Modeling ready biodegradability of fragrance materials

An improved opposition-based crow search algorithm for biodegradable material classification

The development of a reliable quantitative structure-activity relationship (QSAR) classification model with a small number of molecular descriptors is a crucial step in chemometrics. In this study, an improvement of crow search algorithm (CSA) is proposed by adapting the opposite-based learning (OBL) approach, which is named as OBL-CSA, to improve the exploration and exploitation capability of the CSA in quantitative structure-biodegradation relationship (QSBR) modelling of classifying the biodegradable materials. The results reveal that the performance of OBL-CSA not only manifest in improving the classification performance, but also in reduced computational time required to complete the process when compared to the standard CSA and other four optimization algorithms tested, which are the particle swarm algorithm (PSO), black hole algorithm (BHA), grey wolf algorithm (GWA), and whale optimization algorithm (WOA). In conclusion, the OBL-CSA could be a valuable resource in the classification of biodegradable materials.

A Comparative Study of the Performance for Predicting Biodegradability Classification: The Quantitative Structure-Activity Relationship Model vs the Graph Convolutional Network

The prediction and evaluation of the biodegradability of molecules with computational methods are becoming increasingly important. Among the various methods, quantitative structure-activity relationship (QSAR) models have been demonstrated to predict the ready biodegradation of chemicals but have limited functionality owing to their complex implementation. In this study, we employ the graph convolutional network (GCN) method to overcome these issues. A biodegradability dataset from previous studies was trained to generate prediction models by (i) the QSAR models using the Mordred molecular descriptor calculator and MACCS molecular fingerprint and (ii) the GCN model using molecular graphs. The performance comparison of the methods confirms that the GCN model is more straightforward to implement and more stable; the specificity and sensitivity values are almost identical without specific descriptors or fingerprints. In addition, the performance of the models was further verified by randomly dividing the dataset into 100 different cases of training and test sets and by varying the test set ratio from 20 to 80%. The results of the current study clearly suggest the promise of the GCN model, which can be implemented straightforwardly and can replace conventional QSAR prediction models for various types and properties of molecules.

Biodegradation of an essential oil UVCB - Whole substance testing and constituent specific analytics yield biodegradation kinetics of mixture constituents

Testing and assessing the persistency, bioaccumulative and toxic properties of UVCBs (substances of Unknown or Variable composition, Complex reaction products or Biological materials) pose major technical and analytical challenges. The main aim of this study was to combine whole substance biodegradation testing with constituent specific analytics for determining primary biodegradation kinetics of the main UVCB constituents. An additional aim was to link the primary biodegradation kinetics of the main constituents to the bioaccumulation potential and baseline toxicity potential of the UVCB. Two closed biodegradation experiments were conducted using similar test systems but different analyses. The model substance, cedarwood Virginia oil, was tested at a low concentration and wastewater treatment plant effluent served as inoculum. We used microvolume solvent spiking for a quantitative mass transfer of the UVCB, while avoiding that co-solvent degradation would lead to anaerobic conditions. The biodegradation of UVCB constituents was determined with automated solid-phase microextraction coupled to GC-MS/MS using targeted analysis for main constituents and non-targeted analysis for minor constituents and non-polar degradation products. Primary biodegradation kinetics of main constituents, accounting for 73% w/w of the mixture, were successfully determined with degradation rate constants ranging from 0.09 to 0.25 d^-1. Minor constituents were also degraded and non-polar degradation products were not observed. Finally, the bioaccumulation potential and baseline toxicity potential of the mixture at test start were calculated and both parameters decreased then substantially. The strength of the new approach is the possibility of biodegradation testing of a whole UVCB at low concentration while generating constituent specific biodegradation kinetics.

Principles of QSAR Modeling

At the end of her academic career, the author summarizes the main aspects of QSAR modeling, giving comments and suggestions according to her 23 years' experience in QSAR research on environmental topics. The focus is mainly on Multiple Linear Regression, particularly Ordinary Least Squares, using a Genetic Algorithm for variable selection from various theoretical molecular descriptors, but the comments can be useful also for other QSAR methods. The need for rigorous validation, also external, and for applicability domain check to guarantee predictivity and reliability of QSAR models is particularly highlighted. The commented approach is the “predictive” one, based on chemometrics, and is usefully applied to the prioritization of environmental pollutants. All the discussed points and the author's ideas are implemented in the software QSARINS, as a legacy to the QSAR community.

A quantitative structure-biodegradation relationship (QSBR) approach to predict biodegradation rates of aromatic chemicals

The objective of this work was to develop a QSBR model for the prioritization of organic pollutants based on biodegradation rates from a database containing globally harmonized biodegradation tests using relevant molecular descriptors. To do this, we first categorized the chemicals into three groups (Group 1: simple aromatic chemicals with a single ring, Group 2: aromatic chemicals with multiple rings and Group3: Group 1 plus Group 2) based on molecular descriptors, estimated the first order biodegradation rate of the chemicals using rating values derived from the BIOWIN3 model, and finally developed, validated and defined the applicability domain of models for each group using a multiple linear regression approach. All the developed QSBR models complied with OECD principles for QSAR validation. The biodegradation rate in the models for the two groups (Group 2 and 3 chemicals) are associated with abstract molecular descriptors that provide little relevant practical information towards understanding the relationship between chemical structure and biodegradation rates. However, molecular descriptors associated with the QSBR model for Group 1 chemicals (R² = 0.89, Q²_loo = 0.87) provided information on properties that can readily be scrutinised and interpreted in relation to biodegradation processes. In combination, these results lead to the conclusion that QSBRs can be an alternative tool to estimate the persistence of chemicals, some of which can provide further insights into those factors affecting biodegradation.

