Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Phenotypic drug discovery: recent successes, lessons learned and new directions

A Publisher Correction to this article was published on 10 June 2022

This article has been updated

Abstract

Many drugs, or their antecedents, were discovered through observation of their effects on normal or disease physiology. For the past generation, this phenotypic drug discovery approach has been largely supplanted by the powerful but reductionist approach of modulating specific molecular targets of interest. Nevertheless, modern phenotypic drug discovery, which combines the original concept with modern tools and strategies, has re-emerged over the past decade to systematically pursue drug discovery based on therapeutic effects in realistic disease models. Here, we discuss recent successes with this approach, as well as consider ongoing challenges and approaches to address them. We also explore how innovation in this area may fuel the next generation of successful projects.

This is a preview of subscription content

Access options

Buy article

Get time limited or full article access on ReadCube.

$32.00

All prices are NET prices.

Fig. 1: Spectrum of phenotypic drug discovery approaches.
Fig. 2: Chemical structures of low-molecular-weight clinical candidates and drugs derived from phenotypic screening approaches.
Fig. 3: Target identification in phenotypic drug discovery (PDD).
Fig. 4: Utility of active–inactive compound pairs to address safety questions for compound series with unknown target(s) or mechanisms of action (MoAs).
Fig. 5: Schematic overview of an industrialized phenotypic drug discovery (PDD) process.

Change history

References

  1. Swinney, D. C. & Anthony, J. How were new medicines discovered? Nat. Rev. Drug Discov. 10, 507–519 (2011).

    CAS  PubMed  Google Scholar 

  2. Lee, J. A. & Berg, E. L. Neoclassic drug discovery: the case for lead generation using phenotypic and functional approaches. J. Biomol. Screen. 18, 1143–1155 (2013).

    CAS  PubMed  Google Scholar 

  3. Moffat, J. G., Vincent, F., Lee, J. A., Eder, J. & Prunotto, M. Opportunities and challenges in phenotypic drug discovery: an industry perspective. Nat. Rev. Drug Discov. 16, 531–543 (2017).

    CAS  PubMed  Google Scholar 

  4. Eder, J., Sedrani, R. & Wiesmann, C. The discovery of first-in-class drugs: origins and evolution. Nat. Rev. Drug Discov. 13, 577–587 (2014).

    CAS  PubMed  Google Scholar 

  5. Edwards, A. What are the odds of finding a COVID-19 drug from a lab repurposing screen? J. Chem. Inf. Model. 60, 5727–5729 (2020).

    CAS  PubMed  Google Scholar 

  6. Vincent, F. et al. Developing predictive assays: the phenotypic screening “rule of 3”. Sci. Transl. Med. 7, 293ps215 (2015).

    Google Scholar 

  7. Haasen, D. et al. How phenotypic screening influenced drug discovery: lessons from five years of practice. Assay. Drug Dev. Technol. 15, 239–246 (2017).

    CAS  PubMed  Google Scholar 

  8. Comess, K. M. et al. Emerging approaches for the identification of protein targets of small molecules — a practitioners’ perspective. J. Med. Chem. 61, 8504–8535 (2018).

    CAS  PubMed  Google Scholar 

  9. Vincent, F. et al. Hit triage and validation in phenotypic screening: considerations and strategies. Cell Chem. Biol. 27, 1332–1346 (2020).

    CAS  PubMed  Google Scholar 

  10. Zajac, M. et al. Hepatitis C — new drugs and treatment prospects. Eur. J. Med. Chem. 165, 225–249 (2019).

    CAS  PubMed  Google Scholar 

  11. Lemm, J. A. et al. Identification of hepatitis C virus NS5A inhibitors. J. Virol. 84, 482–491 (2010).

    CAS  PubMed  Google Scholar 

  12. Boyle, M. P. & De Boeck, K. A new era in the treatment of cystic fibrosis: correction of the underlying CFTR defect. Lancet Respir. Med. 1, 158–163 (2013).

    PubMed  Google Scholar 

  13. Van Goor, F. et al. Rescue of CF airway epithelial cell function in vitro by a CFTR potentiator, VX-770. Proc. Natl Acad. Sci. USA 106, 18825–18830 (2009).

    PubMed  PubMed Central  Google Scholar 

  14. Van Goor, F. et al. Correction of the F508del-CFTR protein processing defect in vitro by the investigational drug VX-809. Proc. Natl Acad. Sci. USA 108, 18843–18848 (2011).

    PubMed  PubMed Central  Google Scholar 

  15. Middleton, P. G. et al. Elexacaftor–tezacaftor–ivacaftor for cystic fibrosis with a single Phe508del allele. N. Engl. J. Med. 381, 1809–1819 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  16. Singhal, S. et al. Antitumor activity of thalidomide in refractory multiple myeloma. N. Engl. J. Med. 341, 1565–1571 (1999).

    CAS  PubMed  Google Scholar 

  17. Millrine, D. & Kishimoto, T. A brighter side to thalidomide: its potential use in immunological disorders. Trends Mol. Med. 23, 348–361 (2017).

