Key takeaways

ASMS 2024 provided important insights into the latest advances in instrumentation, essential for planning the future development of biocrates’ kit technology. The conference offered an opportunity to explore newly launched MS platforms and their software firsthand. Speaking to behind-the-scenes scientists about their work in developing and testing these technologies allowed us to gather application tips and troubleshooting ideas, surpassing typical interactions with company representatives. Notable advances included Thermo’s new Stellar and Astral mass spectrometers, SCIEX’s Echo MS Plus system, and Waters’ MRT Mass Spectrometer.

Discussions on the translation and validation of biomarker signatures emphasized rigorous quality control in the early stages, aligning with biocrates’ commitment to standardized, quantitative and highly reproducible metabolomics approaches. The consensus was clear: a lack of quantification and quality standards often undermines biomarker projects from the start, reflecting the sentiments of a significant portion of the community.

The poster sessions were equally useful, with information to understand and address challenges, such as insufficient robustness of signals from certain target compounds under chromatographical and/or MS conditions. Insights from discussions with other kit users provided valuable feedback and suggestions for potential target compounds of interest. Additionally, the conference emphasized standardizing metabolomics workflows globally, which is crucial for consistency and reliability in research outcomes.

Advances shared in the poster sessions by biocrates and collaborators included high-resolution metabolomics, artificial intelligence applications in data analysis, urinary metabolomics profiling for biomarker discovery, collaborative ring trials for methodology standardization, and technologies for automated sample preparation. These all highlight ongoing innovations in metabolomics research.

Networking and collaboration

ASMS 2024 was an excellent opportunity to expand our network and foster collaborations. The bustling activity at the biocrates booth was unprecedented, indicating strong interest in metabolomics and quantitative mass spectrometry. Researchers exploring metabolomics or seeking to enhance their platforms found our ready-to-use solutions particularly compelling.

The conference also gave us a chance to meet researchers from our established customer network. It was exciting to hear how our technology simplifies their work from a capacity, budgetary and organizational perspective. For example, one customer shared that they had increased their group size, while being able to move from a considerable budget deficit to a surplus. Another noted that our bile acids kit was more cost-effective than their in-house assay due to reduced reagent needs and increased throughput.

A third customer shared their experience with an alternative metabolomics method that yielded insignificant differences. Initially hesitant to invest in another metabolomics method and adopt our technology, they were thrilled with the results, citing high reproducibility, broad coverage, and confidence in metabolite identification as key factors enabling them to connect results to biological questions.

ASMS is also an opportunity to reconnect with some of our former colleagues and mentors. Celebrating Dr. Elizabeth Neumann’s research award, networking with and mentoring early career scientists and hearing from Jenny Broadbelt within the Females in Mass Spectrometry network, were highlights for members of the biocrates team.

We were pleased to welcome Allumiqs to our Certified Laboratory Program as the only Contract Research Organization (CRO) in North America. Derek Gooderham, CCO at Allumiqs, highlighted, “Working with biocrates enables us to enhance our multiomics customer experience, offering validated and efficient metabolomics data and insights. This collaboration promises to bring significant additional value to our customers’ discoveries”.

Impact

To many in our team, and certainly to many in our network as well, ASMS feels like a family reunion. It offers a fantastic opportunity to catch up with customers, engage face-to-face, and discuss technology across various mass spectrometry applications. The strong emphasis on metabolomics and quantitative mass spectrometry was particularly thrilling. Numerous customers shared how biocrates technology has simplified their work, addressing challenges related to capacity, budget and organizational efficiency.

Overall, ASMS 2024 was a worthwhile event, providing valuable insights, fostering connections, and highlighting the importance of quality control and standardization in metabolomics. The conference reinforced the community’s shared vision for the future of metabolomics and the pivotal role of quantitative and reproducible approaches. We look forward to building on these discussions and advancing the field through continued innovation and collaboration.

NAFLD, characterized by a build-up of fat in the liver. It is generally considered a fairly benign disorder with little impact on health outcomes or quality of life in the short term. However, it is clearly associated with type 2 diabetes and other common cardiometabolic diseases. There is also a well-established epidemiological and pathophysiological link between obesity and type 2 diabetes, and several cancers and neurodegenerative diseases. This suggests a plausible pathophysiological link between NAFLD and those conditions.

As a key metabolic organ, it’s reasonable to hypothesize that the liver could affect metabolic pathophysiology in other organs. However, with the exception of steatosis-related hepatocellular carcinoma, the evidence is not conclusive. This may be because NAFLD is generally underdiagnosed and defined inconsistently between studies. To address this, some argue for the renaming and reclassification of fatty liver diseases, using the term metabolism-associated fatty liver disease (MAFLD) to describe fatty liver disease caused by and accompanied by metabolic dysfunction. In this post, we’ll stick to the term NAFLD.

If we accept that many chronic diseases are preceded by years or even decades of fatty liver pathophysiology, it makes sense to consider whether preventing or treating NAFLD could help prevent chronic diseases.

Nutrition, lifestyle interventions and NAFLD

Today, the main strategies to prevent and treat NAFLD include weight loss, nutrition and exercise. These interventions address the major components of early NAFLD pathophysiology. While the Mediterranean diet is generally recommended, personalized nutrition and lifestyle recommendations are still a matter of debate. Although no specific intervention has been approved for therapeutic purposes, there is growing interest in the role of the microbiome as a potential avenue to determine personalized nutrition recommendations (Hrncir et al., 2021; Koning et al., 2023).

As the white paper, “Chronic diseases have a common origin”, describes at length, many chronic diseases are believed to be endpoints of early metabolic diseases driven by metabolic changes that occur due to a “Western” lifestyle. This typical Western lifestyle is characterized by energy-dense and fiber-deficient diets and a sedentary lifestyle, that together cause changes in the gut microbiome. Early interventions based on nutrition, lifestyle and microbiome could reverse disease progression and reduce the risk of chronic diseases by increasing the availability of chemopreventive substances and reducing the availability of disease-promoting substances.

Pharmacological interventions to prevent and treat NAFLD

Currently, there is no pharmacological treatment for NAFLD once it is established. However, there are a few compounds in clinical development. Many of these are closely related to metabolism. For example:

• Phase III clinical trials are underway for peroxisome proliferator-activated receptor (PPAR) agonists such as Pioglitazone, glucagon-like peptide-1 (GLP-1) agonists, and the farnesoid X receptor (FXR) receptor agonist obeticholic acid;

• Phase II clinical studies are in progress for fibroblast growth factors (FGF) 19/21, fatty acid synthase (FASN) inhibitors, and diacylglycerol acyltransferase (DGAT) inhibitors, which are related to the same metabolic pathways, are in phase II clinical development.

These highlight the importance of fatty acids and lipids, metabolites related to insulin resistance and bile acids. A review on novel targets has recently been published by Parlati et al. (2021).

Besides these promising developments for NAFLD, several therapies that target metabolism might help reduce the risk for NAFLD and its long-term effects. Lange et al. (2021) discuss metabolism-related therapies such as metformin and aspirin as means of chemoprevention for NAFLD, along with the aforementioned interventions.

Prevention of NAFLD and the risk for other chronic diseases

The future of NAFLD therapy very likely lies in combination therapies. Given that other chronic diseases also have complex pathophysiology, intervening in multiple pathways is probably key to make the chemoprevention of complex diseases through NAFLD treatment a viable approach.

When considering chemoprevention for late-onset chronic diseases, concerns revolve around the safety and effectiveness of interventions. The central question is whether it makes sense to subject patients to potential risks and incur costs to prevent diseases that might never have occurred without interventions. We simply don’t have the means to determine whether an individual is likely to develop a specific disease decades into the future.

