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Chemoinformatic Methods for Predicting Interference in Drug of Abuse/Toxicology Immunoassays
Faculty
Not Specified
Year:
2009
Type of Publication:
Article
Pages:
1203-1213
Authors:
Krasowski, Matthew D, Siam, Mohamed G, Iyer, Manisha, Pizon, Anthony F, Giannoutsos, Spiros, Ekins, Sean
DOI:
10.1373/clinchem.2008.118638
Journal:
CLINICAL CHEMISTRY AMER ASSOC CLINICAL CHEMISTRY
Volume:
55
Research Area:
Medical Laboratory Technology
ISSN
ISI:000266687200022
Keywords :
Chemoinformatic Methods , Predicting Interference , Drug , Abuse/Toxicology
Abstract:
BACKGROUND: Immunoassays used for routine drug of abuse (DOA) and toxicology screening may be limited by cross-reacting compounds able to bind to the antibodies in a manner similar to the target molecule(s). To date, there has been little systematic investigation using computational tools to predict cross-reactive compounds. METHODS: Commonly used molecular similarity methods enabled 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 DOA/toxicology screening assays. We used various molecular descriptors (MDL public keys, functional class fingerprints, and pharmacophore fingerprints) and the Tanimoto similarity coefficient. These data were then compared with cross-reactivity data in the package inserts of immunoassays marketed for in vitro diagnostic use. Previously untested compounds that were predicted to have a high probability of cross-reactivity were tested. RESULTS: Molecular similarity calculated using MDL public keys and the Tanimoto similarity coefficient showed a strong and statistically significant separation between cross-reactive and non-cross-reactive compounds. This result was validated experimentally by discovery of additional cross-reactive compounds based on computational predictions. CONCLUSIONS: The computational methods employed are amenable toward rapid screening of databases of drugs, metabolites, and endogenous molecules and may be useful for identifying cross-reactive molecules that would be otherwise unsuspected. These methods may also have value in focusing cross-reactivity testing on compounds with high similarity to the target molecule(s) and limiting testing of compounds with low similarity and very low probability of cross-reacting with the assay. (C) 2009 American Association for Clinical Chemistry
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