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Precision Medicine: How the life science industry can improve outcomes through

Earlier this year, the North American Association of Central Cancer Registries estimated that 1.69 million people in the U.S. alone will be diagnosed with some form of cancer in 2017. With such a huge number of people being diagnosed with complex diseases, such as cancer, the industry must continue to step up efforts to improve the outcomes. One option showing considerable potential is taking a precision medicine approach to determine the patient’s optimal treatment based on their personal molecular makeup and genomic profile.

Precision medicine gained significant attention following the launch of the Precision Medicine Initiative, which saw the White House under President Obama invest $215 million, ‘to broadly support research, development,  innovation’ into the area. Similar projects, such as the Precision Medicine Catapult in the UK, added to the buzz. This interest, combined with technological developments and advances in data mining, has started to show promising results.

The life sciences industry, however, is facing a roadblock when it comes to turning promising research into a practical treatment option. Clinicians and researchers alike are finding it difficult to understand the data available to them and then translate the findings into treatments that will significantly improve clinical outcomes for patients. So, what can the life science industry do to turn precision medicine into reality?

What is holding back advances?

The emergence of new technology, combined with the use of huge knowledge databases, has been largely credited for recent advances in precision medicine. By using Next Generation Sequencing tools alongside gene expression profiling, physicians and researchers can better understand the makeup of the disease and how it is affecting the patient, within minutes.

By using data and research from previous laboratory experiments, researchers can identify the drug on the market with the highest chances of being effective against the particular proliferation mechanism driving the disease. This type of approach will revolutionize the treatment of complex diseases such as cancer, and in theory could saves countless lives around the globe. However, this requires a blend of deep technical and scientific skills, technical know-how to ‘crunch’ the data, and scientific understanding to draw accurate clinical inferences. Without this blended approach, precision therapy for cancer will remain promising yet impractical.

precision-medicine-graph

Case study: Wake Forest Hospital and Elsevier’s R&D Solutions

Elsevier completed a precision medicine pilot with Dr. Francisco Castillos from Wake Forest, a small oncology practice in Winston-Salem, North Carolina (NC). The pilot aimed to treat three late-stage cancer patients, who had exhausted all standard-of-care treatment options, using a two-pronged precision medicine approach.

Castillos was able to understand in detail the pathways activated in the individual’s cancer using Elsevier’s Pathway Studio tools. Then, he could point to FDA-approved drugs that would be effective for the particular molecular mechanism driving the disease, or point to the relevant clinical trials. These analyses were reached through a combination of aggregating and harmonizing data, and Castillos’ scientific understanding and insight. While this study proves the viability of precision medicine in cancer, it is not an approach that can be replicated at scale.

A better use of data is key to precision medicine success

To achieve successful precision medicine at scale and to be able to offer it as an everyday treatment option, researchers in drug R&D need to better understand and manage the reams of unstructured data available to them. The data generated from understanding disease mechanisms is vital to successful drug development. Researchers need to find actionable insights relating to a particular gene, disease or biomarker, which requires searching the relevant published scientific literature, abstracts and clinical trial data and connecting the disparate pieces of information.

The type of approach conducted by Castillos, combining the use of molecular profiling and data mining tools, will help researchers tap into the existing repository of therapeutic drugs already on the market. In the last 25 years, hundreds of FDA-approved drugs have become available, with well-known mechanisms of action. With this approach, it would be possible to identify which approved therapies may be the best choice for a given patient or patient group/sub-group, based on what is driving their disease at a molecular level.

Not limited to the treatment of cancer

Precision medicine is the face of 21st-century medicine and can be used to treat many complex diseases, even at the earliest of stages, when a tissue biopsy is available and a genomics profile can be evaluated. The ‘one treatment fits all’ approach is no longer the only viable option. Those suffering from complex diseases such as multiple sclerosis, schizophrenia, and depression are prescribed medication every day that proves ineffective due to their genetic makeup and individual factors which determine patient response. According to NNT, the 10 top-selling drugs in the U.S. help at best one in four of the patients using them, or in the worst case just one in 24 patients benefit from the drug they’re taking.

There is no denying that challenges involved with precision medicine are complex and there is lots of work to be done by the life science industry. Despite this, precision medicine will inevitably become the expected method of treatment for many diseases. Back in 2006, there were only 13 examples of precision medicine drugs, treatments and diagnostics products available. By 2014, this number had increased to 113, and is only set to grow following a better understanding of what the data generated by technology actually means, and how it can be used.

To do this, physicians and clinical researchers need to better understand the data available to them using digital solutions that accelerate patient analysis and accurately mine the data. Only then can they match patients to an FDA-approved drug, and improve outcomes for patients all over the world.

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(Source: Anton Yuryev, MedCity News)