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Asembia 2025 

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AbbVie Pedro Valencia ADCs

PE: How is AbbVie using novel cancer biomarkers to develop potential first-in-class ADCs for the treatment of advanced solid tumors?

We're constantly looking for biomarkers that can help us to do two key things. The first is to tackle cancer cells. and spare normal cells. We’re looking for specific antigens that are over-expressing tumor cells, and you see limited expression in normal cells. We want to make sure that target that’s over-expressed is critical for the survival of the cancer cell. Once you block it, you can actually drive a cancer cell death. That’s what we're looking for. In that perspective, c-Met is one of those biomarkers that is over expressing multiple tumor types such as lung, colorectal, gastric, and other GA tumors. We as an industry have tried to tackle c-Met in the past but have been unsuccessful. It would actually be successful to tackle what we call met, which is a gene inside the cell.

There’s this thing called met amplification, where the cancer cell produces multiple copies of the gene, and around 30% of patients have it. We can actually tackle that with small molecules. But once the gene becomes a protein and gets over-expressed in the cell, that over-expression is something we haven’t been able to target. For example, we know that on lung cancer, c-Met over-expression is about 25% to 50% in colorectal cancer, and it could be anywhere from 30% to 70% depending how you measure it, so it’s quite prevalent.

With ADCs, we’re trying to tackle c-Met over-expression on the surface of cancer cells. That was novel, and we can do it through ADCs. 706 has a target called SEZ6, and it’s a target that is over expressed in neuroendocrine cells or neuroendocrine neoplasms. When you think about it, multiple tumor types kind of have a subset of cells that are neuroendocrine driven. Small cell lung cancer is one of the few that has everything driven by neuroendocrine cells. The beauty of SEZ6 is over-expressing those cells and limited expression in normal cells. So, we can tackle it with an ADC and drive the results that we're seeing today.

PE: ADCs have redefined the treatment of cancer in recent years. Can you please discuss the promise of this emerging class and what the future may hold?

At the end of the day, ADCs work like a trojan horse. You have a good antibody that cancer cells are happy to receive, but once you hook into the cancer cell receptor, you deliver the payload, which is pretty much chemotherapy. It’s actually a highly potent version of the regular chemotherapy cancer patients are given on a regular basis. So, from that perspective, our goal is to replace chemotherapy. Our grand goal with ADCs is to replace good old, decades old chemotherapy that is really toxic for some patients. That’s how we see the redefinition of cancer treatment in the future with ADCs. So, what do we see in the future? Well, if you think about it, chemotherapy is being given in combination with other agents. In the future, we see ADCs being given in combination with chemotherapy. There will be one with other agents such as immuno-oncology agents. So, if you combine the ADC and the PD1s, you can start seeing that synergy come together and kind of start really dreaming about cures for the patients. All in all, we're super excited about ADCs replacing chemotherapy. 

In an interview with Pharm Exec Associate Editor Don Tracy, Pedro Valencia, VP, Solid Tumor Pipeline Strategy & Execution, AbbVie, discusses using novel cancer biomarkers to develop potential first-in-class antibody drug conjugates (ADCs) and the promise that they offer. 

Utilizing Biomarkers to Develop First-in-Class Antibody Drug Conjugates to Treat Advanced Solid Tumors

AI's Role in Commercial Pharma Strategy


Avinob Roy, Vice President & General Manager of Global Commercial Product Offerings at IQVIA, spoke with 

 about how advancements in AI, particularly agentic AI, are reshaping the future of commercial operations in life sciences. From understanding disease landscapes and identifying patient populations to optimizing brand strategy and field engagement, Avinob outlines how AI companions are augmenting decision-making across the entire commercial value chain. Drawing on real-world experience, Avinob offers an actionable perspective on what it takes to scale AI successfully and how organizations can drive meaningful value in everyday workflows.


How AI is transforming the full spectrum of commercial operations—from market assessment to field execution—by acting as a strategic companion across the value chain.

Distinguishing between hype and reality in the current AI environment, and what it takes to move from proof-of-concept to scalable, production-ready solutions.

The foundational elements required for successful AI adoption in life sciences, including the critical role of data infrastructure, domain expertise, and cross-functional alignment.

Discover how agentic AI is reshaping life sciences commercial operations.

