Open AccessQuantitative bioassay to identify antimicrobial drugs through drug interaction fingerprint analysis
Author(s) -
Zohar Weinstein,
Muhammad H. Zaman
Publication year - 2017
Publication title -
scientific reports
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.24
H-Index - 213
ISSN - 2045-2322
DOI - 10.1038/srep42644
Subject(s) - drug , fingerprint (computing) , computational biology , antimicrobial drug , drug drug interaction , drug interaction , antimicrobial , pharmacology , computer science , biology , artificial intelligence , microbiology and biotechnology
Drug interaction analysis, which reports the extent to which the presence of one drug affects the efficacy of another, is a powerful tool to select potent combinatorial therapies and predict connectivity between cellular components. Combinatorial effects of drug pairs often vary even for drugs with similar mechanism of actions. Therefore, drug interaction fingerprinting may be harnessed to differentiate drug identities. We developed a method to analyze drug interactions for the application of identifying active pharmaceutical ingredients, an essential step to assess drug quality. We developed a novel approach towards the identification of active pharmaceutical ingredients by comparing drug interaction fingerprint similarity metrics such as correlation and Euclidean distance. To expedite this method, we used bioluminescent E. coli in a simplified checkerboard assay to generate unique drug interaction fingerprints of antimicrobial drugs. Of 30 antibiotics studied, 29 could be identified based on their drug interaction fingerprints. We present drug interaction fingerprint analysis as a cheap, sensitive and quantitative method towards substandard and counterfeit drug detection.