Quantitative Structure-Activity Relationships of Aquatic Narcosis: A Review.

BACKGROUND: Prior estimation of toxicity of each and every, existing and yet to be synthesized chemicals is a must to elude their adverse effect on the environment. Experimental determination of such parameters is time consuming, cost effective and above all, it demands the sacrifice of many vertebrates. At this end, the REACH regulations advocate for the use of non-testing predictive methods such as read-across, weight-of-evidence and QSAR (quantitative structure-activity relationship) techniques. Among these methods, QSAR is found to be the best as it is based on molecular structure only. The descriptors used in deriving the model in QSAR vary according to the nature of the narcotics as well as the species used for. The success of a model in predicting the toxicity of a narcotic purely depends on the type of descriptors selected that explains the structural features closely related to the property under study. In this review, we have focused on the different types of descriptors and QSAR models used to explain the narcosis phenomenon. METHODS: Literature was scanned for acute toxicity of chemicals on species like tadpoles, protozoa, planktonic crustaceans, and small fishes like million fish, rainbow fish etc. from different sources. The toxicity and toxicants were classified considering their polarity and specific interactions of the compounds. Due to complex nature of the substrate, the mechanism of action of toxicant is uncertain. However, the overall results obtained from the biological study have been subjected to QSAR studies to obtain various models, which can provide some ideas on the mode of toxicological action. Different types of molecular descriptors derived both experimentally and theoretically have been used in the QSAR studies. RESULTS: Mostly biochemicals have a specific signature on oil/water partition (Ko/w, P), which is the crux in biological activity. Accordingly, the toxicological activities have good correlations with log P. Addition of some more structural descriptors improves the structure-toxicity relationship. Among these, electronic descriptors like EHOMO, ELUMO and ΔE derived from molecular orbitals have been used in the QSAR. ELUMO describing the energy of excited species of the molecule is found to be the most suitable one. Other molecular descriptors used in the QSAR include constitutional, topological and Abraham's solute descriptors. The models derived from the QSAR studies were found to be highly significant to predict the toxicology as well as to throw light on the mechanism. CONCLUSION: The best descriptor for aquatic narcosis is the KO/W or P. Addition of an electronic parameter (ELUMO) improves the QSAR to some extent. However, substitution of ELUMO by other class of molecular descriptors has also some statistical significance. To have a global QSAR model, in addition to P, some more appropriate descriptors are to be derived either experimentally or theoretically, latter being the more cost effective and easy in derivation.

A Short Review of the Generation of Molecular Descriptors and Their Applications in Quantitative Structure Property/Activity Relationships.

BACKGROUND: Synthesis of organic compounds with specific biological activity or physicochemical characteristics needs a thorough analysis of the enumerable data set obtained from literature. Quantitative structure property/activity relationships have made it simple by predicting the structure of the compound with any optimized activity. For that there is a paramount data set of molecular descriptors (MD). This review is a survey on the generation of the molecular descriptors and its probable applications in QSP/AR. METHODS: Literatures have been collected from a wide class of research journals, citable web reports, seminar proceedings and books. The MDs were classified according to their generation. The applications of the MDs on the QSP/AR have also been reported in this review. RESULTS: The MDs can be classified into experimental and theoretical types, having a sub classification of the later into structural and quantum chemical descriptors. The structural parameters are derived from molecular graphs or topology of the molecules. Even the pixel of the molecular image can be used as molecular descriptor. In QSPR studies the physicochemical properties include boiling point, heat capacity, density, refractive index, molar volume, surface tension, heat of formation, octanol-water partition coefficient, solubility, chromatographic retention indices etc. Among biological activities toxicity, antimalarial activity, sensory irritant, potencies of local anesthetic, tadpole narcosis, antifungal activity, enzyme inhibiting activity are some important parameters in the QSAR studies. CONCLUSION: The classification of the MDs is mostly generic in nature. The application of the MDs in QSP/AR also has a generic link. Experimental MDs are more suitable in correlation analysis than the theoretical ones but are more expensive for generation. In advent of sophisticated computational tools and experimental design proliferation of MDs is inevitable, but for a highly optimized MD, studies on generation of MD is an unending process.