Application of Metabolomics in Drug Discovery, Development and Theranostics

Abstract

High throughput analysis of metabolites combined with systems biology is opening new opportunities in medical research and clinical applications. Investigation of metabolic phenotype and metabolic requirements of a disease opens new avenues for treatments. Analysis of metabolic changes in patients and in disease models following treatments provides new methods for investigation of therapeutic response. Combined examination of disease metabolic characteristics and treatment response provides markers for personalization of therapies. Metabolomics finds itself at an increasingly important junction in all of these applications. In this review, we will explore the advantages of combining metabolomics with systems biology to optimize the drug discovery process via in-depth analysis of biological systems and novel metabolism- targeted therapeutics. Assessment of efficacy and specificity of drugs that directly or indirectly target metabolism through analyses of metabolic changes will also be presented. Finally, we will highlight examples of metabolic biomarkers explored as promising theranostics and theranostic imaging tools aimed at therapy optimization and combined therapy and diagnostics. The focus of this review will be directed towards cancer, however, the overarching ideas as well as experimental and analysis approaches are easily transferable to other applications. In cancer research, metabolism is once again raising interest due to a better appreciation of the significance of altered metabolic phenotype in cancer development and progression. Investigation of metabolic changes in cancer following drug applications and identification of clinically relevant metabolic markers for particular cancer subtypes are becoming increasingly appreciated in treatment development and will be explored in this review. Keywords: Bioreactors, drug discovery, drug testing, fluxomics, metabolomics, theragnostics imaging, theragnostics treatment planning, systems biology.