Should Transformation Products Change the Way We Manage Chemicals?

When chemical pollutants enter the environment, they can undergo diverse transformation processes, forming a wide range of transformation products (TPs), some of them benign and others more harmful than their precursors. To date, the majority of TPs remain largely unrecognized and unregulated, particularly as TPs are generally not part of routine chemical risk or hazard assessment. Since many TPs formed from oxidative processes are more polar than their precursors, they may be especially relevant in the context of persistent, mobile, and toxic (PMT) and very persistent and very mobile (vPvM) substances, which are two new hazard classes that have recently been established on a European level. We highlight herein that as a result, TPs deserve more attention in research, chemicals regulation, and chemicals management. This perspective summarizes the main challenges preventing a better integration of TPs in these areas: (1) the lack of reliable high-throughput TP identification methods, (2) uncertainties in TP prediction, (3) inadequately considered TP formation during (advanced) water treatment, and (4) insufficient integration and harmonization of TPs in most regulatory frameworks. A way forward to tackle these challenges and integrate TPs into chemical management is proposed.

Combining predictive and analytical methods to elucidate pharmaceutical biotransformation in activated sludge

While man-made chemicals in the environment are ubiquitous and a potential threat to human health and ecosystem integrity, the environmental fate of chemical contaminants such as pharmaceuticals is often poorly understood. Biodegradation processes driven by microbial communities convert chemicals into transformation products (TPs) that may themselves have adverse ecological effects. The detection of TPs formed during biodegradation has been continuously improved thanks to the development of TP prediction algorithms and analytical workflows. Here, we contribute to this advance by (i) reviewing past applications of TP identification workflows, (ii) applying an updated workflow for TP prediction to 42 pharmaceuticals in biodegradation experiments with activated sludge, and (iii) benchmarking 5 different pathway prediction models, comprising 4 prediction models trained on different datasets provided by enviPath, and the state-of-the-art EAWAG pathway prediction system. Using the updated workflow, we could tentatively identify 79 transformation products for 31 pharmaceutical compounds. Compared to previous works, we have further automatized several steps that were previously performed by hand. By benchmarking the enviPath prediction system on experimental data, we demonstrate the usefulness of the pathway prediction tool to generate suspect lists for screening, and we propose new avenues to improve their accuracy. Moreover, we provide a well-documented workflow that can be (i) readily applied to detect transformation products in activated sludge and (ii) potentially extended to other environmental studies.

Combatting over-specialization bias in growing chemical databases

BACKGROUND

Predicting in advance the behavior of new chemical compounds can support the design process of new products by directing the research toward the most promising candidates and ruling out others. Such predictive models can be data-driven using Machine Learning or based on researchers' experience and depend on the collection of past results. In either case: models (or researchers) can only make reliable assumptions about compounds that are similar to what they have seen before. Therefore, consequent usage of these predictive models shapes the dataset and causes a continuous specialization shrinking the applicability domain of all trained models on this dataset in the future, and increasingly harming model-based exploration of the space.

PROPOSED SOLUTION

In this paper, we propose CANCELS (CounterActiNg Compound spEciaLization biaS), a technique that helps to break the dataset specialization spiral. Aiming for a smooth distribution of the compounds in the dataset, we identify areas in the space that fall short and suggest additional experiments that help bridge the gap. Thereby, we generally improve the dataset quality in an entirely unsupervised manner and create awareness of potential flaws in the data. CANCELS does not aim to cover the entire compound space and hence retains a desirable degree of specialization to a specified research domain.

RESULTS

An extensive set of experiments on the use-case of biodegradation pathway prediction not only reveals that the bias spiral can indeed be observed but also that CANCELS produces meaningful results. Additionally, we demonstrate that mitigating the observed bias is crucial as it cannot only intervene with the continuous specialization process, but also significantly improves a predictor's performance while reducing the number of required experiments. Overall, we believe that CANCELS can support researchers in their experimentation process to not only better understand their data and potential flaws, but also to grow the dataset in a sustainable way. All code is available under github.com/KatDost/Cancels .

