• Preventive medicine | Understanding risks before they manifest
• Predictive medicine | From patterns to forecasting
• Precision medicine | Individuality in context
• Population-based medicine | Power in numbers
• Participatory medicine | Empowered by omics
• The dynamic duo of the post-genomic era

When we speak about 5P medicine – preventive, predictive, precision, population-based, and participatory – the conversation often gravitates toward molecular measures of health. Yet, one essential influence on human biology that deserves a seat at the 5P table is the exposome.

Defined at the Banbury conference as “the integrated compilation of all physical, chemical, biological, and psychosocial influences that impact biology”, the exposome is becoming a necessary part of the omics and medical toolkits, and a particularly promising one when combined with metabolomics.

Metabolomists know that metabolic readouts integrate influences from both our genome and our environment. Exposomics allows us to map the upstream exposures that metabolomics reflects downstream, but it also contributes to the design of impactful metabolomic studies.

Exposomics is defined as “the field that studies the comprehensive and cumulative effects of the exposome on biological systems by integrating data from a variety of interdisciplinary methodologies and data streams” (Miller et al. 2025). These methodologies include mass spectrometry and NMR, as for metabolomics, but also dietary information, health monitoring records, medical questionnaires, geospatial data, meteorological data, and much more.

Because the effects of exogenous factors are known functions of time and intensity of exposure, exposomics is the only omic that emphasizes these parameters in the definition of its scope. There is much here to be learned for metabolomics enthusiasts.

I never tire of explaining how the flexibility and sensitivity of metabolomics is a strength rather than a weakness. But these are characteristics of exposomics too. For this reason, when combined, exposomics and metabolomics form a dynamic duo that leverages the strength of sensitive health measures in all its might.

I got confirmation of this once again recently, while recording an episode of The Metabolomist podcast where Léa Maitre from the Barcelona Institute of Global Health explains the unique strength of metabolomics in a multiomic study of early life exposures: “Metabolomics was the better omic to measure cross associations. [It was the strongest] when we measured the exposure and the omics at the same time in childhood.” You can listen to the full episode here.

This is just one example of the synergies that we unlock when we combine metabolomics and exposomics. In this blog, I will focus on the end applications of these technologies and how our dynamic duo ties to each of the 5Ps. Whether your focus is exclusively on precision medicine or you are looking for a truly holistic view of health, I hope these examples will encourage you to start integrating these two powerful omics in your research.

Preventive medicine | Understanding risks before they manifest

Preventive medicine aims to avoid disease altogether. Thus, prevention is only as strong as our ability to identify risks. Exposomics brings clarity by capturing environmental and behavioral factors such as air pollution, diet, stress, and chemical exposures that influence long-term health trajectories. Environmental and behavioral exposures strongly shape health, including drug response and chronic disease risk. Exposomics thus provides a critical foundation for anticipating and reducing exposure-derived health risks.

Metabolomics contributes here by identifying metabolic signatures linked to exposure-induced biological changes. For example, in a study of the composition of breast milk from mothers with apparently healthy infants versus stunted infants, even a small targeted metabolomic panel could identify signatures pointing to different nutrition levels (Hampel et al 2022). In the study I discuss with Léa Maitre on the podcast, metabolomics helped identify patterns linked to exposures in early childhood (Maitre, Bustamante et al. 2022) that can be followed in longitudinal studies or serve as a basis for mining the catalogue of exposome-related cohorts put together in the IHEN project.

Exposomics combined with metabolomics moves prevention from generic advice to evidence based, exposure and phenotype-specific interventions.

Predictive medicine | From patterns to forecasting

Predictive medicine hinges on data that can forecast health outcomes years before symptoms appear. Exposomics offers exactly that: the ability to quantify the cumulative external pressures shaping one’s biological trajectory. A review by Wan et al. (2025) highlights how exposomics supports diagnosis, disease prediction, early detection, and treatment prediction.

Metabolomics is also well-positioned to reflect the progressive drift of the metabolome from health towards disease outcomes. But one of its best known use is as a source of biomarkers predictive of patient drug response in pharmacometabolomics.

In non small cell lung cancer, quantitative metabolomics has shown that a patient’s baseline metabolic phenotype—shaped not just by genetics but also by diet, microbiome, inflammation and prior exposures—can predict response to immunotherapy, illustrating how the metabolome translates the cumulative exposome into actionable insight for predictive and personalized treatment (Lee et al. 2024).

In other words, exposomics tells us what happened, and metabolomics tells us how the phenotype changed; a powerful predictive duo when we want to leverage the impact of the environment on health.

Precision medicine | Individuality in context

The promise of precision medicine is the ability to tailor treatments to the individual. Genomics contributes the blueprint, but exposomics adds the context; the influences that shape how that blueprint is expressed. Metabolomics, in turn, contributes the resulting phenotype and some of the effectors of this impact on genome expression.

A type of exposure not always recognized by the public but highly relevant in medicine is the intentional exposure to chemicals such as pharmaceutical drugs. Not only do drugs influence our metabolome, but the levels of their downstream metabolic products when they pass through our organs are a powerful way to stratify patients. This is another powerful combination of exposomics and metabolomics.

In the ADNI cohort, metabolomics enabled stratification of individuals not only by disease stage, but also by medication exposure, revealing how drugs act as a critical and often overlooked dimension of the exposome (St John-Williams et al. 2017). By accounting for polypharmacy and treatment effects, this approach demonstrated how metabolomics can support more precise interpretation of molecular phenotypes and more informed patient stratification in clinical research.

In the field of nutrition research, stratification based on metabolomic profile, or “metabotyping” has become a popular tool, as it works well together with variables related to diet, another lesser-known source of deliberate exposures. In a 2023 randomized controlled trial, metabotypes were used to stratify individuals and deliver personalized dietary advice, demonstrating that people with different metabolic phenotypes respond differently to the same nutritional guidance. Leveraging metabolomics for stratification, this study demonstrated how to enable precision nutrition by translating dietary exposures into actionable, metabotype specific interventions rather than population level recommendations (Hillesheim & Brennan 2023). And in this case, the end result most likely will entail the modulation of the very exposures investigated (the diet), turning this knowledge into quickly actionable insights.

Population-based medicine | Power in numbers

The first population-based cohorts were built with genomics in mind, searching for the genetic determinants of disease. This approach opened the door for a new wave of knowledge, but it couldn’t answer all questions. Today, at the population level, exposomics reveals patterns that inform on non-genetic influencers of health especially relevant in the study of complex chronic disease.

Exposures vary dramatically between regions, occupations, socioeconomic backgrounds, and lifestyles, and the study of exposomics quickly takes us to investigate health disparities, environmental injustice, and geographically clustered risks, which are all likely to translate to metabolic differences too.

The HELIX cohort has been a pioneer in the integration of exposomics with other omics, notably combining over 200 measures of exposures with blood and urine metabolomics (Maitre et al. 2022). A follow up study investigated the links between the metabolome, health outcomes and chemical classes with known effects on health, namely endocrine disruptors. The study shows that childhood exposure to endocrine disrupting chemicals, including persistent pollutants, was associated with alterations in the metabolome, including differences in tryptophan derivatives. This work highlights the role of combined exposomics and metabolomics approaches in capturing early life biological responses to chronic environmental exposures at the population level (Fabbri et al. 2023).

Participatory medicine | Empowered by omics

When individuals engage in their own health decisions, this is one of the most direct applications of research that can be. The tenets of participatory medicine are easy-to-use sample collection, ideally performed at home to be extra accessible and reduce discriminations in access to health, and quantitative, robust measures of health that can be compared to reference values from the healthy population.

Today, measures of both exposures and health are already found in many homes, from wearables, to sensors, but also local environmental measures that lead to actionable big data. Tools that combine these measures of the exposome with reliable (metabol)omics measures will provide the solutions that will enable the application of omics-based knowledge in the home, at a scale of n=1.

Today, these offerings largely sit with private companies offering personalized fitness monitoring and advice. Tomorrow, the communities built around exposomics and metabolomics will be the cornerstone of the strategies implemented by healthcare systems providing regular checkups based on samples collected at home and sent in the mail, online questionnaires and exposure data collected by relevant home/health appliances and local exposome mapping.

The dynamic duo of the post-genomic era

To fully realize the goals of 5P medicine, we must integrate data from all layers of the biological and environmental ecosystem. Metabolomics provides the clearest snapshot of a phenotype influenced by both genetics and environment. Exposomics contributes the context in which drivers such as drugs, environmental pollutants, diet and socioeconomic factors influence this phenotype.

The intersection of these two rich omic layers hosts not only a sensitive measure of health outcomes but a wealth of information about determinants of health.
Increasingly used in population-based medicine, driving tailored approaches in preventive, predictive and precision medicine, and soon to enter the realm of participatory medicine, the combination of exposomics and metabolomics is about to revolutionize how we understand and modulate health.

• History & Evolution
• Biosynthesis & dietary uptake
• UDCA and the microbiome
• UDCA and signaling
• UDCA as therapeutic drug
• UDCA and 5P medicine
• References

History & Evolution

1902: discovery in bear bile (Hammarsten) | 1927: isolation and naming (Shoda) | 1980s: approval for liver diseases

Ursodeoxycholic acid (UDCA) is a secondary bile acid with a long history in traditional medicine and modern hepatology. First identified in bear bile by Hammarsten in 1902 and isolated by Shoda in 1927, UDCA takes its name from Ursus, Latin for bear (Lazaridis et al. 2001). It became clinically relevant in the 1980s, when synthetic UDCA was approved for treating cholestatic liver diseases such as primary biliary cholangitis (PBC) (Ishizaki et al. 2005). Today, UDCA is recognized as a key therapeutic bile acid with a range of anti-apoptotic, cytoprotective and immunomodulatory functions (Lazaridis et al. 2001; Amaral et al. 2009).

Biosynthesis vs. dietary uptake

Bile acids are formed from cholesterol in the liver and secreted into the intestine. Gut microbiota metabolize these bile acids through deconjugation, dehydroxylation and epimerization (Keely et al. 2019). In this way, the secondary bile acid UDCA is produced in the colon through microbial transformation of the primary bile acid chenodeoxycholic acid (CDCA), first through deconjugation via bile salt hydrolases, then via epimerization at the 7-position (Daruich et al. 2019; Ridlon et al. 2015). Once formed, UDCA is passively absorbed in the colon and returns to the liver through the portal circulation. There, it is reconjugated with taurine or glycine into glyco- or tauro-ursodeoxycholic acid (GUDCA and TUDCA, respectively) as part of the enterohepatic circulation (Lazaridis et al. 2001). This recycling loop helps maintain bile acid pool stability and amplifies the systemic availability of UDCA. TUDCA and other conjugated forms of UDCA are increasingly recognized for their cytoprotective and anti-inflammatory properties (Daruich et al. 2019).

