Prediction of the Solubility of Medium-Sized Pharmaceutical Compounds Using a Temperature-Dependent NRTL-SAC Model (original) (raw)
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Prediction of pharmaceutical solubilityVia NRTL-SAC and COSMO-SAC
Journal of Pharmaceutical Sciences, 2008
Solid phase solubility is a fundamental parameter in the design of crystallization processes. The development and optimization of crystallization processes requires screening of numerous solvent systems for which the solubility of the compound of interest has to be measured as a function of temperature and solvent composition. Tools that quickly estimate the solubility in different solvents can be very useful in the initial phases of the solvent system selection process. In this paper, we report our experience applying two thermodynamic models in the solubility estimation of pharmaceutical compounds: the NRTL-SAC method (Chen and Song, 2004, Ind Eng Chem Res 43: 8354) which provides a correlative and predictive model from limited solubility measurements, and the COSMO-SAC (Lin and Sandler, 2002, Ind Eng Chem Res 41: 899) method which predicts solubility from ab initio calculations. These theoretical methods, coupled with rapid experimental measurement for verification, provide a powerful solubility screening protocol for the development of crystallization processes.
The importance of the accuracy of the experimental data for the prediction of solubility
Journal of the Serbian Chemical …
Aqueous solubility is an important factor influencing several aspects of the pharmacokinetic profile of a drug. Numerous publications present different methodologies for the development of reliable computational models for the prediction of solubility from structure. The quality of such models can be significantly affected by the accuracy of the employed experimental solubility data. In this work, the importance of the accuracy of the experimental solubility data used for model training was investigated. Three data sets were used as training sets -data set 1, containing solubility data collected from various literature sources using a few criteria (n = 319), data set 2, created by substituting 28 values from data set 1 with uniformly determined experimental data from one laboratory (n = 319), and data set 3, created by including 56 additional components, for which the solubility was also determined under uniform conditions in the same laboratory, in the data set 2 (n = 375). The selection of the most significant descriptors was performed by the heuristic method, using one--parameter and multi-parameter analysis. The correlations between the most significant descriptors and solubility were established using multi-linear regression analysis (MLR) for all three investigated data sets. Notable differences were observed between the equations corresponding to different data sets, suggesting that models updated with new experimental data need to be additionally optimized. It was successfully shown that the inclusion of uniform experimental data consistently leads to an improvement in the correlation coefficients. These findings contribute to an emerging consensus that improving the reliability of solubility prediction requires the inclusion of many diverse compounds for which solubility was measured under standardized conditions in the data set.
Journal of Molecular Liquids, 2017
In this work, solubility of four Active Pharmaceutical Ingredients (APIs) including Butyl Paraben, Fenoxycarb, Fenofibrate and Risperidone were predicted using Hansen Flory Huggins model using two different scenarios. In the first method, activity coefficient of APIs were obtained through fitting the experimental activity coefficients of solvents at particular temperature of 293 K, then components solubility in entire temperature range of study was predicted. In the second scenario, the model parameters were adjusted using experimental data of two selected solvents, then components solubility were predicted in other solvents. In order to check the physical meanings of obtained values, Molecular Dynamic (MD) simulations was utilized and the results were compared. Finally the predictive capabilities of two Hansen Flory Huggins models were compared to temperature-dependent NRTL-SAC model.
Calculation of drug-like molecules solubility using predictive activity coefficient models
Fluid Phase Equilibria, 2012
The A-UNIFAC, UNIFAC, and NRTL-SAC models are used to predict solubility in pure solvents of a set of drug-like molecules. To apply A-UNIFAC, a new set of residual interaction parameters between the ACOH group and six other groups had to be estimated. The solute model parameters of NRTL-SAC were also estimated for this set of molecules. NRTL-SAC showed better performance at 298.15 K, with an average absolute deviation of 37.6%. Solubility dependence with temperature was also studied: all models presented average deviations around 40%. In general, there is an improvement given by the A-UNIFAC over the UNIFAC in aqueous systems, proving the importance of taking association into account.
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International Journal of Pharmaceutics, 1996
The E and C model for hydrogen bonding is used together with nonspecific solubility parameters to predict the solubility of a Lewis base solute in a series of solvents of several chemical classes. A linear relationship between enthalpies of hydrogen bonding calculated from the Drago model and entropies obtained from a few experimental solubilities allows the prediction of the entropy contribution for the other solvents. Correct orders of magnitude are predicted in solvents of all polarities (from benzene to glycerin) which were not used to obtain the empirical relationships. The results suggest that the E and C model may be useful to reduce the experimental work usually needed for predicting solubility of drugs in pure solvents of different acid-base characteristics.
