Technology Convergence in the Digital Era: What Implications for Innovative Medicine Development?

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Pierre Meulien of the Innovative Medicines Initiative (IMI) discusses the potential impact that new technological innovations will have on the drug development process.

 

A more technologically integrated approach is required that looks more holistically at the patient as an individual

Whether we call it “Personalised”, “Precision”, “Stratified”, or “Tailored”, the age of evidence-based medical treatments is upon us. The primary driver for this (r)evolution has been the dramatic technology-driven increase in our understanding of the underlying molecular and genetic bases for common diseases. We now know that diseases like severe asthma, diabetes, or arthritis are not single conditions but a whole spectrum of definable diseases requiring intervention strategies tailored to each situation.

 

The train for this new approach has left the station but many actors in the current system need to adapt – and quickly – to this paradigm shift in order for patients and health systems to benefit fully.

 

The implications for technology providers, regulatory bodies, HTA experts, payers and medical practitioners are profound. If molecular profiling, medical imaging, and digital analysis are now needed for every patient with a serious condition, what kind of technological infrastructure will we need in our hospitals, GP surgeries and homes, and what economic models can make the system financially viable?

 

Certainly, a more technologically integrated approach is required that looks more holistically at the patient as an individual. We also need to consider prevention and early detection of diseases, otherwise, as a society, we will never be able to afford to treat the seriously ill.

 

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From an industrial sector perspective, this new approach will trigger significant changes in how the ecosystem operates as diagnostic, medtech, pharma and of course digital companies will need to work much more closely together in more open collaborative models. Today, we know that trying to reverse end-stage dementia is difficult, if not impossible, as has been demonstrated by the many Phase III trial failures. Now imagine a world where we could intervene early in dementia. This would require complex and sophisticated cognitive measurements and high-end imaging assessments of brain function so that safe pharmacological and/or lifestyle interventions can be administered to prevent disease progression.

 

The digital era adds a further layer of technology onto this already complex mix. With the rapid development of mobile technology, we can carry out remote monitoring and measurement today. Measuring a person’s mobility is a key outcome in many chronic diseases (COPD, Asthma, Rheumatoid Arthritis, Dementia, etc.) and advances will soon allow cognitive assessments to be made using digital devices. This again is impacting regulatory bodies who need to define a framework for the validation of these devices and their use. The layering of all of these technologies with the ability to perform deep analysis of the large data sets that will be produced at a population level will change the way in which medicine is practiced. Machine learning and artificial intelligence are already being applied to help guide medical professionals to make informed decisions. It is becoming clear already that AI technologies applied to digital images in pathology has the potential for more accurate diagnosis than manual analysis. If we can use AI tools effectively in analyzing patients’ responses to specific regimens in a specific disease setting, in real time, and using population-wide data, best practice and new standards of care will evolve much more rapidly.

 

At the same time as the technology developers will need to work together to develop this new ecosystem, health systems will have to dramatically re-engineer their own ways of working. Instead of performing economic modelling on a specific new chemical or biological entity, health systems will also need to look at the holistic picture and be more proactive in understanding the economic value of early detection and prevention of chronic disease.

 

The role of the patient (and citizen) will be a key factor in driving the necessary changes to health systems which are no longer adapted to the needs of society. The patient is clearly the owner of their own data and patient groups will have the power to use collective strength in using this data to drive the so needed systemic change in healthcare systems. These systems need to embrace the technology convergence and take advantage of the opportunities AI will offer in order for them to remain relevant and economically sustainable.

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