Filling the drug discovery gap: is high-content screening the missing link?

Current Opinion in Pharmacology

Volumn 42, Issue , 2018, Pages 40-45

  Dorval, Thierry ^a   Chanrion, Benjamin ^a   Cattin, Marie Elodie ^a   Stephan, Jean Philippe ^a  

^a INSTITUT DE RECHERCHES SERVIER   (France)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; CHEMOINFORMATICS; DATA ANALYSIS; DEEP NEURONAL NETWORK; FLOW CYTOMETRY; FLUORESCENCE MICROSCOPY; GENETIC ENGINEERING; HIGH CONTENT IMAGING; HIGH CONTENT SCREENING; HIGH THROUGHPUT FLOW CYTOMETRY; HIGH THROUGHPUT SCREENING; IMAGE ANALYSIS; IMAGE PROCESSING; IMAGING; IMMUNOCHEMISTRY; INFORMATION PROCESSING; MACHINE LEARNING; MASS SPECTROMETRY; MULTIPLEX IMMUNOCHEMISTRY; PRIORITY JOURNAL; QUANTITATIVE ANALYSIS; REVIEW;

EID: 85050105799     PISSN: 14714892     EISSN: 14714973     Source Type: Journal    
DOI: 10.1016/j.coph.2018.07.002     Document Type: Review

References (44)