If we embrace the premise of the white paper – that multiple chronic diseases share a common origin – it changes how we view the risks and benefits of chemoprevention strategies. The risks of long-term intervention remain unchanged, but the potential rewards increase because they not only reduce the risk of a single chronic disease (e.g. Alzheimer’s disease), but also mitigate the risk of a whole range of age-related diseases.

Of course, this approach only works if we target shared elements those chronic diseases, such as the metabolic pathways and interventions discussed above. In fact, all the interventions mentioned are being investigated for their potential to improve outcomes in indications beyond liver disease. The interest in metabolic targets for diverse chronic diseases supports the hypothesis that tackling NAFLD as an early sign of metabolic disease can help curb the growing burden of aging-related chronic conditions in many societies.

Even without targeting any other specific disease, it’s clear that diagnosing and personalizing treatment of NAFLD based on its underlying pathophysiology could greatly reduce the burden of chronic disease more generally. Simply treating any chronic condition would be a major achievement with far-reaching effects.

Read our white paper, “Complex diseases have a common origin”, for a closer look at the shared pathophysiology of chronic diseases.

References

Hrncir et al.: Gut Microbiota and NAFLD: Pathogenetic Mechanisms, Microbiota Signatures, and Therapeutic Interventions (2021) Microorganisms | https://doi.org/10.3390/microorganisms9050957

Koning et al.: Targeting nonalcoholic fatty liver disease via gut microbiome-centered therapies (2023) Gut Microbes | https://doi.org/10.1080/19490976.2023.2226922

Parlati et al.: New targets for NAFLD (2021) Innovation in Hepatology | https://doi.org/10.1016/j.jhepr.2021.100346

Lange et al.: Prevention of NAFLD-associated HCC: Role of lifestyle and chemoprevention (2021) Journal of Hepatology | https://doi.org/10.1016/j.jhep.2021.07.025

After a relatively quiet post-covid ASMS conference in 2022, with noticeably less participants from outside North America, this year’s conference was back in full force. Over 5,000 people from across the mass spectrometry community representing all continents met together in the George R. Brown Convention center in Houston, Texas, to discuss innovations, solutions, and trends under a multitude of talks and posters. The enormous convention hall hosted more than 170 exhibitors surrounded by more than 700 “fresh” posters every day from Monday through Thursday. From graduate students and postdocs to senior researchers, vendors, and other solutions providers, the scope of the conference provided a perfect event to network and discuss applications under the broad scope of mass spectrometry-related topics.

biocrates experience

We were thrilled by the overall interest in metabolomics and specifically the high awareness and reputation of the biocrates services and kit products among those in attendance. It was a pleasure interacting with the large number of attendees coming from all parts of the world who stopping by our booth.

We were able to have many valuable discussions with people and answer lots of technical questions. Several of our experts were present at the conference and explained the details of our technology and how our kits work. The integrated internal standards, software guided automated workflow, and comprehensive metabolite coverage were very appealing and our decentralized approach to providing standardized metabolomics solutions gives our users control over their own data. Of course, visitors to the booth especially enjoyed the giveaway pens, notebooks, lunchboxes, and thermos bottles.

We were particularly thankful to the number of visitors who completed our survey about metabolomics solutions and research interests which covered questions related to metabolites and indicators of interest, technology and software solutions, as well as researchers’ values, needs, and struggles when working with metabolomics.

We appreciated all the suggestions and feedback we received to expand and improve our portfolio for metabolomics solutions. The results confirmed the value and ease-of-use of our products, and that our technology already meets the needs of many researchers. Our new cloud-based workflow manager WebIDQ perfectly aligns with the trends and increasing number of cloud-based innovations across all application fields. We will continue to innovate and improve our kits and services to tackle solutions and provide high-quality products that resonate with user interests and needs.

In addition to the exhibitor activities, we featured two posters related to our technology. One poster demonstrated the performance and comparability of our most comprehensive MxP® Quant 500 XL kit across mass spectrometer platforms. The results showed high reproducibility and correlation across all laboratories and mass spectrometers used. The other poster applied metabolomics and lipidomics profiling to bioprocessing using our AbsoluteIDQ® p400 HR kit on the Thermo Orbitrap Exploris™ 480 MS system.

It was demonstrated that proper optimization and validation can provide reproducible, accurate, and broad targeted metabolomics profiling for bioprocessing samples and cell culture media optimization. Since that time, we further expanded the AbsoluteIDQ® p400 HR to the Exploris™ 120 to 240 systems.

Omics now and tomorrow

As in the previous years, proteomics topics dominated the conference, followed closely by metabolomics, with improved (and automated) workflows, increased coverage, and better methods for quantification. Within the scope of metabolomics, “exposomics” and the study of the exposome has become increasingly relevant and popular. Identification and annotation of the vast number of molecules that we are exposed to and their implications on human health was of high interest. It was mentioned that only 30% of human diseases can be attributed to genetics, and approximately 70% of diseases are caused by the complex interaction of the exposome (including nutrition, pesticides, PFAS, toxins, etc.) and epigenetics. There will be a lot of movement over the next years in exposome studies.

However, the publication presented more of a mixed bag. Based on surveys with toxicologists in big pharma companies in 2015 and 2020, the authors found wide-ranging perceptions of the potential impact of metabolomics: in 2020, more than one in five respondents considered metabolomics to be a game changer already, up from zero five years earlier. At the same time, almost six in ten did not consider metabolomics a game changer technology either now or in future.

Metabolomics wasn’t the only innovation to be met with a mixed response. The article also discusses challenges in -omics biomarker research more broadly, including miRNA and genetics-based biomarkers. It is a story of excitement, disappointments, and difficulties in translation. For metabolomics, challenges in data interpretation and the amount of time and financial investment required were cited as reasons why metabolomics many respondents did not find major value in metabolomics. Others seem to consider it a highly powerful technology.

While I’m disappointed that the ratio is not the other way around, I do understand the doubters.

Metabolomics can be very powerful, but only if done right. The technology has repeatedly shown to outperform established toxicology biomarkers in their respective fields. The sensitivity of the metabolome to react to challenges is the greatest asset of metabolomics. On the other hand, this also means researchers must pay close attention to potential confounders, such as gender, BMI, or age. Other factors such as dietary factors and exercise should also be considered, if possible. Study design is crucial.

When metabolomics first developed, part of the appeal was that it promised to better reflect the phenotype than the static genotype. After all, the same genotype can produce a caterpillar and a butterfly. Toxicologists, as well as pharmaceutical researchers in general, were among the early adopters of metabolomics. Having spotted its potential early, it’s possible the industry has experienced teething problems that are coloring the current assessment.

During my years in metabolomics, many things have changed considerably, including our understanding of how metabolomics projects should be set up to increase the likelihood of successful translation. For some of the things that you should consider in project setup, check out “7 tips to make your first metabolomics project successful” and “Which sample matrix should I use for my metabolomics study?”

I may be a little biased, but in my opinion it is clear: metabolomics is indeed a game changer in toxicology. For example, thanks to metabolomics, we now know that:

• Bile acids are more sensitive and reliable biomarkers for drug-induced liver injury than routine methods (Slopianka et al. 2017)
• Metabolite profiles differentiate therapeutic dose of acetaminophen from overdose with AUC of 1 (Bhattacharyya et al. 2018)
• Potential biomarkers for unintended effects can be identified, e.g. biomarkers of unintended weight gain during antipsychotic therapy
  (Qiu et al. 2023).
  

The industry was correct to be excited about the opportunities when metabolomics started to gain traction. Now with a mature technology at their disposal, plus a better understanding of the methodological requirements, scientists in pharmaceutical R&D should remain excited about the impact metabolomics is going to make in toxicology.

Are you interested in how metabolomics can enhance safety studies in your R&D program? Reach out to us for more information and support!

