| 1. |
- Dürig, Wiebke, et al.
(författare)
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Development of a suspect screening prioritization tool for organic compounds in water and biota
- 2019
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Ingår i: Chemosphere. - : Elsevier. - 0045-6535 .- 1879-1298. ; 222, s. 904-912
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Tidskriftsartikel (refereegranskat)abstract
- A customizable in silico tool (SusTool) for generating high resolution mass spectrometry (HRMS) suspect screening lists, specifically designed for the detection of hazardous organic compounds in various environmental compartments, was created. A database consisting of similar to 32 000 environmentally relevant organic compounds was constructed, including data on their physicochemical properties, environmental fate characteristics, and endocrine disruption potential, along with emissions and quantity indices. Welldefined customized suspect lists were generated by systematic ranking using a scoring and weighting procedure. For demonstration purposes, three suspect screening lists were created, one for water (SLWater) and two for biota covering less (SLBiota Kow<5 ) or more hydrophobic chemicals (SLBiota Kow>3). Scrutiny of overlaps between compounds within these lists and the SusDat database (20 suspect lists comprising similar to 58 000 compounds compiled by the Norman network) showed that approximately half of the compounds in the three suspect lists were also listed in one of the SusDat database lists. This indicates that SusTool is able to include highly relevant emerging pollutants, but also captures other compounds of potential concern that have been less well studied or not yet investigated. Overall, our in silico prioritization approach enables systematic creation of suspect screening lists and provides new opportunities for suspect screening for environmentally relevant compounds.
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| 2. |
- 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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| 3. |
- Molander, Linda, et al.
(författare)
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Are chemicals in articles an obstacle for reaching environmental goals? : Missing links in eu chemical management
- 2012
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Ingår i: Science of the Total Environment. - : Elsevier BV. - 0048-9697 .- 1879-1026. ; 435, s. 280-289
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Tidskriftsartikel (refereegranskat)abstract
- It is widely acknowledged that the management of risks associated with chemicals in articles needs to be improved. The EU environmental policy states that environmental damage should be rectified at source. It is therefore motivated that the risk management of substances in articles also takes particular consideration to those substances identified as posing a risk in different environmental compartments. The primary aim of the present study was to empirically analyze to what extent the regulation of chemicals in articles under REACH is coherent with the rules concerning chemicals in the Sewage Sludge Directive (SSD) and the Water Framework Directive (WFD). We also analyzed the chemical variation of the organic substances regulated under these legislations in relation to the most heavily used chemicals. The results show that 16 of 24 substances used in or potentially present in articles and regulated by the SSD or the WFD are also identified under REACH either as a substance of very high concern (SVHC) or subject to some restrictions. However, for these substances we conclude that there is limited coherence between the legislations, since the identification as an SVHC does not in itself encompass any use restrictions, and the restrictions in REACH are in many cases limited to a particular use, and thus all other uses are allowed. Only a minor part of chemicals in commerce is regulated and these show a chemical variation that deviates from classical legacy pollutants. This warrants new tools to identify potentially hazardous chemicals in articles. We also noted that chemicals monitored in the environment under the WFD deviate in their chemistry from the ones regulated by REACH. In summary, we argue that to obtain improved resource efficiency and a sustainable development it is necessary to minimize the input of chemicals identified as hazardous to health or the environment into articles.
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| 4. |
- 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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| 5. |
- 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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| 6. |
- 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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| 7. |
- Rybacka, Aleksandra, et al.
(författare)
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Identifying potential endocrine disruptors among industrial chemicals and their metabolites - development and evaluation of in silico tools
- 2015
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Ingår i: Chemosphere. - : Elsevier BV. - 0045-6535 .- 1879-1298. ; 139, s. 372-378
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Tidskriftsartikel (refereegranskat)abstract
- The aim of this study was to improve the identification of endocrine disrupting chemicals (EDCs) by developing and evaluating in silico tools that predict interactions at the estrogen (E) and androgen (A) receptors, and binding to transthyretin (T). In particular, the study focuses on evaluating the use of the EAT models in combination with a metabolism simulator to study the significance of bioactivation for endocrine disruption. Balanced accuracies of the EAT models ranged from 77-87%, 62-77%, and 65-89% for E-, A-, and T-binding respectively. The developed models were applied on a set of more than 6000 commonly used industrial chemicals of which 9% were predicted E- and/or A-binders and 1% were predicted T-binders. The numbers of E- and T-binders increased 2- and 3-fold, respectively, after metabolic transformation, while the number of A-binders marginally changed. In-depth validation confirmed that several of the predicted bioactivated E- or T-binders demonstrated in vivo estrogenic activity or influenced blood levels of thyroxine in vivo. The metabolite simulator was evaluated using in vivo data from the literature which showed a 50% accuracy for studied chemicals. The study stresses, in summary, the importance of including metabolic activation in prioritization activities of potentially emerging contaminants.
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| 8. |
- Rybacka, Aleksandra, et al.
(författare)
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On the Use of In Silico Tools for Prioritising Toxicity Testing of the Low-Volume Industrial Chemicals in REACH
- 2014
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Ingår i: Basic & Clinical Pharmacology & Toxicology. - : Wiley. - 1742-7835 .- 1742-7843. ; 115:1, s. 77-87
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Forskningsöversikt (refereegranskat)abstract
- This study was conducted to evaluate the utility of a selection of commercially and freely available non-testing tools and to analyse how REACH registrants can apply these as prioritisation tool for low-volume chemicals. The analysis was performed on a set of organic industrial chemicals and pesticides with extensive peer-reviewed risk assessment data. Analysed in silico model systems included Derek Nexus, Toxtree, QSAR Toolbox, LAZAR, TEST and VEGA, and results from these were compared with expert-judged risk classification according to the classifying, labelling and packaging (CLP) regulation. The most reliable results were obtained for carcinogenicity; however, less reliable predictions were derived for mutagenicity and reproductive toxicity. A group of compounds frequently predicted as false negatives was identified. These were relatively small molecules with low structural complexity, for example benzene derivatives with hydroxyl-, amino- or aniline-substituents. A rat liver S9 metabolite simulator was applied to illustrate the importance of considering metabolism in the risk assessment procedure. We also discuss outcome of combining predictions from multiple model systems and advise how to apply in silico tools. These models are proposed to be used to prioritise low-volume chemicals for testing within the REACH legislation, and we conclude that further guidance is needed so that industry can select and apply models in a reliable, systematic and transparent way.
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