Other Biodegradation Related Publications
In this section, we are happy to share with you a number of other biodegradation related publications that you may find helpful. They are not affiliated with Aropha.
Trends in predictive biodegradation for sustainable mitigation of environmental pollutants: Recent progress and future outlook
Publication
Singh, A. K.; Bilal, M.; Iqbal, H. M. N.; Raj, A., Trends in predictive biodegradation for sustainable mitigation of environmental pollutants: Recent progress and future outlook. Sci. Total Environ. 2021, 770, 144561. https://doi.org/10.1016/j.scitotenv.2020.144561
Abstract
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.
Source: https://doi.org/10.1016/j.scitotenv.2020.144561
Experimental and in silico assessment of fate and effects of the UV filter 2-phenylbenzimidazole 5-sulfonic acid and its phototransformation products in aquatic solutions
Publication
Westphal, J.; Kummerer, K.; Olsson, O., Experimental and in silico assessment of fate and effects of the UV filter 2-phenylbenzimidazole 5-sulfonic acid and its phototransformation products in aquatic solutions. Water Res. 2020, 171, 115393. https://doi.org/10.1016/j.watres.2019.115393
Abstract
Often ingredients of personal care products are present in treated wastewaters, e. g grey water (GW), and are discharged into aquatic systems. Conventional treatment of GW does not fully eliminate micropollutants such as the UV filter substance 2-phenylbenzimidazole-5-sulfonic acid (PBSA). Photolysis has been proposed as an alternative treatment method for other micropollutants, but it is not clear yet whether it can also be used to eliminate PBSA. One goal of this study was to better understand the basic pathways involved in this process. It aimed to identify photo-transformation products (PTPs) by using, in the test conditions, an initial concentration of PBSA higher than those expected in the environment. The photolysis experiments were carried out using Xenon and UV lamps. Under Xenon irradiation only slight primary elimination was found. UV irradiation resulted in almost complete primary elimination of PBSA but not in full mineralization. Four isomeric mono-hydroxylated PTPs were identified by high resolution mass spectrometry (HRMS) which could be confirmed by other studies. A modified luminescent bacteria test (LBT) with Vibrio fischeri was employed to assess acute and chronic toxic effects of the irradiated photolytic mixtures. A strong correlation was found between the kinetics of two of the PTPs and luminescence inhibition indicating bacterial toxicity. Using a set of in silico quantitative structure-activity relationship (QSAR) models, this study also offered new insights concerning the environmental fate and toxicity of the TPs of PBSA as the TPs generated by UV-treatment are more persistent and partly more toxic than PBSA.
Source: https://doi.org/10.1016/j.watres.2019.115393
Modelling of ready biodegradability based on combined public and industrial data sources
Publication
Lunghini, F.; Marcou, G.; Gantzer, P.; Azam, P.; Horvath, D.; Van Miert, E.; Varnek, A., Modelling of ready biodegradability based on combined public and industrial data sources. SAR QSAR Environ. Res. 2020, 31, (3), 171-186. https://doi.org/10.1080/1062936X.2019.1697360
Abstract
The European Registration, Evaluation, Authorization and Restriction of Chemical Substances Regulation, requires marketed chemicals to be evaluated for Ready Biodegradability (RB), considering in silico prediction as valid alternative to experimental testing. However, currently available models may not be relevant to predict compounds of industrial interest, due to accuracy and applicability domain restriction issues. In this work, we present a new and extended RB dataset (2830 compounds), issued by the merging of several public data sources. It was used to train classification models, which were externally validated and benchmarked against already-existing tools on a set of 316 compounds coming from the industrial context. New models showed good performances in terms of predictive power (Balance Accuracy (BA) = 0.74–0.79) and data coverage (83–91%). The Generative Topographic Mapping approach identified several chemotypes and structural motifs unique to the industrial dataset, highlighting for which chemical classes currently available models may have less reliable predictions. Finally, public and industrial data were merged into global dataset containing 3146 compounds. This is the biggest dataset reported in the literature so far, covering some chemotypes absent in the public data. Thus, predictive model developed on the Global dataset has larger applicability domain than the existing ones.
