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Background:Humans are exposed to thousands of man-made chemicals in the environment. Some chemicals mimic natural endocrine hormones and, thus, have the potential to be endocrine disruptors. Most of these chemicals have never been tested for their ability to interact with the estrogen receptor (ER). Risk assessors need tools to prioritize chemicals for evaluation in costly in vivo tests, for instance, within the U.S. EPA Endocrine Disruptor Screening Program.Objectives:We describe a large-scale modeling project called CERAPP (Collaborative Estrogen Receptor Activity Prediction Project) and demonstrate the efficacy of using predictive computational models trained on high-throughput screening data to evaluate thousands of chemicals for ER-related activity and prioritize them for further testing.Methods:CERAPP combined multiple models developed in collaboration with 17 groups in the United States and Europe to predict ER activity of a common set of 32,464 chemical structures. Quantitative structure–activity relationship models and docking approaches were employed, mostly using a common training set of 1,677 chemical structures provided by the U.S. EPA, to build a total of 40 categorical and 8 continuous models for binding, agonist, and antagonist ER activity. All predictions were evaluated on a set of 7,522 chemicals curated from the literature. To overcome the limitations of single models, a consensus was built by weighting models on scores based on their evaluated accuracies.Results:Individual model scores ranged from 0.69 to 0.85, showing high prediction reliabilities. Out of the 32,464 chemicals, the consensus model predicted 4,001 chemicals (12.3%) as high priority actives and 6,742 potential actives (20.8%) to be considered for further testing.Conclusion:This project demonstrated the possibility to screen large libraries of chemicals using a consensus of different in silico approaches. This concept will be applied in future projects related to other end points.Citation:Mansouri K, Abdelaziz A, Rybacka A, Roncaglioni A, Tropsha A, Varnek A, Zakharov A, Worth A, Richard AM, Grulke CM, Trisciuzzi D, Fourches D, Horvath D, Benfenati E, Muratov E, Wedebye EB, Grisoni F, Mangiatordi GF, Incisivo GM, Hong H, Ng HW, Tetko IV, Balabin I, Kancherla J, Shen J, Burton J, Nicklaus M, Cassotti M, Nikolov NG, Nicolotti O, Andersson PL, Zang Q, Politi R, Beger RD, Todeschini R, Huang R, Farag S, Rosenberg SA, Slavov S, Hu X, Judson RS. 2016. CERAPP: Collaborative Estrogen Receptor Activity Prediction Project. Environ Health Perspect 124:1023–1033; http://dx.doi.org/10.1289/ehp.1510267

Enhancement of hematopoietic recovery after radiation, chemotherapy, or hematopoietic stem cell (HSC) transplantation is clinically relevant. Dipeptidylpeptidase (DPP4) cleaves a wide variety of substrates, including the chemokine stromal cell-derived factor-1 (SDF-1). In the course of experiments showing that inhibition of DPP4 enhances SDF-1–mediated progenitor cell survival, ex vivo cytokine expansion and replating frequency, we unexpectedly found that DPP4 has a more general role in regulating colony-stimulating factor (CSF) activity. DPP4 cleaved within the N-termini of the CSFs granulocyte-macrophage (GM)-CSF, G-CSF, interleukin-3 (IL-3) and erythropoietin and decreased their activity. Dpp4 knockout or DPP4 inhibition enhanced CSF activities both in vitro and in vivo. The reduced activity of DPP4-truncated versus full-length human GM-CSF was mechanistically linked to effects on receptor-binding affinity, induction of GM-CSF receptor oligomerization and signaling capacity. Hematopoiesis in mice after radiation or chemotherapy was enhanced in Dpp4−/− mice or mice receiving an orally active DPP4 inhibitor. DPP4 inhibition enhanced engraftment in mice without compromising HSC function, suggesting the potential clinical utility of this approach.