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Insights

AI Is About to Break the CRO Pricing Model 

Expert insights by Rich Polgar, Managing Director, Danforth Advisors 

For decades, CRO pricing has largely been built on a simple concept: 

Hours × activities = unit price. 

A CRO estimates how many hours it will take a functional expert to complete an activity, applies the relevant labor cost and margin, and converts that calculation into a unit price. 

It is essentially an activity-based costing (ABC) model. 

Sponsors rarely see all of the assumptions underneath that calculation. CROs generally do not provide the hours behind each unit unless specifically asked, and many proposals blend multiple activities into broader units. 

However, AI creates a new challenge to the model. 

What Happens When Hours are No Longer a Good Proxy for Value? 

Consider an activity historically priced assuming 40 hours of work. 

If AI can complete the same deliverable with equivalent or better quality in 15 hours, should the price immediately fall by more than 60%? 

Probably not. 

The sponsor is purchasing the output, expertise, infrastructure, accountability, and risk associated with the deliverable, not simply 15 hours of someone’s time. 

That begins to move the CRO industry away from pure activity-based costing and toward something much closer to value-based pricing. 

And that transition could have enormous implications for CRO economics. 

The Five-Year Pricing Problem 

Clinical development makes this transition particularly interesting because sponsors routinely negotiate prices today for work that will not occur for years. 

Think about a Phase II or Phase III program being contracted today. 

The sponsor may already be locking in unit prices for: 

  • Biostatistical analyses 
  • TLF development 
  • Data review 
  • Database activities 
  • Statistical programming 
  • Clinical Study Report writing 
  • Submission-support activities 

Some of those tasks may not occur for three, four, or even five years. Now consider how much AI-assisted clinical work could change during that period. 

We are effectively negotiating 2026 prices for work that might be performed in 2030 or 2031 using an entirely different operating model. 

If a CRO prices a CSR today based on its current operating assumptions, but five years from now AI enables the organization to produce that CSR using a fraction of the labor, the CRO could generate substantially greater margin on that unit. 

That isn’t necessarily unfair. 

CROs are making substantial investments in technology, AI platforms, data architecture, training, validation, process redesign, and change management. The companies taking the investment risk should expect to capture some of the economic benefit. 

But it changes the pricing conversation. 

AI Doesn’t Automatically Mean Lower CRO Prices 

There is an assumption among some sponsors that AI will simply make CRO services cheaper. 

Eventually, it probably will. But I don’t expect that to happen immediately. 

The first financial impact of AI may actually be margin expansion for the CROs that implement it successfully. 

Imagine two CROs charging the same $100,000 for a deliverable. One still requires 500 labor hours to produce it. The other has redesigned its workflow around AI and requires 250 hours. 

From the sponsor’s perspective, the output may look identical. From the CRO’s perspective, the economics are dramatically different. 

That is why the winners from AI will not necessarily be the CROs with the most AI press releases. They will be the CROs that can systematically reduce the amount of human effort without reducing the value, quality, or accountability of the service. 

The Largest CROs May Have an Early Advantage 

In the short term, I believe the larger CROs may be particularly well positioned to benefit. 

Large CROs have enormous volumes of historical data, repeatable processes, global delivery organizations, and thousands of employees performing similar activities across hundreds of studies. 

A 10% productivity improvement across that infrastructure is meaningful. 

A 30% improvement is transformative. 

And once an AI-enabled process is developed, validated, and deployed, the incremental benefit can be repeated across a very large book of business, creating the potential for substantial operating leverage. 

Smaller and mid-sized CROs can absolutely compete (and in some cases may be able to innovate faster), but they will need to be deliberate about where they invest rather than trying to replicate the technology infrastructure of the largest providers. 

Sponsors Need to Rethink Procurement Too 

This isn’t only a CRO issue. Sponsor procurement and outsourcing teams also need to evolve. 

If we continue evaluating CRO proposals primarily by comparing hundreds or thousands of unit prices, we may increasingly be measuring the wrong thing. 

The better questions may become: 

  • What outcome are we buying? 
  • How much operational risk is the CRO assuming? 
  • How quickly can the work be completed? 
  • What technology and AI capabilities are embedded in the delivery model? 
  • How much of the productivity benefit is being shared with the sponsor? 

And perhaps most importantly: Are we paying for effort, or are we paying for value? 

I don’t think activity-based pricing disappears tomorrow. Sponsors will still need units, assumptions, and transparency to manage budgets and change orders. 

But I do think we are at the beginning of a significant transition. 

What This Means for Biotech Sponsors 

At Danforth Health, we manage clinical outsourcing across multiple biotech companies, applying best practices in CRO sourcing, negotiation, and governance across a broad portfolio of clinical programs. 

That perspective allows us to see how CRO pricing, commercial models, and contracting practices are evolving across the market, not just within a single sponsor or sourcing event. 

As AI changes the economics of clinical delivery, sponsors will need greater transparency into how CROs build their pricing, stronger protections around scope changes and change orders, and more sophisticated ways to assess whether they are receiving appropriate value. 

If you are looking to improve the outcomes, pricing, and transparency of your CRO relationships, contact us to discuss how Danforth Health can help. 

Insights

A BCBS Massachusetts coverage change is hitting current renewals. Is your HR strategy ready?

A policy change from Blue Cross Blue Shield of Massachusetts is showing up in current renewals. Effective January 1, 2027, PPO and EPO plans will no longer be offered to organizations with fewer than 50 employees. For many growing life science companies, this isn’t an abstract market shift. It’s forcing decisions that will shape your employee experience, your talent competitiveness, and your HR infrastructure for years to come.

