Prediction of biodegradability from structure: imidazoles

A project for the development of Structure-Activity Relationship for Biodegradation is presented. The aim of the project is to assemble sets of structural rules governing the potential microbial degradability of (classes of) chemicals. These rules will provide tools to take into account the biodegradation aspects of a product--and all precursors in the production process--early in the product development. The modeling concept is to take all experimental biodegradation data available and combine structural trends in the data with mechanistical information from degradation pathways. The rules that are derived should give insight into the possibility of biodegradation for specific classes of chemicals, thereby revealing why a compound is biodegradable or not. For the class of imidazole derivatives such rules are derived, and a model degradation mechanism is proposed in analogy to the urocanate-hydratase mechanism from histidine metabolism. The model is validated using 12 imidazole-compounds, which are all predicted correctly to be poorly biodegradable. It is demonstrated that both data analysis and information on enzymatic reaction mechanisms are necessary to yield valid Structure-Biodegradation Relationship.     

Biodegradation of Pyridine and Pyridine Derivatives by Soil and Subsurface Microorganisms

Abstract

Large amounts of aromatic compounds are produced by various industries and two thirds of these are heterocyclic chemicals. Compared with the extensive information available on microbial degradation of homocyclic aromatic compounds, relatively little is known on the transformation and biodegradation of heterocyclic chemicals in soil. Recent concerns about the persistence of hazardous pollutants have led to a renewed interest in the biodegradation of heterocyclic compounds. Hence, we investigated the microbial degradation of pyridine and some of its alkylated derivatives under aerobic and anaerobic conditions in groundwater, subsurface sediment, and soil. Results of the investigation revealed that these compounds were degraded predominantly under aerobic conditions and, to a lesser extent, under anaerobic conditions, with nitrate or sulfate serving as electron acceptors. In groundwater polluted with various pyridine derivatives, biodegradation was limited by the absence of oxygen. Therefore, we conclude that, under appropriate conditions, bioremediation is a potentially feasible method for the clean-up of environments contaminated with heterocyclic chemicals and, in particular, pyridine derivatives.     

Probabilistic assessment of biodegradability based on metabolic pathways: CATABOL System

A novel mechanistic modeling approach has been developed that assesses chemical biodegradability in a quantitative manner. It is an expert system predicting biotransformation pathway working together with a probabilistic model that calculates probabilities of the individual transformations. The expert system contains a library of hierarchically ordered individual transformations and matching substructure engine. The hierarchy in the expert system was set according to the descending order of the individual transformation probabilities. The integrated principal catabolic steps are derived from set of metabolic pathways predicted for each chemical from the training set and encompass more than one real biodegradation step to improve the speed of predictions. In the current work, we modeled O 2 yield during OECD 302 C (MITI I) test. MITI-I database of 532 chemicals was used as a training set. To make biodegradability predictions, the model only needs structure of a chemical. The output is given as percentage of theoretical biological oxygen demand (BOD). The model allows for identifying potentially persistent catabolic intermediates and their molar amounts. The data in the training set agreed well with the calculated BODs ( r 2 =0.90) in the entire range i.e. a good fit was observed for readily, intermediate and difficult to degrade chemicals. After introducing 60% ThOD as a cut off value the model predicted correctly 98% ready biodegradable structures and 96% not ready biodegradable structures. Crossvalidation by four times leaving 25% of data resulted in Q 2 =0.88 between observed and predicted values. Presented approach and obtained results were used to develop computer software for biodegradability prediction CATABOL.     

