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Making things personal: Roche and personalised medicine

Published on 13/08/19 at 11:42am

Ann Costello, Global Franchise Lead, Centralised Solutions at Roche Diagnostics, discusses the increasing need for a personalised approach to patient care to tackle today’s health challenges, and how big data and the interplay between pharmaceuticals and diagnostics are key to driving progress in this area.

Can you explain the core idea behind personalised medicine and why it is increasingly gathering traction?

I think the best way to give an overview of personalised healthcare would be to look at it chronologically. Up until the 1990s, there was really a one-size-fits-all approach. If you take cancer as an example, it was pretty much categorised according to the location in the body – lung cancer, breast cancer, and so on – and those patients were treated with the same drug. In some cases the drug worked and in other cases it didn’t.

That prompted questions from both a pharmaceutical industry point of view and also from a clinician’s point of view as to why the drug worked for some people and not for others. The turning point was the introduction of Herceptin at the end of the 1990s. Herceptin became the first drug to be paired with a companion diagnostic – this is really the first personalised medicine.

The test identified only those women that would benefit from Herceptin. This was a turning point. Medicine moved from the one-size-fits-all approach to towards a position in which we could now really target the patients that were going to benefit while finding those who were not before they took the drug. That had multiple impacts because on the clinician side you aren’t giving a very expensive drug to someone who won’t benefit from it, and on the patient side you avoid the side-effects. So that was the starting point of personalised medicine.

Since that time, our knowledge of molecular biology has grown in leaps and bounds. We now know that with breast cancers and other cancers in particular there can be countless mutations that account for cancer; there are many different types of cancer and there’s heterogeneity in tumours as well. Now we’ve got such a better understanding because we understand the disease at a molecular level and we can thus use biomarkers to make much better informed decisions. We can now specifically target therapies and change therapies as we go along; we have many more options for patients.

What is Roche Diagnostics doing to push this area?

At Roche we are fully invested in personalised healthcare. We have two divisions – pharmaceuticals and diagnostics – so we are uniquely positioned from that point of view. We are adamant that because we have this unique setup we should be driving personalised medicine. We are also determined that all of our programmes should have a biomarker programme associated with them, so of course our role as the partner for Roche Pharmaceuticals was very clear.

We are now the leading diagnostic partner of choice for all of the leading pharma companies. We work very closely with them to ensure that we have diagnostics appropriate for their drugs.

What role does big data play in personalised healthcare?

This is becoming front and centre of everything we do. Big data is taking things to the next level. There’s a huge amount of untapped data available and we want to leverage that data for different things. One of those things that is really important for us is to be able to tap into big data to really inform our R&D processes, so that we have better hypotheses about the molecule before we jump into the development process.

It’s also really important that we can use real-world data to understand the clinical trial aspects, as the use of real-world data comes increasingly into play with regards to working with regulatory bodies such as the FDA.

We have also made a number of partnerships: Roche acquired Flatiron, which is an electronic medical record company in the area of oncology. We also have Foundation Medicine, which is a US-based company focused on diagnostic testing and sequencing in cancer. Essentially, what we are doing now is using those resources to help us to better understand how to make better decisions at the R&D level.

That’s on the pharma side, but we also do it in diagnostics as well. We have a very large installed base – more than any of our competitors. We have more than 70,000 instruments worldwide and they are all gathering data. We want to use that data to drive our understanding of how therapies can be even more personalised to the individual’s needs. That means tailoring treatments to individuals rather than to groups of patients.

Having access to big data will allow us to really narrow it down and really individualise therapy compared even to the targeted therapy that we have been doing for the last couple of years. Our ambition and our goal is to make sure that in the future we get to the individualised treatment based on the patient’s own healthcare data.

So how do privacy issues factor in when it comes to working with big data?

We are very interested in having access to patient data in the appropriate way. There are various issues around privacy and that’s incredibly important to us; where we have access to data it can really change everything. We would really like to see the use of real-world data in clinical trials because clinical trials take a long time and huge amounts of money. If there are ways to tap into real-world data to improve this process, that would be really helpful. We have started to see some of that happening already and we certainly want to participate in that.

Privacy issues are absolutely huge for us. At the moment we have only anonymised data and we use it for trending but, of course, the real value of data is when you can pair it with the patient information, and in order to do this there needs to be a build-up of trust. I think that we aren’t there yet by any means, but we hope to take care of that.

How is the field of personalised healthcare diagnostics evolving right now?

More and more we will see the use of digital tools to enhance certain areas, particularly if you look at the Apple Watch, which can measure heartbeat and all sorts of other things. We as a company are looking at the use of potential apps to enhance our products and to use them to identify diseases earlier through constant monitoring. We are also interested in combining different tests together using an algorithm based on data to make better decisions for patients. If you combine data from biomarkers together with in vivo data from sensors, imaging, and so on, we may be able to get more information around the ways in which an individual patient is reacting to a therapy.

What changes do you hope to see in the field in future?

I would like to see data being appropriately used. For us it’s about taking a broader view as to how we can use data appropriately. If we can use it in the regulatory process, that would be great. The FDA has really started to look at this in a very unique way; we are seeing more and more breakthrough therapy designation pathways both for pharma and for diagnostics. That gives me a lot of hope that we can meet regulatory requirements faster and in a cheaper way. That’s the area we’d like to see more progress in, but things are moving in the right direction.

What do you think is the key message from all of this?

Personalised healthcare is here to stay. The days of the blockbuster drug – the shotgun approach, the one-size-fits-all – is gone. I think the really hopeful message is that we’ve got a much better understanding of diseases with these pathways and we are now able to target these disease pathways with our molecules. This is the important part for us as a diagnostics company: the use and value of diagnostics in personalised healthcare comes out through personalised healthcare – you can’t have one without the other. Diagnostics has contributed a huge amount not only to diagnosis but also to therapy selection and therapy prediction, as well as efforts to come up with new therapies and modifying old ones. So it’s absolutely key to have the right diagnostic testing to be able to take data to the next step.

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