Petroleum Hydrocarbon Catabolic Pathways as Targets for Metabolic Engineering Strategies for Enhanced Bioremediation of Crude-Oil-Contaminated Environments

Anthropogenic activities and industrial effluents are the major sources of petroleum hydrocarbon contamination in different environments. Microbe-based remediation techniques are known to be effective, inexpensive, and environmentally safe. In this review, the metabolic-target-specific pathway engineering processes used for improving the bioremediation of hydrocarbon-contaminated environments have been described. The microbiomes are characterised using environmental genomics approaches that can provide a means to determine the unique structural, functional, and metabolic pathways used by the microbial community for the degradation of contaminants. The bacterial metabolism of aromatic hydrocarbons has been explained via peripheral pathways by the catabolic actions of enzymes, such as dehydrogenases, hydrolases, oxygenases, and isomerases. We proposed that by using microbiome engineering techniques, specific pathways in an environment can be detected and manipulated as targets. Using the combination of metabolic engineering with synthetic biology, systemic biology, and evolutionary engineering approaches, highly efficient microbial strains may be utilised to facilitate the target-dependent bioprocessing and degradation of petroleum hydrocarbons. Moreover, the use of CRISPR-cas and genetic engineering methods for editing metabolic genes and modifying degradation pathways leads to the selection of recombinants that have improved degradation abilities. The idea of growing metabolically engineered microbial communities, which play a crucial role in breaking down a range of pollutants, has also been explained. However, the limitations of the in-situ implementation of genetically modified organisms pose a challenge that needs to be addressed in future research.

Chemical reaction networks and opportunities for machine learning

Chemical reaction networks (CRNs), defined by sets of species and possible reactions between them, are widely used to interrogate chemical systems. To capture increasingly complex phenomena, CRNs can be leveraged alongside data-driven methods and machine learning (ML). In this Perspective, we assess the diverse strategies available for CRN construction and analysis in pursuit of a wide range of scientific goals, discuss ML techniques currently being applied to CRNs and outline future CRN-ML approaches, presenting scientific and technical challenges to overcome.

BioTransformer 3.0-a web server for accurately predicting metabolic transformation products

BioTransformer 3.0 (https://biotransformer.ca) is a freely available web server that supports accurate, rapid and comprehensive in silico metabolism prediction. It combines machine learning approaches with a rule-based system to predict small-molecule metabolism in human tissues, the human gut as well as the external environment (soil and water microbiota). Simply stated, BioTransformer takes a molecular structure as input (SMILES or SDF) and outputs an interactively sortable table of the predicted metabolites or transformation products (SMILES, PNG images) along with the enzymes that are predicted to be responsible for those reactions and richly annotated downloadable files (CSV and JSON). The entire process typically takes less than a minute. Previous versions of BioTransformer focused exclusively on predicting the metabolism of xenobiotics (such as plant natural products, drugs, cosmetics and other synthetic compounds) using a limited number of pre-defined steps and somewhat limited rule-based methods. BioTransformer 3.0 uses much more sophisticated methods and incorporates new databases, new constraints and new prediction modules to not only more accurately predict the metabolic transformation products of exogenous xenobiotics but also the transformation products of endogenous metabolites, such as amino acids, peptides, carbohydrates, organic acids, and lipids. BioTransformer 3.0 can also support customized sequential combinations of these transformations along with multiple iterations to simulate multi-step human biotransformation events. Performance tests indicate that BioTransformer 3.0 is 40–50% more accurate, far less prone to combinatorial ‘explosions’ and much more comprehensive in terms of metabolite coverage/capabilities than previous versions of BioTransformer.

Microbial Technologies Employed for Biodegradation of Neonicotinoids in the Agroecosystem

Neonicotinoids are synthetic pesticides widely used for the control of various pests in agriculture throughout the world. They mainly attack the nicotinic acetylcholine receptors, generate nervous stimulation, receptor clot, paralysis and finally cause death. They are low volatile, highly soluble and have a long half-life in soil and water. Due to their extensive use, the environmental residues have immensely increased in the last two decades and caused many hazardous effects on non-target organisms, including humans. Hence, for the protection of the environment and diversity of living organism's the degradation of neonicotinoids has received widespread attention. Compared to the other methods, biological methods are considered cost-effective, eco-friendly and most efficient. In particular, the use of microbial species makes the degradation of xenobiotics more accessible fast and active due to their smaller size. Since this degradation also converts xenobiotics into less toxic substances, the various metabolic pathways for the microbial degradation of neonicotinoids have been systematically discussed. Additionally, different enzymes, genes, plasmids and proteins are also investigated here. At last, this review highlights the implementation of innovative tools, databases, multi-omics strategies and immobilization techniques of microbial cells to detect and degrade neonicotinoids in the environment.

