Artificial Intelligence In Pharmaceuticals: A Golden Opportunity

The pharmaceutical industry is facing numerous challenges, including patent expiries, insufficient pipeline development, evolving regulatory requirements, and the emergence of new job roles. In addition, the industry's internal complexities and lack of agility hinder innovation and progress. However, amidst these challenges, artificial intelligence (AI) has emerged as a game-changer, revolutionizing various aspects of pharmaceutical operations. Let's explore the role of AI in pharma, its impact in different areas, and the potential it holds for transforming the industry.

R&D and Drug Discovery

AI has transformed the traditional drug discovery process by accelerating target identification, target validation, hit identification, lead generation optimization, and preclinical testing. AI can aggregate and synthesize information. It can understand, or help us understand mechanisms of disease by analyzing hundreds if not thousands of databases, connecting published literature, experimental data, clinical data, and so on. It generates data and models, repurposes existing drugs, generates new drug candidates, validates drug candidates and it can actually design drugs.

The growing volume and complexity of medical data require pharmaceutical companies to analyze vast amounts of information to derive actionable insights. AI-powered natural language processing and machine learning algorithms can efficiently scan and summarize clinical studies, publications, guidelines, and more, enabling medical affairs teams to make data-driven decisions.

Clinical Trials

AI plays a crucial role in optimizing clinical trials, from designing preclinical experiments to recruiting participants and analyzing data. By automating tasks such as generating regulatory-compliant clinical study reports and conducting data analysis, AI significantly reduces time and resource utilization.

Case studies demonstrate how AI can complete 80-90% of a clinical study report in less than an hour, saving millions of dollars in lost revenue and improving time-to-market for pharmaceutical products. For example, there was a project where clinical study reports can take an internal team two months to write up, and every day that a drug delays getting on the market costs money. AI solutions that generate regulatory-compliant clinical study reports in less than an hour were implemented. The report-generating AI reduced internal resource utilization by around 250 hours and around $25,000 in team time per day and it stopped the loss of around $9 million per day for six weeks, adding quite a lot of millions to the bottom line of a particular drug that it was used for.

Market Access

Additionally, AI facilitates value pricing strategies by analyzing extensive data sources, monitoring market updates, and identifying optimal price points to maximize profitability. The fastest-growing body of content in both volume and complexity in life sciences is for medical affairs. Analyzing that content, clinical studies, publications, guidelines, and more for medical insights drives better decisions. But it takes a huge amount of human time to do all of this. Using natural language processing intelligence to actually do the scanning and the summarization of what's needed can actually increase productivity of the humans by a thousand percent and decrease the literature monitoring time between 82 and 90%. This is a huge area of savings and profitability because many drugs that have failed did so because the price was wrong. Getting that price right is critical to the success or failure of a drug's market access.

An example case in integrated performance analysis and precision doctor targeting:

A small analytics team that has many products that they are constantly answering questions about and having to do analysis where each of those queries is between a few days to a week of analysis to get the answer creates a huge backlog. A lot of companies outsource to India and have a team there that does these analyses, but still, it takes humans a lot of time. By implementing artificial intelligence with natural language processing and machine learning, you can actually put in your question as you would to a human and the artificial intelligence will take 5-15 minutes to serve up the answer and have it visualized at the same time.

So, now it's ready in 5-15 minutes to gather the needed information, whereas before it was taking weeks and a whole team to churn out patient identification. This is particularly helpful in rare diseases because it's very difficult to find patients with rare diseases.

For example, there was a project involving a third-line cancer product, it was treated like a rare disease because most of the patients died at second-line. Finding those third-line patients was a challenge. Machine learning and natural language processing were used with patient record data to identify any third-line patient and compare all of the patients that died second-line and the third-line alive with the second-line dead to see what are the differences in these two patient groups that can predict from the patients that are currently in second-line and alive, which of these will go onto third-line and actually require this third-line cancer drug.

Now we didn't know who these patients were because all the data is anonymized, however, we did know who the doctor was. We were able to then do a heat map of the exact doctors to approach and the company then deployed their sales force to the right doctors that had patients that would survive to third-line. Those patients would get the drug and the doctors would know this drug existed for those patients that needed it to survive. This drug was expected to be about $3 billion at peak sales, but it reached that goal within the first year because of this targeted approach.

Sales and Marketing

AI empowers pharmaceutical sales and marketing teams by providing personalized, targeted strategies. By combining historical data, temporal sequencing, and real-time analytics, AI can identify high-value physicians, personalize sales messages, and customize detail aids for individual healthcare professionals. These AI-driven approaches result in increased sales, improved customer targeting, and higher productivity for sales representatives.

A specific area where AI is very useful for sales reps is personalized detailing. For example, we did a project in Japan for a large older brand that was declining. There were new brands and generic competition and the sales reps felt they couldn't compete anymore. By combining all the data available and looking at temporal sequencing as well, we used AI techniques to identify physicians who were high prescribers of their drug and were likely to switch to a competitor.

Sending personalized sales messages to the doctor, custom detail aids, and visits were implemented depending on the dynamic activity of the doctor. Knowing the right message for a certain doctor at that point in time and the detail aid, the AI could determine the right sequence that was most appropriate for that doctor at the given time. We did a pilot first and the AI solution drove a 43% increase in sales for the reps using the system compared to the reps that weren't. Then the company rolled it out and integrated it with its CRM system and increased sales by $937 million.

Now you can actually integrate this technology with virtual details, emails, HCP portals, literature, etc. and you can automate it so that the doctor is receiving the right ratio of the different channels and the right content in each channel at the right time. In other words, Dr. X may see a sales rep once every two months, but he has all these other things that are making an impact.

Sustainable Growth with AI

Large markets like cancer and diabetes were historically propelling things forward, but now personalized diagnostics that catch conditions very early are likely to reduce these markets. For instance, GRAIL has developed a test where one blood draw can predict 50 cancers in people with no symptoms whatsoever. There are digital tattoos for monitoring, sensors in clothing, sensors in the home, etc. The advances in diagnostics will greatly change the size of these markets.

In the past, only drugs could treat conditions outside of things like surgery. Now, digital therapeutics and nanobots are the future of treatments and we are moving way beyond the pill, and the pharmaceutical model was always about selling pills. There's a whole heap of new models that can be implemented alongside "the pill" for minimum disruption.

To achieve sustainable growth in the pharmaceutical industry, companies must address individual business challenges using AI solutions that align with their strategic goals. Creating an AI roadmap and recognizing the potential of AI to solve specific problems is crucial. Furthermore, overcoming scalability issues and embracing AI across different areas, such as customer engagement channels and operational processes, will enable pharmaceutical companies to navigate the changing landscape successfully.

Conclusion

The integration of artificial intelligence in pharmaceutical operations presents a significant opportunity for the industry. From drug discovery to clinical trials, market access, and sales and marketing, AI has the potential to revolutionize processes, accelerate timelines, and improve outcomes. By embracing AI technologies and leveraging their capabilities, pharmaceutical companies can overcome challenges, achieve sustainable growth, and deliver innovative therapies to patients more efficiently than ever before. The era of AI in pharmaceuticals is a golden opportunity for the industry to redefine its future with technological advancements.

At Eularis, over 20 years of condensed, tactical, and practical knowledge in pharmaceuticals and healthcare is combined with AI. We deliver intensive in-person training in AI for pharmaceutical professionals. We show you the exact processes, important techniques, and monumental touch points that’ll put you light years ahead of your competition. You will be able to mold, direct, and craft reliable and role-specific AI frameworks – that work every single time!

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