Solubility prediction coefficient

It should be noted that a connection between hydrophobicity and fat-soluble partition coefficients (n-octanol/water) Pow) has been proposed. The ratio is reported as a logarithm (log Pow) that can be considered a quantitative measure of the hydrophobicity of a compound. Hence, log Pow could be used to predict useful operating HPLC conditions. It would be applicable to hydrophobic compounds, but it may not apply to hydrophilic compounds with a log Pow of less than 2 (23). 

The accurate prediction of the aqueous solubility of drugs and drug-like compounds is much further away from a satisfactory solution because the existing QSAR- or group-contribution-based solubility prediction models exhibit quite limited predictive power for new drug classes. Why is it that the extremely important problem of the prediction of aqueous solubility much less solved than the prediction of less important partition coefficients The answer is that the development of prediction models for logarithmic partition coefficients is much simpler, because the molecule X under consideration only acts as a solute at infinite dilution in the two phases. Hence the task is only to calculate the free energy of... 

The solubility prediction equations derived from the three-level tests are given in Table V with their respective correlation coefficients (R2) to assess goodness of fit. 

Calculation of octanol/water partition coefficients. SciLogW for aqueous solubility prediction. SciPredictor for protein secondary structure and homology modeling. SciPolymer for polymer property estimation. PCs under Windows. CAD Gene for gene constructs. Macintosh. 

Keywords Solubility prediction, Pharmaceuticals,, NRTL-SAC Thermodynamic model, Activity coefficient, Solvent screening, Single solvent, Solvent mixture... 

We focus on the thermodynamic models that deal with the liquid mixtures in this chapter. From the two categories of activity coefficient models, the correlative one is not very useful for solubility prediction and solvent screening purposes. The main reason for this is the lack of experimental data for the binary interaction parameters of the solute-solvent, solute-antisolvent, and solvent-antisolvent systems. As an example, the activity coefficient from... 

Hilal SH, Karickhoff SW, Carreira LA. Prediction of the solubility, activity coefficient and liquid/liquid partition coefficient of organic compounds. QSAR Combin Sci 2004 23 709-20. 

Since passive diffusion is the primary driving force behind dermal absorption, physicochemical factors such as molecular weight and structure, lipophilicity, pKa, ionization, solubility, partition coefficients, and diffusivity can influence the dermal absorption of various classes of chemicals. In addition penetration of acidic and basic compounds will be influenced by the skin surface, which is weakly acidic (pH 4.2-5.6), since only the uncharged moiety of weak acids and bases is capable of diffusing though the lipid pathway. Several of these factors (e.g., molecular weight, and partition coefficients) have been used to predict absorption of various drug classes [24-26],... 

Fundamental physicochemical properties (see Chapter 5) such as partition coefficient (log P) and distribution coefficient (log D) and pfCa are well predicted directly from chemical structure [42, 43]. Aqueous solubility may also be predicted reasonably well [44—46] (see Chapter 4), though there are warnings on the accuracy of solubility predictions for in-house pharmaceutical company compounds since these tend to be quite different chemical structures from those used to develop the commercial models [47]. 

The question remains, however, of whether the solution is in fact infinitely dilute at a solute concentration of xi. Only if this is true is it valid to assume that yi = y - Literature values of solubility data for several compounds in water were used to obtain parameters for the UNIQUAC and NRTL excess Gibbs energy expressions, and y values for these compounds were calculated. The calculated values are compared with inverse solubility data in Table I. The inverse solubility predicts lower values of y in all cases. However, the difference becomes smaller as the solubility decreases, and for compounds with solubility less than 0,5% the difference is less than 10%. It has been shown that these excess Gibbs energy expressions, while very useful, are not the exact representation of the composition dependence of activity coefficient all expressions have difficulty in representing liquid-liquid equilibria (43-44). Thus, extrapolating these expressions to infinite dilution may be in error. It is therefore inconclusive as to the correctness of using the inverse solubility to calculate... 

