Toxicity quantum-chemical descriptors

The primary supposition of any toxicological QSAR is that the potency of a compound is dependent upon its molecular structure, which is typically quantified by chemical properties (Schultz et al., 2002). Chemical descriptors include a variety of types, including atom, substituent, and molecular parameters. The most transparent of these are the molecular-based empirical and quantum chemical descriptors. Empirical descriptors are measured descriptors and include physicochemical properties such as hydrophobicity (Dearden, 1990). Quantum chemical properties are theoretical descriptors and include charge and energy values (Karelson et al., 1996). Physicochemical and quantum chemical descriptors are for the most part easily interpretable with regard to how that property may be related to toxicity. The classic example of this, the partitioning of a toxicant between aqueous and lipid phases, has been used as a measure of hydrophobicity for over a century (Livingstone, 2000). 

Karabunarliev, S., Mekenyan, O.G., Karcher, W., Russom, C.L., and Bradbury, S.P., Quantum-chemical descriptors for estimating the acute toxicity of electrophiles to the fathead minnow (Pimephales promelas) an analysis based on molecular mechanisms, Quant. Struct.-Act. Relat., 15, 302-310, 1996a. 

Sixt S, Altschuh J, Briiggemann R (1995) Quantitative structure-toxicity relationships for 80 chlorinated compounds using quantum chemical descriptors. Chemosphere 30 2397-2414... 

Schuurmann G (2004) Quantum chemical descriptors in structure-activity relationships - calculation, interpretation, and comparison of methods. In Cronin MTD (ed) Predicting chemical toxicity and fate. CRC, Boca Raton FL... 

Sixt, S., Altschuh, J. and Brtiggemann, R. (1995). Quantitative Structure-Toxicity Relationships for 80 Chlorinated Compounds Using Quantum Chemical Descriptors. Chemosphere, 30, 2397-2414. 

Quantum-chemical molecular descriptors have been actively used in the quantitative structure-activity relationship studies of biological activities [1,2,72]. In the following, examples of QSARs involving quantum-chemical descriptors and applied on the enzymatic reactivity, pharmacological activity, and toxicity of compounds are discussed. 

Quantum-chemical descriptors have been extensively used in the development of QSARs of various toxic activities of compounds. In principle, the possible interactions that determine toxicity coincide with those determining the pharmacological activity of compounds. Therefore the descriptors may either reflect the direct interaction of toxic agents with the biological targets or they may be related to the bioavailability of such agents. 

The proliferation toxicity toward the algae Scenedesmus vacuolatus in a 24-hr one-generation reproduction assay has been correlated with hydrophobicity (log K0w) and various quantum-chemical descriptors of molecular reactivity using AMI parameterization [119]. The possible mechanism of the toxic action has been proposed in view of the strong correlations with the LUMO and SOMO (singly occupied molecular... 

In another recent study, QSAR models were developed using quantum-chemical descriptors to describe the toxic influence of polychlorinated organic compounds on the rainbow trout (Oncorhynchus mykiss). The logarithm of the bioconcentration factor (BCF) was best correlated with the AM 1 -calculated a-polarizability, energies of the frontier orbitals, and the core-core repulsion energy (CCR), as follows [121] ... 

SchUUrmann, G. (2004). Quantum chemical descriptors in structured-activity relationships—calculation, interpretation and comparison of methods, in M.T.D. Cronin and D.J. Livingstone (eds.). Predicting Chemical Toxicity and Fate, Boca Raton, FL CRC Press, pp. 85-149. 

The effects of 8 transition metals were studied in a nitrifying system to investigate the relationship between the ionic characteristics of metals and their toxicity to nitrifiers. The cumulative oxygen consumption and the cumulative carbon dioxide production were monitored throughout each respirometric batch run to determine the toxicity of metals to nitrifiers. Several QCAR models were developed on the basis of these different toxicity endpoints using quantum chemical descriptors. 

Vectors A series of scalars can be arranged in a column or in a row. Then, they are called a column or a row vector. If the elements of a column vector can be attributed to special characteristics, e.g., to compounds, then data analysis can be completed. The chemical structures of compounds can be characterized with different numbers called descriptors, variables, predictors, or factors. For example, toxicity data were measured for a series of aromatic phenols. Their toxicity can be arranged in a column arbitrarily Each row corresponds to a phenolic compound. A lot of descriptors can be calculated for each compound (e.g., molecular mass, van der Waals volume, polarity parameters, quantum chemical descriptors, etc.). After building a multivariate model (generally one variable cannot encode the toxicity properly) we will be able to predict toxicity values for phenolic compounds for which no toxicity has been measured yet. The above approach is generally called searching quantitative structure - activity relationships or simply QSAR approach. 

In computational chemistry, the main use of PLS is to model the relationship between, on the one hand, computed and measured variables that together characterize the structural variation of a set of N compounds and, on the other hand, biological response variables or other interesting properties measured on the same N substances. " The former comprise the matrix X-of predictor variables, and the latter the matrix Y of response variables. These matrices have dimensions N x K and N X M, respectively. We note that the X variables are usually not independent, and hence they should not be called by that name, but rather predictors or simply X variables. The example used here to illustrate PLS is of this type, where the structural variation of iV = 15 compounds is translated to iV = 15 rows of an X matrix using X = 8 measured and calculated quantum chemical descriptors. The biological response of these compounds is described by a Y matrix comprising M = S aquatic toxicity responses. 

Basak, S. C., Mills, D., Gute, B. D. Predicting bioactivity and toxicity of chemicals from mathematical descriptors A chemical-cum-biochemical approach. In Advances in Quantum Chemistry, Klein, D. J., Brandas, E., Eds., Elsevier, Amsterdam, 2004, in press. 

Toxicity, as with all forms of biological activity, is a result of the molecular structure of the chemical concerned. Given that fact, the computational chemist is presented with a problem that is, at least theoretically, soluble. The tools that have been applied so successfully to rationalizing biological activity in terms of chemical structure can also be used for correlating toxicity with various structural parameters.24 Such structural descriptors may be physicochemical values,25 functions of molecular size and shape, molecular connectivity, and numbers of atoms, or they may be quantum-chemical parameters relating to electronic distribution within the molecule.26-27... 

Basak SC, Mills D, Gute BD (2006) Predicting Bioactivity and Toxicity of Chemicals from Mathematical Descriptors A Chemical-cum-Biochemical Approach. In Klein DJ, Brandas E (eds) Advances in Quantum Chemistry, Elsevier, in press... 

Chemical reactivity and biological activity can be related to molecular structure and physicochemical properties. QSAR models can be established among hydrophobic-lipophilic, electronic, and steric properties, between quantum-mechanics-related parameters and toxicity and between environmental fate parameters such as sorption and tendency for bioaccumulation. The main objective of a QSAR study is to develop quantitative relationships between given properties of a set of chemicals and their molecular descriptors. To develop a valid QSAR model, the following steps are essential ... 

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