References

Slopianka M.et al.: Quantitative targeted bile acid profiling as new markers for DILI in a model of methapyrilene-induced liver injury in rats (2017) Toxicology | https://doi.org/10.1016/j.tox.2017.05.009

Bhattacharyya S.et al.: Targeted metabolomic profiling indicates structure-based perturbations in serum phospholipids in children with acetaminophen overdose (2016) Toxicology Reports | https://doi.org/10.1016/j.toxrep.2016.08.004

Qiu Y. et al.: Metabolic biomarkers of risperidone-induced weight gain in drug-naïve patients with schizophrenia (2023) Front. Psychiatry | https://doi.org/10.3389/fpsyt.2023.1144873

What was discussed?

The symposium covered a lot of ground in exploring how omics research collaborations are shaping a better understanding of health and disease, including:

• how omics technologies are being used to stratify patients with major chronic diseases and better understand rare diseases
• how the largest biobanking projects are contributing to population health
• how experimental research can be integrated with epidemiology to discover even more about individual variability
• the pitfalls and challenges of clinical cohort study design, including data collection, data protection and data integration
• the advantages of using metabolomics and other omics to collect extensive and diverse data sets (in compliance with FAIR data standards)
• the latest developments in data science tools and AI (and how to overcome data protection hurdles)
• new methods to integrate scientific findings to inform drug repurposing efforts and new lines of experimental research.

Reaffirming the value of cohort studies

Chronic disease presents an immense burden internationally, inhibiting individual and community well-being and putting strain on national finances. In the US, around 90 % of the healthcare budget is allocated to chronic diseases (CDC 2022) .The health economic impact for diabetes alone is expected to exceed €20 billion in Germany (Köster et al. 2012) and $300 billion in the US (American Diabetes Association 2018). These effects are likely to become amplified as populations age.

Cohort studies may be resource-intensive, but in this context, they are a worthy investment. Well-run cohort studies can help challenge the rising impact and cost of such diseases by increasing our understanding of these conditions so we can manage them most effectively.

To that end, Prof. Annette Peters’ fantastic keynote lecture demonstrated how cohort studies can help understand the five major groups of non-communicable diseases defined by WHO, and the associated risk factors.

Collaboration: from ideas to impact

Research in complex diseases and large cohort studies requires a wide range of expertise. Through this symposium, we sought to bring together a community of experts, and highlight the essential ingredients for successful epidemiology research, including biobanking standards, analytical expertise, and innovative bioinformatics tools.

Even the largest regional or national cohort cannot cover all factors that contribute to the variability in disease risk and outcomes, so sharing data on common risks and confounders, such as lifestyle, genetic background and socioeconomic status, is vital.
It was great to hear how biobanks and cohort studies in Asia, Europe and North America are emphasizing data interoperability.

We heard about several specific examples of how reproduction of results is achieved by design (such as the EPIC study, the SHIP studies with sister cohorts in Brazil and Poland, or the BRAINcommons platform). Other talks demonstrated successful reproduction of specific results through individual collaboration between cohorts. No one shied away from the challenges, and it was especially useful to see how others overcome the obstacles we may face in our own research.

Thank you!

A huge thank you to everyone who helped make the symposium a reality, especially our 40+ speakers who generously committed their time and expertise. We also owe gratitude to the local hosts, Prof. Masayuki Yamamoto (for Sendai), Prof. Annette Peters and Prof. Markus Gerhard (for Munich), and Prof. Jessica Lasky-Su (for Boston).
We hope you enjoyed the symposium as much as we did. Don’t hesitate to let us know your biggest takeaway and reach out to us if you have any questions about applying omics technologies to your study.

References

American Diabetes Association: Economic Costs of Diabetes in the U.S. in 2017 (2018) National Library of Medicine | http://doi.org/10.2337/dci18-0007

Centers for disease Control and Prevention (CDC): Health and Economic Costs of Chronic diseases (Entered October 18, 2022) | https://www.cdc.gov/chronicdisease/about/costs/index.htm

Köster I. et al.: Follow up of the CoDiM-Study: Cost of diabetes mellitus 2000–2009 (2012) Dtsch Med Wochenschr | http://doi.org/10.1055/s-0032-1304891

Let’s cut to the chase: there is no one-size-fits-all answer to this question. If you were looking for a quick answer, you might want to stop reading now. But if you’re curious about why “it depends” and how you can figure out a good starting point for your study, then read on.

While there’s no single correct approach to figuring out your sample matrix, there certainly are things that could be done wrong. Here, we dive into some of the advantages and disadvantages of the sample matrices you might consider.

The most important question

Before we discuss individual samples, let’s get an even more important question out of the way. What do you want to achieve with your metabolomics study? If you properly define the outcome you desire, the rest often falls neatly into place.

For example, if you want to learn about a biological process that cannot be investigated in human studies, perhaps because it would require multiple tissue samples from sites difficult to access, you have no choice but to move toward animal models or other model systems for the project.

If the goal is to find a stratification biomarker and human biofluids are available, then those biofluids are your logical matrix. If you have other data that seems to make a specific metabolic pathway relevant, such as from transcriptomics experiments, you’d clearly want a metabolomics approach that covers the respective pathways.

The scientific question defines the feasibility of metabolomics from a specific matrix.

An overview of benefits and challenges:

Cells and tissues (incl. novel model systems)

The benefit of using cell and tissue sample matrices is obvious. You can directly investigate the cell or organ type that you are interested in. Any signals should be expected to represent the target organ or cell type. Organoids and organ-on-a-chip technologies offer amazing new opportunities for cell- and tissue-based research as well.

Several factors must be considered to obtain meaningful results from cell- and tissue-based studies:

• Generally, cells and tissues can provide insights into local effects. However, organisms (and biological systems in general) are complex, so one must carefully consider the representativeness of any findings.
  
  
• With tissue samples, variability between locations within organs must be considered. You can counter this problem by using homogenates of samples larger than what the metabolomics method requires, or by measuring tissue samples from multiple sites.
  
  Most metabolomics studies use fresh tissue rather than fixed tissues, mainly because the fixation step has vast consequences on metabolite levels.
  
  
• In organs that are highly perfused, eliminating blood contamination can also be a step to increase validity.
  
  
• Small changes in the conditions for culturing and/or collection can have a major impact on the findings. If cells change their phenotype and behavior in a culture, they may no longer represent what you are intending to investigate.
  
  Co-culturing of cell types can be an interesting option to better simulate physiological conditions. The type of medium can also influence the results.
  
  
• Finally, consider the extraction of metabolites from cells and tissues. Metabolites vary considerably in their chemical and physical properties.
  
  Consequently, one needs to consider whether a single-step extraction works well enough across metabolites of interest, whether a multiple-step extraction protocol could yield better results across metabolite classes, or whether an extraction optimized for polar or apolar is the preferrable option.
  

Biofluids

Blood components

Blood components are probably the most frequently used matrix in metabolomics studies. As a routinely used clinical sample, it’s the go-to matrix for biomarker studies. Blood represents signals from the whole organism, making it a great matrix for research on complex diseases.

While no one doubts the relevance of blood-based samples as a matrix for metabolomics, there’s intense debate around the exact type of sample to use.

Some proponents in the field prefer a serum matrix because it removes a larger proportion of cellular components, leading to slightly higher metabolite concentrations and thus higher sensitivity in biomarker discovery. For some areas of (targeted) metabolite analyses, such as steroid hormone analysis, serum is the conventional matrix of choice.

Others argue that serum is more prone to pre-analytical issues such as oxidation, making serum-based metabolomics vulnerable to effects of impaired sample quality. In addition, some blood cells remain metabolically active during coagulation and may release metabolites into the fluid component of the sample. For example, platelets can metabolize arachidonic acid and release high amounts of eicosanoids during coagulation. This may limit the relevance of such groups of metabolites if analyzed in serum rather than plasma.