UDCA is not only a product of gut microbiota, but also a substrate for further transformation. The major metabolite of UDCA is lithocholic acid (LCA), which has been regarded as the most cytotoxic of the secondary bile acids, especially in the liver (Hofmann 2004). However, this view is increasingly being challenged. Emerging evidence suggests that the beneficial effects of UDCA on epithelial integrity and inflammation may, at least in part, depend on its microbial conversion to LCA (Lajczak-McGinley et al. 2020; Keely et al. 2019).

Additionally, UDCA can be reconverted into isoUDCA by microbial and hepatic enzymes. The ratio of isoUDCA to UDCA has been proposed as a potential biomarker of bile acid pool dynamics and therapeutic efficacy, particularly in cholestatic liver diseases (Beuers et al. 1991). This ratio reflects the delicate interplay between microbial metabolism and host bile acid handling; factors critical for maintaining bile acid homeostasis and preventing liver injury (Marschall et al. 2001).

UDCA and the microbiome

UDCA is both a product of microbial activity and a potent modulator of the gut microbiome itself. While its formation depends on bacterial enzymes, UDCA also feeds back into the intestinal environment, influencing microbial composition and function. This bidirectional relationship underlies many of UDCA’s therapeutic effects.

One of the key microbiome-level impacts of UDCA is its ability to shift microbial communities toward more balanced and less pro-inflammatory configurations (Wahlström et al. 2016). For instance, in experimental models of colitis, UDCA and its taurine-conjugated form TUDCA have been shown to normalize the Firmicutes-to-Bacteroidetes ratio, which can be indicative of microbial balance often disrupted in inflammatory and metabolic conditions. Such rebalancing may reflect reduced bile acid toxicity, improved mucosal barrier function, and downstream effects on immune regulation (Keely et al. 2019).

These microbiome changes are not only compositional but also functional. UDCA treatment is associated with a reduction in microbial pathways linked to harmful metabolites like enterobactin and lactate, while supporting bile acid transformations that favor anti-inflammatory and cytoprotective signaling (Lee et al. 2024).

Altogether, UDCA’s ability to modulate the microbiome reflects a broader mechanism of action that goes beyond its direct effects on bile flow or hepatocyte protection. Its role in shaping a gut ecosystem that supports intestinal and systemic homeostasis – an aspect of its therapeutic profile that is gaining increasing attention.

UDCA and signaling

In addition to its choleretic (promoting bile synthesis and bile flow) and cytoprotective effects, UDCA is increasingly recognized as a signaling molecule that modulates a range of nuclear- and membrane-bound receptors in human tissues (Marchianò et al. 2023). Although a relatively weak agonist compared to more hydrophobic bile acids like LCA or deoxycholic acid (DCA), which passively diffuse into epithelial colonic cells and activate nuclear receptors, UDCA exerts regulatory effects through both direct receptor engagement and indirect metabolic reshaping of the bile acid pool (Hanafi et al. 2018). These interactions contribute to UDCA’s therapeutic profile in liver and intestinal diseases (Lin et al. 2025).

Key receptor and signaling interactions

• Takeda G protein-coupled receptor 5 (TGR5, also known as GPBAR1): UDCA modestly activates this G-protein-coupled receptor, especially in enteroendocrine and immune cells. TGR5 activation enhances secretion of glucagon-like peptide-1 (GLP-1), supports glucose homeostasis, and suppresses pro-inflammatory cytokines such as tumor necrosis factor-alpha (TNF-α) (Hanafi et al. 2018), reinforcing UDCA’s metabolic and anti-inflammatory effects (Lin et al. 2025; Guzior et al. 2021).

• Farnesoid X Receptor (FXR): While UDCA is not a strong FXR agonist, it modulates FXR signaling indirectly by altering bile acid pool composition, notably by reducing antagonists like LCA (Hanafi et al. 2018). In inflammatory settings, UDCA can selectively downregulate FXR expression while upregulating FXR target genes like SHP, in turn influencing genes such as CYP7A1 and BSEP involved in bile acid synthesis and transport (Lin et al. 2025; Guzior et al. 2021).

• Pregnane X and Vitamin D Receptors (PXR and VDR): UDCA does not directly activate these receptors, but by reducing LCA – a potent ligand for both – it may help regulate detoxification pathways, epithelial integrity and immune homeostasis. These are especially relevant in gut-liver disorders and cholestasis (Hanafi et al. 2018; Keely et al. 2019).

There is also an epigenetic layer to UDCA’s anti-inflammatory and anti-tumorigenic mechanisms: UDCA downregulates miRNA-21, a pro-inflammatory and pro-carcinogenic microRNA that is upregulated by lipopolysaccharide (LPS) and in chronic liver inflammation (Peng et al. 2024).

UDCA as therapeutic drug

UDCA is a cornerstone therapy for chronic cholestatic liver diseases such as PBC, with growing relevance in metabolic and immunological conditions (Keely et al. 2019). While its core mechanisms are hepatoprotective and choleretic, UDCA also engages complex signaling and microbiome-modulating pathways that may benefit extrahepatic disorders like inflammatory bowel disease (Lin et al. 2025). As research evolves, combination therapies and system-level understanding will likely expand UDCA’s clinical applications across liver and gut health.

UDCA’s therapeutic effects stem from its ability to balance the bile acid pool, stabilize hepatocyte membranes, reduce oxidative stress and modulate immune responses (Kumar et al. 2001). These actions translate into anti-apoptotic, cytoprotective, choleretic and immunomodulatory benefits (Keely et al. 2019). In PBC, long-term UDCA therapy (13–15 mg/kg/day) improves liver biochemistry, slows histological progression and reduces the need for liver transplantation. It is most effective when started early and has become the standard of care (Kumar et al. 2001).

UDCA is also used in primary sclerosing cholangitis (PSC), though its benefits are modest and may be enhanced when combined with endoscopic therapy. It is the treatment of choice in intrahepatic cholestasis of pregnancy (ICP) and shows benefit in cystic fibrosis–associated liver disease, graft-versus-host disease, and pediatric cholestasis by reducing bile acid toxicity and supporting bile flow (Ted George O. Achufusi et al. 2023).

In metabolic dysfunction-associated steatohepatitis (MASH), UDCA alone has limited therapeutic efficacy but can enhance the effects of FXR/TGR5 agonists. Recent research showed that combining UDCA with such agents led to reversal of liver inflammation and fibrosis, improved bile acid signaling and greater metabolic gene regulation than either treatment alone (Marchianò et al. 2023). This positions UDCA as a synergistic component in multi-targeted approaches for metabolic liver diseases.

UDCA and 5P medicine

UDCA and its derivatives align well with all principles of 5P medicine:

• Predictive and personalized medicine: The isoUDCA/UDCA ratio has emerged as a potential biomarker for therapeutic response and bile acid pool dynamics in cholestatic liver diseases (Beuers et al. 1991). These profiles reflect microbial-host interactions and support individualized monitoring strategies (Marschall et al. 2001)

• Precision medicine: UDCA is an approved treatment for PBC, PSC and ICP (Kumar et al. 2001; Ted George O. Achufusi et al. 2023). It modulates bile acid receptors (FXR, TGR5), transporters and inflammatory responses (Hanafi et al. 2018; Keely et al. 2019). In MASH, UDCA is being tested in combination with receptor agonists like BAR502 (Marchianò et al. 2023). It also reshapes bile acid pools, reducing hepatotoxic intermediates such as LCA (Hanafi et al. 2018), thus acting as both a treatment and response-modifying agent.

• Participatory medicine: UDCA’s oral administration and safety profile allow patients to engage in long-term, proactive care. It is widely used to prevent gallstones in post-bariatric patients (Gideon M. Hirschfield et al. 2017) and treat pregnancy-related cholestasis (Kumar et al. 2001).

• Population-based medicine: UDCA is included in international clinical guidelines for PBC and other liver diseases (Lindor et al. 2019; Gideon M. Hirschfield et al. 2017). Its safety and efficacy support preventive use, although global access and cost remain variable.

References

Amaral, J.D. et al.: Bile acids: regulation of apoptosis by ursodeoxycholic acid (2009) Journal of Lipid Research | DOI: 10.1194/jlr.R900011-JLR200.

Beuers, U. et al.: Formation of iso-ursodeoxycholic acid during administration of ursodeoxycholic acid in man (1991) Journal of hepatology | DOI: 10.1016/0168-8278(91)90870-H.

Daruich, A. et al.: Review: The bile acids urso- and tauroursodeoxycholic acid as neuroprotective therapies in retinal disease (2019) Molecular Vision | PMID: 31700226

Gideon M. Hirschfield et al.: EASL Clinical Practice Guidelines: The diagnosis and management of patients with primary biliary cholangitis (2017) Journal of Hepatology | DOI: 10.1016/j.jhep.2017.03.022.

Guzior, D.V. et al.: Review: microbial transformations of human bile acids (2021) Microbiome | DOI: 10.1186/s40168-021-01101-1.

Hanafi, N.I. et al.: Overview of Bile Acids Signaling and Perspective on the Signal of Ursodeoxycholic Acid, the Most Hydrophilic Bile Acid, in the Heart (2018) Biomolecules | DOI: 10.3390/biom8040159.

Hofmann, A.F.: Detoxification of lithocholic acid, a toxic bile acid: relevance to drug hepatotoxicity (2004) Drug metabolism reviews | DOI: 10.1081/dmr-200033475.

Ishizaki, K. et al.: Hepatoprotective bile acid ‘ursodeoxycholic acid (UDCA)’ Property and difference as bile acids (2005) Hepatology Research | DOI: 10.1016/j.hepres.2005.09.029.

Keely, S.J. et al.: Ursodeoxycholic acid: a promising therapeutic target for inflammatory bowel diseases? (2019) American journal of physiology. Gastrointestinal and liver physiology | DOI: 10.1152/ajpgi.00163.2019.

Kumar, D. et al.: Use of ursodeoxycholic acid in liver diseases (2001) Journal of Gastroenterology and Hepatology | DOI: 10.1046/j.1440-1746.2001.02376.x.

Lajczak-McGinley, N.K. et al.: The secondary bile acids, ursodeoxycholic acid and lithocholic acid, protect against intestinal inflammation by inhibition of epithelial apoptosis (2020) Physiological Reports | DOI: 10.14814/phy2.14456.

Lazaridis, K.N. et al.: Ursodeoxycholic acid ‘mechanisms of action and clinical use in hepatobiliary disorders’ (2001) Journal of hepatology | DOI: 10.1016/S0168-8278(01)00092-7.

