The goal of this challenge was to develop a model based on data provided by the EPA to quantitatively predict a chemical’s systemic lowest effect level (LEL) in a traditional animal toxicity study.
People are exposed to many man-made chemicals throughout their lives. These include food ingredients and additives, pesticides, cosmetics, medicines, cleaners, solvents, etc. Historically, a series of standard animal studies have been used as a means to evaluate whether a chemical can cause a range of different adverse effects and at what dose these effects occur. The term “systemic toxicity” is often used because the effects can occur in different organ systems such as the liver, kidney, lungs, or reproductive system. The systemic Lowest Effect Level or LEL is the lowest dose that shows adverse effects in these animal toxicity tests. The LEL is then conservatively adjusted in different ways by regulators to derive a value that can be used by the Agency to set exposure limits that are expected to be tolerated by the majority of the population.
Ideally, every chemical to which we are exposed would have a well-defined LEL. However, the full battery of animal studies required to estimate the LEL costs millions of dollars and takes many months to complete. As a result, thousands of chemicals lack the required data needed to estimate an LEL. To help fill this gap, the EPA has screened nearly 2,000 chemicals across a battery of more than 700 biochemical and cell-based in vitro assays to identify what proteins, pathways, and cellular processes these chemicals interact with and at what concentration they interact. The goal of this challenge is to develop an algorithm using data from high-throughput in vitro assays, chemical properties, or chemical structural descriptors to quantitatively predict a chemical’s systemic LEL. Chemicals causing toxicity through inhibition of acetylcholinesterase (a common mechanism for neurotoxicity) are excluded from the challenge since they may skew the LEL values and can be identified in other ways.
Project Overview & Stats
This project launched in December, 2013 and was completed in June, 2014. The project was completed using 5 challenges and had 436 registrants from over 32 countries. The model that was created helps to predict a chemical’s systemic LEL.The winning solution to the challenge was scored on the following criteria;
- Strength of prediction as measured against the validation data set
- The fraction of the 1,800 included chemicals the method(s) are able to accurately score
- The scientific supportability of the performance of the prediction methods.
|Generating Chemical Structural Descriptors Idea Generation Contest||12/12/2013||01/09/2014||Conceptualization||Completed|
|Describing High-Throughput Screening Assays Idea Generation Contest||12/30/2013||01/16/2014||Conceptualization||Completed|
|Predictive Capability Tests (Private Contest)||02/24/2014||03/18/2014||Content Creation||Completed|
|Null Effect Level Prediction Challenge||04/23/2014||05/16/2014||Marathon Match||Completed|
|LEL Predictor MM Follow-up Round||05/18/2014||06/08/2014||Content Creation||Completed|
Marathon Match – Winners
Results of Other ToolsThese results are based on existing subject domain tools.