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Predicting drug efficiency and improving drug discovery with ORACLs


Since the dawn of the genomics era in the 1990s, the dominant drug discovery strategy has been to screen compounds for specific activity against known targets associated with disease pathogenesis (P. Imming et al., 2007). A key advantage of this “target-based” screening is that it utilizes uncomplicated biochemical tests that can be carried out with either a simple format or on an automated, rapid screening system of thousands of different compounds. A recent analysis of all first-in-class small molecules medications points to reduced success and higher attrition rates for target-based approaches (D.C. Swinney et al., 2011). The study also showed that the contribution of phenotypic screening to the discovery of first-in-class drugs exceeded that of target based approaches. Historically, drug discovery has mainly employed “phenotypic” methods often characterized by physiological observation on whole animals or organ models. In view of this, phenotypic screening has made a comeback in drug discovery (T. Kodadek, 2010). Phenotypic screening identifies compounds that induce a biological response without making assumptions about the underlying molecular target(s). Phenotypic screening provides a less biased approach to screen molecules that modulate targets and signaling pathways in native cellular context. As a result, bioactive molecules identified in phenotypic screens have higher in vivo therapeutic impact. Phenotypic screening hits constitute better starting points for optimization as they must be cell permeable and engage their targets with sufficient affinity to displace endogenous interacting metabolites or proteins.

Phenotypic screening has proven its usefulness in classifying drug libraries into functional classes and predicting the mechanism of action using guilt by association (D. Houle et al., 2012). A successful phenotypic screen lies in selecting suitable biomarkers (e.g. antibodies, chemical dyes or genetically encoded fluorescent tags) whose phenotypic profiles can classify a set of known drugs in a single-pass. Despite increased adoption, researchers interested in implementing phenotypic screening assays have encountered several challenges. Phenotypic screening assays have lower throughput and access to relevant cell models presents a major challenge. The greatest challenge is the identification of molecular targets of bioactive small molecules. Current phenotypic screenings are based on either too specific or broad readouts and this does not effectively distinguish modes of action in a single-pass screen.

Dr. Kang and colleagues from the University of Texas Southwestern Medical Center addressed these challenges by constructing a library of live-cell reporter cell lines that are fluorescently tagged for genes involved in a wide variety of biological functions. To enable high-content profiling of large-scale compound libraries, they developed a triple labeled live-cell reporter cell line where the first two labels facilitate automated identi¬fication of morphology. The third label allowed each reporter cell line to monitor the expression of a different protein. These reporter cell lines are referred to as ORACLs, optimal reporter cell lines for annotating compound libraries. Proof of concept for this technology comes from large-scale phenotypic screen of small-molecule compound libraries which led to the identification of 175 compound leads. The leads were subsequently validated through literature or experimentation. Currently, there are no established strategies for systematically identifying and classifying compounds across multiple drug classes. As large chemical libraries become increasingly available, methods to efficiently screen for promising compound leads across multiple drug classes would expand our drug repertoire for new targets.

From our long-standing, flagship SCREEN-WELL® Compound library product family to our complex CELLESTIAL® portfolio of fluorescent probes and assay kits for cellular analysis, Enzo Life Sciences provides a complete set of tools for drug discovery, some of which are described below.

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Reference:

  1. P. Imming, et al. Drugs, their targets and the nature and number of drug targets. Nat. Rev. Drug Discov. (2006) 5: 821.
  2. D. C. Swinney, et al. How were new medicines discovered? Nat. Rev. Drug Discov. (2011) 10: 507.
  3. T. Kodadek. Rethinking screening. Nat. Chem. Biol. (2010) 6: 162.
  4. D. Houle, et al. Phenomics: the next challenge. Nat. Rev. Genet. (2010) 11: 855.
  5. J. Kang, et al. Improving drug discovery with high-content phenotypic screens by systematic selection of reporter cell lines. Nat. Biotechnol. (2015).

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