Laboratory Informatics Guide 2020

We have built an AI

platform that models cancer at the molecular level, and tests millions of potential drugs in-silico

easier identify the right patients for those trials, decentralised clinical trials will bring faster and more efficient results and even potentially allow us to predict real-life patient outcomes more precisely. If successful, this could allow the industry to cut development timelines by 30 per cent or more.’ Ultimately, the aim is to use AI and machine

learning to improve diagnosis and also enable more broad adoption of precision medicine. One example of this is Bayer’s work to explore the possibility of developing an AI algorithm to support identification of cancer patients whose tumours express an NTRK gene fusion, which results in production of an altered TRK protein that leads to cancer growth. Although overall rare, this alteration can occur in varying frequencies across various tumour types, in both children and adults. Te AI algorithm aims to help physicians identify all patients who are likely to have TRK fusion cancer by analysing routine tumour pathology slides. ‘We are training an algorithm with the goal

to reach a high degree of precision in identifying correctly NTRK gene fusions, based on basic pathology images,’ Archundia-Pineda explained. A positive result can be confirmed by existing, genomic testing, which is not always used routinely. ‘Ultimately, the AI algorithm could help

support consistent and widespread testing for TRK fusion cancer across the different tumour types, to help identify all appropriate patients who may benefit from a precision oncology treatment that is used to treat solid tumours caused by an NTRK gene fusion. Te algorithm has been trained on an initial dataset. What we are doing now is looking to expand the dataset to further train and refine the algorithm and validate the test at a broader scale,’ Archundia- Pineda added. Other initiatives in Bayer’s AI and machine learning pipeline include the development,


in partnership with Merck and Co’s MSD, of deep learning-aided soſtware to that can help radiologists identify signs of chronic thromboembolic pulmonary hypertension (CTEPH). Te soſtware received FDA Breakthrough Device Designation in 2018. Separately, Bayer is working with Broad

Institute researchers to develop an AI algorithm that can identify patients with a high risk of cardiovascular diseases, based on based on complex individual profiles made up of a unique combination of characteristics (demographics, clinical risk factors e.g. diabetes and genotypes). ‘Ultimately data science and digital

technology will help us to transform patients’ health by diagnosing diseases earlier and better, developing new medicine faster and tailoring treatments to their individual needs,’ said Archundia-Pineda. ‘Tese digital capabilities are transforming the way the pharmaceutical industry brings innovation to patients and illustrate the convergence path of technology, science and medical practice.’

Redefining drug development During the latter part of 2019 Argonne National Laboratory announced joining the ATOM (Accelerating Terapeutics for Opportunities in Medicine) consortium. ATOM is a public-private partnership between national laboratories, academic organisations and industry, which aims to transform cancer drug discovery through the combined use of high performance computing (HPC), biological data and emerging technologies. Argonne is initially bringing into the ATOM environment machine learning algorithms for designing, optimising and predicting efficacy, ADMET (absorption, distribution, metabolism, excretion and toxicity), and key properties of drug candidates. Te ultimate goal is to significantly

streamline, hone and speed the drug discovery and development workflow, explained Rick

Stevens, associate laboratory director for computing, environment and life sciences at Argonne National Laboratory. ‘It’s a high-level vision, but the basic idea is to turn the drug development pipeline upside down and try to compress dramatically – perhaps from six years down to just 12 months – the time it takes from identifying a new compound hit or lead, to starting a clinical trial. To do that we envision replacing most of the experimental steps in the pipeline process with advanced AI-enabled platforms and machine learning algorithms.’ Strip down this AI-enabled drug discovery

workflow and we find it’s basically a two-phase process, Stevens continued. ‘In the first phase you’re using an AI generative network to create molecules. And then you take a molecule and put it into relevant models, so that you can start to predict properties, toxicology, and how different cancers will respond to that molecule. Tese in silico models are trained on existing datasets and then further refined using additional, independent datasets. Tese may be acquired from partners or collaborators, or generated de novo from continued laboratory experimentation.’ Te predictive power of a machine learning

algorithm will depend on the quality of the databases and data sources. ‘Te trick is to keep track of accuracy,’ Stevens added. ‘Accuracy will be greater the closer the model remains to known data.’ Ask the model to move too far away from the training data and confidence will decrease, to the point that you then need more data to continue the training. ‘Tis cycle then continues, so you end up with a series of nested loops, where the core work is being done by machine learning, and you then use other techniques, perhaps classical simulation or high- throughput experiments, to derive more data on which to further train and improve the models.’ Argonne is bringing to ATOM molecular generators and molecular property predictors -

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