Value of p-Value

The goal of this manuscript is to discuss important aspects of external validation of classification and category Quantitative Structure - Activity/Property/Toxicity Relationship QS/A/P/T/R models that to the best of author's knowledge are not addressed in publications. Statistical significance (in terms of p-value) and accuracy of prediction (in terms of Correct Classification Rate (CCR)) of external validation set compounds are among most important characteristics of the models. We assert that in most cases the models built for classification or category response variable should be statistically significant and predictive for each class or category. We show that three thresholds of the number of compounds in each class or category of the external validation sets should be satisfied. 1) The p-value criterion can never be satisfied, if the number of compounds is below the first threshold. 2) If the number of compounds is between the first and the second thresholds, p-value criterion should be used. 3) If it is higher than the third threshold, classification or category accuracy criterion should be used. 4) If the number of compounds is between second and third thresholds, either one or the other criterion should be used depending on the value of p-value. 5) When the number of compounds in the class approaches infinity, the maximum relative error of prediction approaches the relative expected error. The results are of interest in other areas of multidimensional data analysis.

Reducing aquatic micropollutants - Increasing the focus on input prevention and integrated emission management

Pharmaceuticals and many other chemicals are an important basis for nearly all sectors including for example, food and agriculture, medicine, plastics, electronics, transport, communication, and many other products used nowadays. This comes along with a tremendous chemicalization of the globe, including ubiquitous presence of products of chemical and pharmaceutical industries in the aquatic environment. Use of these products will increase with population growth and living standard as will the need for clean water. In addition, climate change will exacerbate availability of water in sufficient quantity and quality. Since its implementation, conventional wastewater treatment has increasingly contributed to environmental protection and health of humans. However, with the increasing pollution of water by chemicals, conventional treatment turned out to be insufficient. It was also found that advanced effluent treatment methods such as extended filtration, the sorption to activated charcoal or advanced oxidation methods have their own limitations. These are, for example, increased demand for energy and hazardous chemicals, incomplete or even no removal of pollutants, the generation of unwanted products from parent compounds (transformation products, TPs) of often-unknown chemical structure, fate and toxicity. In many countries, effluent treatment is available only rarely if at all let alone advanced treatment. The past should teach us, that focusing only on technological approaches is not constructive for a sustainable water quality control. Therefore, in addition to conventional and advanced treatment optimization more emphasis on input prevention is urgently needed, including more and better control of what is present in the source water. Measures for input prevention are known for long. The main focus though has always been on the treatment, and measures taken at the source have gained only little attention so far. A more effective and efficient approach, however, would be to avoid pollution at the source, which would in turn allow more targeted treatment to meet treated water quality objectives globally. New developments within green and sustainable chemistry are offering new approaches that allow for input prevention and a more targeted treatment to succeed in pollution elimination in and at the source. To put this into practice, engineers, water scientists and chemists as well as microbiologists and scientists of other related disciplines need to cooperate more extensively than in the past. Applying principles such as the precautionary principle, or keeping water flows separate where possible will add to this. This implies not minimizing the efforts to improve wastewater treatment but to design effluents and chemicals in such a way that treatment systems and water environments can cope successfully with the challenge of micropollutants globally (Kümmerer et al., 2018). This paper therefore presents in its first part some of the limitations of effluent treatment in order to demonstrate the urgent need for minimizing water pollution at the source and, information on why source management is urgently needed to improve water quality and stimulate discussions how to protect water resources on a global level. Some principles of green and sustainable chemistry as well as other approaches, which are part of source management, are presented in the second part in order to stimulate discussion.

Classification of biodegradable materials using QSAR modelling with uncertainty estimation

The ability to determine the biodegradability of chemicals without resorting to expensive tests is ecologically and economically desirable. Models based on quantitative structure-activity relations (QSAR) provide some promise in this direction. However, QSAR models in the literature rarely provide uncertainty estimates in more detail than aggregated statistics such as the sensitivity and specificity of the model's predictions. Almost never is there a means of assessing the uncertainty in an individual prediction. Without an uncertainty estimate, it is impossible to assess the trustworthiness of any particular prediction, which leaves the model with a low utility for regulatory purposes. In the present work, a QSAR model with uncertainty estimates is used to predict biodegradability for a set of substances from a publicly available data set. Separation was performed using a partial least squares discriminant analysis model, and the uncertainty was estimated using bootstrapping. The uncertainty prediction allows for confidence intervals to be assigned to any of the model's predictions, allowing for a more complete assessment of the model than would be possible through a traditional statistical analysis. The results presented here are broadly applicable to other areas of modelling as well, because the calculation of the uncertainty will clearly demonstrate where additional tests are needed.