enviRule: an end-to-end system for automatic extraction of reaction patterns from environmental contaminant biotransformation pathways

Transformation products (TPs) of man-made chemicals, formed through microbially mediated transformation in the environment, can have serious adverse environmental effects, yet the analytical identification of TPs is challenging. Rule-based prediction tools are successful in predicting TPs, especially in environmental chemistry applications that typically have to rely on small datasets, by imparting the existing knowledge on enzyme-mediated biotransformation reactions. However, the rules extracted from biotransformation reaction databases usually face the issue of being over/under-generalized and are not flexible to be updated with new reactions. We developed an automatic rule extraction tool called enviRule. It clusters biotransformation reactions into different groups based on the similarities of reaction fingerprints, and then automatically extracts and generalizes rules for each reaction group in SMARTS format. It optimizes the genericity of automatic rules against the downstream TP prediction task. Models trained with automatic rules outperformed the models trained with manually curated rules by 30% in the area under curve (AUC) scores. Moreover, automatic rules can be easily updated with new reactions, highlighting enviRule’s strengths for both automatic extraction of optimized reactions rules and automated updating thereof

Towards more Sustainable Peptide- based Antibiotics: Stable in Human Blood, Enzymatically Hydrolyzed in Wastewater?

The emergence and spread of antibiotic resistance is a major societal challenge and new antibiotics are needed to successfully fight bacterial infections. Because the release of antibiotics into wastewater and downstream environments is expected to contribute to the problem of antibiotic resistance, it would be beneficial to consider the environmental fate of antibiotics in the development of novel antibiotics. In this article, we discuss the possibility of designing peptide-based antibiotics that are stable during treatment (e.g. in human blood), but rapidly inactivated through hydrolysis by peptidases after their secretion into wastewater. In the first part, we review studies on the biotransformation of peptide-based antibiotics during biological wastewater treatment and on the specificity of dissolved extracellular peptidases derived from wastewater. In the second part, we present first results of our endeavour to identify peptide bonds that are stable in human blood plasma and susceptible to hydrolysis by the industrially produced peptidase Subtilisin A

Early Assessment of Biodegradability of Small Molecules to Support the Chemical Design in Agro & Pharma R&D

The use of agrochemical and pharmaceutical active ingredients is essential in our modern society. Given the increased concern and awareness of the potential risks of some chemicals, there is a growing need to align with ‘green chemistry’ and ‘safe and sustainable by design’ principles and thus to evaluate the hazards of agrochemical and pharmaceutical active ingredients in early stages of R&D. We give an overview of the current challenges and opportunities to assess the principle of biodegradability in the environment. Development of new medium/high-throughput methodologies, combining predictive tools and wet-lab experimentation are essential to design biodegradable chemicals early in the active ingredient discovery and selection process

Can AI Help Improve Water Quality? Towards the Prediction of Degradation of Micropollutants in Wastewater

Micropollutants have become a serious environmental problem by threatening ecosystems and the quality of drinking water. This account investigates if advanced AI can be used to find solutions for this problem. We review background, the challenges involved, and the current state-of-the-art of quantitative structure-biodegradation relationships (QSBR). We report on recent progress combining experiment, quantum chemistry (QC) and chemoinformatics, and provide a perspective on potential future uses of AI technology to help improve water quality

Can AI Help Improve Water Quality? Towards the Prediction of Degradation of Micropollutants in Wastewater

Micropollutants have become a serious environmental problem by threatening ecosystems and the quality of drinking water. This account investigates if advanced AI can be used to find solutions for this problem. We review background, the challenges involved, and the current state-of-the-art of quantitative structure-biodegradation relationships (QSBR). We report on recent progress combining experiment, quantum chemistry (QC) and chemoinformatics, and provide a perspective on potential future uses of AI technology to help improve water quality

From market to environment – consumption-normalised pharmaceutical emissions in the Rhine catchment

Direct and indirect threats by organic micropollutants can only be reliably assessed and prevented if the exposure to these chemicals is known, which in turn requires a confident estimate of their emitted amounts into the environment. APIs (Active Pharmaceutical Ingredients) enter surface waters mostly through the sewer system and wastewater treatment plants (WWTPs). However, their effluent fluxes are highly variable and influenced by several different factors that challenge robust emission estimates. Here, we defined a dimensionless, theoretically consumption-independent ‘escape factor’ (kesc) for estimating the amount of APIs (expected to be) present in WWTP effluents. The factor is determined as the proportion of marketed and actually emitted amounts of APIs. A large collection of German and Swiss monitoring datasets were analyzed to calculate stochastic kesc values for 31 APIs, reflecting both the magnitude and uncertainty of consumption-normalised emissions. Escape factors provide an easy-to-use tool for the estimation of average API emissions and expected variability from numerous WWTPs given that consumption data are provided, thereby supporting simulation modeling of the fate of APIs in stream networks or exposure assessments