Your broker will help you find a new plan, but that’s only part of the equation.

This is a total rewards strategy moment.

The companies that navigate this well won’t just replace their benefits, they’ll use this inflection point to ask harder questions:

  • Does our benefits package still reflect what talent in this market expects?
  • Are we funding benefits in the most efficient way for our stage of growth?
  • Do our employees understand what they have, and will they trust us through a transition?
  • Are our HR systems and compliance infrastructure ready for what comes next?

That last question matters more than most companies realize. A carrier change triggers a cascade: updated plan documents, ACA reporting considerations, revised employee handbooks, open enrollment communications, and — if your team is in a critical growth phase — potential ripple effects on recruiting and retention.

At Danforth Health, we work alongside life science companies at exactly this intersection, where HR strategy meets operational reality. We help leadership teams think through what this change means for their total rewards philosophy, build the internal communications that keep employees informed and confident, and put the compliance and HR infrastructure in place to execute the transition cleanly.

If you’re a life science company in Massachusetts grappling with what this means for your people strategy, we’d welcome a conversation.

Click here to learn more about how we can help, or scroll down to connect with our team.

Insights

The GrowthCo Playbook: When to Hire, Partner, and Scale

Expert insights contributed by managing directors Candice Cantelli Meklis and Ted Raad

There’s a moment every early-stage biotech leader knows, usually somewhere between a promising preclinical data package and the first IND filing, when the company stops feeling like a scrappy team and starts feeling like a real organization that’s about to get much more complicated.

That moment is exciting. It’s also where a lot of companies make decisions they spend the next three years unwinding.

We’ve lived through this phase more than once. The questions that seem tactical are anything but: Do we hire a head of finance now or wait? Should we build this capability in-house or find a partner? Are we actually ready to scale?  The answers shape your cash runway, your investor story, and your ability to execute when it matters most.

Hire for the Stage You’re In, Not the Stage You’re Aiming For

This sounds obvious. It isn’t.

In the run-up to an IND, the instinct is to start hiring the team you’ll need for Phase 2. You want to show investors a credible bench. But a senior CMO hired eighteen months before you have clinical data is a very expensive listener in a lot of meetings. A VP of Regulatory who’s built global dossiers may be overqualified — and frustrated — running pre-IND interactions with FDA. The mismatch is subtle at first, then it becomes a retention problem, then a leadership problem.

The better question isn’t who do we need eventually but instead what decisions do we need to make in the next twelve months, and who helps us make those well?

Pre-IND to Phase 1, that usually means a hands-on CMO who can own clinical strategy while wearing multiple hats; a finance lead who’s credible in front of Series A/B investors without needing a team underneath them; and regulatory and CMC expertise that’s fractional or partner-based rather than a full internal function.

Hire for judgment, not headcount. At this stage you need people who’ve done it before and can operate without much organizational support around them.

Know What to Build and What to Borrow

Every founding team has a natural instinct to build. We want control. And there’s a legitimate investor narrative around having key capabilities in-house. But in-house capability costs money and management bandwidth, two things almost always in shorter supply than expected.

Before Phase 1 data, there are very few functions where owning the infrastructure outright creates durable competitive advantage. Your edge is in your science, your clinical insight, and your ability to make good decisions fast.

Build in-house when:

☐  The capability is core to your scientific or clinical differentiation

☐  You’ll use it continuously, not in bursts

☐  The knowledge needs to compound inside the organization over time

Partner or outsource when:

☐  You need it now but won’t need it at scale for 18+ months

☐  The external market has deep capacity (biostatistics, regulatory writing, clinical operations)

☐  You don’t yet have the internal expertise to manage a full team doing it


CRO relationships fall into the second bucket, almost always at this stage. So does manufacturing, quality systems, pharmacovigilance, and in most cases, CMC. Where we’ve seen founders get burned is building a clinical ops function before they have a trial to run, or hiring a head of commercial two years before launch because the board wanted to see it.

Are You Actually Ready to Scale?

There’s a version of “scaling” that’s mostly just adding people. It looks like progress. But real scale readiness, the kind that holds up when Phase 1 data is positive and everyone wants to know what’s next, requires more than headcount growth.

Before scaling, check these four things:

☐  Decision infrastructure. Clear ownership at every level. When something goes wrong in a clinical study, there’s a single person accountable, not a committee.

☐  Financial controls that match your ambition. If your accounting is still running on spreadsheets, you’re one financing round away from a real problem. Get audit-ready earlier than you think you need to.

☐  Vendor and partner management discipline. Multiple CRO, CMC, and consulting relationships running simultaneously each need active governance to review deliverables, hold vendors to milestones, and escalate early.

☐  Culture that doesn’t depend on the founders. In a 15-person company, the CEO is the culture. At 40 or 50 people, they’re not. The values and operating norms need to exist somewhere new people can actually find them.

The Trap of Premature Scale

Companies that raise a large Series A on strong preclinical data often feel pressure from the board, from investors, and from their own ambition to quickly “build the organization.”  They hire fast, establish functions, build infrastructure. Within a year, G&A is consuming a meaningful share of burn. Then Phase 1 data comes back with a safety signal requiring a protocol amendment, and suddenly you’re managing a 45-person organization through a six-month delay with a runway that just got a lot more visible.

More people doesn’t mean faster.  In early-stage biotech, coordination costs grow faster than output.