For this reason, many say that plasma should be be preferred over serum. However, it’s not clear which anti-coagulant is most suitable. Ethylenediamine tetraacetic acid (EDTA) plasma is most-commonly used, while citrate plasma is often discouraged for use in LC-MS based metabolomics studies.

Whole blood is rarely used as a matrix for metabolomics because of the potential interference from cellular components. That said, dried blood spot analysis is a well-established metabolomics approach in routine newborn screening (NBS).

Recently, we’ve also seen considerable interest in novel means of biofluid sampling, such as dried plasma spot sampling. This could keep samples (more) stable at room temperature and alleviate logistical barriers to using metabolomics (traditional methods are hampered if blood samples cannot be frozen and transported safely).

Sorted cells or cellular components, plus extracellular vesicles collected from blood samples, can be a very interesting matrix to use to answer specific questions. For example, tumor-secreted extracellular vesicles could provide a non-invasive sample to inform researchers about the metabolome of a tumor. Similarly, metabolomics performed from sorted immune cells can reveal the intricacies of immune regulation.

Urine

Urine is generally considered an interesting matrix for metabolomics due to the easy and non-invasive means of sampling. It’s also highly stable compared to blood-based samples, even at higher storage temperatures.

The gold standard is a 24-hour urine sample, but spot urine is also common. Researchers should watch out for the high salt content in urine when performing analysis. In addition, normalizing for creatinine concentrations is a common strategy to improve quality and reduce variability.

It’s also worth noting that in diagnostic settings, you often look for what is not supposed to be there, e.g. glucose or proteins. Many detectable metabolites are deliberately excreted by the organism to maintain homeostasis within the organism. This is a conceptual issue that is also relevant to the use of feces, as will be discussed later.

Other biofluids

• Saliva and tear fluid are also interesting matrices due to the non-invasive means of sampling, though compared to other biofluids mentioned here, metabolomics in these matrices is in its infancy.
  
• Cerebrospinal fluid (CSF) is an interesting surrogate for the central nervous system, but is difficult to sample and it can be very difficult to obtain healthy control samples for ethical reasons.
  
• Likewise, bronchoalveolar lavage fluid (BALF) can be a great sample for research on respiratory tract disorders, but sampling challenges make sampling at scale impractical.
  
• Sweat could also be a promising matrix due to the easy and non-invasive sampling, but again remains in its infancy.

Other non-invasive sample types

Non-invasive sampling is extremely attractive because it’s accessible, acceptable to study subjects, and easily repeated. Feces and hair are the main matrices to mention here, but we see metabolomics applications described for several other sample types, such as skin lavage, earwax, nasal mucus and nasal lavage, among others.

The use of feces as a matrix for metabolomics studies has seen a steep rise recently. This is partly due to the rise of microbiome studies and acknowledgement by microbiome researchers that metabolomics can enhance functional understanding of host-microbiome interaction. However, ensuring quality in metabolomics studies from fecal samples poses several challenges.

Firstly, the structure and consistency of fecal samples can vary considerably, which affects the abundance of biomolecules. Secondly, both recent and habitual diet can be important confounders. In addition, the method of extraction has a major effect on what is measured in the fecal metabolome.

With separation of fecal water or soft extraction methods, you may be able to mostly base your analysis on the cell-free parts of the fecal sample. With harsher extraction methods you may lyse or rupture microbial cells to varying degrees. This can lead to higher metabolite coverage and be of scientific interest as well.

After all, bacteria interact with the organism extensively and cells may produce and excrete substances that are swiftly taken up by the organism and might thus not be represent in fecal water at significant amounts.

Finally, and most importantly, the fecal metabolome may represent what the organism actively sheds rather than the organism’s metabolic composition. The interaction between the microbiome and the metabolome is more significant in the duodenum than in the lower intestines.

Consequently, the fecal metabolome is a poor surrogate for the metabolome in the upper intestines. The use of cecal duodenal contents may be a better matrix to assess host-microbial interaction, at least in basic research.

In in-vivo human settings, sampling and analyzing samples from the upper intestines has not yet moved beyond proof-of-concept studies and no generally accepted method has evolved yet.

See our article on fecal metabolomics to learn more: Best practices for feces metabolomics – biocrates life sciences gmbh

Hair is another accessible matrix that’s particularly interesting for disease monitoring because it can capture changes that have occurred over weeks or even months prior to sampling.

Although hair analysis has become an established method for measuring xenobiotics and analyzing selected metabolites such as cortisol, its clinical utility remains in question.

The scarcity of metabolomics studies from hair compared to other indications may make it even harder to confirm the relevance of biomarker signatures. Again, variability is a major concern. This can stem from hair characteristics such as pigmentation, but also from external factors such as hair treatment with shampoo and other chemicals.

There are also no accepted standard procedures specific to the analysis of the hair metabolome, which makes comparison between studies even more difficult.

Concluding thoughts

What’s clear is that metabolomics can be and has been done from a vast variety of sample matrices, each with its pros and cons. In that respect, the question of which sample matrix to use remains as relevant as it is difficult to answer.

Thankfully, if you are considering more than one matrix, you don’t always have to choose: why not combine multiple matrices to optimize results and maximize insights?

Here are a few examples of how combining matrices could be of value:

• Combining plasma and urine analyses for research on kidney diseases can add insights compared to using only a single matrix, as relative changes between plasma and urine could be biologically meaningful.

• For biomarker research based on blood components, the question of where the signal originates can be answered by adding tissue of the target organ or multiple organs which are known to contribute to a disease. In other words, combining circulatory biofluids and tissues can provide information on BOTH the local effects and the systemic contribution.

• Combining metabolomics from fecal samples and plasma can provide clues as to the probable effects of microbiome changes directly in the gastrointestinal tract, and which actions it can exert in systemic circulation.

There’s also plenty to say about combining metabolomics with imaging technologies or other -omics technologies – but we’ll save that for another article.

Before you embark on your metabolomics project, you have some serious thinking to do. Choose wisely, and you’ll be rewarded with exciting findings.

What do you think? What is your favorite matrix and why? Have we missed a matrix that you find important? Reach out to us if you have questions about the methodological considerations for metabolomics studies in the matrix of your interest

MxP® Quant 500 kit validated on Sciex Triple Quad 7500 LC MS/MS System – bringing targeted metabolomics to the next generation of SCIEX triple quadrupoles

Metabolomics is an invaluable tool for investigating and understanding the complex mechanisms involved in cellular biology, opening the door to new insights in health, nutrition and disease.

Now, scientists and researchers can use metabolomics techniques with greater ease and efficiency, thanks to the MxP® Quant 500 metabolite profiling kit being validated for use with the new SCIEX 7500 system.

The MxP® Quant 500 is the leading broad metabolite profiling kit for use with liquid chromatography-tandem mass spectrometry (LC/MS). As the most comprehensive metabolomics kit on the market, it offers a ready-to-use solution with a simple automated workflow for the quality-controlled analysis of 630 small molecules and lipids from 26 compound classes.

With more than 230 predefined, biologically relevant metabolite sums and ratios, it facilitates advanced biological data interpretation with increased statistical power.

Validating the MxP® Quant 500 kit with SCIEX 7500

The SCIEX 7500 system is the latest triple quadrupole from SCIEX. In addition to new SCIEX OS software, the ion source and other hardware components have been redesigned. The MxP® Quant 500 kit was validated on earlier SCIEX systems and widely used since its launch in 2019.

After extensive testing and method parameter optimization, we can now confirm that the new 7500 system, with its improved sensitivity and precision, is an ideal match for the Quant 500 kit.

