Estimation of aqueous solubility in drug design

The solubility of drugs in water is of central importance in the process of drug discovery and development from molecular design to pharmaceutical formulation and biopharmacy. The ability to estimate the aqueous solubility and other properties of a promising lead compound affecting its pharmacokinetics is a prerequisite to rational drug design, although it has received much less attention than the prediction of drug-receptor interactions. In this review, methods for the estimation of aqueous solubility of organic compounds are described and limited to approaches, which might be used in the early stage of drug design and development.     

Internet software for the calculation of the lipophilicity and aqueous solubility of chemical compounds

In this paper we describe an Internet Java-based technology that allows scientists to make their analytical software available worldwide. The implementation of this technology is exemplified by programs for the calculation of the lipophilicity and water solubility of chemical compounds available at http://www.lnh.unil.ch/~itetko/logp. Both these molecular properties are key parameters in quantitative structure-activity relationship studies and are used to provide invaluable information for the overall understanding of the uptake distribution, biotransformation, and elimination of a wide variety of chemicals. The compounds can be analyzed in batch or single-compound mode. The single-compound analysis offers the possibility to compare our results with several popular lipophilicity calculation methods, including CLOGP, KOWWIN, and XLOGP. The chemical compounds are analyzed according to SMILES line notation that can be prepared with the JME molecular editor of Peter Ertl. Conversion to SMILES from 56 formats is also available using the molecular structure information interchange hub developed by Pat Walters and Matt Stahl.     

Estimation of aqueous solubility for a diverse set of organic compounds based on molecular topology

An accurate and generally applicable method for estimating aqueous solubilities for a diverse set of 1297 organic compounds based on multilinear regression and artificial neural network modeling was developed. Molecular connectivity, shape, and atom-type electrotopological state (E-state) indices were used as structural parameters. The data set was divided into a training set of 884 compounds and a randomly chosen test set of 413 compounds. The structural parameters in a 30-12-1 artificial neural network included 24 atom-type E-state indices and six other topological indices, and for the test set, a predictive r2 = 0.92 and s = 0.60 were achieved. With the same parameters the statistics in the multilinear regression were r2 = 0.88 and s = 0.71, respectively.     

Prediction of aqueous solubility of organic compounds by the general solubility equation (GSE)

The revised general solubility equation (GSE) is used along with four different methods including Huuskonen's artificial neural network (ANN) and three multiple linear regression (MLR) methods to estimate the aqueous solubility of a test set of the 21 pharmaceutically and environmentally interesting compounds. For the selected test sets, it is clear that the GSE and ANN predictions are more accurate than MLR methods. The GSE has the advantages of being simple and thermodynamically sound. The only two inputs used in the GSE are the Celsius melting point (MP) and the octanol water partition coefficient (K(ow)). No fitted parameters and no training data are used in the GSE, whereas other methods utilize a large number of parameters and require a training set. The GSE is also applied to a test set of 413 organic nonelectrolytes that were studied by Huuskonen. Although the GSE uses only two parameters and no training set, its average absolute errors is only 0.1 log units larger than that of the ANN, which requires many parameters and a large training set. The average absolute error AAE is 0.54 log units using the GSE and 0.43 log units using Huuskonen's ANN modeling. This study provides evidence for the GSE being a convenient and reliable method to predict aqueous solubilities of organic compounds.     

Prediction of n-octanol/water partition coefficients from PHYSPROP database using artificial neural networks and E-state indices

