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What we’ve learned about effective biomarker knowledge management

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This is the first of a 5 installment series covering what we’ve learned about effective biomarker knowledge management over the last few years. The topic will be driven by a “maturity model” that we’ve developed while working with our pharmaceutical, diagnostic and consulting clients. The model outlines the common practice of our clients at various levels of clinical knowledge management maturity, biomarker knowledge management in particular.

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However, I want to start by highlighting the changes in how, as an industry, we track and leverage clinical data.  Since the completion of the Human Genome Project the amount of biomarker information has exploded (right).  Tracking that information has led to the rise of AI and machine learning approaches to deal with the explosion of medical information influencing drug and test development.

The increased attention on biomarkers should not be a surprise. It has long been suspected that effective biomarker usage in drug and diagnostic R&D could be the key to increasing clinical study success rates, servicing smaller patient populations (e.g. rare disease), and delivering on the promise of personalized medicine.

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We recently published the first hard evidence (left) that biomarkers do, in fact, increase the likelihood of approval (LOA) for clinical trials.

We believe the LOA increases so dramatically with appropriate application of biomarkers because the approaches are often aligned with broadly accepted criteria for sufficient strength of biomarker evidence.

Additionally, by empowering all levels of an organization to leverage biomarkers not only can you improve LOA dramatically, but programs commercializing a drug can better understand the commercial landscape and partnership opportunities.

However, our research has shown that the ability to gather and effectively execute on biomarker information varies significantly from organization to organization.  Through our research we’ve identified different levels of biomarker knowledge usage maturity characterized by 5 distinct stages and 4 dimensions contributing to maturity.

The Maturity Model

We are taking this opportunity to introduce the maturity model of organizational biomarker knowledge that we’ve developed while working with our pharma, diagnostic and consulting client base.

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The four dimensions traverse 5 distinct stages, or levels, of biomarker knowledge management maturity. We will cover, in detail, each of the dimensions in subsequent posts.

Each of the dimensions have defining characteristics that help our customers identify how to improve biomarker knowledge management with the expressed intent of more effectively and efficiently applying biomarker knowledge to their projects.