Read time: 15 minutes
The completion of the human genome project in 2003 opened the door to unprecedented insight into the human body through its DNA code. We now know that our genome encodes our proteins, that our proteins are the molecular machinery of certain functions that take place in our cells and that the end products of these functions are our metabolites.
The field of metabolomics—which means monitoring in real time, and at multiple time points, how individuals behave in response to therapy, the environment, or lifestyle choices, for instance—focuses on quantifying these metabolites and exploring how their fluctuations in specific cellular processes can lead to disease. While the technologies and analytical techniques used for this study were inadequate for a long time, they have advanced significantly in recent years as the field has grown. This has opened up the potential for an even deeper understanding of human disease and health, which is particularly valuable in the field of precision medicine.
“Recently, Technology Networks interviewed SCIEX to discuss the role of metabolomics in the field of precision medicine. We share our insights on “the billions of tiny molecules that our cells produce every second,” how we see metabolomics tackling some of the current issues associated with healthcare and how we define and quantitatively measure both wellness and illness. A condensed version of that conversation follows.”
We hear about metabolomics quite often in the context of precision medicine. Can you explain what precision medicine is and its key concepts?
Precision medicine is a term that’s been around for quite a while. Back when I was in pharma R&D, the thinking was that 1 drug could fit every individual. That thinking has shifted to realizing that every individual’s metabolism is different, as are the areas we live in and our lifestyle choices, and therefore the way we respond to medicine is likely to be unique as well. So the pharmaceutical industry is now looking at ways of applying drugs and medicines to cohorts of patients that have similar phenotypes. In other words, how are they going to respond, given their metabolic, proteomic or genomic profile?
At a high level, that’s what precision medicine is, but it can also be used in many other ways—for instance, for stratifying patients in clinical trials during the drug development process. Rather than taking a drug and applying it to a cohort of patients and people, patients are chosen based on what their phenotypic response might be to that medicine. That is why precision medicine is so exciting, because you can take a baseline from an individual, even at a point when they are sick, and tailor medicines to that individual. The metabolome changes a lot over time, so a 1-drug-fits-all approach is not going to work well. Most people won’t fully respond to the treatment, and managing the losses around failed treatments creates a huge burden on the healthcare industry.
In short, precision medicine helps us tackle some major challenges:
- Ensuring we give the right drug to the right patient at the right time
- Addressing lifestyle-related diseases more accurately and precisely
- Overcoming the cost burden around drug development failures, or at least better understanding the toxic effects of drugs on individuals, so those therapies aren’t given to patients in the first place
It’s a very exciting field, and I think we’re finally getting to a place where precision medicine is really achievable.
What are some of the current issues in healthcare and drug discovery that could be addressed through metabolomics research?
The ability to quantify small molecule metabolites is what enables us to unravel the mechanisms of action, which is another way of saying it gives us an understanding of how a drug works. If we give this drug to this person, does it target the right spot? Will it cause the effect that we want it to cause? Will we get the response that we want, given the phenotype of the individual?
As you can imagine, if you try to do that in a rudimentary way, you’ll get a rudimentary answer. But if you do it in a quantitative, precise and accurate way, you will have a much more focused and targeted approach. This will allow you to get more accurate answers, which in turn will allow you to interpret the biology more easily and quickly, and will ultimately allow the drug discovery pipeline to move faster.
I was recently talking to some customers, and they are seeing that more accurate approaches could mean cutting project times by a quarter, which is a huge benefit to the entire drug discovery pipeline.
How can a precise, quantitative approach that speeds up drug discovery also impact clinical diagnostics?
I think this is already happening. Clinical diagnostics is already monitoring small molecules. The term “metabolomics” is just another name for small molecule biochemistry. For example, testing for inborn errors in metabolism is routinely performed on all newborn babies across the US and in most parts of the world. This test enables us to characterize a set of early metabolic disorders (such as defects in phenylalanine and tryptophan metabolism), which lets us know if a baby has a predisposition for certain metabolic diseases.
