title: Automated selection of compounds with physicochemical properties to maximize bioavailability and druglikeness.
abstract: Adequate bioavailability is one of the essential properties for an orally administered drug. Lipinski and others have formulated simplified rules in which compounds that satisfy selected physiochemical properties, for example, molecular weight (MW) ≤ 500 or the logarithm of the octanol-water partition coefficient, log P(o/w) < 5, are anticipated to likely have pharmacokinetic properties appropriate for oral administration. However, these schemes do not simultaneously consider the combination of the physiochemical properties, complicating their application in a more automated fashion. To overcome this, we present a novel method to select compounds with a combination of physicochemical properties that maximize bioavailability and druglikeness based on compounds in the World Drug Index database. In the study four properties, MW, log P(o/w), number of hydrogen bond donors, and number of hydrogen acceptors, were combined into a 4-dimensional (4D) histogram, from which a scoring function was defined on the basis of a 4D dependent multivariate Gaussian model. The resulting equation allows for assigning compounds a bioavailability score, termed 4D-BA, such that chemicals with higher 4D-BA scores are more likely to have oral druglike characteristics. The descriptor is validated by applying the function to drugs previously categorized in the Biopharmaceutics Classification System, and examples of application of the descriptor are given in the context of previously published studies targeting heme oxygenase and SHP2 phosphatase. The approach is anticipated to be useful in early lead identification studies in combination with clustering methods to maximize chemical and structural diversity when selecting compounds for biological assays from large database screens. It may also be applied to prioritize synthetically feasible chemical modifications during lead compound optimization.
authors.affiliation: Department of Pharmaceutical Sciences, School of Pharmacy, University of Maryland, Baltimore, 20 Penn Street, Baltimore, Maryland 21201, United States.
authors.forename: Taiji
authors.initials: T
authors.lastname: Oashi
authors.forename: Ashley L
authors.initials: AL
authors.lastname: Ringer
authors.forename: E Prabhu
authors.initials: EP
authors.lastname: Raman
authors.forename: Alexander D
authors.initials: AD
authors.lastname: Mackerell
mesh_terms: D001331:Automation; D001682:Biological Availability; D055598:Chemical Phenomena; D016208:Databases, Factual; D017277:Drug Approval; D004353:Drug Evaluation, Preclinical / Q000379:methods*; D004791:Enzyme Inhibitors / Q000737:chemistry / Q000493:pharmacokinetics / Q000494:pharmacology; D006860:Hydrogen Bonding; D004364:Pharmaceutical Preparations / Q000737:chemistry*; D011336:Probability; D054592:Protein Tyrosine Phosphatase, Non-Receptor Type 11 / Q000037:antagonists & inhibitors / Q000737:chemistry / Q000378:metabolism; D015203:Reproducibility of Results; D014481:United States; D014486:United States Food and Drug Administration; D018909:src Homology Domains
chemical_list: D004791:Enzyme Inhibitors; D004364:Pharmaceutical Preparations; D054592:Protein Tyrosine Phosphatase, Non-Receptor Type 11