Biomarker: Definition, Examples and Analysis
In clinical value-oriented drug research, the exploration of biomarkers accelerate the speed from drug discovery, preclinical studies, clinical studies to registration and marketing, greatly shortening the drug development cycle.
What is a biomarker?
A biomarker is an accurate and repeatable molecular, cellular, or biochemical change that can be used to identify and monitor physiological and disease-causing processes or responses to pharmacological interventions. These indicators are substances or processes present in the human body, ranging from molecules like proteins, DNA, and metabolites to physiological parameters. Anything that helps identify a disease can serve as a biomarker, whether it's a metabolite, a change in a biological structure or biological process, or a signature.
Types and examples of biomarkers
Diagnostic biomarkers
Diagnostic biomarkers can detect or confirm the presence of a disease or condition, or identify an individual with a subtype of the disease. These biomarkers could not only be used to identify patients with diseases, but could also redefine the classification of diseases. Elevated prostate-specific antigen (PSA) levels in the blood may indicate the presence of prostate cancer. Similarly, C-reactive protein (CRP), as an indicator of inflammation, plays an important role in conditions such as rheumatoid arthritis, cardiovascular disease, and infections.
Susceptibility or risk biomarkers
By detecting specific genetic variants or other biological signals to predict an individual's risk of developing a disease in the future. For example, mutations in the BRCA1/2 gene are associated with a higher risk of breast and ovarian cancer, and identifying these mutations can help take early preventive measures, such as intensive screening procedures or considering preventive surgery.
Monitoring biomarkers
When a biomarker can be continuously measured to assess disease (exposure to medical products or environmental agents), it is a monitoring biomarker. Monitoring markers are a broad concept, so there is overlap in other categories of biomarkers. When treating blood pressure or using low-density lipoprotein (LDL) cholesterol-lowering drugs, blood pressure or LDL cholesterol levels are monitored. Similarly, when treating HIV infection, the CD4 count is monitored.
Pharmacodynamic biomarkers
These biomarkers help determine the pharmacological action of the drug and can inform whether the treatment is working as intended. PI3K inhibitors can be used to treat many cancers. The PI3K signaling pathway includes several downstream targets, including AKT. AKT is phosphorylated when the PI3K pathway is activated, so phosphorylated AKT (pAKT) can be used as a pharmacodynamic biomarker to confirm that PI3K inhibitor therapy does indeed inhibit the PI3K pathway. If the pAKT level drops, it indicates that the inhibitor is working.
Predictive biomarkers
Predictive biomarkers are used to identify individuals most likely to have a good or adverse response to a particular therapy. These biomarkers are key to personalized medicine, where treatments can be tailored to individual patients to achieve maximum efficacy.
Erlotinib maintenance therapy is a therapeutic intervention used to treat advanced non-small cell lung cancer. For patients with EGFR mutations in their tumors, progression-free survival after erlotinib treatment was significantly lower than in patients who received the same treatment but did not develop EGFR mutations. This makes EGFR mutation status a biomarker for predicting response to erlotinib therapy.
Prognostic biomarker
Prognostic biomarkers provide information about the likely progression of the disease or the likelihood that the disease will recur in an already diagnosed patient. An example of a prognostic biomarker is the mutated status of PIK3CA in HER-2 positive metastatic breast cancer, where individuals with PIK3CA mutations have been found to have lower disease-free survival. Ki-67 acts as an indicator of cell proliferation, and its high levels are often associated with more aggressive cancer behavior, such as breast and prostate cancer.
Safety biomarker
Safety biomarkers are used to monitor potential harm to the body as a result of therapeutic interventions or environmental factors. For many treatments, monitoring liver, kidney, or cardiovascular toxicity is critical to ensure that a given treatment can be safely maintained. When antiarrhythmic drugs are used, prolonging the ECG QT interval serves as a safe biomarker because it predicts the risk of developing fatal arrhythmias and can be used to identify patients in need of effective therapeutic countermeasures. Liver function tests (LFTs) can detect abnormal liver enzyme and protein levels, which can be caused by drug toxicity. Creatinine clearance is one of the important parameters to evaluate the kidney health status, especially in the use of chemotherapy drugs that may cause renal toxicity.
