Today, biomarkers play a crucial role in drug development, helping measure the effects of investigational treatments in humans during clinical trials. Recently, advancements in biomarker discovery and validation methods, spurred on by innovations in instrumentation and in silico predictive tools , are improving diagnosis, prognosis and disease monitoring.
Let’s take a closer look at biomarker discovery and validation.
In medicine, a biomarker describes a characteristic of the human body which can be measured and reproduced, usually as an objective indicator of a normal biological process or a disease state.
There are four types of biomarkers: molecular, histologic, radiographic, or physiologic.
Molecular Biomarkers contain biophysical properties that can be further measured through a biological sample (e.g. blood, plasma, serum, bodily fluid).
Histologic Biomarkers focus on the toxicological effects and pharmacokinetic processes of chemical interaction among the various bodily systems (e.g. tissue sample).
Radiographic Biomarkers use feature an image or sequence of images used to diagnose conditions (e.g. MRI, X-Ray, and CT scan).
Physiologic Biomarkers measure processes throughout the body and are incorporated into many routine diagnostic exams (e.g. vital signs monitoring and electrocardiograms).
For the pharmaceutical industry, biomarkers can serve as intermediate indicators of disease in clinical trials, and also as possible drug targets. Biomarkers can also be used in clinical diagnostics, as a way of confirming the effectiveness of a treatment, or to continuously monitor treatment safety. After regulatory approval, biomarkers can be used to find and identify as well as stratify patient populations that can benefit from a specific treatment or therapy.
This type of biomarker indicates the potential for developing a disease or medical condition (e.g. BRCA 1/2 mutations used to identify predisposition to breast cancer).
This type of biomarker is used to determine if an individual has a particular medical condition or subtype of a disease (e.g. blood sugar levels used to identify Type 2 diabetes).
This type of biomarker repeatedly measures the status of a disease or medical condition, usually for evidence of the effects of a medical product or environmental agent (e.g. blood concentrations of an addictive drug used to measure compliance).
This type of biomarker identifies the likelihood of a clinical event, disease recurrence or progression in a patient who has been diagnosed with a disease or medical condition (e.g. chromosome deletions and mutations used to assess likelihood of death when evaluating patients with chronic lymphocytic leukemia).
This type of biomarker identifies individuals more likely to experience a favorable or unfavorable effect from exposure to a medical product or an environmental agent (e.g. the appearance of certain mutations may help select the most effective treatments for cystic fibrosis).
This type of biomarker is used to show that a biological response (either beneficial or harmful) has occurred in an individual who has been exposed to a medical product or an environmental agent (e.g. INR used to evaluate patient response to warfarin treatment for prevention of thrombosis).
This type of biomarker is measured before or after an exposure to a medical product or an environmental agent to indicate the likelihood, presence, or extent of an adverse effect (e.g. serum creatinine used to monitor for nephrotoxicity).
Biomarker discovery refers to the process through which novel biomarkers are found. Biomarker discovery can be a time- and resource-intensive process (studies can take 7-8+ years to conclude and tens of millions of dollars), requiring hypothesis generation, sample collection, data collection, data analysis, assay development, assay validation and regulatory approval before the biomarker can be used in the clinic or commercialized.
Typically, this process begins with defining the target (a biological, pathogenic or pharmacological response highlighted by the biomarker), after which multiple candidates will be identified. Each of these candidates then requires validation before moving into the clinical study phase.
To deliver the highest quality research data for effective use of biomarkers, biomarker validation is essential. Biomarker validation describes the process of assessing a biomarker and measuring its performance in order to determine the range of conditions for which it will give reproducible, accurate data. This evidentiary process is sometimes also called evaluation or qualification.
Biomarkers must go through three evidentiary stages in order to achieve complete acceptance according to regulatory guidelines: exploratory, probable valid, and known valid or fit-for-purpose. Validation is a multi-step process and not all-or-nothing, it may, therefore, vary during drug development as new information and data become available.
There are a range of technologies and methodologies presently being used to study and discover new biomarkers.
Proteomic methods use technologies like protein microarray and mass spectrometry to analyze many proteins at once. The post-translational state of proteins are monitored and can yield important biomarkers and indicate tumor progression.
Genomic methods measure DNA/RNA expression or characteristics that might indicate normal biologic or pathogenic processes, and/or the response to a specific therapeutic or intervention. This is done via the northern blot (or RNA blot), gene expression techniques, a transcriptome technique known as SAGE, DNA microarray measurements, and PCR testing.
Metabolomic methods aim to analyze the metabolic markers of a disease or metabolic responses to drugs or diseases, for example, blood-glucose strips for diabetes testing. Other systems such as high-resolution nuclear magnetic resonance spectroscopy (NMR) and mass spectrometry (MS), and additional analytical techniques may also be used.
Machine learning methods apply computational approaches for biomarker discovery, particularly around classification and feature selection. In one recent example, researchers trained a machine learning algorithm to analyze the blood vessels around a tumor using only a non-invasive CT scan. The AI-calculated biomarker predicted how lung cancer patients would respond to immunotherapy.
New and innovative technologies will continue to enhance biomarker discovery automation and validation for clinical use. These advancements make possible the earlier-stage diagnosis of diseases and can mitigate late-stage disease progression.
Want to learn more about the function and advancement of biomarker discovery? Check out VeriSIM Life’s article on Biomarker Discovery: Function and Advancement.