Biopharma 2025: Efficiency, Optional? No More!

By 2030, patents will have expired for 190 drugs on the market today, including 69 current blockbusters, impacting the revenues of almost every major biopharma company by $236 billion annually. Since most biopharma companies invest nearly 20% of their revenue in R&D, replenishing the pipeline with blockbuster hopefuls is critical to growth and, in some cases, survival.

Given the potential impact of IRA price controls, projections suggest that nearly $160 billion could be cut from R&D budgets at a time when the opportunities for breakthroughs are more promising than ever.

Historically, the biopharma industry has relied on high-value blockbusters to remain viable, counteracting the R&D costs for 90 percent of assets that fail during development as well as the high proportion that don’t recoup the cost expended to get them to market.

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However, biopharma companies continue to struggle to replenish their R&D pipeline with new assets at the pace and value of the assets, leaving their pipelines with a burgeoning deficit. With rising costs, long cycle times, looming patent expiries, a complex M&A landscape, and changing regulations, biopharma is nearing the point where its commercial portfolios can no longer sustain innovative R&D to support long-term growth.

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Biopharma has navigated patent cliffs before...

What’s different this time?

Insiders have always known this, but this may surprise outsiders: biopharma is a boom or bust business. Where blockbuster drugs determine fortunes, and companies that consistently fail to launch blockbusters are eventually either merged or sold.

Executive tenures and their strategic decisions are often inextricably linked to the patent life of their blockbuster medicines. Before so-called patent cliffs, executives typically seek to aggressively restock their pipelines by increasing investments in R&D, licensing experimental therapies, or acquiring other biopharma companies.

Until now, the biopharma playbook for dealing with patent cliffs has been to rapidly add more drugs to the pipeline (in multiple R&D phases) — either organically or by buying promising external assets, reducing costs through layoffs, outright acquisitions, or mergers, and relentlessly extending the patent life of in-market blockbusters.

For example, AbbVie followed the lessons of the previous patent cliff by merging with Allergan in a 2019 deal that gave it enough new revenue to withstand the expected erosion of Humira’s sales. Its approach was similar to that of large pharmaceutical companies ahead of the 2010 patent cliff, which spurred megamergers like Pfizer with Wyeth and Merck with Schering-Plough.

But, will these traditional strategies be enough for biopharma companies to climb out from this steepest yet patent cliff in the industry’s history?

Given the pace of politically charged regulatory changes, the impending and unprecedented scale of the loss of exclusivity of high-value assets, pricing pressures in both the US & EU, the rapid pace of scientific and technological advances, and rising protocol design complexity affecting cycle times all suggest — it may not be business as usual this time around.

Why? For one, megamergers are no longer the answer this time. Current pharma executives have expressed reluctance to do big deals because of the complexity of combining two global companies. There is much debate over the merits of such deals on combined market valuation and R&D productivity afterward. Besides, after years of industry consolidation, few major drugmakers remain attractive merger targets and those that do have patent cliffs of their own. Instead, the hunt for new products continues unabated in internal laboratories of big biopharma companies or those of smaller biotechs.

But then, in biotech, there are not enough blockbuster hopefuls for everyone, so it will understandably be a fight for high-value derisked assets fueled by extensive cash holdings. According to some estimates, big biopharma currently has $500 billion in cash for acquisitions and other pipeline-building transactions. Small and mid-sized biotechs have simultaneously seen their valuations drop, limiting their financial options. We are potentially entering an era of fewer blockbusters and many more smaller products. Consequently, and related to this shift, biopharma needs to become super efficient with executing multiple and parallel global launches.

Essentially, this time, the biopharma candle is burning on both ends. The new reality of winning in the pharma business is not just innovating newer drugs faster; it is continually becoming more operationally efficient than the competitors.

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Why should you care?

While the average R&D cost of an asset from discovery to launch in 2022-2023 has remained flat at $2.3 Billion per pipeline asset, the corresponding metric of average forecast peak sales per pipeline asset has fallen from $389 million in 2022 to $362 million in 2023. In other words, today, each launched product must sustain peak sales for 6.3 years to recover its development costs, let alone make any profits. And, in the face of evolving circumstances (above), achieving sustainable profitability is likely to get much more challenging.

For R&D leaders, the additional challenges now, beyond their day job of discovering high-potential assets, include addressing the delayed response rates to operational problems, plugging persistent efficiency gaps, and reducing organizational reliance on incomplete or uninsightful data sources to accelerate decision-making.

