Air-liquid partition coefficient for a diverse set of organic compounds: Henry's Law Constant in water and hexadecane.

The SPARC vapor pressure and activity coefficient models were coupled to successfully estimate Henry's Law Constant (HLC) in water and in hexadecane for a wide range of organic compounds without modification to, or additional parametrization of, either SPARC model. The vapor pressure model quantifies the solute-solute intermolecular interactions in the pure liquid phase, whereas the activity coefficient model quantifies the solute-solvent and solvent-solvent (in addition to the solute-solute) interactions upon placing solute, i, in solvent, j. These intermolecular interactions are factored into dispersion, induction, dipole-dipole, and H-bonding components upon moving a solute molecule from the gas to the liquid phase. The SPARC HLC calculator so produced was tested and validated on the largest experimental HLC data set to date: 1356 organic solutes, spanning a wide range of functional groups, dipolarities and H-bonding capabilities, such as PAHs, PCBs,VOCs, amides, pesticides, and pharmaceuticals. The rms deviation errors for the calculated versus experimental log HLCs for 1222 compounds in water and 563 in hexadecane were 0.456 and 0.192 log [(mol/L)/(mol/L)] units, respectively, spanning a range of more than 13 and 20 log HLC dimensionless units for the compounds in water and hexadecane, respectively. The SPARC calculator web version is available for public use, free of charge, and can be accessed at http://sparc.chem.uga.edu.

In silico prediction of ionization constants of drugs.

Most pharmacologically active molecules contain one or more ionizing groups, and it is well-known that knowledge of the ionization state of a drug, indicated by the pKa value, is critical for understanding many properties important to the drug discovery and development process. The ionization state of a compound directly influences such important pharmaceutical characteristics as aqueous solubility, permeability, crystal structure, etc. Tremendous advances have been made in the field of experimental determination of pKa, in terms of both quantity/speed and quality/accuracy. However, there still remains a need for accurate in silico predictions of pKa both to estimate this parameter for virtual compounds and to focus screening efforts of real compounds. The computer program SPARC (SPARC Performs Automated Reasoning in Chemistry) was used to predict the ionization state of a drug. This program has been developed based on the solid physical chemistry of reactivity models and applied to successfully predict numerous physical properties as well as chemical reactivity parameters. SPARC predicts both macroscopic and microscopic pKa values strictly from molecular structure. In this paper, we describe the details of the SPARC reactivity computational methods and its performance on predicting the pKa values of known drugs as well as Pfizer internal discovery/development compounds. A high correlation (r2=0.92) between experimental and the SPARC calculated pKa values was obtained with root-mean-square error (RMSE) of 0.78 log unit for a set of 123 compounds including many known drugs. For a set of 537 compounds from the Pfizer internal dataset, correlation coefficient r2=0.80 and RMSE=1.05 were obtained.