Estimation of drug solubility in water, PEG 400 and their binary mixtures using the molecular structures of solutes

With the aim of solubility estimation in water, polyethylene glycol 400 (PEG) and their binary mixtures, quantitative structure-property relationships (QSPRs) were investigated to relate the solubility of a large number of compounds to the descriptors of the molecular structures. The relationships were quantified using linear regression analysis (with descriptors selected by stepwise regression) and formal inference-based recursive modeling (FIRM). The models were compared in terms of the solubility prediction accuracy for the validation set. The resulting regression and FIRM models employed a diverse set of molecular descriptors explaining crystal lattice energy, molecular size, and solute-solvent interactions. Significance of molecular shape in compound's solubility was evident from several shape descriptors being selected by FIRM and stepwise regression analysis. Some of these influential structural features, e.g. connectivity indexes and Balaban topological index, were found to be related to the crystal lattice energy. The results showed that regression models outperformed most FIRM models and produced higher prediction accuracy. However, the most accurate estimation was achieved by the use of a combination of FIRM and regression models. The results also showed that the use of melting point in regression models improves the estimation accuracy especially for solubility in higher concentrations of PEG. Aqueous or PEG/water solubilities can be estimated by these models with root mean square error of below 0.70. © 2010 Elsevier B.V

Can small drugs predict the intrinsic aqueous solubility of ‘beyond Rule of 5’ big drugs?

The aim of the study was to explore to what extent small molecules (mostly from the Rule of 5 chemical space) can be used to predict the intrinsic aqueous solubility, S0, of big molecules from beyond the Rule of 5 (bRo5) space. It was demonstrated that the General Solubility Equation (GSE) and the Abraham Solvation Equation (ABSOLV) underpredict solubility in systematic but slightly ways. The Random Forest regression (RFR) method predicts solubility more accurately, albeit in the manner of a ‘black box.’ It was discovered that the GSE improves considerably in the case of big molecules when the coefficient of the log P term (octanol-water partition coefficient) in the equation is set to -0.4 instead of the traditional -1 value. The traditional GSE underpredicts solubility for molecules with experimental S0 < 50 µM. In contrast, the ABSOLV equation (trained with small molecules) underpredicts the solubility of big molecules in all cases tested. It was found that the errors in the ABSOLV-predicted solubilities of big molecules correlate linearly with the number of rotatable bonds, which suggests that flexibility may be an important factor in differentiating solubility of small from big molecules. Notably, most of the 31 big molecules considered have negative enthalpy of solution: these big molecules become less soluble with increasing temperature, which is compatible with ‘molecular chameleon’ behavior associated with intramolecular hydrogen bonding. The X‑ray structures of many of these molecules reveal void spaces in their crystal lattices large enough to accommodate many water molecules when such solids are in contact with aqueous media. The water sorbed into crystals suspended in aqueous solution may enhance solubility by way of intra-lattice solute-water interactions involving the numerous H‑bond acceptors in the big molecules studied. A ‘Solubility Enhancement–Big Molecules’ index was defined, which embodies many of the above findings.</p

Thermodynamic Properties of Aqueous Species Calculated Using the HKF Model: How Do Different Thermodynamic and Electrostatic Models for Solvent Water Affect Calculated Aqueous Properties?

Thermodynamic properties of aqueous species are essential for modeling of fluid-rock interaction processes. The Helgeson-Kirkham-Flowers (HKF) model is widely used for calculating standard state thermodynamic properties of ions and complexes over a wide range of temperatures and pressures. To do this, the HKF model requires thermodynamic and electrostatic models of water solvent. In this study, we investigate and quantify the impact of choosing different models for calculating water solvent volumetric and dielectric properties, on the properties of aqueous species calculated using the HKF model. We identify temperature and pressure conditions at which the choice of different models can have a considerable effect on the properties of aqueous species and on fluid mineral equilibrium calculations. The investigated temperature and pressure intervals are 25–1000°C and 1–5 kbar, representative of upper to middle crustal levels, and of interest for modeling ore-forming processes. The thermodynamic and electrostatic models for water solvent considered are: Haar, Gallagher and Kell (1984), Wagner and Pruß (2002), and Zhang and Duan (2005), to calculate water volumetric properties, and Johnson and Norton (1991), Fernandez and others (1997), and Sverjensky and others (2014), to calculate water dielectric properties. We observe only small discrepancies in the calculated standard partial molal properties of aqueous species resulting from using different water thermodynamic models. However, large differences in the properties of charged species can be observed at higher temperatures (above 500°C) as a result of using different electrostatic models. Depending on the aqueous speciation and the reactions that control the chemical composition, the observed differences can vary. The discrepancy between various electrostatic models is attributed to the scarcity of experimental data at high temperatures. These discrepancies restrict the reliability of the geochemical modeling of hydrothermal and ore formation processes, and the retrieval of thermodynamic parameters from experimental data at elevated temperatures and pressures.ISSN:1468-8115ISSN:1468-812

An evaluation of thermodynamic models for the prediction of drug and drug-like molecule solubility in organic solvents

Prediction of solubility of active pharmaceutical ingredients (API) in different solvents is one of the main issue for crystallization process design. Experimental determination is not always possible because of the small amount of product available in the early stages of a drug development. Thus, one interesting perspective is the use of thermodynamic models, which are usually employed for predicting the activity coefficients in case of Vapour–Liquid equilibria or Liquid–Liquid equilibria (VLE or LLE). The choice of the best thermodynamic model for Solid–Liquid equilibria (SLE) is not an easy task as most of them are not meant particularly for this. In this paper, several models are tested for the solubility prediction of five drugs or drug-like molecules: Ibuprofen, Acetaminophen, Benzoic acid, Salicylic acid and 4-aminobenzoic acid, and another molecule, anthracene, a rather simple molecule. The performance of predictive (UNIFAC, UNIFAC mod., COSMO-SAC) and semi-predictive (NRTL-SAC) models are compared and discussed according to the functional groups of the molecules and the selected solvents. Moreover, the model errors caused by solid state property uncertainties are taken into account. These errors are indeed not negligible when accurate quantitative predictions want to be performed. It was found that UNIFAC models give the best results and could be an useful method for rapid solubility estimations of an API in various solvents. This model achieves the order of magnitude of the experimental solubility and can predict in which solvents the drug will be very soluble, soluble or not soluble. In addition, predictions obtained with NRTL-SAC model are also in good agreement with the experiments, but in that case the relevance of the results is strongly dependent on the model parameters regressed from solubility data in single and mixed solvents. However, this is a very interesting model for quick estimations like UNIFAC models. Finally, COSMO-SAC needs more developments to increase its accuracy especially when hydrogen bonding is involved. In that case, the predicted solubility is always overestimated from two to three orders of magnitude. Considering the use of the most accurate equilibrium equation involving the ΔCp term, no benefits were found for drug predictions as the models are still too inaccurate. However, in function of the molecules and their solid thermodynamic properties, the ΔCp term can be neglected and will not have a great impact on the results

A new view on gold speciation in sulfur-bearing hydrothermal fluids from in situ X-ray absorption spectroscopy and quantum-chemical modeling

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