(Q)SPR Models for Prediction of Hydrophobicity of Isatins

(Q)SPR Models for Prediction of Hydrophobicity of Isatins

A.K. Madan (Pt. B.D. Sharma University of Health Sciences, Rohtak, India) and Rohit Dutt (School of Medical of Allied Sciences, G D Goenka University, Gurgaon, India)
DOI: 10.4018/IJQSPR.2018010105
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Abstract

In the present study, the application of a wide variety of topological descriptors was investigated for predicting hydrophobicity (clogP) of isatin analogues. A total of four topochemical indices selected through decision tree (DT) were used for the development of single index based models using moving average analysis (MAA). The overall accuracy of prediction varied from a minimum of 95% to a maximum of 98% with regard to hydrophobicity.The values of sensitivity, specificity and Mathew's correlation coefficient for all MAA based models with regard to hydrophobicity (clogP) was found to be =78%, =94% and =0.85 respectively, suggesting robustness of proposed models. Since the compounds with high clogP values were found effective in carboxylesterases (CEs) inhibition, therefore, highly hydrophobic ranges of proposed MAA models can easily be exploited for the design and development of potent CEs inhibitors.
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Introduction

The significant attrition of new therapeutic candidates due to safety or suboptimal pharmacokinetics findings has been frequently observed. This simply results in increased costs and extended timelines for the drug development process (Wager et al., 2010; Leeson and Empfield, 2010). It is now very well recognized that physicochemical, pharmacokinetic and biopharmaceutical properties are required to be addressed during initial stages of drug development/discovery process (de Waterbeemd et al., 2001; Edwards and Price, 2010). A medicinal chemist is the only architect to sketch drug like properties and desirable safety attributes at the design stage before undertaking synthesis of molecules (Wager et al., 2010; Lipinski et al., 1997). Thus, prioritization by insilico methods prior to intensive experimentation is of utmost importance in order to ensure that valuable resources are exclusively utilized for most promising drug candidates.

The modern drug discovery era has now come to a stage where structure-based design and property-based design constitute an integral approach in drug development process in pharmaceutical industry (Arnott and Planey, 2012). The properties of a molecule are inherent in its structure, and once synthesized, all further studies of a drug candidate during development are essentially focused on understanding of its biological activities, metabolism, pharmacokinetics, toxicological profile and pharmaceutical properties (Meanwell, 2011). Two physicochemical parameters having the most profound influence on drug-like properties of a molecule are aqueous solubility and hydrophobicity or lipophilicity. The partitioning of drugs between aqueous and lipophilic phases or a balanced hydrophilic-lipophilic character is of utmost importance for drug potency as well as for drug absorption, transport and distribution (Tsantili-Kakoulidou, 2009). The octanol/water partition coefficient (log PO/W) constitutes a quantitative, and easily accessible measure of hydrophobicity/lipophilicity. Hydrophobicity describes the ability for aggregation of organic compounds in water whereas the lipophilicity is determined by intermolecular relationships between an organic substance and solvent (Sangster, 1997). This coefficient is usually quantified as log P and is an important molecular characteristic in medicinal chemistry and in silico drug design as well (Kujawski et al., 2012a). The hydrophobic drugs with high partition coefficients are favorably distributed into the hydrophobic domains such as lipid bilayers of cells, while hydrophilic drugs (low partition coefficients) are preferentially confined in hydrophilic domains, such as blood serum (Kujawski et al., 2012b). The importance of hydrophobic interactions on atomic or molecular scale has long been recognized and is currently used to assess biological properties relevant to drug action (Ghose et al., 1998; Sarkar and Kellogg, 2010).

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