Advanced technology is now commonplace in pharma, where innovations like artificial intelligence (AI) and machine learning accelerate and improve the accuracy of drug discovery and development efforts. Add to the list the supercomputer 鈥� a powerful class of computer, vastly superior to general-purpose computers in terms of speed and performance, that are commonly used for data-intensive scientific purposes.
But what does this ultra-powerful technology mean for medicine?
US tech company NVIDIA this year , the UK鈥檚 most powerful supercomputer, to enable UK healthcare researchers to solve pressing, large-scale medical challenges using powerful AI and simulation.
The product of a $100m investment by , Cambridge-1 will enable scientists to better understand brain diseases like dementia, use AI to design new drugs, more accurately identify disease-causing variations in human genomes, and more. The projects will be pursued in collaboration with the computer鈥檚 first users: , GlaxoSmithKline, Guy鈥檚 and St Thomas鈥� NHS Foundation Trust, King鈥檚 College London, and .
Boosting the UK鈥檚 life sciences
Craig Rhodes, NVIDIA鈥橲 EMEA industry lead for and life sciences, says medical research was the natural target for Cambridge-1鈥檚 processing capabilities.
鈥淲e’ve got enough data 鈥� sometimes too much data 鈥� but now computation is the bottleneck,鈥� Rhodes explains. 鈥淪o what we wanted to do as part of our investment into the cure for Covid, and now looking at cures for cancer, was to remove that computational bottleneck. With all the data that we’ve got, we can do things much more accurately; we can produce results that are much more efficient and effective for dealing with clinical change, drug discovery change, and different clinical practices.
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By GlobalData鈥淯s deploying Cambridge-1 is showing clinical and drug discovery organisations and organisations doing large, national population sequencing that supercomputing 鈥� removing the computational bottleneck 鈥� will give them an answer much faster and much more accurately,鈥� he says.
鈥淚f you take all of those dimensions: the UK being such a hotbed for AI, NVIDIA鈥檚 investment in the key organisations in the UK, as well as wanting to accelerate [finding cures for] Covid and now cancer 鈥� all of those things put together is such a unique opportunity to NVIDIA to go and do something really, really good and worthwhile.鈥�
Enhancing drug discovery with data
Supercomputing has immense value, Rhodes says, in even the earliest stages of drug development, where it can give researchers better insights into cells and potential compounds.
鈥淭o look at microscopy data, look at the structure of compounds, identify how that compound might change subtly when it when it locks into a target, is really important,鈥� he explains. 鈥淏ut it takes enormous computational power to be able to do that.鈥�
That鈥檚 where the advanced technology comes in. A by researchers at the Institute for Bioengineering of Catalonia, Barcelona, found that using machine learning over conventional computational methods reduced microscope data processing time from months to mere seconds.
Cambridge-1 also has the potential to drastically accelerate and optimise the later stages of drug research, enabling large volumes of valuable pathology and genomics data to be processed, enhancing researchers鈥� ability to generate genetically validated targets for drug candidates.
鈥淚f we just had two whole genomes, and we’re looking at a drug that we’re trying to use for a particular condition, then it’s not a very good representation of the whole world,鈥� Rhodes says. 鈥淚t won’t give us a very accurate understanding of, if we take this particular drug with this particular human with this type of genetic data, how that human might interact with that drug.
鈥淲e’ve seen this an awful lot; we saw it with the Covid-19 vaccine, where certain vaccines work very, very well on certain demographics, and some just didn’t,鈥� he explains. 鈥淯nderstanding the genetic makeup allows us to understand some of the subtleties in those differences 鈥� but again, computationally doing that is massively challenging.鈥�
One human genome equals around one gigabyte 鈥� roughly the size of a short film, Rhodes says. He compares processing large volumes of genomic data to having a computer review and analyse 鈥渆very single scene, voice, motion, interaction鈥� in thousands of Netflix movies all at once. Tasks like these 鈥� highly challenging for general-purpose computing systems 鈥� are a breeze for supercomputers like Cambridge-1.
Because the processing capabilities of supercomputers make it possible for scientists to process millions more samples than a traditional computing system could handle, diverse populations will be better represented, and researchers can more accurately determine which drugs are likely to be successful. This saves drug companies both time and money; the computer鈥檚 data-based recommendations will allow researchers to create clinical trial cohorts that are more diverse, more precise, and therefore more likely to respond well to the drug candidate.
