Thursday, July 20, 2017

Precision medicine: a deep breakthrough in life sciences paradigm

Bioscience - Lost in Translation? How precision medicine closes the innovation gap

It is not so easy to translate knowledge into practice, and this is the case of biosciences into clinical applications. However, recently this trend is accelerating and precision medicine is emerging. A new book gives us the highlights to understand precisely what's going on: Bioscience - Lost in Translation? How precision medicine closes the innovation gap.

Richard Barker (the author of 2030 - The future of medicine) says:
The classic definition of diseases has been in terms of the symptoms they cause and/ or where in the body they appear. This was the best that medicine could do when external observation of the patient was the only or primary means of diagnosing disease. The  powerful new tools of molecular biology are reinterpreting disease in terms of aberrant,
defective, or unbalanced molecular mechanisms at the cellular, organ, or organism level. Molecular level diagnosis becomes a real possibility. Such an approach brings effective therapy immediately closer. Molecular diagnostics can separate diseases with similar symptoms but different underlying causes— and often suggest a different starting point for intervention.
If this is so, what should we do?
The seven changes of mindset and of practice are:
1. Advance the molecular definition of disease and the application of systems biology. We need a more decisive move from a classic definition of diseases— in terms of the symptoms they cause and/ or where in the body they appear— to a definition in terms of aberrant, defective, or unbalanced molecular mechanisms at the cellular level. And we need to marry this with a recognition that singular target- based innovation rarely works: we need a systems biology approach.
2. Partner academia and industry in more collaborative, impact- oriented research. We need to extend the ‘open innovation’ approach in which academia and companies invest together and share IP. We need to define new pre- or non- competitive spaces, especially in work on disease mechanisms and disease models. And we need to provide for new types of links and incentives to break down the barriers between these two worlds. 
3. Move decisively to a more adaptive approach to development, trial and approval design. We need to build on successful experiments in more flexible trial design, development pathways, and regulatory appraisal to a globally accepted adaptive approach. This involves collaborative design of the evidence package needed to secure approval and reimbursement, and greater teamwork through the process. 
4. Create new reward and financing vehicles for leading edge innovation. We need to move from reward systems based purely on unit sales of products, irrespective of outcome, to rewarding innovators for positive outcomes, patient by patient. We also need to design financing mechanisms that bridge between cost- effectiveness and affordability. We must be able to accommodate high- cost precision therapies that offer cures and so generate long- term returns for the system.
5. Engineer tools and systems for faster and better innovation adoption and adherence. We need to move from reliance solely on promotion to doctors and passive patient participation to a disciplined approach to establishing new pathways of care. These will be based on modern behavioural science, clinical decision support, and other digital technologies.
6. Develop an infrastructure for real- world data- driven learning. We now have the opportunity to study in large populations how lifestyle and treatment choices lead
to outcomes, learning from every patient as if in a clinical trial. New analytical tools will empower this.
7. Bring patients into the mainstream of decision- making and engage them  hole heartedly throughout the process. It is time to move from a process and mindset in which patients are regarded as passive subjects for clinical trials and recipients of products and procedures. Their input and engagement needs to be sought along the whole innovation chain: on treatment benefits, acceptable risks, optimal clinical trial design, adherence support, and outcomes.

Highly recommended.

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