Recommended by: Philip Bourne, University of California, San Diego
After finishing his postdoc, Lei Xie started working at a pharmaceutical company where he saw firsthand the weaknesses of the one-drug-one-gene-one disease approach. Upon leaving industry, Xie returned to academia, set upon finding ways to improve the success rate of investigational treatments through so-called polypharmacology, an approach that aims to advance drugs that target disease-causing networks instead of inhibiting individual receptors.
In his research at CUNY's computational systems biology, molecular modeling, and bioinformatics lab, Xie aims to identify the targets of drugs across the whole genome and then map them into biological pathways and networks.
In one such effort, Xie and his colleagues are working with Limerick Biopharm to identify the molecular and cellular mechanisms of action underlying a molecule that has shown activity against treating diabetes. "We're trying to figure out how this drug works so we can optimize its efficacy and reduce its side effects," Xie says.
Xie's research also focuses on applying structural bioinformatics and systems biology approaches to characterize protein function and their association with drug efficacy and adverse reactions.
For example, in a paper in PLOS Computational Biology, Xie, his mentor Philip Bourne, and several others from the University of California, San Diego, used an integrated computational approach to model and investigate how the human kidney metabolizes the drug torcetrapib.
The hypercholesterolemia treatment torcetrapib failed late-stage clinical trials at Pfizer several years ago due to increased drug-related mortality and heart events. The analysis by Xie and his colleagues suggested that PTGIS and ACOX1 are off-targets of torcetrapib and their inhibition could cause hypertension. If these biomarkers are validated as associated with torcetrapib's side effects, then, in the future, researchers can try to avoid similar adverse reactions in studies of other drugs in the CETP inhibitor class, Xie and others wrote in the paper.
Although Xie is focused on improving the drug development strategies, he notes that regulatory barriers and the complexity of the computational approaches has kept most pharmaceutical companies from integrating these types of strategies throughout their pipelines.
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Xie says his work developing structural systems biology approaches to identify genetic adaptations to complex diseases stands to have the most impact on the future of medicine. "As the central dogma of molecular biology shapes 20th century biology, I believe that advance of biology in the 21st century requires multi-scale modeling of biological systems so that we can understand the information flow from atomic details of molecular interaction to emergent properties of organisms," he says.
Using a structural systems biology approach, Xie and his colleagues are mapping single point mutations to protein structure to gauge how they alternate protein activity. Then they are studying the mutated protein as part of a biological interaction network to figure out which pathways are dysregulated. "The genetic variance will be linked to the phenotype through proteins structure and biological pathway," Xie adds.