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Applications of AI in Drug Commercialization

There is no doubt that AI represents an opportunity to transform the drug development and commercialization process and value that is derived from it. With the rise of computing power, machines have played a role for decades in data-crunching, extending the view that humans had on, for example, molecules to consider for a specific drug development need.

As AI has begun to evolve, so too has their role in aiding human decision making on where to place bets, how to manage risk, and how to ensure that everything you develop is optimized for what the market needs. AI already has a proven track record in its applications for drug development, both in identifying new applications of existing compounds and in guiding humans to create new-to-world compounds. Beyond these development activities, we’ve outlined five other key areas where AI in drug commercialization is driving transformational change for important life sciences activities today.

Optimizing Targeting and Messaging

AI and machine learning have the potential to efficiently analyze layers of HCP, patient, and payer data, providing actionable predictions for improved decision making. Part of the challenge for the life sciences industry today lies in leveraging the vast amounts of data for actionable business intelligence. Through artificial intelligence, sales and marketing teams can identify the most critical insights and optimize their targeting and messaging efforts to be more impactful than ever before.

Machine learning can quickly separate millions of stakeholders into highly specific segments, organizing HCPs and key opinion leaders by area of expertise, availability, and location, or even by factors such as their preferred method of communication or their openness to considering new treatments for patients. Using this method, we have worked with companies to create tailored, customer-level engagement strategies and optimized messaging, deriving requests for more product and even increasing HCP engagement.’

Going Beyond Basic Segmenting and Engagement 

Going beyond an existing consumer base, AI can identify new target populations for novel and existing products. An example involves Pfizer, who used public data to find patients who had successfully quit smoking, and after pairing this data with AI, discovered similar populations through which the company re-energized Chantix marketing efforts. Other major companies, including GSK, Novartis, and Teva have also used this approach with success.

The most powerful messages are received when they’re needed most, and AI can predict just what a customer needs to hear. A Eularis report on AI analytics found that using sales message customization can increase prescribing by up to 43%. AI can also better process stakeholder information so that content can be disseminated in the most efficient manner, speeding up market delivery and refining content strategy.

Streamlining Content and Marketing Efforts 

While typically time consuming, insights gained from AI can increase the likelihood of return of investment from content efforts. Knowing which message to send is one thing, but knowing how to format, prescribe, and dose this message is key to its delivery. Although the capabilities are still developing, AI can transform sets of time and numbered data into human-readable articles, hinting at the future capabilities of AI in content creation. AI can also greatly mitigate the time, labor, and resources that go into compliance review, enabling sales teams to redirect effort to other marketing activities.

AI exists everywhere in the digital realm, but harnessing it for digital marketing gives an extra boost to your company’s digital presence. Where SEO and advertising meet, programmatic advertising allows swift and targeted publication so that campaigns reach the right individuals at just the right times. A report by Gartner details further use cases for AI in web content management and digital experience platforms, highlighting the importance of digital channels as a key arm of customer engagement.

Pricing Strategies 

When pricing a new compound, it’s clear that recouping investment and hitting profit goals isn’t enough. AI can aid in the pricing decision by accelerating the analysis of clinical evidence and market information that support the drug’s value case, all while accounting for the predicted effects on various stakeholders. In addition, AI is being explored in a number of contexts to support negotiation with payer organizations and streamline critical market access activities.

Pharmacovigilance and Compliance

Most importantly, AI’s applications in pharmacovigilance can protect long-term product growth and ensure compliance. Current AI capabilities can streamline narrative analysis and causality assessment, and in some instances it can even automate case processing. Bayer implemented AI last year in its pharmacovigilance process, allowing for earlier detection and thus faster reaction to new side effects.

In a world where 20-30% of trials fail because of non-adherence and 90% of outpatient behavior remains unknown to any health system, the opportunity for AI to drive transformation in adherence is significant. UK-based AiCure is building an AI platform to monitor when a patient takes a drug to improve adherence. It includes HIPAA compliant compliance support features such as facial recognition, medication identification, ingestion confirmation, fraud detection, and assistive technologies.

Slow But Steady Adoption

As with any new capability, designing an approach to implementing AI, and actualizing value takes time, resources, and a tolerance for risk. Companies should approach adoption as they would approach the creation of new drugs—purposefully and methodically. As the development of AI in drug commercialization continues, understanding avenues for application is the first step to deriving value from any AI investment. As steep competition and pricing pressures solidify in the market, pharmaceutical sales and marketing teams should invest in AI analytics as a forward-looking strategy to deliver for patients.

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Coauthor and contributions by Kyleigh Andries

Tags: Artificial Intelligence
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