The Future for Clinical Studies

Attend any research meeting, conference or clinical congress and you will hear about developments that are occurring so rapidly we could be forgiven for concluding that we are living in a future that is happening now. The pace of change seems unprecedented in my 30+ years working in clinical trials. Is it possible to predict what clinical trials will look like in the next 30 years, or 10 or even 5 years time? This was the challenge posed for me recently.

The philosopher Confucius tells us that to divine the future we must first study the past. Any scientist can appreciate the value of extrapolation. Looking back, the last three decades have seen many changes, including the introduction of novel trial designs, information parameters and transformative technologies. These modifications have come as a series of bolt-on’s, and you might be forgiven for thinking that the clinical framework we established in the terms of Good Clinical Practice and ICH is struggling to remain fit for purpose. So where do we go next?

If 30 years in clinical research has taught me anything it is that understanding is about asking the right questions. Below are five questions that will sit at the heart of future clinical trials:

• What diseases will we be focusing on?
• What will our future medicines look like?
• How can we make the drug development process more efficient? 
• Who will pay for these new drugs?
• Who owns the data and the ever more exotic analytical methods we use to interrogate it and who will validate our interpretations?

Future of disease

We don’t expect new and exotic diseases to appear from outer space. However, for most of our medical history we have applied a symptomatic approach to its classification, diagnosis and treatment. Our understanding has been based on what we could measure, what we could observe – high blood pressure as hypertension and increased body mass as obesity, etc. Technology is changing what we know of disease and we are starting to see many in a new light. It seems certain that the future will no longer rely on ‘symptoms’ and dusty textbook descriptions, our new ‘electronic dissection kit’ is more subtle than the scalpels of Edwardian clinicians. I suspect we will be re-writing the (electronic) textbooks for years to come.

We are collecting more data at every level. We are gaining access to more and more historic patient data as it is scanned and transcribed into databases. Following calls for transparency we are also sharing more and more clinical study data. With the explosion in biomarkers we are monitoring more and more parameters, we have also seen increasing technological granularity as our ‘measuring’ devices are becoming more sensitive, providing continuous multi-layered data streams. Technology has removed any restriction there might have been to our ability to process these volumes of data including the gigabytes generated daily by our mobile phones, health apps, wearables and computers. 

A broad variety of computer-based systems are looking for ‘understanding’ (and I don’t use that term lightly) at a level that we are not equipped to comprehend. We already have a veritable armoury of analytical tools to use in our clinical studies and the computer power to run them (Figure 1). In short, our technological wizardries are providing new insights as well as earlier and more precise diagnoses. Their utility is already being reported across the clinical spectra – from acute kidney disease to automatic imaging assessment and patient triaging.

Therapies of the future

If the way we understand disease is changing then we must assume our approach to treatment will also change. Even if you ignore the issue of antibacterial resistance, we are in desperate need new medicines as our population itself is changing. Estimates suggest that more than 1.4 billion people will be over 60 years of age by 2030. Although many of us can expect to remain active, old age is associated with a plethora of chronic, non-communicable diseases and associated disabilities that we have failed to understand for decades – hypertension, obesity, diabetes … ageing itself? By 2030, one in three people over 50 will be suffering from chronic disease, diseases that will account for 70% of deaths. 

In the last 100 years we have pursued small molecule solutions to symptomatic relief through the receptor theory of pharmacology. We have had some notable successes, but we have long known its limitations. People’s responses to medicines are variable – medicines just don't work in everyone. All drugs are potential poisons and we have long battled with the challenge of getting the right amount of that poison to the right place at the right time. The failure of trial subjects to all respond in exactly the same way has meant that the only rational option has been to establish ‘optimal’ doses based on group responses established in relatively small, homogeneous cohorts.

