title: Molecular similarity methods for predicting cross-reactivity with therapeutic drug monitoring immunoassays.
abstract: Immunoassays are used for therapeutic drug monitoring (TDM), yet may suffer from cross-reacting compounds able to bind the assay antibodies in a manner similar to the target molecule. To our knowledge, there has been no investigation using computational tools to predict cross-reactivity with TDM immunoassays. The authors used molecular similarity methods to enable calculation of structural similarity for a wide range of compounds (prescription and over-the-counter medications, illicit drugs, and clinically significant metabolites) to the target molecules of TDM immunoassays. Utilizing different molecular descriptors (MDL public keys, functional class fingerprints, and pharmacophore fingerprints) and the Tanimoto similarity coefficient, the authors compared cross-reactivity data in the package inserts of immunoassays marketed for in vitro diagnostic use. Using MDL public keys and the Tanimoto similarity coefficient showed a strong and statistically significant separation between cross-reactive and non-cross-reactive compounds. Thus, 2-dimensional shape similarity of cross-reacting molecules and the target molecules of TDM immunoassays provides a fast chemoinformatics methods for a priori prediction of potential of cross-reactivity that might be otherwise undetected. These methods could be used to reliably focus cross-reactivity testing on compounds with high similarity to the target molecule and limit testing of compounds with low similarity and ultimately with a very low probability of cross-reacting with the assay in vitro.
authors.affiliation: Department of Pathology, University of Pittsburgh, Scaife Hall S-737, 3550 Terrace Street, Pittsburgh, PA 15261, USA. mdk24@pitt.edu
authors.forename: Matthew D
authors.initials: MD
authors.lastname: Krasowski
authors.forename: Mohamed G
authors.initials: MG
authors.lastname: Siam
authors.forename: Manisha
authors.initials: M
authors.lastname: Iyer
authors.forename: Sean
authors.initials: S
authors.lastname: Ekins