Modeling Ecotoxicity as Applied to some Selected Aromatic Compounds: A Conceptual DFT Based Quantitative-Structure-Toxicity-Relationship (QSTR) Analysis

Modeling Ecotoxicity as Applied to some Selected Aromatic Compounds: A Conceptual DFT Based Quantitative-Structure-Toxicity-Relationship (QSTR) Analysis

Santanab Giri (Indian Institute of Technology Kharagpur, India), Arindam Chakraborty (Indian Institute of Technology Kharagpur, India), Ashutosh Kumar Gupta (Udai Pratap Autonomous College, India), Debesh Ranjan Roy (Indian Institute of Technology Kharagpur, India), Ramadoss Vijayaraj (Central Leather Research Institute Chennai, India), Ramakrishnan Parthasarathi (Central Leather Research Institute Chennai, India), Venkatesan Subramanian (Central Leather Research Institute Chennai, India) and Pratim Chattaraj (Indian Institute of Technology Kharagpur, India)
DOI: 10.4018/978-1-60960-860-6.ch001


In the present chapter, density functional theory based reactivity indices are applied as chemical descriptors in QSAR analysis for ecotoxicological studies on a group of aromatic compounds. Two sets of aromatic compounds have been chosen to model ecotoxicity. First set comprises 97 electron-donor aromatic compounds and 77 electron-acceptor aromatic compounds studied on Tetrahymena pyriformis. The second set consists of 19 chlorophenol compounds studied for Daphnia magna, Brachydanio rerio and Bacillus. It is observed that a very simple descriptor like atom counting (number of non-hydrogenic atoms) along with other descriptors like electrophilicity index and (ground state) energies of the molecule, provide the best QSAR model for the toxicity of the first set of compounds. For the second set of compounds, it is found that the descriptors consisting of atom counting and group philicities together give the best QSAR models.
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The quantitative structure activity relationship (QSAR) is a mathematical representation of biological activity in terms of structural descriptors of a series of homologue molecules (Hansch, Leo & Taft, 1991)(Gao, Katzenellenbogen, Garg & Hansch, 1999)(Franke, 1984)(Gupta, Singh & Bindal, 1983)(Gupta, 1991)(Karelson, Lobanov & Katritzky, 1996). The main objective of QSAR is to look for new molecules with required properties using chemical intuition and experience transformed into a mathematically quantified and computerized form (Karelson, Lobanov & Katritzky, 1996). Once a correlation is established, the structure of any number of compounds with desired properties can be predicted. Thus QSAR methodology saves resources and expedites the process of development of new molecules and drugs (Hansch, Hoekman, Leo, Weininger & Selassie, 2002). Success of QSAR not only rests on the development of new drug molecules but, also in exploring the prediction of toxicological and, ecotoxicological activities, biological activity, biodegradability and environmental activities of various molecules (Hansch, Hoekman, Leo, Weininger & Selassie, 2002)(Hansch, Maloney, Fujita & Muir, 1962). Because the experimental determination is time-consuming and expensive, QSAR is widely used as an alternative.

QSAR models are also utilized for the regulation of industrial chemicals. In many countries including United States, legislation allows for the wide use of QSAR (Zeeman, Auer, Clements, Nabholz & Boethling, 1995). These observations have prompted the search for new QSAR tools and therefore the study of applications of molecular modeling methods, from pharmaceutical sciences to ecotoxicology. Use of quantum chemical descriptors in the development of QSAR has received rave attention due to their reliability and versatility of prediction (Parr & Yang, 1989)(Chermette, 1999)(Geerling, De Proft & Langenaeker, 2003)(Chattaraj, Nath & Maiti, 2003). Specifically, toxicity of various chemical compounds and associated biochemical processes have been related to their molecular structures. In this context, the structure-activity relationships (SARs) based on electrophilicity (ω) is shown to be promising. It has been found (Parthasarathi, Padmanabhan, Subramanian, Maiti & Chattaraj, 2003) (Parthasarathi, Padmanabhan, Subramanian, Sarkar, Maiti & Chattaraj, 2003) that the interaction between a toxin and a bio-system is essentially through a charge-transfer process supplemented by a variety of non-covalent interactions including stacking. Hence, the importance of global and local electrophilicities as well as the conformational flexibility of the toxins in understanding the toxicity of those molecules is well acclaimed.

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