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Business


Improving productivity with better predictivity


Changing demands in global healthcare over the past 15 years have led to greater complexity and spiralling costs in drug development. The average price tag of taking a new drug from discovery to completion of Phase III clinical trials is now $2.87 billion1, which means informed decisions need to be made early on about which compounds to pursue. AstraZeneca’s new R&D framework, termed the ‘5Rs’, has introduced an increased scientific rigour and emphasis on quality, driving an almost five-fold increase in R&D productivity. AstraZeneca has surpassed the industry norm in recent years, moving from a 4% success rate in molecules progressing from candidate nomination to completion of Phase III trials in 2010, to more than 19% in 20172. In our pursuit for continual improvement, the question now becomes how far can we push an even greater improvement in drug discovery productivity? One approach is by adopting new and evolving preclinical technologies to further improve clinical translation and reduce clinical attrition.


By Dr Lorna C.


Ewart, Dr Richard J. A. Goodwin,


Dr Stephen E. Fawell, Dr Pernille B.


Laerkegaard Hansen, Dr Catherine C.


Priestley, Steve Rees and Dr Menelas N. Pangalos


R


&D productivity across the industry has declined from 9% in the 1990s to 4% in 20093. AstraZeneca recognised the need


to make bold changes; in 2010, we conducted an in-depth review of our R&D to identify critical ‘success factors’. As a result, we launched the ‘5R framework’, which champions quality over quanti- ty and has transformed the culture of our organi- sation. This is a new model of working, based on ensuring each project team focuses on improving their understanding around a key set of criteria which we believe increase the probability of suc- cess: right target, right tissue, right safety, right patient, and right commercial (see Table 1). Between 2005 and 2010, AstraZeneca’s preclini-


cal pipeline contained around 200 projects at any time. After the implementation of the 5R frame- work, the number of projects halved, with the remaining projects having stronger validation as deemed by the 5R criteria; while the number of pro-


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jects decreased, the probability of success increased. This quality-over-quantity approach led to decreased clinical attrition, a fuller clinical pipeline and an increase in overall R&D productivity. Today, our projects rarely fail for safety reasons, or missing a proof of mechanism. Instead, the major cause of failure is a scientific hypothesis that turns out to be incorrect. If we can improve our under- standing of disease biology and how best to modu- late validated biological pathways, we believe this will further improve our R&D productivity. In recent years, we have adopted methodologies


based on advanced ‘omics’ and humanised models, allowing us to develop our ability to rigorously test hypotheses and translate science into medicine. Here we review four technologies that we are investing in to improve predictability of preclinical data: CRISPR-Cas9 system for efficient target vali- dation; patient-derived xenograft (PDX) models to model human tumours; mass spectrometry imaging


Drug Discovery World Summer 2018


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