07 Oct WisBusiness, WisPolitics Health Care Report for Oct. 7
Machine learning systems are helping researchers in Wisconsin discover promising drug candidates through more efficient data analysis.
Tony Gitter is an associate professor in the Department of Biostatistics and Medical Informatics at UW-Madison, and an investigator at the Morgridge Institute for Research. Speaking today at the Wisconsin Biohealth Summit in Madison, he explained how his research team worked with other scientists at the university to identify new potential antibiotics.
“There’s a lot of interest and need to develop novel types of antibiotics because there’s increasing bacterial resistance to some of the classic antibiotics that we have on the market,” he said.
Scientists working under UW-Madison Professor James Keck in the Biomolecular Chemistry Department started with a focus on a bacterial strain of pneumonia, and determined that breaking apart two structural proteins would kill the bacteria. From there, they began looking for chemicals that could do so through the traditional screening process. After months of research, scientists and graduate students working at the Keck Lab had tested over 427,000 chemicals.
“Their reward is finding that 99.9 percent of what they tested completely failed. There’s a tiny, tiny sliver of chemicals that might be promising, but most of those are also not very good,” Gitter said. “But what this generates is a lot of data, so now we have an area that’s really ripe for machine learning to step in.”
Gitter’s research group trained machine learning models on the data in hopes of finding out what makes that small number of candidate chemicals different from all the others that failed.
After scoring a billion more combinations that were commercially available, the system came up with a list of just 68 chemicals “that looked very appealing,” he said. After acquiring and testing those candidates, they found that about half of them showed promise for killing the bacteria.
“So we go from 99.9 percent complete failures, to an almost 50 percent hit rate, because the machine learning system is guiding our decisions about what to test,” he said. “We can have a much more customized view of which chemicals might actually work.”
While artificial intelligence applications like this are having a large impact on the early stages of drug discovery, Gitter said future developments might improve late-stage efforts as well.
“Machine learning is not yet reducing our animal testing needs, it’s not yet reducing the number of failed clinical trials that we have,” he said. “Those are going to be some grand challenges that we might think about going forward.”
An assistant professor at Marquette University is getting a $1.9 million federal grant to study long-term health factors in amateur athletes.
The funding is being provided through the National Institutes of Health “High-Risk, High-Reward Research” program. These grants support “innovative research proposals that, due to their inherent risk, may struggle in the traditional peer-review process,” according to a release from the university.
Jacob Capin is an assistant professor of physical therapy in Marquette University’s College of Health Sciences. His research will focus on the impact of prior injury and levels of physical activity as they relate to certain health measures in amateur athletes. The five-year research effort aims to inform health care providers’ approaches to rehabilitation and educating athletes. Ultimately, Capin wants to help reduce chronic disease later in life for these individuals.
The program will include two cohorts including former athletes ages 45-64 and current college athletes, both with or without prior traumatic knee injuries, and non-athlete control groups. The study will entail physical testing and participants completing questionnaires.
In the release, Capin explains that most research to date has focused on neurocognitive health and the “elite few” professional male athletes. He says research on the long-term health and wellness of former athletes has been limited.
“This study will evaluate physical activity patterns, musculoskeletal function, cardiometabolic health, and dietary intake in both male and female amateur athletes — a much larger, ubiquitous group,” he said.
UW-Madison has clarified that additional mental health providers it hired won’t serve students based on race.
The move follows the conservative Wisconsin Institute for Law & Liberty threatening to sue after the campus’s announcement last month stated three of nine new mental health counselors “will exclusively serve students of color.”
The announcement was updated yesterday to state three will join eight current providers “who have special expertise addressing issues that students of color often experience.”
“Research shows that students from minority backgrounds on college campuses often experience unique stresses,” the university said in a statement. “Research also shows that clients are more satisfied with counseling when it is provided by a counselor who is culturally responsive.”
WILL attorney Dan Lennington said the group will continue to monitor the situation because it remains “concerned that such ‘expertise’ will consist of little more than stereotypes and worry about the disparate treatment that such stereotypical thinking might beget.”
See the revised announcement.
State agencies will host a series of virtual listening sessions next month as they develop a new environmental and public health mapping tool.
The Wisconsin Environmental Equity Tool is being created by the departments of Administration, Health Services and Natural Resources, as well as the Wisconsin Economic Development Corporation. The data visualization platform aims to help users identify communities in the state that are most impacted by environmental and health inequities, a release shows.
“We can do better, and we want everyone to be a part of our work to better understand and address the environmental and health inequities facing Wisconsinites every day and build a healthier, more equitable future for our state,” Gov. Evers said in the release.
Three listening sessions will be held in early November to give participants a chance to weigh in on the tool’s development.
Register for the listening sessions here.