Computational Approaches for the Prediction of Environmental Transformation Products: Chlorination of Steroidal Enones

There is growing interest in the fate and effects of transformation products generated from emerging pollutant classes, and new tools that help predict the products most likely to form will aid in risk assessment. Here, using a family of structurally related steroids (enones, dienones, and trienones), we evaluate the use of density functional theory to help predict products from reaction with chlorine, a common chemical disinfectant. For steroidal dienones (e.g., dienogest) and trienones (e.g., 17β-trenbolone), computational data support that reactions proceed through spontaneous C4 chlorination to yield 4-chloro derivatives for trienones and, after further reaction, 9,10-epoxide structures for dienones. For testosterone, a simple steroidal enone, in silico predictions suggest that C4 chlorination is still most likely, but slow at environmentally relevant conditions. Predictions were then assessed through laboratory chlorination reactions (0.5-5 mg Cl₂/L) with product characterization via HRMS and NMR, which confirmed near exclusive 4-chloro and 9,10-epoxide products for most trienones and all dienones, respectively. Also consistent with computational expectations, testosterone was effectively unreactive at these same chlorine levels, although products consistent with in silico predictions were observed at higher concentrations (in excess of 500 mg Cl₂/L). Although slight deviations from in silico predictions were observed for steroids with electron-rich substituents (e.g., C17 allyl-substituted altrenogest), this work highlights the potential for computational approaches to improve our understanding of transformation products generated from emerging pollutant classes.

Holistic evaluation of biodegradation pathway prediction: assessing multi-step reactions and intermediate products

The prediction of metabolism and biotransformation pathways of xenobiotics is a highly desired tool in environmental sciences, drug discovery, and (eco)toxicology. Several systems predict single transformation steps or complete pathways as series of parallel and subsequent steps. Their performance is commonly evaluated on the level of a single transformation step. Such an approach cannot account for some specific challenges that are caused by specific properties of biotransformation experiments. That is, missing transformation products in the reference data that occur only in low concentrations, e.g. transient intermediates or higher-generation metabolites. Furthermore, some rule-based prediction systems evaluate the performance only based on the defined set of transformation rules. Therefore, the performance of these models cannot be directly compared. In this paper, we introduce a new evaluation framework that extends the evaluation of biotransformation prediction from single transformations to whole pathways, taking into account multiple generations of metabolites. We introduce a procedure to address transient intermediates and propose a weighted scoring system that acknowledges the uncertainty of higher-generation metabolites. We implemented this framework in enviPath and demonstrate its strict performance metrics on predictions of in vitro biotransformation and degradation of xenobiotics in soil. Our approach is model-agnostic and can be transferred to other prediction systems. It is also capable of revealing knowledge gaps in terms of incompletely defined sets of transformation rules.

Trends in predictive biodegradation for sustainable mitigation of environmental pollutants: Recent progress and future outlook

The feasibility of in-silico techniques, together with the computational framework, has been applied to predictive bioremediation aiming to clean-up contaminants, toxicity evaluation, and possibilities for the degradation of complex recalcitrant compounds. Emerging contaminants from different industries have posed a significant hazard to the environment and public health. Given current bioremediation strategies, it is often a failure or inadequate for sustainable mitigation of hazardous pollutants. However, clear-cut vital information about biodegradation is quite incomplete from a conventional remediation techniques perspective. Lacking complete information on bio-transformed compounds leads to seeking alternative methods. Only scarce information about the transformed products and toxicity profile is available in the published literature. To fulfill this literature gap, various computational or in-silico technologies have emerged as alternating techniques, which are being recognized as in-silico approaches for bioremediation. Molecular docking, molecular dynamics simulation, and biodegradation pathways predictions are the vital part of predictive biodegradation, including the Quantitative Structure-Activity Relationship (QSAR), Quantitative structure-biodegradation relationship (QSBR) model system. Furthermore, machine learning (ML), artificial neural network (ANN), genetic algorithm (GA) based programs offer simultaneous biodegradation prediction along with toxicity and environmental fate prediction. Herein, we spotlight the feasibility of in-silico remediation approaches for various persistent, recalcitrant contaminants while traditional bioremediation fails to mitigate such pollutants. Such could be addressed by exploiting described model systems and algorithm-based programs. Furthermore, recent advances in QSAR modeling, algorithm, and dedicated biodegradation prediction system have been summarized with unique attributes.

