Alexander D. Blanchette, Ph.D.
Scientist III


Phone(281) 809-0118
Address31 College Place
Suite B118
Asheville, NC 28801




View Publications

Professional Profile

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. This expertise 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.

Dr. Blanchette’s technical laboratory experience includes conducting cell culture and in vitro assays and data collection, as well as conducting TempO-Seq assays, operating a Flipr Tetra High-Throughput Cellular Screening System, high-content imaging, and conducting genotyping experiments using PCR and agarose gels.

Dr. Blanchette earned a Doctorate in Toxicology from Texas A&M University. His doctoral dissertation work involved the development and application of a novel in vitro–in silico model, the combination of human induced-pluripotent stem cell–derived cardiomyocytes and a hierarchical Bayesian population dose-response model, to screen environmental and pharmacological substances for cardiotoxic hazard and risk, and to quantify toxicodynamic variability. His list of student awards includes the George T. Edds Award in 2021 at the College of Veterinary Medicine and Biomedical Sciences at Texas A&M, as well as the Student Section award from the Society of Toxicology’s Computational Toxicology Specialty Section in 2021. He was also a finalist in 2020 for the Best Abstract award from SOT’s Biological Modeling Specialty Section.