Lee, J. et al.: The gut microbiome predicts response to UDCA/CDCA treatment in gallstone patients: comparison of responders and non-responders (2024) Scientific Reports | DOI: 10.1038/s41598-024-53173-2.

Lin, X. et al.: Crosstalk Between Bile Acids and Intestinal Epithelium: Multidimensional Roles of Farnesoid X Receptor and Takeda G Protein Receptor 5 (2025) International Journal of Molecular Sciences | DOI: 10.3390/ijms26094240.

Lindor, K.D. et al.: Primary Biliary Cholangitis: 2018 Practice Guidance from the American Association for the Study of Liver Diseases (2019) Hepatology | DOI: 10.1002/hep.30145.

Marchianò, S. et al.: Combinatorial therapy with BAR502 and UDCA resets FXR and GPBAR1 signaling and reverses liver histopathology in a model of NASH (2023) Scientific Reports | DOI: 10.1038/s41598-023-28647-4.

Marschall, H.-U. et al.: Isoursodeoxycholic acid: metabolism and therapeutic effects in primary biliary cirrhosis (2001) Journal of lipid research | DOI: 10.1016/S0022-2275(20)31635-7.

Peng, C.-Y. et al.: Ursodeoxycholic Acid Modulates the Interaction of miR-21 and Farnesoid X Receptor and NF-κB Signaling (2024) Biomedicines | DOI: 10.3390/biomedicines12061236.

Ridlon, J.M. et al.: The human gut sterolbiome: bile acid-microbiome endocrine aspects and therapeutics (2015) Acta pharmaceutica Sinica. B | DOI: 10.1016/j.apsb.2015.01.006.

Ted George O. Achufusi et al.: Ursodeoxycholic Acid (2023) | PMID: 31424887

Wahlström, A. et al.: Intestinal Crosstalk between Bile Acids and Microbiota and Its Impact on Host Metabolism (2016) Cell Metabolism | DOI: 10.1016/j.cmet.2016.05.005.

• History & Evolution
• Biosynthesis
• Spermidine, autophagy and aging
• Spermidine and cancer
• Spermidine and neurology
• Spermidine and hair
• References

History & Evolution

1924: first synthesis by Otto Rosenheim (Bachrach 2010)

Spermidine is a biogenic amine of the polyamine family, which contribute to cell division and growth. It’s also the downstream metabolite of putrescine, which is associated with bodily odors. Spermidine and its precursor, spermine, get their name from semen, after crystals of spermine were first identified in human semen in the seventeenth century (Bachrach 2010). Once considered a uremic toxin, spermidine has been put in a more positive light by recent research pointing to its role in cell homeostasis, cancer treatment, longevity, and hair growth, making it a hot topic for 5P medicine applications.

Biosynthesis and dietary uptake

Spermidine is present in large amounts in mammals. It can also be readily absorbed in the small intestine from foods such as wheat germ, soy bean, aged cheese and mushrooms (Bardócz et al. 1995). In humans, it can be synthesized by commensal bacteria in the large intestine from putrescine or arginine (Matsumoto et al. 2012; Hanfrey et al. 2011; Tofalo et al. 2019). Mammalian cells are also capable of synthesizing spermidine from putrescine, itself a product of the metabolism of arginine, linking polyamines to the urea cycle (Madeo et al. 2018). In humans, circulating levels of spermidine are often in the low micromolar range, although they show a strong inter-individual variability. This is most likely due to the effect of diet on overall spermidine concentration (Soda et al. 2009).

Spermidine, autophagy and aging

In the last decade, spermidine has attracted interest as a promoter of longevity. In 2009, Eisenberg et al. linked spermidine to increased lifespan, reduced oxidative stress, and reduced necrosis across species. The effect of spermidine on acetylation of proteins and chromatin was identified as a key mechanism for the modulation of autophagy, increasing the lifespan of yeast, flies, worms, and human cells (Eisenberg et al. 2009). Autophagy, the mechanism by which cells remove malfunctioning proteins and organelles, is an essential player in cellular homeostasis. This makes it a major target of drug development for conditions as varied as cancer, Alzheimer’s disease, and cardiovascular diseases (Onorati et al. 2018; Ren et al. 2018; Uddin et al. 2018). As an endogenous activator of autophagy, spermidine has attracted a lot of attention for dietary intervention and drug development.

Spermidine and cancer

Polyamines were originally relevant to cancer research because of their role in cell proliferation and growth. Disturbance of polyamine metabolism was observed in several types of cancer, including skin, breast, lung, prostate, and colon (Madeo et al. 2018; Nowotarski et al. 2013). However, more recent cancer research studies have focused on spermidine as a caloric restriction mimetic (CRM) with a positive impact. CRMs are molecules that limit cancer cells’ access to nutrients, thus making them more vulnerable to anti-cancer treatment. As an autophagy activator, spermidine has potential for both cancer prevention and treatment, especially to limit tumor growth (Fan et al. 2020; Kocaturk et al. 2019). In 2018, a prospective population-based study linked high levels of spermidine intake to lower mortality, including mortality from cancer (Kiechl et al. 2018). Researchers are therefore exploring both spermidine supplementation and more elaborate treatments such as the use of spermidine analogues targeting the DNA of tumor cells (Wang et al. 2018).

Spermidine and neurology

Circulating spermidine levels decrease with age (Pekar et al. 2020). This, and the fact that autophagy is of interest in the treatment of neurodegenerative diseases (De Risi et al. 2020), makes spermidine an interesting candidate for further investigation. In patients with Parkinson’s disease (PD), the spermine to spermidine ratio in plasma was strongly decreased, suggesting an inhibition of the enzyme spermine synthase (Saiki et al. 2019). Interestingly, PD patients were found to have elevated levels of acetylated forms of spermidine and putrescine, and a general hyperacetylation consistent with autophagy activation. In a randomized controlled Phase IIa trial, supplementation with spermidine showed a positive impact on memory performance after just three months of administration in older adults at risk of dementia (Wirth et al. 2018). In the senescence accelerated mouse model SAMP8, spermidine was shown to slow neurodegeneration, restoring impaired mitochondrial function and reducing inflammation (Xu et al. 2020). Spermine and rapamycin (a classical activator of autophagy via the mTOR pathway), induced similar effects, although spermine tended to be less effective.

Spermidine and hair

A more exotic field for the application of spermidine research is hair growth and hair loss. In vitro studies on human scalp and hair follicle epithelial stem cells revealed the effect of spermidine on hair growth, but also on the regulation of epithelial stem cells (Ramot et al. 2011). A 90-day study showed that spermidine supplementation promoted hair growth and resistance in human subjects (Rinaldi et al. 2017). Immunohistochemical staining in rat hair follicles revealed the distribution patterns of putrescine, spermidine and spermine in the regions most linked to hair growth (Yamamoto et al. 2018). However, only the polyamines were present in high amounts in the epidermis and fibroblasts.

References

Bachrach et al. : The early history of polyamine research (2010) Plant Physiology and Biochemistry | doi: 10.1016/j.plaphy.2010.02.003

Bardócz; et al. : The importance of dietary polyamines in cell regeneration and growth (1995) British Journal of Nutrition | doi: 10.1079/BJN19950087

Eisenberg et al. : Induction of autophagy by spermidine promotes longevity (2009) Nature cell biology | doi: 10.1038/ncb1975

Fan et al.: Spermidine as a target for cancer therapy (2020) Pharmacological Research | doi: 10.1016/j.phrs.2020.104943

Hanfrey et al.: Alternative Spermidine Biosynthetic Route Is Critical for Growth of Campylobacter jejuni and Is the Dominant Polyamine Pathway in Human Gut Microbiota * (2011) Journal of Biological Chemistry | doi: 10.1074/jbc.M111.307835

Kiechl et al.: Higher spermidine intake is linked to lower mortality: a prospective population-based study (2018) The American journal of clinical nutrition | doi: 10.1093/ajcn/nqy102

Kocaturk et al.: Autophagy as a molecular target for cancer treatment (2019) European journal of pharmaceutical sciences : European Federation for Pharmaceutical Sciences | doi: 10.1016/j.ejps.2019.04.011

Madeo et al.: Spermidine in health and disease (2018) Science | doi: 10.1126/science.aan2788

De Risi et al.: Mechanisms by which autophagy regulates memory capacity in ageing (2020) Aging Cell | doi: 10.1111/acel.13189

Matsumoto et al.: Impact of Intestinal Microbiota on Intestinal Luminal Metabolome (2012) Scientific Reports |  doi: 10.1038/srep00233

Nowotarski et al.: Polyamines and cancer: implications for chemotherapy and chemoprevention (2013) Expert Reviews in Molecular Medicine | doi: 10.1017/erm.2013.3

Onorati et al.: Targeting autophagy in cancer (2018) Cancer | doi: 10.1002/cncr.31335

Pekar et al.: Spermidine in dementia (2020) Wiener klinische Wochenschrift | doi: 10.1007/s00508-019-01588-7

Ramot et al.: Spermidine promotes human hair growth and is a novel modulator of human epithelial stem cell functions (2011) PloS one | doi: 10.1371/journal.pone.0022564

Ren et al.: Metabolic Stress, Autophagy, and Cardiovascular Aging: from Pathophysiology to Therapeutics (2018) Trends in endocrinology and metabolism: TEM | doi: 10.1016/j.tem.2018.08.001

Rinaldi et al.: A spermidine-based nutritional supplement prolongs the anagen phase of hair follicles in humans: a randomized, placebo-controlled, double-blind study (2017) Dermatology practical & conceptual | doi: 10.5826/dpc.0704a05

Saiki et al. : A metabolic profile of polyamines in parkinson disease: A promising biomarker (2019) Annals of neurology | doi: 10.1002/ana.25516 .

Soda et al.: Long-Term Oral Polyamine Intake Increases Blood Polyamine Concentrations (2009) Nutritional Science and Vitaminology | doi: 10.3177/jnsv.55.361

Tofalo et al.: Polyamines and Gut Microbiota (2019) Frontiers in Nutrition | doi: 10.3389/fnut.2019.00016

Uddin et al.: Autophagy and Alzheimer’s Disease: From Molecular Mechanisms to Therapeutic Implications (2018) Frontiers | doi: 10.3389/fnagi.2018.00004

Wang et al.: Isothermal Self-Assembly of Spermidine-DNA Nanostructure Complex as a Functional Platform for Cancer Therapy (2018) ACS | doi: 10.1021/acsami.8b03464

Wirth et al.: The effect of spermidine on memory performance in older adults at risk for dementia: A randomized controlled trial (2018) Cortex | doi: 10.1016/j.cortex.2018.09.014

Xu et al.: Spermidine and spermine delay brain aging by inducing autophagy in SAMP8 mice (2020) Aging | doi: 10.18632/aging.103035

Yamamoto et al.: Expression and distribution patterns of spermine, spermidine, and putrescine in rat hair follicle (2018) Histochemistry and Cell Biology | doi: 10.1007/s00418-017-1621-1

Genome-wide association studies (GWAS) investigate genomic variants (single-nucleotide polymorphisms (SNPs)) across different individuals to identify associations with a specific trait or phenotype. Over the past 15 years, GWAS have uncovered many variants associated with complex traits, but identifying the causal gene(s) is still a major challenge. More recently, integrating the GWAS dataset with other omics data has emerged as a promising approach to identify these causal genes (Mountjoy, E. et al. 2021).

