Daniele Zink
Institute of Bioengineering and Nanotechnology, Singapore
Title: Accurate high-throughput prediction of human organ-specific toxicities
Biography
Biography: Daniele Zink
Abstract
Evaluating the toxicity of chemicals, drug candidates and other compounds requires predictive methods. There is a steeply increasing demand for alternative methods due to various problems associated with animal experiments and changes in legislation (e.g. animal bans for cosmetics testing). However, many alternative methods are of unknown predictivity, and accepted alternative methods for predicting toxicity for human internal organs are not available. This problem is addressed by our work, which was initially focused on the kidney. Recently, we have developed the first animal-free platforms for the accurate prediction of nephrotoxicity in humans. These technologies have received various international awards, including the Lush Science Prize 2016. Our methods include the only available predictive methods based on human induced pluripotent stem cell-derived renal cells and a predictive high-throughput platform. The high-throughput platform is currently applied in collaboration with the US Environmental Protection Agency to predict the human nephrotoxicity of ToxCast compounds. The test balanced accuracies of our predictive methods range between ~80% - 90%, and these methods also reveal injury mechanisms and compound-induced cellular pathways. Based on a similar methodology we are now developing high-throughput platforms for predicting toxicity for other human organ systems, including liver and vasculature. Furthermore, we are establishing predictive organ-on-chip platforms for efficient repeated dose testing and dose-response assessment.