Before every significant hire or function-build, ask: If we got a bad Phase 1 result tomorrow, would we still make this decision? If the answer is no, it’s probably premature.

The best-run pre-Phase 1 companies we’ve encountered are lean but not thin. They have the right people in the right seats, rigorous cash management, explicit choices about what they outsource, and boards that add real operational value. They plan in scenarios rather than pretending their operational plan is more certain than it is.

The goal at this stage isn’t to build the organization you’ll eventually become. It’s to make the right bets with the capital you have, generate the data that expands your options, and stay flexible enough to respond when the science surprises you, which it always does.

Insights

Built to Transact: Aligning for IPO, M&A, or Strategic Deals in Biotech

Expert insights contributed by Managing Directors Ted Raad and Candice Cantelli Meklis

For biotech companies, few moments are as pivotal as the period following meaningful clinical data. Positive results don’t just validate a program — they unlock optionality, whether an IPO, licensing deal, acquisition, or strategic partnership.

But optionality isn’t automatic.

The companies that successfully capitalize on this inflection point aren’t just clinically promising — they’re built to transact. They’ve aligned their operations, development strategy, and commercial story in a way that stands up to scrutiny from investors, partners, and acquirers alike.

Here’s what that alignment really requires.

1. Build Infrastructure that Withstands Diligence

Transaction readiness starts long before a deal process begins.

Investors and acquirers aren’t just evaluating your science — they’re assessing whether your company can operate at scale under public market or partner scrutiny. That means:

  • Financial rigor: Clean, auditable financials and forecasting discipline
  • Operational scalability: Systems and processes that won’t break under growth
  • Governance maturity: A board structure, controls, and documentation that hold up in diligence

Too often, companies wait until a financing or deal is imminent to address these areas. By then, gaps become risks — and risks can become discounts.

Just as importantly, transaction-ready companies are disciplined about where they spend. Sophisticated investors and acquirers increasingly reward lean operating models that direct capital toward value-generating activities — particularly high-quality clinical execution and meaningful data generation — rather than excessive fixed overhead. Companies that stay focused on milestone-driving investments often preserve more flexibility, stronger capitalization profiles, and broader strategic optionality.

2. Craft a Deal-Ready Asset Narrative

Great data doesn’t speak for itself.

Different stakeholders — public investors, large pharma partners, strategic acquirers — evaluate assets through different lenses. What they all need, however, is a clear, coherent, and defensible story.

That story should:

  • Translate clinical results into meaningful differentiation
  • Address risk head-on, not obscure it
  • Connect the asset to a larger strategic vision
  • Anticipate the questions diligence teams will ask — and answer them proactively

A strong narrative doesn’t just describe what you’ve done. It makes the case for why your asset will create value in the hands of the next owner or investor.

3. Align Development Strategy with Value Creation

Transaction readiness is shaped by the decisions you make before data readouts.

Program prioritization, indication selection, and trial design all influence how buyers and investors perceive value and flexibility.

Key considerations include:

  • Are you advancing the right indications to maximize strategic interest?
  • Do your studies generate data that is decision-grade for partners or regulators?
  • Are you preserving the ability to expand into additional indications or combinations?

Well-designed development programs don’t just answer scientific questions — they expand strategic options.

4. Demonstrate Real Market Potential

Clinical success alone is rarely enough to drive a premium outcome.

Stakeholders increasingly expect to see evidence that a therapy can win in the real world — not just in a trial.

That means translating clinical outcomes into:

  • Clear product positioning relative to standard of care
  • Evidence of payer relevance and reimbursement potential
  • Insights from key opinion leaders and patients that validate adoption potential

Early commercial thinking signals that your asset isn’t just viable — it’s valuable.

5. Preserve Optionality Across Strategic Paths

Perhaps the most overlooked element of transaction readiness is intentionality around optionality.

The goal isn’t to commit early to a single path — it’s to stay credible across multiple: IPO, licensing or co-development partnerships, and M&A.

This requires:

  • Disciplined capital allocation and timing around capital raises and partnerships
  • Avoiding decisions that limit future flexibility
  • Continuously evaluating how new data shifts your best strategic path

Companies that maintain optionality don’t just react to opportunities — they create leverage.

Transaction Readiness Is a Cross-Functional Effort

Being “built to transact” isn’t the responsibility of a single team. It sits at the intersection of:

  • Operational excellence
  • Clinical strategy
  • Commercial insight
  • Strategic storytelling

When these elements are aligned, companies don’t just reach inflection points — they’re prepared to act on them.

And in today’s environment, that preparation is often what separates a good outcome from a great one.

Are You Built to Transact?

Many companies don’t realize where their gaps are until they’re already in the middle of diligence — when it’s hardest to fix them.

At Danforth, we work with biotech teams across the full corporate lifecycle to assess and strengthen transaction readiness — from financial and operational infrastructure to asset positioning, development strategy, and commercial validation.

If you’re approaching a key inflection point, we can help you evaluate where you stand and what it will take to maximize your options.

Insights

Why HCP Engagement Can Make or Break an Ultra-Rare Disease Launch 

Insights from Chris Mycek, Head of Commercialization at Benchworks, a Danforth Health Company 

Launching in an ultra-rare disease market is not a volume game. Success depends on whether the right physicians have the confidence, clarity, and support to identify patients, initiate treatment, and continue prescribing. 

That is what makes healthcare professional (HCP) engagement so critical. 