Going forward we can expect to see more and more ‘smart’ medicines – self-regulating therapies that adjust to the dynamic nature of our own pathophysiologies. We have been running trials for some time in novel ‘devices’ that employ creative release mechanisms to provide more subtle and targeted delivery. We are also seeing medications that exploit mechanisms that have progressed far beyond the receptor theory – CRISPR, targeted protein degradation, immuno-oncology treatments, silencing and cellular therapies. These new medicines raise some interesting questions for clinical pharmacologists. Are we equipped to validate these treatments before they are used in actual patients? Would such clinical trials tell us anything more than a sanitised safety and tolerability profile? It might seem a bit of a leap but if we are investigating treatments for diseases we didn’t previously understand and those medicines are exploiting subtle mechanisms we can’t measure, our technology becomes the only way to ‘detect’ changes indicative of benefit (and this only in patients). 

Innovations in registration

Even if we continue with our current rate of identifying new therapies we still have the problem of getting them into the patients. If anything needs addressing it is the process of drug development – it is slow, expensive and inefficient. We all know the statistics; it takes 10 years or more to bring a drug to market and the best estimates suggest that only (about) 1 in 10,000 candidates pass the finishing line. This bleak assessment remains unchanged despite us living at a time when we are seeing unprecedented advances in fundamental science. So, what is going wrong?

Despite our best efforts we have not been able to address the dual curse of attrition and protracted development times. Modifications like those here (Figure 2) from the US Food and Drug Agency have made small, incremental improvements and more are coming:

• Synthetic control groups – no more healthy controls, we can just use historical or real-world data;
• Virtual clinical trials – collecting data remotely from wearables, monitored containers etc.;
• Apps and social media – will help make patients aware of trials and speed recruitment. 

And yet estimates tell us that these developments won’t make a substantial impact on either timelines or attrition. So, is the system we are using at fault?

The clinical trial process dictated to us by tradition and regulatory agencies employs a ‘block’ approach (Figure 3). We currently segment development into individual phases and trials, each incorporating different treatment groups, often with the focus of the study being to establish some statistical significance. This approach ties up considerable resources. For example, the process of writing and approval for each clinical study protocol can take anything from 5 to 20 weeks, on average each protocol has at least four substantial amendments (more delay) and, once you’re done you need to perform data review and analysis before writing and publishing the final report. If we say each drug development programme involves anything from 5 – 10 clinical studies we can estimate that regulatory administration alone takes up 3 years – a third of the current clinical development time. 

Obviously, this is a rather simplistic estimation, but I hope you get the idea. So, what is the alternative? In a world of ever-tightening regulations and oversight it may be time for regulatory authorities to take a more proactive approach to regulation. Spend any time in clinical studies in the last decade and you will have experienced increasing pressure to register the details of your study protocols and findings on regulatory agency and other databases. Could data collection coupled with integrated data management systems be better used to empower regulators to actually regulate?

Reviewing the European Medical Agency’s current stated regulatory strategy (Figure 4) – it appears to contain many of the necessary components for real-time programme coordination and monitoring. With a little development it could be made possible for protocols to become less rigid, even infinitely amendable online and approved in an ‘as required’ almost real-time basis. This introduces the possibility of a whole development programme being incorporated into a single, infinitely adaptive clinical trial. The saving in administration time alone would be significant. 

Future reimbursement

Our expenditure on healthcare is increasing and is expected to have doubled by 2030 – just 10 years’ time. Although we appreciate that this increase will not solely be due to the cost of medicine it has certainly put focus on the pharmaceutical industry. If we take a quick look at some of the medicines at the upper end of the market we can see that the numbers are many times greater than any single patient could ever afford (Table 1) – and this for treatments that are often no more than life extender therapies. Both on an individual and a healthcare service level these costs are unsustainable.

