Second-generation antibacterial benzimidazole ureas: discovery of a preclinical candidate with reduced metabolic liability.

Compound 3 is a potent aminobenzimidazole urea with broad-spectrum Gram-positive antibacterial activity resulting from dual inhibition of bacterial gyrase (GyrB) and topoisomerase IV (ParE), and it demonstrates efficacy in rodent models of bacterial infection. Preclinical in vitro and in vivo studies showed that compound 3 covalently labels liver proteins, presumably via formation of a reactive metabolite, and hence presented a potential safety liability. The urea moiety in compound 3 was identified as being potentially responsible for reactive metabolite formation, but its replacement resulted in loss of antibacterial activity and/or oral exposure due to poor physicochemical parameters. To identify second-generation aminobenzimidazole ureas devoid of reactive metabolite formation potential, we implemented a metabolic shift strategy, which focused on shifting metabolism away from the urea moiety by introducing metabolic soft spots elsewhere in the molecule. Aminobenzimidazole urea 34, identified through this strategy, exhibits similar antibacterial activity as that of 3 and did not label liver proteins in vivo, indicating reduced/no potential for reactive metabolite formation.

High-throughput screening for glutathione conjugates using stable-isotope labeling and negative electrospray ionization precursor-ion mass spectrometry.

RATIONALE: It has been proposed that the increase in the instances of idiosyncratic adverse drug reactions (IADRs) and black box warnings may be attributed to the occurrence of reactive metabolites. Consequently, a high-throughput screen for reactive metabolites formed from liver microsome extracts with added glutathione (GSH) was developed for use in the early stages of drug discovery. METHODS: To enhance sensitivity and specificity, as well as accelerate data processing, a mixture of a stable-isotope probe consisting of natural GSH (light GSH) and stable-isotope-labeled [(15) N,(13) C(2)] GSH (heavy GSH) at a ratio of 1:1 was used. Any metabolite that reacted with the GSH results in the formation of light and heavy GSH conjugates with a 3 Da difference. Employing a precursor-ion scan using negative ion electrospray ionization (ESI) corresponding to the expected fragments, signals with the appropriate ratio in the precursor ion scan are then further examined. RESULTS: The new method greatly simplifies data collection by assuming molecules containing GSH will fragment to form specific ions. As such, this approach accelerates data processing (and collection) at the risk of missing compounds that do not fragment as expected. The assay was validated with 33 diverse drugs known to form GSH conjugates, 5 drugs known to not form GSH adducts and over 100 samples containing components of the normal in vitro matrix. In all cases data collected matched the expected result. CONCLUSIONS: The observed sensitivity, specificity, and fast data processing make this assay an excellent fit for high-throughput screening of reactive metabolites in the early stages of drug discovery. This method is not intended to eliminate compounds or terminate their development. Instead, it is to bring forward molecules with one less liability and thus a greater probability of ultimate success.

Quantitative chemical proteomics for identifying candidate drug targets.

We have developed a systematic strategy for drug target identification. This consists of the following sequential steps: (1) enrichment of total binding proteins using two differential affinity matrixes upon which are immobilized positive and negative chemical structures for drug activity, respectively; (2) covalent labeling of the proteins with a new cleavable isotope-coded affinity tag (ICAT) reagent, followed by proteolysis of the combined proteins; (3) isolation, identification, and relative quantification of the tagged peptides by liquid chromatography-mass spectrometry; (4) array-based transcription profiling to select candidate proteins; and (5) confirmation of direct interaction between the activity-associated structure and the selected proteins by using surface plasmon resonance. We present a typical application to identify the primary binding protein of a novel class of anticancer agents exemplified by E7070. Our results suggest that this approach provides a new aspect of quantitative proteomics to find specific binding proteins from protein mixture and should be applicable to a wide variety of biologically active small molecules with unidentified target proteins.