Consider this, the two most important aspects of the biopharma business are the (tangible) drug itself and the (intangible) information about the drug — indication, usage, dosage, side effects, precautions, pharmacology, toxicology, and epidemiology, among others — to get a drug from discovery to regulatory approval. Not to mention all the information needed to ensure, sustain, and expand a successful launch — healthcare system, patient, and market insights to assure prescription generation, access and reimbursement, and therapy compliance.

Biopharma is as much in the medical innovation business as it is in the information collection and analysis business. When vital information flow is bottlenecked or impeded by functional or technology silos, it directly affects the quality and pace of decision-making, impacting successful outcomes of discovering new approvable drugs and launching them globally to realize the full extent of their societal and financial value.

With the explosion of data and tools in the last two decades, the amount of information produced by biopharma companies continues to grow exponentially. Yet biopharma organizations have only scratched the surface of what their data can tell and help them do. Mainly because their data is still very fragmented and situated in functional silos or legacy tools, some companies have proactively created data lakes to integrate disparate data, yet without contextually understanding how one type of data affects another in real-time within their live ecosystem, leaders are limited in their capacity to drive operational efficiency and decision quality.

Biopharma leaders today are sitting on a data goldmine, primed to create and drive a sustainable R&D innovation and launch execution engine. But not until their tools to extract value from their goldmine remain in their infancy. The benefits of all available information can only be realized if the data is managed, processed, and utilized in real-time to generate actionable just-in-time insights through systemic integration of tools and automation of processes — all without the extensive need for manual human effort.

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What can you do?

AI-enabled digital transformation is fast becoming a strategic imperative for leaders in life sciences. To date, the most common use of AI is transforming how pharma companies decide which disease areas to invest in. And to identify targets, develop molecules, and improve drug discovery accuracy, predictability, and speed.

However, augmenting internal human effort with AI has the potential to slow rising costs, accelerate tasks across the entire R&D value chain, and bring a significant proportion of external services back within the walls of biopharma companies while improving experiences for employees and patients alike, and ultimately contributing to more efficacious therapies.

Many companies’ early forays into AI involve experimenting with use cases that drive productivity and efficiency—a natural choice as a low-risk play to gain confidence and trust in this new technology. Companies use AI to solve problems they already know how to solve using legacy techniques, yet AI helps them do it faster and with the added security of using humans to check the work.

However, most companies today are in a transitional phase with AI where enthusiasm outstrips impact. Companies have invested in disjointed use cases that have created productivity gains in isolated pockets but have failed to drive the business objectives that executives target. Also, the early enthusiasm for new tools fizzles in the face of poor experiences, oversaturation, and tenuous results. In addition, rank-and-file employees feel alienated from their companies’ AI efforts because leaders haven’t sufficiently articulated their vision.

To begin truly scaling AI for value, companies need to shift focus beyond seeking productivity and efficiency gains toward delivering fundamental shifts in what they do and how they do it, which requires a clear vision aligned with business goals and the leadership support to develop whole chains of use cases that make that vision a reality.

Novel scientific breakthroughs are not sufficient alone to guarantee market success. Many R&D leaders are acutely aware of the operational silos between their teams across the value chain, without insights from across the company about the product, market landscape, regulations, and patient population characteristics to drive success when the drug progresses through to launch, strategic decisions about asset progression and launch and commercial strategies are not as fully informed as they can be.

When companies understand the commercial potential of a drug at the earliest possible stage in R&D, they focus their resources on drugs most likely to succeed. Harnessing advanced analytics to evaluate different scenarios supports the development of alternate strategies that empower the ‘go or no-go’ decisions at various stages across the R&D cycle.

Biopharma leaders committed to raising R&D productivity and fully realizing the commercialization potential of their pipeline assets would do well to think seriously about their functional and technological information silos. Busting information silos not only enhances functional alignment, it accelerates decision-making, improves decision quality, and facilitates proactive action at all levels of the organization.

While improving productivity in biopharma R&D has never been easy, today, the opportunity to apply AI solutions to balance cost efficiency with value creation, to influence multiple critical levers simultaneously to drive change, is unprecedented in biopharma history.

Fragmented information and divided people drive inefficiencies and thwart growth—instead, Unite and Prosper (Unipr).

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Unipr is an ecosystem of tools and technologies for unifying disparate data and generating predictive insights to transform portfolio and people operations in real-time across evolving scenarios.

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References:

1. Drug Patents Protect Pharma Profits
2. Pharma's Patent Cliff of Tectonic Magnitude
3. Measuring Return from Pharmaceutical Innovation

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