Using High-Performance Supercomputing to Find Endocrine Disruptors: A Fast Track to Discovering New Medicines and Protecting the Environment
Endocrine disruptors are a class of chemicals that can alter the normal activity of hormones in plants and animals. They are found in manufactured products such as plastics, metal containers, detergents, pesticides, cosmetics, and medicines—but they also exist widely in natural forms. They can either cause or suppress a range of developmental, neuronal, and immune diseases. This is why pharmaceutical, environmental, and health organizations are so concerned with identifying, regulating, and using these substances.
In the past, endocrine disruptors have been difficult to discover and manage, for two main reasons:
1) Vast varieties of chemicals need to be tested as disruptors, but traditional experiments are slow and costly.
2) Many chemicals are not even considered to be disruptors until after they are structurally changed from within an organism to a secondary form—so, many methods of discovery do not identify chemicals that act disruptively beyond their primary form.
The goal of this project is to address these two concerns using an integrated and tiered approach. First, we will evaluate chemical binding activity by using high-performance supercomputing to create virtual-binding simulations. The virtual methods will rank chemicals for likely primary or secondary disruptor activity, allowing us to then prioritize these chemicals for validation in traditional in vitro or cell-based assays.
In other words: using advanced computer simulations, we will be able to quickly and comprehensively categorize potential endocrine disruptors, allowing us to put them onto a fast track for experimental validation—and thus for possible applications in medicine or environmental regulation.