In recent years, the use of artificial intelligence (AI) and machine learning (ML) in the healthcare industry has gone from science fiction to science fact. From medical image analysis to robot-assisted surgery and virtual nurses, AI-driven innovations are revolutionizing healthcare.
The biopharma industry is no different. There are a growing number of pharmaceutical companies considering–or already using–AI-based solutions in their research, discovery, and manufacturing processes.
We explore many of these use cases in our latest e-book, but if you want a quick overview of the key takeaways, then read on!
We’ll cover how AI and ML are helping biopharma companies:
- Accelerate drug discovery and development
- Make clinical trials more successful
- Streamline the pharmaceutical supply chain
- Boost marketing and sales efforts
1. Accelerate drug discovery and development
The drug discovery and pre-clinical phase of the development journey is long, expensive, and challenging. On average, it takes an investment of $1.3 billion and between seven to 10 years for a new medicine to complete the journey from initial discovery to the marketplace. To make matters more frustrating, the probability of FDA approval is a meager 13.8%.
Put simply, the drug discovery process is fraught with risk, making the early stages of research and development absolutely critical. AI and ML have the potential to revolutionize this phase of the journey by helping scientists gain a deeper understanding of disease pathology and accelerating the development process.
Biopharma companies can leverage AI and ML to help:
- Identify viable molecular targets faster. A crucial first step in the early drug discovery process, finding the right molecular target and compound is a slow and labor-intensive process that involves the manual curation of large amounts of biological and chemical data. An AI system can save the research team possibly weeks or months by automating the analysis of large datasets, leading to the discovery of potential targets based on their biological relevancy, potential efficacy, and molecular behavior.
- Predict side effects. Understanding how molecules interact with one another can offer valuable insights. For example, a research team can use an AI system to analyze how similar, known proteins interact to make predictions on how the molecule they’re targeting may be affected by their drug, including side effects or adverse reactions.
- Maintain regulatory compliance. Biopharma companies may be able to use machine learning algorithms to potentially improve the quality of regulatory submissions and more easily maintain compliance. According to Pfizer, ML may help the company predict in advance what regulators are likely to ask and come prepared with those answers ahead of time, possibly saving weeks of time spent back and forth with regulators.
2. Make clinical trials more successful
Clinical trials offer biopharma companies valuable information, such as how a treatment improves the quality of life in a patient, a treatment’s cost-effectiveness, the clinical value of a diagnostic test, and more. These results have an incredible impact on the company’s decision-making moving forward.
However, researchers must navigate a series of barriers to conduct a successful clinical trial—the greatest of which isn’t regulatory or biomedical at all. Rather, the ability to enroll a full panel of candidates and keep them for the duration of the study is one of the most critical obstacles to success.
Difficulty enrolling patients in a clinical trial has shown to result in costly delays or even termination of the trial. In fact, multiple analyses report that about 80% of all trials fail to meet their original enrollment deadline and 55% of trials are terminated for failure to achieve full enrollment.
This is another area where AI can help.
In fact, several studies have found that AI can reduce patient screening time by 34% and increase trial enrollment by 11%. This is made possible through a combination of machine learning algorithms, natural language processing, and optical character recognition.
Together, these applications dramatically shorten the time spent reviewing patient criteria and determining eligibility. Researchers may even consider using AI to automate the review process in the future, as one clinical trial team found that their AI matching system was able to determine eligibility with an accuracy of 95%.
3. Streamline the pharmaceutical supply chain
It’s common to read about new medical treatments in the news and the fascinating research that has led to their development. What you’re less likely to hear about are the complex processes involved in ensuring that medicines, both new and established, reach patients when they need them.
The pharma supply chain is dizzying in scope, usually involving several independent stakeholders ranging from manufacturers and wholesalers to hospitals, group purchasing organizations (GPOs), and many regulatory agencies. Ensuring that the biopharma supply chain runs smoothly and is free of obstructions is vital to getting patients the medicines they need when they need them.
