Interview with Dr Hui Cao

How are precision medicine strategies overhauling the entire biopharma business model?

You definitely see precision medicine as a main topic in the academic field, at least in the US, but pharma companies have been holding the attitude of wait and see. Everybody understands that precision medicine is very important, but how to translate that into the biopharma business model is not very clear. Early adopters and biotech companies who work in oncology are more involved in precision medicine than other therapeutic areas. Precision medicine seems to carry more weight in the immunotherapy/oncology field.

If you look at the multinational pharmaceutical companies, precision medicine has not been much explored in the core business model. In my opinion, the pharma industry are not overhauling their business model due to the emerging trend of precision medicine, but some are piloting it in certain therapeutic areas. A few years ago we discussed personalised medicine, now we talk about precision medicine. The changes that came from personalised medicine were targeted oncology drugs with companion diagnostics for specific patient populations. This is true for precision medicine. This concept is being implemented mostly in the area of oncology. In terms of other therapeutics areas I believe traditional drug development and business models will likely remain in the near future.  

By giving research teams the ability to transfer more data sources into evidence, how will this drive more efficiency in drug development?

Precision medicine would definitely drive the drug development more efficiently and that’s an area I don’t think  the pharmaceutical companies are doing enough in, especially in the areas outside of oncology. I’m in the respiratory area and familiar with asthma and COPD. Severe asthma is such a heterogeneous disease.  Simply grouping patients under the asthma umbrella term is not the most efficient way to develop a targeted therapy for these patients. We need to dive deeper into the different pathways involved in asthma. Asthma, is just an umbrella term. There are many different subgroups and different endotypes and phenotypes. Endotypes are what we notice from different disease pathways, and phenotypes are what we observe from the patterns of a clinical practice. We’re trying to understand how those different endotypes correspond to the phenotypes that we see in clinical practice.  However, we don’t have a lot of data and evidence to support us to make the linkage. We can’t make a direct linkage between what we know about the pathway and how that pathway impact on various phenotypes that we see in clinical practice. I see the trend of doing more, larger-scale registries that have a harmonised data collection, to help to build the linkage. For example, the European Respiratory Society is doing a severe asthma programme called SHARP. This programme is trying to build a harmonised platform to build a multi-country severe asthma registry so we can better understand the different phenotypes and endotypes and how they interconnect.  With such insights, we can apply a precision medicine approach to develop more targeted treatment for different subgroups of severe asthma.

How have strategic initiatives changed our ability to set research objectives with the recent development of large-scale biological databases, new methods that characterise patients and computational tools for analysing large sets of data?

Both the US and EU have started building large databases that include both biological data and clinical data of patients. Most pharmaceutical companies participate in these efforts. But often the number of patients enrolled is still not big enough for us to really understand the biological pathways of a very complex disease.

When you do deep molecular profiling of patients you end up with a huge amount of data about each patient. However, if you don’t have enough patients to participate in the study, for a specific endotype you may end up with a very small number of patients and analysis on such a small sample cannot provide a confirmative answer. Those biological databases have provided  a huge amount of hypotheses, but we still need to do more to understand how these hypotheses can be verified with subsequent research or database development.

What is the importance of precision medicine in the new value-based care environment?

It’s very important. If you look at traditional drug development, you run studies to demonstrate that the drug is efficacious and safe. These trials do not tell you for which patients the drug works. This is why we often run post approval observational studies to further understand how our drugs work for what patients in the real-world setting. We are very outcomes oriented at Novartis. Novartis has entered into value-based contracting for several compounds with government payers. These contracts allows the government payers to determine the future access or the price based on outcomes improvement seen in patients. Efficacy in trials does not translate into real world effectiveness. Therefore, value-based contracting bears a certain level of risk. I think, knowing precision medicine and knowing how the patients are doing in the real world, relative to either their biological endotype or phenotype cluster they are in, we can further reduce this kind of risk. We need to apply a precision medicine approach to quantify the improvement in patient outcome. This is very needed for the future value-based healthcare environment.

Finally, what are you hoping to get out of the Big Data in Precision Medicine in this fall in DC?

I want to see more conversation on phenotypes, data from large epidemiology studies and reverse genomics. The Precision Medicine Summit I attended last year largely focused on omics. I would like to see more on improving actual patient outcomes. Precision medicine has two components, in my view. The first is to understand the disease progress, disease mechanism, and the disease progression in patients. The second is to estimate the outcome we will see once you put the patient on a given therapeutic. I do feel that the outcome aspect is not highlighted enough. It is a big component of precision medicine, even though it is a much later part of the value chain.

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