PHARMACEUTICALS analysis was based on real-world clinical programmes supported through Accelerator Drug Development *, a framework designed to unify process development, manufacturing, clinical supply capabilities and clinical studies within a single partnership structure.
Reexamining the status quo
Outsourcing has long been a core strategy for drug developers, enabling access to specialised capabilities and global infrastructure without the cost of building it in-house. But the traditional approach— contracting separately for manufacturing, clinical trials, lab services and distribution— requires significant coordination across different organisations that may not share systems, timelines or priorities.
In practice, this can lead to duplicated effort, miscommunication and delays. It also limits the ability to manage drug development efficiently as a connected whole. These issues are especially pronounced in therapeutic areas where development is complex and speed is critical, such as oncology and rare diseases.
Figure 1- Key findings of Tufts study
A fragmented model also introduces risk. When different service providers operate in silos, sponsors may face challenges ranging from incomplete data handovers and redundant documentation requirements to misaligned timelines and inconsistent quality oversight. These problems are exacerbated when clinical programmes involve multiple sites or countries, or when shipping conditions are complex, as is often the case with vaccines and cell-based therapies.
Consider, for example, an emerging biotech preparing for its first-inhuman trial. Without coordinated support, it may struggle to align API characterisation, regulatory documentation, clinical protocol development and supply chain planning, leading to costly delays. Similarly, a global biopharma company managing hundreds of clinical sites may find that fragmented and redundant governance slows start-up activities and introduces variability across regions.
In both cases, even minor missteps can escalate into significant timeline disruptions. An integrated model, by contrast, consolidates multiple
Up to 113.1x ROI and $ 62.9M expected Net Present Value( eNPV) gains for Phase Ill oncology programmes( mAbs)
Compounding benefits from multiphase integration, with eNPV gains of $ 16.4M for Phase II + Phase Ill mAbs
Up to 46.9x ROI and $ 25.1M eNPV gains for small molecules in Phase Ill functions within a single partner network, creating shared oversight and accountability. While this strategy is not universally adopted, the Tufts study set out to evaluate its potential benefits using quantitative analysis.
Methodology & findings
The Tufts team used a risk-adjusted financial model called expected net present value( eNPV), which estimates the lifetime costs, risks and returns of development programmes. The model included industry-standard assumptions for costs, success rates and timelines, as well as operational data drawn from real-world trials.
To test the model, researchers focused on oncology, a therapeutic area known for its high costs and compressed timelines. They assessed a range of integration scenarios across Phases I through III and conducted sensitivity analyses to test how results held up under changing assumptions. The operational data supporting the model was compiled from integrated programmes executed through Accelerator Drug Development.
The results showed clear financial and operational gains from singlepartner integration, particularly in later clinical phases. In Phase III monoclonal antibody( mAb) programmes, integration was associated with an average eNPV gain of $ 62.9 million and a return on investment( ROI) of 113.1. For small molecules in the same phase, the model estimated $ 25.1 million in eNPV gain and a 46.9 ROI( Figure 1).
Benefits were evident in earlier phases as well, though more modest. For example, integrating services in a Phase II mAb programme yielded a 5.8 ROI and an eNPV gain of $ 3.14 million. Gains increased when integration extended across multiple phases( Figure 2).
These timeline and cost improvements stemmed from reduced handoffs, fewer delays, more efficient use of resources and tighter alignment across development functions. The analysis
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