Laboratory Informatics Guide 2020

Data is just as important as money when you work with AI in drug research

particularly the efficacy models – for cancer. ‘We have additional technologies that are not yet in the hopper for our work with ATOM, including new approaches to accelerating simulations, and we are working on automated high throughput experiments.’ One of the nearer term goals is to build a

common, model-based infrastructure with tools as pluggable components, Stevens said. Te ATOM founders, including GSK, Lawrence Livermore National Laboratory, Frederick National Laboratory for Cancer Research, and the University of California, San Francisco, as well as the National Cancer Institutes, and startups, are all contributing their expertise. ‘What ATOM allows us to do is to integrate

everything into something that is greater than the sum of the parts. It’s trying to build a working infrastructure that organisations can plug into, for the benefit of all members. Tis will result in a scale of endpoint that is well above what most groups can hope to achieve on their own.’ Scientists aren’t short of computing power,

but one of the main bottlenecks is having enough of the right sort of data to generate true AI- enabling technologies for drug discovery. ‘In fact, data is just as important as money

when you work with AI in drug research. For some areas of drug development money is probably easier to acquire than data, and we are developing our algorithms and AI-enabled technologies faster than we can access enough data on which to train the resulting models. For example, some types of tumour haven’t been screened against enough drug types to generate sufficient data on which to build thorough efficacy models,’ states Stevens. Tere’s plenty of cell line data, Stevens said,

but cell lines don’t behave exactly like tumours, and while there are increasing amounts of data from human cancers transplanted in animal models, this is also not necessarily easily accessible, non-proprietary data.

10 ‘So, creating the right databases is still a key

objective, and this will require experimentation. We can use simulations to predict some properties, but for things like measuring toxicity, or evaluating binding affinity on real targets, we do need to have more open or semi-open databases, and this will require experimentation,’ Stevens added. Te situation is different in the clinical arena,

Stevens pointed out. In countries where there are nationalised systems, such as in the UK, some very large datasets are already available. ‘It’s a little bit easier to integrate data across the enterprise, although there still won’t be molecular and genetic data for every patient. We don’t have genetic data for every patient or their tumours, for example, and this is partly a cost issue.’ ‘Tere does need to be a step change if we are

going to be able to harness data from national healthcare databanks. And that will involve making molecular tests cheaper and faster, so that they can become a part of routine patient care. We have to rationalise healthcare data exchange models, while still protecting privacy.’ Making big, high-quality databases available

springboards possibilities for machine learning and AI, Stevens said. He cites the UK Biobank as one example. ‘Te Biobank has accelerated a lot of machine learning-based, clinically relevant research because its a large enough, high-quality dataset to work with. It’s not tied to a company’s drug development programs, it has been generated purely out of the clinical space. Te U.S. Department of Veterans Affairs has a similar mindset and is collecting a large dataset across the entire population. Tese kinds of datasets aren’t just relevant to clinical research, but to drug discovery and development as well.’ So what will likely emerge as the first

tangible benefits and game-changers for drug development? Stevens suggests that work by Argonne National Laboratory, ATOM and other pioneers will generate solid proof-of-principle

evidence that it is possible to optimise drug leads using existing machine learning technologies. ‘We need to run through multiple cycles

of generating leads, and working through simulation and/or experiments, which will enable you to further refine those compounds. It’s still a relatively slow process, but that’s because we need to work through the mathematics of uncertainty in these models, and how to optimise simulations and experiments,’ added Stevens Optimise these cycles and you are then

presented with a raſt of compounds that will need prioritising. ‘You will always will have more targets than you can do experiments on, or that you can do simulations on, so another opportunity is to use optimal experimental design mathematics to prioritise not just the compounds from the biology standpoint or from the drug-like standpoint, but from the practicality of actually doing high throughput measurements on them. And that is what we hope to accomplish during this next year: to get that entire end-to-end system working’. Stevens says the ultimate goal is to

dramatically cut the drug development timeline, and costs – and to generate candidates that are more likely to show improved efficacy and safety. Importantly, AI-enabled refinement of the process could have an impact not just on the development of drugs for major diseases such as cancer or cardiovascular disorders, but on drug development and clinical research for orphan diseases with smaller patient populations. Orphan diseases have traditionally not been

a major focus for mainstream pharma due to much more limited potential revenues. ‘If we can reduce development timelines (and so potentially patent-protected time on the market) and also development costs, then developing drugs for orphan diseases, as well as neglected tropical diseases, become more attractive propositions commercially, as well as from a discovery and development perspective,’ concluded Stevens.

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