A new method, ALOGPS v 2.0 (http://www.lnh.unil.ch/~itetko/logp/), for the assessment of n-octanol/water partition coefficient, log P, was developed on the basis of neural network ensemble analysis of 12 908 organic compounds available from PHYSPROP database of Syracuse Research Corporation. The atom and bond-type E-state indices as well as the number of hydrogen and non-hydrogen atoms were used to represent the molecular structures. A preliminary selection of indices was performed by multiple linear regression analysis, and 75 input parameters were chosen. Some of the parameters combined several atom-type or bond-type indices with similar physicochemical properties. The neural network ensemble training was performed by efficient partition algorithm developed by the authors. The ensemble contained 50 neural networks, and each neural network had 10 neurons in one hidden layer. The prediction ability of the developed approach was estimated using both leave-one-out (LOO) technique and training/test protocol. In case of interseries predictions, i.e., when molecules in the test and in the training subsets were selected by chance from the same set of compounds, both approaches provided similar results. ALOGPS performance was significantly better than the results obtained by other tested methods. For a subset of 12 777 molecules the LOO results, namely correlation coefficient r(2)= 0.95, root mean squared error, RMSE = 0.39, and an absolute mean error, MAE = 0.29, were calculated. For two cross-series predictions, i.e., when molecules in the training and in the test sets belong to different series of compounds, all analyzed methods performed less efficiently. The decrease in the performance could be explained by a different diversity of molecules in the training and in the test sets. However, even for such difficult cases the ALOGPS method provided better prediction ability than the other tested methods. We have shown that the diversity of the training sets rather than the design of the methods is the main factor determining their prediction ability for new data. A comparative performance of the methods as well as a dependence on the number of non-hydrogen atoms in a molecule is also presented.     

Estimation of the aqueous solubility of organic molecules by the group contribution approach

Several group contribution methods to estimate the aqueous solubility of organic molecules are proposed and evaluated for their ability to predict the water solubility of new molecules. The learning set consisted of 1168 organic compounds with experimental data taken from the literature after critical evaluation. The best method, based on a new fragment atom scheme, leads to a squared correlation coefficient of 0.95 and an average absolute calculation error of 0.50 log unit, which is superior to other group contribution methods currently available. One of the advantages of this model is that it has upper and lower limits so that the predicted solubilities cannot be unrealistily high or low.     

Estimation of aqueous solubility of organic molecules by the group contribution approach. Application to the study of biodegradation.

A reliable and generally applicable aqueous solubility estimation method for organic compounds based on a group contribution approach has been developed. Two models have been established based on two different sets of parameters. One has a higher accuracy, while the other has a more general applicability. The prediction potentials of these two models have been evaluated through cross-validation experiments. For model I, the mean cross-validated r2 and SD for 10 such cross-validation experiments were 0.946 and 0.503 log units, respectively. While for model II, they were 0.953 and 0.546 log units, respectively. Applying our models to estimate the water solubility values for the compounds in an independent test set, we found that model I can be applied to 13 out of 21 compounds with a SD equal to 0.58 log unit and model II can be applied to all the 21 compounds with a SD equal to 1.25 log units. Our models compare favorably to all the current available water estimation methods. A program based on this approach has been written in FORTRAN77 and is currently running on a VAX/VMS system. The program can be applied to estimate the water solubility of the water solubility of any organic chemical with a good or fairly good accuracy except for except for electrolytes. Applying our aqueous solubility estimation models to biodegradation studies, we found that although the water solubility was not the sole factor controlling the rate of biodegradation, ring compounds with greater solubilities were more likely to biodegrade at a faster rate. The significance of the relationship between water solubility and biodegradation activity has been illustrated by predicting the biodegradation activity of 27 new chemicals based solely on their estimated solubility values.     

Experimental determination of the solubility of aromatic compounds in aqueous solutions of various amines

A new apparatus to measure solubility data of aromatics in aqueous solutions has been designed. It is based on a static-analytic method with Rolsi™ pneumatic samplers for on line gas chromatograph analysis. Operating pressures and temperatures are between 0.3 and 10 MPa and between 293 and 393 K.

Solubility measurement results are reported for several aromatic compounds (benzene, toluene, ethylbenzene and xylene) in different amine aqueous solutions (monoethanolamine (MEA), diethanolamine (DEA), methyldiethanolamine (MDEA), diglycolamine (DGA)). Several influent parameters are studied (temperature, total pressure, etc.).     

Comparison of two methods for predicting aqueous solubility.