Accelerating Pharma Commercial Strategy with Smarter, Faster, and Bolder AI

How Can the Pharma Industry Prepare for Enforcement of the MFN Order?

Peter Rubin, executive director of No Patient Left Behind, spoke with 

 about President Trump’s Most-Favored Nation (MFN) order and the widespread impact it could have on the industry. While the order seeks to reduce drug costs in the United States by enforcing a rule based on foreign prices, it’s likely that this rule could force pharma companies, large and small, to adjust the way they approach innovation. According to Rubin, the ruling is likely to impact more than just pricing. He also believes that it will be hard to even find a fair price for the drugs using the MFN order as determining a drug’s true value is more difficult that it first appears.

Pharmaceutical Executive: How can the pharma industry prepare for enforcement of the MFN order?

 When you think about the biopharma industry, it’s important to think about it as an entire ecosystem. You have the specialist investors that fund seed stage and small bio-pharmas that comprise two-thirds of the drugs before FDA. There’s the large pharmas that are very successful at bringing new drugs to market successfully.

The industry is not a monolith, it’s an ecosystem where everybody plays an important role. Specialist investors and small bio-pharmas have done a good job of raising their hands and warning of the impact of imposing foreign price controls based on ex-US health technology entities that’s going to impact US competitiveness and impact our ability to bring new drugs to market five to 10 years from now.

As we talk to people over the course of the year, I’ve told people is that this is really a 16-quarter presidency. Each quarter will have its own volatility and you should expect that. Just because deal with MFN in the first quarter doesn’t mean that it’s not going to pop up in a whole new way in the second quarter. Even if you resolve it in the third, it’s going pop up between now and the rest of the 16 quarters. It’s hard to get a sense of what’s actually going to happen.

PE: How is oncology R&D already being impacted by existing regulatory concerns?

 It’s a one-two-punch with MFN and the Inflation Reduction Act (IRA) with the small molecule penalty. That’s already caused a lot of retreat in the small molecule oncology. Prior to the IRA’s passage, half of the molecules that were approved in oncology were small molecules. As of the IRA and the pill penalty, a lot of investors, large pharmas, and everybody inbetween switching to large molecule solutions. Unfortunately, small and large molecules are just as difficult to innovate, but small molecules are easier and less expensive to manufacture. They don’t require physician administration (in most cases), they’re interchangeable at the pharmacy, and they’re easily genericized.

Many of the large molecules aren’t covered by the IRA’s $2,000 out-of-pocket cap. It’s really only Part D drugs, which are primarily small molecule drugs in most instances. From a societal and environmental perspective, oncology and small molecule drugs have been severely impacted by the IRA’s pill penalty. If you throw imposing foreign price controls (via the MFN process) on top of that, you’re going to see another negative impact on oncology R&D for both small and large molecules.

Peter Rubin discusses how the industry is an ecosystem and each of the parts will feel the impact differently.

How is Oncology R&D Already Being Impacted by Existing Regulatory Concerns?

Alongside regulations from the IRA, oncology R&D is set to face unique challenges when MFN is enforced.

Like humans, biotechnology startups begin small. When they are born, they’re bold and the sky is the limit. Yes, they are small and still need to be nurtured, but at a young age they’re already focused on a big idea built around discovery that could have real impact on the world.

In the earliest of days, it’s all about focus and going deep on complex topics. First steps in R&D and early-stage financing with limited clinical evidence or no proof of concept (POC). Challenges are steep, with nearly half of clinical trials failing to progress beyond Phase I,

 costing an average of $7.1 million (I would challenge this is low by today’s standards) and lasting as long as two years. Even when companies reach the market, hurdles persist, with only 28% of first-time biotech product launches exceeding analysts’ pre-launch forecasts, compared to nearly 50% for experienced companies.

 Furthermore, about 90% of biotech startups end up failing,

 making the commercialization journey especially daunting. A strong foundation is critical.

The focus shift: From depth to depth and breadth

In early R&D stages, a high level of depth and focus is critical to succeed. Clinical and research teams dive deeply into the science, solving new problems daily. In a successful discovery process, quality wins over quantity. They need to ensure precision in the entire R&D process while keeping the big picture of how “wonder science” will be applied in day-to-day medicine. Before you know it, you go from “keeping this baby alive” to dealing with a toddler and then a teenager in search of their own identity and hungry for autonomy. As the saying goes: Little kids, little challenges; Big kids, big challenges.