Identifying causal genes for genetic diseases is not only of academic interest but pivotal for the development of therapeutic approaches. Traditional drug development is a knowledge-based approach that starts with a hypothesis based on limited data. The hypothesis is then put to the test in cell lines to qualify a potential target, before moving to preclinical research in animal models that only moderately represent human disease. More than 90% of all drug candidates fail in the preclinical stage (Sun et al. 2022), mostly because of safety concerns or lack of efficacy. In contrast, precision medicine uses a human-centric, hypothesis-free approach to drug discovery grounded in real-world data, using multiomics datasets from large populations evaluated with deep learning for target relevance and causality. Switching to this approach increased the success rate of Astra Zeneca’s drug development (from drug candidate to approval) from 4% to 19% (Morgan et al. 2018).

Why metabolomics?

Combining GWAS with metabolomics (mGWAS) shows particular promise. Metabolomics is used to analyze pathophysiological processes and map metabolites to biochemical pathways. When combined with GWAS datasets, metabolite profiling helps establish functional links to genes associated with clinical disease phenotypes. This way, mGWAS identifies genetically influenced metabotypes that correspond to phenotype-converting genetic variations. mGWAS qualifies genetic associations by effect size — indicating the phenotype-converting potential — and thus reveals potential drug targets for drug development.

Including metabolomics has an advantage over other omics in GWAS due to a marked p-value gain when using metabolite ratios. Metabolite ratios represent the flux through a biochemical pathway when a pair of metabolites is connected. For example, the ratio of an enzymatic reaction product to the source metabolite characterizes enzyme activity much better than either metabolite concentration alone. This comes with statistical benefits:

• Metabolite ratios increase the statistical power by reducing the overall biological variability.
• They reduce the impact of systematic experimental errors.

The p-gain statistic is a measure for whether a ratio between two metabolite concentrations carries more information than the two corresponding metabolite concentrations alone (Petersen et al. 2012). When this is the case, and metabolite concentrations are affected by the gene in question, including metabolite ratios in the GWAS analysis will lead to markedly lower p-values, highlighting the relevant associations.

Including metabolite sums instead of single metabolites can also make sense when studying lipids, as lipid-converting enzymes usually process several lipids with similar configuration such as similar fatty acid side chain lengths. The biocrates MetaboINDICATOR™ is a useful tool exploring metabolite sums and ratios relevant to health and disease.

Discovery of potential drug targets with mGWAS

Montasser and colleagues published a study on cardiovascular diseases that illustrates the potential of mGWAS (Montasser et al. 2022). They performed a GWAS on a discovery cohort of 650 individuals from the Old Order Amish founder population. The chance of identifying previously unknown disease associations is elevated in founder populations because certain variants are enriched to a higher frequency due to genetic drift. They discovered about eight million genetic variants.

Trying to identify the relevant ones is like looking for a needle in a haystack. To narrow down the results, they also performed lipidomics and integrated their concentration results on 355 lipid species from 14 different classes with their GWAS outcomes. This mGWAS analysis resulted in 12 significant associations. Seven of these were already known, and the five remaining significant associations represented novel associations between SNPs and cardio protection, cholecystitis, atherosclerosis, blood pressure, and inflammation, respectively. Integrating metabolomics in their GWAS thus resulted in five novel potential drug targets instead of hundreds of associations with uncertain relevance.

Naturally, the chances for identification of relevant variants increase with the number of metabolites covered. For example, metabolomics results obtained with the MxP Quant 500 XL kit, which covers up to 1,019 metabolites from 39 biochemical classes, bear a higher likelihood of success than a smaller kit or assay.

From weak associations to causality

The full power of mGWAS is realized when combined with Mendelian randomization. Mendelian randomization uses the measured variation in candidate genes to assess the causal effect a gene variant has on a disease by affecting metabolite concentrations or ratios, diminishing the need for an additional randomized controlled validation study. Mendelian randomization assumes that genetic variants are:

• Associated with the disease
• Not related to confounders
• Not associated with the disease through an alternative pathway in which the metabolite is not involved.

The causal effect is calculated as the variable ß̂ for each association:

Significant results in Mendelian randomization mean that a metabolite or a metabolite ratio is related to a specific disease via a specific genetic variant (Davies et al. 2018). Thus, that variant may become a target for that disease.

In other words, Mendelian randomization refines mGWAS data by testing for causality. The direct link between a disease or other phenotype and the corresponding genetic variant is usually quite weak, due to environmental influences. However, metabolites act as an intermediate phenotype involved in the development of the disease. The link between genetic variant and metabolite concentration is usually stronger than the association between genetic variant and disease because the relationship is more direct and independent of environmental exposures. The strength of an association is further increased when using metabolite ratios.

The same is true for the association of a certain metabolite concentration or metabolite ratio with a certain phenotype. Mendelian randomization uses the strong association between genetic variant and metabolite and the strong association between metabolome and clinical phenotype instead of the weak association between genetic variant and clinical phenotype to calculate causality. Because the environmental factors play a smaller role for these associations, they can be ignored for the causality calculation (Genetics meets metabolomics 2012). This also minimizes the risk of reverse causation, where the disease itself might appear to cause the differences in metabolite concentrations (Bowden et al. 2019; Le Chang et al. 2023).

A few examples for mGWAS studies combined with Mendelian randomization are provided in the following table:

A new general strategy for analyzing genomic variance

Given the robust causal relationships found between certain clinical phenotypes, metabolites, and genetic variants, combining GWAS and metabolomics into mGWAS, followed by evaluation with Mendelian randomization, should become the new standard for identifying genomic variance and potential therapeutic targets. The key steps should include the following:

1. In a population study with available genomic, metabolomic and longitudinal clinical information, select a phenotype of interest (e.g. myocardial infarction, type 2 diabetes, cancer).
2. Conduct mGWAS by jointly evaluating GWAS and metabolomic data (especially metabolite ratios) to uncover highly relevant associations and/or new functional relationships to underlying genetic variance.
3. Use Mendelian randomization to establish causality between genetic variant and phenotype. The shortlist of associations with proven causality can then be mined for feasible therapeutic drug targets.
4. Repeat the Mendelian randomization with subgroups defined by specific longitudinal clinical information, such as drug exposure, to identify causal relationships between treatment and outcome (phenotype), and enable prediction of drug intervention effects.

Where to start

Despite the undisputable advantages of combining GWAS with metabolomics and Mendelian randomization, it is still not a common approach. Among the more than 9000 GWAS that have been conducted, there are less than 70 mGWAS, and only about a dozen have incorporated Mendelian randomization, as shown in the table above. These numbers suggest that many existing GWAS could be combined with metabolomics to qualify results and derive causal links between gene variants and a clinical phenotype.

Many researchers performing GWAS may be unfamiliar with how to integrate metabolomics and conduct Mendelian randomization. To bring out this untapped potential, here are some helpful links:

• Prof. Karsten Suhre explains the principle behind mGWAS and Mendelian randomization very well in the book “Genetics meets metabolomics” (Genetics meets metabolomics 2012). For a preview, you can listen to Prof. Suhre talking about mGWAS and metabolite ratios in our podcast “The Metabolomist”.
• The mGWAS-Explorer lists details of 65 manually curated mGWAS studies, along with an mGWAS R package for download.
• biocrates’ multiomics data analysis service enables integration of metabolomics with genomics and other omic datasets. 

With these resources, you should be able to use metabolomics to significantly improve fine-mapping of genomic data to phenotypes and identify causally supported phenotype-converting therapeutic targets.

Interested in conducting a broad metabolomics analysis to be integrated with your GWAS? Find out more about the biocrates MxP® Quant 500 XL kit.

References

Mountjoy, E. et al.: An open approach to systematically prioritize causal variants and genes at all published human GWAS trait-associated loci. (2021). Nat Genet | DOI: 10.1038/s41588-021-00945-5.

Sun, D. et al.: Why 90% of clinical drug development fails and how to improve it? 2022. Acta Pharm Sin B | DOI: 10.1016/j.apsb.2022.02.002.

Morgan, P. et al.: Impact of a five-dimensional framework on R&D productivity at AstraZeneca. 2018. Nat Rev Drug Discov | DOI: 10.1038/nrd.2017.244.

Petersen, A-K. et al.: On the hypothesis-free testing of metabolite ratios in genome-wide and metabolome-wide association studies. 2012. BMC Bioinformatics | DOI: 10.1186/1471-2105-13-120.

Montasser, ME. et al.: An Amish founder population reveals rare-population genetic determinants of the human lipidome. 2022. Commun Biol | DOI: 10.1038/s42003-022-03291-2.

Davies, NM. et al.: Reading Mendelian randomisation studies: a guide, glossary, and checklist for clinicians. 2018. BMJ | DOI: 10.1136/bmj.k601.

Genetics meets metabolomics: From experiment to systems biology. 2012. New York, Heidelberg: Springer | DOI: 10.1007/978-1-4614-1689-0.

Bowden, J. et al.: Meta-analysis and Mendelian randomization: A review. 2019. Res Synth Methods | DOI: 10.1002/jrsm.1346.

Chang, L. et al.: mGWAS-Explorer 2.0: Causal Analysis and Interpretation of Metabolite–Phenotype Associations. 2023. Metabolites | DOI: 10.3390/metabo13070826.

Yin, X. et al.: Genome-wide association studies of metabolites in Finnish men identify disease-relevant loci. 2022. Nat Commun | DOI: 10.1038/s41467-022-29143-5.

Karjalainen, MK. et al.: Genome-wide characterization of circulating metabolic biomarkers reveals substantial pleiotropy and novel disease pathways. 2022. medRxiv | DOI: 10.1101/2022.10.20.22281089.

Surendran, P. et al.: Rare and common genetic determinants of metabolic individuality and their effects on human health. 2022. Nat Med | DOI: 10.1038/s41591-022-02046-0.

