AI In Pharma: How Artificial Intelligence Is Changing Drug Discovery & Clinical Trial Design

When we think of the future of any industry, it is nearly impossible to ignore the role artificial intelligence (AI) will play. Whether it is the future of healthcare, pharmaceuticals, or the entertainment industry, there are use cases for AI.

Before we talk about AI in pharma, let’s look at how it is changing the world as a whole.

How AI Is Changing The World

McKinsey and Company’s most recent Global Survey on AI shows how organizations across industries are using AI as a tool for generating value—and how they plan to invest even more in response to the COVID-19 pandemic.

In 2020, the global corporate investment in AI totaled nearly $68 billion. This figure marks an increase of 39% from the year before. This represents the highest level of growth since the 149% leap from 2016 to 2017. The increase in investment has pushed up the research and development expenditure of firms worldwide. As a result, it has sparked conversations about whether the benefits of the investment outweigh the costs. When you examine the numbers that are presented, the increase in R&D spending by firms doesn’t seem to be slowing down.

Here’s how this investment in AI is playing out in the pharmaceutical industry.

AI In Pharma

Pharmaceuticals is an industry that is most reliant on discovery and trial. These are the steps to identify a solution to a specific problem and figure out how to treat, distribute and commercialize that solution.

Current methods used to find a new drug are over a hundred years old. Enter: AI in pharma.

To predict what may work, humans need to analyze vast amounts of data with only a surface understanding of what the problem is. AI tools enhance these predicative efforts by being able to understand text and information and recognize complex relationships, providing more robust analytics.

AI In Drug Discovery

The state of the art for new drug discovery is being done by chemists, both on-the-job and through highly advanced AI programs. In fact, money is continuing to pour into AI-assisted drug discovery—nearly $14 billion in 2020.

Using artificial intelligence, chemists can draw out a drug design that is then screened against clinical trial data to evaluate its safety and efficacy.

Unlike older generations, current chemists are developing methods for creating drugs to address genetic and epigenetic problems by combining drugs from different fields. Chemists use proteins and gene expression techniques to produce the best possible medication.

With the availability of large-scale DNA sequencing, we are learning more about our own genomes and biology and using that knowledge to create better treatments for all people.

AI In Clinical Trial Design

While we are still in the early stages of AI in clinical trials, the applications are already being used to improve results.

Today, to identify the best study design or what type of patients would be ideal for a study, physicians have to rely on spreadsheets. This often leads to hours of wasted time and analysis. In turn, patients are denied access to treatments that could improve their health.

We are not just talking about patient surveys or other subjective methods but rather functional assessments like MRIs. If physicians are unable to calculate the best treatment for each patient based on their abilities and the results of their exams, it greatly limits their ability to provide the best care for patients.

AI has the potential to help physicians move away from spreadsheets and deliver more insights about patients, allowing physicians to provide the best care. 

AI In Pharma: Conclusion

Artificial intelligence is a powerful way to make better business decisions. Whether your company finds itself limited by its data or looking for an edge, AI will be able to give you new ways to compete in your industry.

The future of AI in pharma is looking bright, as traditional methods of data analytics are gaining momentum. The pharmaceutical industry has started incorporating AI to improve their research and production. Although the process has yielded only limited or marginal improvements in terms of productivity, quality, safety and safety-of-life measures, AI will have a profound effect on the way work is done in the life sciences industry.