Tremendous technological advancements in automated biotechnology and combinatorial chemistry have led to the widespread implementation of high throughput screening (HTS) for drug discovery since the early 1990s. However, the drug discovery process is a long and costly journey, often requiring many years and millions of dollars to reach the market.
Moreover, while HTS may identify a couple of hundred lead compounds from a panel of hundreds of thousands or even millions of compounds, only a small fraction of these will ever be tested in a clinical trial, and an even smaller number will ever be approved by the Food and Drug Administration (FDA). Despite these difficulties, HTS remains a viable, but cumbersome, strategy for success.
The two major strategies for HTS include biochemical and cell-based approaches. Pharmaceutical mediated biochemical approaches are typically target-based, utilizing enzyme inhibition or ligand-receptor interactions. However, as these biochemical HTS platforms are dependent on purified proteins, there are significant limiting issues related to protein stability and scalability. Furthermore, these approaches require subsequent analysis to test in-vivo or tissue-specific effects that cannot be obtained from initial biochemical screens.
Thus, the HTS field has continued to shift toward the development and utilization of in-vitro cell-based assays as a means for obtaining biologically relevant hits while simultaneously evaluating toxicity profiles early in the drug discovery process 1. By doing this, researchers aim to increase the success rate of the cell-based HTS approach by emphasizing ADMET (absorption, distribution, metabolism, excretion and toxicity) analysis of lead compounds earlier in the drug development process.
Therefore, there has been a recent emphasis in the design and development of increasingly efficient and reliable HTS platforms for early phase drug discovery. In this whitepaper, we will outline some of the historical and contemporary strategies of cell-based HTS, give a detailed description of some of the technical challenges to overcome in successful HTS methods as well as discuss some of the critical statistical concerns in HTS platforms, including common problems with false positives and strategies to increase statistical yield in cell-based HTS.