NMR spectroscopy-based metabolic profiling of drug-induced changes in vitro can discriminate between pharmacological classes.

Drug-induced changes in mammalian cell line models have already been extensively profiled at the systemic mRNA level and subsequently used to suggest mechanisms of action for new substances, as well as to support drug repurposing, i.e., identifying new potential indications for drugs already licensed for other pharmacotherapy settings. The seminal work in this field, which includes a large database and computational algorithms for pattern matching, is known as the "Connectivity Map" (CMap). However, the potential of similar exercises at the metabolite level is still largely unexplored. Only recently, the first high-throughput metabolomic assay pilot study was published, which involved screening the metabolic response to a set of 56 kinase inhibitors in a 96-well format. Here, we report results from a separately developed metabolic profiling assay, which leverages (1)H NMR spectroscopy to the quantification of metabolic changes in the HCT116 colorectal cancer cell line, in response to each of 26 compounds. These agents are distributed across 12 different pharmacological classes covering a broad spectrum of bioactivity. Differential metabolic profiles, inferred from multivariate spectral analysis of 18 spectral bins, allowed clustering of the most-tested drugs, according to their respective pharmacological class. A more-advanced supervised analysis, involving one multivariate scattering matrix per pharmacological class and using only 3 spectral bins (3 metabolites), showed even more distinct pharmacology-related cluster formations. In conclusion, this type of relatively fast and inexpensive profiling seems to provide a promising alternative to that afforded by mRNA expression analysis, which is relatively slow and costly. As also indicated by the present pilot study, the resulting metabolic profiles do not seem to provide as information-rich signatures as those obtained using systemic mRNA profiling, but the methodology holds strong promise for significant refinement.