Why the Most Important Phase 3 Decisions Should Be Made Before Phase 3

Even after regulatory approval, therapies must overcome what many refer to as the fourth hurdle: reimbursement.

While regulators focus primarily on safety and efficacy, payers and HTAs evaluate additional factors such as comparative effectiveness, durability of benefit, and overall value relative to cost, routinely using approaches such as cost-effectiveness modelling and indirect treatment comparisons.

These differences in evidentiary expectations can create a disconnect. Trials that meet regulatory requirements may not provide the information needed to demonstrate value to payers.

For sponsors, the consequences can be significant. Delays in reimbursement can limit patient access, reduce market uptake, and shorten the effective commercial window during a product’s exclusivity period.

Addressing this challenge increasingly requires reconsidering when and what payer-relevant evidence is generated during development.

Forward-looking organizations are beginning to rethink how evidence strategies are designed.

Rather than treating reimbursement as a downstream milestone, they are incorporating payer evidence needs into development planning in early phases.

Within a Model-Informed Drug Development (MIDD) framework, approaches such as QSP, MBMA, pharmacokinetic/pharmacodynamic (PK/PD) modeling, and early health economic (HEOR) analyses enable teams to integrate internal and external evidence as early as late Phase 1 or early Phase 2.

When used together, these methods create a connected evidence strategy. Early biological insights can inform mechanistic models, which then feed into predictive trial and real-world effectiveness simulations as well as early economic analyses that guide later development decisions and data collection strategies.

This integrated approach allows teams to:

• anticipate comparative efficacy and real-world effectiveness before head-to-head trials
• identify evidence gaps relevant to payer evaluation
• explore long-term outcomes relevant to payers beyond the clinical trial duration
• inform key evidence gaps via refined Phase 3 trial designs and through early planning of appropriate real-world evidence studies

It also encourages closer collaboration between clinical development, HEOR, and market access teams, helping break down traditional silos across organizations.

QSP models combine biological knowledge, experimental data, and clinical insights to simulate how therapies interact with complex disease systems.

While QSP offers powerful insights, many organizations can further improve decision-making by prioritizing best-in-disease candidates, increasing the probability of success through optimized clinical trial design, and accelerating development by complementing clinical data with QSP modeling.

Because these models link drug mechanisms to clinical outcomes, they allow researchers to explore questions that would otherwise be difficult to address early in development.

For example:

• Which biological targets are most likely to translate into meaningful clinical benefit?
• Which patient populations are most likely to respond to therapy?
• How might combination therapies influence outcomes?

In diseases such as inflammatory bowel disease, QSP simulations can help evaluate how targeting specific biological pathways may influence clinical endpoints.

These insights allow development teams to evaluate therapeutic targets earlier, prioritize drug candidates that have high real-world potential more effectively, and design clinical programs that better reflect underlying disease biology.

While QSP provides mechanistic insight, MBMA helps teams understand how therapies perform within the broader clinical landscape.

If you’re comparing MBMA to traditional meta-analysis approaches, this quick guide breaks down the key differences and when each method applies.

MBMA integrates results from multiple clinical trials to build predictive models of treatment effects across drugs, doses, and patient populations.

By synthesizing data across studies, MBMA can simulate how a new therapy might perform relative to existing treatments, even before head-to-head trials are conducted.

This enables development teams to evaluate questions such as:

• How might a new therapy compare with current standards of care?
• What level of efficacy is required to differentiate in the market?
• What outcomes might be expected in Phase 3 trials and subsequently in real world settings based on early data?

Because MBMA combines internal and publicly available evidence, it provides a quantitative framework for understanding the competitive landscape and guiding decisions about dose selection, development prioritization, trial design, and a treatments ultimate place in therapy.

As healthcare systems place increasing emphasis on comparative effectiveness and value, development programs must be designed with both regulatory and payer expectations in mind.

This may require selecting comparators that reflect real-world standards of care, ensuring endpoints are relevant to Health Technology Assessment (HTA) evaluations, and designing studies capable of supporting subgroup analyses important for reimbursement discussions.

Modeling approaches can help teams explore these decisions earlier. By simulating different development scenarios, organizations can evaluate how trial design choices may influence both regulatory outcomes and payer acceptance.

In this way, modeling transforms Phase 3 planning into a broader strategic process, one that considers not only whether a therapy can be approved, but also how it will demonstrate value within the healthcare system.

Healthcare decision-makers are placing increasing emphasis on comparative effectiveness, value, and real-world outcomes.

Advanced modeling approaches such as QSP and MBMA allow sponsors to integrate internal and external evidence earlier in development, reducing uncertainty and strengthening strategic decision-making across the program.

For clinical development, HEOR, and market access teams, this integrated approach provides a way to align development strategy with payer expectations long before pivotal trials are completed.

When applied effectively, modeling can help sponsors:

• anticipate payer evidence requirements
• design more informative clinical trials
• proactively plan for real-world evidence collection where required to fill anticipated payer evidence requirements
• quantify differentiation against competing therapies
• strengthen the value narrative supporting reimbursement

Ultimately, integrating modeling earlier in development shifts reimbursement from a downstream hurdle to a parallel strategic objective, improving the likelihood that innovative therapies achieve both regulatory approval and timely patient access.