title: In silico prediction of ionization constants of drugs.
abstract: 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.
authors.affiliation: Computer Assisted Drug Discovery, Research Formulation, Pharmaceutical Science, Chemical Technology, Lead Discovery Group, Pfizer Global Research and Development, 2800 Plymouth Road, Ann Arbor, Michigan 48105, USA. pilhlee@gmail.com
authors.forename: Pil H
authors.initials: PH
authors.lastname: Lee
authors.forename: Saravanaraj N
authors.initials: SN
authors.lastname: Ayyampalayam
authors.forename: Lionel A
authors.initials: LA
authors.lastname: Carreira
authors.forename: Marina
authors.initials: M
authors.lastname: Shalaeva
authors.forename: Shobha
authors.initials: S
authors.lastname: Bhattachar
authors.forename: Rachel
authors.initials: R
authors.lastname: Coselmon
authors.forename: Salwa
authors.initials: S
authors.lastname: Poole
authors.forename: Eric
authors.initials: E
authors.lastname: Gifford
authors.forename: Franco
authors.initials: F
authors.lastname: Lombardo
mesh_terms: D003198:Computer Simulation; D019075:Electrophoresis, Capillary; D006860:Hydrogen Bonding; D007477:Ions / Q000737:chemistry*; D008956:Models, Chemical; D015394:Molecular Structure; D004364:Pharmaceutical Preparations / Q000737:chemistry*; D012984:Software; D055672:Static Electricity; D013237:Stereoisomerism; D013816:Thermodynamics
chemical_list: D007477:Ions; D004364:Pharmaceutical Preparations