1. Drews, J., Ryser, S., The role of innovation in drug development. Nature Biotech 15 (1997), 1318–1319.
2. Horrobin, D.F., Realism in drug discovery – could Cassandra be right?. Nature Biotech 19 (2001), 1099–1100.
3. Ruffolo, R.R., Why has R&D productivity declined in the pharmaceutical industry?. Expert Opin Drug Discov 1 (2006), 99–102.
4. Munos, B., Lessons for 60 years of pharmaceutical innovation. Nature Rev Drug Discov 8 (2009), 959–968.
5. Paul, S.M., Mytelka, D.S., Dunwiddie, C.T., Persinger, C.C., Munos, B.H., Lindborg, S.R., Schacht, A.L., How to improve R&D productivity: the pharmaceutical industry's grand challenge. Nature Rev Drug Discov 9 (2010), 203–214.
6. Bennani, Y.L., Drug discovery in the next decade: innovation needed ASAP. Drug Discov Today 16 (2011), 779–792.
7. Pammolli, F., Magazzini, L., Riccaboni, M., The productivity crisis in pharmaceutical R&D. Nature Rev. Drug Discov 10 (2011), 428–438.
8. Swinney, D.C.V., Anthony, J., How were new medicines discovered?. Nature Rev Drug Discov 10 (2011), 507–519.
9. Scannell, J.W., Blanckley, A., Boldon, H., Warrington, B., Diagnosing the decline in pharmaceutical R&D efficiency. Nature Rev Drug Discov 11 (2012), 191–200.
10. Sams-Dodd, F., Is poor research the cause of the declining productivity of the pharmaceutical industry? An industry in need of a paradigm shift. Drug Discov Today 18 (2013), 211–217.
11. Lee, J.A., Uhlik, M.T., Moxham, C.M., Tomandl, D., Sall, D.J., Modern phenotypic drug discovery is a viable, neoclassic pharma strategy. J Med Chem 55 (2012), 4527–4538.
12. Hoelder, S., Clarke, P.A., Workman, P., Discovery of small molecule cancer drug: successes, challenges and opportunities. Mol Oncol 6 (2012), 155–176.
13. Wagner, B.K., Schreiber, S.L., The power of sophisticated phenotypic screening and modern mechanism-of-action methods. Cell Chem Biol 23 (2016), 3–9.
14. Scannell, J.W., Bosley, J., When quality beats quantity: decision theory, drug discovery, and the reproducibility crisis. PLoS ONE, 11, 2016 e0147215.
15. Eder, J., Sedrani, R., Wiesmann, C., The discovery of first-in class drugs: origins and evolution. Nature Rev. Drug Discov 13 (2014), 577–587.
16. Moffat, J.G., Vincent, F., Lee, J.A., Eder, J., Prunotto, M., Opportunities and challenges in phenotypic drug discovery: an industry perspective. Nature Rev Drug Discov 16 (2017), 531–543 The authors provide a realistic overview of the therapeutic areas which could benefit the best from phenotypic drug discovery approach; challenges and uncertainties that one can face when starting a phenotypic drug discovery program and summarize the risks, costs and potential rewards of this type of drug-discovery pipeline.
17. Zheng, W., Thorne, N., McKew, J.C., Phenotypic screens as a renewed approach for drug discovery. Drug Discov Today 18 (2013), 1067–1073.
18. Auld, D., Thorne, N., Molecular sensors for the transcriptional and post-transcriptional assays. Fu, H., (eds.) Chemical Genomics, 2012, Cambridge University Press, 173–197.
19. Palacino, J., Swalley, S.E., Song, C., Cheung, A.K., Shu, L., Zhang, X., Van Hoosear, M., Shin, Y., Chin, D.N., Keller, C.G., et al. SMN2 splice modulators enhance U1-pre-mRNA association and rescue SMA mice. Nat Chem. Biol 11 (2015), 511–517.
20. Lamb, J., Crawford, E.D., Peck, D., Modell, J.W., Blat, I.C., Wrobel, M.J., Lerner, J., Brunet, J.P., Subramanian, A., Ross, K.N., et al. The connectivity Map: using gene-expression signatures to connect small molecules, genes, and disease. Science 313 (2006), 1929–1935.
21. Znella, F., Lorens, J.B., Link, W., High-content screening: seeing is believing. Trends Biotechnol 28 (2010), 237–245.
22. Barabasz, A., Foley, B., Otto, J.C., Scott, A., Rice, J., The use of high-content screening for the discovery and characterization of compounds that modulate mitotic index and cell cycle progression by differing mechanisms of action. Assay Drug Dev Technol 4 (2006), 153–163.
23. Gasparri, F., Ciavolella, A., Galvani, A., Cell-cycle inhibitor profiling by high-content analysis. Adv Exp Med Biol 604 (2007), 137–148.
24. Thaunat, O., Granja, A.G., Barral, P., Filby, A., Montaner, B., Collinson, L., Martinez-Martin, N., Harwood, N.E., Bruckbauer, A., Batista, F.D., Asymmetric segregation of polarized antigen on B cell division shapes presentation capacity. Science. 335 (2012), 475–479.
25. Tsujikawa, T., Kumar, S., Borkar, R.N., Azimi, V., Thibault, G., Chang, Y.H., Balter, A., Kawashima, R., Choe, G., Sauer, D., et al. Quantitative multiplex immunohistochemistry reveals myeloid-inflamed tumor-immune complexity associated with poor prognosis. Cell Rep 19 (2017), 203–217.
26. Gessel, M.M., Norris, J.L., Caprioli, R.M., MALDI imaging mass spectrometry: spatial molecular analysis to enable a new age of discovery. J Proteomics 107 (2014), 71–82.
27. Boutros, M., Heigwer, F., Laufer, C., Microscopy-based high-content screening. Cell 163 (2015), 1314–1325 The authors provide a comprehensive overview of imaging-based cellular phenotypic screening and discuss general concepts and key considerations for constructing such assays.
