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.
Keywords: pKa; in silico prediction; SPARC; macroscopic (microscopic) ionization constants; drugs;
tautomer model; prediction error