In broader specialty markets, commercial teams can often rely on scale and repetition to build awareness and drive adoption. In ultra-rare disease, the equation is different. Prescriber universes are small, treatment decisions are highly nuanced, and each account can materially influence launch performance. When uptake falls short, the issue is often not awareness alone. More often, it is a gap in confidence, coordination, or capability. 

A recent case study involving the launch of a first-in-class therapy for a rare pediatric metabolic disorder highlights this dynamic. 

When Awareness is Not Enough 

A mid-sized biotechnology company launched a therapy for a rare pediatric metabolic disorder affecting roughly 2,000 patients in the United States. The product entered the market with strong Phase 3 efficacy data and favorable payer coverage. On paper, the launch appeared well-positioned. 

Yet in the first 18 months, revenue significantly underperformed projections. 

Commercial analysis showed the company had correctly identified its core prescriber base: approximately 85 pediatric metabolic specialists. But actual prescribing was concentrated among just 22 of them. 

The remaining specialists were aware of the therapy but hesitant to act. Their concerns centered on patient selection, timing of treatment initiation, comfort with the novel mechanism of action, potential long-term safety considerations, and uncertainty around insurance coverage and out-of-pocket costs. Some also viewed the therapy as relevant only for the most severe phenotypes rather than the broader eligible population. 

This pattern is common in ultra-rare disease.  

Awareness may open the door, but it rarely drives adoption on its own. In highly specialized markets, prescribing decisions require deeper clinical confidence, practical support, and coordinated engagement across functions. 

A More Targeted Intervention 

Working with Benchworks, a Danforth Health Company, the organization implemented a six-month HCP engagement program focused on sharper targeting and stronger field coordination.  

The strategy our experts facilitated centered on five key moves. 

1. Refining segmentation beyond simple prescriber lists 

The team developed a more dynamic targeting model based on patient volume, growth potential, and alignment with treatment philosophy. In ultra-rare disease, effective segmentation must go beyond static prescriber lists to identify the accounts most likely to drive near-term adoption. 

2. Enabling peer-to-peer learning 

The company launched small-group dinners and virtual roundtables featuring active prescribers sharing real-world clinical experience. This peer-to-peer model helped move physicians from awareness to greater confidence in patient selection and treatment initiation. 

3. Tightening MSL and field integration 

Weekly account planning sessions improved coordination between MSLs and field teams, enabling more aligned and responsive engagement. In ultra-rare disease, seamless medical-commercial collaboration is essential to delivering credible, timely support. 

4. Addressing access concerns directly 

The company developed payer-specific guidance and hub services talking points to address coverage and affordability questions. Clear access communication helped reduce friction between prescription intent and therapy start. 

5. Supporting treatment initiation with practical tools 

The launch team created clinical support materials, including dosing algorithms, monitoring protocols, and adverse event guidance. These resources helped physicians feel more prepared to initiate treatment with confidence. 

The Impact of Coordinated HCP Engagement 

Within six months of engagement with our experts, the company saw meaningful improvement across key launch metrics: 

  • Active prescribers increased from 22 to 58  
  • New patient starts rose 47% quarter over quarter  
  • Prescription abandonment fell from 23% to 11%  
  • Time from first prescription to second prescription dropped from 8.3 months to 4.7 months  
  • Net revenue increased 12% year over year despite flat pricing  

The takeaway is clear: when commercial teams address the specific barriers holding physicians back, adoption can accelerate meaningfully even in a small, highly specialized market. 

Looking to Strengthen Launch Performance? 

From launch planning to HCP engagement strategy, Danforth Health’s commercial readiness experts help life sciences companies build smarter launch strategies, align cross-functional teams, and accelerate adoption in complex markets. 

Reach out to our team today. 

Insights

Get Ahead of Risk: How Integrating Regulatory Early Drives Commercial Impact in Life Sciences

Expert insights from Lisa Maffei Luther, Founder and Head of Regulatory Strategy at Advyzom, a Danforth Health company

For many biopharma companies, regulatory is often viewed as a critical – but contained – function: essential for approval, but less central once a product reaches the market.

In reality, regulatory plays an increasingly strategic role in how a product is communicated, adopted, and sustained over time.

From the earliest stages of commercialization planning through post-approval lifecycle management, regulatory sits at the intersection of science, compliance, and commercial impact. And when that connection isn’t fully integrated, companies can face real consequences — slower launches, inconsistent messaging, or increased regulatory risk.

The growing complexity of commercial regulatory

Today’s commercialization environment is more complex than ever:

  • Promotional claims must be not only compelling, but fully aligned with evolving labeling and guidance
  • Pre-launch scientific exchange requires careful navigation to avoid crossing into promotion
  • Global considerations introduce additional layers of variability and risk
  • Post-approval changes — from new indications to safety updates — require ongoing coordination across teams

At the same time, commercial teams are under pressure to move quickly, differentiate clearly, and engage stakeholders effectively.

Balancing these dynamics isn’t easy, and it’s no longer something that can be managed in silos.

Regulatory as a bridge — not a checkpoint

The most effective organizations are shifting their mindset:  Regulatory isn’t just a checkpoint at the end of the process — it’s a strategic partner throughout.

When regulatory is embedded early and works closely with commercial, medical, and market access teams, it can:

  • Shape messaging strategies that are both impactful and compliant
  • Translate complex labeling into clear, usable claims
  • Anticipate and mitigate risk before it slows down execution
  • Streamline review processes and reduce rework
  • Support consistent, aligned communication across functions and markets

In this model, regulatory becomes a driver of efficiency and clarity, not just a gatekeeper.

