Analysis of Water Solubility Data on the Basis of HYBOT Descriptors. Part 3. Solubility of Solid Neutral Chemicals and Drugs

Abstract

A detailed QSPR investigation of the water solubility (logS) of 1063 solid neutral chemicals, agrochemicals, drugs and prodrugs has been carried out. The application of the ™General Solubility Equation∫ of Yalkowsky et al. resulted in a correlation between experimental and calculated solubility with rather modest statistic criteria. It was found that 191 compounds (18%) have calculated logS with deviations above one logarithmic unit. A comparison of experimental values with those calculated by an equation previously derived for liquid chemicals demonstrates that a part of the total set of compounds containing certain substructures such as chloroalkyls, phenyls, biphenyls, nitrogen (acyclic and in cycles), benzanthracenes, phenols, and chemicals with ether, ester, and N,N-disubstituted carboxamide groups obey to the equation for liquids. Not unexpectedly, the other part of compounds containing both strong H-bond acceptor and donor groups is essentially less soluble than calculated for compounds in the liquid state. It is proposed that this low solubility is connected with a specific H-bond association of these compounds in their crystal lattice.Two approaches were tested for the derivation of quantitative models for prediction of the solubility of solid chemicals and drugs. The first approach is based on the QSPR for liquids extended by several indicator variables for different functional groups. The second approach is based on the combination of chemical similarity and traditional QSAR techniques. In the framework of this approach different numbers of structural and physicochemical neighbors of a compound-of-interest were considered together with the corresponding HYBOT descriptor data. This method enables to calculate the solubility of a compound-of-interest by using the solubility of nearest neighbor compounds and the difference between descriptor data of those neighbors and the corresponding data of the compound-of-interest. It was found that the use of nearest neighbor compounds with high Tanimoto index value (i.e. good structural similarity) and close H-bond acceptor and donor factors (good physicochemical similarity) ensures good prediction of water solubility with average absolute errors on the level of the error of experimental logS determination.

Recent progress in the computational prediction of aqueous solubility and absorption

“…They implicitly capture the crystal lattice energy using the observed solubility of several highly similar compounds with an adjustment based on the differences in the physicochemical descriptors identifi ed as important for predicting the solubility of liquids. 36 The resulting model performance is very competitive with those obtained for the GSE, but without the requirement for a measured melting point. Roughly 7 of the 14 outliers in this model fall in the FFP range, based on a plot in the paper.…”

“…36 They are both discussed in greater detail below, in the section about crystal effects on solubility prediction.…”

“…In the follow-up paper that considers solid chemicals and drugs, 36 the authors apply the GSE to 1063 neutral solidstate compounds with known melting point. The statistical performance ( R 2 = 0.85, RMSe = 0.81) was substantially less impressive than for previous data sets that included a large number of liquid-state compounds.…”

Acute intravenous toxicity to mice calculations on the basis local regression models in superoverlapping clusters (LRMSC)

“…Указанный подход был успешно применен для расчета липофильности [2], растворимости в воде [3,4], адсорбции химических соединений в организме человека [5,6], и токсичности по отношению к Guppy [7]. Опыт использования уравнения (2) показывает, что результаты расчета становятся лучше в случае, когда структурный сосед имеет близкие физико-химические свойства по отношению к рассматриваемому соединению.…”

QSPR Prediction of Aqueous Solubility of Drug-Like Organic Compounds

“…[20][21][22] Several articles have published with MLR models for the prediction of aqueous solubility. [23][24][25][26] In a QSPR study, a mathematical model is developed which relates the structure of a set of compounds to a physical property such as aqueous solubility. In a QSPR study is that there is some sort of relationship between the physical property of interest and structural descriptors.…”