Transforming Pharmaceutical R&D with Data
|出版日||ページ情報||英文 31 Pages
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.