For the analysis, 13 polar small molecule classes were analyzed by ultra-high-performance liquid chromatography-electrospray ionization-tandem mass spectrometry (UHPLC-ESI-MS/MS), while hexoses and 12 lipid classes were analyzed by flow injection analysis-tandem mass spectrometry (FIA-MS/MS).

The Quant 500 kit was validated on a UHPLC coupled to a SCIEX 7500 platform using the high flow rate ion source. The validation was carried out across three kit plates run on three separate days, consisting of three concentration levels of plasma, fecal, and urine quality control (QC) samples.

Pooled samples of each matrix type representing typical unknown samples were also run on each plate. The scope of the validation provided detailed information regarding intra and inter day kit performance, accuracy, reproducibility and stability. The pooled matrix samples provided the expectations for metabolite coverage in real study samples.

The results were impressive, demonstrating improved compatibility between the Quant 500 kit and the new SCIEX platform.

Summary of exceptional performance:

• Using the biocrates standard level QC sample as a reference, excellent precision was observed across all three plates with <6% average CV within each plate and <15% average CV across all plates for analytes detected above the limit of detection (LOD).
• Analyte coverage detectable above LOD compared favorably with other instruments. In a pooled human plasma sample, 499 metabolites were detected above LOD. n a pooled human fecal sample, 447 metabolites were detected above LOD. In pooled urine, 66 metabolites were detected above LOD, owing to the low lipid abundance in this matrix.
• Calibration standards showed good accuracy across the full range of concentrations for each analyte with quadratic curves fittings of r²=0.99.

The MxP® Quant 500 adaptation on SCIEX 7500 system is the latest advance in the longstanding use of biocrates kits on SCIEX triple quadrupole mass spectrometers.

As with its predecessors, the kit performs exceptionally well on the latest SCIEX instrument, offering an effective platform for those looking to upgrade and those just getting started into quantitative metabolomics.

It’s well known that only 20–30 % of patients experience a long-term benefit from immunotherapy, but the high rate of adverse events in immunotherapies is also a matter of intense research and scientific debate. Biomarkers that can stratify patients and identify whether they are at risk for severe toxicities are an important medical need. Here, I make the case for metabolomics as a tool for discovering such biomarkers.

Earlier this month, Eschweiler and colleagues reported on a failed phase II trial for a phosphoinositide 3-kinase delta (PI3Kδ) inhibitor in head and neck cancer. (Eschweiler et al. 2022) The drug had previously been tested successfully for B cell lymphomas. Specifically, the authors reported severe immune-related adverse events (irAEs) in about 50 % of patients. Most of the adverse events were reported as colitis, but the lungs and skin were also affected.

Using single-cell sequencing, the authors identified specific T cell populations that were affected and suggested intermittent dosing as a potential way to reduce toxicity while retaining therapeutic response. Fewer toxicities were reported in trials for B cell lymphoma. This was attributed to the fact that the lymphoma patients had been immunocompromised through previous therapies, while the patients with head and neck tumors were treatment naïve, which allowed a stronger immune response.

While I don’t challenge those findings, I do think there’s probably more to the story. To me, a reductionist view on pharmaceutical research is problematic – we should aim to investigate comprehensively any factors that could have contributed to outcomes in clinical trials.

How does metabolism factor in here?

We know from another recent study showed that previous therapy does affect metabolism, which might affect the response to subsequent lines of therapy. (Nees et al 2022) Several other factors further convince me that this type of research calls for a metabolomics approach to be included in the investigation.

Interdependencies of metabolism and immune regulation

Immunometabolism is rapidly gaining traction as an interesting research topic. Reviewing the complex interactions between metabolism and the immune system is beyond the scope of this article, however, several recent reviews have addressed the topic. (Voss et al. 2021, Lercher et al. 2020, Traba et al. 2021) Here, we shall briefly address the role of PI3K in the regulation of metabolism and metabolic regulation of immune responses:

The role of PI3K in regulating metabolism

PI3K is a key member of the PI3K / protein kinase B (AKT) / mammalian target of rapamycin (mTOR) pathway. This pathway is involved in a huge number of cellular mechanisms, including immune regulation as well as metabolism. It’s therefore possible that endogenous and/or tumor metabolism might be involved in the effect and side effects of therapeutics targeting this pathway. For more on the interplay between PI3K and metabolism in the context of cancer, see Hoxhaj and Manning 2020. (Hoxhaj et al. 2020)

The importance of metabolism in regulating immune cell function and local immune responses

Immune cells are highly metabolically active and their function depends on systemic metabolism and nutritional status.(Alwarawrah et al. 2018) This means that if we’re looking at a therapy designed to affect immune cells, we need to know as much as possible about how metabolism determines the observed effects. At the level of specific metabolic pathways, the most well-known interaction is probably that of Tryptophan (Moffett et al. 2003, Han et al. 2021, Modoux et al. 2021, Wang et al. 2020, Gargaro et al. 2021, Kim et al. 2021, Wu et al. 2018) but it’s far from the only one that’s vital for keeping the immune system in balance.

For example, cholesterol metabolites and their main signaling routes (i.e. nuclear receptors) are also well-established in regulating T cells and other types of immune cells. (Bietz et al. 2017, Park et al. 2015, Lee et al. 2022, Bilotta et al. 2020)

Finally, it’s well known that the metabolism of immune cells is relevant to cancer biology. In this case, the tumor uses metabolic competition to dampen local immune responses, and so targeting immune cell metabolism might improve the response to immunotherapy. (Le Bourgeois et al. 2018, Shyer et al. 2020, Leone et al. 2020, Talty et al. 2021, Xia et al. 2021, Madden et al. 2021, Wei et al. 2021)

Evidence for microbiome-metabolome-immune interaction in inflammatory bowel disease and other immune-mediated diseases

Metabolism and immune metabolism are one of the major pathways through which the microbiome interacts with the host metabolism. Many therapies are known to affect the intestinal microbiota, (Forslund et al. 2021) and the role of the microbiome as a determinant of and target for improving immunotherapeutic outcomes is a matter of increasingly intense scientific inquiry. (Zhou et al. 2021, Li et al. 2021, Renga et al. 2022, Hayase et al. 2021)

For a therapy that induces adverse (auto-)immune effects in the gut, it makes sense to consider the involvement of microbial metabolism. We’ve already seen links between microbial composition and the toxicity of immunotherapies.(Andrews et al. 2021)

Several of the pathways and metabolites mentioned here have been associated with inflammatory bowel diseases, autoimmunity, and other immune-mediated diseases. (Bilotta et al. 2020, Ding et al. 2020, Haq et al 2021, Hu et al. 2021, Visekruna et al. 2021, Qiu et al. 2021, Jutley et al. 2021, Nardone et al. 2021)

Immune metabolism as predictor of response to immunotherapies in oncology

Here, I’ve discussed how metabolism shapes the interaction between the immune system and cancer. There’s also published evidence for the role of metabolism in determining the efficacy of immunotherapeutic drugs. The biomarkers described as relevant include metabolites from microbiome-associated, as well as immune modulatory pathways.(Malczewski et al. 2020, Nie et al. 2021, Hatae et al. 2020, Mock et al. 2019, Kocher et al. 2021)

Conclusion

Metabolism plays an essential role in regulating immune responses, and perturbations in immunometabolic pathways contribute to a large variety of diseases including cancer. In addition, targets in the immunotherapy of cancer are involved in cellular and immunometabolic pathways. Finally, there is growing scientific evidence that metabolomics can provide biomarkers indicative of patient outcome in immunotherapy. If we want to understand why a patient responds to immunotherapy favorably, with adverse effects, or not at all, metabolomics could be instrumental in doing so.

What do you think? Have I missed a vitally important metabolic pathway? Do you already have experience with research in immunometabolism, and/or metabolomics in the context of immunotherapies? Please share your thoughts with us and write to stefan.ledinger@biocrates.com.

