The Next Generation of R&D

By Eiry Roberts, Vice President of Project/Program Medical, Eli Lilly

It’s important to stop and think about what have we, as a pharmaceutical industry, achieved so far. Where are we right now? Where are we with the innovation that pharmaceutical research and development has been able to deliver in the past decade and, say, in the last few years?

If you think about the health of the general public, we have made great strides through innovation: curing many childhood leukemia’s – that’s a huge step forward; curing and eradicating infectious diseases in many parts of the world; the mortality improvements that we’ve seen in areas like myocardial infarction; the fact that we’ve been able to transform, universally, life-threatening diseases such as cancer into chronic therapies that can be managed over a period of time.

So it’s not all doom and gloom for the pharma industry, but it does face us with many challenges moving forward because, although we’ve made huge breakthroughs, the result of that is that we have a population that is aging – which comes with added costs associated with it. We all understand that the cost pressure on healthcare is huge now, and as a result, the discussion around understanding what healthcare technologies and which new medicines really add value in terms of improving patient outcomes is becoming a much more concerning conversation right around the world.

We also need to ask ourselves are we really a lot healthier than we have been historically. We’re certainly living longer, but if we look around and see the pandemic of obesity, or the situation we have with control and treatment of diabetes, we have worse figures for the treatment, particularly of type I diabetes in young teenage girls, now than we had 20 or 30 years ago, and so despite the fact that we’ve made a great deal of progress, we still have a huge amount of unmet medical need.

In addition to that our prescribing habits are still pretty empiric in most circumstances. In common diseases, we adopt a one-size-fits-all approach to the medicines that we use. We don’t have personalized medicines and as a result, we have gaps that are perceived in terms of how people see our medicines, both in terms of the efficacy and the safety profile. If you take common diseases for example – and this has been published quite widely and routinely – the response rate in diseases such as depression to any particular intervention are anywhere between 30 and 50 percent, so response rates of greater than 50 percent in common diseases to any single therapeutic agent are actually very uncommon. We also know that far too many people still get serious adverse drug events. People still die from iatrogenic disease or from drug prescribing errors and there are many things and many areas in which we would still like to make great progress in terms of how we manage our prescribing environment and how we prescribe medicines for patients.

Escalating R&D costs
The average cost of a therapy now is quoted anywhere between $1.5 billion and sometimes even as high as $2 billion per new chemical entity coming to the marketplace. The success rates are pretty poor. If you look across the board, success rates of greater than 10 percent in any therapeutic area are pretty much unheard of. And a lot of those therapeutic failures are occurring in phase II, phase III and even phase IV settings, so when the drug is already on the market, as we’ve seen with several of the recent drug withdrawals.

Now, that’s incredibly expensive for the companies involved in the development of those molecules. It’s also expensive from a patient perspective in terms of understanding that we’ve not delivered the value to patients that we need to and not been able to decide early enough whether a medicine is useful or not.

We have an escalating cost of development, we have failure rates that are too high and failure rates that occur too late in development and we have this one-size-fits-all approach. In the main we’re making progress in that regard, but across many common diseases, that is still the case.

So, what do we think about the previous, or even some of the current pharmaceutical R&D models? What is it that creates that environment for us? Why is it that things cost so much? Why is it that things don’t fail early enough? Why is the overall success rate poor? And what are we going to do about it? Because essentially that’s what we need to change in the new R&D environment.

I have a few thoughts in that regard. Firstly, we’ve seen during the last few years a significant growth in technology in the chemistry space and how we actually design therapies, design drugs and understand how to test drug targets. What that has led us to is very much in the discovery arena rather than a biology-based approach, which is focused on understanding the disease from the biological perspective. In many cases we actually treat syndromes rather than diseases because our understanding of the biology or systems underlying that are so poor. In that regard, in many circumstances, we do not necessarily understand the functional relevance of some of the targets that we’re choosing and the patient populations that we’re interested in treating.

Secondly, much of our focus in the development arena has been on speed. Speed is very good when it enables you to deliver a valuable product to a patient in the most efficient and effective way. When speed is used to skip phases of development, to skip understanding dose response of any new therapeutic or the development of any accompanying biomarkers or diagnostic tests that ought to be used in combination with that therapy or to actually skip the period of evaluation of the therapy in different patient populations in order to define the right patient for that drug, then that is actually false speed. It leads to more late failures, and it leads to an environment where we don’t necessarily deliver the value that we’d like to for patients.