The overlooked importance of post-approval strategy

Just as critical, and often underappreciated, is the role of regulatory after approval.

Maintaining a product’s value in-market requires continuous attention to:

  • Labeling updates and evolving safety information
  • Post-marketing requirements and commitments
  • New data, indications, or formulations
  • Shifts in the regulatory landscape

Without a proactive approach, these factors can create friction, delay opportunities, or even impact patient access.

With the right strategy in place, however, they become opportunities to extend value, strengthen positioning, and ensure continuity of care.

A more integrated path forward

As commercialization models evolve, the line between regulatory and commercial execution continues to blur.

Companies that succeed will be those that treat regulatory not as a constraint — but as an enabler of smarter, more effective commercialization.

That means building more integrated teams, aligning earlier in the process, and taking a lifecycle view of both compliance and value creation.

Because in today’s environment, getting regulatory right isn’t just about avoiding risk — it’s about unlocking the full potential of your product.

Insights

5 Steps Biopharma Companies Should Take Now to Prepare for MFN Drug Pricing

What Is MFN?

A Most Favored Nation (MFN) drug pricing policy would tie certain U.S. drug prices (most likely within Medicare) to the lowest price paid for the same product in a basket of comparable, economically advanced countries.

Rather than relying solely on domestic benchmarks such as Average Sales Price (ASP), MFN models use international reference pricing (IRP) to cap U.S. reimbursement levels

MFN has moved rapidly from policy proposal to operational reality. The Trump Administration has reached voluntary MFN pricing agreements with 17 of the largest pharmaceutical manufacturers, representing 86% of the branded drug market, and is actively working with Congress to codify those agreements into law. In parallel, CMS has proposed two mandatory Medicare MFN payment models–GLOBE (Part B) and GUARD (Part D)–with implementation beginning as early as October 2026. In a post-IRA environment where Medicare negotiation is already reshaping pricing dynamics, MFN is no longer a future risk to model–it is a present-tense strategic challenge.

Why MFN Matters for Market Access

MFN is not simply a pricing policy. It fundamentally changes the interplay between global pricing strategy and U.S. access dynamics.

Historically:

  • U.S. pricing has been largely insulated from international pricing decisions
  • Market access strategy in the U.S. focused primarily on payer mix, contracting, and value demonstration domestically

Under MFN:

  • Global pricing decisions directly influence U.S. reimbursement
  • Ex-U.S. launch sequencing and pricing strategy become U.S. access decisions
  • Market access planning must shift from domestic optimization to global coordination

This creates both operational complexity and strategic risk.

Key Market Access Impacts

1. Global Launch Sequencing Becomes a U.S. Access Lever

Now that U.S. Medicare reimbursement is being pegged to the lowest international price:

  • Early lower-price agreements in EU markets could reduce U.S. reimbursement ceilings
  • Delays in ex-U.S. launches may become more common
  • Manufacturers may prioritize price stability over speed to global access

Market Access Implication:
Launch sequencing, traditionally a commercial strategy decision, becomes a critical component of U.S. access preservation.

This dynamic is already playing out. The December 2025 U.S.-UK pharmaceutical pricing agreement increased UK net drug spending through reduced clawback rates and a higher cost-effectiveness threshold–an early, real-world example of MFN exerting upward price pressure in reference markets. Manufacturers should monitor whether foreign market launch sequencing shifts as this pricing environment evolves.

2. Increased Pressure on Gross-to-Net Strategy

MFN is now effectively capping top-line pricing. The White House projects a 30% decrease in net prices for drugs in the U.S. over 10 years, with $529 billion in aggregate domestic savings–with the largest impact concentrated in roughly 150 single-source products in antipsychotics, antiretrovirals, antineoplastics, anti-inflammatory biologics, and antidiabetics. Specific implications include:

  • Reduced flexibility to offset domestic rebates with higher list prices
  • Potential compression of net revenue if international reference prices are significantly lower
  • Greater scrutiny of contracting structures across both Medicare and commercial segments

Market Access Implication:
Manufacturers will need tighter integration between pricing, contracting, and policy teams to manage cross-market impacts

3. Impact on Medicare Negotiation Leverage

As MFN is now layered onto IRA price negotiation:

  • The “maximum fair price” ceiling could be influenced by international pricing floors
  • Negotiation dynamics may shift toward even greater federal leverage
  • Therapeutic classes with strong ex-U.S. HTA pressure (e.g., oncology, immunology) could see disproportionate exposure

Market Access Implication:
Access teams must prepare for negotiations that incorporate international comparators more explicitly and aggressively.

4. Formulary and Utilization Management Ripple Effects

Lower Medicare reimbursement rates could:

  • Change buy-and-bill economics in Part B
  • Affect provider margin and prescribing behavior
  • Influence payer formulary positioning in Part D

If margins compress:

  • Providers may prefer alternative therapies with better economics
  • Access hurdles may increase if plans attempt to offset reimbursement compression elsewhere.

Market Access Implication:
Manufacturer field reimbursement and provider engagement strategies will need to adapt quickly

5. The GLOBE and GUARD Models: Mandatory MFN Coming to Medicare

Beyond the voluntary manufacturer agreements, CMS has proposed two mandatory MFN payment models that will directly affect manufacturers regardless of whether they have negotiated deals with the administration:

  • GLOBE (Global Benchmark for Efficient Drug Pricing): A mandatory Part B model launching October 1, 2026. Covers single-source drugs and biologics in seven USP therapeutic categories with annual Part B spending above $100 million. CMS estimates GLOBE will cover approximately 55% of annual Part B FFS drug spending. Manufacturers whose products exceed the international benchmark must pay an incremental rebate to Medicare.
  • GUARD (Guarding U.S. Medicare Against Rising Drug Costs): A mandatory Part D model launching January 1, 2027. Covers 17 USP therapeutic classes with annual Part D spending above $69 million. Applies to approximately 25% of Part D enrollees.

