Personalised medicine is generating significant interest in the healthcare industry as standard blanket approaches to patient therapies metamorphose into individualised treatment plans.

Whether it’s for a type of cancer, a neurodegenerative disease, or a rare disease, some treatments inherently work better in some patients and less successfully in others due to underlying genetics.

With the advent of modern genetic testing technologies, more integrated patient support networks, and the current buzzword on everyone’s lips – artificial intelligence (AI) – science is beginning to personalise treatment for patients. Understanding the genetic framework in an individual living with a disease gives clinicians a holistic picture needed to provide the optimal path for the prevention, diagnosis, or treatment of a disease.

Roche, one of the largest pharmaceutical companies in the world, has been particularly focused on personalised medicine and genomics development.

In an exclusive interview with Medical Device Network, Roche’s head of health system shaping and personalised healthcare Nicole Arming and Roche Lietuva (Lithuania)’s general manager Stefano Volonté, delve into the potential of personalised medicine, the hurdles that need to be overcome, and the ever-present role of AI. This interview has been edited for length and clarity.

Robert Barrie (RB): How will personalised medicine impact the look of healthcare provision in 20 years time?

Nicole Arming (NA): What we will see in the future is better prevention. I think we will be aware of a person’s personal risk factors which will help us detect diseases patients potentially before patients experience symptoms and treat them in a way that they can stay where they’re supposed to be, which is with their families or at work. I think we will see an exponential increase in the speed at which innovation is going to be delivered and technology such as sensors and digital applications will be seamlessly integrated in the way we deliver care. At the moment, we’re sporadically leveraging health data. I think this will become the norm rather than the exception.”

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Stefano Volonté (SV): The speed of innovation is much faster and more convenient than what we have ever experienced before. That gives me hope that the solution is around the corner, so we need to believe in that. We need to create a coalition so we see the benefits in a way that it’s not just accessible to a few people around the world.

RB: What treatment space does Roche see as having the greatest potential?

NA: The biggest spaces where there’s a high disease burden and unmet medical need are in the cardiometabolic space, neuroscience, ophthalmology, and oncology. There are many diseases where we are still far away from being able to treat and cure patients, like Alzheimer’s disease, and we are committed to be part of finding solutions for these devastating diseases.

SV: Our aim is not only prevention and early detection but to extend life at a good quality of living. There’s a difference between starting with sub-optimal treatments at the onset of the disease and then, after disease progression, doing very powerful precision medicine compared to doing that right away. In many healthcare systems, the latter approach is considered expensive and may not be suitable for everyone. Healthcare starts with the basic and cheapest solution and then during the progression, you might see benefits in using those more powerful treatments. Why wait?

Using multiple sclerosis as an example, we have very powerful drugs, and they work wonderfully, they slow down the progression the quality of living is much better. It’s not curative, but it makes a difference. Unfortunately, it’s not the first line. It’s perceived too often as a cost for society rather than an investment to make sure that those people don’t need time from specialists and time in clinics. They can be self-sufficient for longer and that’s an investment. As an industry, that’s what we are aiming to share with policymakers and governments.

RB: Speaking of policymakers and governments, how important will they be going forward?

SV: They’re absolutely key. A good example is the CONNECT consortium in Norway, where they connect with different industries, startups, and academia, under the leadership of government policymakers that support precision oncology from a regulatory and policy standpoint. At the start, there was no interoperability and no standardisation of information and the use of data was very limited.

In Lithuania, we are still in this early stage, hospitals are willing to share information in an anonymised way and they see a lot of value in terms of decision-making for healthcare providers and can see how the quality of patient lives can improve if symptoms are diagnosed early on. However, at the national level, there’s no standard. There’s no exchange of information on a common platform. So, there are still lots of opportunities to invest to change this and make that precision medicine vision a reality. People understand the importance of it but it’s another thing translating it into reality.

NA: What is important is to set the right incentives. If today we discuss with a payor, and we discuss about approval of medicine, they will look at the budget impact that might have, but to take Stefano’s example of multiple sclerosis, you could treat a patient and then that patient has eight, nine, ten years out of a wheelchair and can go to work and contribute to society not needing care and support which is all very costly. But if we look at the impact in isolation and not as a whole, then we’re never going to have the right incentives and the right decisions being taken.

RB: How do you tackle the lack of understanding in patients around personalised medicine?

SV: A lot of cooperation is needed and ongoing discussions with patient associations. They are instrumental and they sensitise a lot of the community. Our role is to provide the knowledge and create programs in partnership with these associations. This means we can go out and do early screening programmes and support the education of people. There are a lot of resources that are really targeted for specific needs of patients. Too often people go on Google which not only scares patients, it is also not specific and does not reflect the real situation. Helping people who go and get a more targeted education – that is very helpful. The next step is to reach out to people in need, with some kind of point of care or home delivery of treatment combined with this additional information, because that is very convenient and reassuring. Making that simple and accessible is key to making sure people get what they need to manage that disease.

RB: As with all segments in healthcare, artificial intelligence (AI) is generating a lot of buzz. Does Roche use AI in its personalised medicine approach, and how key is it in the field going forward?

NA: Yes, we use algorithms and machine learning along the value chain. It helps us to identify potential therapeutic targets. In clinical trials, it can help with predicting the responses of patients and aid trial design. It can identify different subpopulations of patients and whether or not they might respond to a certain treatment. Those are all things that we’re looking into in our clinical trials because we want to make sure that we develop the right medicine for the right person. It’s a big topic all along the value chain for us.

SV: There’s a real need to go in the AI direction – not just because it’s what everyone expects, but because the cost and the time to develop new drugs is not sustainable anymore. This new technology is possibly one of the most powerful answers to that problem. Out of 100 new molecules, only one will make it to the patient, and that takes on average ten years. A brand-new molecular entity costs about $2bn until it gets to the patient – it’s all quite risky. So that’s not sustainable if we want to target in a personalised manner. The answer is ultimately faster with real-world data. [AI] technologies are a huge accelerator for managing that information. Of course, we develop our own AI engines, but we also want to combine them with powerful data information companies. We don’t do it alone – and that’s good!