References 1 DiMasi, J et al (2016). Innovation in the pharmaceutical industry: New estimates of R&D costs. Journal of Health Economics, 47: 20-33. 2 Morgan, P et al (2018). Impact of a five-dimensional framework on R&D productivity at AstraZeneca. Nature Reviews Drug Discovery, 17: 167-181. 3 O’Hagan, P et al (2009, March 30). Bringing Pharma R&D back to health. Retrieved from Bain & Company: ns/articles/bringing-pharma-r- and-d-back-to-health.aspx. 4 Greene, J et al (2017). The novel ATM inhibitor (AZ31) enhances antitumor activity in patient derived xenografts that are resistant to irinotecan monotherapy. Oncotarget, 8 (67): 110904-110913. 5 Bruna, A et al (2016). A Biobank of Breast Cancer Explants with Preserved Intra- tumor Heterogeneity to Screen Anticancer Compounds. Cell, 167 (1): 260-274. 6 Huh, D et al (2010). Reconstituting organ-level lung functions on a chip. Science, 328(5986): 1662-1668. 7 Bauer, S et al (2017). Functional coupling of human pancreatic islets and liver spheroids on-a-chip: Towards a novel human ex vivo type 2 diabetes model. Scientific Reports, doi: 10.1038/s41598- 017-14815-w. 8 Nguyen, DO-H (2018). Humanizing Miniature Hearts through 4-Flow Cannulation Perfusion Decellurazition and Recellurization. Scientific Reports, DOI:10.1038/s41598- 018-25883-x. 9 Homan, K et al (2016). Bioprinting of 3D Convoluted Renal Proximal Tubules on Perfusable Chips. Scientific Reports, 6, 34845.

predicting responses to therapy. Massive datasets of spatially-resolved molecular information will be combined to create Google Earth-style multi-level rendered maps of tumours. Users will be able to use these to explore tumour architecture from a whole-tumour overview, down to individual molecules. The amount of detail captured using MSI results

in immense datasets. We are working with leaders in this field including the National Centre of Excellence in Mass Spectrometry Imaging (NiCE- MSI) at the National Physical Laboratory (NPL), to explore how to mine this high-dimensionality data to predict how drugs will behave in a patient. As with other high-content techniques, the MSI development must be coupled with advances in data storage, artificial intelligence (AI) algorithms and processing power. As improvements in compu- tational power expand to accommodate and make sense of these datasets, MSI can be used to build more informative clinical packages than ever before.

CRISPR-Cas9 genome editing In 2013, two seminal studies published in Science described how CRISPR-Cas9 system could be har- nessed for gene editing in mammalian cells11,12. Since then, it has revolutionised gene editing for biomedical research. Thanks to its ease of use and implementation, CRISPR is a powerful tool in tar- get selection and validation. It can be used to cre- ate precise genetic disease models, often involving the change of a single nucleotide. We have applied CRISPR to create more than

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120 cellular disease models in which we have delet- ed or introduced single nucleotide changes to genes to examine the effect of specific genes on disease pathways. For example, we used CRISPR to reveal a new drug target in chronic obstructive pul- monary disease (COPD). Previous research had shown that salt-inducible kinase (SIK) was a factor in inflammatory response in some COPD patients. SIK exists in three isoforms, and it was not clear which particular isoform was responsible for the pathological effect. Using CRISPR-Cas9, we creat- ed iPSC-derived macrophages expressing mutants of the three isoforms13. By creating kinase-dead versions of each of the three isoforms, we identified which isoform was responsible for disease. This has led to initiation of a programme to develop a specific inhibitor for the target isoform. While a broad-spectrum SIK inhibitor might have been effective, we decided to hone in on the precise tar- get, maximising the specificity of the potential ther- apy and chance of success.

We have also used CRISPR to rule out what oth-

erwise appeared to be promising targets before investing in screening programmes. In 2014, two Nature papers suggested that MTH-1 inhibitors killed cancer cells, making it a ‘hot topic’ at the time14,15. We subsequently used CRISPR to delete MTH-1 – precisely knocking it down was a clean, definitive means of assessing its role. Surprisingly, this had a much smaller effect on cancer cell sur- vival than had been predicted, invalidating it as a target and saving on further clinical development only for it to fail later on16. A core application at AstraZeneca is in the cre-

ation of libraries of CRISPR reagents to delete, upregulate or downregulate expression of every gene in the genome17. Alongside partners at the Sanger Institute and the Innovative Genomics Institute, we have tested these reagents to identify new disease targets – a major priority in creating preclinical validation packages and, arguably, the most important decision made during research. In terms of target identification, CRISPR offers a pre- cise, efficient new paradigm compared to a small molecule screen. To put this into context, a genome-wide CRISPR screen may use 20,000 assay wells when a small molecule screen may use hun- dreds of thousands of compounds. Identifying the actual target of a small molecule screen post-hit can take years, whereas CRISPR screening directly identifies genes of interest. Target identification and validation are arguably some of the most crucial elements of drug discovery – at AstraZeneca, 83% of projects fail for reasons related to target valida- tion, so adopting CRISPR to optimise this early on will deliver on driving pipeline efficiency and thera- peutic prediction of successful candidates.

Summary Our 5R framework has been a major factor in transforming R&D productivity, and supporting AstraZeneca’s return to growth. We now set our- selves the challenge to continue our journey of R&D productivity improvement. By testing our hypotheses in preclinical models that more closely resemble the human conditions we are aiming to treat, we believe we will select better drug targets and make better predictions for which candidates to test clinically. Looking to the future, there is great interest in

the potential of machine learning and artificial intelligence algorithms to make the most of data. Constructing ‘knowledge graphs’ using all our pre- clinical and clinical datasets, including data from humanised models, MSI and CRISPR combined with public-domain data, will layer information in

Drug Discovery World Summer 2018

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