Recent advances in computational metabolite structure predictions and altered metabolic pathways assessment to inform drug development processes

Many drug candidates fail during preclinical and clinical trials due to variable or unexpected metabolism which may lead to variability in drug efficacy or adverse drug reactions. The drug metabolism field aims to address this important issue from many angles which range from the study of drug-drug interactions, pharmacogenomics, computational metabolic modeling, and others. This manuscript aims to provide brief but comprehensive manuscript summaries highlighting the conclusions and scientific importance of seven exceptional manuscripts published in recent years within the field of drug metabolism. Two main topics within the field are reviewed: novel computational metabolic modeling approaches which provide complex outputs beyond site of metabolism predictions, and experimental approaches designed to discern the impacts of interindividual variability and species differences on drug metabolism. The computational approaches discussed provide novel outputs in metabolite structure and formation likelihood and/or extend beyond the saturated field of drug phase I metabolism, while the experimental metabolic pathways assessments aim to highlight the impacts of genetic polymorphisms and clinical animal model metabolic differences on human metabolism and subsequent health outcomes.

Prioritizing Direct Photolysis Products Predicted by the Chemical Transformation Simulator: Relative Reasoning and Absolute Ranking

The United States Environmental Protection Agency's Chemical Transformation Simulator (CTS) platform implemented the first freely available reaction library to predict direct photolysis products of organic contaminants in aquatic systems. However, the initial version of the reaction library did not differentiate the formation likelihood of each predicted product, and therefore, the number of predicted products that are not observed tended to exponentially increase with the prediction generation. To alleviate this problem, we first employed relative reasoning algorithms to remove unlikely products. We then ranked different reaction schemes according to their transformation kinetics and removed slowly forming products. Applying the two strategies improved the precision (the percentage of correctly predicted products over all predicted products) by 34% and 53% for the internal evaluation set and the external evaluation set, respectively, when products from three generations were considered. This improved library also revealed new research directions to improve predictions of the dominant phototransformation products.

Using computer tools for the evaluation of biodegradability, toxicity, and activity on the AT1 receptor of degradation products identified in the removal of valsartan by using photo-electro-Fenton process

This work deals with the theoretical approach of biodegradability, lipophilicity, and physiological activity of VAL and four degradation products (DPs) detected after 20 min of the photo-electro-Fenton (PEF) process. The biodegradability calculation, taking into account the change in the theoretical oxygen demand, showed that the four DPs had a more negative value than VAL, indicating that they are more susceptible to oxidation. However, these results do not imply more accessible biotransformation pathways than VAL, as observed using the EAWAG-BBD program, through which neutral biotransformation pathway prediction for VAL and DPs was made. Subsequently, by calculating the theoretical lipophilicity of the molecules (log P), the theoretical toxicity of the DPs was proposed, where the DPs had log P values between 1 and 3, lower values than those of VAL (log P = 4), indicating that DPs could be less toxic than the original compound (VAL). Both results suggest that VAL degradation (by photo-electro-Fenton process proposed) yields a positive effect on the environment. Finally, when molecular dynamic simulations were carried out, it was observed that DP1, DP2, and DP3 maintained similar interactions to those of VAL with the binding site of the AT1R. DP4 did not show any interaction. These results indicated that, despite the presence of DPs, generated after 20 min of the treatment, they could not exert a physiological activity in any organism the same way that does VAL.

The ever-expanding limits of enzyme catalysis and biodegradation: polyaromatic, polychlorinated, polyfluorinated, and polymeric compounds

Biodegradation is simply the metabolism of anthropogenic, or otherwise unwanted, chemicals in our environment, typically by microorganisms. The metabolism of compounds commonly found in living things is limited to several thousand metabolites whereas ∼100 million chemical substances have been devised by chemical synthesis, and ∼100 000 are used commercially. Since most of those compounds are not natively found in living things, and some are toxic or carcinogenic, the question arises as to whether there is some organism somewhere with the enzymes that can biodegrade them. Repeatedly, anthropogenic chemicals have been denoted ‘non-biodegradable,’ only to find they are reactive with one or more enzyme(s). Enzyme reactivity has been organized into categories of functional group transformations. The discovery of new functional group transformations has continually expanded our knowledge of enzymes and biodegradation. This expansion of new-chemical biodegradation is driven by the evolution and spread of newly evolved enzymes. This review describes the biodegradation of widespread commercial chemicals with a focus on four classes: polyaromatic, polychlorinated, polyfluorinated, and polymeric compounds. Polyaromatic hydrocarbons include some of the most carcinogenic compounds known. Polychlorinated compounds include polychlorinated biphenyls (PCBs) and many pesticides of the twentieth century. Polyfluorinated compounds are a major focus of bioremediation efforts today. Polymers are clogging landfills, killing aquatic species in the oceans and increasingly found in our bodies. All of these classes of compounds, each thought at one time to be non-biodegradable, have been shown to react with natural enzymes. The known limits of enzyme catalysis, and hence biodegradation, are continuing to expand.

