Integrating artificial intelligence and advanced analytics throughout operations offers pharma companies a pathway to offset impending drug patent expirations.
The average cost for a pharmaceutical company to bring a new drug to market in the US has reached a staggering $2 billion, according to a recent study by Deloitte.1 The path is made even more challenging by a series of additional obstacles. From growing regulatory complexity to the difficulty of communicating the nuanced benefits of specialized therapies to niche audiences, launching a new product can feel like Sisyphus pushing a rock uphill. It’s not surprising that more than one third of all product launches fail, often resulting in serious (if not catastrophic) revenue losses.
As if product launches weren’t already daunting enough, there are several factors on the horizon that will intensify challenges in the years ahead. The Inflation Reduction Act, signed into law last year, will empower the federal government to negotiate lower prices on a number of key drugs. When that process begins in this fall, revenues will likely drop for many companies. In addition, the industry is facing the prospect of losing more than $200 billion in annual revenue from patents that are set to expire. As a result, nearly 200 drugs (including 69 blockbusters) will lose exclusivity by 2030. For example, Johnson & Johnson’s top earner, Stelara, which treats plaque psoriasis, psoriatic arthritis, Crohn’s disease, and ulcerative colitis, will lose US exclusivity this year along with AstraZeneca’s star respiratory medicine, Symbicort.
This grim reality has the potential to impact almost every major pharmaceutical company, forcing many to rush to replenish their research pipelines and skillfully manage future product launches to replace lost revenue.
All of this is happening amid strong financial headwinds, including a weakening biotech investment sector, a decrease in available capital, general banking woes, and overall economic uncertainty. The entire sector is feeling a tightening grip around their budgets, driving companies to trim the proverbial fat, streamline processes, and get serious about improving commercial effectiveness.
These uneasy factors mean that life sciences companies are faced with a dichotomy: as launching new products grows more difficult, it also becomes more critical to shore up finances. Like it or not, companies need to wade into these choppy waters to remain profitable. As such, what can the industry do?
Part of the solution can be found in data and analytics. Today’s data analytics technologies enable companies to collect, manage, analyze, and synthesize more data and information faster than ever. Traditionally, it has taken companies months to uncover important insights on the effectiveness of a launch strategy midstream and then another few months to pivot—far too late to properly tune a strategy that might be faltering. It doesn’t have to be that way anymore. Now, companies can make informed, data-driven responses nearly in real time, which can make all the difference in launch success.
As the life sciences industry seeks to chart a course through these turbulent times, key industry figures have been weighing in with their expert insights. Among them is Andree Bates, PhD, chairman, CEO, and founder of Eularis, a company specializing in harnessing the power of artificial intelligence (AI) to boost efficiencies within the biopharma and healthcare sectors. In an exclusive interview, Bates emphasizes the crucial role of AI in confronting today’s multifaceted market challenges.
“AI-driven strategies are the key to solving today’s complex market problems,” she asserts. “In fact, life sciences companies should be doing this in general—not just in the face of the patent cliff or the current challenging environment.”
As the pharma industry looks to navigate these difficult waters, here are three ways forward.
More than ever, pharma companies need to develop broader product pipelines so that they can position themselves for the market as quickly as possible. Advanced AI and large language model (LLM) analytics can help organizations accelerate clinical research and get the most out of the work that they’ve already completed.
“There are so many different areas where technology can speed up processes to save time and money in R&D, and clinical trials in particular,” says Bates. “AI and machine learning can predict the outcome of a trial before it even starts, for instance. Theoretically, in 10 years, the whole process of getting a new product to market could go from 10–15 years to only one to three years using these technologies.”
One example of an organization taking advantage of AI in R&D is GRAIL, a healthcare company that focuses on early detection of cancer. GRAIL recently introduced Galleri, the first multi-cancer early detection test that can find a shared cancer signal across more than 50 types of cancer. Through AI-driven, next-generation sequencing and machine learning algorithms, Galleri can isolate cell-free DNA and analyze its methylation patterns to detect if a cancer signal is present. If it is, then the test predicts the cancer signal origin to help guide diagnostic evaluation.
This unique combination of AI, rich data, and quality practitioners has resulted in a breakthrough test that could potentially impact millions of lives in the years ahead—and it’s a prime example of leveraging advanced analytics to speed the research process.
Companies cannot leave anything on the table in the current economy. Brand teams must double down on cultivating relationships with existing customers and continue to develop loyalty ahead of a product losing exclusivity. Identify the practitioners who are most loyal. The physicians who have been loyal prescribers for years and have seen positive results in their patients are more likely to forgo the generic alternative when it becomes available. The more life sciences companies embrace these high-volume prescribers, the better results they will see, both prior to losing exclusivity and after.
It’s worth noting, too, that extending prescribing behavior even for just one week beyond patent expiration and before a physician starts to prescribe the generic due to consumer demand can make a difference. Every little bit helps. Extra loyalty-building efforts now could be worth millions of dollars in revenue down the road.
