Improving drug discovery with new technology of high-content phenotypic screens

Developers

Jungseog Kang, Chien-Hsiang Hsu, Steven J Altschuler, Lani F Wu, etc.

Description of the technology

This technology is a version of high-content phenotypic screen. High-content, image-based screens allows to carry out the identification of compounds that induce cellular responses similar to those of known drugs but through different chemical structures or targets. A main task in the development of phenotypic screens is to choose suitable imaging biomarkers.

The technology provides a method for systematically identifying optimal reporter cell lines for annotating compound libraries (ORACLs), whose phenotypic profiles most accurately classify a training set of known drugs. This method is realized in three steps. First, a library of live-cell reporter cell lines is being constructed, where the cell lines are fluorescently tagged for genes involved in a wide variety of biological functions. Second, analytical criteria is being used to identify the reporter cell line in this library whose phenotypic profiles most accurately classify training drugs across multiple drug classes. In third step, it is necessary to demonstrate that this single reporter cell line, in a single-pass screen, can accurately identify lead compounds across diverse drug classes.

The technology showed its applicability in the process of validation with use of various compounds. The high overall validation rate of 85% demonstrates that this approach can produce high quality compound «leads» across diverse drug classes.

Practical application

The technology can be applicable and highly useful for drug discovery. Main instrument of this technology − optimal reporter cell lines for annotating compound libraries (ORACL) − can functionally annotate large compound libraries across diverse drug classes in a single-pass screen and confirm high prediction accuracy by means of orthogonal, secondary validation assays. The technology can increase the efficiency, scale and accuracy of phenotypic screens by maximizing their discriminatory power.

Laboratories

• Green Center for Systems Biology, University of Texas Southwestern Medical Center, Dallas (USA)
• Department of Arts and Science, New York University-Shanghai, Shanghai (China)
• Simmons Cancer Center, University of Texas Southwestern Medical Center, Dallas (USA)
• Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco (USA)

Links

Publications

• Kang, J. et al. «Improving drug discovery with high-content phenotypic screens by 
• Loo, L.H., Wu, L.F., Altschuler, S.J. «Image-based multivariate profiling of drug responses from single cells." 4.5 Nat Methods. (2007): 445−453.
• Rajaram, S. et al. «PhenoRipper: software for rapidly profiling microscopy images." 9.7 Nat Methods. (2012): 635−637