary standards applied by payors to ADCs.

To address payer expectations, teams must move beyond clinical trial data and adopt a more comprehensive evidence strategy.

As Roman Casiano explained: “Integrated evidence… is central to translating clinical trial results into real-world value.”

This includes combining modeling and simulation with real-world evidence and comparative analyses to build a stronger, more complete value story.

Across all stages of development, model-informed drug development for ADCs provides a consistent foundation for decision-making.

As Ananth summarized: “MIDD strategies can… reduce uncertainty in key decisions like dose selection, patient selection, and trial design.”

By integrating data across sources and stages, MIDD enables:

• More efficient trial design
• Stronger regulatory alignment
• Earlier identification of evidence gaps
• More confident decisions across development

ADC development is complex, and that complexity extends beyond the science into regulatory strategy and market access.

Teams that adopt model-informed drug development approaches for ADCs early will be better positioned to not only achieve approval, but to optimize value, support access, and ultimately reach more patients.