Eastern Nazarene College and The University of Connecticut School of Pharmacy have jointly received a grant from the National Institutes of Health (NIH). The four-year research project will work to develop a method for identifying metabolites – chemical compounds produced in the human body – and holds possible long-term applications in the diagnosis of multiple sclerosis (MS) and other diseases.
“Chemical compounds resulting from human metabolism play a major role in our overall physical well being,” said ENC Chemistry Professor Lowell Hall, who is collaborating on the research project with Principal Investigator UConn Professor David Grant. “But only a small fraction of these metabolite compounds have been identified. Separating and identifying the thousands of individual chemical compounds present in tiny quantities in biofluids is extremely difficult.”
Finding a way to quickly identify specific compounds in complex mixtures may ultimately help researchers and pharmaceutical companies develop new, lifesaving drugs more quickly and easily, Hall said.
The joint ENC and UConn research represents a new approach to identifying unknown compounds in a complex mixture through the development of mathematical, computational models. “Separation science has improved dramatically in recent years, enabling chemists to use chromatography to separate complex mixtures and use mass spectrometry to provide some chemical information,” Hall said. “While chromatography and mass spectrometry provide useful information on chemical substances in a complex mixture, those methods usually do not uniquely identify them.
“Our joint research is based on a really novel idea,” Hall continued. “By analyzing the chromatography data, we can develop a mathematical model whose predictions could help narrow down the field of possible compounds to a reasonable number for final identification.
In a second phase, we will use data from mass spectrometry to develop a second model which will further improve the structure filtering process.”
Through the research grant, Hall will work to develop mathematical modeling tools, which will then be tested in experiments at the University of Connecticut, where the extensive experimental work and data measurement will be done. Most of the $1.2 million grant’s funding will support these UConn experiments, Hall said.
Once developed, the computational tools will be integrated into a computer software program capable of sorting through hundreds of thousands of possible candidates and returning just a handful of possible compounds for chemical structure identification. The final phase of the project will involve testing the model and software in experiments related to multiple sclerosis.
“Currently, there is no reliable diagnostic test for multiple sclerosis – rather it is diagnosed by process of elimination,” Hall said. “In the final stage of our project, we will apply the integrated software program to cerebral spinal fluid from rats with MS in laboratory experiments at UConn, in an attempt to determine the profile of metabolites in MS. Any differences found may be useful in early diagnosis.
“Because of the potential long-term benefits,” Hall added, “we are very pleased to be a part of this research program with David Grant and the graduate students at UConn.”
Hall graduated from ENC in 1959 with a degree in Chemistry and went on to receive a Ph.D. in Physical Chemistry from The Johns Hopkins University (1963). He has taught chemistry at ENC for more than 40 years, publishing several books and more than 100 research papers in peer-reviewed journals.
Dr. Hall’s son and fellow ENC graduate, Mark Hall of Hall Associates Consulting, also will be involved in the NIH research project as the major developer of the modeling technology. Mark Hall’s work in developing artificial neural networks and computational methodologies will be central to the project’s mathematical modeling.
“This is an entirely new way of approaching the problem (of identifying chemical compounds),” Lowell Hall said. “We’re not aware of anyone else applying mathematical models this way, or applying it to research into MS.”
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Related at ENC: ENC Chemistry Department