AI-based next-best-action (NBA) solutions help supercharge loyalty-building efforts by unearthing valuable insights about how to best tailor each engagement with healthcare professionals (HCPs). For example, in Italy, one Aktana customer had invested substantially in developing loyalty-building customer journeys. The pharma company used Aktana’s contextual intelligence platform to design and choreograph these programs via the NBA engine, delivering the recommended actions through sales and marketing automation systems that personalized experiences for HCPs. As a result, the company increased strengthened relationships with their HCP customers.”
Further, NBA platforms support personalized HCP engagement executed across all customer-facing teams—sales, marketing, and medical—at scale. This is critical. NBA technologies not only deliver critical insights about HCPs’ preferences but also empower the entire commercial organization to orchestrate an expertly coordinated customer experience in real time. It’s the key to delivering a better, synchronized HCP experience that makes them repeat prescribers. It’s no longer a nice-to-have thing but a necessity.
“Here’s one example of the power of intelligence solutions. We used AI to analyze the value of all commercial engagement channels used for a product with only three years left under its patent,” says Bates. “By prioritizing the channels and messages that had the best results, the product doubled its market share in only six months, essentially helping to maximize revenues for the remaining patent life.”
Life sciences companies that are exploring new indications for products going off-patent can also use AI to identify new HCPs and begin to cultivate those relationships early. Advanced NBA solutions can then help companies strategically coordinate pre-commercial sales and marketing efforts with medical science liaison engagement of leading physicians to pave the way toward a more effective product launch post-regulatory approval and establish long-term loyalty with influential key opinion leaders and digital opinion leaders.
Once a product has gone to market, companies must be able to quickly and effectively pinpoint their target customers to maximize value. Being able to effectively analyze and synthesize enormous amounts of data can often mean the difference between success and failure. This delicate and time-consuming process—the who, what, when, where, and how of commercializing a product—can be greatly improved with data analytics and AI-powered intelligence systems. For example, Eularis helped a top 10 pharmaceutical company plan the launch of a new drug like their existing blockbuster, but with a new “mode of action” and without eroding the blockbuster’s market share.
“We used both big and small data, including claims data, patient registry data, and market research data to map how the target disease space was changing as well as how the patient journey and treatment approaches were evolving with the new market entrants,” explains Bates. “We did this analysis across all relevant therapeutic areas for the drug.”
Among many other analyses, Eularis also mapped each disease area for all existing drugs in the market, to identify the strongest opportunity spaces for the new drugs.
“Through this detailed work—aided by AI—the pharmaceutical company was able to create highly effective sales and marketing strategies using the insights gained. The new drug was launched and performed exceedingly well, reaching around $3 billion in sales in the first year without cannibalizing the original blockbuster,” says Bates.
Such comprehensive AI-driven data analysis, done in coordination with machine learning and human analysis, made a big difference. Without the data, technology, or human input, this meticulous work would not have been possible, potentially leaving large amounts of revenue on the table.
Additionally, NBA platforms help sales, marketing, and medical teams target the right HCPs in the way that they like to be contacted and with the content that will move them to action. Such platforms can help companies wade through data to provide solutions that improve the customer experience. NBA systems that learn, adapt, and coordinate effective execution allow commercial teams to adjust strategic priorities and chart new pathways for execution in real time. For example, facing the imminent launch of a new vaccine, one global pharma company working with Aktana used an NBA platform to optimize launch strategy execution in real time. Although reps were armed with call plans and targets for their new product, it was difficult to plan out a strategic route without an established visit pattern to guide activity. By delivering NBA suggestions and insights directly within sales reps’ daily workflow, the teams optimized their schedules, effectively incorporated new targets into existing routes, and surfaced the right messaging for every HCP detail. As a result, sales reps who used the platform outperformed reps who did not by more than 30%.
Companies should be investing in technology now that will provide real-time launch feedback and enable continuous improvement in preparation for the deluge of patent expiry that’s coming. Only AI-powered platforms empower teams with ongoing, up-to-the-minute insights that inform adjustments to a strategy with a continuous feedback loop: “insight, action, learn, adjust.” Rinse and repeat.
Over the next decade, pharmaceutical companies will have to contend with a host of obstacles standing in the way of their success. While there is much that these companies will have to do to mitigate the challenges before them, fully embracing AI, machine learning, and large-language model analytics will provide a way forward. Integrating these technologies into every phase of their business—from pipeline development to sales and marketing—will empower companies to weather the storm of waning patent exclusivity and emerge stronger than before.
“The life sciences industry needs to accelerate their adoption of AI and generative AI, including LLMs, across all areas—from R&D to commercial,” says Bates. “Urgently, too, companies should embrace these technologies for product launch success. AI tells teams exactly who to engage and when, how to engage them most meaningfully, and what content motivates them to prescribe. That’s the magic formula.”