Combining In Silico Tools with Multicriteria Analysis for Alternatives Assessment of Hazardous Chemicals: Accounting for the Transformation Products of decaBDE and Its Alternatives

Transformation products ought to be an important consideration in chemical alternatives assessment. In this study, a recently established hazard ranking tool for alternatives assessment based on in silico data and multicriteria decision analysis (MCDA) methods was further developed to include chemical transformation products. Decabromodiphenyl ether (decaBDE) and five proposed alternatives were selected as case chemicals; biotic and abiotic transformation reactions were considered using five in silico tools. A workflow was developed to select transformation products with the highest occurrence potential. The most probable transformation products of the alternative chemicals were often similarly persistent but more mobile in aquatic environments, which implies an increasing exposure potential. When persistence (P), bioaccumulation (B), mobility in the aquatic environment (M), and toxicity (T) are considered (via PBT, PMT, or PBMT composite scoring), all six flame retardants have at least one transformation product that can be considered more hazardous, across diverse MCDA. Even when considering transformation products, the considered alternatives remain less hazardous than decaBDE, though the range of hazard of the five alternatives was reduced. The least hazardous of the considered alternatives were melamine and bis(2-ethylhexyl)-tetrabromophthalate. This developed tool could be integrated within holistic alternatives assessments considering use and life cycle impacts or additionally prioritizing transformation products within (bio)monitoring screening studies.

Electrochemical Reduction of Azo Dyes Mimicking their Biotransformation to More Toxic Products

Introduction

Some azo dyes, including Sudans I–IV and Para Red, are genotoxic and may be biotransformed to cancerogenic aromatic amines. They are banned as food and feed additives, but their presence has been detected in food. Aromatic amines are also considered potentially toxic. Online EC–MS is a promising tool to study the transformation mechanisms of xenobiotics such as azo dyes. The aim of the study was to investigate emulation of how azo dyes are enzymatically transformed to amines with EC–MS.

Material and Methods

The reduction reactions of five azo dyes (Sudans I–IV and Para Red) were conducted using a glassy carbon working electrode and 0.1% formic acid in acetonitrile. Reduction results were compared with the literature and in silico to select preliminary candidates for metabolites. The LC-MS/MS method was used to confirm results obtained by electrochemical reactor.

Results

A limited number of pre-selected compounds were confirmed as azo dyes metabolites – aniline for Sudan I, aniline and 4-aminoazobenzene for Sudan III, o-toluidine for Sudan IV, and 4-nitroaniline for Para Red. No metabolites were found for Sudan II.

Conclusions

Electrochemistry–mass spectrometry was successfully applied to azo dyes. This approach may be used to mimic the metabolism of azo dyes, and therefore predict products of biotransformation.

BioTransformer: a comprehensive computational tool for small molecule metabolism prediction and metabolite identification

Background

A number of computational tools for metabolism prediction have been developed over the last 20 years to predict the structures of small molecules undergoing biological transformation or environmental degradation. These tools were largely developed to facilitate absorption, distribution, metabolism, excretion, and toxicity (ADMET) studies, although there is now a growing interest in using such tools to facilitate metabolomics and exposomics studies. However, their use and widespread adoption is still hampered by several factors, including their limited scope, breath of coverage, availability, and performance.

