Transforming Pharmaceutical R&D with Data

  • ID: 4331628
  • Report
  • 31 pages
  • Datamonitor Healthcare
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In the current drug pricing environment, biopharmaceutical firms cannot afford to continue spending billions of dollars on development programs that are more than 90% likely to fail. Raising prices to compensate for expensive, risky research and development (R&D) is no longer an option amid a global payer backlash against drug costs. Drug R&D needs to become more efficient, faster, and cost-effective in order for biopharma firms to be sustainable and to maintain a supply of innovative treatments.

Fortunately, multiple new tools are emerging to help streamline R&D. Most of these involve more intelligent and targeted use of existing data, and exploiting multiple new kinds of data and analytical methods. They are enabling efforts along the R&D value chain, from discovery through late-stage trials and approval.

Several Big Pharma companies have started to invest in more efficient processes such as e-sourcing clinical data and virtual trial recruitment. Precision medicine, which is growing rapidly in oncology, in theory allows smaller, more targeted trials with a higher chance of success. Meanwhile, technology giants like IBM, as well as a new generation of biotechs, are using artificial intelligence and machine learning to accelerate and improve R&D; many are seeking partners as well as developing their own pipelines. Regulators are very open to new, faster, data-driven approaches to drug development.

Making R&D more efficient will not solve the drug pricing challenge; however, it will help by allowing biopharma to run a wider set of programs and make faster, wiser decisions about when and whether to engage in expensive late-stage trials.
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Executive Summary
  • Current R&D economics are unsustainable
  • Accelerating discovery
  • Accelerating development
  • Accelerating approval, access, and adherence
  • Challenges to data-driven R&D streamlining
Current R&D Economics are Unsustainable
  • The drug pricing environment is forcing more efficient R&D
  • New tools are emerging from discovery through to commercialization
  • Driving R&D efficiency requires partners
  • Squeezing value out of forgotten assets
  • Cost and time savings may reach 20-50%
  • Bibliography
Accelerating Discovery
  • Faster identification and validation of promising drug targets and leads
  • Accelerating discovery with artificial intelligence
  • Augmenting, not replacing, the work of scientists
  • Up-ending drug R&D
  • Big Pharma is signing up for computer-backed discovery
  • How predictive is your AI algorithm?
  • Machine-accelerated drug discovery is still only a promise
  • Precision medicine and biomarkers
  • Bibliography
Accelerating Development
  • Improving and accelerating clinical trials
  • Data driven site-selection
  • Accelerating trial recruitment
  • Electronic trial data capture
  • Bibliography
Accelerating Approval, Access, and Adherence
  • Expediting the regulatory process, drug uptake, and adherence
  • Accelerating regulatory review
  • Faster commercial uptake
  • Outcomes data inform R&D as part of a broader, data-driven disruption
  • Bibliography
Challenges to Data-Driven R&D Streamlining
  • Regulatory uncertainty
  • Data compatibility
  • Organizational and cultural change
  • Pharma must upgrade its data skills to stay competitive
  • Bibliography
Appendix
  • About the author
  • Scope
  • Methodology
List of Tables:
Table 1: Selected approaches to accelerating drug discovery
Table 2: Selected approaches to accelerating development
Table 3: Analytics tools for accelerating approval, access, and adherence
Table 4: Challenges to data analytics-driven R&D streamlining
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