Model modeling quantitative structure-activity relationship

The Danish EPA has developed an advisory list for self-classification of dangerous substances including 20 624 substances. The substances have been identified by means of QSAR models (Quantitative Structure-Activity Relationship) as having acute oral toxicity, sensitization, mutagenicity, carcinogenicity, and/or danger to the aquatic environment. 

QSAR models Quantitative Structure-Activity Relationship-statistical models that relate biological activity to features of a molecule... 

Keywords Skin permeability Percutaneous absorption Skin penetration Mathematical model Quantitative structure-activity relationships Permeability coefficient Human skin... 

Single property prediction through QSAR modeling (Quantitative Structure-Activity Relationship). 

Browne, K. A., Tamburri, M. N., and Zimmer-Faust, R. K., Modelling quantitative structure-activity relationships between animal behaviour and environmental signal molecules, J. Exp. Biol., 201, 245, 1998. 

Hammad, A.M.A. and Taha, M.O. (2009) Pharmacophore modeling, quantitative structure-activity relationship analysis, and shape-complemented in silica screening allow access to novel influenza neuraminidase inhibitors. Journal of Chemical Information and Modeling, 49, 978-996. 

Sutherland JJ, O Brien LA, Weaver DF (2004) A comparison of methods for modeling quantitative structure-activity relationships. J Med Chem 47(22) 5541-5554 Gohlke H, Klebe G (2002) DrugScore meets CoMFA adaptation of fields for molecular comparison (AFMoC) or how to tailor knowledge-based pair-potentials to a particular protein. J Med Chem 45(19) 4153 170. doi 10.1021/jm020808p... 

A challenging task in material science as well as in pharmaceutical research is to custom tailor a compound s properties. George S. Hammond stated that the most fundamental and lasting objective of synthesis is not production of new compounds, but production of properties (Norris Award Lecture, 1968). The molecular structure of an organic or inorganic compound determines its properties. Nevertheless, methods for the direct prediction of a compound s properties based on its molecular structure are usually not available (Figure 8-1). Therefore, the establishment of Quantitative Structure-Property Relationships (QSPRs) and Quantitative Structure-Activity Relationships (QSARs) uses an indirect approach in order to tackle this problem. In the first step, numerical descriptors encoding information about the molecular structure are calculated for a set of compounds. Secondly, statistical and artificial neural network models are used to predict the property or activity of interest based on these descriptors or a suitable subset. 

Furthermore, QSPR models for the prediction of free-energy based properties that are based on multilinear regression analysis are often referred to as LFER models, especially, in the wide field of quantitative structure-activity relationships (QSAR). 

The fundamental assumption of SAR and QSAR (Structure-Activity Relationships and Quantitative Structure-Activity Relationships) is that the activity of a compound is related to its structural and/or physicochemical properties. In a classic article Corwin Hansch formulated Eq. (15) as a linear frcc-cncrgy related model for the biological activity (e.g.. toxicity) of a group of congeneric chemicals [37, in which the inverse of C, the concentration effect of the toxicant, is related to a hy-drophobidty term, FI, an electronic term, a (the Hammett substituent constant). Stcric terms can be added to this equation (typically Taft s steric parameter, E,). 

Quantitative Structure—Activity Relationships (QSAR). Quantitative Stmcture—Activity Relationships (QSAR) is the name given to a broad spectmm of modeling methods which attempt to relate the biological activities of molecules to specific stmctural features, and do so in a quantitative manner (see Enzyme INHIBITORS). The method has been extensively appHed. The concepts involved in QSAR studies and a brief overview of the methodology and appHcations are given here. 

Among others, 11 was included in a series of drugs to study quantitative structure-activity relationships (96KFZ(6)29, 98MI7, 99BMC2437). A statistically significant CoMFA model was developed for describing the... 

Risperidone (11) was also included among a a 1-adrenergic receptor antagonists to study a quantitative structure-activity relationship (99BMC2437). A pharmacophore model for atypical antipsychotics, including 11, was established (00MI41). An increased plasma level of 11 and 9-hydroxyrisperidone (12) was observed in combination with paroxetine (01 MI 13). The effect of vanlafaxine on the pharmacokinetics of 11 was reported (99MI13). 

Ekins S, De Groot MJ, Jones JP. Pharmacophore and three-dimensional quantitative structure activity relationship methods for modeling cytochrome P450 active sites. Drug Metab Dispos 2001 29 936-44. 

Smith PA, Sorich MJ, McKinnon RA, Miners JO. Pharmacophore and quantitative structure-activity relationship modeling complementary approaches for the rationalization and prediction of UDP-glucuronosyltransferase 1A4 substrate selectivity. J Med Chem 2003 46 1617-26. 

Wang YW, Liu HX, Zhao CY, Liu HX, Cai ZW, Jiang GB. Quantitative structure-activity relationship models for prediction of the toxicity of polybrominated diphenyl ether congeners. Environ Sci Technol 2005 39 4961-6. 

Enslein K, Gombar VK, Blake BW, Maibach HI, Hostynek JJ, Sigman CC et al. A quantitative structure-activity relationships model for the dermal sensitization guinea pig maximization assay. Food Chem Toxicol 1997 35 1091-8. 

More recently (2006) we performed and reported quantitative structure-activity relationship (QSAR) modeling of the same compounds based on their atomic linear indices, for finding fimctions that discriminate between the tyrosinase inhibitor compounds and inactive ones [50]. Discriminant models have been applied and globally good classifications of 93.51 and 92.46% were observed for nonstochastic and stochastic hnear indices best models, respectively, in the training set. The external prediction sets had accuracies of 91.67 and 89.44% [50]. In addition to this, these fitted models have also been employed in the screening of new cycloartane compounds isolated from herbal plants. Good behavior was observed between the theoretical and experimental results. These results provide a tool that can be used in the identification of new tyrosinase inhibitor compounds [50]. 

Ghose, A. K., Crippen, G. M. Atomic physicochemical parameters for three-dimensional strucmre directed quantitative structure-activity relationships. II. modeling dispersive and hydrophobic interactions. 7. Chem. Inf. Comp. Sci. 1987, 27, 21-35. 

Kubinyi, H., Quantitative structure-activity relationships. IV. Nonlinear dependence of biological activity on hydrophobic character a new model, Arzneim. Forsch. (Drug Res.) 26, 1991-1997 (1976). 

In a study by Andersson et al. [30], the possibilities to use quantitative structure-activity relationship (QSAR) models to predict physical chemical and ecotoxico-logical properties of approximately 200 different plastic additives have been assessed. Physical chemical properties were predicted with the U.S. Environmental Protection Agency Estimation Program Interface (EPI) Suite, Version 3.20. Aquatic ecotoxicity data were calculated by QSAR models in the Toxicity Estimation Software Tool (T.E.S.T.), version 3.3, from U.S. Environmental Protection Agency, as described by Rahmberg et al. [31]. To evaluate the applicability of the QSAR-based characterization factors, they were compared to experiment-based characterization factors for the same substances taken from the USEtox organics database [32], This was done for 39 plastic additives for which experiment-based characterization factors were already available. 

Among the possible alternative methods, in vitro assay (for ATMs) and quantitative structure-activity relationships (QSARs) models (for ANTMs) are the most applied approaches in the toxicological and ecotoxicological evaluation of chemicals profiles, even in the field of environmental research and risk assessment. 

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