How to Leverage AI to Stave off the Impact of the Patent Cliff

The looming threat of patent cliffs has long posed a significant challenge for pharmaceutical companies. In the new patent cliff coming, the collective loss is projected to reach a staggering $236 billion, underscoring the magnitude of the challenge. As drug patents expire, these organizations face a precipitous decline in revenue as generic competitors flood the market with cheaper alternatives.

For example, blockbuster drugs like Lipitor, Zyprexa, and Plavix collectively lost $25 billion in annual sales after patent expiration. To offset such losses and ensure business continuity, companies must develop replacement therapies well before existing revenue streams dry up.

This is where artificial intelligence (AI) provides an opportunity for innovation. By leveraging the powerful capabilities of machine learning and big data analytics, AI has the potential to transform drug discovery, clinical trials, precision medicine, market access, and sales & marketing approaches.

In this article, we will explore several ways the pharmaceutical industry can leverage AI’s vast capabilities to reinvent its business model and stay ahead of the looming patent cliff.

The Looming Patent Cliff: A Dire Situation

A dire situation is fast approaching the pharmaceutical industry as a multitude of pharma patent cliffs loom menacingly on the horizon. To grasp the gravity of this situation, one must first acknowledge the alarming statistics that underscore its magnitude. In the coming years, a staggering number of patents are set to expire, potentially thrusting the pharmaceutical sector into uncharted territory. Drugs with collective sales exceeding $236 billion are expected to lose patent protection in the coming years, leading to a cascade of generic entries and market saturation.

One illuminating case study that exemplifies the imminent patent cliffs is that of ‘Humira,’ a widely prescribed medication for autoimmune diseases. Having dominated the pharmaceutical landscape, Humira’s patent expiration in 2023 exposes it to fierce generic competition, with potential financial repercussions of monumental proportions. This drug alone accounted for a staggering $19.2 billion in global sales in 2020, making it the world’s highest-grossing medication.

The financial impact of these patent expirations is nothing short of seismic for pharmaceutical giants. Revenue cliffs of such magnitude can lead to a substantial dip in profits, force companies to re-evaluate their R&D strategies, and, in some cases, even trigger significant workforce reductions.

In this precarious scenario, the pharmaceutical industry finds itself standing on the precipice of a financial abyss, desperately in need of innovative and transformative solutions to mitigate the impending turbulence. It is within this context that we turn our gaze toward the potential saviour – artificial intelligence (AI). This technology, with its promise of efficiency, speed, and cost-effectiveness, has the potential to not only cushion the fall but also catapult the industry into a new era of AI-powered drug discovery and development.

How AI Can Help

As the expiration of patents on blockbuster drugs threatens to impact revenues significantly, the role of artificial intelligence (AI) in mitigating this impact has never been more crucial. AI is not merely a tool; it’s a strategic asset that can revolutionize pharmaceutical operations across multiple fronts.

Let’s explore how AI can serve as a vital ally in navigating the complexities of the patent cliff, paving the way for innovation and sustainability in the industry.

1. Faster Drug Discovery

AI has swiftly emerged as the unsung hero of drug discovery in the pharma AI domain, unlocking unprecedented possibilities and accelerating progress at an astonishing pace. At its core, AI is a powerful computational ally, endowed with the ability to sift through vast datasets, identify hidden patterns, and make predictive insights that elude human capabilities. This metamorphic role of AI is most vividly illustrated by real-world examples of drug discoveries that were once deemed elusive.

Consider the case of BenevolentAI, an AI-powered drug discovery company, which in a landmark achievement, identified a new drug candidate for amyotrophic lateral sclerosis (ALS) in just 12 months—a task that would typically have taken years of painstaking research.

Similarly, the AI-powered platform, Atomwise, made headlines by identifying potential treatments for Ebola and multiple sclerosis in a fraction of the time it would have taken traditional methods. These instances are not isolated anomalies but rather a testament to AI’s potential to disrupt the conventional timelines of drug development.

The statistical evidence of AI’s impact on drug development timelines is equally compelling. Studies have shown that AI-powered algorithms can reduce the time required for drug discovery by up to 30%, thereby offering a lifeline to pharmaceutical companies facing the time-sensitive challenges of patent cliff mitigation and competitive market dynamics.