Surrogate biomarker
These are surrogate indicators that can substitute for the clinical outcome of the disease and also help predict the effect of therapeutic interventions. Such measures may be associated with clinical endpoints, but are not necessarily related. Elevated levels of cholesterol in heart disease, a surrogate biomarker for heart disease, are associated with an increased risk of heart disease, but this relationship does not always exist. To be effective, alternative markers should be able to effectively substitute for clinical outcomes, and the impact on alternatives should be able to predict clinical outcomes. However, because the biomarker is only a substitute, the effects of drug therapy on the substitute may not affect the outcome, limiting its usefulness.
Applications of biomarkers
Biomarkers in drug development
Biomarkers play an important role in the drug development cycle. Biomarker guided trial design improves clinical trial success rate. In the drug discovery stage: select the drug target and clarify the mechanism of action. In the preclinical stage: for dose selection, preclinical safety assessment, to clarify the mechanism of action. In the clinical study stage: for diagnostic stratification, patient selection, dose selection, pharmacokinetics, safety evaluation, efficacy evaluation. In the registration and marketing stage, monitor clinical safety and effectiveness, drug quality control, etc.
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Biomarkers in cancer
Biomarkers are crucial in oncology for the early detection, diagnosis, staging, and monitoring of treatment responses. Tumor markers like PSA, HER2, and Ki-67 are widely used to guide clinical decisions and predict patient outcomes. Molecular biomarkers allow for the detection of specific mutations or genetic alterations that inform treatment options, while imaging biomarkers help to monitor tumor growth and spread. PSA can be used to detect and treat prostate cancer. In the treatment of breast cancer, HER2/neu status can guide the use of targeted drugs such as trastuzumab.
Biomarkers for chronic diseases
Chronic diseases such as diabetes, cardiovascular disease, and autoimmune diseases rely heavily on biomarkers for early detection, monitoring, and treatment optimization. HbA1c can be used for diabetes management and to evaluate the effects of long-term blood sugar control.
Biomarker data analysis
Biomarker data refers to the quantitative and qualitative measurements obtained from biological samples (e.g., blood, tissue, urine) that provide information about the presence or progression of diseases, therapeutic responses, or safety risks. The accurate collection and interpretation of biomarker data are essential for making informed decisions in both clinical practice and drug development.
The analysis of biomarker data involves advanced bioinformatics and statistical techniques to extract meaningful patterns and correlations. The integration of machine learning models and bioinformatics tools can uncover novel biomarkers, predict treatment outcomes, and enable precision medicine strategies.
Role of bioinformatics: Bioinformatics tools enable the mining of large-scale biomarker datasets, identifying correlations between biomarkers and disease outcomes.
Clinical trials: Biomarker data analysis in clinical trials can help identify patient subgroups most likely to benefit from a particular treatment, optimizing clinical trial designs and improving the chances of success.
How are biomarkers identified?
Biomarker discovery
Biomarker discovery involves high-throughput technologies like genomics, proteomics, and metabolomics, which allow researchers to identify new biomarkers that may be used in diagnosis, prognosis, or treatment monitoring. Genomic sequencing, mass spectrometry, and imaging technologies are essential tools in identifying new biomarkers.
High-throughput screening
High-throughput screening enables the simultaneous analysis of thousands of biomarker candidates. This process accelerates the discovery of potential biomarkers for a wide range of diseases.
Validation of biomarkers
Once discovered, biomarkers must undergo rigorous validation to confirm their clinical relevance. This involves demonstrating that the biomarker accurately predicts disease presence, progression, or response to treatment.
Biomarker databases and platforms
Databases like TCGA (The Cancer Genome Atlas) and COSMIC (Catalogue of Somatic Mutations in Cancer) provide comprehensive repositories for researchers to validate biomarkers and explore correlations with clinical outcomes.