For further information about how biocrates can help with biomarker research in pharmaceutical R&D, see also the article Therapy resistance: could metabolomic biomarkers remove this major roadblock to successful pharmaceutical research and development programs?

Read more about metabolomics on pharmacology

Watch webinar Advancing cancer treatment by targeting dysregulated metabolism – A roadmap (YouTube Link)

References

Alwarawrah Y. et al.: Changes in Nutritional Status Impact Immune Cell Metabolism and Function. (2018) Frontiers in Immunology | https://doi.org/10.3389/fimmu.2018.01055

Andrews M. et al.: Gut microbiota signatures are associated with toxicity to combined CTLA-4 and PD-1 blockade. (2021) Nature Medicine | https://doi.org/10.1038/s41591-021-01406-6

Bietz A. et al.: Cholesterol Metabolism in T Cells. (2017) Frontiers in Immunology  | https://doi.org/10.3389/fimmu.2017.01664

Bilotta M. et al.: Liver X Receptors: Regulators of Cholesterol Metabolism, Inflammation, Autoimmunity, and Cancer. (2020) Frontiers in Immunology | https://doi.org/10.3389/fimmu.2020.584303

Ding X. et al.: Tryptophan Metabolism, Regulatory T Cells, and Inflammatory Bowel Disease: A Mini Review. (2020) Mediators of Inflammation | https://doi.org/10.1155/2020/9706140

Eschweiler S. et al.: Intermittent PI3Kδ inhibition sustains anti-tumour immunity and curbs irAEs ( 2022) Nature | https://www.nature.com/articles/s41586-022-04685-2

Forslund S. et al.: Combinatorial, additive and dose-dependent drug-microbiome associations. (2021) Nature | https://doi.org/10.1038/s41586-021-04177-9

Gargaro M. et al.: Tryptophan Metabolites at the Crossroad of Immune-Cell Interaction via the Aryl Hydrocarbon Receptor: Implications for Tumor Immunotherapy. (2021) International Journal of Molecular Science |  https://doi.org/10.3390/ijms22094644

Han C. et al.: T-cell Antitumor Immunity: Amino Acid Metabolism Revisited. (2021) Cancer Immunology Research |  https://doi.org/10.1158/2326-6066.CIR-21-0459

Haq S. et al.: Tryptophan-derived serotonin-kynurenine balance in immune activation and intestinal inflammation. (2021) The FASEB Journal | https://doi.org/10.1096/fj.202100702R

Hatae R. et al.: Combination of host immune metabolic biomarkers for the PD-1 blockade cancer immunotherapy. (2020) JCI Insight. | https://doi.org/10.1172/jci.insight.133501

Hayase E. et al.: Role of the intestinal microbiome and microbial-derived metabolites in immune checkpoint blockade immunotherapy of cancer. (2021) Genome Medicine | https://doi.org/10.1186/s13073-021-00923-w

Hoxhaj G. et al.: The PI3K-AKT network at the interface of oncogenic signalling and cancer metabolism. (2020) Nature Reviews Cancer. | https://doi.org10.1038/s41568-019-0216-7

Hu J. et al.: Gut microbiota-mediated secondary bile acids regulate dendritic cells to attenuate autoimmune uveitis through TGR5 signaling. (2021) Cell Reports | https://doi.org/10.1016/j.celrep.2021.109726

Jutley G. et al.: Relationship Between Inflammation and Metabolism in Patients With Newly Presenting Rheumatoid Arthritis. (2021) Frontiers in Immunology | https://doi.org/10.3389/fimmu.2021.676105

Kim M. et al.: A Rheostat of Cancer Immune Escape Mediated by Immunosuppressive Enzymes IDO1 and TDO. (2021) Frontiers in Immunology | https://doi.org/10.3389/fimmu.2021.636081

Kocher F. et al.: High indoleamine-2,3-dioxygenase 1 (IDO) activity is linked to primary resistance to immunotherapy in non-small cell lung cancer (NSCLC). (2021) Translational Lung Cancer Research | https://doi.org/10.21037/tlcr-20-380

Le Bourgeois T. et al.: Targeting T Cell Metabolism for Improvement of Cancer Immunotherapy. (2018) Frontiers in Oncology | https://doi.org/10.3389/fonc.2018.00237

Lee M. et al.: Reprogramming cholesterol metabolism in macrophages and its role in host defense against cholesterol-dependent cytolysins. (2022) Cellurar&Molecular Immunology | https://doi.org/10.1038/s41423-021-00827-0

Leone R. et al.: Metabolism of immune cells in cancer. (2020) Nature Reviews Cancer | https://doi.org/10.1038/s41568-020-0273-y

Lercher A. et al.: Systemic Immunometabolism: Challenges and Opportunities. (2020) Immunity | https://doi.org/10.1016/j.immuni.2020.08.012

Li B. et al.: Mining the Gut Microbiota for Microbial-Based Therapeutic Strategies in Cancer Immunotherapy. (2021) Frontiers in Oncology  | https://doi.org/10.3389/fonc.2021.721249

Modoux M. et al.: Tryptophan Metabolism as a Pharmacological Target. (2021) Trends in Pharmacological Science |  https://doi.org/10.1016/j.tips.2020.11.006

Moffett J. et al.: Tryptophan and the immune response. (2003) Immunology Cell Biology | https://doi.org/10.1046/j.1440-1711.2003.t01-1-01177.x

Madden M. et al.: The Complex Integration of T-cell Metabolism and Immunotherapy. (2021) Cancer Discovery | https://doi.org/10.1158/2159-8290.CD-20-0569

Malczewski A. et al.: Microbiome-derived metabolome as a potential predictor of response to cancer immunotherapy. (2020) Journal for Immuno Therapie for Cancer. | https://doi.org/10.1136/jitc-2020-001383

Mock A. et al.: Serum very long-chain fatty acid-containing lipids predict response to immune checkpoint inhibitors in urological cancers. (2019) Cancer Immunology, Immunotherapie | https://doi.org/10.1007/s00262-019-02428-3

Nardone O. et al.: Inflammatory Bowel Diseases and Sarcopenia: The Role of Inflammation and Gut Microbiota in the Development of Muscle Failure. (2021) Frontiers in Immunology | https://doi.org/10.3389/fimmu.2021.694217

Nees J. et al.: How previous treatment changes the metabolomic profile in patients with metastatic breast cancer (2022) Archive of Gynecology and Obstetrics | https://doi.org/10.1007/s00404-022-06558-5

Nie X. et al.: Serum Metabolite Biomarkers Predictive of Response to PD-1 Blockade Therapy in Non-Small Cell Lung Cancer. (2021) Frontiers in Molecular Biosciences | https://doi.org/10.3389/fmolb.2021.678753

Park B. et al.: The role of nuclear receptors in regulation of Th17/Treg biology and its implications for diseases. (2015) Cellurar&Molecular Immunology | https://doi.org/10.1038/cmi.2015.21

Qiu J. et al.: Metabolic Control of Autoimmunity and Tissue Inflammation in Rheumatoid Arthritis. (2021) Frontiers in Immunology | https://doi.org/10.3389/fimmu.2021.652771

Renga G. et al.: Optimizing therapeutic outcomes of immune checkpoint blockade by a microbial tryptophan metabolite. (2022) Journal for Immuno Theraphy of Cancer | https://doi.org/10.1136/jitc-2021-003725

Shyer J. et al.: Metabolic signaling in T cells (2020) Cell Research | https://doi.org/10.1038/s41422-020-0379-5

Talty R. et al.: Metabolism of Innate Immune Cells in Cancer. (2021) Cancers | https://doi.org/10.3390/cancers13040904