Quantitative prediction of biodegradability,metabolite distribution and toxicity of stable metabolites

An evaluation of the capability of organic chemicals to mineralize is an important factor to consider when assessing their fate in the environment. Microbial degradation can convert a toxic chemical into an innocuous one, and vice versa , or alter the toxicity of a chemical. Moreover, primary biodegradation can convert chemicals into stable products that can be difficult to mineralize. In this paper, we present some new results obtained on the basis of a recently developed probabilistic approach to modeling biodegradation based on microbial transformation pathways. The metabolic transformations and their hierarchy were calibrated by making use of the ready biodegradability data from the MITI-I test and expert knowledge for the most probable transformation pathways. A model was developed and integrated into an expert software system named CATABOL that is able to predict the probability of biodegradation of organic chemicals directly from their structure. CATABOL simulates the effects of microbial enzyme systems, generates the most plausible transformation pathways, and quantitatively predicts the persistence and toxicity of the biodegradation products. A subset of 300 organic chemicals were selected from Canada's Domestic Substances List and subjected to CATABOL to compare predicted properties of the parent chemicals with their respective first stable metabolite. The results show that most of the stable metabolites have a lower acute toxicity to fish and a lower bioaccumulation potential compared to the parent chemicals. In contrast, the metabolites appear to be generally more estrogenic than the parent chemicals.     

Regulatory use of (Q)SARs in toxicological hazard assessment strategies

In 2001, the European Commission published a policy statement ("White Paper") on future chemicals regulation and risk reduction that proposed the use of non-animal test systems and tailor-made testing approaches, including (Q)SARs, to reduce financial costs and the number of test animals employed. The authors have compiled a database containing data submitted within the EU chemicals notification procedure. From these data, (Q)SARs for the prediction of local irritation/corrosion and/or sensitisation potential were developed and published. These (Q)SARs, together with an expert system supporting their use, will be submitted for official validation and application within regulatory hazard assessment strategies. The main features are: ? two sets of structural alerts for the prediction of skin sensitisation hazard classification as defined by the European risk phrase R43, comprising 15 rules for chemical substructures deemed to be sensitising by direct action with cells or proteins, and three rules for substructures acting indirectly, i.e., requiring biochemical transformation; ? a decision support system (DSS) for the prediction of skin and/or eye lesion potential built from information extracted from our database. This DSS combines SARs defining reactive chemical substructures relevant for local lesions to be classified, and QSARs for the prediction of the absence of such a potential. The role of the BfR database, and (Q)SARs derived from it, in the use of current and future (EU) testing strategies for irritation and sensitisation is discussed.     

Structural Effects on the Biodegradation of Aliphatic Polyesters

In advance of a discussion on structural effects on biodegradation, aliphatic polyesters as biodegradable structural materials were classified into four types regarding chemical structure, that is poly(ω-hydroxy acid), poly(β-hydroxyalkanoate), poly(ω-hydroxyalkanoate) and poly(alkylene dicarboxylate), and reviewed on synthesis route, thermal and physical properties, and biodegradability. The biodegradation mechanism of these aliphatic polyesters were discussed on the major mode of hydrolysis reaction in regard whether it was enzyme-catalyzed or not, and the substrate specificities of enzymes, such as lipases or PHA depolymerases, were discussed on the hydrolysis of the aliphatic polyesters in respect of primary structure. Moreover, the biodegradation behaviors were exceedingly influenced by solid-state morphology in addition to primary structure. The rate of enzymatic degradation of polycaprolactone fibers drawn with various draw ratios was dependent on draw ratios, suggesting that crystallinity and orientation of them affected biodegradability by lipase. In the study of enzymatic degradation of films made from butylene succinate – ethylene succinate copolymer, the dependence of degradation rate on polymeric compositions was ascribed to the degree of crystallinity rather than the primary structure. These studies revealed that the degree of crystallinity was the major rate-determining factor of biodegradation of solid polymers. © 1997 John Wiley & Sons, Ltd.     

