43-44 - ROTY - Targeting treatment

COVID-19 has thrown the entire world into chaos. Within just a few weeks a handful of cases became a global pandemic. The virus attacks indiscriminately but its effects are not distributed evenly – and a utilitarian approach will inevitably lead to fatal consequences for some. But without the technology and data required for individual level responses what choice do we have? Crisis breeds innovation, and how governments and health services choose to address the pandemic will have long-lasting practical and philosophical implications for other areas of medicine and society as a whole.

Protecting people from a new and deadly virus is hard, harder still if it has the ability to spread rapidly over a long, asymptomatic incubation period. With an exponentially growing number of cases and limited ability to diagnose, treat and cure the sick, public health authorities are left with the unenviable task of making trade-offs between lives lost to the disease and those lost to the long-lasting adverse socio-economic effects caused by heavy-handed interventions. Put simply, are the costs of tackling COVID-19 worth the benefit?

The brutal reality of such cost-benefit trade-offs are already made by healthcare professionals every day. Today, proxy calculations, like QALYs (quality of life years), are used to weigh up the benefits of new treatments against existing practices. In the UK, we have NICE (The National Institute for Health and Care Excellence) to maintain a standard of consistent, transparent decisions and invest NHS resources accordingly. As a tax funded system (as opposed to the US) this utilitarian approach to healthcare is to be expected – the greatest impact for society is prioritised over any individual. Which means, even with increased investment on end-of-life drugs where additional time is all the more important, expensive cancer drugs are routinely rejected by this system despite treating patients successfully elsewhere in the world.

COVID-19 presents a threat to us all. But, by early 2020, it became apparent that it’s more likely to adversely affect some more than others. Older people are more vulnerable than younger ones. Certain comorbidities have been highlighted as increased risk factors too. A one-size-fits all approach just cannot work for everyone but decisions need to be made at national and global levels to save the greatest number of lives. We’ve now seen every country stretch its healthcare resources, under greater scrutiny than ever before. As new data are emerging all the time any cost-benefit analysis will be inevitably imperfect. Modelling and simulating outcomes are the only tools we have to try to predict how different scenarios may play out and help us to invest our resources wisely.

Innovation and creativity thrive in times of crisis. The response from the global modelling community has been overwhelming, with more than 50% increase in the number of academic papers on ‘modelling viruses’ published in 2020 than in the whole of 2019. However, real progress in the field has come from harnessing the knowledge of academics with the skills and technology of industry.

At Hadean, we responded to the call from RAMP (Rapid Assistance in Modelling the Pandemic), coordinated by the Royal Society to bring academia and industry together. We volunteered our distributed computing skills and technology to disease dynamics experts from Imperial College London and Oxford University – and together created massive geospatial social networks that can be harnessed by the Oxford Big Data Institute’s city-level agent based model of COVID-19 (OpenABM). By helping the model better reflect reality – and therefore increasing the accuracy of the resulting simulation – we’re hoping to arm policymakers with the necessary information to make impactful decisions.

At the other end of the scale, we’re using our spatial simulation engine to create a multiscale model of the virus’ transmission in the human lung network with scientists at the Francis Crick Institute. As the disease grows over time and interplays with the immune system, a war-game ensues. Computationally this is analogous to simulating thousands of entities in a multiplayer game, or the virtual world of a complex synthetic training environment.

Applying cutting-edge technologies from one industry to another is a common practice, and interdisciplinary fields are where some of the most exciting innovations occur. The response to the pandemic will accelerate other computationally challenging areas of life sciences, in particular, personalised medicine.

There is a future reality where every person receives truly individual level healthcare. Personalised medicine will combine biological, clinical and lifestyle data to create a unique picture for each patient and help clinicians decide which treatment methods are the most suitable for their patients.

It is currently unclear how these large volumes of disparate data can be easily combined into meaningful simulations. Current infrastructure was not designed for the sheer volume of these data, and harnessing the potential of cloud computing requires a redesign of the tech stack to eliminate operational bottlenecks. Technical solutions today (eg, HPC, high performance computing) have restrictions such as availability, cost and customisability, and require scientists to learn specialised computing skill sets. When available, significant time is lost on devops (a set of practices combining software development and IT operations) – orchestrating swarms of servers, programming massively distributed applications, and capturing petabytes of data – rather than focusing on the science.

However, many of these problems already have found solutions outside the scientific community. A number of industries – from online gaming through to biomolecular modelling – are facing similar computational bottlenecks. As a result of the pandemic, we’re now seeing tools that natively understand massively distributed workloads, removing the need for complex programming and engineering overhead traditionally associated with HPC simulation, being taken up by scientists.

Individual level diagnoses and treatments will lead to a shift in strategy from payers like the NHS, incentivising them towards the patient-centred, deontological approach, personalised healthcare provides. Because, while hyper-targeted drugs may be expensive to develop and administer, the data shows the current costs of generalised drug regimes that offer no benefits to patients are huge. Studies have shown that as much as 60% of drugs prescribed in the US today are completely ineffective. Payers and insurers would happily divert those same funds to efficacious drugs for smaller populations. Furthermore, by allowing pharmaceuticals to run hyper-targeted clinical trials with reduced duration, size and, most importantly, cost it will bring down the substantial investments required to bring drugs to market today.

The ramifications of COVID-19 will clearly be tragic and long-lasting. It has brought into sharp focus a number of difficult questions that healthcare professionals grapple with throughout their careers. If we are able to use the pandemic to accelerate technological breakthroughs and alleviate some of the stark choices we are faced with, it will have far-reaching benefits beyond the here and now, creating a new more personalised approach to medicine that doesn’t result in difficult tradeoffs between the individual and society.