Traba J. et al.: Immunometabolism at the Nexus of Cancer Therapeutic Efficacy and Resistance. (2021) Frontiers in Immunology |  https://doi.org/10.3389/fimmu.2021.657293

Visekruna A. et al.: The Role of Short-Chain Fatty Acids and Bile Acids in Intestinal and Liver Function, Inflammation, and Carcinogenesis. (2021) Frontiers in Cell and Developmental Biology | https://doi.org/10.3389/fcell.2021.703218

Voss K. et al.: A guide to interrogating immunometabolism. (2021) Nature Reviews Immunology | https://doi.org/10.1038/s41577-021-00529-8

Wang W. et al.: Amino Acids and Their Transporters in T Cell Immunity and Cancer Therapy. (2020) Molecular Cell | https://doi.org/10.1016/j.molcel.2020.09.006

Wei J. et al.: T cell metabolism in homeostasis and cancer immunity. (2021) Current Opinion in Biotechnology | https://doi.org/10.1016/j.copbio.2021.02.003

Wu H. et al.: Indoleamine 2, 3-dioxygenase regulation of immune response. (2018) Molecular Medicine Report | https://doi.org/10.3892/mmr.2018.8537

Xia L. et al.: The cancer metabolic reprogramming and immune response. (2021) Molecular Cancer. | https://doi.org/10.1186/s12943-021-01316-8

Zhou C. et al.: Gut Microbiota in Cancer Immune Response and Immunotherapy. (2021) Trends in Cancer | https://doi.org/10.1016/j.trecan.2021.01.010

Resistance to available drug therapies represents a major problem in pharmaceutical therapy. The issue affects a broad field of indications including (but not limited to) diabetes, depression and multiple sclerosis (Schork 2015). Non-response is not just an issue in clinical care, but is also a major obstacle in clinical trials for new pharmaceuticals, and may help explain why 50% to 80% of clinical development programs are discontinued at each stage of clinical trial (Pammolli et al. 2020).

In some disease groups, such as neurodegenerative diseases, failure at the clinical stage has unfortunately been the norm, with success stories a rare exception. Late-stage attrition may be driven by greater patient variability, due to different lifestyle factors, genetic backgrounds, comorbidities, and other factors. In this context, a higher proportion of patients must be expected to be therapy-resistant. Stratifying patients using novel biomarkers that reflect this complexity is likely to help differentiate the therapy-responsive patients and identify those who are likely to benefit from a particular therapeutic approach.

Stratification can be useful in other areas, too. In many diseases, therapies tend to become ineffective as the disease progresses, or because resistance mechanisms are activated. For example, acquired resistance is the subject of intense discussion in oncology (Floros et al. 2020, Bueschbell et al. 2022) and in relation to immunological therapies in inflammatory bowel disease (Fine et al., 2019). In Type 2 Diabetes, it is estimated that 5% to 10% of patients need to change therapy due to a loss of efficacy (Wexler et al. 2022).

Another significant challenge is the lack of actionable biomarkers that would enable clinical therapy management to optimize care with respect to co-medication and lifestyle therapies that could reduce non-response rates and improve clinical outcomes. Without such biomarkers, therapeutic choices must be made based on trial and error, although evidence in support of nutritional interventions is growing (Shastri et al. 2021, Schuetz 2017, Kaegi-Braun et al. 2021, Fan et al. 2022).

Figure 1: Areas of need for biomarker research

Why do metabolomic biomarkers hold promise?

Classical markers assessed in pharmaceutical research and development focus on concepts such as target engagement and monitoring of drug levels. In the clinical setting, only limited biomarkers are available, many of which are concentrated in the oncology space. Therapy choices are typically based on target (over-)expression of oncogenes (in “classical” targeted therapies) or of immune checkpoints (in cancer immunotherapies).

While such approaches are undoubtedly of great value, high rates of primary and/or acquired resistance remain a problem. This can be attributed to pathophysiological complexities that such markers do not properly consider. Tumor biology consists of much more than single oncogenic drivers, and the immune system is much too complex to determine by a single factor. (For more information about the pathophysiological process at play in oncology, Hanahan 2022 is a great choice.).

Metabolomics can help combat these issues in several ways. Comprehensive biochemical (i.e. metabolic) characterization of patients can reveal a lot of additional information about the various pathophysiological processes that contribute to the patient’s condition. These analyses can take account of the individual’s genetic background, age, comorbidities, and lifestyle factors, as well as interactions between organs that may contribute to the condition.

They can also be performed using easily accessible blood samples. Metabolomics can thus greatly enhance the information that is available from classical biomarker approaches and routine laboratory parameters. Finally, metabolic profiles are expected to evolve as the disease does, capturing the dynamics of the disease during a patient’s journey.

Consequently, metabolomics can provide actionable biomarkers that inform suitable approaches for co-medication and supportive nutrition therapy and can guide early adaptation of the therapeutic approach in the event of disease progression, development of acquired resistance, and/or toxicities.

A recent study has shown that biomarkers significantly improve the outcome of clinical trial success rates, particularly in the field of oncology (Parker et al. 2021). Exploratory biomarkers were found to bring a benefit to clinical trial success rates even before their proper validation.

The study did not refer to metabolomics specifically, but the factors discussed above suggest that the establishment of research programs for metabolomic biomarkers could constitute an opportunity for further improved success rates in clinical pharmaceutical research. As such, metabolic profiles may constitute a new cornerstone of precision therapy approaches.

Evidence for metabolic signatures as relevant and actionable biomarkers in pharmacological therapy

This discussion shows that in theory, metabolomics-based biomarker signatures have great potential. But is there proof that it works? According to several peer-reviewed papers, there is scientific evidence that it does.

Pharmacometabolomics in targeted cancer therapy

In a study of one of the most prominent examples of targeted cancer therapy, pharmacometabolomics showed promise as a tool for patient stratification in breast cancer patients treated with trastuzumab-paclitaxel (Miolo et al. 2015). While this treatment was an important milestone, but a relatively high proportion of recipients are non-responders.

The study showed that a simple ratio between spermidine and tryptophan is predictive of response. Spermidine interacts with pathways affected by the paclitaxel component of the treatment, while tryptophan is probably related to immunocytotoxicity of trastuzumab. Representing two chemically related and biophysically similar analytes (i.e. an amino acid and an amino acid metabolite), such ratios have the potential to serve as predictive biomarkers and could be easily implemented into clinical routine.

Several examples of translational biomarker research in targeted therapies have been published by the Cancer Therapeutics Unit at The Institute of Cancer Research in London by Dr. Raynaud and colleagues (Ang et al. 2016, Ang et al. 2017, Pal et al. 2020). As a similar experimental approach has been used in several projects, the results shall be discussed together. Figure 2 below shows that MEK inhibitors, PI3K inhibitors and AGC-kinase inhibitors act on interacting pathways. In mouse xenografts, differential dose-dependent metabolic responses have been observed.

The altered metabolites were considered candidate pharmacodynamic biomarkers, and the signals observed in preclinical research have largely been confirmed in Phase-I clinical trials. Moreover, the most important signals have associated with clinically relevant outcomes. In PI3K inhibitor therapy, branched-chain amino acids (BCAA) were associated with dose-limiting insulin resistance. In AGC-kinase inhibition, changes in nitric oxide (NO) metabolism were associated with dose-limiting hypotension. For MEK inhibitors, metabolic patterns were predictive of objective response and progression.

Figure 2: Signaling pathways, targets and metabolites (ICR London studies)

– Find out more about the AGC kinase inhibitor project and how circulating metabolites shed light on mechanism of action.

Pharmacometabolomics in cancer immunotherapy

Targeted therapies in oncology can have high initial response rates but high rates of acquired resistance. By contrast, immunotherapy often has long-lasting effects but only shows a response in a minority of patients. A group from the Heidelberg University Hospital in Germany found that lipids containing very long-chain fatty acids (VLCFA) were predictive to immunotherapy response (Mock et al. 2019). This suggests that supplementation with VLCFAs might increase a person’s response to immune checkpoint inhibition.