AI and SAR approaches for predicting chemical carcinogenicity: survey and status report

A wide variety of artificial intelligence (AI) and structure-activity relationship (SAR) approaches have been applied to tackling the general problem of predicting rodent chemical carcinogenicity. Given the diversity of chemical structures and mechanisms relative to this endpoint, the shared challenge of these approaches is to accurately delineate classes of active chemicals representing distinct biological and chemical mechanism domains, and within those classes determine the structural features and properties responsible for modulating activity. In the following discussion, we present a survey of AI and SAR approaches that have been applied to the prediction of rodent carcinogenicity, and discuss these in general terms and in the context of the results of two organized prediction exercises (PTE-1 and PTE-2) sponsored by the US National Cancer Institute/National Toxicology Program. Most models participating in these exercises were successful in identifying major structural-alerting classes of active carcinogens, but failed in modeling the more subtle modifiers to activity within those classes. In addition, methods that incorporated mechanism-based reasoning or biological data along with structural information outperformed models limited to structural information exclusively. Finally, a few recent carcinogenicity-modeling efforts are presented illustrating progress in tackling some aspects of the carcinogenicity prediction problem. The first example, a QSAR model for predicting carcinogenic potency of aromatic amines, illustrates that success is possible within well-represented classes of carcinogens. From the second example, a newly developed FDA/OTR MultiCASE model for predicting the carcinogenicity of pharmaceuticals, we conclude that the definitions of biological activity and nature of chemicals in the training set are important determinants of the predictive success and specificity/sensitivity characteristics of a derived model.     

Biodegradation of aliphatic and aromatic polycarbonates

Polycarbonate is one of the most widely used engineering plastics because of its superior physical, chemical, and mechanical properties. Understanding the biodegradation of this polymer is of great importance to answer the increasing problems in waste management of this polymer. Aliphatic polycarbonates are known to biodegrade either through the action of pure enzymes or by bacterial whole cells. Very little information is available that deals with the biodegradation of aromatic polycarbonates. Biodegradation is governed by different factors that include polymer characteristics, type of organism, and nature of pretreatment. The polymer characteristics such as its mobility, tacticity, crystallinity, molecular weight, the type of functional groups and substituents present in its structure, and plasticizers or additives added to the polymer all play an important role in its degradation. The carbonate bond in aliphatic polycarbonates is facile and hence this polymer is easily biodegradable. On the other hand, bisphenol A polycarbonate contains benzene rings and quaternary carbon atoms which form bulky and stiff chains that enhance rigidity. Even though this polycarbonate is amorphous in nature because of considerable free volume, it is non-biodegradable since the carbonate bond is inaccessible to enzymes because of the presence of bulky phenyl groups on either side. In order to facilitate the biodegradation of polymers few pretreatment techniques which include photo-oxidation, gamma-irradiation, or use of chemicals have been tested. Addition of biosurfactants to improve the interaction between the polymer and the microorganisms, and blending with natural or synthetic polymers that degrade easily, can also enhance the biodegradation.     

External validation of EPIWIN biodegradation models

The BIOWIN biodegradation models were evaluated for their suitability for regulatory purposes. BIOWIN includes the linear and non-linear BIODEG and MITI models for estimating the probability of rapid aerobic biodegradation and an expert survey model for primary and ultimate biodegradation estimation. Experimental biodegradation data for 110 newly notified substances were compared with the estimations of the different models. The models were applied separately and in combinations to determine which model(s) showed the best performance. The results of this study were compared with the results of other validation studies and other biodegradation models. The BIOWIN models predict not-readily biodegradable substances with high accuracy in contrast to ready biodegradability. In view of the high environmental concern of persistent chemicals and in view of the large number of not-readily biodegradable chemicals compared to the readily ones, a model is preferred that gives a minimum of false positives without a corresponding high percentage false negatives. A combination of the BIOWIN models (BIOWIN2 or BIOWIN6) showed the highest predictive value for not-readily biodegradability. However, the highest score for overall predictivity with lowest percentage false predictions was achieved by applying BIOWIN3 (pass level 2.75) and BIOWIN6.     

