Biodegradability Prediction

For most organic chemicals, biodegradation is the dominant transformation pathway, contributing significantly to the attenuation of their environmental concentrations. When biodegradation is complete and mineralisation to carbon dioxide or methane occurs, organic carbon and other elements from the substance, like for instance nitrogen and sulphur, are released and reassimilated into natural elemental cycles. To understand the fate of a chemical and to assess its ecological impact, information on the biodegradability is critical. Basically two types of biodegradation data are required. The first type of information is on whether the substance is completely biodegradable or if persistent metabolites are formed. In the case of formation of persistent metabolites an additional impact assessment will need to be conducted for them and the long-term potential for impacts due to accumulation evaluated. The second type of data is on the rate of biodegradation in relevant environmental compartments. This information is used to predict the concentrations to which organisms will be exposed in the environment. The rate data can also be used to assess the potential for accumulation.

Industrial chemicals like halogenated, sulphonated and nitrated aliphatics and aromatics, many of which represent xenobiotics, persist in the biosphere and need to be eliminated from the environment once they have entered as pesticides or other technical end products, industrial effluents, or unintentionally through accidents. Although one can find numerous articles in the literature on the biodegradation of such compounds, there is only little information on the biodegradability of most of the chemical structures synthesized up to now. The establishment of predictive models is an urgent need but this faces fundamental problems. The first will be the definition of biodegradability in terms of transformation and/or mineralization, and the biological processes involved, and the second is the goal one wishes to attain.

An overview is given of the main aspects related to the biodegradability of chemicals. As such, two approaches to the evaluation of biodegradability are distinguished: a microbial approach (usually based on experiments with pure bacterial cultures at optimum conditions)and an environmental approach (based on experiments with mixed cultures grown under conditions approaching field conditions). Several types of biodegradability tests are reviewed, and testing strategies are discussed.

As a basis for developing Quantitative Structure Activity Relationships for biode-gradation, detailed results of biodegradation testing are reported for several classes of chemicals.

The operation and purpose of the META-CASETOX suite of computer programs is presented. CASETOX evaluates the toxic potential of organic molecules while META, outfitted with a biodegradation module evaluates the nature of the biodegradation products of organic molecules subjected to aerobic biodegradation. Together, they offer the ability to assess the toxic hazard posed by the disposal of organic molecules in the environment.

The inductive machine learning method coupled with a set of experts’ judgements and evaluated experimental biodegradation data were used to develop structural rules for ultimate biodegradation. Developed rules have been tested on BIODEG and MITI data. Both external validation tests have shown that the rules have solid predictive ability and performed better than other available methods. BIODEG and MITI data-bases were also used to develop new improved biodegradation rules. Two sets of developed biodegradation rules have very good classification ability, up to 90% for poorly biodegradable chemicals, and disclose structural features that either stimulate or hinder environmental biodegradation of organic chemicals.

Significant differences have been observed in the 2,3,7,8-substituted residue patterns of polychlorinated dibenzo-p-dioxins (PCDD) in freshwater, estuarine and marine sediments. Whereas these patterns can, to some degree, be explained by source identification, PCDD at environmental concentrations were recently found to be dechlorinated via microbial and chemical processes. Both peri- (1,4,6,9-substituted chlorines) and lateral (2,3,7,8-substituted chlorines) dechlorination patterns, as well as differences in extent of dechlorination were found to be correlated to specific abiotic and biotic catalytic activities. Qualitative relationships were based on isomer-specific analysis and the appearance of selective congeners under different conditions. The relevance of these processes to sediment biogeochemistry indicates that microbial dechlorination contributes significantly to the natural weathering of these types of pollutants. Whereas the lack of knowledge on the catalytic nature of the dechlorination reaction precludes the establishment of QSARs, characterization of microbial activities in combination with geochemical indicators may eventually present a means to describe the potential for microbial PCDD dechlorination in a given sediment environment and allow for a scientifically justified interpretation of patterns observed.

BESS is a software system that simulates the action of biodegradation pathways on compounds. It does so by encoding biodegradation pathways in a knowledge base and applying those pathways in sequence to the compound, breaking it down into metabolites. We describe BESS, its knowledge-base, some preliminary validation results and an approach to learning that will be used to improve the knowledge base.