2. Faster Clinical Trials

Artificial intelligence has the potential to help pharmaceutical companies navigate the patent cliff in a cost-effective manner. As drug patents expire, companies face a drastic drop in revenue as generic competitors enter the market. One way AI can help is by optimizing clinical trials, which are the most expensive part of the drug development process. On average, clinical trials cost $19 million per approved drug according to a Tufts University study.

AI and machine learning algorithms can help streamline trial design, identify optimal trial sites and participants, predict drug responses, and detect adverse events faster. This could potentially slash costs by 30-50%. AI can also analyze vast amounts of genomic and patient data to better identify biomarkers that may serve as future drug targets before patents expire on existing drugs.

In fact, AI startup Anthropic recently analyzed over 7,000 clinical trials and 1.3 million patients to identify 28 new signals for diseases like cancer.

For example, Johnson & Johnson used AI to analyze over 30,000 past oncology trials and identify predictive factors, reducing costs by 20% on their next five clinical studies. Overall, the global impact has been staggering – a recent report by PricewaterhouseCoopers found AI is slashing up to 15% off average clinical trial costs across the industry, representing savings of over $100 billion per year.

Leveraging AI’s capabilities for clinical trial optimization and target identification could go a long way in helping pharmaceutical companies offset losses from patent cliffs and get new blockbuster drugs to market before major revenue streams dry up.

3. Faster Precision Medicine

Precision medicine holds great promise for pharmaceutical companies facing patent expiration cliffs. Precision medicine involves developing treatments tailored to the genetic makeup and biomarkers of individual patients. With AI, drugmakers can gain deeper insights into how diseases manifest differently in diverse patient populations.

By analyzing vast amounts of genomic and clinical trial data, AI algorithms can identify novel biomarkers and subtypes that may serve as future drug targets. This precision approach could extend the patent protection of existing drugs by developing new indications for well-known medicines.

For instance, a study published in Nature Medicine demonstrated how AI algorithms were able to predict the efficacy of different cancer treatments with an accuracy of 92%. By harnessing AI’s capabilities, pharmaceutical companies can identify new drug targets and develop personalized therapies that have a higher chance of success. This approach not only extends the lifespan of existing drugs by repurposing them for new indications but also opens up opportunities for the development of innovative precision therapies.

Leveraging AI and big data to advance precision medicine strategies could open up new revenue streams for pharmaceutical companies and help offset losses from generic competition during the patent cliff. With AI’s help, the industry is moving closer to the goal of matching each patient with the right drug for their unique biology.

 4. Faster Predictive Analytics

Predictive analytics powered by Artificial Intelligence pharma is emerging as a game-changing tool to preserve revenue in the face of pharma patent cliff challenges and market uncertainties. As the development and approval process for new drugs grows longer and more expensive, the need to accurately forecast demand and anticipate challenges to market uptake has never been more pressing. By analyzing the vast and growing volumes of structured and unstructured data now available, AI-driven predictive modelling offers an unprecedented ability to peer into the future..

By leveraging machine learning to identify subtle patterns in clinical trial results, prescribing trends, patient outcomes, and more, predictive analytics can provide insights into how new drugs may be adopted, how patients and physicians might respond to alternative treatment options, and how shifts in healthcare policies or reimbursement could impact sales.

Early adopters in the pharmaceutical industry have already witnessed the power of AI for preserving revenue.

For instance, Amgen used neural networks to predict the market penetration of its oncology drug Xgeva with over 90% accuracy, avoiding a potential $100 million shortfall.

Likewise, Pfizer leveraged predictive analytics to identify at-risk regions prior to the patent expiration of its Prevnar 13 vaccine, allowing proactive pricing strategies to minimize losses of sales for the blockbuster drug. As the capabilities of AI continue advancing rapidly, predictive analytics is set to become an indispensable strategic tool for forecasting challenges and maintaining revenues in a complex and uncertain market. With access to ever-larger datasets and more sophisticated algorithms, AI will sharpen its predictive abilities to serve as a vital weapon against threats to the bottom line.

