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How are metabolite biomarkers improving drug discovery and development?

BY YUNING WANG, PHD · ILLUSTRATED BY ASHLEIGH CAMPSALL

By offering a rich source of insights into disease and drug action, metabolite biomarkers are at the forefront of therapeutic exploration.

As early as the 5th century BC, greek phyician Hippocrates and Indian surgeon Sushruta noted that a sweet taste of the patient’s urine could indicate what we now recognize as diabetes (1,2). Though ancient physicians lacked the scientific tools to explain it, they were unknowingly relying on metabolites to diagnose diseases. Today, the study of these small molecules has grown into the field of metabolomics — the systematic analysis of all metabolites in biological systems. Scientists have identified over 220,000 distinct metabolites in the human body, and their role in drug discovery and development is becoming increasingly critical (3).

What are metabolite biomarkers?

Metabolites are intermediates and end products of metabolism. These small molecules, including carbohydrates, lipids, amino acids, and organic acids, typically under 1500 Da in size, flow through cells, tissues, and body fluids, acting as fuel and messengers in virtually all biological activities (4). They power energy production, enable cellular communication, and provide building blocks for proteins, DNA, and other macromolecules.

Because metabolites directly reflect the body’s biochemical activity, they are useful biomarkers for tracking physiological and pathological states. Their concentrations fluctuate dynamically in response to diet, stress, disease, and medications, and their abnormal levels can indicate metabolic imbalances and disease progression. By quantifying specific metabolites in sources such as blood or urine, clinicians can detect and monitor various conditions. For example, elevated blood glucose signals diabetes, and increased serum lactate can indicate tissue hypoxia or sepsis.

Metabolites have unique properties compared to other biological biomarkers. Genomic biomarkers reveal DNA variations linked to disease risk, while transcriptomic and proteomic biomarkers capture gene expression patterns or protein-level changes in specific contexts. Metabolite biomarkers, in contrast, are the biochemical endpoints of these processes, linking genetic activity, environmental influences, and the final phenotype (4). For this reason, metabolite biomarkers play a pivotal role in drug discovery and development by helping researchers understand disease mechanisms, identify drug targets, and optimize therapeutic strategies.

How does metabolic research help scientists understand disease mechanisms?

When diseases occur, metabolism often goes haywire. Analyzing this alteration offers clues into diseases’ biochemical mechanisms. In cancer research, for example, studying metabolism has helped explain how cancer cells sustain uncontrolled growth. Researchers have identified critical pathways, such as the Warburg effect, where cancer cells preferentially use aerobic glycolysis to produce energy. In this process, cancer cells convert glucose into pyruvate, which then mostly turns into lactate and exits the cell instead of entering the tricarboxylic acid (TCA) cycle (5). This metabolic shortcut allows cancer cells to generate ATP rapidly while producing essential building blocks like nucleotides, amino acids, and lipids for survival and proliferation. Meanwhile, cancer cells actively take up glutamine from their surroundings to fuel the TCA cycle, which further supports ATP production and biosynthetic pathways (6).

Similarly, research on neurological diseases like Alzheimer’s disease (AD) has revealed connections between metabolism and neurodegeneration. In patients with AD, scientists have found high levels of certain lipids, such as cholesterol and ceramides, in the cerebrospinal fluid. These metabolic changes drive amyloid accumulation and tau hyperphosphorylation, two hallmark processes of AD (7). Additionally, growing evidence suggests that impaired glucose uptake in neurons, which occurs years before clinical symptoms emerge, leads to decreased energy production, brain insulin resistance, neuro inflammation, and oxidative stress (8). These factors collectively disrupt neuronal function and survival, leading to disease progression.

Beyond cancer and neurodegenerative diseases, metabolic studies are helping scientists pinpoint the root causes of various other conditions. In cardiovascular diseases, researchers have linked heart failure to disrupted metabolism of branched-chain amino acids, including leucine, isoleucine, and valine, inducing insulin resistance and cardiac dysfunction (9). In diabetes, altered lipid and amino acid profiles, such as elevated ceramides and reduced glycine, contribute to insulin signaling defects (10). In autoimmune disorders like rheumatoid arthritis, the accumulation of metabolites such as lactate, citrate, and succinate act as inflammatory signals, fueling immune cell hyperactivity in the inflamed joint (11).

How does metabolic research help scientists understand disease mechanisms?

In the early stages of drug development, identifying promising drug targets is crucial yet complex. Traditionally, researchers have leaned on genetic and proteomic data to link specific genes or proteins to disease mechanisms. But in recent years, metabolomics has emerged as a powerful tool for guiding and refining this search.

