Biomarkers are measurable characteristics that indicate a healthy or pathological process or response to an intervention or exposure. Biomarker testing involves the analysis of the histological, molecular, physiological, or radiographic characteristics. Notably, biomarkers differ from a clinical outcome and an endpoint. An endpoint is a predefined variable indicating the desired effect, which can be statistically analyzed to assess the research of interest. On the other hand, clinical outcome evaluates how a patient feels, functions, or survives. Due to its inherent attributes, biomarkers can help improve clinical outcomes while evaluating new drug products. Let us understand more about how biomarker testing of new drug development will improve clinical outcomes.
Biomarker testing for improving clinical outcomes
In clinical use, a measurable predictive factor should correlate to a health-related outcome. There are three principal medically related predictive attributes, demographic, anatomic/cellular, and molecular. A biomarker can further supplement these three clinical outcomes. For example, a molecular biomarker is a measurable molecular element that predicts a health or disease-related outcome over a specified period. Such is the case with cytokine testing, where biomarker labs test cytokines to assess disease mechanism or progression. However, each predictive biomarker must have a specific time interval. For current clinical outcomes, the time interval is instantaneous, whereas, for future outcomes, it has a pre-specified duration.
As biomarkers can supplement clinical outcome measures, sponsors should focus on adequate biomarker assay development and validation. Sponsors must follow a systematic and documented path where the level of biomarker assay validation should adequately support the intended use in clinical practice. Moreover, biomarker CROs must continuously modify the approach as the biomarker progresses through the discovery and development phases.
The ability of a biomarker to be used for diagnosis depends on its positive and negative predictive value (PPV and NPV). PPV and NPV are real-world true positives and true negatives based on the prevalence of a disease in the population. Both PPV and NPV are influenced by biomarker specificity and disease prevalence. Additionally, accuracy, transparency, explainability, ease of use, cost-effectiveness, and validation protocol are vital factors for a biomarker to diagnose patients.
Furthermore, standard operating procedures are crucial while using biomarkers in a new situation. For example, a composite biomarker consisting of proteins, genes, and EEG leads will need defined criteria mentioning which variables or combinations are used for analysis. This necessity is because patients may often require a file explaining the algorithm and data processing steps instead of just a written description. Such an approach provides transparency and eases its use for supplementing clinical outcome measures.
The road ahead for biomarker analysis services
Biomarkers are increasingly used to assess the risk, diagnosis, and prognosis of diseases and medical conditions. Soon biomarkers will be the primary targets for robust molecular therapies and personalized medicines. It is not a distant reality where predictions based on biomarkers will be used in clinician-patient interactions. They will be an integral component of clinical outcomes and decision-making processes.
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