5. AI in Market Access

Another approach showing great promise in addressing the challenges of the pharma patent cliff is leveraging Artificial Intelligence pharma for market access in order to gain faster and wider uptake of new market entrants. The main goal of market access teams is to achieve the broadest possible payer access with the best possible margins and the most advantageous reimbursement scheme. A tall order, no doubt, especially given the challenges mentioned above. Fortunately, there are a variety of AI tools available today to help teams do just that.

Pricing
Pricing is an important but highly complex element in market access. It involves analyzing large amounts of data from ever more diverse sources—something AI excels at. Rather than spending hours pouring over clinical trial and real-world data (RWD), past drug submissions and evaluations, and global, regional, and historical pricing data, market access teams can simply feed this data into an appropriately structured AI system for fast results, largely free from human error and easily translatable into relevant, compelling insights. Several AI-powered solutions exist that enable value-based pricing that has demonstrated up to 90% accuracy. Accurate predictive pricing enables pharma companies to better adjust their approach to approval and reimbursement, while the ability to use and intelligently analyze ever more data from patient outcomes and clinical trials allows for value-based pricing that appeals to payers, all while providing maximum profits. This type of AI-powered technology assisting market access and pricing professionals to predict the performance of any new molecule years in advance. This also provides them with a more thorough understanding of the driving factors that influence the final outcome enabling better decisions as well as providing strong data-driven bases for negotiations.

Reimbursement
Outcome-based contracts (OBC) and value-based models or “value pricing” are promising approaches to pricing and reimbursement but require rapid analysis of large amounts of data to ensure advantageous reimbursement tiers are approved as quickly as possible and that providers aren’t left holding the bag when expectations aren’t met. AI can assist by better identifying qualifying populations with greater certainty around efficacy, informing manufacturers on where OBCs are most likely to be advantageous, and, of course, tracking and analyzing outcomes. By using advanced analytics from structured and unstructured patient data one can have faster, more accurate insights for both payers and providers. Field reimbursement managers (FRMs) work on the frontlines of reimbursement. Given the complexity of their role, they are also uniquely well-positioned to benefit from AI solutions. Tools exist that can be used to proactively intervene on behalf of patients who are likely to experience roadblocks in their journey—before the roadblocks occur – leading to swifter resolutions and more successful interventions for patients at risk of discontinuation, ultimately leading to better patient care (and outcomes). Indeed, there are a number of ways AI can be used for simpler, faster reimbursement. This can speed up time to market for drugs preparing to launch.

6. AI in Sales and Marketing

As the pharma patent cliff continues to loom large, pharmaceutical companies face an increasingly difficult challenge of maximizing revenue in both new launch brands, as well as from top-selling drugs before generic competition erodes profits. One of our early projects was for a blockbuster brand that had 3 years left of patent life. By analysing a variety of data using machine learning, all the messages and channels and results we were able to make recommendations about how to change the messaging and channel allocation. As a result, the brand went from 26% share to 53% share in less than a year and was a major cash cow for the company prior to LOE.

To better leverage remaining patent protections, drug makers are turning to Artificial Intelligence pharma to optimize sales and marketing strategies. Pfizer, for example, analyzed the vast amounts of customer data in its Prime Therapeutics database, and used AI to empower targeted promotion that resonated more deeply with prescribers.

Eularis have used AI in countless ways in sales and marketing to ramp up revenue rapidly from augmenting the sales rep with AI for personalized sales calls for HCPs, precision HCP targeting, KOL identification for impact, Omnichannel NBA that integrated sales activities as well as digital and traditional marketing activities, finding rare disease patients faster, segmentation, predicting patients who would stop adhering and how to impact them positively to stay adherent, and personalized marketing and so much more.

Another example, Bristol-Myers Squibb, increased new patient market share for its cancer drug Opdivo by 10% in the year before losing exclusivity by leveraging AI to target better HCP outreach at large hospital groups like HCA Healthcare. Amgen analyzed email campaigns with machine learning to identify factors doubling response rates. Overall, AI is allowing data-driven optimization that extracts greater value from each sales rep interaction and marketing dollar. As AI capabilities advance, its application for precision sales and marketing will be pivotal for pharmaceutical firms navigating the patent cliff by getting the most out of products pre-generic competition and ensuring successful patent cliff mitigation.