Aqueous solubility prediction continues to be an active area of research, with a wide variety of approaches being applied to this important and challenging area. To date, group contribution approaches as well as correlation with physicochemical properties (partition coefficient) appear to be the most promising (29, 32). It is important to keep in mind that correlations that are developed from structurally related analogs would consistently yield more accurate predictions. 

The addition of AMIFS values calculated from acMIES to the MIFS values improved the correlation with the log k values. The correlation coefficient was 0.973 ( = 18) as demonstrated in Figure 6.38. Even though solubility prediction is impossible, the MI energy values with a model solvent phase improved the precision of the relationship between the log k values and the molecular interaction energy values. The correlation coefficients in 90 and 70% aqueous acetonitrile were 0.967 n = 18) and 0.971 n = 17), respectively. 

The HYBOT descriptors were successfully applied to the prediction of the partition coefficient log P (>i--octanol/water) for small organic componnds with one acceptor group from their calculated polarizabilities and the free energy acceptor factor C, as well as properties like solubility log S, the permeability of drugs (Caco-2, human skin), and for the modeling of biological activities. 

Two approaches to quantify/fQ, i.e., to establish a quantitative relationship between the structural features of a compoimd and its properties, are described in this section quantitative structure-property relationships (QSPR) and linear free energy relationships (LFER) cf. Section 3.4.2.2). The LFER approach is important for historical reasons because it contributed the first attempt to predict the property of a compound from an analysis of its structure. LFERs can be established only for congeneric series of compounds, i.e., sets of compounds that share the same skeleton and only have variations in the substituents attached to this skeleton. As examples of a QSPR approach, currently available methods for the prediction of the octanol/water partition coefficient, log P, and of aqueous solubility, log S, of organic compoimds are described in Section 10.1.4 and Section 10.15, respectively. 

The partition coefficient and aqueous solubility are properties important for the study of the adsorption, distribution, metabolism, excretion, and toxicity (ADME-Tox) of drugs. The prediction of the ADME-Tox properties of drug candidates has recently attracted much interest because these properties account for the failure of about 60 % of all drug candidates in the clinical phases. The prediction of these properties in an early phase of the drug development process could therefore lead to significant savings in research and development costs. 

Multiple linear regression analysis is a widely used method, in this case assuming that a linear relationship exists between solubility and the 18 input variables. The multilinear regression analy.si.s was performed by the SPSS program [30]. The training set was used to build a model, and the test set was used for the prediction of solubility. The MLRA model provided, for the training set, a correlation coefficient r = 0.92 and a standard deviation of, s = 0,78, and for the test set, r = 0.94 and s = 0.68. 

If the mutual solubilities of the solvents A and B are small, and the systems are dilute in C, the ratio ni can be estimated from the activity coefficients at infinite dilution. The infinite dilution activity coefficients of many organic systems have been correlated in terms of stmctural contributions (24), a method recommended by others (5). In the more general case of nondilute systems where there is significant mutual solubiUty between the two solvents, regular solution theory must be appHed. Several methods of correlation and prediction have been reviewed (23). The universal quasichemical (UNIQUAC) equation has been recommended (25), which uses binary parameters to predict multicomponent equihbria (see Eengineering, chemical DATA correlation). 

The solubihty coefficients are more difficult to predict. Although advances are being made, the best method is probably to use a few known solubility coefficients in the polymer to predict others with a simple plot of S vs ( poiy perm Y where and are the solubility parameters of the polymer and permeant respectively. When insufficient data are available, S at 25°C can be estimated with equation 19 where k = 1 and the resulting units of cal/cm are converted to kj /mol by dividing by the polymer density and multiplying by the molecular mass of the permeant and by 4.184 (16). 

A.queous Solubility. SolubiHty of a chemical in water can be calculated rigorously from equiHbrium thermodynamic equations. Because activity coefficient data are often not available from the Hterature or direct experiments, models such as UNIFAC can be used for stmcture—activity estimations (24). Phase-equiHbrium relationships can then be appHed to predict miscibility. Simplified calculations are possible for low miscibiHty however, when there is a high degree of miscibility, the phase-equiHbrium relationships must be solved rigorously. 

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