28. Calif, B.L., Maunze, B., Krueger, N.L., Simpson, M.T., Blackmore, M.G., The application of CRISPR technology to high content screening in primary neurons. Mol. Cell Neurosci 80 (2017), 170–179.
29. Ryall, K.A., Bezzerides, V.J., Rosenzweig, A., Saucerman, J.J., Phenotypic screen quantifying differential regulation of cardiac myocyte hypertrophy identifies CITED4 regulation of myocyte elongation. J Mol Cell Cardiol 72 (2014), 74–84.
30. Desbordes, S.C., Placantonakis, D.G., Ciro, A., Socci, N.D., Lee, G., Djaballah, H., Studer, L., High-throughput screening assay for the identification of compounds regulating self-renewal and differentiation in human embryonic stem cells. Cell Stem Cell 2 (2008), 602–612.
31. Eulalio, A., Mano, M., Dal Ferro, M., Zentilin, L., Sinagra, G., Zacchigna, S., Giacca, M., Functional screening identifies miRNAs inducing cardiac regeneration. Nature 492 (2012), 376–381.
32. Viotti, M., Wilson, C., McCleland, M., Koeppen, H., Haley, B., Jhunjhunwala, S., Klijn, C., Modrusan, Z., Arnott, D., Classon, M., et al. SUV420H2 is an epigenetic regulator of epithelial/mesenchymal states in pancreatic cancer. J Cell Biol 217 (2018), 763–777 RNAi-HCS targeting 300 epigenetic factors to identify upstream regulators of epithelial/mesenchymal transition, modifying the invasiveness and chemoresistance of pancreatic cancer cells.
33. Usaj, M.M., Styles, E.B., Verster, A.J., Friesen, H., Boone, C., Andrews, B.J., High-content screening for quantitative cell biology. Trend in Cell Biol 26 (2016), 598–611 The authors provide a broad overview of biological questions that can be addressed using HCS approaches and discuss overall images and data analysis that can be used to extract biologically meaningful results out of a HCS dataset.
34. Garippa, R.J., Hoffman, A.F., Gradl, G., Kirsch, A., High-throughput confocal microscopy for beta-arrestin-green fluorescent protein translocation G protein-coupled receptor assays using the Evotec Opera. Methods Enzymol 414 (2006), 99–120.
35. Chin, R.M., Panavas, T., Brown, J.M., Johnson, K.K., Optimized mitochondrial targeting of proteins encoded by modified mRNAs rescues cells harboring mutations in mtATP6. Cell Rep 22 (2018), 2818–2826.
36. Panarella, A., Bexiga, M.G., Galea, G., O'Neill, E., Salvati, A., Dawson, K.A., Simpson, J.C., A systematic High-Content Screening microscopy approach reveals key roles for Rab33b, OATL1 and Myo6 in nanoparticle trafficking in HeLa cells. Sci Rep, 6, 2016, 28865 First RNAi-HCS large-scale assessment of the proteins responsible for nanoparticle trafficking in cells, paving the way for the design of more efficient intracellular drug-delivery strategies.
37. Gough, A., Shun, T.Y., Taylor, D.L., Schurdak, M., Integrating analysis of cellular heterogeneity in high-content dose-response studies methods. Mol Biol 1745 (2018), 25–46.
38. Garvey, C.M., Spiller, E., Lindsay, D., Chiang, C.T., Choi, N.C., Agus, D.B., Mallick, P., Foo, J., Mumenthaler, S.M., A high-content image-based method for quantitatively studying context-dependent cell population dynamics. Sci Rep, 6, 2016, 29752.
39. Horvath, P., Aulner, N., Bickle, M., Davies, A.M., Del Nery, E., Ebner, D., Montoya, M.C., Östling, P., Pietiäinen, V., Price, L.S., Shorte, S.L., Turcatti, G., Vo Schantz, C., Carragher, N.O., Screening out the irrelevant cell-based models of disease. Nature Rev 15 (2016), 751–769.
40. Kang, J., Hsu, C.H., Wu, Q., Liu, S., Coster, A.D., Posner, B., Improving drug discovery with high-content phenotypic screens by systematic selection of reporter cell lines. Nat Biotechnol 34 (January 2016), 70–77 This study reports the development of optimal reporter cell lines for annotating compound libraries (ORACLs) and demonstrates that ORACL can be used to functionally annotate large compound libraries in a single-pass screen and to predict compound leads accurately across multiple drug classes.
41. Pointon, A., Pilling, J., Dorval, T., Wang, Y., Archer, C., Pollar, C., From the cover: High-throughput imaging of cardiac microtissues for the assessment of cardiac contraction during drug discovery. Toxicol Sci 155 (2017), 444–457.
42. Simm, J., Klambauer, G., Arany, A., Steijaert, M., Wegner, J.K., Gustin, E., Chupakhin, V., Chong, Y.T., Vialard, J., Buijnsters, P., Velter, I., Vapirev, A., Singh, S., Carpenter, A.E., Wuyts, R., Hochreiter, S., Moreau, Y., Ceulemans, H., Repurposing high-throughput image assays enables biological activity prediction for drug discovery. Cell Chem Biol 25 (2018), 1–7 Proof of concept that pre-existing HCS dataset can complement chemistry-based knowledge to build compound biological fingerprint and predict compounds activities in independent and unrelated screening assays.
43. Rawat, W., Wang, Z., Deep convolutional neural networks for image classification: a comprehensive review. Neural Comput 29 (2017), 2352–2449.
44. Caicedo, J.C., Singh, S., Carpenter, A.E., Applications in image-based profiling of perturbations. Curr Opin Biotechnol 39 (2016), 134–142.