Personalised medicine is defined as: ‘A medical model using molecular profiling technologies for tailoring the right therapeutic strategy, for the right person, at the right time to determine the predisposition to disease at the population level and to deliver timely and stratified prevention’.-(European Commission, 2010).
Research into the sphere of personalised health treatment using molecular profiling technologies is quickly gaining traction in the Pharma industry. Traditional drug discovery and development attempted to find medicines that showed benefit across an entire disease population. Personalized Medicine however, attempts to define molecular predictors of drug safety and efficacy up front and to stratify patients and diseases into subsets based on their unique characteristics. This approach allows for an optimal response to therapy by enabling physicians to prescribe therapies more likely to benefit patients and minimizes patient exposure to drugs that are not likely to provide a benefit.
Personalised health therapies focus more on emerging and more accurate molecular definitions of ‘health’ and ‘ill-health’ and bio-markers are being used as an indicator between the two states. It is expected that this combination results in significant cost-savings at a societal level. So instead of having a broad ‘one size fits all’ policy this new concept of personalised health therapy seeks to define a group within a population of patients suffering from the same sub-type of illness and treat them based on their own individual genotype.
At Java Clinical Research Ltd we pride ourselves on our expertise in this specialist field. We develop methodologies for the acquisition of ‘–omic’ data from clinical samples, to allow the discovery of biomarkers for disease or drug treatment to be determined in future studies. We add value to clinical trials by providing clients with better information about the effects of new treatments in early stages of clinical development, making the clinical trial process less uncertain and more efficient, potentially avoiding costly late stage clinical trial failures.
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