Improving diagnosis and treatment
Two of NVIDIA鈥檚 partners, King鈥檚 College London and Guy鈥檚 and St Thomas鈥� NHS Foundation Trust, are leveraging Cambridge-1 to 鈥渢each AI models to generate synthetic brain images by learning from tens of thousands of MRI brain scans, from various ages and diseases鈥�. It鈥檚 hoped the AI-based data model, which can generate an infinite amount of never-seen brain images with chosen characteristics, will allow a better understanding of brain diseases and enable earlier diagnosis and treatment.
Enhanced imaging processing is valuable in a number of therapeutic areas. Oncology, Rhodes says, is an area that can benefit hugely from NVIDIA鈥檚 technology.
鈥淟et’s say we’re trying to cure a particular type of lung cancer. There are many types of lung cancers; we know some lung cancers, and we don’t know a lot about other lung cancers,鈥� he explains. 鈥淪o people still die because we don’t know what that cancer was.
鈥淲e want to be looking at hundreds of thousands of [pathology] images of lung cancer, so that system has to look through hundreds of thousands of images and identify the variations in them 鈥� what’s the same? What’s different? What seems normal? What seems abnormal?鈥�
According to Rhodes, these are simple activities, but for traditional computers the task of processing pathology images is laborious and slow.
鈥淚f we then say we want to look at the genetic data for this drug, for lung cancer, we need to look at hundreds of thousands of specimens,鈥� he adds. 鈥淪o that we can identify that this group of patients, with this particular type of lung cancer, have this particular trait within the DNA signature.
鈥淚f they have something that’s very specific, does this mean this drug is going to work well with that? Or does this mean that it won’t work well, or won’t work at all?
鈥淎nd now, what’s becoming more advanced is to actually look at the image and then predict on the image where to look in the DNA data.鈥�
While a pathologist can identify cancerous cells on an image within seconds, what isn鈥檛 always obvious is the kind of cancer being shown, or what stage the disease has progressed to.
鈥淏ut if we can go into the DNA sequence data of that patient and identify where the particular markers we鈥檙e expecting for these types of cancer are, we can then go, 鈥榓ctually, we can see there’s a genetic issue here, or this genetic makeup would determine that it’s this particular type of cancer鈥�,鈥� Rhodes explains.
Even if analysis of a patient鈥檚 DNA doesn鈥檛 return any relevant or useful information, he says, this result could indicate that researchers are dealing with a type of cancer they鈥檝e never seen before. This finding, too, would be valuable for investigating or assessing which treatment protocols may or may not work for a particular patient.
鈥淭he activities that we’re doing at this level, they seem very, very simple 鈥� it’s watching, it’s looking 鈥� but they’re hugely important to the outcome, whether it’s a patient or whether it’s a drug going through a particular process,鈥� Rhodes says.
Is supercomputing the future of research?
Cambridge-1 isn鈥檛 the only supercomputer being applied to medical research. This year, the was granted access to Marconi100, a supercomputer developed by IBM and NVIDIA and based in Italy. The professor leading the project, Jonathan Hirst, said it will 鈥渉arness the power of both machine learning and of physics-based molecular simulation to accelerate the discovery of compounds with predicted therapeutic values as leads in drug discovery efforts鈥�.
Elsewhere, Japanese scientists are harnessing the world鈥檚 most powerful supercomputer, , to accelerate the discovery and development of personalised therapies. Despite only officially launching in March this year, Fugaku has already to identify existing medicines that can be used to fight Covid-19, narrowing over 2000 potential drug candidates down to just a few dozen.
Back in 2019, were selected to host supercomputing centres aimed at supporting development in personalised medicine and drug and material design, among other applications. The centres were allocated by the European High-Performance Computing Joint Undertaking, which was set up to equip the EU with 鈥渁 world-class supercomputing infrastructure by the end of 2020鈥�.
If supercomputers are to transform medical research for good, the high-performance computing needed to truly optimise drug discovery and development must be widely available. Democratising advanced technology across the industry is crucial to discovering the most effective medicines, faster 鈥� and high-performance computers like Cambridge-1, designed specifically to aid researchers in their efforts, are a big step in the right direction.
鈥淲e want people to realise that computational blockages, or challenges with computation, shouldn’t affect scientific research,鈥� Rhodes says. 鈥淲e’ve done this to show the UK government, look at what could be done, and also look at the enormously valuable assets that are now being produced in the UK, by UK companies.
鈥淗ow will [supercomputing] fundamentally change clinical research in the future? I don’t know the answer to that, but I know we’re giving them the assets to really make significant change,鈥� he says. 鈥淟ooking back on my life, I will really have thought that we have affected clinical change, we have improved patient lives, we have improved the NHS here 鈥� and NVIDIA鈥檚 been at the heart of it.鈥�
