Putting safety in the discovery stage

For researchers working in drug discovery and development, AI has the potential to do wonders, from increasing safety to moving their work forward at a faster pace. But, before you apply advanced AI, it’s important to ask: What is the specific problem that I want to address?

“It’s very motivating to work on a project related to patient safety,” says Dr. Catherine Noban. “And it’s a natural fit since Elsevier also helps organizations accelerate the development of drugs to treat diseases. We are always exploring how we can support our customers in their innovation efforts to better predict adverse drug reactions using various Elsevier solutions — while also streamlining their R&D and reducing the need for animal testing.”

With a PhD in organic chemistry and years of working with researchers in drug discovery to develop Elsevier’s data and information tools, Catherine is now applying her know-how to advanced AI. As Lead Product Manager for Life Sciences Biomedical Innovation at Elsevier, she’s exploring innovative ways to better predict adverse drug reactions.

The work sits at the intersection of patient safety and tailored therapy — two areas she’s found to be intrinsically related. And she says it’s essential to understand this connection to get the most out of AI.

“Patient safety and tailored therapy are really two sides of the same coin,” she says. “But for now, we need to stay focused. It’s important to really understand the specific problem you want to address when you apply advanced AI techniques.”

Elsevier Life Science Solutions supports various phases of drug discovery and development. For instance, customers may choose to search the drug approval documents and extracted data of PharmaPendium , visualize biological pathways via EmBiology , or go deeper into the bioactivity or molecular level via Reaxys .

“One of the key challenges of preclinical development is predicting how and why a certain adverse event will play out in a specific individual,” Catherine explains. “For example, what does this particular compound do to a dog? What does it do to a human — or different humans? When does the information correlate? When does it not correlate? So you try to understand the why using as much historical data as possible. And then you can draw your conclusion, make a hypothesis and test it.”

This not only helps preclinical development teams with their hypothesis generation, she adds; it ultimately helps them improve healthcare outcomes in an ever-evolving healthcare landscape.

“Basically, we want to help scientists understand the effect of a certain compound,” she explains. “We do this by helping them compare the new molecules they are making with ones we may already know a lot about. In other words, it’s about putting safety in the discovery stage — and thereby, down the road, decreasing the need for animal testing while increasing the chances of a clinical trial’s success.”

The importance of drug safety in drug development is clear. Drug safety issues are also one of the major causes of late-stage drug failure in pharmaceutical development.

It’s also clear that AI and machine learning are ideally suited to assess the possibility of connecting any relational dots of a drug's effects after it enters the market — and the even more dizzying implications of when this drug interacts, perhaps adversely, with those drugs already on the market. It’s simply impossible for mere mortals to cross-test the thousands of medicines currently on the market.

It’s also become clear that animal testing is often ineffective, and AI offers tremendous opportunities to contribute to the efforts related to the 3R principles: Replacement, Reduction and Refinement of animal testing. “I really hope our project contributes to this quickly changing landscape of assessing whether a medicine is safe or not,” Catherine says. She’s confident the project will be a success and hopes to have a proof-of-concept soon. “There are immense challenges, but we have the required expertise to work through them,” she says. “So it’s just a matter of time.”

Catherine got her “proper data schooling” while helping to develop what’s now known as the Reaxys Target and Bioactivity (RTB) platform, which was originally formulated to facilitate better connections between biological and chemical data.

“This was really the school I graduated from when it comes to knowing the data industry,” Catherine says. “I really learned it from scratch: how to produce such a massive and complex data set and what comes with it. It’s not enough to understand the data; you need to understand how the customers can best use it. You really need to understand both to make it work.”

More recently, she led the development of a prediction tool for safety margin risk assessment. A collaboration with a large pharmaceutical company, the project was centered around using different Elsevier datasets and systematically applying scientific rules to data to support very early safety pharmacology assessment. Now, the idea is to take these efforts to a whole new level.

“As we explore how to support our customers in their preclinical assessments and continuous scientific monitoring,” Catherine says, “it’s important to investigate the possibility of looking at things more holistically using new technological advances — bringing together different elements to create a bigger picture. In this way, we can help scientists to better deduce and prove correlations, compare substances and properties, and do predictions.

“But first, you must still understand the specific problem you want to address,” she adds. “As technology and science advance, there is a greater need to deep-dive into the actual problems. This is really key to data integration and applications of advanced AI techniques. Only then can you look for the relevant Life Science datasets and how to combine them to address your particular question.”

Read the full article at Elsevier Connect to find out more about how this work relates to personalized medicine.