Implications of microbial adaptation for the assessment of environmental persistence of chemicals
Publication
Poursat, B. A. J.; van Spanning, R. J. M.; de Voogt, P.; Parsons, J. R., Implications of microbial adaptation for the assessment of environmental persistence of chemicals. Crit. Rev. Environ. Sci. Technol. 2019, 49, (23), 2220-2255. https://doi.org/10.1080/10643389.2019.1607687
Abstract
Persistency of organic chemicals is a key property in their environmental risk assessment. Information on persistency is often derived from the results of biodegradability screening tests such as the ready biodegradability tests (RBTs). RBTs are, however, not designed for this purpose and suffer from several problems that lead to a high variability of the results and, hence, to difficulties in their interpretation. The origin and exposure history of the inocula used for biodegradability testing can lead to highly variable outcomes. Microbial adaptation to chemicals and its impact on biodegradation needs further investigation in order to have a better understanding of their effects on persistency assessments of chemicals. It is well described that microbial adaptation stimulates biodegradation of organic chemicals. Several mechanisms responsible for these phenomena have been described, amongst which are i) shifts in community composition or abundances, ii) mutations within populations, iii) horizontal gene transfer or iv) recombination events. These adaptation processes may well be mimicked under laboratory conditions, but the outcome remains difficult to predict as we lack a fundamental understanding of the adaptive responses. This review aims to bring together our current knowledge regarding microbial adaptation and its implication for the testing of biodegradation of chemicals.
The experimental determination of reliable biodegradation rates for mono-aromatics towards evaluating QSBR models
Publication
Acharya, K.; Werner, D.; Dolfing, J.; Meynet, P.; Tabraiz, S.; Baluja, M. Q.; Petropoulos, E.; Mrozik, W.; Davenport, R. J., The experimental determination of reliable biodegradation rates for mono-aromatics towards evaluating QSBR models. Water Res. 2019, 160, 278-287. https://doi.org/10.1016/j.watres.2019.05.075
Abstract
Quantitative Structure Biodegradation Relationships (QSBRs) are a tool to predict the biodegradability of chemicals. The objective of this work was to generate reliable biodegradation data for mono-aromatic chemicals in order to evaluate and verify previously developed QSBRs models. A robust biodegradation test method was developed to estimate specific substrate utilization rates, which were used as a proxy for biodegradation rates of chemicals in pure culture. Five representative mono-aromatic chemicals were selected that spanned a wide range of biodegradability. Aerobic biodegradation experiments were performed for each chemical in batch reactors seeded with known degraders. Chemical removal, degrader growth and CO2 production were monitored over time. Experimental data were interpreted using a full carbon mass balance model, and Monod kinetic parameters (Y, Ks, qmax and μmax) for each chemical were determined. In addition, stoichiometric equations for aerobic mineralization of the test chemicals were developed. The theoretically estimated biomass and CO2 yields were similar to those experimentally observed; 35% (s.d ± 8%) of the recovered substrate carbon was converted to biomass, and 65% (s.d ± 8%) was mineralised to CO2. Significant correlations were observed between the experimentally determined specific substrate utilization rates, as represented by qmax and qmax/Ks, at high and low substrate concentrations, respectively, and the first order biodegradation rate constants predicted by a previous QSBR study. Similarly, the correlation between qmax and selected molecular descriptors characterizing the chemicals structure in a previous QSBR study was also significant. These results suggest that QSBR models can be reliable and robust in prioritising chemical half-lives for regulatory screening purposes.
Source: https://doi.org/10.1016/j.watres.2019.05.075
Widespread microbial adaptation to l-Glutamate-N,N-diacetate (L-GLDA) following its market introduction in a consumer cleaning product
Publication
Itrich, N. R.; McDonough, K. M.; van Ginkel, C. G.; Bisinger, E. C.; LePage, J. N.; Schaefer, E. C.; Menzies, J. Z.; Casteel, K. D.; Federle, T. W., Widespread microbial adaptation to l-Glutamate-N,N-diacetate (L-GLDA) following its market introduction in a consumer cleaning product. Environ. Sci. Technol. 2015, 49, (22), 13314-21. https://doi.org/10.1021/acs.est.5b03649
Abstract
l-Glutamate-N,N-diacetate (L-GLDA) was recently introduced in the United States (U.S.) market as a phosphate replacement in automatic dishwashing detergents (ADW). Prior to introduction, L-GLDA exhibited poor biodegradation in OECD 301B Ready Biodegradation Tests inoculated with sludge from U.S. wastewater treatment plants (WWTPs). However, OECD 303A Activated Sludge WWTP Simulation studies showed that with a lag period to allow for growth (40-50 days) and a solids retention time (SRT) that allows establishment of L-GLDA degraders (>15 days), significant biodegradation (>80% dissolved organic carbon removal) would occur. Corresponding to the ADW market launch, a study was undertaken to monitor changes in the ready biodegradability of L-GLDA using activated sludge samples from various U.S. WWTPs. Initially all sludge inocula showed limited biodegradation ability, but as market introduction progressed, both the rate and extent of degradation increased significantly. Within 22 months, L-GLDA was ready biodegradable using inocula from 12 WWTPs. In an OECD 303A study repeated 18 months post launch, significant and sustained carbon removal (>94%) was observed after a 29-day acclimation period. This study systematically documented field adaptation of a new consumer product chemical across a large geographic region and confirmed the ability of laboratory simulation studies to predict field adaptation.