Results

To address these limitations, we have developed BioTransformer, a freely available software package for accurate, rapid, and comprehensive in silico metabolism prediction and compound identification. BioTransformer combines a machine learning approach with a knowledge-based approach to predict small molecule metabolism in human tissues (e.g. liver tissue), the human gut as well as the environment (soil and water microbiota), via its metabolism prediction tool. A comprehensive evaluation of BioTransformer showed that it was able to outperform two state-of-the-art commercially available tools (Meteor Nexus and ADMET Predictor), with precision and recall values up to 7 times better than those obtained for Meteor Nexus or ADMET Predictor on the same sets of pharmaceuticals, pesticides, phytochemicals or endobiotics under similar or identical constraints. Furthermore BioTransformer was able to reproduce 100% of the transformations and metabolites predicted by the EAWAG pathway prediction system. Using mass spectrometry data obtained from a rat experimental study with epicatechin supplementation, BioTransformer was also able to correctly identify 39 previously reported epicatechin metabolites via its metabolism identification tool, and suggest 28 potential metabolites, 17 of which matched nine monoisotopic masses for which no evidence of a previous report could be found.

Conclusion

BioTransformer can be used as an open access command-line tool, or a software library. It is freely available at https://bitbucket.org/djoumbou/biotransformerjar/. Moreover, it is also freely available as an open access RESTful application at www.biotransformer.ca, which allows users to manually or programmatically submit queries, and retrieve metabolism predictions or compound identification data.

Electronic supplementary material

The online version of this article (10.1186/s13321-018-0324-5) contains supplementary material, which is available to authorized users.

Tracing the Biotransformation of Polycyclic Aromatic Hydrocarbons in Contaminated Soil Using Stable Isotope-Assisted Metabolomics

Biotransformation of organic pollutants may result in the formation of oxidation products more toxic than the parent contaminants. However, to trace and identify those products, and the metabolic pathways involved in their formation, is still challenging within complex environmental samples. We applied stable isotope-assisted metabolomics (SIAM) to PAH-contaminated soil collected from a wood treatment facility. Soil samples were separately spiked with uniformly ¹³C-labeled fluoranthene, pyrene, or benzo[a]anthracene at a level below that of the native contaminant, and incubated for 1 or 2 weeks under aerobic biostimulated conditions. Combining high-resolution mass spectrometry and automated SIAM workflows, chemical structures of metabolites and metabolic pathways in the soil were proposed. Ring-cleavage products, including previously unreported intermediates such as C₁₁H₁₀O₆ and C₁₅H₁₂O₅, were detected originating from fluoranthene and benzo[a]anthracene, respectively. Sulfate conjugates of dihydroxy compounds were found as major metabolites of pyrene and benzo[a]anthracene, suggesting the potential role of fungi in their biotransformation in soils. A series of unknown N-containing metabolites were identified from pyrene, but their structural elucidation requires further investigation. Our results suggest that SIAM can be successfully applied to understand the fate of organic pollutants in environmental samples, opening lines of evidence for novel mechanisms of microbial transformation within such complex matrices.

Multidimensional monitoring of anaerobic/aerobic azo dye based wastewater treatments by hyphenated UPLC-ICP-MS/ESI-Q-TOF-MS techniques

Sulfonated reactive azo dyes, such as Reactive Orange 107, are extensively used in textile industries. Conventional wastewater treatment systems are incapable of degrading and decolorizing reactive azo dyes completely from effluents, because of their stability and resistance to aerobic biodegradation. However, reactive azo dyes are degradable under anaerobic conditions by releasing toxic aromatic amines. To clarify reaction mechanisms and the present toxicity, the hydrolyzed Reactive Orange 107 was treated in anaerobic-aerobic two-step batch experiments. Sulfonated transformation products were identified employing coupled ICP-MS and Q-TOF-MS measurements. Suspected screening lists were generated using the EAWAG-BBD. The toxicity of the reactor content was determined utilizing online measurements of the inhibition of Vibrio fischeri. The OCHEM web platform for environmental modeling was instrumental in the estimations of the environmental impact of generated transformation products.