Once a biotech company delivers a POC, part of bringing it to life will depend on how key elements of commercialization are integrated and if they’re integrated early enough in the clinical development and manufacturing process. Delayed focus on strategy—dosing, patient profiling, market fit—can jeopardize a promising drug’s future. As Keith Woods, chief operating officer at Scholar Rock explains, “Clinical teams need to work side-by-side with peers from early commercialization strategy teams, ensuring that science fits realistically into clinical practice, health systems, how it reaches patients, and how it aligns with payers, providers, and regulators.”

At this stage, financial stability becomes even more crucial. In 2023, biotech bankruptcies reached a10-year peak,

 highlighting the industry’s financial volatility and the challenges many companies face in sustaining operations long enough to reach commercialization.

Overnight, the teenager is out of college, and her world suddenly expands with opportunities and complexities. Vital decisions need to be made (where to live, what to become in a few years). A biotech at this stage, private or public, will grow exponentially in size, complexity, and capital requirements. Its risk and opportunity profile shifts from success in science to success in future science and commercial P&Ls.

While science should remain at the heart of the company, the opportunity to reach patients on a commercial scale and generate its own revenue is where true transformation takes shape.

In the same way parents guide their kids to unleash their full potential, company leaders and boards of directors need to drive change and evolution for a biotech company to deliver on its promise to patients, healthcare providers, employees, other stakeholders, and shareholders.

This is a moment of truth, and not all companies are able or willing to embrace the opportunity to evolve. Purist scientists have a tendency to “fall in love” with inventions they’ve spent years nurturing– rightfully so.

As a father of a wonderful teenage daughter, Violeta, I can assure you that it is not easy to see them grow and leave the “nest.” It’s a deeply emotional journey for both the kid and the father, but you know, letting go is critical for their growth.

Speed: From clinical research to clinical practice

R&D operates under a different sense of urgency and scale. Clinical trials are always a simplified and partial view of clinical practice. The company is in full control with protocols in place; they are meticulously planned, they know when patients are showing up days in advance, and progress is measured in years. Precision and patience are key.

Commercialization moves at the speed of life—patients, providers, and competitors won’t wait. Once approved, the company operates 24/7, with patient demands and compliance hurdles requiring agility, and many organizations struggle to adapt quickly. Moving from a deliberate, controlled setting to a more proactive, fast-moving, and uncertain environment requires a mindset change starting at the top. David Epstein, chairman and CEO of Ottimo Pharma says, “For biotech companies, the transition from an R&D-focused organization to a commercial entity is one of the deepest, most challenging yet rewarding transformations in the industry.

Control: Letting go while staying grounded

One of the most jarring changes during this transition is the loss of control. In R&D, you are the one steering the ship. You design clinical trials, control the environment, and interact with a small circle of stakeholders.

The arrival of early commercialization strategy teams should not dramatically change the script if the “voice of the customer” was included in the early days. Woods notes, “Once the product is approved, the market dictates its use—payers, prescribers, patients, and policymakers are in the driver’s seat.” This shift is a major hurdle faced by nearly half of the surveyed professionals,

 making reimbursement and patient access unpredictable. The increased complexity and perceived randomness can feel overwhelming for companies accustomed to the precision of R&D alone.

A path forward? Learning to regain control in new ways. It’s about building trust with external stakeholders, creating robust processes, and being proactive rather than reactive. Thriving in uncertainty becomes a skill—and a necessity.

The success of this transformation hinges on leadership. Epstein adds, “moving from R&D to commercial isn’t just a shift in operations—it’s a cultural transformation if it was not already considered in the early stages. Organizations risk misallocating resources or failing to meet critical regulatory milestones without strategic leadership and a cohesive development strategy. Strategy, vision, and decision-making all evolve.”

More often than not, companies wait too long to bring in the right leadership. Founders, who are often brilliant scientists, may struggle to move from two-variable equations (science and funding) to more complex organizations. Leading and running an R&D-focused organization requires a different mindset and skillset than leading a cross-functional commercial operation.

Successful biotech companies recognize this early on. They recruit experienced leaders and build teams capable of bridging science and business, and foster cultures that encourage collaboration and innovation.