In addition, much of our drug development – in the clinical trial environment – is focused on identifying very well controlled patient populations and outcomes that are regulatory driven. It is very important that we demonstrate efficacy and preliminary safety in our Phase I, II and III clinical trials but it doesn’t always mean that we’ve delivered data that supports the outcomes that are important for patients. Real world outcomes that are often associated with longer clinical trials in a less-controlled setting are, in part, also a reason why we’ve hit challenges in the later phases of development in the recent past and more historically.

Evolving
If we think about this as being the current situation, and about how we’re trying to innovate and evolve from that environment then, in my opinion, we should focus on a move from being focused on the chemistry of the models that we bring forward and the chemistry in the context of high throughput screening, toward our desire to understand much more deeply the diseases that we’re trying to address.

This is seen, first and foremost, in oncology and immunology areas. Truly understanding the biology, the systems, the pathways and using many of the novel technologies in genomics, proteomics and other areas that will allow us to convert many of these so-called syndromes or otherwise – for example, we know that cancer, rather than being defined as an anatomical disease now and into the future, is much more defined on the basis of the pathways and systems involved in that tumor. And as a result of that, we can be much more focused on understanding how to intervene in terms of how we choose our drug targets such that we know that the targets, pathways or systems, once they’re interrupted, will actually lead to a more favorable outcome, and we can measure those in terms of biomarkers and diagnostics that go alongside those drugs.

Another key area is understanding how we integrate all the information that is currently available to us around diseases. Taking a complex disease as an example, how do we take those vast amounts of data that exist in health technology – health records, clinical trial data – and integrate those. That’s a huge informatics challenge, but as soon as we can start to address that in a more effective manner, than we can make further steps forward in our understanding of disease.

A key learning for us moving forward is to focus a lot of our effort on understanding the diseases that we’re trying to serve in a different way from the ways in which we’ve done before. We also need to understand our patients better. We know from a genetic, biological and psychosocial perspective what outcomes are important to patients and the significant focus that we have as an industry now is completing trials that are based on real world outcomes. Linkage of those outcomes to surrogate of biomarkers is really going to bear fruit for us as we move forward, and certain diseases are leading the way in that regard, but over time, we’ll see even some of the more challenging areas such as neuroscience moving into that area.

Finally, we need to understand our drugs better. Profiling of our therapies, understanding extensive use of PKPD modeling, extensive use of exposure response modeling and linkage of that to the disease models that we’re developing again allows us to understand how the use of each of these therapies may necessarily be different in different patients, even within a given disease area.

Transparency and collaboration
There are many things that we’re currently working on across all of those technologies, all of the different efforts and disciplines within the academic environment, within the regulatory environment, and the pharmaceutical R&D environment that will facilitate us in getting a better outcome for patients.

One of the key challenges for us as a pharmaceutical R&D organization is the trust and understanding of the customers that we’re trying to serve. If you don’t have the trust of your customers, and you don’t have a transparent and open dialogue with them then you have no basis to be able to be successful. In the current environment, we have a situation where we don’t communicate as well as we need to with patients, payers or prescribers, in a fashion that allows us to be open and transparent about the common goals that we have.

And so, as we work together over the coming weeks, months and years, it’s going to be very important that we understand how to share information and data in an environment that still supports the business model that we need, how we gain transparency around communication with patients, and how we express benefit risk in a different way from that which we have done before. If we can achieve that transparency and partnership, then again, we’ll have an opportunity to truly be able to describe and explain the value of the innovation that we bring to patients.

Dr. Eiry Roberts is a physician who trained in pharmacology and medicine in the UK, qualifying from the University of London in 1987. Her post-graduate clinical training was in clinical pharmacology and cardiology at St. Bartholomew’s Hospital and the Royal London Hospital. She obtained membership of the Royal College of Physicians of the UK in 1990. At Lilly, as Vice President of Project/Program Medical, Robert’s has responsibility for all phase I and II clinical development across the five therapeutic areas supported by Lilly.