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Whitepaper Jan 16, 2018

Using Real-World Evidence to Optimize Clinical Trials

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Introduction

This paper highlights how biopharmaceutical companies can better leverage existing real-world data (RWD) to improve the costs and efficiencies of R&D and accelerate the timelines of clinical trials without compromising the quality of evidence development, and instead greatly enhance trial feasibility, implementation, and data analysis.

Here, we explore and outline how RWE can be practically applied to pre-clinical research and subsequent trial design and implementation. After reading this whitepaper, you will have a better understanding of how to:

  • Use RWE to generate a feasible and clinically relevant
    hypotheses and refine your patient cohort
  • Leverage genomic data to further tune your patient cohort
  • Optimize clinical trial design
  • Ensure patient availability
  • Assess site feasibility
  • Reduce control arm and recruitment burden
  • And bolster data analysis using a patient-centric tool

Background
For decades, the costs of biopharmaceutical research and development (R&D) have been rising unsustainably and it has become glaringly clear that the current drug development model is broken. The current cost of developing a prescription drug that gained market approval has been estimated to be around $2.6 billion, which is a 145 percent increase from the early 2000s (DiMasi 2016). It has also been estimated that some top pharmaceutical companies have spent nearly $12 billion per approved drug (Sax 2012). Though costs have been climbing, the likelihood of a drug successfully getting from Phase 1 to approval has not meaningfully budged from around 10 percent (Thomas et al 2016).

Because of the massive divide between the development costs and likelihood of approval, drug manufacturers have historically priced their
drugs at prohibitive levels to recoup portions of their investment. For rare disease treatments in particular, these fixed developmental costs must be recovered from a limited patient population. Spinal muscular
atrophy (SMA), for instance, is a genetic disorder that affects 1 in 11,000 newborns yearly for which there had been no effective treatment despite intensive research since the discovery of associated gene mutation in 1997 (Smith 2017). In 2016, the FDA approved an SMA treatment developed by Biogen which was shown to be a remarkably

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