The R&D-to-commercial transition is messy, tough, fascinating, challenging—and fun. It’s what turns an early spark of innovation into something real—something that changes lives at scale and over time. However, the reality remains that only a fraction of biotech companies

 successfully bring their first product to market, reinforcing the importance of strategic leadership, financial discipline, and early commercial planning.

Getting a product to market is just one part of the transition. The true goal is to build a resilient organization—one that can sustain innovation, deliver value, and leave a lasting impact.

The journey from lab to market isn’t a “stroll in the park,” and meaningful growth rarely is. It’s about embracing change and opportunity, learning from challenges, and growing into what’s next. And in biotech, that next step can change the world in magical but very real ways, just like science.

Carlos Martin is a Strategic Advisor for Rocket Pharmaceuticals

1. Early Phase Challenges for Biotechs. ICON. 

https://www.iconplc.com/sectors/biotech/early-phase-challenges-biotechs

2. Making the Leap from R&D to Fully Integrated Biotech for First Launch. McKinsey & Company. February 15, 2023. 

https://www.mckinsey.com/industries/life-sciences/our-insights/making-the-leap-from-r-and-d-to-fully-integrated-biotech-for-first-launch

3. Thornewood, R. How Often do Startups Fail? Fisheriesalliance.eu blog. August 12, 2024. 

https://fisheriesalliance.eu/how-often-do-startups-fail/

4. Armstrong, A.; Mason, G. Biotech Bankruptcies Hit 10-Year Peak in 2023. Fierce Biotech. February 12, 2024. 

https://www.fiercebiotech.com/special-reports/biotech-bankruptcies-break-10-year-record-2023?utm_source=chatgpt.com

5. First-Time Launchers in the Pharmaceutical Industry. McKinsey & Company. February 12, 2021. 

https://www.mckinsey.com/industries/life-sciences/our-insights/first-time-launchers-in-the-pharmaceutical-industry

Biotechnology startups, akin to human growth, begin with bold ideas and face significant challenges, including high failure rates and financial volatility. Initially, these companies focus deeply on R&D, but as they progress, they must integrate commercialization strategies early to ensure success. The transition from R&D to commercialization involves a cultural shift, requiring strategic leadership and collaboration. Despite the difficulties, this transformation is crucial for delivering impactful innovations to patients. Ultimately, successful biotech companies build resilient organizations capable of sustaining innovation and delivering long-term value.

Outlining the key touchpoints for embracing change and opportunity in the “messy, tough—and fun” R&D-to-commercial transition journey. 

From Lab to Market: Tracking Today’s Shifting Pathways for Biotech

The pharmaceutical industry has moved well beyond asking whether artificial intelligence (AI) can transform drug development. According to a survey by the Pistoia Alliance,

 68% of life sciences R&D professionals are using AI and machine learning in their work. Yet the same survey reveals that 52% cite poor data quality as the biggest barrier to AI implementation.

This paradox captures exactly where the pharma industry finds itself in 2025. Companies have moved from wondering “what can AI do?” to conducting meaningful experiments. But most remain stuck between proof-of-concept success and enterprise-scale value.

That hurdle is not a technology problem. AI models have improved dramatically, with new capabilities emerging every few months as access becomes increasingly democratized. The potential of AI has always been there, and that fundamental promise hasn’t changed.

But McKinsey research says generative AI models account for only about 15% of a typical project effort.

 The remaining 85% involves adapting models to a company’s internal knowledge base and use cases—and this is where most drugmakers struggle.

The real barriers are about business transformation. Consider a common scenario in pharmaceutical manufacturing: Drug test results are still shared via email attachments and PDFs between contract manufacturers and testing laboratories. These documents often contain handwritten notes that get scanned into PDFs. Then someone has to manually type out the handwritten notes so they can enter them into their own system. It’s slow and error-prone.

Working with numerous pharmaceutical companies on AI implementation has revealed a clear framework for moving from successful experiments to enterprise-scale value. The approach centers on business outcomes rather than technology capabilities.

Step 1: Start with clear business outcomes

Too many organizations begin with the technology and then search for applications. Business outcomes must drive the digitization and use of tools such as AI. A single IT function can’t effectively solve problems across all the different business domains.

