Scientist III

Dr. Alexander Blanchette is a toxicologist specializing in the use of computational methods and other new-approach methods (NAMs) to aid in all phases of risk assessment, with broad experience in using the R programming language. He received my bachelors of science in Forensic Chemistry from Western New England University where he worked to develop an alternate and improved field test for marijuana. After receiving hos undergraduate degree, he joined the Toxicology Ph.D. program at the University of Maryland Baltimore and after a year, hr decided to transfer to Texas A&M, where he recieved his Ph.D in Toxicology as a member of Dr. Weihsueh Chiu's lab, where he developed his computational toxicology skillset.This skillset extends to other aspects of risk assessment, including in vitro –to–in vivo extrapolation, high-throughput toxicokinetic modeling with the httk package in R, meta-analysis and meta-regression, machine-aided literature selection and other machine learning applications, and population variability assessment. His work has incorporated data from multiple sources, including exposure, high-content imaging, and other in vitro data for Bayesian population dose-response models built using Stan, integrated in R as the rstan package. He also has experience conducting analyses, such as ANOVA, mixed-effects modeling, and T-tests, and creating clear and compelling visualizations in R, Graphpad Prism, Microsoft Excel, and ToxPi with structurally diverse and heterogeneous data sets. His toxicological skill set includes general toxicology, systematic literature review across a broad range of subjects, cardiovascular toxicology with a more specific focus in environmental cardiology, the use of induced pluripotent stem cells in toxicology, high-throughput screening, and population variability, including both toxicokinetic and toxicodynamic variability.