Experimental verification of structural alerts for the protein binding of cyclic compounds acting as Michael acceptors

This study outlines how a combination of and in vitro data can be used to define the applicability domain of selected structural alerts within the protein binding profilers of the Organisation for Economic Co-operation (OECD) Quantitative Structure–Activity Relationship (QSAR) Toolbox. Thirty chemicals containing a cyclic moiety were profiled for reactivity using the OECD and Optimised Approach based on Structural Indices Set (OASIS) protein binding profilers. The profiling results identified 22 of the chemicals as being reactive towards proteins. Analysis of the experimentally data showed 19 of these chemicals to be reactive. Subsequent analysis allowed refinements to be suggested to improve the applicability domain of the structural alerts investigated. The accurate definition of the applicability domain for structural alerts within in silico profilers is important due to their use in chemical category in predictive and regulatory toxicology.     

Macromolecular Architecture-Nature as a Model for Degradable Polymers

Abstract

Nature usually combines polymers with short degradation times with polymers having long degradation times in an energy and material optimized process involving hierarchical systems. Sometimes a natural system of polymers has evolved to degrade in a month, sometimes in many years. The building blocks of the plant and animal kingdom are biopolymers which are either oxidizable or hydrolyzable. In natural composites, combinations of the two are common, e.g., in wood. Current trends in polymer research and marketing of plastics indicate an increasing demand for the development of a diversity of degradable polymer products with a predetermined service-life. We identify four main routes to design degradable polymers. The goal is to tailor-make a material which is more susceptible to environmental degradation factors (e.g., hydrolysis, biodegradation, photooxidation). The most convenient route is to use cheap synthetic bulk polymers and add a biodegradable or photooxidizable component. A more expensive solution is to change the chemical structure by introducing hydrolyzable or oxidizable groups in the repetitive chain of a synthetic polymer. The third route to degradable polymers is to use biopolymers or derivatives of these where the bacterial polyhydroxyalkanoates are perhaps the most studied material of them all. The fourth route is to tailor-make new hydrolyzable structures e.g., polyesters, polyanhydrides, and polycarbonates.     

Frequency distribution and selection of space groups in inorganic crystal chemistry

Statistical analysis of modern structural databases on inorganic crystals and minerals is carried out to elucidate the classification of crystal structure distributions over space groups. The known fact about extremely uneven occupation of space groups is confirmed; the relative abundance of all and, in particular, 24 most frequent groups (>1%) is derived, and the latter are noted to belong to holohedral and centrosymmetric classes. The data on occupation of extremely rare and empty space groups are analyzed and refined separately, which allows the reduction of a number of unreliable structural data. The theoretical analysis of empirical regularities is performed from the standpoint of filling rules for special structural positions (Wyckoff positions) and the symmetry of lattice complexes. It is shown that only combined consideration of different symmetry rules and restrictions provides an explanation for most regularities observed in occupation of symmetry point groups.     

Investigation of Degradation of Structural Adhesives under Influence of Chemicals

Structural adhesives are used for joining materials also under conditions, where they through the application will be influenced by many different chemicals. The adhesives can – if not protected from the chemical influence – be degradated of the chemicals. The degradation can because of the different structures of the polymers in the adhesives result in lower strength of the joining area, but can also give higher strength but brittleness. Information of the structures of the structural adhesives used in the project have been taken from the data sheets from the manufacturers and have been compared with investigation of the structures by FTIR and DSC. In the laboratory the HSP's (Hansen Solubility Parameters) of the adhesives has been determined and compared with the theoretically estimated HSP's. The estimation has mainly been done by Lydersens group contribution method. The chemical resistance of the adhesives have been foreseen by using HSP's of the adheisves and compared them with the HSP's of the chemicals. The structural adhesives were most of the epoxy types and of the polyurethane types with different curing systems. The structural adhesives should all have high strength and an opening time of more than 30 minutes. They were in the laboratory cured up after the specifications from the manufacturer and were stored one week after curing before they were influenced by hte chosen chemicals. The chemicals were chosen from their functional groups. In the laboratory the adhesives were influenced by different chemicals at room temperarture and under elevated temperature and under different periods to develope the degradation curves for the different chemicals and to foresee the degradation time of the adhesives before their properties were not acceptable any more. The structure after influence of the chemicals is studied by FTIR. The results of the investigations have been that it is possible to estimate the degradation by using the HSP's of the adhesives and the chemicals, but to estimate the time before degradation has been so serious that the properties of the adhesives are not acceptable any more, it is necessary to add laboratory investigations to the HSP comparisons of adhesives and chemicals. The comparison of the HSP and of the chemicals by which the adhesives can be in its lifetime has seen to be usefull especially if the chemicals are pesticides.     

