A kinetic model for predicting biodegradation

Biodegradation plays a key role in the environmental risk assessment of organic chemicals. The need to assess biodegradability of a chemical for regulatory purposes supports the development of a model for predicting the extent of biodegradation at different time frames, in particular the extent of ultimate biodegradation within a ‘10?day window’ criterion as well as estimating biodegradation half-lives. Conceptually this implies expressing the rate of catabolic transformations as a function of time. An attempt to correlate the kinetics of biodegradation with molecular structure of chemicals is presented. A simplified biodegradation kinetic model was formulated by combining the probabilistic approach of the original formulation of the CATABOL model with the assumption of first order kinetics of catabolic transformations. Nonlinear regression analysis was used to fit the model parameters to OECD 301F biodegradation kinetic data for a set of 208 chemicals. The new model allows the prediction of biodegradation multi-pathways, primary and ultimate half-lives and simulation of related kinetic biodegradation parameters such as biological oxygen demand (BOD), carbon dioxide production, and the nature and amount of metabolites as a function of time. The model may also be used for evaluating the OECD ready biodegradability potential of a chemical within the ‘10-day window’ criterion.     

A kinetic model for predicting biodegradation

Biodegradation plays a key role in the environmental risk assessment of organic chemicals. The need to assess biodegradability of a chemical for regulatory purposes supports the development of a model for predicting the extent of biodegradation at different time frames, in particular the extent of ultimate biodegradation within a '10 day window' criterion as well as estimating biodegradation half-lives. Conceptually this implies expressing the rate of catabolic transformations as a function of time. An attempt to correlate the kinetics of biodegradation with molecular structure of chemicals is presented. A simplified biodegradation kinetic model was formulated by combining the probabilistic approach of the original formulation of the CATABOL model with the assumption of first order kinetics of catabolic transformations. Nonlinear regression analysis was used to fit the model parameters to OECD 301F biodegradation kinetic data for a set of 208 chemicals. The new model allows the prediction of biodegradation multi-pathways, primary and ultimate half-lives and simulation of related kinetic biodegradation parameters such as biological oxygen demand (BOD), carbon dioxide production, and the nature and amount of metabolites as a function of time. The model may also be used for evaluating the OECD ready biodegradability potential of a chemical within the '10-day window' criterion.     

QSAR models for P450 (2D6) substrate activity

Human Cytochrome P450 (CYP) is a large group of enzymes that possess an essential function in metabolising different exogenous and endogenous compounds. Humans have more than 50 different genes encoding CYP enzymes, among these a gene encoding for the CYP isoenzyme 2D6, a CYP able to metabolise drugs and other chemicals. A training set of 747 chemicals primarily based on in vivo human data for the CYP isoenzyme 2D6 was collected from the literature. QSAR models focusing on substrate/non-substrate activity were constructed by the use of MultiCASE, Leadscope and MDL quantitative structure–activity relationship (QSAR) modelling systems. They cross validated (leave-groups-out) with concordances of 71%, 81% and 82%, respectively. Discrete organic European Inventory of Existing Commercial Chemical Substances (EINECS) chemicals were screened to predict an approximate percentage of CYP 2D6 substrates. These chemicals are potentially present in the environment. The biological importance of the CYP 2D6 and the use of the software mentioned above were discussed.     

Estimation of aqueous solubility of organic molecules by the group contribution approach. Application to the study of biodegradation.

A reliable and generally applicable aqueous solubility estimation method for organic compounds based on a group contribution approach has been developed. Two models have been established based on two different sets of parameters. One has a higher accuracy, while the other has a more general applicability. The prediction potentials of these two models have been evaluated through cross-validation experiments. For model I, the mean cross-validated r2 and SD for 10 such cross-validation experiments were 0.946 and 0.503 log units, respectively. While for model II, they were 0.953 and 0.546 log units, respectively. Applying our models to estimate the water solubility values for the compounds in an independent test set, we found that model I can be applied to 13 out of 21 compounds with a SD equal to 0.58 log unit and model II can be applied to all the 21 compounds with a SD equal to 1.25 log units. Our models compare favorably to all the current available water estimation methods. A program based on this approach has been written in FORTRAN77 and is currently running on a VAX/VMS system. The program can be applied to estimate the water solubility of the water solubility of any organic chemical with a good or fairly good accuracy except for except for electrolytes. Applying our aqueous solubility estimation models to biodegradation studies, we found that although the water solubility was not the sole factor controlling the rate of biodegradation, ring compounds with greater solubilities were more likely to biodegrade at a faster rate. The significance of the relationship between water solubility and biodegradation activity has been illustrated by predicting the biodegradation activity of 27 new chemicals based solely on their estimated solubility values.     

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.     

