How to Identify Clinically Successful Biomarkers?

The decisive goal of clinical biomarker discovery should be intended for developing high quality and low-cost disease detection/monitoring assays with high diagnostic accuracy. Innovative approaches are warranted for the discovery of clinical biomarkers, with faster bench to clinics timeline, to provide high quality and efficient patient care.  At the same time, if we continue with the strategic and technological approaches that are currently being adopted for biomarker discovery and validation, most likely, it may take several years to find clinically viable biomarker/s for complex diseases like cancer or neurodegenerative or autoimmune or other human diseases. Therefore, an ideal clinical biomarker discovery platform, which can lead to the development of reliable and robust clinical diagnostics assays, should adopt an integrated approach that consists of comprehensive understanding of patients’ phenotypic, genetic and socio-environmental characteristics as well as biological and functional relevance of all biomolecules. In order to achieve these goals, two models for the discovery, selection and validation of clinically viable biomarkers are proposed in this scientific blog.  Read the full blog: http://www.sciclips.com/sciclips/blogArticle.do?id=1022&blog=How%20to%20Identify%20Clinically%20Successful%20Biomarkers?

Strategies for Rational and Personalized Cancer Biomarker Discovery

This scientific blog critically analyzes potential complexities associated with current biomarker discovery approaches. According to the scientific arguments that have been put forward in this blog, thousands of biomarkers that are currently being reported may not be true biomarkers of the target disease, rather it may be a complex mixture of biomarkers, which may include target disease specific biomarker as well as biomarkers or biomolecules associated with other diseases, infections, gender, race/ethnic backgrounds, geographic-environmental factors, psychiatric condition/diseases and nutritional factors.  Based on our analysis, we believe that an ideal biomarker discovery platform, which can lead to the development of reliable and robust diagnostics assays, should be developed by integrating comprehensive understanding of patients’ phenotypic, genetic and socio-environmental characteristics along with biological and functional relevance of all biomolecules that may be potentially identified and called as biomarkers. Several innovative strategies for developing rational and personalized biomarker discovery platforms have been suggested in this blog. These strategies include 1) Comprehensive genome-scale analysis based rational genetic biomarker discovery 2) Cell or tissue or organ specific function based rational or targeted biomarker discovery 3) Use of validated tissue/organ specific biomarkers or therapeutic drug targets for identifying non-invasive biomarkers, 4) Epidemiology-driven biomarker discovery for developing personalized diagnostic tools and 5) Integrated bioinformatics approaches for rational biomarker discovery. The relevance of disease prevalence and predictive value in biomarker discovery for personalized medicine, utility of rational or personalized biomarkers in clinical trials and applications of rationally identified biomarkers for diagnostics imaging or theranostics have also been discussed. Link to the full blog: http://www.sciclips.com/sciclips/blogArticle.do?id=1021&blog=Strategies%20for%20Rational%20and%20Personalized%20Cancer%20Biomarker%20Discovery

Potential Use of Drug Response-Efficacy Biomarkers for Predicting Life-Threatening Disease Causing Side Effects of Therapeutic Drugs

This scientific blog analyzes potential applications of drug response-efficacy biomarkers for predicting future onset of drug therapy induced life-threatening diseases, such as cardiovascular diseases, infectious diseases and cancer. Enabling applications of drug response-efficacy biomarkers for predicting drug-induced side effects may lead to the development of “smart biomarkers”, which can reduce morbidity and mortality in patients, and can revolutionize personalized medicine approaches. In order to achieve this goal, we proposed a model for the discovery of drug response-efficacy biomarkers and the translational utilization of these biomarkers for personalized therapies. In an exploratory analysis, anti-TNF therapy response markers of rheumatoid arthritis (RA) were analyzed, by intelligent data mining and data analysis, to demonstrate that these biomarkers may be potentially used for predicting the risk in developing cardiovascular diseases (CVDs), like atherosclerosis, in RA patients.  Please follow this link to read the blog:

http://www.sciclips.com/sciclips/blogArticle.do?id=Potential Use of Drug Response-Efficacy Biomarkers for Predicting Life-Threatening Disease Causing Side Effects of Therapeutic Drugs