Alan Smith

Credentials: PhD in Plant Genetics

Position title: Study Director
Labcorp

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Dr. Smith completed his Bachelor’s and Master’s Degrees in Biology at Purdue University Fort Wayne where he used functional genomics to research aspects of branched-chain acid metabolism in plants. He earned his Ph.D. from the University of Wisconsin-Madison in Plant Breeding and Plant Genetics for his research on epigenetic changes that led to reactivation of transposable elements. Dr. Smith completed his Postdoctoral training in the Department of Animal Sciences at the University of Wisconsin-Madison where he developed metabolomics-based approaches to identify putative metabolic biomarkers in in vitro and in vivo disease models that led to three patents.

Dr. Smith went on to be a founding scientist at Stemina Biomarker Discovery (“Stemina”), a small biotech company in Madison, WI, where he transferred the metabolomics technology and data analysis pipeline he developed at the University of Wisconsin. During his tenure at Stemina, he developed expertise in funding a small biotechnology company using the Small Business Innovation Research (SBIR) program to transition complex scientific discoveries into commercial products. He worked on research strategies for three Phase I and four Phase II funded SBIRs that led to the development of four metabolomics based commercial products and four patents.

As Associate Director of Bioinformatics at Stemina, his research focused on developing computational and experimental methodologies in metabolomics for the identification of predictive biomarkers. These biomarkers were used for clinical diagnostic applications and in vitro new approach methodologies (NAMs) for accurately predicting the potential toxicity of a chemical. Dr. Smith has created predictive models based on changes in metabolism in human induced pluripotent stem cells (iPSC) to predict the potential for developmental toxicity and cardiotoxicity using iPSC-derived cardiomyocytes. He also worked on identification of metabolic biomarkers able to stratify individuals with autism spectrum disorder into distinct metabolic phenotypes as well as identify metabolic phenotypes associated with individuals who respond to a pharmaceutical.