Drug Development Pipeline Running Low, What’s Data Got to Do with It?

Abstract

The per capita cost of health care in the US, by far the highest in the world, is driven in part by the high cost of pharmaceuticals. The low conversion rate of promising agents into successful clinical therapeutics is an important contributor to the high cost of pharmaceuticals. For example, all of the ~150 drugs developed in the last 15 years in mouse models to treat sepsis have failed in clinical trials. Several NIH institutes and other funding agencies have recently eliminated or significantly curtailed their funding for animal-based studies. A number of in vitro models of living tissues, especially organoids and microphysiological systems, are playing an increasingly significant role in prescreening of promising therapeutics for safety, efficacy and toxicity prior to expensive animal and human trials, thus offering the promise of accelerated drug development. However, a data-based understanding of how and the degree to which these assays reproduce the biological signals of interest, as well as drug-cell interactions, is critical to their successful deployment in the field of drug discovery. It is therefore critical to decipher omic and other changes to map known response pathways/networks so that in silico models can be used to determine which components of the biological signaling in human cells is preserved in mouse cells to guide further optimization of in vitro assays. Development of appropriate analytical tools will be critical to the success of this hybrid approach to drug development.