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This Article Full Text Full Text (PDF) Supplemental Data All Versions of this Article: clinchem.2008.118638v1 55/6/1203    most recent Alert me when this article is cited Alert me if a correction is posted Citation Map Services Email this article to a friend Similar articles in this journal Similar articles in PubMed Alert me to new issues of the journal Download to citation manager Google Scholar Articles by Krasowski, M. D. Articles by Ekins, S. PubMed PubMed Citation Articles by Krasowski, M. D. Articles by Ekins, S.

Chemoinformatic Methods for Predicting Interference in Drug of Abuse/Toxicology Immunoassays

¹ Department of Pathology, University of Pittsburgh, Pittsburgh, PA; ² Department of Pathology, Division of Clinical Chemistry, Toxicology and Therapeutic Drug Monitoring Laboratory, University of Pittsburgh Medical Center Presbyterian/Shadyside, Pittsburgh, PA; ³ Department of Forensic Medicine and Toxicology, Zagazig University, Zagazig, Egypt; ⁴ Department of Emergency Medicine, Division of Medical Toxicology, University of Pittsburgh Medical Center, Pittsburgh, PA; ⁵ Collaborations in Chemistry, Jenkintown, PA; ⁶ Department of Pharmacology, University of Medicine and Dentistry of New Jersey, Robert Wood Johnson Medical School, Piscataway, NJ; ⁷ Department of Pharmaceutical Sciences, University of Maryland, Baltimore, MD.

^aAddress correspondence to this author at: University of Pittsburgh, Department of Pathology, Scaife Hall S-737, 3550 Terrace Street, Pittsburgh, PA, 15261. Fax 412-647-5934; e-mail mdk24{at}pitt.edu.

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.