One key way metabolomics facilitates drug target identification is by defining the natural substrates of target proteins. Many drug targets discovered through genomic or transcriptomic studies have unclear biochemical roles. Researchers may know a protein is involved in a disease but not how it functions at the molecular level or what molecules it engages with. Metabolomics helps bridge this gap by tracking metabolic changes between healthy and diseased states and identifying which metabolites accumulate or decrease in response to the target protein’s activity. This clarifies how a target protein affects cellular metabolism, participates in specific pathways, and interacts with other molecules. With this knowledge, researchers can focus on targets that are more likely to produce meaningful therapeutic effects.

A good example comes from cancer research. Scientists have long known that mutations in isocitrate dehydrogenase (IDH) enzymes are common in certain brain and blood cancers. Through metabolomics research, researchers discovered that these mutations result in the accumulation of a harmful metabolite called D-2-hydroxyglutarate (D-2HG) (12). This discovery established D-2HG as a diagnostic and prognostic biomarker and led to two FDA-approved drugs, Ivosidenib and Enasidenib, which target mutated IDH enzymes, lower D-2HG levels, and slow cancer progression (13,14).

Once researchers identify a target, metabolic studies help validate its role as they provide clear, measurable indicators of how a drug interacts with it. By mapping cellular metabolic changes onto known pathways, researchers can see which key enzymes or metabolites are most affected. Comparing metabolic profiles between wild-type and genetically modified models, as well as treated versus untreated samples, further helps confirm whether a drug is effectively influencing the intended pathways. This validation process helps researchers reduce the risk of expensive failures down the road (15).

How do metabolite biomarkers provide insights into drug efficacy and safety?

Metabolite biomarkers help scientists understand how the body processes a drug by revealing whether it is effectively converted into its active form. Some medications need to be metabolized before they can work, and measuring their metabolites allows researchers to confirm if this process happens as expected. By analyzing these biomarkers, scientists can determine whether a drug is likely to be effective in a patient, optimize dosing, and identify any potential variations in metabolism that might affect treatment outcomes.

Metabolism is a key factor in why patients respond differently to the same drug. By integrating metabolomics with pharmacokinetics (how a drug moves through the body) and pharmacodynamics (how a drug exerts its effects), researchers can link specific metabolic pathways to drug behavior. For example, this approach has helped clarify how Metformin, a frontline diabetes drug, works at a molecular level. Studies have shown that Metformin’s pharmacokinetics are tied to pathways like arginine, proline, and branched-chain amino acid metabolism (16). Identifying these metabolic signatures helps explain why some patients respond better than others and could lead to more tailored treatments.

Metabolite biomarkers also serve as early warning signals for drug toxicity. The Consortium for Metabonomic Toxicity project, one of the largest studies on drug safety, analyzed 150 compounds and showed that metabolomics can detect signs of liver and kidney toxicity of drug candidates (17). More recently, metabolomics has shed light on chemotherapy responses. By analyzing serum samples from patients with breast cancer undergoing chemotherapy, researchers identified significant shifts in sphingolipid and amino acid metabolism linked to treatment response and hematological toxicity. These findings suggest that serum metabolite profiles could help predict a patient’s response to chemotherapy and its adverse effects on the blood (18). In addition to drug efficacy and toxicity, scientists are also using metabolomics to investigate drug mechanisms of action, identify drug-drug interactions, and repurpose existing drugs for new therapeutic applications (19).

How do scientists identify and assess metabolite biomarkers?

Finding metabolite biomarkers starts with collecting biological samples such as blood, cerebrospinal fluid, saliva, urine, or tissue extracts. Researchers carefully prepare these samples to extract metabolites while keeping them intact, using methods like centrifugation or chemical extraction.

The next step involves choosing a suitable metabolomics approach — targeted or untargeted analysis — based on the study goals (15). Targeted metabolomics focuses on preselected metabolites of interest, allowing for precise quantification and verification against known standards. This approach works best when researchers already have candidates in mind. In contrast, untargeted metabolomics detects as many metabolites as possible, generating a broad metabolic profile that can reveal novel biomarkers.

To analyze the metabolites, scientists mainly rely on mass spectrometry (MS) and nuclear magnetic resonance (NMR). MS, often combined with liquid or gas chromatography, can detect low abundance metabolites with high sensitivity. On the other hand, NMR helps determine metabolite structures and concentrations, making it useful for studying abundant metabolites involved in key metabolic pathways like glucose metabolism and the TCA cycle (15).

