Method for predicating bovine serum albumin-water distribution coefficient of organic compound on basis of molecular structures and model establishment method

Abstract

The invention relates to a method for rapidly predicating the bovine serum albumin-water distribution coefficient (log K [BSA/W]) of an organic compound on the basis of molecular structures and a model establishment method. According to the method, ovine serum albumin-water distribution coefficient prediction is conducted according to the following model, namely, log K [BSA/W]=0.747*log[Kow]+0.476*E[HOMO]-0.024*CCR+0.870*q<+>+0. 007*CSEV+4.342, wherein the log[Kow] represents the n-caprylic alcohol-water distribution coefficient, the E[HOMO] represents the maximum occupied molecular orbital energy, the CCR represents nucleus-nucleus repulsive energy, the q<+> represents the most positive net charge, and the CSEV presents the Connolly solvent repulsion volume. The method has the advantage that the distribution coefficients of various types of neutral organic compounds in the bovine serum albumin and water can be rapidly predicted according to the method. Descriptors of the molecular structures which are used can be easily obtained, the regression analysis method is simple, and therefore the method is convenient and rapid to use, low in cost and excellent in performance.

Description

technical field [0001] The invention belongs to the technical field of quantitative structure-activity relationship (QSAR) for environmental ecological risk assessment, and in particular relates to the development of a rapid prediction of organic compound bovine serum albumin-water partition coefficient (log K BSA / W ) and model building methods can be applied to various types of neutral organic compounds log K BSA / W Prediction. Background technique [0002] After organic compounds enter the organism, their distribution in tissues and organs will significantly affect their bioaccumulation, toxicokinetics and toxic effects. In addition to the absorption and adsorption of organic compounds by fat components in the body, the enrichment of protein components also plays an important role in the absorption and metabolism of compounds in the body. Compounds that enter the bloodstream bind to serum albumin, thereby affecting its distribution, absorption, transport, and metabolism...

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Problems solved by technology

However, the currently reported QSAR model for KBSA / W still has problems in terms of the scope of application of the compound, the predictive ability of the model, and the operability of practical applications.

Wichmann et al. (Wichmann, K., Diedenhofen, M., Klamt, A. Prediction of blood-brain partitioning and human serum albumin binding based on COSMO-RS σ-moments. Journal of Chemical Information Modeling, 2006, 47: 228– 233) Calculated 5 quantum chemical parameters using the solvent model and established a QSAR model of 92 drug molecules and serum albumin binding constants. The prediction performance of this model is not high, and the types of compounds that can be applied are limited and cannot be used for environmental organic compounds. Prediction

deBruyn et al. (deBruyn, A. M. H., Gobas, F. A. P. The sorptive capacity of animal protein. Environmental Toxicology and Chemistry, 2007, 26: 1803–1808) use the n-octanol-water partition coefficient logKow established a QSAR model for 36 compounds KBSA / W, but the model covers a small range of compounds and has poor predictive performance

Endo et al. (Endo S., Goss, K-U. Serum Albumin Binding of Structurally Diverse Neutral Organic Compounds: Data and Models. Chemical Research in Toxicology, 2011, 24, 2293–2301) based on the multiparameter linear free energy relationship (pp-LFER ) established the QSAR model of 83 organic compounds KBSA / W, and there is also the problem of low performance of the model, which is used for KBSA / W predictive reliability is low

Using the same compound, Golmohammadi et al. (Golmohammadi H., Dashtbozorgi Z., Acree Jr W. E. Prediction of bovine serum albumin-water partition coefficients of a wide variety of neutral organic compounds by means of support vector machine. Molecular Informatics, 2012, 31 : 867-878) developed a new QSAR model based on the support vector machine using topological index, the model uses a nonlinear method, the transparency is not high, and the portability is poor

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