This study compares the solubility predictions of the two parameter general solubility equation (GSE) of Jain and Yalkowsky with the 171 parameter Klopman group contribution approach. Melting points and partition coefficients were obtained for each of the compounds from Klopman's test set. Using these two variables, the solubility of each compound was calculated by the GSE and compared to the values predicted by Klopman. Both methods give reasonable solubility predictions. The data of Klopman produced an average absolute error (AAE) of 0.71 and a root-mean-square error (RMSE) of 0.86, while the GSE had an AAE of 0.64 and a RMSE of 0.92.     

Determination of the solubility of organic compounds. II

Summary In general, solid particles liquify when they are exposed to air laden with the vapor of a liquid in which they are soluble. There are exceptions, however, which may be explained by assuming the formation of a solvate.

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Zusammenfassung Feste Teilchen verflüssigen sich im allgemeinen, wenn sie Luft ausgesetzt sind, die mit dem Dampf einer Flüssigkeit geladen ist, in der sie löslich sind. Es wurden aber Ausnahmen gefunden, die möglicherweise durch die Bildung von Solvaten erklärt werden können.

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Résumé Généralement, les particules solides se liquéfient si elles sont exposées à l'air chargé de la vapeur d'un liquide dans lequel elles sont solubles. Il existe cependant des exceptions que l'on peut expliquer en supposant la formation de solvates.

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On the occasion of the hundredth return ofFriedrich Emich's birthday.

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The authors wish to express their appreciation to ProfessorO. F. Steinbach for his suggestions and advice.     

The solubility of ozone and kinetics of its chemical reactions in aqueous solutions of sodium chloride

The solubility of ozone and the kinetics of its decomposition and interaction with chloride ions in a 1 M aqueous solution of NaCl at 20°C and pH 8.4–10.8 were studied. The ratio between the concentration of O₃ in solution and the gas phase was found to be 0.16 at pH 8.4–9.8. The concentration of dissolved ozone decreased sharply as pH increased to 10.8 because of a substantial increase in the rate of its decomposition. It was observed for the first time that the interaction of O₃ with Cl^? in alkaline media resulted in the formation of ClO ₃ ^? chlorate ions. The dependence of the rate of formation of ClO ₃ ^? on pH was determined; its maximum value was found to be 9.6 × 10^?6 mol l^?1 min^?1 at pH 10.0 and the concentration of ozone at the entrance of the reactor 30.0 g/m³. A spectrophotometric method for the determination of chlorate ions (concentrations 1 × 10^?5?3 × 10^?4 M) in aqueous solutions was suggested.     

Enhancement of cyclosporine aqueous solubility using α- and hydroxypropyl β-cyclodextrin mixtures

Cyclodextrins (CDs) are cyclic oligosaccharides that form inclusion complexes with lipophilic molecules through their hydrophobic central cavity. In this study, the effect of α-CD, hydroxylpropyl-β-CD (HP-β-CD) and mixtures of these two CDs on the aqueous solubility of cyclosporine A (CyA) was investigated. Infrared spectroscopy and thermal analysis were used to confirm CyA-CD complex formation. CyA aqueous solubility was increased by 10 and 80 fold in the presence of α-CD and HP β-CD, respectively. The phase-solubility profile for HP-β-CD was linear while that for α-CD had positive deviation from linearity. In the presence of constant concentration of α-CD (15% w/v), aqueous solubility of CyA was further increased upon addition of HP-β-CD up to a concentration of 20% w/v. At higher HP-β-CD concentrations, aqueous solubility of CyA was observed to decrease. Addition of sodium acetate (up to 5% w/v) to aqueous solutions containing 20% w/v HP-β-CD and increasing concentrations of α-CD resulted in a significant reduction in CyA solubility. Complex formation between CyA and both α-CD and HP-β-CD was confirmed by differential scanning calorimetry (DSC). No significant changes were observed in the IR spectra of either CyA or CD following complex formation suggesting chemical interaction between CyA and the CD was unlikely. Phase-solubility studies showed that α-CD had a much greater effect on the solubility of CyA than HP-β-CD. Addition of HP-β-CD to aqueous solutions of α-CD affected the solubility of CyA in these systems. A mixture of 15% w/v α-CD and 20% w/v HP-β-CD was optimal for increasing aqueous solubility of CyA.