Received on October 1, 2008
Accepted on February 19, 2009

Drug Monitoring and Toxicology

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

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

^* To whom correspondence should be addressed. 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 (mapping description language) 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.