König, E. et al.: Whole Exome Sequencing Enhanced Imputation Identifies 85 Metabolite Associations in the Alpine CHRIS Cohort. 2022. Metabolites | DOI: 10.3390/metabo12070604.

Feofanova, EV. et al.: A Genome-wide Association Study Discovers 46 Loci of the Human Metabolome in the Hispanic Community Health Study/Study of Latinos. 2020. Am J Hum Genet | DOI: 10.1016/j.ajhg.2020.09.003.

Rueedi, R. et al.: Genome-wide association study of metabolic traits reveals novel gene-metabolite-disease links. 2014. PLoS Genet | DOI: 10.1371/journal.pgen.1004132.

Lotta, LA. et al.: Genetic Predisposition to an Impaired Metabolism of the Branched-Chain Amino Acids and Risk of Type 2 Diabetes: A Mendelian Randomisation Analysis. 2016. PLoS Med | DOI: 10.1371/journal.pmed.1002179.

Qin, Y. et al.: Genome-wide association and Mendelian randomization analysis prioritizes bioactive metabolites with putative causal effects on common diseases. 2020. medRxiv | DOI: 10.1101/2020.08.01.20166413.

Wang, Z. et al.: Genome-wide association study of metabolites in patients with coronary artery disease identified novel metabolite quantitative trait loci. 2021. Clin Transl Med | DOI: 10.1002/ctm2.290.

Panyard, DJ. et al.: Cerebrospinal fluid metabolomics identifies 19 brain-related phenotype associations. 2021. Commun Biol | DOI: 10.1038/s42003-020-01583-z.

Using mass spectrometry in metabolic profiling

Metabolomics relies on mass spectrometry (MS), a prevalent methodology known for its high sensitivity, reproducibility, and versatility. MS is a quantitative and qualitative analytical technique that uses the mass-to-charge (m/z) ratio of previously ionized molecules to provide insights into biochemical pathways, metabolic fluxes, and the physiological status of organisms. Due to the diverse physical and chemical properties of compounds such as lipids, amino acids, and organic acids, as well as their widespread and fluctuating distribution across biological matrices, different MS-based technologies are used (Ren et al. 2018; Zhang et al. 2015).

Mass spectrometry, which consists of three main components: an ion source, a mass analyzer, and a detector. Ionization techniques such as electrospray ionization (ESI) and matrix-assisted laser desorption/ionization (MALDI) ionize molecules from solutions/solid-state, mixtures, while electron ionization (EI) is used to ionize molecules from the gas phase (after gas-chromatography (GC) separation).

Mass analyzers like time-of-flight (TOF), quadrupole, and ion trap separate ions based on their m/z ratios. Detectors such as electron multipliers and photomultiplier tubes then amplify ion signals for analysis. Liquid chromatography-mass spectrometry (LC-MS) combines liquid chromatography separation with MS detection, suitable for complex mixtures, whereas GC-MS offers high resolution for volatile compounds but requires derivatization for non-volatile ones. Each platform has advantages and limitations, guiding their selection for metabolomics studies based on the nature of the analytes and analytical requirements (Ahmad et al. 2020, Hoffmann et al. 2007).

Sample preparation

Effective metabolomic analysis calls for meticulous sample preparation to extract metabolites from the biological matrices, enhance detectability, and mitigate potential interference in subsequent mass spectrometry (MS) analysis.

If you’re unsure about the optimal matrix for your study, consider referring to ‘Which sample matrix should I use for my metabolomics study?’.

While preparation techniques play a crucial role in improving the detection and stability of metabolites, they also introduce complexities and variability into the sample preparation workflow. Sample clean-up procedures are necessary to eliminate unwanted matrix components, ameliorate matrix effects and enhance the sensitivity and selectivity of MS detection (Gong et al. 2017). Despite efforts to optimize sample preparation protocols, striking a balance between maximizing metabolite detection and minimizing sample variability and matrix effects remains challenging (Fan et al. 2012).

Extraction and analysis

Metabolomics analysis relies on various extraction techniques, each tailored to specific metabolite proprieties and sample matrices. Liquid-liquid extraction (LLE), solid-phase extraction (SPE), and protein precipitation are a few examples, each offering distinct advantages and applicability across different sample types (Nováková et al. 2024).

LLE involves partitioning analytes between two immiscible liquid phases, making it ideal for extracting non-polar and moderately polar metabolites. SPE uses solid-phase sorbents to selectively retain analytes, making it applicable to a wide range of sample types with high selectivity (Schomakers et al. 2022, Chetwynd et al. 2015). By contrast, protein precipitation removes proteins using organic solvents or acids, providing a rapid and effective approach for high-throughput analyses.

Derivatization methods enhance metabolite detection and stability, particularly those with low volatility or poor ionization efficiency, but as with preparation techniques, they can complicate the sample preparation workflow (Skov et al 2015).

Data analysis and interpretation

After data preprocessing, which involves peak picking, retention time alignment, and normalization, researchers strive to mitigate systematic variations that may be lurking in the raw data. To facilitate the identification of differences between experimental conditions, the data interpretation phase uses statistical methods, which may include the following (Lamichhane et al. 2018, Cambiaghi et al. 2017): 

• t-tests
• analysis of variance (ANOVA)
• linear mixed models
• multivariate analyses such as principal component analysis (PCA) and partial least-squares discriminant analysis (PLS-DA)
• metabolite set analysis such as the hypergeometric test and Fisher Exact test
• data fusion techniques such as joint and individual explained analysis (JIVE) and deeply integrated single-cell omics (DISCO).

A few common challenges to look out for at this stage are handling missing values, managing high data dimensionality, and controlling false discovery rates. Researchers address these through various strategies such as imputation techniques, dimensionality reduction methods, and stringent statistical corrections. Quality control measures and validation strategies are also imperative for ensuring the robustness and reproducibility of results (Perez de Souza et al. 2024, Anwardeen et al. 2023). Multivariate data analysis techniques help find patterns and correlations within metabolomics datasets. Limonciel, 2023, introduces the “STORY Principle”, a systematic framework for interpreting metabolomics data and guiding researchers through each step.

Applications of MS-based metabolomics in recent research publications

Metabolomics can be applied in diagnostics, prognostics, biomarker discovery, treatment monitoring, and understanding disease mechanisms across various fields such as cardiometabolic diseases, diabetology, oncology, and toxicology.

The study of cardiometabolic diseases, including diabetes, is an especially important application of MS in metabolomics research. Huang et al. 2024, investigated the protective effect of Liu-Wei-Di-Huang-Wan (a traditional Chinese formulation composed of six commonly used Chinese herbs) in mice with type 2 diabetes mellitus. They identified 30 endogenous metabolites including 3-hydroxybutyric acid, citric acid, hexadecanoic acid, and octadecanoic acid. They found that treatment with Liu-Wei-Di-Huang-Wan improved metabolic patterns in diabetic kidney disease mice, comparable to gliquidone, by regulating metabolites associated with key metabolic pathways. In another study, Matter-Rieck et al. 2024, showcase the potential role of omentin in regulating systemic metabolism, particularly lipid metabolism, particularly lipid metabolism, and influencing insulin sensitivity, further exploring its involvement in type 2 diabetes. Omentin levels were positively associated with lipids such as phosphatidylcholines (PCs) and acylcarnitines, and negatively associated with certain amino acids.

Epidemiology and genetics offer insights into disease etiology and progression. Rosario et al. 2023, identified significant metabolic and transcriptional dysregulation associated with gastrointestinal adenocarcinomas. The findings reveal alterations in pathways such as steroid metabolism and tryptophan/kynurenine metabolism, with sex-specific differences. Investigating gut microbiota stability, Frost et al. 2021, found that instability, characterized by an increase in potential pathogens like Enterobacteriaceae and a decrease in beneficial bacteria such as Bifidobacteria, was significantly associated with metabolic disorders including diabetes mellitus and fatty liver disease.

Metabolomics is also an important investigative tool in oncology, presenting the metabolic signatures linked to cancer development and treatment response. Zhang N et al. 2024, used LC-MS and GC-MS analysis of plasma metabolites to study biomarkers for neoadjuvant therapy efficacy in HER2 + breast cancer. Significant differences were found between responders and non-responders, with 100 metabolites identified and enriched in 40 pathways. Area under the curve (AUC) values for discriminating groups exceeded 0.910, and 18 metabolites showed potential for monitoring efficacy. In another study, Decker et al. 2024, described connections between cholesterol precursors, oxysterols, and diverse factors in women diagnosed with breast cancer. Their research shows the linkage of cholesterol precursors to metabolic factors like body mass index (BMI), cardiovascular disease (CVD) and oxysterols, particularly those generated via reactive oxygen species (ROS), and key characteristics of breast cancer tumors, including obesity.

Metabolic profiling is increasingly recognized in cardiology studies. A study by Kretzschmar et al. 2024, analyzed plasma metabolites in patients with acute decompensated heart failure (ADHF) and chronic heart failure (CHF). They found distinct metabolic signatures associated with each phenotype, including potential novel biomarkers for heart failure such as 1-methyl histidine and 3-indolepropionic acid. Another interesting study by Andrews et al. 2024, highlights significant changes in lipid metabolism in mice, specifically focusing on alterations in acylcarnitines, amino acids, bile acids, ceramides, sphingomyelins, and triacylglycerols. These findings pinpoint the mechanisms underlying hepatic steatosis and atherosclerosis development in mice fed the hyperhomocysteinemic diet.

Finally, in neuroscience, Ge et al. 2024, conducted a metabolomic analysis from 48 patients with disorders of consciousness (DoC), showing distinct metabolic profiles linked to etiology, consciousness levels, and prognosis. Their findings highlight the critical role of phospholipid metabolism and identify potential biomarkers for improved diagnosis and treatment of DoC.

As mass spectrometry evolves, we’ll see more opportunities to apply the methodology within metabolomics studies. If you are considering using this technique in your own study, feel free to contact the biocrates team with any questions.

