McMaster researchers create human tissue models to study disease and accelerate drug discovery

“From early preclinical testing all the way to phase 3 clinical trials – the drug development pipeline tends to be long, arduous, and expensive. But what if there was a way to reduce development times while simultaneously making more accurate predictions on the likelihood of success? Boyang Zhang, associate professor of chemical engineering at McMaster University, has been leading the development of organ-on-a-chip technology, which aims to accelerate drug discovery by constructing in vitro human tissue models for use in preclinical testing. The technology is complex, but the idea is simple – Zhang and his team are building tissue models that simulate parts of the human body, which can be used to study how drugs affect various tissues. They use real human cells obtained from patients and healthy donors or even derived from stem cells. “Before ever starting a clinical trial, our organ-on-a-chip technology allows us to gain valuable insights on drug interactions in human tissue,” said Zhang. The team at OrganoBiotech – Zhang’s startup based at McMaster Innovation Park – has modelled a variety of human tissues, such as blood vessels, lung airways and air sacs, kidney and intestinal tissues, and even placenta. Previously, drug discovery involving the placenta has been challenging – this organ only exists during pregnancy and there are potential risks to the mother and fetus. The “placenta-on-a-chip” model is a safe and accessible alternative that will help scientists study how drugs pass through the placenta barrier during pregnancy and offer insights into disease conditions like preeclampsia. View this post on Instagram Unlike other organ-on-a-chip providers, OrganoBiotech uses an innovative 3D printing method to produce tissue models at scale. These models are built on special plates that are capable of simulating blood flow. “Because these systems use human cells assembled in a controlled microenvironment, they can capture human-specific tissue-level responses that are often difficult to reproduce in animal models,” said Zhang. Historically, drug discovery research has involved animals before moving into human trials, but success in animal models rarely translates to humans. “Our organ-on-a-chip technology complements existing animal research, offering different types of information to the researchers,” said Zhang. The more data scientists can gather, the more insightful their predictions will be, allowing them to better allocate resources to drugs that are more likely to succeed in human trials. “Building large datasets with only animal models is challenging and time consuming – there’s an upper limit to how much we can speed things up,” said Zhang. “Animal models are costly and much more limited by space restraints.” Conversely, efficiency and scalability are prime advantages of OrganoBiotech’s organ-on-a-chip technology. With the ability to examine up to 128 samples on a single plate, researchers can screen different drugs at different dosages and under different experimental conditions all at once. These advantages are even more apparent when coupled with automation. A fluorescence micrograph showing close-up detail of an intestinal tissue model. Protruding colon crypts (tube-like glands) are visible. (Photo by Alexandrer Sotra, courtesy Boyang Zhang/OrganoBiotech) “We designed our platform to be compatible with robotic liquid handling and other automated systems,” said Zhang. “This allows us to automate the tissue production and the screening process to further improve efficiency and throughput.” Using their automated technology, Zhang and other researchers will be able to generate vast quantities of data that can be used with artificial intelligence (AI) and machine learning to guide the development of new drugs. His team has previously shown that machine learning can be used for accurate image analysis of their tissue samples, a process that involves classifying cells according to their quantity, shape, and size. This process is time consuming when performed manually by a human looking through a microscope, but automation makes quick work of it. Researchers are now using these growing datasets to train AI models designed to help make better predictions on whether a drug is likely to succeed or fail. The post McMaster researchers create human tissue models to study disease and accelerate drug discovery appeared first on McMaster News .

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