Molecular dynamics simulation solubility prediction

Subject areas for the Series include solutions of electrolytes, liquid mixtures, chemical equilibria in solution, acid-base equilibria, vapour-liquid equilibria, liquid-liquid equilibria, solid-liquid equilibria, equilibria in analytical chemistry, dissolution of gases in liquids, dissolution and precipitation, solubility in cryogenic solvents, molten salt systems, solubility measurement techniques, solid solutions, reactions within the solid phase, ion transport reactions away from the interface (i.e. in homogeneous, bulk systems), liquid crystalline systems, solutions of macrocyclic compounds (including macrocyclic electrolytes), polymer systems, molecular dynamic simulations, structural chemistry of liquids and solutions, predictive techniques for properties of solutions, complex and multi-component solutions applications, of solution chemistry to materials and metallurgy (oxide solutions, alloys, mattes etc.), medical aspects of solubility, and environmental issues involving solution phenomena and homogeneous component phenomena. 

Gupta, J., Nunes, C., Vyas, S., and Jonnalagadda, S. 2011. Prediction of solubility parameters and miscibility of pharmaceutical compounds by molecular dynamics simulations. Journal of Physical Chemistry B 115 2014—2023. 

Molecular simulations have been used to obtain thermodynamic properties and phase equilibria data of ionic liquid systems (i) Monte Carlo simulation techniques were employed to predict the solubility of gases and water in ionic liquids and (ii) molecular dynamics simulations were used to investigate the solvation dynamics of water and various organics in ionic liquids. ... 

A computational model based on molecular dynamics was developed to predict the miscibility of indomethacin in the carriers polyethylene oxide (PEO), glucose, and sucrose (Gupta et al. 2011). The cohesive energy density and the solubility parameters were determined by simulations using the condensed-phase optimized molecular potentials for atomistic simulation studies (COMPASS) force field. The simulations predicted miscibility for indomethacin with PEO (A5 < 2), borderline miscibility with sucrose (A5 < 7), and immiscibility with glucose (A5 > 10 Table 2.2). 

There are several parameters to consider for quick assessments. The detailed theoretical explanation can be found elsewhere. The most commonly used criteria are solubility parameter and lipophilicity (LogP). When the solubility parameter is used, a difference below 2.8 is empirically recognized as well miscible (Yoo et al. 2009). LogP value differences between the component pairs below 1.7 tend to stay amorphous however, this rule is not strictly followed (Yoo et al. 2009). A more complicated miscibility estimation is to employ molecular dynamic (MD) simulation to predict the miscibility of a drug candidate and its carrier (Gupta et al. 2011). The MD approach can provide molecular level insight. 

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