References

Andrews, S.G. et al.: Diet-Induced Severe Hyperhomocysteinemia Promotes Atherosclerosis Progression and Dysregulates the Plasma Metabolome in Apolipoprotein-E-Deficient Mice (2024) Nutrients | DOI: 10.3390/nu16030330

Anwardeen, N.R.et al.: Statistical methods and resources for biomarker discovery using metabolomics (2023) BMC Bioinformatics 24 | DOI:10.1186/s12859-023-05383-0

Cambiaghi A et al.: Analysis of metabolomic data: tools, current strategies and future challenges for omics data integration (2017) Briefings in Bioinformatics | DOI: 10.1093/bib/bbw031

Causon J.: Review of sample preparation strategies for MS-based metabolomic studies in industrial biotechnology (2016) Analytica Chimica Acta | DOI: 10.1016/j.aca.2016.07.033

Chetwynd J. et al.: Solid-Phase Extraction and Nanoflow Liquid Chromatography-Nanoelectrospray Ionization Mass Spectrometry for Improved Global Urine Metabolomics (2014) Analytical Chemistry | DOI: doi.org/10.1021/ac503769q

Decker, N.S et al.: Associations between lifestyle, health, and clinical characteristics and circulating oxysterols and cholesterol precursors in women diagnosed with breast cancer: a cross-sectional study. (2024) Sci Rep 14 | DOI: 10.1038/s41598-024-55316-x

Fan, T.WM.: Considerations of Sample Preparation for Metabolomics Investigation. (2012) The Handbook of Metabolomics. Methods in Pharmacology and Toxicology | DOI: 10.1007/978-1-61779-618-0_2

Frost F. et al.: Long-term instability of the intestinal microbiome is associated with metabolic liver disease, low microbiota diversity, diabetes mellitus and impaired exocrine pancreatic function (2021) Gut | DOI: 10.1136/gutjnl-2020-322753

Ge Q, et al.: Serum metabolism alteration behind different etiology, diagnosis, and prognosis of disorders of consciousness (2024) Chin Neurosurg J. | DOI: 10.1186/s41016-024-00365-4

Gong Z-G. et al.: The Recent Developments in Sample Preparation for Mass Spectrometry-Based Metabolomics (2017) Critical Reviews in Analytical Chemistry, 47(4), 325–331 | DOI: 10.1080/10408347.2017.1289836

Hoffmann E. et al.: Mass Spectrometry: Principles and Applications (2007) Hoboken, NJ: John Wiley & Sons. | ISBN: 978-0-470-03310-4

Huang JH. et al.: A GC-MS-Based Metabolomics Investigation of the Protective Effect of Liu-Wei-Di-Huang-Wan in Type 2 Diabetes Mellitus Mice (2020) Int J Anal Chem. Aug | DOI: 10.1155/2020/1306439

Javed A et al.: Basic Principles and Fundamental Aspects of Mass Spectrometry (2022) Mass Spectrometry in Food Analysis | DOI: 10.1201/9781003091226-2

Kretzschmar T. et al.: Metabolic Profiling Identifies 1-MetHis and 3-IPA as Potential Diagnostic Biomarkers for Patients With Acute and Chronic Heart Failure With Reduced Ejection Fraction. (2024) Circ Heart Fail | DOI: 10.1161/CIRCHEARTFAILURE.123.010813

Lamichhane S.:Chapter Fourteen – An Overview of Metabolomics Data Analysis: Current Tools and Future Perspectives (2018) Comprehensive Analytical Chemistry | DOI: 10.1016/bs.coac.2018.07.001

Limonciel A. The STORY principle A guide to the biological interpretation of metabolomics (2023) biocrates life sciences gmbh | DOI: 10.1016/bs.coac.2018.07.001

Nováková S. et al.: Comparison of Various Extraction Approaches for Optimized Preparation of Intracellular Metabolites from Human Mesenchymal Stem Cells and Fibroblasts for NMR-Based Study (2024) Metabolites | DOI: 10.3390/metabo14050268

Perez de Souza L. et al.: Computational methods for processing and interpreting mass spectrometry-based metabolomics. (2024) Essays Biochem | DOI: 10.1042/EBC20230019

Ratter-Rieck J.: Omentin associates with serum metabolite profiles indicating lower diabetes risk: KORA F4 Study (2024) BMJ Open Diabetes Research and Care | DOI: 10.1136/bmjdrc-2023-003865

Ren J-L. et al.: Advances in mass spectrometry-based metabolomics for investigation of metabolites (2018) Royal Society of Chemistry | DOI: 10.1039/C8RA01574K

Rosario D. et al.: Systematic analysis of gut microbiome reveals the role of bacterial folate and homocysteine metabolism in Parkinson’s disease (2021) Cell Reports 34 | DOI: 10.1016/j.celrep.2021.108807

Schomakers V. Et al.: Polar metabolomics in human muscle biopsies using a liquid-liquid extraction and full-scan LC-MS (2022) TAR Protocols | DOI: 10.1016/j.xpro.2022.101302

Skov K. et al.: LC–MS analysis of the plasma metabolome—A novel sample preparation strategy (2015) Journal of Chromatography B | DOI: 10.1016/j.jchromb.2014.11.033

Zhang A. et al.: Metabolomics for Biomarker Discovery: Moving to the Clinic (2015) Biomed Res Int. | DOI: 10.1155/2015/354671

Zhang N et al.: Metabolomics assisted by transcriptomics analysis to reveal metabolic characteristics and potential biomarkers associated with treatment response of neoadjuvant therapy with TCbHP regimen in HER2 + breast cancer. (2024) Breast Cancer Res. | DOI: 10.1186/s13058-024-01813

2007: atmospheric fingerprinting of pinene enantiomers | 2010: pinenes considered as renewable alternative to fuel | 2014: engineering of pinene-producing E. coli.

As one of the metabolites responsible for the smell of conifers (Schreiner et al. 2018), alpha-pinene is a fitting choice for our festive Metabolite of the month. This chiral molecule has two enantiomers that are found in varying proportions in different plants, as does its isomer, beta-pinene (Stephanou 2007).

Pinenes are unsaturated monoterpenes characterized by two rings fused to each other, making them quite reactive when released in the atmosphere. For instance, photooxidation of pinenes generates the metabolite 3-hydroxyglutaric acid (Claeys et al. 2007). Despite the clue in their name, their synthesis is not limited to pine trees and occurs in many plant species and some microorganisms.

A 2010 study identified beta-pinene as a potential renewable energy source, with properties comparable to those of jet fuel (Harvey et al. 2010). To enable industrial production of pinenes, bacterial strains were engineered to synthesize pinene by expressing pinene synthase and geranyl diphosphate synthase genes (Sarria et al. 2014). Beta-pinene was also used as a substrate to synthesize a high molecular weight polymer of interest for optoelectronics (Satoh et al. 2014; Winnacker 2018).

Pinenes also serve many endogenous functions in plants, with some interesting applications for human health, as discussed below.

Biosynthesis vs. dietary uptake

In plants, pinene biosynthesis starts with the activation of isoprene units that exist in two forms that react together to form geranyl pyrophosphate (GPP). GPP is transformed to its isomer linaloyl pyrophosphate, which undergoes cyclization and a nucleophilic attack, leading to a pinane cation intermediate. Alpha-pinenes are synthesized by methylene proton elimination while beta-pinenes arise from methyl proton elimination (Nyamwihura et al. 2022). Pinenes also serve as intermediates in the synthesis of other plant metabolites such as carvone (a common component of essential oils used in the food industry).

In human males, ingestion of oils containing pinenes resulted in absorption of alpha- and beta-pinene, with both metabolites detectable in plasma after 24 hours (Papada et al. 2020). A 2012 study showed that alpha-pinene and other terpenes were detectable in the plasma and milk of goats after ingestion, suggesting that these metabolites may be useful in tracing the consumption of certain foods by farm animals (Poulopoulou et al. 2012). However, terpene levels in cheese made from these goats’ milk were found to vary, which may limit their use as a feed tracer in processed milk products.

Alpha-pinene functions in plants

Pinenes appear to serve several functions in the plants that synthesize them. Studies have shown that pinenes help protect plants from pests, such as the red turpentine beetle, (Dendroctonus valens) (Xu et al. 2016) and the white pine weevil (Pissodes strobi) (McKay et al. 2003). The study by McKay et al. describes how traumatic resin ducts form in the Sitka spruce (Picea sitchensis) upon attack by pests that activate the tree’s terpenoid defense systems.

Pinenes also play a role in plant-to-plant communication. The release of pinenes into the atmosphere is light- and temperature-dependent (Stephanou 2007), and headspace exposure to these pinenes can cause strong reactions. For example, when Arabidopsis thaliana is exposed to a mixture of alpha and beta-pinenes, this triggers the plant’s defense responses, accumulation of reactive oxygen species, and changes in gene expression that are consistent with systemic acquired resistance (Riedlmeier et al. 2017). Thus, pinenes and other monoterpenes are infochemicals, supporting plant-to-plant signaling and propagating defense signals between neighboring plants.

Lastly, pinenes influence the growth of their host plant. In Cicer arietinum, alpha-pinene increases solute leakage from roots and increases the levels of proline, malondialdehyde and hydrogen peroxide, inhibiting radicle growth (Singh et al. 2006). Beta-pinene has been found to inhibit germination in several weed species (Chowhan et al. 2013).

Alpha-pinene activity in animals

Pinenes are not only relevant to plant biology.They have been found to have several properties that are of interest in human health and disease. Pinenes have been studied for their antimicrobial properties (Da Rivas et al. 2012). They have also been described as having anticoagulant, antitumor, antimalarial, antioxidant, anti-inflammatory, and analgesic (Salehi et al. 2019). Some of these effects were uncovered through the study of active ingredients in the plants used in traditional Chinese medicine. For example, a 2011 study showed that alpha-pinene derivatives isolated from Angelica sinensis inhibited platelet aggregation and exhibited weak antithrombin activity (Yang et al. 2011).

Pinenes are also studied for their effects on cancer and cancer therapies. Both alpha- and beta-pinenes have shown synergistic antitumor effects with paclitaxel in the treatment of non-small-cell lung carcinoma (Zhang et al. 2015). Interestingly, in a murine model, exposure to a fragrant environment rich in alpha-pinene prior to and after tumor implantation resulted in 40% smaller tumors, increased plasma leptin levels, and neurological and immune differences compared to mice who were not exposed to alpha-pinene (Kusuhara et al. 2019).

In various models, pinenes were also shown to increase the skin penetration of drugs (Almirall et al. 1996), provide anti-inflammatory effects via inhibition of mitogen-activated protein kinases (MAPK) and nuclear factor-kappa B (NF-κB) in macrophages (Kim et al. 2015), influence vascular tone (Jin et al. 2023), and have a protective effect on the gastrointestinal tract (Marcelo de Almeida et al. 2015).

Alpha-pinene in the atmosphere

Researchers collect samples of the air over forests to identify the enantiomeric composition of the volatile compounds emitted by trees. A 2007 comparison of the ambient air over tropical (South America) and boreal (Northern Europe) forests showed that (-) alpha-pinene and (+) beta-pinene were predominant in the tropical forest while (+) alpha-pinene and (-) beta-pinene were predominant in the boreal forest (Williams et al. 2007). These results indicate a fingerprint-like profile of pinene enantiomers between different ecosystems and raise questions about the effects of such enantiomeric mixtures in the atmosphere.

To continue learning about seasonal metabolites, read our article on cinnamaldehyde.

