The regulation and monitoring of pharmaceutical pollution in Europe lag behind that of more prominent groups. However, the repurposing of sales data to predict surface water environmental concentrations is a promising supplement to more commonly used market-based risk assessment and measurement approaches. The Norwegian Institute of Public Health (NIPH) has since the 1980s compiled the Drug Wholesale Statistics database - covering all sales of both human and veterinary pharmaceuticals to retailers, pharmacies, and healthcare providers. To date, most similar works have focused either on a small subset of Active Pharmaceutical Ingredients (APIs) or used only prescription data, often more readily available than wholesale data, but necessarily more limited. By using the NIPH’s product wholesale records, with additional information on API concentrations per product from, we have been able to calculate sales weights per year for almost 900 human and veterinary APIs for the period 2016–2019. In this paper, we present our methodology for converting the provided NIPH data from a public health to an ecotoxicological resource. From our derived dataset, we have used an equation to calculate Predicted Environmental Concentration per API for inland surface waters, a key component of environmental risk assessment. We further describe our filtering to remove ecotoxicological-exempt and data deficient APIs. Lastly, we provide a limited comparison between our dataset and similar publicly available datasets for a subset of APIs, as a validation of our approach and a demonstration of the added value of wholesale data. This dataset will provide the best coverage yet of pharmaceutical sales weights for an entire nation. Moreover, our developed routines for processing 2016–2019 data can be expanded to older Norwegian wholesales data (1974–present). Consequently, our work with this dataset can contribute to narrowing the gap between desk-based predictions of exposure from consumption, and empirical but expensive environmental measurement.
We have created a knowledge graph based on major data sources used in ecotoxicological risk assessment. We have applied this knowledge graph to an important task in risk assessment, namely chemical effect prediction. We have evaluated nine knowledge graph embedding models from a selection of geometric, decomposition, and convolutional models on this prediction task. We show that using knowledge graph embeddings can increase the accuracy of effect prediction with neural networks. Furthermore, we have implemented a fine-tuning architecture which adapts the knowledge graph embeddings to the effect prediction task and leads to a better performance. Finally, we evaluate certain characteristics of the knowledge graph embedding models to shed light on the individual model performance.
Extrapolation of adverse biological (toxic) effects of chemicals is an important contribution to expand available hazard data in (eco)toxicology without the use of animals in laboratory experiments. In this work, we extrapolate effects based on a knowledge graph (KG) consisting of the most relevant effect data as domain-specific background knowledge. An effect prediction model, with and without background knowledge, was used to predict mean adverse biological effect concentration of chemicals as a prototypical type of stressors. The background knowledge improves the model prediction performance by up to 40\% in terms of $R^2$ (\ie coefficient of determination). We use the KG and KG embeddings to provide quantitative and qualitative insights into the predictions. These insights are expected to improve the confidence in effect prediction. Larger scale implementation of such extrapolation models should be expected to support hazard and risk assessment, by simplifying and reducing testing needs.
Emamectin benzoate (EMB) is an antisea lice chemical widely used in the aquaculture that may also unintentionally affect nontarget crustaceans in the environment. Although the adverse effects of this compound are well documented in various species, the full modes of action (MoAs) are still not well characterized. The current study was therefore conducted to characterize the MoAs of EMB and link perturbations of key toxicological pathways to adverse effects in the model freshwater crustacean Daphnia magna. Effects on molting and survival were determined after 48 h exposure to EMB, whereas global transcriptional changes and the ecdysone receptor (EcR) binding potency was determined to characterize the MoA. The results showed that the molting frequency and survival of D. magna decreased in a concentration-dependent manner, and the observed changes could not be attributed to direct interactions with the EcR. Major MoAs such as activation of glutamate-gated chloride channels and gamma-aminobutyric acid signaling, disruption of neuroendocrine regulation of molting, perturbation of energy homeostasis, suppression of DNA repair and induction of programmed cell death were observed by transcriptional analysis and successfully linked to the adverse effects. This study has demonstrated that acute exposure to intermediate and high pM levels of EMB may pose hazards to nontarget crustaceans in the aquatic environment.
The 6th Norwegian Environmental Toxicology Symposium (NETS, www.niva.no/nets2016) was organized in Oslo, October 18–20 (2016), by Knut Erik Tollefsen, at the Norwegian Institute for Water Research ...
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