U.S. Environmental Protection Agency Office of Research and Development EPA www.epa.gov Development of QSAR Models for Systemic Toxicity Points of Departure with Variability in Experimental Data Prachi Pradeep1,2 and Richard Judson2 1Oak Ridge Institute for Science and Education, Oak Ridge, Tennessee 2National Center for Computational Toxicology, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina INTRODUCTION TYPES OF MODELS DATA PREPARATION METHODS Prachi Pradeep l pradeep.prachi@epa.gov l ORCID iD: 0000-0002-9219-4249 l Phone: 919-541-5150 CONCLUSIONS Human health risk assessment associated with environmental chemical exposure is limited by the tens of thousands of chemicals with little or no experimental in vivo toxicity data. Data gap filling techniques, such as quantitative structure activity relationship (QSAR) models based on chemical structure information, are commonly used to predict hazard in the absence of experimental data. However, variability in the experimental data leads to uncertainty in QSAR model predictions and impacts model quality estimates. This study presents three sets of QSAR models developed for systemic toxicity in vivo points of departure (POD, the point on the dose-response that marks the beginning of a low-dose extrapolation). The in vivo data is taken from the EPA’s ToxValDB, a compilation of information on ~3000 chemicals from a variety of public data sources. The first set of QSAR models were developed and evaluated to predict point estimates of POD values using structural and physicochemical descriptors. The second set of models were built to account for skewness in the training data. The third set of models were built to account for the known lab to lab variability in experimental POD values. The QSAR models were also evaluated for enrichment of most potent chemicals. These models will inform chemical screening and prioritization efforts. • Point-estimate model results demonstrate that independent study type and species combinations did not result in significantly better models than combining the data for all the classes and species together. - The RMSE for the all the models are within the variance in the underlying POD data (Figures 2 and 6). - Enrichment analysis results demonstrate the utility of these models for chemical screening and prioritization efforts. • Point-estimate with balanced dataset model results show improvement in the training set results but did not show improved results on the external test sets. • Point-estimate with confidence interval models presented a technique to estimate uncertainty associated with model predictions. The results demonstrate the impact of variability in training data (experimental POD) on uncertainty associated with model results. Disclaimer: The views expressed are those of the authors and do not necessarily reflect the views or policies of the U.S. Environmental Protection Agency. 1. Point-estimate Models • A single POD value predicted for each chemical. • Experimental POD = Median POD value from all studies. 2. Point-estimate with Balanced Dataset Models • Training data re-constructed to reduce skewness. • A single POD value predicted for each chemical using the re-constructed data. The process was repeated 1000 times. • Experimental POD = Median POD value from all studies. 3. Point-estimate with Confidence Interval Models • A POD distribution was constructed for each chemical (µ = Median experimental POD value from all studies, σ = 0.5 log-units). • 1000 bootstrap models were built with random sampling of POD values for each chemical from the pre-generated POD distribution. • Predicted POD = mean of 1000 bootstrap predictions • Confidence interval of POD = ±1 standard deviation of 1000 bootstrap predictions Given the variability and skewness in the training dataset, 3 types of models were developed: Study Type Species Total number of POD values (studies) Number of unique chemicals