References

Almirall M. et al.: Effect of d-limonene, alpha-pinene and cineole on in vitro transdermal human skin penetration of chlorpromazine and haloperidol. (1996) In Arzneimittel-Forschung | Available online at https://pubmed.ncbi.nlm.nih.gov/8842336/

Chowhan, N. et al.: β-Pinene inhibited germination and early growth involves membrane peroxidation. (2013) In Protoplasma http://doi.org/10.1007/s00709-012-0446-y

Claeys M. et al.: Hydroxydicarboxylic acids: markers for secondary organic aerosol from the photooxidation of alpha-pinene. (2007) Environmental Science & Technology | http://doi.org/10.1021/es0620181

Da Rivas S. et al.: Biological activities of α-pinene and β-pinene enantiomers. (2012) In Molecules 17 | http://doi.org/10.3390/molecules17066305

Harvey B. et al.: High-Density Renewable Fuels Based on the Selective Dimerization of Pinenes. (2010) Energy Fuels 24 | http://doi.org/10.1021/ef900799c

Jin L. et. al.: Endothelial-dependent relaxation of α-pinene and two metabolites, myrtenol and verbenol, in isolated murine blood vessels. In American journal of physiology. (2023) Heart and circulatory physiology 325 | http://doi.org/10.1152/ajpheart.00380.2023

Kim D. et al.: Alpha-Pinene Exhibits Anti-Inflammatory Activity Through the Suppression of MAPKs and the NF-κB Pathway in Mouse Peritoneal Macrophages. (2015) The American journal of Chinese medicine 43 | http://doi.org/10.1142/S0192415X15500457

Kusuhara M. et al.: (2019): A Fragrant Environment Containing α-Pinene Suppresses Tumor Growth in Mice by Modulating the Hypothalamus/Sympathetic Nerve/Leptin Axis and Immune System.(2019) Integrative cancer therapies 18 | http://doi.org/10.1177/1534735419845139

Marcelo de Almeida P. et al.: Gastroprotective effect of alpha-pinene and its correlation with antiulcerogenic activity of essential oils obtained from Hyptis species. (2015) In Pharmacognosy Magazine | Available online at https://phcog.com/article/view/2015/11/41/123-130

McKay S. et al.: Insect attack and wounding induce traumatic resin duct development and gene expression of (-)-pinene synthase in Sitka spruce. (2003) Plant physiology | http://doi.org/10.1104/pp.103.022723

Nyamwihura R. et al.: The pinene scaffold: its occurrence, chemistry, synthetic utility, and pharmacological importance. (2022) RSC advances | http://doi.org/10.1039/d2ra00423b

Papada E. et al.: An Absorption and Plasma Kinetics Study of Monoterpenes Present in Mastiha Oil Humans. (2020) Foods 9 | http://doi.org/10.3390/foods9081019

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Riedlmeier M. et al.: Monoterpenes Support Systemic Acquired Resistance within and between Plants. (2017) The Plant cell | http://doi.org/10.1105/tpc.16.00898

Salehi B. et al.: Therapeutic Potential of α- and β-Pinene: A Miracle Gift of Nature. (2019) Biomolecules | http://doi.org/10.3390/biom9110738

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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

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

Cell culture metabolomics is an important tool in pharmacology and drug development since it allows researchers to gain a more comprehensive understanding of the complex metabolic processes that occur within cells. Equipped with this knowledge, researchers can identify new drug targets, optimize drug dosing regimens, evaluate the efficacy and safety of drugs including their toxicity profile, understand drug metabolism, and ultimately develop safer and more effective therapies for a wide range of diseases and conditions.

How metabolomics can help to reduce costs in drug development
What to consider when conducting cell culture metabolomics
Advantages for pharmaceutical R&D
How metabolomics can be applied in pharmacology
Drug discovery and basic research
Preclinical drug development
One step closer to clinical trials
Drug repurposing

How metabolomics can help to reduce cost of drug development

The pharmaceutical industry is one of the largest sectors of the global economy, creating drug revenues of more than $1,000 billion in 2020 alone (Mullard 2022). Research and development (R&D) investments range from $0.9 billion to $2.8 billion for each new drug, with costs expected to rise over time (Simoens et al. 2021). The high costs of failure throughout the drug development process are a significant issue, especially given the extremely low approval rate: only 9% in phase 1 and just 46% even in phase 3 clinical trials (Fogel 2018).

Although large pharmaceutical businesses may endure this loss, smaller companies can experience serious financial problems. For example, biopharma company NewLink Genetics was forced to fire 43% of its entire workforce (100 people) following the failure of an immunotherapeutic agent in phase 3 trial (Lo 2017). Recently, the US Food and Drug Administration (FDA) has committed to exploring alternative methods to help reduce costs and drive results, such as the use of cutting-edge cell cultures to replace laboratory animals in developing new drugs (Nuwer 2022).

In recent years, metabolomics has emerged as a powerful tool in the pharmaceutical industry for biomarker discovery, drug target identification, mechanism of action studies, safety assessment, and personalized medicine (Wishart 2008). Metabolomics is the comprehensive analysis of small molecule and lipid metabolites in biological systems, providing excellent snapshots of a patient’s metabolic profile from biofluids, tissues, stem cells, cell culture lysates, and even cell culture supernatants. Cell culture metabolomics can contribute to pharmacological studies along the entire drug discovery pipeline.

What to consider when conducting cell culture metabolomics

Cell culture metabolomics is a valuable tool for drug discovery and development. The main objective of cell culture metabolomics is to examine as many metabolites as possible in a defined cell state or condition, and provide a snapshot of metabolic status. This is achieved by halting all enzymatic and chemical reactions to prevent them from affecting results (Halama 2014).

Metabolite concentrations in humans may differ from those in animal or cell culture models, but the broad trends are widely preserved (Wishart 2008, Kleinstreuer et al. 2011) including in the case of the blood-brain barrier (Schultz 2015). Standardization is crucial in metabolomic analyses, because several factors, such as the culture conditions, cell density, sample size, and culture media composition, may affect the results (Fritsche-Guenther et al. 2022). To get the best results, it is useful to consider some important technical parameters:

• Cell number – we recommend starting analysis with a pellet of approximately 1-3 x 10⁶ cells, but this depends on the cell line and concentrations of metabolites of interest and should be tested before the actual investigation begins.

• Sample collection – the nature and the concentration of metabolites in a sample should remain as unchanged as possible from the time of sampling. It is crucial to ensure the best possible quenching and harvesting technique to stop further breakdown of metabolites by intracellular enzymes and changes in sample composition. When working with adherent cell cultures, cells must be detached by scraping. This is a stressful procedure for the cells and may change the metabolome, so it is important to work quickly and cool the cells (Fritsche-Guenther et al. 2022).
  
  The use of trypsin or other enzymes for harvesting is not recommended since there is a high a risk of cell membrane injury and metabolite leakage which can affect the metabolome (Batista et al. 2019). For suspension cell cultures and 3D cell cultures such as spheroids, cells can be easily collected through centrifugation.

• Metabolite extraction – extraction method is a crucial factor in reliable metabolite quantification. Andresen et al. (2022) compared ten extraction protocols in four human-derived sample types (liver tissue, bone marrow, HL60, and human embryonic kidney cells) for metabolome analysis. The results showed that extraction efficiency and reproducibility vary greatly between methods, tissues, and metabolite chemical groups. Metabolite levels obtained by mass spectrometry can also be altered significantly by washing the samples before extraction and adding acid to the methanol extraction solvent (Ser et al. 2015).

• Special sample types – in addition to cell lysates and cell culture supernatants, various materials are suitable for metabolomic analyses, including embryonic stem cells (Kleinstreuer et al. 2011, Schultz et al. 2015), induced pluripotent stem cells (Fritsche-Guenther et al. 2022), tissue cultures (Batista et al. 2019), organs-on-a-chip, as well as 3D cell cultures like spheroids and organoids (Artati et al. 2019, Andresen et al. 2022)

Advantages for pharmaceutical R&D

Cell culture metabolomics offers many advantages in pharmacological studies:

• Controlled experimental conditions – cell culture metabolomics allows for the study of metabolites in a controlled experimental environment, free from the variability of in vivo systems. This makes it easier to manipulate and control experimental conditions, which supports more accurate and reproducible results.

• High-throughput screening – cell culture metabolomics can be used for high throughput screening of drug candidates, allowing for the rapid identification of compounds with specific metabolic effects. This can save time and resources in the drug discovery process.

• Easy sample preparation – cell culture metabolomics requires only a small amount of sample material. Preparation is relatively straightforward compared to other sample types. This makes it a cost-effective and efficient method for analyzing metabolites.

• Reproducibility – because cells can be cultured under controlled conditions, experiments can be easily replicated, leading to increased confidence in the results.

• Potential for personalized medicine – cell culture metabolomics can be used to study the metabolic profiles of individuals, which can be used to develop personalized medicine approaches that are tailored to the unique metabolic needs of patients.

• Lower cost – cell culture metabolomics is less expensive than in vivo studies. There are no animal costs, and the cell culture equipment and reagents are generally less expensive than those required for in vivo studies.

• Ethical considerations – cell culture metabolomics may decrease the use of animals as well as patient harm.

How metabolomics can be applied in pharmacology

Metabolomics has been used to create a more comprehensive understanding of metabolism and disease- or treatment-related metabolic changes. In pharmacology, metabolomics has taken center stage in gaining an in-depth understanding of underlying metabolic processes and identifying the mechanism of action (MoA) of a drug. Metabolomics applications have the potential to accelerate drug discovery and development at several stages which are critical for progressing good drug candidates through the development process.

Drug discovery and basic research

In the early phases of drug development, potential drug targets may be identified from data derived from phenotypic screening or genetic mutation analysis, without an in-depth understanding of their biochemical features. This makes it more challenging to understand the MoA and analyze target engagement. Metabolomics is well suited to investigating endogenous enzyme substrates in this context and has an important role in biomarker discovery and screening studies.

For example, metabolic characterization of same-patient-derived cell lines from primary colon adenocarcinoma and its metastatic derivatives showed that the metastatic cell lines were selectively vulnerable to the inhibition of cystine import and folate metabolism, two key pathways in redox homeostasis. As a result, two proteins from these pathways were identified as potential therapeutic targets for combating metastatic colorectal cancer (Tarragó-Celada et al. 2021).

Metabolomics has also been used to assess the effects of chemical exposure on female germ cells and infertility in a human ovarian tissue culture model (Hao et al. 2018). Furthermore, Kleinstreuer et al. used the supernatant of human embryonic stem cells to predict the teratogenic effect of known chemicals. They achieved 73% accuracy, which was consistent with their animal data (Kleinstreuer et al. 2011).

