| 1. |
- 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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| 2. |
- Norinder, Ulf, 1956-, et al.
(författare)
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Conformal prediction to define applicability domain : A case study on predicting ER and AR binding
- 2016
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Ingår i: SAR and QSAR in environmental research (Print). - : Taylor & Francis. - 1062-936X .- 1029-046X. ; 27:4, s. 303-316
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Tidskriftsartikel (refereegranskat)abstract
- A fundamental element when deriving a robust and predictive in silico model is not only the statistical quality of the model in question but, equally important, the estimate of its predictive boundaries. This work presents a new method, conformal prediction, for applicability domain estimation in the field of endocrine disruptors. The method is applied to binders and non-binders related to the oestrogen and androgen receptors. Ensembles of decision trees are used as statistical method and three different sets (dragon, rdkit and signature fingerprints) are investigated as chemical descriptors. The conformal prediction method results in valid models where there is an excellent balance in quality between the internally validated training set and the corresponding external test set, both in terms of validity and with respect to sensitivity and specificity. With this method the level of confidence can be readily altered by the user and the consequences thereof immediately inspected. Furthermore, the predictive boundaries for the derived models are rigorously defined by using the conformal prediction framework, thus no ambiguity exists as to the level of similarity needed for new compounds to be in or out of the predictive boundaries of the derived models where reliable predictions can be expected.
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| 3. |
- Rybacka, Aleksandra, 1987-
(författare)
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A step forward in using QSARs for regulatory hazard and exposure assessment of chemicals
- 2016
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- According to the REACH regulation chemicals produced or imported to the European Union need to be assessed to manage the risk of potential hazard to human health and the environment. An increasing number of chemicals in commerce prompts the need for utilizing faster and cheaper alternative methods for this assessment, such as quantitative structure-activity or property relationships (QSARs or QSPRs). QSARs and QSPRs are models that seek correlation between data on chemicals molecular structure and a specific activity or property, such as environmental fate characteristics and (eco)toxicological effects.The aim of this thesis was to evaluate and develop models for the hazard assessment of industrial chemicals and the exposure assessment of pharmaceuticals. In focus were the identification of chemicals potentially demonstrating carcinogenic (C), mutagenic (M), or reprotoxic (R) effects, and endocrine disruption, the importance of metabolism in hazard identification, and the understanding of adsorption of ionisable chemicals to sludge with implications to the fate of pharmaceuticals in waste water treatment plants (WWTPs). Also, issues related to QSARs including consensus modelling, applicability domain, and ionisation of input structures were addressed.The main findings presented herein are as follows:QSARs were successful in identifying almost all carcinogens and most mutagens but worse in predicting chemicals toxic to reproduction.Metabolic activation is a key event in the identification of potentially hazardous chemicals, particularly for chemicals demonstrating estrogen (E) and transthyretin (T) related alterations of the endocrine system, but also for mutagens. The accuracy of currently available metabolism simulators is rather low for industrial chemicals. However, when combined with QSARs, the tool was found useful in identifying chemicals that demonstrated E- and T- related effects in vivo.We recommend using a consensus approach in final judgement about a compound’s toxicity that is to combine QSAR derived data to reach a consensus prediction. That is particularly useful for models based on data of slightly different molecular events or species.QSAR models need to have well-defined applicability domains (AD) to ensure their reliability, which can be reached by e.g. the conformal prediction (CP) method. By providing confidence metrics CP allows a better control over predictive boundaries of QSAR models than other distance-based AD methods.Pharmaceuticals can interact with sewage sludge by different intermolecular forces for which also the ionisation state has an impact. Developed models showed that sorption of neutral and positively-charged pharmaceuticals was mainly hydrophobicity-driven but also impacted by Pi-Pi and dipole-dipole forces. In contrast, negatively-charged molecules predominantly interacted via covalent bonding and ion-ion, ion-dipole, and dipole-dipole forces.Using ionised structures in multivariate modelling of sorption to sludge did not improve the model performance for positively- and negatively charged species but we noted an improvement for neutral chemicals that may be due to a more correct description of zwitterions. Overall, the results provided insights on the current weaknesses and strengths of QSAR approaches in hazard and exposure assessment of chemicals. QSARs have a great potential to serve as commonly used tools in hazard identification to predict various responses demanded in chemical safety assessment. In combination with other tools they can provide fundaments for integrated testing strategies that gather and generate information about compound’s toxicity and provide insights of its potential hazard. The obtained results also show that QSARs can be utilized for pattern recognition that facilitates a better understanding of phenomena related to fate of chemicals in WWTP.
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| 4. |
- Rybacka, Aleksandra, et al.
(författare)
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Considering ionic state in modelling sorption of pharmaceuticals to sewage sludge
- 2016
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Ingår i: Chemosphere. - : Elsevier. - 0045-6535 .- 1879-1298. ; 165, s. 284-293
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Tidskriftsartikel (refereegranskat)abstract
- Partitioning of chemicals between particular matter and water in sewage treatment plants provide essential information on fate of chemicals and is particularly challenging for pharmaceuticals that frequently are present in ionized form. The aim of this study was to investigate how ionization state affects partitioning to sludge of active pharmaceutical ingredients (APIs). In addition, we investigated the use of chemical descriptors based on ionized structures to improve our understanding of the underlying mechanisms of sludge sorption and for use in quantitative structure-property relationship (QSPR) models. We collected KD values for 110 APIs, which were classified as neutral, positive, or negative at pH 7. The models with the highest performance exceeded 0.75 R2Y and 0.65 Q2. We found that neutral and positively charged APIs share dominant intermolecular forces with sludge, i.e., hydrophobic, Pi-Pi and dipole-dipole interactions. In contrast, hydrophobicity driven interactions for negatively charged APIs was of little importance and sorption was mainly driven by covalent bonding, and ion-ion, ion-dipole, and dipole-dipole interactions. The performance of the models increased by 5-10% by adding charge-related descriptors, implying importance of electrostatic interactions. Using descriptors calculated for ionized structures did not improve model statistics for positive and negative APIs, however, the model statistics of the neutral APIs increased. We believe that this increase resulted from a better description of neutral zwitterions present in the dataset.
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