The application of a mechanistic approach for the study of mechanisms of microbial degradation processes and the development of Quantitative Structure-Biodegradability models are outlined in this contribution. The dehalogenation of haloaliphatic compounds was used as a case study and an attempt was made: (i) to determine the rate-limiting sub-process, (ii) to quantitatively estimate interspecies variability in substrate specificity and (iii) to investigate structure-activity relationships leading towards development of QSBR models.

A comparison of dehalogenation rates obtained in testing systems at different organization levels, intact cells and isolated enzyme, revealed that penetration of the halogenated aliphatic compounds into the cells of dehalogenase competent microbes is not the rate limiting step in the hydrolytic dehalogenation. Consequently, the enzymatic reaction is to be considered slower than the penetration process. Multivariate analysis of the substrate profiles of the haloalkane dehalogenases of different microbial strains was performed to tackle the problem of the utility of single species testing for the prediction of environmental rate constants. At least two groups of haloalkane dehalogenases were formulated based on their ability to dehalogenate various haloaliphatic compounds.

A preliminary QSBR model for terminally substituted mono- and dihalogenated alkanes was developed. All three types of descriptors: hydrophobicity, steric, and electronic were necessary to obtain a good description of the process studied. Three outliers from the model, all short-chain chlorinated compounds, were detected and the reasons for discrepancy between predictions and observation are discussed. Although more research is needed to better understand structure-biodegradability relationships for hydrolytic dehalogenation, the mechanistic approach is shown to provide beneficial information for model interpretation.

Risk is a function of both intrinsic hazard and exposure. Knowledge of the persistence of compounds in the environment is thus of prime importance in the assessment of risk; clearly, therefore, ability to predict persistence (or lack of persistence, i.e. biodegradability) can be of great help in this respect.

Biodegradation is an important process in the environmental fate of substances. Therefore, biodegradation is considered for several legislative purposes, such as Priority Setting, Risk Assessment and Classification and Labelling of substances within the European Union. For these purposes, it is important whether a substance is readily or not-readily biodegradable according to EU and OECD test guidelines. For many existing substances, the experimental biodegradation data have been derived under non-standard test conditions which complicates the interpretation of test results. Furthermore, results from different studies may not be consistent. (Quantitative) Structure Activity Relationships ((Q)SARs) may assist in the evaluation of experimental data. Therefore, a model has been developed that predicts whether a substance is readily biodegradable or not-readily biodegradable under MITII test conditions. The model is based on a set of 600 substances, all having MITI I test results and 111 predefined structural fragments. The model is generated by Partial Least Squares (PLS) discriminant analysis. The model is both internally cross validated and externally validated with 198 substances that were not used in the model development. Both readily and not-readily biodegradable substances were predicted correctly for >86 % of the substances in the external validation set. The model has been developed in line with the principles on Use of QSARs laid down in the EU Technical Guidance Documents on Risk Assessment.

Knowledge of half-lives or biodegradation rate constants in soil is useful for estimating the natural attenuation rates of contaminants due to microbial transformations and to make decisions regarding treatment action or no treatment with isolation of the contaminated site to minimize exposure to animal and human life. Half-life is defined as the time required for 50 % of the contaminant to be biodegraded. Soil treatment is time consuming and expensive, and often for large isolated contaminated sites, relying on natural attenuation may be the most cost-effective solution. In this paper, a neural network is trained to estimate the range of half-lives for organic contaminants in soil. Soil half-life data, obtained from the literature for 258 chemicals is correlated with 14 molecular fragments or indicators using a back-propagation neural network with 14 input nodes, 12 nodes in the hidden layer and 2 output nodes. A cross-validation method was used to test the neural network. The converged neural network produced less than 50 % relative error for more than 80 % of the chemicals in the training set. Using a classification scheme of fast (half-life range of 1 to 7 days), moderately fast (half-life range from 7 to 28 days), slow (half-life range from 28 to 180 days) and resistant (half-life range from 180 to 365 days), the neural network was able to correctly classify more than 95 % of the 258 chemicals in the database.