Tool could Improve Success in Translating Drugs from Animal Studies to Humans

About 50% of people who take the drug infliximab for inflammatory bowel diseases, such as Crohn's disease, end up becoming resistant or unresponsive to it.

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.

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."

Douglas K Brubaker, Manu P Kumar, Evan L Chiswick, Cecil Gregg, Alina Starchenko, Paige N Vega, Austin N Southard-Smith, Alan J Simmons, Elizabeth A Scoville, Lori A Coburn, Keith T Wilson, Ken S Lau, Douglas A Lauffenburger.
An interspecies translation model implicates integrin signaling in infliximab-resistant inflammatory bowel disease
Science Signaling, 2020. doi: 10.1126/scisignal.aay3258

Most Popular Now

Using Data and AI to Create Better Healt…

Academic medical centers could transform patient care by adopting principles from learning health systems principles, according to researchers from Weill Cornell Medicine and the University of California, San Diego. In...

AI Medical Receptionist Modernizing Doct…

A virtual medical receptionist named "Cassie," developed through research at Texas A&M University, is transforming the way patients interact with health care providers. Cassie is a digital-human assistant created by Humanate...

Northern Ireland Completes Nationwide Ro…

Go-lives at Western and Southern health and social care trusts mean every pathology service is using the same laboratory information management system; improving efficiency and quality. An ambitious technology project to...

AI Tool Set to Transform Characterisatio…

A multinational team of researchers, co-led by the Garvan Institute of Medical Research, has developed and tested a new AI tool to better characterise the diversity of individual cells within...

AI Detects Hidden Heart Disease Using Ex…

Mass General Brigham researchers have developed a new AI tool in collaboration with the United States Department of Veterans Affairs (VA) to probe through previously collected CT scans and identify...

Human-AI Collectives Make the Most Accur…

Diagnostic errors are among the most serious problems in everyday medical practice. AI systems - especially large language models (LLMs) like ChatGPT-4, Gemini, or Claude 3 - offer new ways...

MHP-Net: A Revolutionary AI Model for Ac…

Liver cancer is the sixth most common cancer globally and a leading cause of cancer-related deaths. Accurate segmentation of liver tumors is a crucial step for the management of the...

Highland Marketing Announced as Official…

Highland Marketing has been named, for the second year running, the official communications partner for HETT Show 2025, the UK's leading digital health conference and exhibition. Taking place 7-8 October...

Groundbreaking TACIT Algorithm Offers Ne…

Researchers at VCU Massey Comprehensive Cancer Center have developed a novel algorithm that could provide a revolutionary tool for determining the best options for patients - both in the treatment...

The Many Ways that AI Enters Rheumatolog…

High-resolution computed tomography (HRCT) is the standard to diagnose and assess progression in interstitial lung disease (ILD), a key feature in systemic sclerosis (SSc). But AI-assisted interpretation has the potential...