Accelerating Pharma Research with AI (Complete Guide)

Pharmaceutical research is a critical process that helps identify and develop new drugs to improve human health. In recent years, artificial intelligence (AI) has emerged as a powerful tool that can be used to support this process. In this guide, explore how AI can be used to strengthen and accelerate pharma research and the benefits it can bring to this field.

Ways AI can help enhance pharma research processes:

  • Leverage quantitative data and export it from content more efficiently through table extraction innovations.
  • More easily pull financials and identify addressable market data, patient segments, and unmet needs.
  • Understand how technology, drug class, regional market, is performing.
  • Understand the identified commercial risks and what are unknowns.
  • Quickly decipher the clinical landscape, identify development, and regulatory risks for pipeline products.
  • Many, many more.
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For those in the healthcare industry, knowledge of regulatory activities and FDA drug approvals can be critical to competitive intelligence.

In the pharmaceutical space, for example, it’s imperative to know as soon as a competitor’s drug is approved in order to manage company strategy accordingly. Information about approvals, warnings, and other insights are readily available through regulatory websites that govern the pharmaceutical and medical device industries, and changes in status of a drug or device are typically reflected on the regulator’s site.

Unfortunately, regulatory sites are notoriously difficult to navigate. Teams are often left combing through pages and pages of data in search for the information they need. This can make finding something extremely difficult, and time-consuming. It’s a lot of CTRL+F, and it’s often not as fruitful as it needs to be.

No matter the size of the company, a nearly universal KPI for business professionals is improving speed and efficiency, and artificial intelligence is proving to deliver on both. 

The current state of pharmaceutical research

The current state of pharmaceutical research is highly competitive, with companies constantly vying for the latest breakthrough. This can be seen in the billions of dollars that are invested each year in research and development. However, this level of competition has also led to some negative consequences. For example, many companies are now reluctant to share information or collaborate with others, as they fear that their rivals will gain an advantage. As a result, progress in pharmaceutical research has often been slow and incremental.

Nevertheless, there have been some major breakthroughs in recent years. Thanks to advances in technology, we are now able to develop new drugs more quickly and efficiently than ever before. In addition, we are also beginning to understand more about the causes of disease, which is helping us to develop more targeted treatments. As a result, the current state of pharmaceutical research is very exciting, and there is much to be optimistic about for the future.

Hesitation around AI in pharma research 

If we think back on the various automations that have been developed for the lab, images of robotic wielding arms on factory floors or equipment that would run an automated hematoxylin and eosin stain come to mind. This is what we consider to be simple automation. However, when it comes to the professionals, we are talking about human judgment and interpretation of information, areas that have been historically challenging to automate. 

Historically, there has been hesitation around utilizing artificial intelligence to enhance research methods due to fear of it replacing that human element. But with the pharma market continuing to grow more competitive by the day, can research professionals continue living in fear of replacement?

AI-powered research has proven to be key in taming the influx of ever-changing information, empowering professionals to gather insights faster. Machine learning and NLP algorithms can not only read reports and understand language variance, but they can also identify and extract themes, tease out nuance in sentiment, pull out KPIs, chart them against peers, and summarize all of those findings into written text reports ready for consumption by the most senior levels of the organization. 

The current landscape for research professionals

The pace of business and therefore the competitive landscape is increasing. There is a greater desire to understand the vast amount of patient, research, and diagnostic data to make better investment decisions. Departments responsible for analytics and insights are flourishing. 

The amount of moving pieces is becoming even more difficult to track, especially given the ever-increasing complexities of information. The need to separate signal from noise, summarize information, and analyze the data is far more important than the hunt for that information.

Creating an ability to stay a step ahead of the information needs of senior leadership is critical to success

AI investment in pharma research 

Now more than ever, there is mounting pressure from investors, patients, and society to make quicker discoveries and developments, ultimately leading to more competition. The COVID-19 pandemic of 2020 plunged the world into uncertainty, leaving pharma professionals with the nearly impossible task of planning their next steps for months and years beyond. Information came at a rapid clip, guidance changed on a weekly basis, and new challenges surfaced. Pharma research needs to move even faster than before. This is where AI comes in.

AI can help track competitor market developments and help professionals deeply understand competitor clinical developments, financials, news, and partnerships.

9 ways AI can help enhance pharma research processes:

  1. Leverage quantitative data and export it from content more efficiently through table extraction innovations.
  2. More easily pull financials and identify addressable market data, patient segments, and unmet needs.
  3. Understand how technology, drug class, regional market, is performing.
  4. Understand the identified commercial risks and what are unknowns.
  5. Quickly decipher the clinical landscape, identify development, and regulatory risks for pipeline products.
  6. Understand what moves peers are making.
  7. Apply AI solutions to estimate development costs and success probabilities.
  8. Apply AI to the rich historical content your organization has created to date, and overlay structured data.
  9. Create a single AI-powered knowledge management platform.

Fight data fatigue and decision paralysis

Professionals in the pharma space must find key information, trends, and themes across a wide variety of documents. 

Having the ability to quickly comb through 13 million research articles, understanding the variation in clinical and scientific language, all while parsing out the competitive intensity of drugs in clinical development for a particular indication, is crucial for competitive analysis. 

Professionals need to be able to chart sentiment across those competing companies based on the language that those management teams are using to describe their efforts. Access to broker reports and drilling down into extracted KPIs can show whether their overall performance is matching the sentiment.

 

Understand market impact

It is imperative to have a breadth of coverage across all industries and all sectors. Seeing the bigger picture and gaining that competitive advantage is absolutely crucial for understanding market impact. 

Some executive projects can be esoteric or market events can be so massive that they force one to look outside their standard data sets. The impact of covid across the world, across every industry and business sector is a great example. Suddenly clients were forced to understand not only disease and treatments but global supply chains, remote work environments, financial impacts, new partnership opportunities, new sales and marketing strategies, even new regulations for everything from clinical studies to personal protective equipment.

Track new developments

It is a requirement for professionals to track new developments. This is the difference between being reactive versus proactive. 

Conducting point-in-time research is reactive, setting a company up for failure. New advancements in research through AI allow professionals to monitor broad topical areas or dense content sets as the technology suggests critical data sets, new trends, and new entrant companies. As this technology evolves the best ones will even have a robust recommendation engine that learns which research is more desired.

A wealth of information on companies is readily available but it can be a double-edged sword. Too much info creates noise, making it difficult to hone in on the most relevant information. Information overload also leads to decision paralysis, and in a constantly changing environment, inaction or taking too long to make a decision can be deadly.

 

The promise of AI

It is no longer enough for pharma companies and professionals to simply acknowledge market shifts. They need to position themselves in a way that empowers their operational models and fuels innovation. 

If you’re relying on point-in-time data, manual searches, and gathering info from disparate sources, you’ll find yourself stuck in a reactive loop just trying to make sense of the new information that falls on your lap. As you identify the business model that best aligns with your company goals, incorporating artificial intelligence in research methodologies will be critical for sustained competitive advantage and growth. 

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