Laboratory Informatics Guide 2020

July 2019, focused on leveraging the Sensyne platform to develop new treatments for cardiovascular disease. Both partnerships embody the global drive to harness machine learning, and AI to develop more accurate, insightful diagnostics as well as to accelerate drug discovery and development, reduce pipeline attrition rate, and make the development of patient-focused precision medicines a tangible goal for many diseases. While one-size-fits-all treatments have

traditionally had to suffice for many diseases, the ability to use computing power to analyse data sets collated from different disciplines means it should be possible to develop more effective, safer personalised medicines. ‘Te wealth of data now available through our digital lives – think smartphone apps and wearables – in combination with genetic and molecular data, and traditional blood and tissue-based laboratory tests and health record information, can feasibly be used to help more accurately understand the interplay between genetics, environment and lifestyle on health and disease,’ Archundia Pineda suggested. It can take about 12 to 15 years for a potential

drug candidate to make it to market, and less than one per cent of projects successfully negotiate early research, preclinical, clinical and regulatory milestones. ‘Developments in experimental techniques and high throughput and high content tests at the cellular and molecular level mean that we now have greater amounts of high-quality data, which, together with advanced analytics and computer algorithms, will help us to identify new targets for innovative medicines much faster, more accurately and more efficiently than ever before. Tis could dramatically cut the development timeline and attrition rate.’ Bayer has realised these opportunities, and

is working with global technology partners to developing AI-enabled solutions for drug development. ‘Together with Budapest-based startup Turbine, for example, we have built an AI platform that models cancer at the molecular level, and tests millions of potential drugs in silico.’ Te development of such models does rely

on the quality, reliability and breadth of data, and this brings us back to the potential to exploit real-time, real-world information that may originate from the patients themselves. Today’s activity trackers contain biometric sensors that monitor exercise, heart rate and sleep, for example. ‘What’s important is that these devices are collecting all of this data moment- to-moment, on an online basis, and they are all connected. So all of this data that is being generated and stored is unique to each one of us. Te big opportunity is to harness machine learning and AI to understand how it impacts

on health and disease, so that we can identify patterns and relationships and uncover precision opportunities for maintaining and sustaining a long, healthy life.’ Data gives us a real opportunity now to drill

down into the biology of disease, to start to understand why, for example, one person who has smoked heavily all their lives doesn’t develop lung disorders, but another does, Archundia- Pineda suggested. ‘And then we can harness machine learning algorithms to better predict who will develop diseases, use imaging and other existing techniques to better diagnose disease. Ultimately, this will allow us to develop better drugs for treating, managing or preventing diseases from developing.’ It doesn’t take a great leap of imagination to

envisage a smartwatch that will be able to devise the best exercise and diet program for the wearer based on daily real-time metrics, or that tells the patient what the optimum dose of a particular drug will be on that day, based on their exercise, diet and other measurable physiological and biological parameters. ‘We can even start to think about a drug-delivery patch that dispenses the optimum amount of drug, automatically, based on a collection of day-to-day measurements.’ Upstream of the clinical impact of using

machine learning and AI to maintain and even improve health at the level of the individual, such technologies are massively impacting on drug discovery and development, Archundia- Pineda explained. ‘Ultimately, we will link all the experiments that we design in the laboratory today to enable the faster, more informed development of precision medicines.’ In effect, leveraging huge datasets that are

now being collected from patients – for example, through initiatives such as Sensyne’s partnership with the NHS trusts – and from clinical trials and preclinical research, will all feed back into the discovery and development engine to inform R&D at an early stage. ‘Tis is going to be revolutionary with respect to the way physicians can treat their patients.’ Te massive computing power available today

makes it possible to take all this data, and use it to model not just how, when and why diseases develop, but to rapidly identify molecular structures against optimum targets. ‘Given the right quality as well as quantity of data, computer algorithms can be trained to understand and simulate the way that a disease progresses, pick out the best molecular structures and properties for specific disease targets, and how best to deliver a molecule to specific cell types.’ We should also blur the demarcation lines

that traditionally separate internal R&D from clinical partners, states Archundia-Pineda. ‘Scientists working on synthetic molecule discovery and optimisation might be steps removed from the clinical application of these

molecules in clinical trials, or their ultimate prescription for patients, so it’s important that through our collaborations, the supporting organisations can work with key scientists, clinical practitioners and investigators together.’ ‘We can plug huge datasets into these

algorithms to test a model’s response, but that data has to be in a usable format, and complete and reliable. Our partner, Sensyne Health, has come up with a really interesting business model, which leverages large anonymised datasets that belong to the UK people, through the NHS.’ Lord Paul Drayson added, ‘It is important

to recognise that AI is only as good as the data it analyses. Bigger datasets mean more robust findings, particularly when looking at rare diseases or analysing complex conditions. Te UK has some of the richest health data sets in the world, collected across an extremely large national healthcare network. Sensyne Health believes NHS data is a sovereign asset that will help to make dramatic improvements in healthcare for patients in the UK and abroad, and sustain our NHS for future generations. We have developed a unique business model partnering with NHS Trusts where they receive equity and a financial return from the data we analyse.’ Sensyne Health currently partners with five NHS Trusts. ‘Our NHS partners remain the data controllers and all requests we submit to analyse the anonymised datasets are subject to the approval of an independent ethics committee. We strongly believe that patients have the right to expect that their data will be ethically sourced and responsibly analysed.’ Sensyne Health effectively acts as an enabler

for the analysis of patient data on behalf of its commercial partners, Archundia-Pineda commented. While ownership of data remains with the Trusts, through Sensyne it can now be used to power and train the machine learning algorithms and AI technologies that are helping to develop the treatments and diagnostics that will hopefully ultimately be used to treat those patients. ‘Trough its partnership with Bayer, Sensyne also provides a platform that allows researchers to assemble, leverage and track the data, so that the insights continue to improve over time. Ultimately it will help us at Bayer develop better drugs, faster.’ It might even be possible to carry out clinical

trials in silico, to predict treatment effects on patients according to their disease specifics, and suggest optimum dosage. ‘We are working on an entirely new design of clinical trial, which is based entirely on computer-aided steps, with the expertise of principal investigators,’ Archundia-Pineda noted. ‘Te internet of things and connected devices will enable decentralised clinical trials, where patients can stay at home instead of going to clinical research sites. While AI-based patient stratification can help us to


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