We know from experience that children learn and interact with the world around them in profoundly different ways than adults. But another, not-so-obvious difference happens out of sight, on a molecular level, and it influences how children respond to medicines.
In fact, from a biological perspective children are “more than just adults in smaller bodies,” says S. Joshua Swamidass, MD, PhD, an assistant professor in the Division of Laboratory and Genomic Medicine at Washington University in St. Louis. “Children are made up of different parts. They can have different levels of different drug metabolizing enzymes — the enzymes that modify medicines.”
Because of this, a child can metabolize a medicine differently than an adult, and this variation in metabolism can lead to harmful reactions. And unfortunately, it’s not clear exactly how or why this happens — a gap in knowledge that Dr. Swamidass is taking on.
With a four-year grant from the National Library of Medicine, Dr. Swamidass and a team of researchers will use mathematical models to analyze decades of research to understand how children metabolize drugs differently. The hope is this will help researchers develop safer and more effective medicines for children.
Neural Networks Make Comprehensive Review Possible
Neural networks — a tool of rising importance in computer science that can unlock patterns in big data — will be integral to the success of the project, says Dr. Swamidass, who is being assisted by students Na Le Dang, Tyler Hughes and Matthew Matlock. In a typical clinical study, the focus would be on a single medicine or enzyme. However, in this case, Dr. Swamidass and his team are conducting a comprehensive review, looking at many medicines and enzymes that have been the subjects of other studies.
The sheer volume of data would be more than anyone could review. To solve this problem, his group uses neural networks to review the data instead. “It’s a big computational mind — that’s one way to think of it,” Dr. Swamidass says. “Essentially, we’re getting a computer to understand [the data], to summarize it, to quantify it in a way that allows us to think about new medications and new drugs and new situations, based on all the collective knowledge we have from the past.”
These models will be checked using three medicines that children are already known to metabolize in different ways than adults: phenytoin, dextromethorphan and midazolam. “We want the system we build to be able to correctly predict that those three molecules are metabolized differently,” Dr. Swamidass says. Once this is achieved, the model will be applied to new medicines to predict whether they are safe or could cause harm.
“Because we’re a computational group, we can ask questions about all medications, instead of just a few,” he says. “We’re interested in asking about the 2,000 drugs that are on the market right now, and really seeing what the underlying principles of metabolism are, so that when scientists and physicians ask questions about a specific drug, we have tools to help them.”
Seeking Confirmation Through a Unique Collaboration
Dr. Swamidass and his team are collaborating with Grover P. Miller, PhD, of the University of Arkansas for Medical Sciences. Ethical considerations make it difficult to conduct clinical studies on children, says Dr. Swamidass. To overcome this, the researchers are looking to create a model of a child’s liver in a test tube, mixing enzymes in the right proportions, to see if it will correctly predict how children metabolize differently than adults.
This coordination between computational and experimental researchers is a unique aspect of the project. “This is a real, true collaboration where there are two groups working together to solve this problem with complementary approaches,” Dr. Swamidass says.
How will the research ultimately impact pediatricians? “Hopefully in the long run, there will be more drugs that we can confidently prescribe to children,” Dr. Swamidass says, “and we will have more confidence that the drugs that we use really are safe for children, too.”
“Hopefully in the long run, there will be more drugs that we can confidently prescribe to children.”