by Lawrence F. Arnstein, PhD
Volume 4, Issue 1 (January/February 2005)
R&D output continues to climb with advances in laboratory automation and data analytics but, when measured in terms of the number of drug approvals versus expenditures, the productivity of R&D has been in decline. This paradox confounds many organizations as they struggle to improve. A common strategy has been to invest in informatics. These investments are meant to increase the effectiveness and speed of the decision-making process in which validated findings are compared against predicted outcomes — to close the loop between design and experimentation. But the loop has not been closed in a broad and coherent way, and increased productivity has not been achieved. Instead, the information management landscape in life sciences R&D has remained fragmented, with barriers between disciplines and geographies that prevent broad-based productivity gains…
Citation:
Arnstein LF. Modeling Platforms: The Enabler for Closed-Loop R&D. BioProcess J, 2005; 4(1): 47-51.