Source: https://doi.org/10.1021/acs.est.5b03649
Quantitative structure-activity relationship models for ready biodegradability of chemicals
Publication
Mansouri, K.; Ringsted, T.; Ballabio, D.; Todeschini, R.; Consonni, V., Quantitative structure-activity relationship models for ready biodegradability of chemicals. ‎J. Chem. Inf. Model 2013, 53, (4), 867-78. https://doi.org/10.1021/ci4000213
Abstract
The European REACH regulation requires information on ready biodegradation, which is a screening test to assess the biodegradability of chemicals. At the same time REACH encourages the use of alternatives to animal testing which includes predictions from quantitative structure-activity relationship (QSAR) models. The aim of this study was to build QSAR models to predict ready biodegradation of chemicals by using different modeling methods and types of molecular descriptors. Particular attention was given to data screening and validation procedures in order to build predictive models. Experimental values of 1055 chemicals were collected from the webpage of the National Institute of Technology and Evaluation of Japan (NITE): 837 and 218 molecules were used for calibration and testing purposes, respectively. In addition, models were further evaluated using an external validation set consisting of 670 molecules. Classification models were produced in order to discriminate biodegradable and nonbiodegradable chemicals by means of different mathematical methods: k nearest neighbors, partial least squares discriminant analysis, and support vector machines, as well as their consensus models. The proposed models and the derived consensus analysis demonstrated good classification performances with respect to already published QSAR models on biodegradation. Relationships between the molecular descriptors selected in each QSAR model and biodegradability were evaluated.
Source: https://doi.org/10.1021/ci4000213
In silico assessment of chemical biodegradability
Publication
Cheng, F.; Ikenaga, Y.; Zhou, Y.; Yu, Y.; Li, W.; Shen, J.; Du, Z.; Chen, L.; Xu, C.; Liu, G.; Lee, P. W.; Tang, Y., In silico assessment of chemical biodegradability. ‎J. Chem. Inf. Model 2012, 52, (3), 655-69. https://doi.org/10.1021/ci200622d
Abstract
Biodegradation is the principal environmental dissipation process. Due to a lack of comprehensive experimental data, high study cost and time-consuming, in silico approaches for assessing the biodegradable profiles of chemicals are encouraged and is an active current research topic. Here we developed in silico methods to estimate chemical biodegradability in the environment. At first 1440 diverse compounds tested under the Japanese Ministry of International Trade and Industry (MITI) protocol were used. Four different methods, namely support vector machine, k-nearest neighbor, naive Bayes, and C4.5 decision tree, were used to build the combinatorial classification probability models of ready versus not ready biodegradability using physicochemical descriptors and fingerprints separately. The overall predictive accuracies of the best models were more than 80% for the external test set of 164 diverse compounds. Some privileged substructures were further identified for ready or not ready biodegradable chemicals by combining information gain and substructure fragment analysis. Moreover, 27 new predicted chemicals were selected for experimental assay through the Japanese MITI test protocols, which validated that all 27 compounds were predicted correctly. The predictive accuracies of our models outperform the commonly used software of the EPI Suite. Our study provided critical tools for early assessment of biodegradability of new organic chemicals in environmental hazard assessment.
Source: https://doi.org/10.1021/ci200622d
Expert systems survey on biodegradation of xenobiotic chemicals
Publication
Boethling, R. S.; Gregg, B.; Frederick, R.; Gabel, N. W.; Campbell, S. E.; Sabljic, A., Expert systems survey on biodegradation of xenobiotic chemicals. Ecotoxicol. Environ. Saf. 1989, 18, (3), 252-267. https://doi.org/10.1016/0147-6513(89)90019-5
Abstract
To determine the feasibility of developing an expert system for biodegradability assessment, a survey was conducted in which biodegradation experts were asked to estimate rates and products of degradation for 50 chemicals. These chemicals, which varied widely in structure, were considered representative of the spectrum of premanufacture notice chemicals subject to EPA review under the Toxic Substances Control Act. There was substantial agreement among the 22 experts on both sites of initial attack and rates of degradation. The approximate order in which various groups were viewed as contributing to aerobic biodegradability is as follows: ester, amide, anhydride > hydroxyl > carboxyl, epoxide, site of unsaturation > benzene ring, methyl methylene. Hydrolyzable groups, azo bonds, halogens, and nitro groups were preferred sites of anaerobic attack. Among the negative influences on aerobic biodegradability were molecular mass, branching, halogenation, and nitrogen heterocycles. Results also indicate that estimates of removal by biodegradation in aerobic wastewater treatment and time for aerobic ultimate and primary degradation were well correlated, and that the predictive value of such correlations could be improved using correction factors for certain classes of chemicals. The results lend support to existing rules of thumb, but also offer additional insight that will prove useful in designing a prototype system.