Dr. Todor Antonijevic is a toxicologist, physicist, and mechanical engineer in ToxStrategies’ Houston, Texas, office. He has seven years of experience specializing in computational toxicology, machine learning, and nanomaterials.
He implemented a novel quantitative analytical model to predict a chemically specific critical concentration (“toxicological tipping point”) as a threshold in biological systems between adaptation and adversity from time-course concentration-response high-throughput screening (HTS) data. Dr. Antonijevic uses pharmacokinetics (PBPK) modeling—precisely, quantitative in vitro–to–in vivo extrapolation (qIVIVE)—to translate critical phenomena at a cellular level to apical outcomes and to compare these results with subchronic, repeat-dose animal studies.
Dr. Antonijevic integrated machine learning and qIVIVE to predict hepatotoxicants by developing a new approach to connect high-content imaging (HCI) concentration-response data to adverse hepatic effects observed in animal studies. He also developed a new machine learning algorithm to infer Boolean network responses from multidimensional concentration- and time-course-dependent RNA-seq and HCI data following chemical treatments.
He obtained his Ph.D. in Nanoscience from the University of North Carolina at Greensboro, where he studied phase transition in low-density lipoproteins (LDLs) by applying molecular dynamics analysis and Metropolis Monte Carlo simulations.