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AI-Driven Strategies: Pharma’s Answer to Patent Expirations

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Copyright © DrugPatentWatch. Originally published at https://www.drugpatentwatch.com/blog/

In a recent article in Pharmaceutical Executive, Alan Kalton, Senior Vice President of Commercial Strategy at Aktana, sheds light on the critical challenges facing pharmaceutical companies as they confront impending drug patent expirations and escalating costs in the United States market. With a journalistic perspective, this report delves into the dire financial landscape that pharmaceutical enterprises find themselves in, accentuating the urgency of adopting data-driven strategies, particularly leveraging artificial intelligence (AI) and advanced analytics, as a lifeline to navigate these treacherous waters.

The article highlights a startling statistic: the average cost for a pharmaceutical company to bring a new drug to market in the U.S. has soared to a staggering $2 billion, a revelation drawn from a recent study by Deloitte. This formidable price tag is compounded by an array of formidable challenges that confront drug manufacturers. Regulatory complexity is on the rise, while the ability to effectively communicate the nuanced advantages of specialized therapies to niche audiences remains elusive. As a result, launching a new pharmaceutical product often feels akin to the Greek myth of Sisyphus pushing a boulder uphill—an arduous and uncertain endeavor. Furthermore, the article underscores the grim reality that more than one third of all product launches ultimately fail, causing substantial, if not catastrophic, revenue losses.

The situation is poised to worsen with impending changes in the pharmaceutical landscape. The Inflation Reduction Act, recently signed into law, empowers the federal government to negotiate lower prices for key drugs, a move that will likely lead to decreased revenues for many companies. Additionally, the industry faces the looming specter of losing over $200 billion in annual revenue due to expiring drug patents. Nearly 200 drugs, including 69 blockbusters, are slated to lose exclusivity by 2030. Notable examples include Johnson & Johnson’s top-earning drug, Stelara, and AstraZeneca’s star respiratory medicine, Symbicort.

This daunting scenario has cast a shadow over major pharmaceutical players, compelling them to expedite research pipelines and master the art of adeptly managing future product launches to offset impending revenue losses. However, these efforts are being hampered by strong financial headwinds, including a weakening biotech investment sector, dwindling available capital, banking challenges, and overall economic uncertainty, all of which are constricting budgets across the sector.

In the face of this dichotomy, the pharmaceutical industry is compelled to seek solutions. The article posits that part of the answer lies in harnessing the power of data and analytics. Advanced data analytics technologies enable companies to amass, manage, analyze, and synthesize vast amounts of data at an unprecedented speed. The traditional months-long process of uncovering critical insights on the effectiveness of a launch strategy and pivoting in response is no longer acceptable. The article emphasizes that AI-driven strategies hold the key to tackling the multifaceted challenges of today’s pharmaceutical market. Andree Bates, CEO, and founder of Eularis, underscores the vital role of AI in addressing these complexities, emphasizing that life sciences companies should be integrating AI across their operations as a general practice, not just in response to the looming patent cliff.

As the pharmaceutical industry navigates these tumultuous waters, the article delineates three crucial strategies for the way forward:

  1. Expand Product Pipelines: In a rapidly evolving landscape, pharmaceutical companies are urged to broaden their product pipelines. Advanced AI and large language model (LLM) analytics are identified as tools to expedite clinical research and maximize the value of completed work. The potential for AI and machine learning to predict trial outcomes and potentially reduce the time to market from 10-15 years to just one to three years is highlighted. GRAIL, a healthcare company specializing in early cancer detection, is cited as an exemplar of AI-driven research acceleration.
  2. Embrace Current (and New) Customers: Focusing on cultivating relationships with existing customers, especially healthcare practitioners, is deemed paramount. The article underlinesthe significance of identifying loyal prescribers who are more likely to remain loyal even after a drug loses exclusivity. It points to the power of AI-based next-best-action (NBA) solutions in tailoring engagements and fostering loyalty. Real-world success stories, such as the impact of AI on a pharma company’s market share, are presented as evidence of the efficacy of these approaches.
  3. Adopt Data Analytics for Targeting: After a product launch, companies must swiftly and effectively target their customers to maximize value. Data analytics and AI-powered intelligence systems are recommended as indispensable tools for this purpose. Real-world examples of AI-driven data analysis enabling highly effective sales and marketing strategies are cited, showcasing the tangible benefits of data-driven decision-making.

Pharmaceutical companies must embrace AI, machine learning, and large language model analytics across all facets of their operations. These technologies are portrayed as the keys to not only weathering the storm of patent expiry and escalating costs but emerging from it stronger than before. As Andree Bates asserts, AI empowers teams with real-time insights and a continuous feedback loop—a formula for success in an industry grappling with unprecedented challenges. The message is clear: the future of pharmaceutical success hinges on embracing AI and data-driven strategies as essential components of the industry’s arsenal.

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