Scientists might be able to catch problems like this one earlier in
the drug development process, when drugs move from testing in
animals to clinical trials, with a new computational model developed
by researchers from Purdue University and Massachusetts Institute of
Technology.
The researchers call the model “TransComp-R.” In a study published
in Science Signaling, they used the model to identify an overlooked
biological mechanism possibly responsible for a patient’s resistance
to infliximab.
Such a mechanism is hard to catch in preclinical testing of new
drugs because animal models of human diseases may have different
biological processes driving disease or a response to therapy. This
makes it difficult to translate observations from animal experiments
to human biological contexts.
“This model could help better determine which drugs should move from
animal testing to humans,” said Doug Brubaker, a Purdue assistant
professor of biomedical engineering, who led the development and
testing of this model as a postdoctoral associate at MIT.
“If there is a reason why the drug would fail, such as a resistance
mechanism that wasn’t obvious from the animal studies, then this
model would also potentially detect that and help guide how a
clinical trial should be set up,” he said.
TransComp-R consolidates thousands of measurements from an animal
model to just a few data coordinates for comparing with humans. The
dwindled-down data explain the most relevant sources of biological
differences between the animal model and humans.
From there, scientists could train other sets of models to predict a
human’s response to therapy in terms of those data coordinates from
an animal model.
For infliximab, data from a mouse model and human hadn’t matched up
because they were different types of biological measurements. The
mouse model data came in the form of intestinal proteins, whereas
data from patients were only available in the form of expressed
genes, a discrepancy TransComp-R was able to address.
TransComp-R helped Brubaker’s team to find links in the data
pointing toward a resistance mechanism in humans.
The team collaborated with researchers from Vanderbilt University to
test the predicted mechanism in intestinal biopsies from a Crohn’s
disease patient and then with experiments in human immune cells.
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The researchers used single-cell sequencing of a sample from an infliximab-resistant
Crohn’s disease patient to identify the cell types expressing the genes related
to the resistance mechanism predicted by TransComp-R.
They then treated immune cells with infliximab and an inhibitor of the receptor
identified by the model to be part of the resistance mechanism. The experiment
showed that inhibiting the receptor enhanced the anti-inflammatory effects of
infliximab, enabling the drug to be more effective because it could better
control inflammation.
With additional testing to figure out a way to more precisely measure the
markers of this resistance mechanism, doctors could use information about the
drug response to determine if a patient needs a different course of treatment.
Since this study, Brubaker has been working with his former research group at
MIT to apply the mathematical framework behind TransComp-R to identify mouse
models predictive of Alzheimer’s disease biology and immune signatures of
vaccine effectiveness in animal studies of COVID-19 vaccine candidates.
“The modeling framework itself can be repurposed to different kinds of animals,
different disease areas and different questions,” Brubaker said. “Figuring out
when what we see in animals doesn’t track with what’s happening in humans could
save a lot of time, cost and effort in the drug development process overall.”
The TransComp-R code is available at MathWorks File Exchange (ID: 77987).
Brubaker received funding for this research from the Strategic Hub for
Innovation New Therapeutic Concept Exploration program of Boehringer Ingelheim
Pharmaceuticals.
About Purdue University
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solutions to today’s toughest challenges. Ranked the No. 6 Most Innovative
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debt-free. See how Purdue never stops in the persistent pursuit of the next
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[Writer Kayla Wiles,
Source: Doug Brubaker] |