Preclinical drug development

To achieve successful drug development, researchers must obtain accurate information on pharmacokinetics and metabolism as early as possible. This information directly affects the compound’s success or failure in the long run. Early screening of absorption, distribution, metabolism, and excretion (ADME) has significantly reduced the percentage of drugs that fail in clinical trials. Preclinical ADME aims to identify and reject weak drug candidates early in the drug development process, so that resources can be focused on promising candidates.

The distinct metabolic characteristics of cancer cells offer promising opportunities for drug development, particularly in precision medicine. However, surprisingly few new drugs have been developed for therapeutic targeting of cancer metabolism to date. One of the first studies to evaluate the utility of pharmacological inhibition of glutamine transport successfully inhibited cell growth and proliferation both in vitro and in vivo (Schulte et al. 2018).

Using metabolomics, Caiola et al. (2016) demonstrated that there are distinct metabolic responses induced by mutant and wildtype KRAS in non-small cell lung cancer (NSCLC) cells after pharmacological impairment of PI3K signaling by inhibitor candidates. Another study targeting glycolytic flux in hematologic cancer cell lines showed the antitumor activity of the drug candidate both as monotherapy and in combination with some common therapeutics and verified these results in vivo (Curtis et al. 2017).

In addition to these current advancements, an emerging research area known as activity metabolomics suggests that metabolomics may also directly identify bioactive metabolites involved in the MoA (Alarcon et al. 2022). For example, indole-3-propionic acid is an unusual antibiotic that combines anti-inflammatory and antioxidant effects (Negatu et al. 2020). Metabolomics is also being used to research the effects of nutrition on pharmacological therapy. For example, analysis of the amino acid and central carbon metabolism of breast cancer cells treated with palbociclib, letrozole, and xenoestrogens revealed that dietary estrogens influence the metabolic and anti-oncogenic drug response (Warth et al. 2017).

One step closer to clinical trials

The translational gap between drug discovery and clinical development is one of the main bottlenecks for drug development. This can reduce the overall success rate of pharmaceuticals during clinical development, despite significant investments in drug development. Analyzing the metabolic effects of drug candidates in vitro and in vivo may offer a better understanding of their metabolic changes, MoA, and potential toxic effects before clinical trials.

Metabolomics is a useful tool for investigating the MoA and therapeutic response of drug candidates. For example, a study monitoring over 750 intracellular metabolites revealed that metabolism played a direct role in modulating the response to antibiotic stress, leading to cell death or escape. The metabolic changes were found to be drug- and dosage-specific. In a subsequent study, the same team developed a method to categorize the MoA of antimicrobial substances by quick and meticulous metabolic profiling (Zampieri et al. 2018).

Another study investigated the MoA of destruxins, suggesting that the underlying mechanism of resistance was not efflux through ABC transporters, commonly known as chemoresistance mediators, but cholesterol-mediated plasma membrane re-organization (Heilos et al. 2018). In erlotinib-resistant pancreatic cancer cells, changes in acetyl‑CoA‑associated and phosphatidylcholine metabolism (Lee et al. 2017), ornithine decarboxylase (ODC) and its major metabolite putrescine were identified as contributing factors. Putrescine administration was found to reverse the effect (Jang et al 2017).

Recently, nanomaterials have become essential tools in healthcare, with therapeutic uses ranging from contrast media in imaging to vehicles for the transport of drugs and genes into tumors. However, the toxicity of nanoparticles raises concerns about their environmental and societal impact. Metabolomics has been used to study the effects of some well-characterized nanomaterials with various chemical compositions, sizes, and chemical surface modifications.

These studies identified relationships between molecular pathways, physico-chemical properties, and toxicological endpoints (Bannuscher et al. 2020a, Karkossa et al. 2021). Metabolic profiling of nanomaterial-treated cell lines resulted in time- and concentration-dependent changes of the metabolome related to oxidative stress, which contributed to a better understanding of the MoA (Bannuscher et al. 2020b).

Drug repurposing

Drug repurposing is the process of finding new therapeutic uses for drugs that already exist. Given that the safety profile of these drugs is already established, this method of drug development is likely to be faster and more cost-effective (Pulley et al. 2020). For example, the cholesterol-lowering statin drug lovastatin used to reduce the risk of cardiovascular disease was shown to have an antitumor effect both in vitro and in vivo, by reducing the Warburg effect and lactate production in cancer cells (Kobayashi et al. 2017). Additionally, the clinically authorized antidepressant sertraline has been proposed as an adjuvant in the treatment of cancer (Geeraerts et al. 2021).

The radioprotective agent amifostine was also shown to have anti-angiogenic effects using 3D cell metabolomics (Katsila et al. 2021). Considering all these results, cell culture metabolomics has proven to be useful for developing hypotheses and validating targets for drug repurposing.

If you need help with your cell preparation or want to obtain our technical guide for cell sample preparation for metabolic phenotyping please contact us.

References

Alarcon-Barrera JC et al.: Recent advances in metabolomics analysis for early drug development. (2022) Drug Discov Today | https://doi.org/10.1016/j.drudis.2022.02.018

Andresen C et al.: Comparison of extraction methods for intracellular metabolomics of human tissues. (2022) Front Mol Biosci | https://doi.org/10.3389/fmolb.2022.932261

Artati A et al.: LC-MS/MS-Based Metabolomics for Cell Cultures. (2019) Methods Mol Biol | https://doi.org/10.1007/978-1-4939-9477-9_10

Bannuscher A et al.: A multi-omics approach reveals mechanisms of nanomaterial toxicity and structure-activity relationships in alveolar macrophages. (2020a) Nanotoxicology | https://doi.org/10.1080/17435390.2019.1684592

Bannuscher A et al.: Metabolomics profiling to investigate nanomaterial toxicity in vitro and in vivo. (2020b) Nanotoxicology | https://doi.org/10.1080/17435390.2020.1764123

Batista U et al.: Effects of different detachment procedures on viability, nitroxide reduction kinetics and plasma membrane heterogeneity of V-79 cells. (2010) Cell Biology International | https://doi.org/10.1042/CBI20090276

Caiola E et al.: Different metabolic responses to PI3K inhibition in NSCLC cells harboring wild-type and G12C mutant KRAS. (2016) Oncotarget | https://doi.org/10.18632/oncotarget.9849

Curtis NJ et al.: Pre-clinical pharmacology of AZD3965, a selective inhibitor of MCT1: DLBCL, NHL and Burkitt’s lymphoma anti-tumor activity. (2017) Oncotarget | https://doi.org/10.18632/oncotarget.18215

Fogel DB.: Factors associated with clinical trials that fail and opportunities for improving the likelihood of success: A review (2018) Contemp Clin Trials Commun | https://doi.org/10.1016/j.conctc.2018.08.001

Fritsche-Guenther R et al.: Analysis of adherent cell culture lysates with low metabolite concentrations using the Biocrates AbsoluteIDQ p400 HR kit. (2022) Sci Rep | https://doi.org/10.1038/s41598-022-11118-7

Geeraerts SL et al.: Repurposing the Antidepressant Sertraline as SHMT Inhibitor to Suppress Serine/Glycine Synthesis-Addicted Breast Tumor Growth. (2021) Mol Cancer Ther | https://doi.org/10.1158/1535-7163.MCT-20-0480

Halama A.: Metabolomics in cell culture–a strategy to study crucial metabolic pathways in cancer development and the response to treatment. (2014) Archives of Biochemistry and Biophysics | https://doi.org/10.1016/j.abb.2014.09.002

Hao J et al.: Resveratrol supports and alpha-naphthoflavone disrupts growth of human ovarian follicles in an in vitro tissue culture model. (2018) Toxicol Appl Pharmacol | https://doi.org/10.1016/j.taap.2017.11.009

Heilos D et al.: Altered membrane rigidity via enhanced endogenous cholesterol synthesis drives cancer cell resistance to destruxins. (2018) Oncotarget | https://doi.org/10.18632/oncotarget.25432

Jang W-J et al.: Multi-omics analysis reveals that ornithine decarboxylase contributes to erlotinib resistance in pancreatic cancer cells. (2017) Oncotarget | https://doi.org/10.18632/oncotarget.21572

Karkossa I et al.: Nanomaterials induce different levels of oxidative stress, depending on the used model system: Comparison of in vitro and in vivo effects. (2021) Sci Total Environ | https://doi.org/10.1016/j.scitotenv.2021.149538

Katsila T et al.: Three-Dimensional Cell Metabolomics Deciphers the Anti-Angiogenic Properties of the Radioprotectant Amifostine. (2021) Cancers | https://doi.org/10.3390/cancers13122877

Kleinstreuer NC et al.: Identifying developmental toxicity pathways for a subset of ToxCast chemicals using human embryonic stem cells and metabolomics. (2011) Toxicol Appl Pharmacol | https://doi.org/10.1016/j.taap.2011.08.025

Kobayashi Y et al.: Drug repositioning of mevalonate pathway inhibitors as antitumor agents for ovarian cancer. (2017) Oncotarget | https://doi.org/10.18632/oncotarget.20046

Lee S et al.: Comparative metabolomic analysis of HPAC cells following the acquisition of erlotinib resistance. (2017) Oncol Lett | https://doi.org/10.3892/ol.2017.5940

Lo C.: Counting the cost of failure in drug development (2017) URL: https://www.pharmaceutical-technology.com/features/featurecounting-the-cost-of-failure-in-drug-development-5813046/

Mullard A.: FDA approvals. (2022) Nat Rev Drug Discov 2023 | https://doi.org/10.1038/d41573-023-00001-3

Negatu DA et al.: Indole Propionic Acid, an Unusual Antibiotic Produced by the Gut Microbiota, With Anti-inflammatory and Antioxidant Properties. (2020) Front. Microbiol. | https://doi.org/10.3389/fmicb.2020.575586

Nuwer R.: US agency seeks to phase out animal testing. (2022) Nature | https://doi.org/10.1038/d41586-022-03569-9

Pulley JM et al.: Using What We Already Have: Uncovering New Drug Repurposing Strategies in Existing Omics Data. (2020) Annu Rev Pharmacol Toxicol | https://doi.org/10.1146/annurev-pharmtox-010919-023537

Schulte ML et al.: Pharmacological blockade of ASCT2-dependent glutamine transport leads to antitumor efficacy in preclinical models. (2018) Nat Med | https://doi.org/10.1038/nm.4464

Schultz L et al.: Evaluation of drug-induced neurotoxicity based on metabolomics, proteomics and electrical activity measurements in complementary CNS in vitro models. (2015) Toxicol In Vitro 2 | https://doi.org/10.1016/j.tiv.2015.05.016

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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)

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