Instead, start by identifying specific, measurable business problems. Consider clinical study report generation: from the last patient’s final visit to the clinical study report generation, it takes anywhere from a few weeks to multiple months. AI can handle 60%-70% of document creation much faster than humans, while still maintaining human oversight. Don’t shoot for 90%-95% at the beginning. Begin with modest automation goals and iterate toward optimization.

One of the biggest barriers to AI scaling is the traditional divide between business and IT functions. Siloed approaches consistently fail to deliver enterprise value. According to McKinsey, 70% of transformations fail, in part because of the disconnect between what the business wants and what the IT teams deliver.

Gartner recently suggested putting the chief information officers (CIOs) and the chief experience officers (CXOs) of the business functions together and holding them jointly accountable for outcomes.

 Each leader owns their domain, but shares ownership of results. This creates true partnership rather than traditional handoffs. Gartner says that nearly three-quarters of CXOs who co-lead delivery meet or exceed targets from digital investments.

Step 3: Implement hub-and-spoke governance

Successful AI scaling requires balancing centralized governance with federated execution. Central functions should maintain oversight of safety, regulatory compliance, and enterprise-wide standards—particularly crucial in the heavily regulated pharmaceutical sector.

However, use case development should be federated to business units. Some responsibility should be co-owned between the CIO and CXO, while safety and regulatory aspects remain centralized at the enterprise level. This ensures that business units can move quickly while maintaining necessary controls. Eventually, companies should work toward democratized AI access where business users can build their own solutions, test them, and either scale them to production or discard them based on results.

Rather than attempting wholesale digital transformation, focus on bolt-on solutions that don’t require overhauling entire systems. The document processing example, for instance, doesn’t need any fundamentally new system. It can be a bolt-on with possibilities of what AI brings to the table.

Merck and McKinsey co-developed a platform that generates clinical study reports.It reduced the time to produce a first draft from 180 hours to 80 hours while cutting errors in half.

It’s a clear opportunity for AI augmentation without requiring complete infrastructure replacement.

The ultimate goal is creating infrastructure in which business users can access data, compute power, and AI models directly. With that infrastructure comes a drastic change in how organizations operate. Anyone with the right credentials should be able to access data, compute, storage, and AI models, connect analytics, and make informed business decisions in real-time. The impact is almost instantaneous. Instead of three-month wait times for simple dashboards, business users can create solutions over an afternoon. But this democratization must be built on solid data foundations—achieving a true single source of truth rather than maintaining data silos.

While many organizations remain focused on experimenting with models and use cases—the 15% of the effort—the real value lies in solving the 85% challenge of business adaptation and integration. Companies that master this business-first approach to AI scaling will create sustainable competitive advantages.

The goal is enabling organizations to operate at the speed of business decisions rather than the speed of IT implementation. Doing so can bring life-saving treatments to patients faster, moving from months-long processes to real-time insights and actions.

Success requires moving beyond the technology-first thinking that has characterized much of the industry’s AI journey. The companies willing to do the hard work of business transformation will be the ones that capture that potential at enterprise scale.

Ramji Vasudevan is Business Unit Head - Life Sciences, Altimetrik

1. What will the Labs of 2030 Look Like? Pistoia Alliance. 

https://marketing.pistoiaalliance.org/hubfs/Lab%20Of%20The%20Future%20Reports/Lab%20Of%20The%20Future%20Survey%20Results%202024%20.pdf

2. Generative AI in the Pharmaceutical Industry: Moving From Hype to Reality. McKinsey & Company. January 9, 2024. 

https://www.mckinsey.com/industries/life-sciences/our-insights/generative-ai-in-the-pharmaceutical-industry-moving-from-hype-to-reality 

3. Perspectives on Transformation. McKinsey & Company. 

https://www.mckinsey.com/capabilities/transformation/our-insights/perspectives-on-transformation

4. Fostering CIO Partnerships With CxOs Takes on New Urgency. Gartner. 

https://www.gartner.com/en/chief-information-officer/insights/cio-partnerships


5. With Gen AI, Merck and McKinsey Transform Clinical Authoring. McKinsey & Company. 

https://www.mckinsey.com/about-us/new-at-mckinsey-blog/with-gen-ai-merck-and-mckinsey-transform-clinical-authoring

The key steps for pharma in pivoting from proof-of-concept experimenting  
to enterprise-scale value.

Scott Berghoff

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