Eawag-Soil in enviPath: a new resource for exploring regulatory pesticide soil biodegradation pathways and half-life data

Developing models for the prediction of microbial biotransformation pathways and half-lives of trace organic contaminants in different environments requires as training data easily accessible and sufficiently large collections of respective biotransformation data that are annotated with metadata on study conditions. Here, we present the Eawag-Soil package, a public database that has been developed to contain all freely accessible regulatory data on pesticide degradation in laboratory soil simulation studies for pesticides registered in the EU (282 degradation pathways, 1535 reactions, 1619 compounds and 4716 biotransformation half-life values with corresponding metadata on study conditions). We provide a thorough description of this novel data resource, and discuss important features of the pesticide soil degradation data that are relevant for model development. Most notably, the variability of half-life values for individual compounds is large and only about one order of magnitude lower than the entire range of median half-life values spanned by all compounds, demonstrating the need to consider study conditions in the development of more accurate models for biotransformation prediction. We further show how the data can be used to find missing rules relevant for predicting soil biotransformation pathways. From this analysis, eight examples of reaction types were presented that should trigger the formulation of new biotransformation rules, e.g., Ar-OH methylation, or the extension of existing rules, e.g., hydroxylation in aliphatic rings. The data were also used to exemplarily explore the dependence of half-lives of different amide pesticides on chemical class and experimental parameters. This analysis highlighted the value of considering initial transformation reactions for the development of meaningful quantitative-structure biotransformation relationships (QSBR), which is a novel opportunity offered by the simultaneous encoding of transformation reactions and corresponding half-lives in Eawag-Soil. Overall, Eawag-Soil provides an unprecedentedly rich collection of manually extracted and curated biotransformation data, which should be useful in a great variety of applications.

In Silico Identification of Bioremediation Potential: Carbamazepine and Other Recalcitrant Personal Care Products

Emerging contaminants are principally personal care products not readily removed by conventional wastewater treatment and, with an increasing reliance on water recycling, become disseminated in drinking water supplies. Carbamazepine, a widely used neuroactive pharmaceutical, increasingly escapes wastewater treatment and is found in potable water. In this study, a mechanism is proposed by which carbamazepine resists biodegradation, and a previously unknown microbial biodegradation was predicted computationally. The prediction identified biphenyl dioxygenase from Paraburkholderia xenovorans LB400 as the best candidate enzyme for metabolizing carbamazepine. The rate of degradation described here is 40 times greater than the best reported rates. The metabolites cis-10,11-dihydroxy-10,11-dihydrocarbamazepine and cis-2,3-dihydroxy-2,3-dihydrocarbamazepine were demonstrated with the native organism and a recombinant host. The metabolites are considered nonharmful and mitigate the generation of carcinogenic acridine products known to form when advanced oxidation methods are used in water treatment. Other recalcitrant personal care products were subjected to prediction by the Pathway Prediction System and tested experimentally with P. xenovorans LB400. It was shown to biodegrade structurally diverse compounds. Predictions indicated hydrolase or oxygenase enzymes catalyzed the initial reactions. This study highlights the potential for using the growing body of enzyme-structural and genomic information with computational methods to rapidly identify enzymes and microorganisms that biodegrade emerging contaminants.

Root Uptake of Pharmaceuticals and Personal Care Product Ingredients

Crops irrigated with reclaimed wastewater or grown in biosolids-amended soils may take up pharmaceuticals and personal care product ingredients (PPCPs) through their roots. The uptake pathways followed by PPCPs and the propensity for these compounds to bioaccumulate in food crops are still not well understood. In this critical review, we discuss processes expected to influence root uptake of PPCPs, evaluate current literature on uptake of PPCPs, assess models for predicting plant uptake of these compounds, and provide recommendations for future research, highlighting processes warranting study that hold promise for improving mechanistic understanding of plant uptake of PPCPs. We find that many processes that are expected to influence PPCP uptake and accumulation have received little study, particularly rhizosphere interactions, in planta transformations, and physicochemical properties beyond lipophilicity (as measured by Kow). Data gaps and discrepancies in methodology and reporting have so far hindered development of models that accurately predict plant uptake of PPCPs. Topics warranting investigation in future research include the influence of rhizosphere processes on uptake, determining mechanisms of uptake and accumulation, in planta transformations, the effects of PPCPs on plants, and the development of predictive models.

enviPath--The environmental contaminant biotransformation pathway resource

The University of Minnesota Biocatalysis/Biodegradation Database and Pathway Prediction System (UM-BBD/PPS) has been a unique resource covering microbial biotransformation pathways of primarily xenobiotic chemicals for over 15 years. This paper introduces the successor system, enviPath (The Environmental Contaminant Biotransformation Pathway Resource), which is a complete redesign and reimplementation of UM-BBD/PPS. enviPath uses the database from the UM-BBD/PPS as a basis, extends the use of this database, and allows users to include their own data to support multiple use cases. Relative reasoning is supported for the refinement of predictions and to allow its extensions in terms of previously published, but not implemented machine learning models. User access is simplified by providing a REST API that simplifies the inclusion of enviPath into existing workflows. An RDF database is used to enable simple integration with other databases. enviPath is publicly available at https://envipath.org with free and open access to its core data.

