The potential to use QSAR to populate ecotoxicity characterisation factors for simplified LCIA and chemical prioritisation

Abstract

Purpose Today's chemical society use and emit an enormous number of different, potentially ecotoxic, chemicals to the environment. The vast majority of substances do not have characterisation factors describing their ecotoxicity potential. A first stage, high throughput, screening tool is needed for prioritisation of which substances need further measures. Methods USEtox characterisation factors were calculated in this work based on data generated by quantitative structure-activity relationship (QSAR) models to expand substance coverage where characterisation factors were missing. Existing QSAR models for physico-chemical data and ecotoxicity were used, and to further fill data gaps, an algae QSAR model was developed. The existing USEtox characterisation factors were used as reference to evaluate the impact from the use of QSARs to generate input data to USEtox, with focus on ecotoxicity data. An inventory of chemicals that make up the Swedish societal stock of plastic additives, and their associated predicted emissions, was used as a case study to rank chemicals according to their ecotoxicity potential. Results and discussion For the 210 chemicals in the inventory, only 41 had characterisation factors in the USEtox database. With the use of QSAR generated substance data, an additional 89 characterisation factors could be calculated, substantially improving substance coverage in the ranking. The choice of QSAR model was shown to be important for the reliability of the results, but also with the best correlated model results, the discrepancies between characterisation factors based on estimated data and experimental data were very large. Conclusions The use of QSAR estimated data as basis for calculation of characterisation factors, and the further use of those factors for ranking based on ecotoxicity potential, was assessed as a feasible way to gather substance data for large datasets. However, further research and development of the guidance on how to make use of estimated data is needed to achieve improvement of the accuracy of the results.

Synthesis of 1-Substituted 1H-1,2,3,4-Tetrazoles Using Biosynthesized Ag/Sodium Borosilicate Nanocomposite

“…A wide range of chemical entities are continuously released into the environment from a variety of sources. These discharges and the ensuing pollution of the atmosphere and the potential damage to existing beings and humans cause risk to human health and the ecosystem . To effectively reduce this exposure by instigating measures or substitutions, it is obligatory to recognize the chemical releases of concern; , the main reason should be grounded on the probable undesirable impact of the chemicals rather than only the emitted amount.…”

“…Relative to other data collection methods, QSARs are expeditious and have the prospective to include diverse entities that are reliant on the model domain. Additionally, the appraised facts, for example, by QSARs, are expected to upsurge in significance as authorizations such as the European Chemicals Agency promote the reduced use of animal testing. , In this study, the QSAR method was explored to investigate the formation plausibility of the synthesized chemicals in Table and predict the effect of different functional groups attached to the main structure for their toxicity hazard and protein binding (Figure ).…”

“…65 The characteristics of a chemical structure are obtained by a set of chemical descriptors that are utilized to Additionally, the appraised facts, for example, by QSARs, are expected to upsurge in significance as authorizations such as the European Chemicals Agency promote the reduced use of animal testing. 60,66 In this study, the QSAR method was explored to investigate the formation plausibility of the synthesized chemicals in Table 2 and predict the effect of different functional groups attached to the main structure for their toxicity hazard and protein binding (Figure 15). Table 4 shows the detailed structural information for 1-phenyl-1H-tetrazole, which is the main scaffold for all synthesized compounds.…”

An (Eco)Toxicity Life Cycle Impact Assessment Framework for Per- And Polyfluoroalkyl Substances

“…To reduce uncertainty in the CFs, empirical data were given priority. The use of in silico methods to populate substance data parameters adds uncertainty to the CFs, and in the case of PFAAs this uncertainty may be high given that PFAAs are ionic, amphiphilic surfactants. In silico models available were listed (Section S6) to explore the possibility of their use in cases where experimental data were missing.…”

Predicting Chemical End-of-Life Scenarios Using Structure-Based Classification Models

“…Song et al 36 developed an ML QSAR approach via artificial neural networks for performing a rapid life cycle impact assessment for chemicals. Holmquist et al 37 developed an ML QSAR for obtaining characterization factors for simplified life cycle impact assessment. Holmquist et al 37 developed a framework that can be used to rapidly assess perand polyfluoroalkyl substances.…”