Following data collection, researchers process and analyze the data to identify disease-related metabolite biomarkers. This involves multivariate statistical analysis to determine significant metabolic alterations, bioinformatics tools to map identified metabolites to biological pathways, and advanced techniques like machine learning and network analysis to further refine biomarker selection, improving their diagnostic and prognostic power (4).

After identifying potential biomarkers, scientists assess their biological relevance. This step involves integrating metabolomics data with other omics approaches, such as transcriptomics and proteomics, to connect metabolic changes to broader biological processes. Experimental validation through pharmacological studies, small interfering RNA experiments, or isotope tracing helps confirm the biomarker’s role in disease progression or drug response. For clinical applications, validation studies in independent cohorts are necessary to confirm biomarker reliability across different populations (15). By combining metabolomics with advanced computational tools and multiomics integration, researchers continue to refine the discovery and assessment of metabolite biomarkers, paving the way for more effective, personalized treatments.

REFERENCES

  1. Karamanou, M., Protogerou, A., Tsoucalas, G.,Androutsos, G. & Poulakou-Rebelakou, E. Milestones in the history of diabetes mellitus: The main contributors. World J. Diabetes 7, 1–7 (2016).
  2. Fournier, A. Diagnosing diabetes. J. Gen. Intern. Med. 15, 603–604 (2000).
  3. Human Metabolome Database. at https://hmdb.ca/.
  4. Qiu, S. et al. Small molecule metabolites: discovery of biomarkers and therapeutic targets. Signal Transduct. Target. Ther. 8, 1–37 (2023).
  5. Yang, J., Shay, C., Saba, N. F. & Teng, Y. Cancer metabolism and carcinogenesis. Exp. Hematol. Oncol. 13, 10 (2024).
  6. Jin, J., Byun, J.-K., Choi, Y.-K. & Park, K.-G. Targeting glutamine metabolism as a therapeutic strategy for cancer. Exp. Mol. Med. 55, 706–715 (2023)
  7. Yin, F. Lipid metabolism and Alzheimer’s disease: clinical evidence, mechanistic link and therapeutic promise. FEBSJ. 290, 1420–1453 (2023).
  8. Ardanaz, C. G., Ramírez, M. J. & Solas, M. BrainMetabolic Alterations in Alzheimer’s Disease. Int. J. Mol.Sci. 23, 3785 (2022).
  9. Karwi, Q. G. & Lopaschuk, G. D. Branched-Chain Amino Acid Metabolism in the Failing Heart. Cardiovasc. Drugs Ther. 37, 413–420 (2023).
  10. Hameed, A. et al. Altered Metabolome of Lipids and Amino Acids Species: A Source of Early Signature Biomarkers of T2DM. J. Clin. Med. 9, 2257 (2020).
  11. Hanlon, M. M., Canavan, M., Barker, B. E. & Fearon, U. Metabolites as drivers and targets in rheumatoid arthritis. Clin. Exp. Immunol. 208, 167–180 (2022).
  12. Dang, L. et al. Cancer-associated IDH1 mutations produce 2-hydroxyglutarate. Nature 462, 739–744 (2009).
  13. DiNardo, C. D. et al. Durable Remissions with Ivosidenib in IDH1-Mutated Relapsed or Refractory AML. N. Engl. J. Med. 378, 2386–2398 (2018).
  14. Yen, K. et al. AG-221, a First-in-Class Therapy Targeting Acute Myeloid Leukemia Harboring Oncogenic IDH2 Mutations. Cancer Discov. 7, 478–493 (2017).
  15. Alarcon-Barrera, J. C., Kostidis, S., Ondo-Mendez, A. & Giera, M. Recent advances in metabolomics analysis for early drug development. Drug Discov. Today 27, 1763–1773(2022).
  16. Tee, K. B. et al. Pharmacokinetic–Pharmacometabolomic Approach in Early-Phase Clinical Trials: A Way Forward for Targeted Therapy in Type 2 Diabetes. Pharmaceutics 14, 1268 (2022).
  17. Lindon, J. C. et al. The Consortium for Metabonomic Toxicology (COMET): Aims, Activities and Achievements. Pharmacogenomics 6, 691–699 (2005).
  18. Fang, Z. et al. Serum metabolomic profiling for predicting therapeutic response and toxicity in breast cancer neoadjuvant chemotherapy: a retrospective longitudinal study. Breast Cancer Res. 27, 2 (2025).
  19. Pang, H. & Hu, Z. Metabolomics in drug research and development: The recent advances in technologies and applications. Acta Pharm. Sin. B 13, 3238–3251 (2023).