Development of an information-intensive structure–activity relationship model and its application to human respiratory chemical sensitizers

Structure–activity relationship (SAR) models are recognized as powerful tools to predict the toxicologic potential of new or untested chemicals and also provide insight into possible mechanisms of toxicity. Models have been based on physicochemical attributes and structural features of chemicals. We describe herein the development of a new SAR modeling algorithm called cat-SAR that is capable of analyzing and predicting chemical activity from divergent biological response data. The cat-SAR program develops chemical fragment-based SAR models from categorical biological response data (e.g. toxicologically active and inactive compounds). The database selected for model development was a published set of chemicals documented to cause respiratory hypersensitivity in humans. Two models were generated that differed only in that one model included explicate hydrogen containing fragments. The predictive abilities of the models were tested using leave-one-out cross-validation tests. One model had a sensitivity of 0.94 and specificity of 0.87 yielding an overall correct prediction of 91%. The second model had a sensitivity of 0.89, specificity of 0.95 and overall correct prediction of 92%. The demonstrated predictive capabilities of the cat-SAR approach, together with its modeling flexibility and design transparency, suggest the potential for its widespread applicability to toxicity prediction and for deriving mechanistic insight into toxicologic effects.     

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.     

QSPR modelling of the soil sorption coefficient from training sets of different sizes

Quantitative structure–property relationship (QSPR) modelling has been used in many scientific fields. This approach has been extensively applied in environmental research to predict physicochemical properties of compounds with potential environmental impact. The soil sorption coefficient is an important parameter for the evaluation of environmental risks, and it helps to determine the final fate of substances in the environment. In the last few years, different QSPR models have been developed for the determination of the sorption coefficient. In this study, several QSPR models were generated and evaluated for the prediction of log K_oc from the relationship with log P. These models were obtained from an extensive and diverse training set (n = 639) and from subsets of this initial set (i.e. halves, fourths and eighths). The aim of this study was to investigate whether the size of the training set affects the statistical quality of the obtained models. Furthermore, statistical equivalence was verified between the models obtained from smaller sets and the model obtained from the total training set. The results confirmed the equivalence between the models, thus indicating the possibility of using smaller training sets without compromising the statistical quality and predictive capability, as long as most chemical classes in the test set are represented in the training set.     

Computer‐aided prediction of toxicity with substructure pattern and random forest

Toxicity of chemicals induced by different factors is an important consideration, especially during the drug research and development process. Thus, there is urgent need to develop computationally effective models that can predict the toxicity or adverse effects of chemicals for a specific class of chemicals. In this study, random forest (RF) was used to classify five toxicity data sets from Distributed Structure‐Searchable Toxicity database network, using substructure fingerprints calculated directly from simple molecular structure. Three model validation approaches, out‐of‐bag validation incorporated in RF, fivefold cross‐validation, and an independent validation set, were used for assessing the prediction capability of our models. The chemical space analysis of data sets was explored by multidimensional scaling plots, and outlying molecules were also detected by the proximity measure in RF. At the same time, the important substructure fingerprints, recognized by the RF technique, gave some insights into the structure features related to toxicity of chemicals. The results obtained showed that these in silico classification models with substructure patterns and RF are applicable for potential toxicity prediction of chemical compounds. Copyright © 2012 John Wiley & Sons, Ltd.     

Development of an information-intensive structure-activity relationship model and its application to human respiratory chemical sensitizers

Structure-activity relationship (SAR) models are recognized as powerful tools to predict the toxicologic potential of new or untested chemicals and also provide insight into possible mechanisms of toxicity. Models have been based on physicochemical attributes and structural features of chemicals. We describe herein the development of a new SAR modeling algorithm called cat-SAR that is capable of analyzing and predicting chemical activity from divergent biological response data. The cat-SAR program develops chemical fragment-based SAR models from categorical biological response data (e.g. toxicologically active and inactive compounds). The database selected for model development was a published set of chemicals documented to cause respiratory hypersensitivity in humans. Two models were generated that differed only in that one model included explicate hydrogen containing fragments. The predictive abilities of the models were tested using leave-one-out cross-validation tests. One model had a sensitivity of 0.94 and specificity of 0.87 yielding an overall correct prediction of 91%. The second model had a sensitivity of 0.89, specificity of 0.95 and overall correct prediction of 92%. The demonstrated predictive capabilities of the cat-SAR approach, together with its modeling flexibility and design transparency, suggest the potential for its widespread applicability to toxicity prediction and for deriving mechanistic insight into toxicologic effects.     

Predicting hexadecane–air equilibrium partition coefficients (L) using a group contribution approach constructed from high quality data

A group contribution-based quantitative structure–property relationship (QSPR) for the hexadecane–air equilibrium partition coefficients (L) of organic chemicals is developed using the iterative fragment selection (IFS) approach. This new QSPR includes in its training and external validation data sets L values for a large number of structurally complex chemicals measured by the same group using consistent methods. The resulting QSPR has better predictive power than other prediction methods trained primarily using data for chemicals of simpler structures, and measurements of L values from diverse sources. For a subset of chemicals in which the L values have non-additive effects caused by intramolecular hydrogen bonds, the new QSPR gives much better performance in comparison to the most commonly used prediction method.