The cost of developing new drugs has been increasing and whichever number you believe, 1 or even 3 billion US dollars, it represents a significant investment for a single organisation, especially seeing that success is not guaranteed. In 2016 for example, the US FDA approved only 22 novel drugs of the 41 new filings. A 50% success rate is a considerable risk when you are spending a billion dollars. How have Sponsor companies responded over the last few decades? We have seen many mergers and acquisitions as companies have attempted to minimise overheads. There has been reduced investment in maintaining internal teams and increased use of outsourcing models. Sponsors continue to investigate ‘shared risk’ models with contact research organisations that have taken over responsibility for performing much of the research work, and there has been increased investment in specialty/orphan submissions. But perhaps the most newsworthy has been the drive to increase reimbursement.

In response we have seen movement to drive change, first in assessing an overall ‘value’ for new drugs – particularly in the actions of NICE– and now thoughts are turning to creating reimbursement models based on efficacy – that is, if your drug doesn’t work on a patient you don’t get paid for that patients treatment. Such models could see a significant swing in the development landscape. Shifting the 'efficacy' relationship to one between payers and Sponsors has the potential to reduce the burden on regulatory agencies to one of monitoring safety. This has the potential to slash the time it takes to get drugs into patients.

Drowning in data

Data sits firmly at the centre of the future of clinical trials. In reality, this article is really about data rather than clinical studies – what is a clinical study other than the collection of data under specific conditions? This infogram (Figure 5), taken from the ‘Future for All’ website [1] gives a taste of the sort of data all of us will soon be collecting – irrespective of whether or not we are taking part in a clinical study. It is clear that we will be heavily monitored and whole populations will be available to serve as trial participants (with or without our knowledge or permission). 

Following the revolution in data collection the clinical study teams that devise and run studies must also change. The amount and type of data we can expect dictates that it won’t be clinical pharmacologists or clinicians, but more likely algorithms managed by data scientists that will be driving drug development. It seems likely that the true leadership in development will lie with those who own the algorithms that we will rely on to establish both safety and efficacy.

So, where does this lead us? Mergers such as that between Quintiles and IMS appear to reflect growing acceptance of the concept of exploiting end-to-end (development to reimbursement) solutions for pharmaceutical companies. But look closer. More significant change is coming. Over the last few decades we have seen pharmaceutical companies divest expertise, reducing their employee base and adopting outsourcing models. They have coupled this with an emphasis on asset acquisition over in-house development. In focusing on recouping profit over investing in the engine to drive future development we can conclude that the pharmaceutical industry has taken its eye off the ball. Clinical development was always about data – and now the Gods of data are coming to call. Try as they might Blue-chip pharmaceutical companies and mega CRO conglomerates have already lost the data initiative. We are seeing technology companies come to the fore as pharma’s trillion dollar spend attracts predators like Google, IBM and Microsoft. 

Where next?

In conclusion, in less than 10 years if you are working in clinical trials and you aren’t the janitor then you are a data scientist, probably working at Google. We can expect to see a new three-phase registration model (Figure 6). The first phase will involve an aspect of quality evaluation and biological proof-of-concept probably involving more preclinical modelling and engineered human cell lines and fewer animal studies. Once a new product is registered it will enter into a period of adaptive clinical development (delivered as a single study) intended to establish safety. This phase should take around 1 – 2 years and explore tailored options for administration. Investigations will most likely be conducted in patients, possibly in a ‘patient-in-a-box’ setting (hospital or healthcare centre, virtual or microsite). 

On completion of safety licensing drugs will begin assessment of efficacy in partnership with those responsible for reimbursement – testing will be performed in patients, possibly where individual patient involvement in safety testing will offer some reimbursement deal for future treatment. Change is coming, though its precise form will likely depend on the creativity and vision of sponsors, regulator and payers. In the words of Vladimir Lenin: “It is impossible to predict the time and progress of revolution as it is governed by its own mysterious laws.”

This is a partial transcript of a presentation given at the “Innovation and Technology in Early Clinical Trials” in London on 19 September, 2019.

Tim Hardman is Managing Director of Niche Science & Technology Ltd., a bespoke services CRO based in the UK. He is also Chairman of the Association of Human Pharmacology in the Pharmaceutical Industry and an occasional commentator on science, business and the process of drug development.