Quantitative bioassay to identify antimicrobial drugs through drug interaction fingerprint analysis

Zohar B Weinstein,Muhammad H Zaman

doi:10.1038/srep42644

Abstract

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.

Highlights

Novel means to address the issue of substandard medicines may decrease the health and financial burden associated with long-term treatment of all forms of tuberculosis
In order to determine the feasibility of drug interaction profiling for API identification, we first analyzed a previously published dataset and unpublished data of all pairwise interactions among 25 antibacterial drugs in E. coli for unique drug interaction profiles[21]
Drug interaction fingerprints can be utilized for drug identification if the same drug tested against an array of other drugs is more similar to biological replicates than to the profile of other drugs

Summary

Introduction

We are able to generate unique profiles of bacterial response to varying combinations of drugs to create unique fingerprints for four anti-mycobacterial agents and a major rifampicin degradation product. Drug interaction fingerprints can be utilized for drug identification if the same drug tested against an array of other drugs is more similar to biological replicates than to the profile of other drugs. Distance metrics alone could identify up to 8/25 antibiotics using drug interaction scores with a single drug partner (Fig. 1d).

Results

Conclusion