Large-scale assessment of organic contaminant emissions from chemical and pharmaceutical manufacturing into Swiss surface waters

This study presents a nation-wide assessment of the influence of chemical and pharmaceutical manufacturing (CPM) wastewaters on synthetic organic contaminant (SOC) emissions to Swiss surface waters. Geographic Information System (GIS) based analysis of the presence of CPM in wastewater treatment plant (WWTP) catchments revealed wide distribution of this industrial sector across Switzerland, suggesting that one-third of the 718 Swiss WWTPs may be influenced by CPM wastewaters. To reflect the diversity of this type of wastewaters, we investigated the effluents of 11 WWTPs of diverse sizes and technologies, which treated 0-100% wastewater from a variety of CPM activities. In an extensive sampling campaign, we collected temporally high resolved (i.e., daily) samples for 2-3 months to capture the dynamics of CPM discharges. The > 850 samples were then measured with liquid chromatography high-resolution mass spectrometry (LC-HRMS). Non-target characterization of the LC-HRMS time series datasets revealed that CPM wastewaters left a highly variable and site-specific signature in the effluents of the WWTPs. Particularly, compared to WWTPs with purely domestic input, a larger variety of substances (up to 15 times more compounds) with higher maximum concentrations (1-2 orders of magnitude) and more uncommon substances were found in CPM-influenced effluents. Moreover, in the latter, highly fluctuating discharges often contributed to a substantial fraction of the overall emissions. The largely varying characteristics of CPM discharges between different facilities were primarily related to the type of activities at the industries (i.e., production versus processing of chemicals) as well as to the pre-treatment and storage of CPM wastewaters. Eventually, for one WWTP, LC-HRMS time series were correlated with ecotoxicity time series obtained from bioassays and major toxic components could be identified. Overall, in view of their potential relevance to water quality, a strong focus on SOC discharges from CPM is essential, including the design of situation-specific monitoring, as well as risk assessment and mitigation strategies that consider the variability of industrial emissions. Keywords: Chemical and pharmaceutical industry; High-resolution mass spectrometry; Industrial wastewater; Micropollutants; Non-target analysis; Temporal data

Increasing the Environmental Relevance of Biodegradation Testing by Focusing on Initial Biodegradation Kinetics and Employing Low-Level Spiking

The environmental relevance of standard biodegradation tests such as OECD 309 has been questioned. Challenges include the interpretation of changing degradation kinetics over the 60–90 incubation days and the effects of chemical spiking on the microbial community. To ameliorate these weaknesses, we evaluated a modified OECD 309 test using water and sediment from three Swedish rivers. For each river, we had three treatments (no spiking, 0.5 μg L–1 spiking, and 5 μg L–1 spiking). The dissipation of a mixture of 56–80 spiked chemicals was followed over 14 days. Changes in dissipation kinetics during the incubation were interpreted as a departure of the microbial community from its initial (natural) state. The biodegradation kinetics were first-order throughout the incubation in the no spiking and 0.5 μg L–1 spiking treatments for almost all chemicals, but for the 5 μg L–1 treatment, more chemicals showed changes in kinetics. The rate constants in the no spiking and 0.5 μg L–1 treatments agreed within a factor of 2 for 35 of 37 cases. We conclude that the environmental relevance of OECD 309 is improved by considering only the initial biodegradation phase and that it is not compromised by spiking multiple chemicals at 0.5 μg L–1. KEYWORDS: biodegradation river water sediment micropollutants OECD 30

Temperature, phytoplankton density and bacteria diversity drive the biotransformation of micropollutants in a lake ecosystem

For most micropollutants (MPs) present in surface waters, such as pesticides and pharmaceuticals, the contribution of biotransformation to their overall removal from lake ecosystems is largely unknown. This study aims at empirically determining the biotransformation rate constants for 35 MPs at different periods of the year and depths of a meso-eutrophic lake. We then tested statistically the association of environmental parameters and microbial community composition with the biotransformation rate constants obtained. Biotransformation was observed for 14 out of 35 studied MPs for at least one sampling time. Large variations in biotransformation rate constants were observed over the seasons and between compounds. Overall, the transformation of MPs was mostly influenced by the lake's temperature, phytoplankton density and bacterial diversity. However, some individual MPs were not following the general trend or association with microorganism biomass. The antidepressant mianserin, for instance, was transformed in all experiments and depths, but did not show any relationship with measured environmental parameters, suggesting the importance of specific microorganisms in its transformation. The results presented here contribute to our understanding of the fate of MPs in surface waters and thus support improved risk assessment of contaminants in the environment. Keywords: Biotransformation; Microbial community; Pesticides; Pharmaceuticals; Phytoplankton; Random fores