Using measures of metabolic flux to align screening and clinical development: Avoiding pitfalls to enable translational studies

Elucidation of Mechanism of Action in Drug Invention: Using Stable Isotope Tracers to Unravel Biochemical Kinetics

The invention of a therapeutic begins by characterizing features that differentiate healthy versus diseased states; this often presents as changes in the concentration of an analyte. Examples include elevated blood glucose in diabetes, high cholesterol in heart disease, and protein aggregation in neurodegeneration. Analyte concentrations reflect the (im)balance of synthetic and degradation rates; as such, aberrant biochemical kinetics drive the changes in endpoint concentration that define disease biology. Therapeutics aim to reset the concentration of a disease marker via modulation of biochemical kinetics. This is easy to understand for drugs directly targeting an enzyme in a pathway but, although less obvious, this can also be at the core of protein: protein interactions. For instance, stimulation of the insulin receptor changes the flux of several biochemical substrates (across multiple tissues); similarly, modulation of proprotein convertase subtilisin/kexin type 9-low density lipoprotein (PCSK9-LDL) receptor interactions alters cholesterol trafficking. These classic examples underscore the importance of studying biochemical kinetics at a clinical level. Here, we discuss how kinetic studies link disease biology with mechanism of action elucidation and screening. This has an immediate impact on (i) enabling in vitro-in vivo correlations in early discovery, (ii) enhancing exposure-response models aiding in human dose prediction, and (iii) providing support for biomarker plans, including clinical diagnostics. Mechanism of action studies can also influence modality selection; e.g., knowledge regarding target kinetics is needed when making decisions surrounding the development of a reversible inhibitor vs. an irreversible covalent modifier, or an intervention that affects target levels such as those which enhance protein degradation or reduce protein synthesis.

Metabolomics and lipidomics strategies in modern drug discovery and development

Metabolomics and lipidomics have an increasingly pivotal role in drug discovery and development. In the context of drug discovery, monitoring changes in the levels or composition of metabolites and lipids relative to genetic variations yields functional insights, bolstering human genetics and (meta)genomic methodologies. This approach also sheds light on potential novel targets for therapeutic intervention. In the context of drug development, metabolite and lipid biomarkers contribute to enhanced success rates, promising a transformative impact on precision medicine. In this review, we deviate from analytical chemist-focused perspectives, offering an overview tailored to drug discovery. We provide introductory insight into state-of-the-art mass spectrometry (MS)-based metabolomics and lipidomics techniques utilized in drug discovery and development, drawing from the collective expertise of our research teams. We comprehensively outline the application of metabolomics and lipidomics in advancing drug discovery and development, spanning fundamental research, target identification, mechanisms of action, and the exploration of biomarkers.

Measurement of lipid flux to advance translational research: evolution of classic methods to the future of precision health

Over the past 70 years, the study of lipid metabolism has led to important discoveries in identifying the underlying mechanisms of chronic diseases. Advances in the use of stable isotopes and mass spectrometry in humans have expanded our knowledge of target molecules that contribute to pathologies and lipid metabolic pathways. These advances have been leveraged within two research paths, leading to the ability (1) to quantitate lipid flux to understand the fundamentals of human physiology and pathology and (2) to perform untargeted analyses of human blood and tissues derived from a single timepoint to identify lipidomic patterns that predict disease. This review describes the physiological and analytical parameters that influence these measurements and how these issues will propel the coming together of the two fields of metabolic tracing and lipidomics. The potential of data science to advance these fields is also discussed. Future developments are needed to increase the precision of lipid measurements in human samples, leading to discoveries in how individuals vary in their production, storage, and use of lipids. New techniques are critical to support clinical strategies to prevent disease and to identify mechanisms by which treatments confer health benefits with the overall goal of reducing the burden of human disease.

Personalized tracking of how lipid (fat) metabolism changes over time could lead to improvements in the diagnosis and treatment of several diseases. Elizabeth Parks and colleagues from the University of Missouri, Columbia, USA, discuss the ways in which researchers use stable isotope labeling to monitor the kinetics of fatty acids and other lipids in the body. Usually, lipid quantities are measured only at a single timepoint, however the tracking of lipid turnover over time provides further diagnostic information. Aided by new techniques such as high-throughput mass spectrometry and machine learning, researchers are now able to continuously map total lipid contents in individual patients. The transition of measurements of lipid flux from the research laboratory to the doctor’s office will likely play a role in a new era of precision medicine.