Biology subjects

Svedberg, P.

Publications and source records attributed to Svedberg, P..

3 recordsLinked to original sources

bioRxiv2024

AI-aided chronic mixture risk assessment along a small European river reveals multiple sites at risk and pharmaceuticals being the main risk drivers

The vast amount of registered chemicals leads to a high diversity of substances occurring in the environment and the creation of new substances outpaces chemical risk assessment as well as monitoring strategies. Hence, risk assessment strategies need to be modified ensuring that they remain aligned with the rapid development and marketing of new substances. Here we performed a longitudinal chronic mixture risk assessment considering a real-world case study scenario with diverse anthropogenic impact types characterised by different land uses along a river in Central Germany. We sampled river water using large-volume solid phase extraction at six selected sampling sites. Following chemical analysis using liquid chromatography-high resolution mass spectrometry, we quantified 192 substances. For 34% of them, we obtained empirical chronic effect data for freshwater organisms. Furthermore, we used the open-source artificial intelligence (AI) model TRIDENT to predict chronic toxicity for all substances. A multi-scenario mixture risk assessment was conducted for three taxonomic groups, using the concentration-addition concept and considering various hazard and exposure scenarios. The results showed that the chronic risk estimates for all taxonomic groups were considerably higher when the empirical data was amended with data from in silico modelling. We identified hot spots of chemical pollution and our analysis indicated that fish were the most vulnerable taxonomic group, with pharmaceuticals being the most relevant risk drivers. Our study exemplifies the application of an AI model to predict chronic risk for aquatic organisms in combination with the consideration of multiple risk scenarios, that may complement future risk assessment strategies. HighlightsO_LI192 organic chemicals were quantified in six surface water samples along a river. C_LIO_LIMultiple hazard and exposure scenarios were considered in mixture risk assessment. C_LIO_LIArtificial intelligence was used to fill data gaps and predict chronic ecotoxicity. C_LIO_LIFish were identified as the most vulnerable taxonomic group for chronic toxicity. C_LIO_LIPharmaceuticals were the most prevalent mixture risk drivers. C_LI Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=81 SRC="FIGDIR/small/623722v1_ufig1.gif" ALT="Figure 1"> View larger version (32K): org.highwire.dtl.DTLVardef@1836b85org.highwire.dtl.DTLVardef@107eeaaorg.highwire.dtl.DTLVardef@1c60caaorg.highwire.dtl.DTLVardef@16995d0_HPS_FORMAT_FIGEXP M_FIG C_FIG

pharmacology and toxicology↗

bioRxiv2023

Defining the data gap: what do we know about environmental exposure, hazards and risks of pharmaceuticals in the European aquatic environment?

Active pharmaceutical ingredients (APIs) and their transformation products inevitably enter waterways where they might cause adverse effects to aquatic organisms. Identifying the potential risks of APIs in the environment is therefore a goal and current strategic direction of environmental management described in the EU Strategic Approach to Pharmaceuticals in the Environment and the Green Deal. This is challenged by a paucity of monitoring and ecotoxicity data to adequately describe risks. In this study we analyze measured environmental concentrations (MECs) of APIs from 5933 sites in 25 European countries as documented in the EMPODAT database or collected by the German Environment Agency for the time period between 1997 to 2020. These data were compared with empirical data on the ecotoxicity of APIs from the U.S. EPA ECOTOX database. Although 1763 uniquely identifiable APIs are registered with the European Medicines Agency (EMA) for sale in the European Economic Area (EEA), only 312 (17.7%) of these are included in publicly available monitoring data, and only 36 (1.8%) compounds have sufficient ecotoxicological data to perform an EMA-compliant ERA. Among the 27 compounds with sufficient exposure and hazard data to conduct a single substance risk assessment according to EMA guidelines, four compounds (14.8%) had a median risk quotient (RQ) > 1. Endocrine disruptors had the highest median RQ, with 7.0 and 5.6 for 17-ethinyl-estradiol and 17{beta}-estradiol respectively. A comparison of in-silico and empirical data for 72 APIs demonstrated the high protectiveness of the current EMA guidelines, with predicted environmental concentrations (PECs) exceeding median MECs in 98.6% of cases, with a 100-fold median increase. This study describes the data shortfalls hindering an accurate assessment of the risk posed to European waterways by APIs, and identifies 68 APIs for prioritized inclusion in monitoring programs, and 66 APIs requiring ecotoxicity testing to fill current data gaps. HighlightsO_LI1763 medicines are EMA-approved for sale in the EEA C_LIO_LIThe data gap is 1201 APIs (68%) that have no ecotoxicity or public monitoring data C_LIO_LIOnly 27 APIs (1.5%) have sufficient empirical data for risk assessment. C_LIO_LIERA using 23 years of EU monitoring data shows four compounds with a median RQ > 1 C_LIO_LIData gap APIs prioritized for monitoring programs (68) and ecotoxicity testing (66) C_LI

pharmacology and toxicology↗

bioRxiv2023

Transformers enable accurate prediction of acute and chronic chemical toxicity in aquatic organisms

Environmental safety assessments, as mandated by many regulations, require that toxicity data is generated for up to three trophic levels, algae, aquatic invertebrates, and fish. Conducting these tests in vivo is resource-intensive, time-consuming, and causes undue suffering. Computational methods are fast and cost-efficient alternatives, however, their adaptation in regulatory settings has been slow, both due to low accuracy and narrow applicability domains. Here we present a new method for predicting chemical toxicity based on molecular structure. The method is based on a transformer, capturing structural features associated with toxicity, followed by a deep neural network that predicts the corresponding effect concentrations. After training on data from tens of thousands of exposure experiments, the model shows high predictive performance for each of the three trophic levels. Compared to commonly used QSAR methods, the model has both a larger applicability domain and a considerably lower error. In addition, training the model on data that combines multiple types of effect concentrations further improves the performance. We conclude that transformer-based models have the potential to significantly advance computational predictions of chemical toxicity and make in silico approaches a more attractive alternative when compared to animal-based exposure experiments.

pharmacology and toxicology↗