Biomedical subjects

E Rorije

Publications and source records attributed to E Rorije.

5 recordsLinked to original sources

PubMed2002

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.

Bacteria↗

PubMed2001

Structure-specificity relationships for haloalkane dehalogenases.

A structural analysis of the substrate specificity of hydrolytic dehalogenases originating from three different bacterial isolates has been performed using the multiple computer-automated structure evaluation methodology. This methodology identifies structural fragments in substrate molecules that either activate or deactivate biological processes. The analysis presented in this contribution is based on newly measured dehalogenation data combined with data from the literature (91 substrates). The enzymes under study represent different specificity classes of haloalkane dehalogenases (haloalkane dehalogenase from Xanthobacter autotrophicus GJ10, Rhodococcus erythropolis Y2, and Sphingomonas paucimobilis UT26). Three sets of structural rules have been identified to explain their substrate specificity and to predict activity for untested substrates. Predictions of activity and inactivity based on the structural rules from this analysis were provided for those compounds that were not yet tested experimentally. Predictions were also made for the compounds with available experimental data not used for the model construction (i.e., the external validation set). Correct predictions were obtained for 28 of 30 compounds in the validation set. Incorrect predictions were noted for two substrates outside the chemical domain of the set of compounds for which the structural rules were generated. A mechanistic interpretation of the structural rules generated provided a fundamental understanding of the structure-specificity relationships for the family of haloalkane dehalogenases.

Biodegradation, Environmental↗

PubMed1999

Evaluation and application of models for the prediction of ready biodegradability in the MITI-I test.

Three existing models and one newly developed model for the prediction of ready biodegradability of organic compounds are evaluated by comparing the descriptors they use, and the consistency of the models when applied to the set of High Production Volume Chemicals (HPVC) in the European Union. Linear regression models developed for the OECD showed the best performance in the external validation (84.7% correct), although comparison with the other three models is flawed because of the class specificity of these models. With these models 567 of the 894 compounds could be predicted in the validation. The multivariate statistical model showed the best performance in the external validation (82.7% correct) combined with the broadest applicability of the model. The evaluation of the predictions of the models for the HPVC shows that all models are highly consistent in their prediction of not-ready biodegradability, but much less consistency is seen in the prediction of ready biodegradability. This complies with the observation that all 4 models show better performance in their predictions of not-ready biodegradability.

Analysis of Variance↗

PubMed1995

On the use of backpropagation neural networks in modeling environmental degradation.

In this study a systematic analysis of the predictive capabilities of models built with backpropagation neural networks (BPNN) is made to corroborate the hypothesis that BPNN is capable of modeling the interaction terms in group contribution models, without explicitly adding these as descriptors. The data used for comparison are reactivities of 275 organic compounds towards the atomospheric OH-radical. This dataset was selected because of the internal consistency, reliability and relatively large size of this dataset. While training the network, the minimal Mean Squared Error (MSE) on a test set was used as the stop criterion. This avoids overfitting on the training data, and is most likely to give the best generalizing network. A network trained with a designed training and test set is compared with networks trained on randomly constructed training and test sets. The BPNN model based on designed training and test set not only gives the best model, but also the best predictability on an external validation set, compared both to linear models built with the same training and validation sets, and BPNN models based on randomly constructed training and test sets. The performance of the designed BPNN model is comparable to an existing model which includes interaction terms.

Environmental Pollutants↗

PubMed1995

Modeling reductive dehalogenation with quantum chemically derived descriptors.

Existing models for the reductive dehalogenation reaction under environmentally relevant conditions use Hammett and Taft coefficients as descriptors. Drawbacks of these descriptors are the limited possibilities for interpretation in terms of reaction mechanisms, and the limited availability of these descriptors for more "exotic' substituents. Therefore, in this study new descriptors are tested, using semi-empirical molecular orbital calculations. These descriptors are based on the energetic and electronic properties of the reaction sites and should be able to account for the systematics of the rate constants in a better way than substituent coefficient models. This approach is expected to give reliable estimates of the rate constants even for compounds containing less common structural features. Several relationships for a series of halogenated aromatics are presented here, relating the experimental rate constants to, among others, the calculated activation energy of the rate limiting step in the reductive dehalogenation process. Results show that semi-empirical molecular orbital descriptors are capable of describing the reaction kinetics within a homologous series of compounds. All descriptors can be explained for in terms of reaction mechanisms, thus corroborating the hypothesis about mechanisms taking place in the environment.

Chemical Phenomena↗