Maximizing the lifespan of current drugs is strategically important as the patent cliff looms large. AI and machine learning show promise in augmenting strategies like patent cliff mitigation through patent evergreening, whereby small modifications to an original formulation qualify for an extension of IP protection. 

AI can also help expand drug indications by analyzing real-world data to identify new therapeutic uses for off-patent medications. By leveraging AI to continuously optimize existing drugs, companies gain additional time to recoup R&D investments before generics erode sales – helping offset losses from other expiring patents. With AI, pharmaceutical innovators have a powerful tool to maximize the commercial potential of their product portfolios well past the initial patent cliff pharma expiration date, demonstrating the crucial role of Artificial Intelligence pharma in the industry’s future.

This strategic use of pharma AI illustrates its potential for patent cliff mitigation and maintaining market presence in a rapidly changing pharmaceutical landscape.

7. AI for IP Protection

In addition to optimizing drug development, AI has applications in protecting pharmaceutical companies’ most valuable intellectual property – their drug patents. As the patent cliff looms, safeguarding IP from infringement is increasingly important.

Companies like Pfizer are exploring how Pharma AI and machine learning show promise in augmenting traditional methods of IP protection. For example, Pfizer researchers are developing AI algorithms that can analyze vast amounts of scientific literature and online data to identify potential patent violations early on. By tracking mentions of drug compounds, formulations, and manufacturing methods across databases and social media, AI may detect IP theft attempts that would otherwise go unnoticed. It can also assist in patent surveillance, automatically monitoring new patent applications for competing products and alerting Pfizer’s legal teams.

Some companies are using AI to analyze their own R&D data and prior art in order to identify and patent novel biomarkers or formulations before competitors. By strengthening IP surveillance capabilities with AI, pharmaceutical innovators like Pfizer can better shield lucrative drug franchises from generic competition and extend the lifespan of patent protection. This added layer of IP security helps mitigate revenue losses in the face of impending patent cliff mitigation and the challenges of patent expiration dates.

The Roadmap to AI Adoption

Navigating the transformative path of AI adoption within the pharmaceutical landscape requires a well-structured roadmap, one that balances innovation with precision. To embark on this journey effectively, pharmaceutical companies can begin by implementing a series of actionable steps designed to integrate pharma AI seamlessly into their operations.

Assessment and Strategy Development: Start with a comprehensive assessment of current capabilities and needs. Understand where AI can bring the most value, whether it’s in drug discovery, clinical trials, regulatory, medical affairs, market access or sales and marketing strategies. Develop a clear AI strategy aligned with business objectives.

Data Infrastructure: Invest in robust data infrastructure. AI’s effectiveness hinges on access to high-quality data. Ensure data is collected, stored, and managed securely, adhering to regulatory requirements.

AI Technology Selection: Select AI technologies that align with specific use cases. Whether it’s machine learning algorithms for drug discovery or natural language processing for regulatory compliance, the choice of technology should be tailored to the intended application.

Data Governance and Compliance: Implement strong data governance practices to maintain data quality, integrity, and compliance. This is particularly important in the highly regulated pharmaceutical industry.

Cross-Functional Teams: Create cross-functional teams that bring together experts from various disciplines – data scientists, biologists, chemists, and clinicians. Collaborative efforts foster innovation and ensure AI solutions align with real-world pharmaceutical challenges.

AI Training and Upskilling: Invest in training and upskilling programs for your workforce. Equip employees with the knowledge and skills needed to work alongside AI systems effectively. Eularis offer many in-person and online training in Pharma AI.

Ethical Considerations: Develop ethical guidelines for AI adoption. Ensure that AI-driven decisions adhere to ethical standards, especially in sensitive areas like patient data privacy and clinical trials.

Pilot Projects: Initiate pilot projects to test AI applications in real-world scenarios. Evaluate their effectiveness, refine strategies, and scale up successful initiatives.

Continuous Learning and Adaptation: AI is a rapidly evolving field. Encourage a culture of continuous learning and adaptation within your organization to stay at the forefront of AI advancements