QSPR Prediction of Aqueous Solubility of Drug-Like Organic Compounds
CHEMICAL & PHARMACEUTICAL BULLETIN, 2007
The aqueous solubility of organic compounds is an important molecular property, playing a large role in the behavior of compounds in many areas of interest. Given the importance of solubility, a means of prediction based solely on molecular structure should prove a useful tool, as many compounds exist for which the solubility simply is not available. The solubility of chemicals and drugs in the water phase has an essential influence on the extent of their absorption and transport in a body. That is why solubility is considered to be a very important parameter in current ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) research. Water solubility plays a key role in areas such as drug dosage, anesthesiology, corrosion of metals, transport fate of pollutants in terrestrial, aquatic and atmospheric ecosystems, deposition of minerals and composition of ground waters, and availability of oxygen and other gases in life support systems. The widespread relevance of water solubility data to many branches and disciplines of science, medicine, technology, and engineering has led to the development of several models to predict water solubility. Hence, it was deemed advantageous to develop a model to predict water solubility using only theoretically derived descriptors. 6-9) Comparing with the time-consuming experimental procedures to determine aqueous solubility directly, reliable computational methods to predict aqueous solubility are more popular in today's research. There are some reports about the applications of QSPR approaches to predict the aqueous solubility of organic compounds. In our previous papers, we reported on the application of QSPR techniques in the development of a new, simplified approach to prediction of compounds properties. 20-22) Several articles have published with MLR models for the prediction of aqueous solubility. In a QSPR study, a mathematical model is developed which relates the structure of a set of compounds to a physi-cal 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. These descriptors are numerical representations of structural features of molecules that attempt to encode important information that causes structurally different compounds to have different physical property values. Even though the descriptors used to build a QSPR model can be empirical, it is generally more useful to use descriptors derived mathematically from the 3D molecular structure, since this allow any relationship so derived to be extended to the prediction of the property for unavailable compounds. In this work a QSPR study is performed, to develop model that relate the structures of a heterogeneous group of 150 drug-like compounds to their aqueous solubility. The stepwise MLR was used to select the most informative descriptors from the calculated descriptors by Molecular Modeling Pro Plus software. The selected descriptors were used to develop a MLR model for predicting the solubility for 40 drug compounds in water at 25°C. The aim of this work was to investigate molecular descriptors important in determining aqueous solubility.
2019
Solubility data of solid in aqueous and different organic solvents are very important physicochemical properties considered in the design of the industrial processes and the theoretical studies. In this study, experimental solubility data of 666 pharmaceutical compounds in water and 712 pharmaceutical compounds in organic solvents were collected from different sources. Three different artificial neural networks including multilayer perceptron, radial basis function and support vector machine were constructed to predict the solubility of these different pharmaceutical compounds in water and different solvents. Molecular weight, melting point, temperature and the number of each functional group in the pharmaceutical compound and organic solvents were selected as the input variables of these three different neural network models. The neural network predictions were compared with the experimental data and the SVR-PSO model with the Average Absolute Relative Deviation equal to 0.0166 for...
Solubility Prediction of Drugs in Mixed Solvents Using Partial Solubility Parameters
Journal of Pharmaceutical Sciences, 2011
Solubility of drugs in binary and ternary solvent mixtures composed of water and pharmaceutical cosolvents at different temperatures were predicted using the Jouyban-Acree model and a combination of partial solubility parameters as interaction descriptors in the solution. The generally trained version of the model produced the overall mean percentage deviation values for the back-calculated solubility of drugs in binary solvents of 34.3% and the predicted solubilities in ternary solvent mixtures of 38.0%. In addition, the applicability of the trained model for predicting the solvent composition providing the maximum solubility of a drug was investigated. The results of collected solubility data of drugs in various mixed solvents and the newly measured solubility data of five drugs in ethanol + propylene glycol + water mixtures at 25 • C showed that the model provided acceptable predictions and could be used in the pharmaceutical industry.
Recent Advances in Thermo and Fluid Dynamics, 2015
In this chapter, the applicability of two predictive activity coefficient-based models will be examined. The experimental data from five different types of VLE (vaporliquid equilibrium) and VLLE (vapor-liquid-liquid equilibrium) systems that are common in industry are used for the evaluation. The nonrandom two-liquid segment activity coefficient (NRTL-SAC) and universal functional activity coefficient (UNI-FAC) were selected to model the systems. The various thermodynamic relations existing in the open literature will be discussed and used to predict the solubility of active pharmaceutical ingredients and other small organic molecules in a single or a mixture of solvents. Equations of states, the activity coefficient, and predictive models will be discussed and used for this purpose. We shall also present some of our results on solvent screening using a single and a mixture of solvents.