A competitive advantage is necessary for success in all industries, but perhaps nowhere more so than in the pharmaceutical industry, where companies spend millions of dollars and thousands of hours researching how to bring their developments through clinical trials and to market first of the competitors.
But they don’t do it alone.
Behind major pharmaceutical companies, as well as smaller biotech companies, consultancies like Lifescience Dynamics provide third-party credibility thanks to dozens of academic scholars and analysts, and, more importantly, provide valuable tools to provide pharmaceutical companies with insights and recommendations to accelerate the development process of their products and obtain FDA approval.
“Pharma is a data-driven business,” explains Hussein Jaafar, senior consultant at Lifescience Dynamics, who has largely led the team’s adoption of AI. “In order to consult our customers, we need to have access to as much data as possible.”
The power of Lifescience Dynamics comes from its five core technology products, which incorporate elements of artificial intelligence, including machine learning, large language models and generative AI, to process large data sets, accumulate insights and provide informed recommendations.
On average, it takes eight to twelve years to discover, develop and finally launch a drug. Along the way, pharma teams make different decisions, often based on “conflicting, limited or fragmented data,” explains Lifescience Dynamics founder and president Rafaat Rahmani. To minimize risks, pharmaceutical companies are required to seek out third-party research firms to validate their data and decision-making. That’s why Rahmani, who previously worked for Eli Lilly and other healthcare consulting firms, started Lifescience Dynamics two decades ago.
Until recent years, with the explosion of artificial intelligence capabilities, many of this team’s tasks were still performed manually, accumulating thousands of hours of work every year. With more than 130 customers hailing from most of the world’s top 20 pharmaceutical companies, this was a daunting task but one that also left more opportunities for human error – a major challenge for an industry as regulated as the pharmaceutical industry.
Now, with the help of artificial intelligence, some tasks take as little as 10 minutes, and confidence in the task is often 100%. While Rahmani has long viewed Lifescience Dynamics as a tech-savvy company, the real benefit of that mindset has proven itself in its use of artificial intelligence.
The areas of business where Jaafar has seen the greatest impact are perhaps less attractive but unprecedented in terms of value for customers and his own team: data collection, data analysis and data visualization. Monitoring of clinical trials, especially by competitors, is fundamental for the pharmaceutical industry. Jaafar explains that the team had “giant” Excel spreadsheets that a team member would have to physically click on, read the updates online, and then update the sheet. In 2021, they implemented a machine learning model that does this for the team by automatically pulling information from online registries like clinicaltrials.gov and continuously adding updates. Live feed automation, he says, has been key to optimizing processes and increasing their effectiveness in meeting customer expectations.
Likewise, he spearheaded a project that gathers valuable insights into sessions and drug updates from the medical industry’s premier conference. Many of these events attract more than 70,000 people with sometimes more than 5,000 sessions. It was a beast for a team to consolidate and analyze data before AI; Now, the Lifescience Dynamics model automatically extracts abstracts and details, while also summarizing and recommending sessions for attendance.
The information collected by Lifescience Dynamics all resides in a customer portal, allowing customers to log in at any time for a comprehensive look at their competitive intelligence projects, clinical trial data and drug data. Jaafar explains that they are currently building AI models based on that data to help customers querying using natural language better understand the results. He not only adds transparency to the client-adviser relationship, but saves the Lifescience team from having to answer time- and resource-intensive client questions.
More recently, Jaafar and his team have been examining the benefits of generative AI, specifically on online surveys created to allow independent doctors to weigh in on criticisms and recommendations for a particular drug. An important component of the peer review process, pharmaceutical companies turn to doctors to get real, patient-friendly opinions on potential drugs. For Jaafar, generative AI and broad language models allowed him to produce survey templates for online discussions between doctors and identify relevant experts for a specific survey.
“Previously this was done entirely manually and we just had to use our experience and expertise to put something together,” says Jaafar. “But with AI, we can provide the background of the discussion guide that we would like to have, and it produces a very useful model that gets us 80% of the way to definitive guidance.”
The team works manually on the remaining 20%.
While the team celebrates their success with AI, Jaafar and Rahmani know that bigger challenges await. Jaafar would like to build his own AI models specific to his art. While Lifescience Dynamics can draw on its own historical data, the real value would come from more data shared by the industry. Unfortunately, he explains, the regulatory nature of healthcare and patient privacy, combined with the competitive nature of the pharmaceutical industry, causes companies to keep their data confidential for a variety of reasons. The fear is that companies will continue to isolate themselves in development fields rather than share collective data globally so that AI can learn at an exponential rate. There is simply less shareable data than in other fields.
Rahmani predicts it will take more years to resolve pharmaceutical debates over artificial intelligence. Despite all the euphoria and excitement, there are old up-and-comers and leaders who simply aren’t supportive of the technology, he says. He, however, is confident in the future of AI as a tool for the collective success of the industry.
“I can understand why they aren’t willing to connect, but that limits the usefulness of AI,” Rahmani says. “Our clients engage us to provide them with information and convert it into foresight, in the shortest possible time and in the least expensive way. These AI tools make the most of our data and bring it to life.”