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  • Dr. Elizabeth O'Day

Metabolomics is the next "-omics"

Like fashion, different trends in science cycle in and out of style. And right now, metabolomics is en vogue. In the last 18 months, I have seen several new metabolomics companies pop up. In fact, I’ve been asked to perform due diligence on many of them. Overwhelmingly I endorse the company thesis. Metabolomics is the next ‘omics’ and if executed correctly this will not be a fad but lead to the long-lasting development of clinically impactful products.

I have spent more than a decade advocating for the pivotal role metabolism plays in understanding human health and how metabolomics can be leveraged to develop tools (therapeutics and diagnostics) to combat disease. Already a majority of clinical diagnostics measure a metabolite (think of glucose for diabetes or cholesterol for heart disease) and nearly all FDA-approved drug targets hit a metabolic enzyme (1). There are 190 clinically approved metabolite biomarkers, which is more than all other ‘-omics’ technologies combined (1). Many of these applications were results of early discoveries in the field, the “low-hanging fruit” so to speak, where a singular metabolite was shown to have a strong correlation with disease. Metabolomics, the measurement of the full set of metabolites in a patient or organism, will expand upon these early success stories. Akin to the genomic revolution which started with single genetic mutations associated with disease and has moved to multi-gene panels or even full genome sequencing, we are now witnessing a metabolomics revolution where multi-metabolite panels or full metabolome profiling is providing a more comprehensive picture of health and disease.

I firmly believe metabolomics will (finally) deliver on the promise of precision medicine enabling us to get the right drug to the right patient at the right time (2). However, to optimize the field for success, it’s important to ensure knowledge of the fundamentals and address the unique challenges of metabolomics.

First the basics- metabolomics is the study of metabolites and metabolic pathways. Metabolites are the small molecules that swim around in our bodies that provide the energy and the biomass to support all cellular processes. Measuring the metabolome is poised to reveal a more holistic understanding of an individual than any other data set. Unlike genomics which provides information about what “could happen” or proteomics which relays information on the cell machinery that “makes things happen”, metabolites are a functional readout for what is “actually happening” inside a patient (3). Metabolites are made by enzymes- so the presence or absence of a metabolite informs you of activity. Furthermore, metabolites are influenced by both the genome and the environment, thus providing a “chemical fingerprint” of an individual’s overall health and lifestyle. In fact, the metabolites in your urine are a more unique identifier than your fingerprint- they reveal sex, age, ethnicity, recent travels, diet, predisposition to disease, and response to therapy (4). Metabolomics provides a treasure trove of information.

Yet metabolomic studies are incredibly challenging to execute. The chemical diversity (lipids to acids), the dynamic range of metabolites (femtomolar to millimolar), constant flux, and the potential confounding factors (age, sex, diet, sleep, exercise, etc), has made it nearly impossible to measure the metabolome accurately and reproducibly (5). The reproducibility challenge has been a major limiting factor for the field. A big pharma executive once told me he ‘flat out hated metabolomics. When I pushed that was unfair for the molecules and pathways that literally sustain his life, he clarified he hated having a metabolomics line item in his P&L—as it always delivered reams and reams of data, but it was not replicable nor was clear how it helped their drug discovery efforts. This is critical and frankly quite common feedback for the field. I urge new companies and all metabolomic investigators to take note. In fact, it is in part why in Olaris’ early days we have focused so heavily on optimizing our platform for reproducibility (6) (7) and making sure every analysis we do is rooted in biology.

Study design, analytical measurements, statistics, machine learning, and biological interpretation are all essential elements of a well-executed metabolomics study. While it is now possible to outsource different elements of this pipeline, understanding each step is critical, as something seemingly benign like a change in the type of test tube can completely obscure results. I believe if you want to harness the full power of the metabolome you need eyes on the whole process. It took us 7 years to get our platform truly ready for prime time. We tested different biofluids, different collection tubes, we interrogated the role of confounders, we explored in which disease areas we could have the biggest impact, and more (8) (9) (10). This served to both de-risk our platform and empowered us to know what works and also what doesn’t- both of which have extreme value.

Now the Olaris platform is set to “rinse and repeat” where we have confidence the metabolite signatures we discover are associated with disease and response to therapy (and not something else). Furthermore, we link the differential metabolites back to metabolic pathways to uncover novel insights into disease etiology or resistance mechanisms. Numerous times Olaris has been asked to help “make sense” of other companies’ metabolomics data. Biological insight cannot be undervalued as it can provide a mechanism of action, explain off-target effects, lead to new therapeutic targets and so much more.

Metabolomics is extremely tough to do but when done well, it is worth it. It reveals the underpinnings supporting all of life. If we do this right the current excitement in metabolomics will absolutely lead to the development of novel diagnostics and therapeutics that transform human health. And patients deserve this- we have the potential to create a future where metabolite-based precision medicine tools can end a long and confusing diagnostic odyssey and ensure optimal treatment at a critical time in patients' (and their families) lives.

  1. Wishart, D. S. Emerging applications of metabolomics in drug discovery and precision medicine. Nat Rev Drug Discov 15, 473–484 (2016).

  2. Abrahams, E. Right Drug- Right Patient- Right Time: Personalized Medicine Coalition. Clin Trans Sci. May 1 (1) 11-12 (2008).

  3. Patti, G. J., Yanes, O. & Siuzdak, G. Metabolomics: the apogee of the omics trilogy. Nat Rev Mol Cell Biol 13, 263–269 (2012).

  4. Beger, R. D. et al. Metabolomics enables precision medicine: “A White Paper, Community Perspective”. Metabolomics 12, 149 (2016).

  5. Johnson, C. H. & Gonzalez, F. J. Challenges and Opportunities of Metabolomics. J Cell Physiol 227, 2975–2981 (2012).

  6. Zhang, B., Powers, R. & O’Day, E. M. Evaluation of Non-Uniform Sampling 2D 1H–13C HSQC Spectra for Semi-Quantitative Metabolomics. Metabolites 10, 203 (2020).

  7. Honrao, Chandrashekhar, Nathalie Teissier, Bo Zhang, Robert Powers, and Elizabeth M. O’Day. Gadolinium-based Paramagnetic Relaxation Enhancement Agent Enhances Sensitivity for NUS Multidimensional NMR-based Metabolomics. Molecules 26(17), 5115;(2021).

  8. O'Day, E.M., K. Leizel, A. Lipton, et al. Pretreatment serum metabolome predicts PFS in first-line trastuzumab-treated metastatic breast cancer [poster'. In: Proceedings of the 2020 San Antonio Breast Cancer Symposium; San Antonio (TX). Olaris, Inc., Waltham (MA), Penn State Hershey Medical Center, Hershey (PA): SABC, 2021. P4-10-35.;

  9. Honrao, Chandrashekharm Srihari Raghavendra Rao, Nathalie Teissier, S. Greg Call, Elizabeth M. O'Day, Filip Janku. Plasma-based metabolic profiling in metastatic gastrointestinal stromal tumors (GIST) [abstract]. In: Proceedings of the 112th Annual Meeting of the American Association for Cancer Research; 2021 April 10-15; Virtual. Olaris, Inc., Waltham (MA), MD Anderson Cancer Center, Houston (TX): AACR, 2021. Abstract nr LB031.!/9325/presentation/4540;

  10. Zhang, B., Warner, J., Pinto, C., Juric, D. & ODay, E. NMR-metabolite-resonance signature to predict HR+ breast cancer patient response to CDK4/6 inhibitors. JCO37, 3043–3043 (2019).


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