This is the connective tissue between metabolomics and clinical diagnostics: the space in between that is “translational science.” We analyze many biomarkers during the discovery phase and then triage them down as we start to validate our assays. As you start the validation, you might be left with only a handful of markers, and that is when you get into that space: when you are starting to ramp up the validation and starting to implement a diagnostic test in the clinic.
What kind of work are you seeing in the metabolomics space from a healthcare industry perspective?
We have a customer from Virginia Commonwealth University, Dr. Dayanjan Wijesinghe, who is doing sepsis-based research that takes him directly into the hospital unit. He’s not making diagnostic decisions right there and then, because that would require an assay cleared by the US Food and Drug Administration (FDA). However, he is using data to try and understand the metabolic output and phenotypic responses of a patient to a certain drug, how they are reacting to it and what stage they’re at in the sepsis diagnosis, which can enable him to understand whether the patient will return to the clinic. He’s using a lot of technology from SCIEX, and he is creating some phenomenal artificial intelligence and augmented reality tools to take this research to the next level and overcome barriers on the data side.
What role does mass spectrometry play in metabolomics research?
Mass spectrometry plays a major role in the healthcare industry. These systems are used on the testing side with companies such as Labcorp and Quest Diagnostics, and at large research facilities like those in the pharmaceutical industry. Mass spectrometry also plays a huge role in the drug development pipeline I mentioned earlier, from discovery to development and ultimately through clinical trials.
However, the most profound impact of metabolomics-based mass spectrometry is in the early discovery phase, where it allows researchers to understand mechanisms of action, pursue their target identification work and determine which drug to push through to development. In particular, SWATH® Acquisition is important for discovery work, since it is both quantitative and qualitative. Once biomarkers of interest are found, they need to be accurately, sensitively and reproducibly detected to validate and move them into the QA/QC stage. Accurate quantification along the discovery and development pathway is vital to monitoring the efficacy of the drug.
The word “wellness” is used quite frequently now, and we hear a lot about how humans essentially move along a scale between wellness and illness. How does metabolomics research contribute to our understanding of wellness, and how we can measure it?
There is a huge area of academic research that seeks to address wellness and illness. For instance, how do we know when somebody is sick or healthy? Metabolomics allows you to interrogate that because if you can monitor someone repeatedly over time, you can get a useful metabolic profile of how that individual is responding phenotypically. There are studies that show major metabolome differences, for example, if you had a large steak for dinner and took a blood test 30 minutes after—you would see a huge increase in lipids. Similarly, if you went out for a bike ride and took a test when you came back, you would see changes in the amino acids and energy metabolism.
That direct readout of biology is having a real impact in the wellness testing space. One of our customers at Stanford University, Professor Michael Snyder, is doing a lot of omics studies that incorporate the use of wearable technology. He is monitoring various types of omics (i.e., metabolomics, lipidomics and proteomics) using mass spectrometry, and he is using sensors and sensing technology to understand how a person changes over time and whether that data can be tied to a person’s wellness. He’s pushing the barriers of what’s possible to measure and using mass spectrometry-based techniques—in particular, SWATH Acquisition approaches—to do it.
What are some of the challenges that exist within metabolomics research and implementing this research in healthcare practices?
Metabolomics generates a huge amount of data, so overcoming or reducing the complexity of that data and trying to get a more consolidated result is a significant challenge in the field. This bottleneck is consistent across the industry, and the community is putting a lot of effort into building tools to move beyond that. I think that’s one of the major barriers: How do we take this mountain of data and gain some real biological insight from it?
Another barrier is the standardization of methods and technology and making mass spectrometry, which was still novel to the field 10–15 years ago, routine for metabolomics research. Nuclear magnetic resonance (NMR) is still applicable today, but I would say that mass spectrometry has taken over as the preferred technique.
Those are some of the main barriers: the data challenge and getting buy-in that metabolomics is an omics that can deliver, even more than genomics does today. But it’s really not a standalone approach. I think the future is combining multiple omics data and integrating genomics with proteomics and metabolomics, and I think we will see a real shift in our entire understanding of a system. Some labs are already doing that, and while I don’t think we are there yet collectively as a field, I do think that those in the field are really excited about it, and about ultimately getting there.