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S O U R C E : Genetic Engineer and Biotechnolgy News
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 “An interspecies translation model implicates integrin signaling in infliximab-resistant inflammatory bowel disease” 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.
“Anti–tumor necrosis factor (anti-TNF) therapy resistance is a major clinical challenge in inflammatory bowel disease (IBD), due, in part, to insufficient understanding of disease-site, protein-level mechanisms. Although proteomics data from IBD mouse models exist, data and phenotype discrepancies contribute to confounding translation from preclinical animal models of disease to clinical cohorts. We developed an approach called translatable components regression (TransComp-R) to overcome interspecies and trans-omic discrepancies between mouse models and human subjects. TransComp-R combines mouse proteomic data with patient pretreatment transcriptomic data to identify molecular features discernible in the mouse data that are predictive of patient response to therapy. Interrogating the TransComp-R models revealed activated integrin pathway signaling in patients with anti–TNF-resistant colonic Crohn’s disease (cCD) and ulcerative colitis (UC),” write the investigators.
“As a step toward validation, we performed single-cell RNA sequencing (scRNA-seq) on biopsies from a patient with cCD and analyzed publicly available immune cell proteomics data to characterize the immune and intestinal cell types contributing to anti-TNF resistance. We found that ITGA1 was expressed in T cells and that interactions between these cells and intestinal cell types were associated with resistance to anti-TNF therapy. We experimentally showed that the a1 integrin subunit mediated the effectiveness of anti-TNF therapy in human immune cells. Thus, TransComp-R identified an integrin signaling mechanism with potential therapeutic implications for overcoming anti-TNF therapy resistance. We suggest that TransComp-R is a generalizable framework for addressing species, molecular, and phenotypic discrepancies between model systems and patients to translationally deliver relevant biological insights.”
“This model could help better determine which drugs should move from animal testing to humans,” said Doug Brubaker, PhD, 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.”
TransComp-R consolidates thousands of measurements from an animal model to just a few data coordinates for comparing with humans, he continued, adding that 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. The model helped Brubaker’s team find links in the data pointing toward a resistance mechanism in humans.
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