– For a deeper insight into this topic, take a look at our blog post: Make sure your drug fits to your cancer.

Tryptophan metabolism is a major regulator of immune processes. For this reason, indoleamine 2,3-dioxygenase (IDO) has attracted interest for its therapeutic potential. Although attempts to target IDO have largely failed thus far, the pathway remains of interest for the development of novel therapeutics (Peyraud et al. 2022).

Besides being a potential therapeutic target, the pathway also shows promise as a predictor of therapeutic outcomes. In a study of non-small cell lung cancer (NSCLC) patients, Kocher et al. (2021) describe an association between tryptophan metabolism and primary resistance to immune checkpoint inhibitors. Here, tryptophan is also suggested as a surrogate parameter for the IDO activity as a predictive biomarker for immune checkpoint inhibitor therapy, and as an informative trait for future investigations of therapeutic approaches targeting IDO directly.

Pharmacometabolomics in other indications

As noted, therapy resistance is a hot topic in virtually all areas of medicine, from neurodegenerative and neuropsychiatric diseases to cardiometabolic diseases. Beyond oncology, metabolomics also has proven potential as a stratification biomarker technology in other indications. For example, researchers from the Mayo Clinic in Rochester, US, have identified multiple genotype-metabolite interactions that are predictive of the response to antidepressant drugs escitalopram/citalopram (Joyce et al. 2021).

Selected acylcarnitines, lipids and amino acids showed pre-treatment differences between responders and non-responders. In addition, on-treatment changes in circulatory metabolite levels provided novel insights into the mechanism of action of those drugs, besides their actual target as selective serotonin reuptake inhibitors (SSRIs) (MahmoudianDehkordi et al. 2021).

Unsurprisingly, the prospect of improving therapeutic outcomes and patient stratification through metabolomics technologies has also attracted attention in the field of cardiometabolic diseases. For example, a research group around University Medical Center Groningen has found a signature consisting of 21 metabolites that predicts mircoalbuminuria as major endpoint of angiotensin II receptor blockers in patients with type 2 diabetes. The signature includes asymmetric dimethylarginine (ADMA), which may be related to the nitric oxide metabolism and endothelial function associated with the underlying pathophysiology (Pena et al. 2016).

Beyond stratification biomarkers, metabolomics offers significant benefits to pharmaceutical researchers through improved understanding of therapeutics’ mechanisms of action, and in the translation of results from discovery and preclinical research to clinical sciences.

– For more insights into the potential of pharmacometabolomics, see “Pharmacometabolomics provides biomarkers for precision medicine”.

References

Ang J. et al.: Plasma Metabolomic Changes following PI3K Inhibition as Pharmacodynamic Biomarkers: Preclinical Discovery to Phase I Trial Evaluation. (2016) Molecular Cancer Therapheutics | https://doi.org/10.1158/1535-7163.MCT-15-0815

Ang J. et al et al.: Modulation of Plasma Metabolite Biomarkers of the MAPK Pathway with MEK Inhibitor RO4987655: Pharmacodynamic and Predictive Potential in Metastatic Melanoma. (2017) Molecular Cancer Therapheutics | https://doi.org/10.1158/1535- 7163.MCT-16-0881

Bueschbell B. et al.: Network biology and artificial intelligence drive the understanding of the multidrug resistance phenotype in cancer.  (2022) Drug Resistance Updatates | https://doi.org/10.1016/j.drup.2022.100811

Fan K. et al.: Targeting Nutrient Dependency in Cancer Treatment. (2022) Front. Oncol. | https://doi.org/10.3389/fonc.2022.820173

Fine S.: Etiology and Management of Lack or Loss of Response to Anti-Tumor Necrosis Factor Therapy in Patients With Inflammatory Bowel Disease. (2019) Gastroenterol Hepatol | https://pubmed.ncbi.nlm.nih.gov/31892912/

Floros K.: Investigating New Mechanisms of Acquired Resistance to Targeted Therapies: If You Hit Them Harder, Do They Get Up Differently? (2020) Cancer Research | https://doi.org/10.1158/0008-5472.CAN-19-3405

Hanahan D.: Hallmarks of Cancer: New Dimensions. (2022) Cancer Discov. | https://doi.org/10.1158/2159-8290.CD-21-1059

Joyce J. et al.: Multi-omics driven predictions of response to acute phase combination antidepressant therapy: a machine learning approach with cross-trial replication. (2021) Translational Psychiatry  | https://doi.org/10.1038/s41398-021-01632-z

Kaegi-Braun N.:  Association of Nutritional Support With Clinical Outcomes in Malnourished Cancer Patients: A Population-Based Matched Cohort Study. (2021) frontiers in Nutrition | https://doi.org/10.3389/fnut.2020.603370

Kocher F. et al.: High indoleamine-2,3-dioxygenase 1 (IDO) activity is linked to primary resistance to immunotherapy in non-small cell lung cancer (NSCLC). (2021) Translational Lung Cancer Research | https://doi.org/10.21037/tlcr-20-380

MahmoudianDehkordi S. et al.: Alterations in acylcarnitines, amines, and lipids inform about the mechanism of action of citalopram/escitalopram in major depression. (2021) Translational Psychiatry  | https://doi.org/10.1038/s41398-020-01097-6

Miolo G. et al.: Pharmacometabolomics study identifies circulating spermidine and tryptophan as potential biomarkers associated with the complete pathological response to trastuzumab-paclitaxel neoadjuvant therapy in HER-2 positive breast cancer. (2016) Oncotarget | https://doi.org/10.18632/oncotarget.9489

Mock A.et al.: Serum very long-chain fatty acid-containing lipids predict response to immune checkpoint inhibitors in urological cancers. (2019) Cancer Immunological Immunotheraphy | https://doi.org/10.1007/s00262-019-02428-3

Pal A. et al.: Metabolomic changes of the multi (-AGC-) kinase inhibitor AT13148 in cells, mice and patients are associated with NOS regulation. (2020) Metabolomics | https//doi.org/10.1007/s11306-020-01676

Pammolli F. et al.: The endless frontier? The recent increase of R&D productivity in pharmaceuticals. (2020) Journal of Translational Medicine | https://doi.org/10.1186/s12967-020-02313-z

Parker J. et al.: Does biomarker use in oncology improve clinical trial failure risk? A large-scale analysis. (2021) Cancer Medicine |  https://doi.org/10.1002/cam4.3732

Pena M.et al.: Serum metabolites predict response to angiotensin II receptor blockers in patients with diabetes mellitus. (2016)  Journal of  Transl ational Medicine | https://doi.org/10.1186/s12967-016-0960-3

Peyraud F. et al.: Targeting Tryptophan Catabolism in Cancer Immunotherapy Era: Challenges and Perspectives. (2022) froniers in Immunology | https://doi.org/10.3389/fimmu.2022.807271

Schork, N.: Personalized medicine: Time for one-person trials. (2015) Nature |  https://doi.org/10.1038/520609a

Schuetz, P.: Food for thought: why does the medical community struggle with research about nutritional therapy in the acute care setting? (2017) BMC Med  | https://doi.org/10.1186/s12916-017-0812-x

Shastri A. et al.: Personalized Nutrition as a Key Contributor to Improving Radiation Response in Breast Cancer. (2022) International Journal of Molecular Sciences | https://doi.org/10.3390/ijms23010175

Wexler, D. et al.: Management of persistent hyperglycemia in type 2 diabetes mellitus. (2022) Up to Date | https://www.uptodate.com/contents/management-of-persistent-hyperglycemia-in-type-2-diabetes-mellitus#H7554297