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- Mansouri, Kamel, et al.
(författare)
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CoMPARA : Collaborative Modeling Project for Androgen Receptor Activity
- 2020
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Ingår i: Journal of Environmental Health Perspectives. - 0091-6765 .- 1552-9924. ; 128:2, s. 1-17
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Tidskriftsartikel (refereegranskat)abstract
- BACKGROUND: Endocrine disrupting chemicals (EDCs) are xenobiotics that mimic the interaction of natural hormones and alter synthesis, transport, or metabolic pathways. The prospect of EDCs causing adverse health effects in humans and wildlife has led to the development of scientific and regulatory approaches for evaluating bioactivity. This need is being addressed using high-throughput screening (HTS) in vitro approaches and computational modeling.OBJECTIVES: In support of the Endocrine Disruptor Screening Program, the U.S. Environmental Protection Agency (EPA) led two worldwide consortiums to virtually screen chemicals for their potential estrogenic and androgenic activities. Here, we describe the Collaborative Modeling Project for Androgen Receptor Activity (CoMPARA) efforts, which follows the steps of the Collaborative Estrogen Receptor Activity Prediction Project (CERAPP).METHODS: The CoMPARA list of screened chemicals built on CERAPP's list of 32,464 chemicals to include additional chemicals of interest, as well as simulated ToxCast (TM) metabolites, totaling 55,450 chemical structures. Computational toxicology scientists from 25 international groups contributed 91 predictive models for binding, agonist, and antagonist activity predictions. Models were underpinned by a common training set of 1,746 chemicals compiled from a combined data set of 11 ToxCast (TM)/Tox21 HTS in vitro assays.RESULTS: The resulting models were evaluated using curated literature data extracted from different sources. To overcome the limitations of single-model approaches, CoMPARA predictions were combined into consensus models that provided averaged predictive accuracy of approximately 80% for the evaluation set.DISCUSSION: The strengths and limitations of the consensus predictions were discussed with example chemicals; then, the models were implemented into the free and open-source OPERA application to enable screening of new chemicals with a defined applicability domain and accuracy assessment. This implementation was used to screen the entire EPA DSSTox database of similar to 875,000 chemicals, and their predicted AR activities have been made available on the EPA CompTox Chemicals dashboard and National Toxicology Program's Integrated Chemical Environment.
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- Mansouri, Kamel, et al.
(författare)
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CERAPP : Collaborative Estrogen Receptor Activity Prediction Project
- 2016
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Ingår i: Journal of Environmental Health Perspectives. - : Environmental Health Perspectives. - 0091-6765 .- 1552-9924. ; 124:7, s. 1023-1033
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Tidskriftsartikel (refereegranskat)abstract
- 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.
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- Casiraghi, F, et al.
(författare)
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Amnion epithelial cells are an effective source of factor H and prevent kidney complement deposition in factor H-deficient mice
- 2021
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Ingår i: Stem cell research & therapy. - : Springer Science and Business Media LLC. - 1757-6512. ; 12:1, s. 332-
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Tidskriftsartikel (refereegranskat)abstract
- Complement factor H (FH) is the main plasma regulator of the alternative pathway of complement. Genetic and acquired abnormalities in FH cause uncontrolled complement activation amplifying, with the consequent accumulation of complement components on the renal glomeruli. This leads to conditions such as C3 glomerulopathy (C3G) and atypical hemolytic uremic syndrome (aHUS). There is no effective therapy for these diseases. Half of the patients progress to end-stage renal disease and the condition recurs frequently in transplanted kidneys. Combined liver/kidney transplantation is a valid option for these patients, but the risks of the procedure and donor organ shortages hamper its clinical application. Therefore, there is an urgent need for alternative strategies for providing a normal FH supply. Human amnion epithelial cells (hAEC) have stem cell characteristics, including the capability to differentiate into hepatocyte-like cells in vivo.Here, we administered hAEC into the livers of newborn Cfh−/− mice, which spontaneously developed glomerular complement deposition and renal lesions resembling human C3G. hAEC engrafted at low levels in the livers of Cfh−/− mice and produced sufficient human FH to prevent complement activation and glomerular C3 and C9 deposition. However, long-term engraftment was not achieved, and eventually hAEC elicited a humoral immune response in immunocompetent Cfh−/− mice.hAEC cell therapy could be a valuable therapeutic option for patients undergoing kidney transplantation in whom post-transplant immunosuppression may protect allogeneic hAEC from rejection, while allogeneic cells provide normal FH to prevent disease recurrence.
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- Sushko, Iurii, et al.
(författare)
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Applicability Domains for Classification Problems : Benchmarking of Distance to Models for Ames Mutagenicity Set.
- 2010
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Ingår i: Journal of chemical information and modeling. - : American Chemical Society (ACS). - 1549-9596 .- 1549-960X. ; 50:12, s. 2094-2111
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Tidskriftsartikel (refereegranskat)abstract
- The estimation of accuracy and applicability of QSAR and QSPR models for biological and physicochemical properties represents a critical problem. The developed parameter of "distance to model" (DM) is defined as a metric of similarity between the training and test set compounds that have been subjected to QSAR/QSPR modeling. In our previous work, we demonstrated the utility and optimal performance of DM metrics that have been based on the standard deviation within an ensemble of QSAR models. The current study applies such analysis to 30 QSAR models for the Ames mutagenicity data set that were previously reported within the 2009 QSAR challenge. We demonstrate that the DMs based on an ensemble (consensus) model provide systematically better performance than other DMs. The presented approach identifies 30-60% of compounds having an accuracy of prediction similar to the interlaboratory accuracy of the Ames test, which is estimated to be 90%. Thus, the in silico predictions can be used to halve the cost of experimental measurements by providing a similar prediction accuracy. The developed model has been made publicly available at http://ochem.eu/models/1 .
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- Chavan, Swapnil, et al.
(författare)
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Towards Global QSAR Model Building for Acute Toxicity : Munro Database Case Study
- 2014
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Ingår i: International Journal of Molecular Sciences. - : MDPI AG. - 1661-6596 .- 1422-0067. ; 15:10, s. 18162-18174
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Tidskriftsartikel (refereegranskat)abstract
- A series of 436 Munro database chemicals were studied with respect to their corresponding experimental LD50 values to investigate the possibility of establishing a global QSAR model for acute toxicity. Dragon molecular descriptors were used for the QSAR model development and genetic algorithms were used to select descriptors better correlated with toxicity data. Toxic values were discretized in a qualitative class on the basis of the Globally Harmonized Scheme: the 436 chemicals were divided into 3 classes based on their experimental LD50 values: highly toxic, intermediate toxic and low to non-toxic. The k-nearest neighbor (k-NN) classification method was calibrated on 25 molecular descriptors and gave a non-error rate (NER) equal to 0.66 and 0.57 for internal and external prediction sets, respectively. Even if the classification performances are not optimal, the subsequent analysis of the selected descriptors and their relationship with toxicity levels constitute a step towards the development of a global QSAR model for acute toxicity.
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