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Harnessing AI for Pharma Sales

Pharmaceutical ExecutivePharmaceutical Executive: July 2023
Volume 43
Issue 7

Artificial intelligence is steadily becoming a game changer for the pharma industry, particularly in commercialization activities, such as sales and marketing—and efforts to help highly sought treatments reach the right patients.

Doron Aspitz

Doron Aspitz

Six months ago, ChatGPT wowed the world as a powerful tool with the ability to transform the way we live and work. Its growing capabilities show how artificial intelligence (AI) is reshaping all industries from journalism, to finance, to healthcare. In the pharmaceutical industry, AI and machine learning are driving important insights and breakthroughs, including identifying potential drug targets and biomarkers, accelerating the drug discovery process, and making sure the right drugs reach the right patients.

As a result, the interest in AI from pharma organizations is growing faster than ever. Industry giants like Pfizer, Merck & Co., and GSK have already acquired or collaborated with companies developing AI technologies. According to industry estimates, the pharma industry’s spending on AI is expected to reach $3 billion by 2025.

Pharma companies partnering with AI companies to develop platforms tailored to optimizing the drug commercialization process is one of the fastest-growing areas of collaboration—in addition to biopharma organizations launching AI projects for drug discovery. In both cases, as consulting firms such as McKinsey & Company have reported, challenges abound, including integrating AI into routine processes in a repeatable and scalable manner.

Currently, AI is often seen as a separate project rather than part of the overall workflow, according to a 2022 McKinsey & Company article, which hinders its effectiveness.

As pharma companies are partnering with expert AI providers or adopting services to use AI in various parts of the business—from scientific processes to marketing, sales, and business development—spending on data analytics by pharma companies is growing 27% each year, according to ABI Research. Regulators are keeping a close eye on the growing use of AI and advanced data analytics in the industry, with FDA calling for more oversight of AI systems, especially when it comes to data privacy and cybersecurity, which increases the challenges of leveraging data without breaching regulations in a highly regulated industry.

There is no doubt that over the next decade AI will continue to be a growing part of the pharma industry, helping to address challenges in research and development, manufacturing, and commercialization. In commercialization, especially in sales and marketing activities, AI will play a transformative role in bringing the latest in pharma innovation to patients.

Although a fully AI-designed drug has yet to reach the market, this is likely the direction the industry is headed. Several AI-designed medications, including those for amyotrophic lateral sclerosis and cancer, are now in the human-trial phase, with researchers and regulators awaiting data on their efficacy. The growing availability of (anonymized) patient and medical data, critical for running these AI-powered discovery and development platforms, will only accelerate the AI-powered drug development process.

In recent years, AI and predictive analytics have already played a large role in getting drugs to market faster as well as improving the drug discovery process. AI-powered algorithms are helping identify which genes, proteins, and molecules to target, allowing computer simulations to replace some time-consuming lab testing, and predicting side effects. In some cases, AI is helping drug companies bypass the animal-testing stage, allowing them to use computer models of humans instead. Not only does this save time and money, but researchers say the results are potentially more accurate and relevant. The tests are focused on models of humans, which are the eventual audience, rather than animals, whose systems and reactions can differ significantly from those of people.

AI also helps repurpose existing drugs to treat other or new conditions by using advanced data analytics to identify other pathogens that may respond to the treatment.

Personalization and speed

This improved understanding of how exactly drugs work and which kinds of patients they work best for is changing the commercialization and marketing process even more significantly by providing pharma companies with precise information that should be communicated to doctors, caregivers, and patients. When AI is added to the sales and marketing stages of the commercialization process, the increased data and knowledge about how medications work can more effectively be used to better match treatments with patients and help identify the right healthcare providers to target with the most relevant information.

Until recently, drug companies relied on routine sales representative visits to reach physicians and healthcare providers with general information about the newest medications and treatments as well as traditional marketing methods. This includes using key opinion leaders (KOLs), who are often experienced physicians and also reach out to medical practices with information and recommendations for the latest products. Digital marketing and social media have also come to play a larger role, including the use of digital opinion leaders, who interact with physicians on behalf of pharma organizations.

Sales reps and outreach by KOLs and others often focused on the largest practices, as that was the most efficient way to reach the highest number of doctors whose patients would most likely benefit from these drugs. But the demanding schedules of physicians as well as the pandemic shifting many of these visits to a virtual format has challenged this approach. It is clear that sales need to be more focused, and sales reps need to add real value when meeting with health practitioners.

AI is increasingly offering a more efficient way to reach physicians with more relevant information, allowing pharma companies to target specific practices based on their individual patient pools. AI tools can collect real-time data on physicians and health practices, helping sales teams target the right customers with the right products. Instead of playing a numbers game and prioritizing sales and marketing for the largest practices, pharma companies can use data to identify which practices have patients who can benefit from certain drugs. For example, rather than simply reaching out to cardiologists, pharma sales reps can reach out to cardiologists who have patients especially suited for a specific drug based on their age, a combination of health conditions, the risk for side effects, and many more factors.

In addition, with quick access to detailed data analysis, drug manufacturers can more easily reach out to smaller medical practices they may have previously overlooked. Targeted outreach to these smaller, niche practices not only optimizes resource allocation for pharma companies, but it also enhances patient care, ensuring timely access to necessary medications and thereby promoting improved health outcomes.

As stated previously, AI can help pharma organizations provide more relevant information to doctors. With AI and data from physicians, drugmakers can more accurately anticipate the specific concerns that certain patients may have about therapies and make sure marketing materials help doctors address these concerns. This is especially valuable in an era of the empowered patient when an increasing amount of patients want more information, involvement, and control in their healthcare and medical decisions. This will help doctors better serve patients and meet the growing demand for more personalized and patient-centric care.

This is an area that is sure to grow, especially as more patients become more proactive about their healthcare and the drugs they take and as medicines become more personalized. As AI advances, pharma companies will be able to consider even more aspects of patients’ health, demographics, and overall drug efficacy to further tailor the sales and marketing of medications and treatments.

Providing more relevant and personalized information can also go a long way in helping to ensure that patients take their prescribed medications correctly as well as promote adherence. Currently, about half of patients do not take their drugs as prescribed—due partly to fears about side effects, mistrust, and misunderstanding of instructions, according to a 2023 American Medical Association article. But if doctors can provide more comprehensive and digestible information from pharma companies, perhaps patients would feel more comfortable or knowledgeable about the drugs they take and be more likely to take them as prescribed.

AI and real-world evidence

AI and advanced data analysis have the potential to monitor which patients respond best to which drugs. Drug companies have long engaged in real-world evidence (RWE), following the performance of medications on the commercial market. But AI allows for a more comprehensive, accurate, and efficient way to track this RWE.

This is especially applicable as more ultra-expensive cell and gene therapies and other specialty drugs enter the market.

Regulators are more likely to require RWE after approval for these products.

In addition, with the rise of new contracting and pricing models, including value-based contracting (where drug prices and payment schedules are tied to patient outcomes and other factors), RWE will play a larger role. AI will prove key to handling and processing detailed post-launch patient data, ultimately providing valuable insights to pharma companies on how their products work in the real world and informing the development of new drugs.

There is no doubt that AI will become a standard tool for pharma marketers and promote a transformation of science and human health, enabling life-saving drugs to reach their full potential by improving efficacy as well as ensuring that these important treatments get to the right patients at the right time to improve outcomes across populations.

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