I recently had the privilege of discussing the future of drug development with CMPI advisory council member, Joseph A. DiMasi, PhD, Director of Economic Analysis Tufts Center for the Study of Drug Development, Tufts University.
Here are some tidbits you may find of interest —
Q: How will 21st century science help advance the future of drug development?
A: There are hopeful signs that new technologies and analytical approaches will improve the pharmaceutical R&D process in the 21st century. If successful, they will reduce costs and facilitate getting the right drugs to the right people at the right time. There is a growing recognition that genetic and other biomarkers that predict efficacy and toxicity responses or measure disease progression need to be developed, validated, standardized and included in drug development programs. The resulting increase in predictive power can eventually permit smaller and more informative clinical trials. Sharing of blinded clinical outcomes data from failed as well as successful trials across companies can also improve the efficiency of the clinical development process. Bioinformatics, data mining, and Bayesian statistical analysis can also help. Given the high costs of investigating the numerous drugs that eventually fail in testing, advances in discovery and preclinical development technologies that result in a higher hit rate for successful drugs can substantially reduce development costs.
Q: Will these advances impact cost?
A: All of the above technologies and techniques can lower the overall cost of drug development. It is hard to know how much they will do so, but reductions of one-quarter or one-third in average costs seem attainable from increases in success rates or reductions in development times.
Q: What can the FDA do to facilitate these improvements?
A: There is much that the FDA can do, in conjunction with industry, academia, and other government agencies, to modernize the development and regulation of new medicines. These include collaborations to develop and validate biomarkers, facilitating the analysis of pooled data on outcomes across clinical trials, developing guidance on how pharmacogenetic data can be used in labeling, how to utilize outcomes data from post-marketing drug surveillance, where and when it is appropriate to use Bayesian analysis and observational studies, and the evaluation of and standards for electronic medical records submissions.