Both models are mandatory–not voluntary–and CMS has not explicitly exempted manufacturers who have already negotiated voluntary agreements with the administration. Drugs with an active IRA-negotiated maximum fair price are excluded, but all other qualifying single-source products are in scope. Biosimilars and generics are excluded.

Market Access Implication:
Manufacturers with qualifying Part B or Part D products face near-term mandatory obligations regardless of voluntary agreement status. The window to adjust international pricing to mitigate future U.S. rebate risk under GLOBE is open now–GLOBE benchmarks will be based on Q2 2026 pricing data. Dual-track revenue forecasting (model geographies vs. non-model geographies) will become essential for affected portfolios.

    Manufacturer Decision-Making Under MFN Risk

    With mandatory models launching in Q4 2026 and voluntary agreements already covering 86% of the branded market, MFN is no longer a risk to model–it is an operational constraint to manage. The following areas require immediate attention:

    1. Portfolio Risk Stratification

    Companies should assess:

    • Which assets are most exposed to international price referencing?
    • Which therapeutic areas face the largest EU-U.S. price deltas?
    • Which products are most Medicare-dependent?

    This enables:

    • Prioritized mitigation planning
    • Revenue-at-risk modeling
    • Earlier lifecycle management interventions

    2. Integrated Global Pricing Governance

    MFN requires tighter alignment across:

    • U.S. market access
    • Global pricing & reimbursement
    • Government affairs
    • Legal and compliance

    Decisions that were once siloed (e.g., a German price agreement) may have direct downstream U.S. impact

    Prepared organizations will:

    • Establish formal cross-market price governance committees
    • Model international pricing agreements before execution
    • Create scenario playbooks for reference price compression

    3. Enhanced Economic Value Demonstration

    As pricing ceilings tighten, value demonstration becomes even more critical:

    • Stronger real-world evidence (RWE)
    • More robust health economic modeling
    • Outcomes-based contract readiness
    • Clear differentiation from therapeutic alternatives

    Under MFN, margin compression increases the importance of maintaining favorable access tiers and minimizing utilization restrictions.

    4. Contracting Innovation and Risk Sharing

    If top-line pricing flexibility narrows:

    • Manufacturers may shift toward outcomes-based agreements
    • Indication-specific pricing may gain traction
    • Population health-based contracting could become more common

    Access teams should be developing infrastructure now to support:

    • Data collection and analytics
    • Outcomes measurement
    • Performance-based reimbursement models

    5. Scenario Planning and Financial Modeling

    MFN preparedness requires:

    • Modeling various international basket configurations
    • Estimating price floors under different country mixes
    • Stress-testing gross-to-net assumptions
    • Evaluating provider economics impact (especially in Part B)

    This modeling should inform:

    • Investor communications
    • Portfolio prioritization
    • Pipeline investment decisions

    Preparedness Checklist for Market Access Teams

    Manufacturers should consider the following actions:

    Strategic Planning

    ☐ Conduct MFN exposure modeling by product

    ☐ Quantify Medicare revenue at risk

    ☐ Map international price differentials across key markets

    ☐ Assess GLOBE and GUARD model exposure by product (Part B: Oct. 2026; Part D: Jan. 2027)

    ☐ Evaluate April 2026 Executive Order tariff implications and compliance with MFN domestic production commitments

    Governance

    ☐ Formalize cross-market pricing review processes

    ☐ Establish escalation protocols before signing major ex-U.S. agreements

    ☐ Align U.S. and global access leadership

    Evidence & Value

    ☐ Strengthen RWE generation plans

    ☐ Prepare enhanced HEOR dossiers

    ☐ Expand outcomes-based contracting readiness

    Operational Readiness

    ☐ Assess provider reimbursement impact

    ☐ Update field reimbursement training

    ☐ Develop payer communication strategies

    MFN has arrived. With 17 voluntary manufacturer agreements covering 86% of the branded drug market, mandatory GLOBE and GUARD models launching in late 2026 and early 2027, an April 2026 Executive Order tying tariff relief to MFN compliance, and active Congressional codification efforts, the policy landscape has moved from “what if” to “what now.” Organizations that have not yet built MFN into their pricing governance, portfolio risk models, and access strategy are already behind.

    Is your portfolio exposed to MFN risk?

    At Danforth Health, we work with manufacturers to model policy exposure, align global and U.S. pricing strategy, pressure-test access assumptions, and build practical readiness plans. If MFN, or broader international reference pricing, would materially affect your portfolio, now is the time to assess your exposure and build a coordinated response.

    We welcome the opportunity to help you think through the implications for your assets, pipeline, and access strategy. Schedule a conversation.

    Insights

    AI in Clinical Outsourcing: What’s Signal, What’s Noise? 

    Written by Rene Stephens, Managing Director, Clinical Business Operations, Danforth Health  

    AI in clinical development is everywhere right now. Headlines promise transformation. Vendors promise acceleration. Teams are running pilots. 

    But beneath the noise, where does the industry actually stand? 