External validation of structure-biodegradation relationship (SBR) models for predicting the biodegradability of xenobiotics

Biodegradation is an important mechanism for eliminating xenobiotics by biotransforming them into simple organic and inorganic products. Faced with the ever growing number of chemicals available on the market, structure–biodegradation relationship (SBR) and quantitative structure–biodegradation relationship (QSBR) models are increasingly used as surrogates of the biodegradation tests. Such models have great potential for a quick and cheap estimation of the biodegradation potential of chemicals. The Estimation Programs Interface (EPI) Suite? includes different models for predicting the potential aerobic biodegradability of organic substances. They are based on different endpoints, methodologies and/or statistical approaches. Among them, Biowin 5 and 6 appeared the most robust, being derived from the largest biodegradation database with results obtained only from the Ministry of International Trade and Industry (MITI) test. The aim of this study was to assess the predictive performances of these two models from a set of 356 chemicals extracted from notification dossiers including compatible biodegradation data. Another set of molecules with no more than four carbon atoms and substituted by various heteroatoms and/or functional groups was also embodied in the validation exercise. Comparisons were made with the predictions obtained with START (Structural Alerts for Reactivity in Toxtree). Biowin 5 and Biowin 6 gave satisfactorily prediction results except for the prediction of readily degradable chemicals. A consensus model built with Biowin 1 allowed the diminution of this tendency.     

A Noncongeneric Model for Predicting Toxicity of Organic Molecules to Vibrio Fischeri

Abstract

A general Quantitative Structure-Activity Relationship (QSAR) model on Vibrio fischeri (Microtox^? test) was derived using the autocorrelation method for describing the molecules and a neural network as statistical tool. From a training set of 1068 organic chemicals described by means of four different autocorrelation vectors, it was possible to obtain valuable models but presenting some large outliers. Addition of the time of exposure as variable allowed us to derive a more powerful model from 2795 toxicity results. The predictive power of this 36/26/1 neural network model was tested on an external testing set of 385 toxicity data and compared with the performances of linear models designed for polar narcotic amines and for weak acid respiratory uncouplers.     

In-Silico Screening of High Production Volume Chemicals for Mutagenicity using the mcase QSAR Expert System

Computational screening is suggested as a way to set priorities for further testing of high production volume (HPV) chemicals for mutagenicity and other toxic endpoints. Results are presented for batch screening of 2484 HPV chemicals to predict their mutagenicity in Salmonella typhimurium (Ames test). The chemicals were tested against 15 databases for Salmonella strains TA100, TA1535, TA1537, TA97 and TA98, both with metabolic activation (using rat liver and hamster liver S9 mix test) and without metabolic activation. Of the 2484 chemicals, 1868 are predicted to be completely nonmutagenic in all of the 15 data modules and 39 chemicals were found to contain structural fragments outside the knowledge of the expert system and therefore suggested for further evaluation. The remaining 616 chemicals were found to contain different biophores (structural alerts) believed to be linked to mutagenicity. The chemicals were ranked in descending order according to their predicted mutagenic potential and the first 100 chemicals with highest mutagenicity scores are presented. The screening result offers hope that rapid and inexpensive computational methods can aid in prioritizing the testing of HPV chemicals, save time and animals and help to avoid needless expense.