Bibliometric analysis of phytotechnologies for remediation: global scenario of research and applications

Phytoremediation is often a low cost alternative to conventional remediation. To assess trends, a bibliometric approach using data from SciVerse Scopus, SciVerseHub, and GoogleTM Trends was used. Globally there is a linear increase in publications containing the word phytoremediation as a percent of all published papers in SciVerse Hub, with China, India, and the Philippines concentrating relatively more research in phytoremediation. Furthermore there was an inverse correlation between a country's Human Development Index (HDI) and a country's phytoremediation research output as a percent of total research. Results show a focus on phytoremediation in countries with low HDI values. This suggests that academic experts are available for advancing phytoremediation applications in countries where the majority of the effected population do not have the education, finances, and political leverage to obtain expensive conventional remediation efforts on their land. Phytoremediation can combine expert advice with affected parties commitment and labor to help mitigate the harms of polluted landscapes.

An algorithm for rapid computational construction of metabolic networks: a cholesterol biosynthesis example

Alternative pathways of metabolic networks represent the escape routes that can reduce drug efficacy and can cause severe adverse effects. In this paper we introduce a mathematical algorithm and a coding system for rapid computational construction of metabolic networks. The initial data for the algorithm are the source substrate code and the enzyme/metabolite interaction tables. The major strength of the algorithm is the adaptive coding system of the enzyme-substrate interactions. A reverse application of the algorithm is also possible, when optimisation algorithm is used to compute the enzyme/metabolite rules from the reference network structure. The coding system is user-defined and must be adapted to the studied problem. The algorithm is most effective for computation of networks that consist of metabolites with similar molecular structures. The computation of the cholesterol biosynthesis metabolic network suggests that 89 intermediates can theoretically be formed between lanosterol and cholesterol, only 20 are presently considered as cholesterol intermediates. Alternative metabolites may represent links with other metabolic networks both as precursors and metabolites of cholesterol. A possible cholesterol-by-pass pathway to bile acids metabolism through cholestanol is suggested.

Simulation of chemical metabolism for fate and hazard assessment. I: approach for simulating metabolism

Information regarding the metabolism of xenobiotic chemicals plays a central role in regulatory risk assessments. In regulatory programmes where metabolism studies are required, the studies of metabolic pathways are often incomplete and the identification of activated metabolites and important degradation products are limited by analytical methods. Because so many more new chemicals are being produced than can be assessed for potential hazards, setting assessment priorities among the thousands of untested chemicals requires methods for predictive hazard identification which can be derived directly from chemical structure and their likely metabolites. In a series of papers we are sharing our experience in the computerized management of metabolic data and the development of simulators of metabolism for predicting the environmental fate and (eco)toxicity of chemicals. The first paper of the series presents a knowledge-based formalism for the computer simulation of non-intermediary metabolism for untested chemicals, with an emphasis on qualitative and quantitative aspects of modelling metabolism.

Biology of fluoro-organic compounds

Investigations on diverse aspects of fluoro-organic compounds have rapidly increased during the past decades. Because natural sources of fluoro-organic compounds are extremely rare, the industrial synthesis of fluorinated organic compounds and production of fluorinated natural product derivatives have greatly expanded in recent years because of their increasing importance in the agrochemical and pharmaceutical industries. Due to structural complexity or instability, synthetic modification is often not possible, and various biofluorination strategies have been developed in recent years for applications in the anti-cancer, anti-viral and anti-infection fields. Despite the industrial importance of fluorinated compounds, there have been serious concerns worldwide over the levels and synthetic routes of certain fluorinated organic compounds, in particular perfluorinated chemicals (PFCs). PFCs are emerging and recalcitrant pollutants which are widely distributed in the environment and have been detected in humans and wildlife globally. PFCs have been demonstrated to be potentially carcinogenic, adversely affect the neuroendocrine and immune systems, and produce neurotoxicity, heptatotoxicity and endocrine disrupting effects in vertebrate animals. Here, we provide an overview of recent advances in our understanding of the biology of various fluoro-organic compounds and perspectives for new enzymes and metabolic pathways for bioremediation of these chemicals.