    To better understand the reality, Danforth Health conducted a focused industry pulse survey both before the 2026 SCOPE Summit Conference and during a live panel discussion with Outsourcing, Clinical Operations, and healthcare Innovation experts on The Impact of AI on Outsourcing and Clinical Trial Execution. The findings provide a candid snapshot of adoption, constraints, and where real value is (and isn’t) emerging.  

    View the survey results here and continue reading for the 5 biggest takeaways… 

    1. AI Adoption Is Real — but Still Fragmented 

    Survey respondents confirmed that AI is no longer theoretical in clinical development, yet enterprise-wide adoption remains the exception, not the rule. 

    • 22% report actively using AI in a subset of trials 
    • 30% are implementing or formally allowing AI use 
    • 37% are still evaluating 

    Nearly 90% of organizations are somewhere on the adoption curve, but most remain in pilot mode, functional silos, or narrowly defined use cases rather than scaled transformation. 

    The takeaway: AI credibility has arrived. Operational maturity has not. 

    2. Deployment Is Concentrated Where Risk Is Measurable 

    Where AI is being deployed, the survey shows strong clustering around activities with: 

    • High data density 
    • Clear quality metrics 
    • Auditable outputs 

    Respondents reported AI usage primarily in: 

    • Data capture and quality oversight 
    • Protocol design and optimization 
    • Patient population and cohort identification 
    • CSR preparation and submission support 

    Adoption is split between internally built tools and vendor/CRO-provided solutions, raising a strategic question many sponsors are still debating:

    Is AI a capability to own, or a service to buy?  

    3. The Business Case Is Speed — Not Headcount Reduction 

    One of the clearest signals from both survey responses and the panel discussion: 

    AI’s value is being measured in cycle-time compression, not FTE elimination. 

    External benchmarks point to an average six-month reduction in development timelines per asset driven by: 

    • Improved protocol feasibility 
    • Faster cohort identification 
    • Earlier quality signal detection 

    Sponsors are not viewing AI as a replacement for CROs or clinical teams. Instead, it’s emerging as an execution accelerator, a tool to reduce rework, friction, and downstream risk. 

    4. Governance, Not Technology, Is the Primary Bottleneck 

    The biggest obstacles aren’t algorithmic; they’re organizational and are likely more difficult to overcome without a deliberate approach to change management.  

    Transparent positioning of the impact to current and future functions, both positive and potentially negative, and honest discussions with key stakeholders to address changes to operating processes and team roles, among others, are key considerations for POC pilots as well as full implementation. 

    Despite optimism, respondents expressed clear confounders to value: 

    • Unclear ownership across Clinical Ops, IT, Data Science, and Procurement 
    • Variability in CRO AI maturity and transparency 
    • Questions around validation, auditability, and regulatory defensibility 

    At the same time, ICH E6(R3) and the FDA Diversity Action Plan (effective January 1, 2026) will increasingly force sponsors to integrate AI into risk-based quality management and enrollment analytics, whether they feel operationally ready or not. 

    In short: AI adoption is no longer optional, but unmanaged adoption is risky. 

    5. 2026–2027 Will Separate Experimenters from Operators 

    The survey reinforces a forward-looking conclusion: 

    • Organizations can realize measurable gains in speed and predictability, and possibly see cost savings when AI is embedded into core trial design and operations (i.e., feasibility, site ID, data management, safety reporting, etc.) 
    • Those that treat AI as an “innovation sidecar” risk falling behind, even if they run more pilots 

    This is less about tools and more about operating model design: 

    • Sponsor-led vs. CRO-led AI 
    • Centralized vs. functional deployment 
    • Governance that enables scale without slowing execution 

    My Thoughts? 

    This industry pulse survey’s results tell a clear story: 

    AI in clinical trials has crossed the credibility threshold—but not the execution threshold. 

    The next phase will be defined less by better algorithms and more by better integration: 

    • Into outsourcing strategy 
    • Into vendor governance 
    • Into clinical operating models 

    That is where real competitive advantage will be created. 

    If your team is navigating similar decisions around AI adoption and governance, we’d welcome the opportunity to compare perspectives. Reach out for a brief follow-up discussion to explore how your approach aligns with broader industry trends.   

    Insights

    Commercial Planning: Building the Right Launch Team & Governance Model

    Expert insights from Asymmetry Group, a Danforth Health Company

    Product launches are amongst the most complex undertakings for biopharma companies—and there’s no shortcut to success. No matter how strong your strategy or detailed your plan, success ultimately depends on people. If you don’t have the right team in place, even the best-designed launch will fall short. That’s why launch governance and team structure aren’t just operational necessities; they’re strategic levers. Organizations that invest in building a high‑performing launch team and a governance model tailored to their unique needs don’t just execute—they create a competitive advantage.

    Several considerations will shape your launch team structure and governance—and directly influence who you select, how the team operates, and what support they’ll need:

    1) Launch Experience 

    Is this your company’s first launch? How much launch experience exists within your organization?

    Implications: Internal experience will impact the level of expertise required on the team, whether you need to bring in external support, and how much structure and oversight your governance model must provide.

    2) Desired Launch Team Dynamics

    To what extent do you expect your team to operate as an integrated, cross‑functional group? Is this consistent with your current culture?

    Implications: Your desired team dynamic will shape role selection, behavioral expectations, and whether new ways of working are needed to enable strong cross‑functional collaboration. Even if you have a highly experienced launch team, it’s likely the first time THIS TEAM is launching together.