AI presents new opportunities for companies navigating the pharma supply chain. In particular, companies are exploring the potential around predictive analytics, the process of analyzing historical healthcare data to identify patterns and trends and then make predictions. Using AI in such a way can help professionals make more informed decisions with near real-time insights, mitigate risks, and automate certain processes. Below are just a few use cases companies are exploring:
- Making predictions and forecasts with confidence. Companies can’t make accurate forecasts or confident decisions without clear visibility into the entire biopharma market. AI can aggregate across multiple sources, like drug orders and weather info, to provide 360-degree visibility into each touchpoint along the supply chain. This can help logistics coordinators and plant managers predict hurdles along the way and properly adapt to changes in the market.
- Minimizing mistakes. Maintenance issues often lead to expensive disruptions to business. Knowing how expensive it is to develop a drug, losing a batch because of employee negligence or equipment failure is simply not an option. Warehouse managers can fight against these costly mistakes by implementing AI-powered predictive maintenance technology. Using a combination of historical operations data and predictive modeling, the technology informs maintenance crews which equipment is most likely to fail and when, keeping operations running smoothly and maximizing machine uptime.
- Enhancing efficiency. Instead of having employees move boxes of medicine around a warehouse like a giant game of Tetris, AI can help the plant manager make smarter decisions about where items are placed from the start. A predictive analytics model can identify which treatments are in highest demand and which need to be stored longest, helping improve operational efficiency and reducing storage-related errors.
There are many, many more solutions healthcare companies across the industry are exploring to build more resilient supply chains. To learn more, you can check out episode 22 of the Definitively Speaking podcast: “Can we pandemic-proof the healthcare supply chain?”
4. Boost marketing and sales efforts
The biopharma landscape is incredibly complex and fiercely competitive. Before a new drug or treatment hits the market, marketing and sales teams must untangle a web of facilities, providers, payors, and patients, all connected by multiple affiliations, partnerships, technologies, and treatments.
With a vast array of data to sift through, companies cannot afford to spend precious time searching for insights, and decisions can’t be made confidently without a clear picture of the market.
This is where AI steps in, offering marketing and sales teams the power to unify and analyze large amounts of data from across the healthcare ecosystem. This intelligence can then be used to determine the best market tactics and the most efficient use of resources. More specifically, AI and ML can help marketing and sales teams:
- Gain a comprehensive look into the market. Marketing and sales teams can use AI to analyze claims data, prescriber behaviors, geographic and demographic data, executive contact info, and more to get a deeper understanding of the market. These insights combine to make a meaningful map of the market and show how providers and organizations work together.
- Find the most influential experts. AI can assist medical affairs professionals in identifying influential experts within specific treatment areas. By analyzing various data sources such as published research, speaking engagements, grants, and all-payor claims, AI can recommend experts and KOLs who possess extensive knowledge and impact in their respective fields.
- Navigate the competitive landscape. Medical and prescription claims, reference, and affiliation data uncover which providers are prescribing competitor therapies, helping marketing and sales teams uncover opportunities and assess threats—and then adjust strategies accordingly.
- Enhance omnichannel strategies. AI helps marketing teams gain a better understanding of their target audience, including customers, influencers, and decision-makers and helps life science teams develop an omnichannel approach. By analyzing customer engagement patterns, AI can identify the most effective channels for engagement and provide insights into customer interests, preferences, and pain points.
Altogether, these use cases improve campaign performance, lead to more effective outreach, help teams make more informed decisions, and ultimately drive business growth.
The future of biopharma is in AI
It’s clear there is a lot of momentum behind AI applications in biopharma. It is being harnessed to accelerate each step of the R&D process and to improve diagnostics, provide predictive analytics, fulfill the promise of personalized medicine, and much more.
Be sure to check out the e-book for even more in-depth exploration of the ways pharma and biotech companies are leveraging AI. Or read our blog on the applications of AI in the world of medical devices. And to read industry perspectives on the usage of AI in the broader healthcare industry, catch the findings of our AI survey here.
