Derisking Future Agrochemicals before They Are Made: Large-Scale In Vitro Screening for In Silico Modeling of Thyroid Peroxidase Inhibition

Abstract

Inhibition of thyroid peroxidase (TPO) is a known molecular initiating event for thyroid hormone dysregulation and thyroid toxicity. Consequently, TPO is a critical off-target for the design of safer agrochemicals. To date, fewer than 500 structurally characterized TPO inhibitors are known, and the most comprehensive result set generated under identical conditions encompasses approximately 1000 compounds from a subset of the ToxCast compound collection. Here we describe a collaboration between wet lab and data scientists combining a large in vitro screen and the subsequent development of an in silico model for predicting TPO inhibition. The screen encompassed more than 100,000 diverse drug-like agrochemical compounds and yielded more than 6000 structurally novel TPO inhibitors. On this foundation, we applied different machine learning techniques and compared their performance. We discuss use cases for in silico TPO models in agrochemical research and explain that model recall is of particular importance when selecting compounds from large virtual compound collections. Furthermore, we show that due to the higher structural diversity of our training data, our final model allowed better generalization than models trained on the ToxCast data set. We now have a tool to predict TPO inhibition even for molecules that are only available virtually, such as hits from virtual screenings, or compounds under consideration for inclusion in our screening collection. Structures and activity data for 34,524 compounds are provided. This data set includes almost all inhibitors, including more than 3000 proprietary structures, and a large proportion of the inactives.