    3) Organizational Decision-Making

    How does your organization collaborate and make decisions? Is authority centralized or shared across empowered teams? Are decisions made quickly or more slowly?

    Implications: Existing decision-making patterns will inform governance design—defining authority levels, escalation pathways, meeting cadence, and required functional representation to maintain momentum. And then it’s up to the organization to live these decisions to create trust and efficiency.

    4) Partnerships 

    Are external partners involved in the launch? What is the structure of the relationship, and how could it affect planning and execution?

    Implications: Partnership arrangements will influence team composition, require clearly defined roles and responsibilities, and may necessitate additional coordination or joint governance mechanisms.

      While there’s no single approach to structuring your launch team and governance, four best practices consistently position organizations for success:

      1) Appoint a Launch Lead Who Bridges Strategy and Execution

      This role varies by company and launch, but the Launch Lead must flex between strategic and execution‑focused work while maintaining a strong cross‑functional lens. Launch success requires alignment across all functions—not the efforts of just one or two teams.

      2) Establish a Small, Expert Launch Management Team (LMT) to Drive Progress 

      Typically composed of the Launch Lead and a few launch‑experienced resources, the LMT drives critical work: shaping strategy, coordinating across teams, tracking progress, and flagging risks. Acting as the “quarterback” of the launch, the LMT ensures visibility, timely communication, and cross‑functional alignment.

      3) Create a Lean, Empowered Governance Structure that Can Scale

      Your model should include a Core Launch Team, Extended Launch Team, Steering Committee, and functional working teams. Core Launch Team members—one per key function—must be empowered decision‑makers who effectively communicate back to their functions. Keep the Core Team lean; leverage the Extended Launch Team to involve additional functions without slowing down execution. The Steering Committee plays a critical role by resolving escalations and making timely decisions that cannot be addressed within the Core Team, Extended Team, or individual functions.

      4) Define Roles and Ways of Working to Enable Alignment and Decision‑Making 

      Roles and responsibilities should be clearly defined across all governance groups. Equally important is aligning on ways of working early—how decisions will be made, how escalations will be handled, how information will be shared, and what each governance member is accountable for.

        Planning your next launch? 

        Building the right team and governance model is just the beginning. Whether you’re launching for the first time or bringing deep experience to a new organizational or market context, our workshop offers actionable insights, proven best practices, and practical tools to set you up for success.

        Join us on March 26th and 27th and learn from our team of launch experts. Reserve your spot today: http://bit.ly/4eP0153 or reach out to our experts here.

        Insights

        Biotech Goal Setting: Why It’s a Mistake to Wait for the Board Meeting

        We hear it all the time in biotech goal setting: “We can’t set goals until the Board signs off on our corporate strategy.”

        The problem? That board meeting often doesn’t happen until February. By then, you’ve lost two of your twelve months and momentum.

        At Danforth Health, we’ve worked with more than 1,8000 life science companies. One of the most common missteps we see is delaying biotech goal setting until everything is “finalized”. This hesitation leads to operational drift and signals to your team that alignment and accountability are negotiable. They aren’t.

        Start Early. Adjust Later.

        Even if your corporate strategy isn’t finalized yet, you can, and should, begin laying the groundwork for effective goal setting in biotech well before the calendar flips to January. 

         “You can always adjust. You can always modify. But if you wait to begin until everything is finalized, you’ve already fallen behind.” Danforth Advisors HR Experts 

        A Rolling Approach to Biotech Goal Setting

        Begin in December: Host departmental and individual goal-setting sessions using current KPIs, performance metrics, and known priorities. This helps create momentum while keeping teams focused. 

        Use a Rolling Framework: When corporate goals are finalized post-board meeting, refine existing goals- don’t restart the process. This prevents duplication of effort and supports better alignment. 

        Train Managers to Guide the Process: Not everyone is naturally skilled at facilitating effective goal-setting conversations. And in biotech, where teams are lean and leaders wear many hats, that matters. Invest in manager enablement so that goal conversations are structured, consistent, and clear.  

        “A lot of the conversations we see around goal setting are vague. You want clarity on outcomes, not just tasks. Managers need to know how to distinguish between the two.” – Danforth Advisors HR Experts 

        Best Practices for Biotech Goal Setting

        Use SMART Goals: Make them Specific, Measurable, Achievable, Relevant, and Time-bound. In biotech, metrics matter- especially when tied to funding, product development, and hiring plans. 

        Limit Company-Wide Goals to 3–5: Especially at the early stage, less is more. Trying to pursue too many priorities dilutes focus. Danforth Health consistently sees better execution when companies narrow down to a handful of core, high-impact goals. 

        Distinguish Goals from Tasks: A goal is a measurable outcome (e.g., “Complete IND submission by Q3”). A task is a step toward that goal (e.g., “Draft protocol outline”). Confusing the two can muddy accountability and derail timelines. 

        Cascade and Align: Once corporate goals are in place, ensure every department and employee understands how their work contributes to the larger mission. Alignment isn’t automatic- it’s built through communication and iteration.  

        What Happens After You Set Goals?

        Effective biotech goal setting doesn’t stop once objectives are written down. Make it a living process:

        • Revisit them monthly or quarterly 
        • Identify gaps, changing priorities, or resource constraints 
        • Adjust without starting from scratch 
        • Review progress transparently with leadership and staff 

        Need Help Setting Goals in Biotech?

        At Danforth Health, we help biotech companies operationalize clarity-turning strategic priorities into measurable, achievable goals at every level. Don’t let the calendar or boardroom delay your momentum. Reach out today.