    CAS  PubMed  Google Scholar 

  18. Lindner, S. & Kronke, J. The molecular mechanism of thalidomide analogs in hematologic malignancies. J. Mol. Med. 94, 1327–1334 (2016).

    CAS  PubMed  Google Scholar 

  19. Urquhart, L. Top companies and drugs by sales in 2020. Nat. Rev. Drug Discov. 20, 253 (2021).

    CAS  PubMed  Google Scholar 

  20. Lu, G. et al. The myeloma drug lenalidomide promotes the cereblon-dependent destruction of Ikaros proteins. Science 343, 305–309 (2014).

    CAS  PubMed  Google Scholar 

  21. Schreiber, S. L. The rise of molecular glues. Cell 184, 3–9 (2021).

    CAS  PubMed  Google Scholar 

  22. Palacino, J. et al. SMN2 splice modulators enhance U1-pre-mRNA association and rescue SMA mice. Nat. Chem. Biol. 11, 511–517 (2015).

    CAS  PubMed  Google Scholar 

  23. Naryshkin, N. A. et al. Motor neuron disease. SMN2 splicing modifiers improve motor function and longevity in mice with spinal muscular atrophy. Science 345, 688–693 (2014).

    CAS  PubMed  Google Scholar 

  24. Sivaramakrishnan, M. et al. Binding to SMN2 pre-mRNA–protein complex elicits specificity for small molecule splicing modifiers. Nat. Commun. 8, 1476 (2017).

    PubMed  PubMed Central  Google Scholar 

  25. Campagne, S. et al. Structural basis of a small molecule targeting RNA for a specific splicing correction. Nat. Chem. Biol. 15, 1191–1198 (2019).

    CAS  PubMed  Google Scholar 

  26. Hopkins, A. L. Network pharmacology: the next paradigm in drug discovery. Nat. Chem. Biol. 4, 682–690 (2008).

    CAS  PubMed  Google Scholar 

  27. Reddy, A. S. & Zhang, S. Polypharmacology: drug discovery for the future. Expert Rev. Clin. Pharmacol. 6, 41–47 (2013).

    CAS  PubMed  Google Scholar 

  28. Keiser, M. J. et al. Predicting new molecular targets for known drugs. Nature 462, 175–181 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  29. Mestres, J., Gregori-Puigjane, E., Valverde, S. & Sole, R. V. The topology of drug-target interaction networks: implicit dependence on drug properties and target families. Mol. Biosyst. 5, 1051–1057 (2009).

    CAS  PubMed  Google Scholar 

  30. Lin, A. et al. Off-target toxicity is a common mechanism of action of cancer drugs undergoing clinical trials. Sci. Transl. Med. 11, eaaw8412 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  31. Lotsch, J. & Geisslinger, G. Low-dose drug combinations along molecular pathways could maximize therapeutic effectiveness while minimizing collateral adverse effects. Drug Discov. Today 16, 1001–1006 (2011).

    PubMed  Google Scholar 

  32. Gitelman, S. E. et al. Imatinib therapy for patients with recent-onset type 1 diabetes: a multicentre, randomised, double-blind, placebo-controlled, phase 2 trial. Lancet Diabetes Endocrinol. 9, 502–514 (2021).

    CAS  PubMed  Google Scholar 

  33. Capdeville, R., Buchdunger, E., Zimmermann, J. & Matter, A. Glivec (STI571, imatinib), a rationally developed, targeted anticancer drug. Nat. Rev. Drug Discov. 1, 493–502 (2002).

    CAS  PubMed  Google Scholar 

  34. Wong, S. et al. Sole BCR-ABL inhibition is insufficient to eliminate all myeloproliferative disorder cell populations. Proc. Natl Acad. Sci. USA 101, 17456–17461 (2004).

    CAS  PubMed  PubMed Central  Google Scholar 

  35. Cohen, P., Cross, D. & Janne, P. A. Kinase drug discovery 20 years after imatinib: progress and future directions. Nat. Rev. Drug Discov. 20, 551–569 (2021).

    CAS  PubMed  Google Scholar 

  36. Crystal, A. S. et al. Patient-derived models of acquired resistance can identify effective drug combinations for cancer. Science 346, 1480–1486 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  37. Ianevski, A. et al. Identification and tracking of antiviral drug combinations. Viruses 12, 1178 (2020).

    CAS  PubMed Central  Google Scholar 

  38. van Hasselt, J. G. C. & Iyengar, R. Systems pharmacology: defining the interactions of drug combinations. Annu. Rev. Pharmacol. Toxicol. 59, 21–40 (2019).

    PubMed  Google Scholar 

  39. Morphy, R. Selectively nonselective kinase inhibition: striking the right balance. J. Med. Chem. 53, 1413–1437 (2010).

    CAS  PubMed  Google Scholar 

  40. Roth, B. L., Sheffler, D. J. & Kroeze, W. K. Magic shotguns versus magic bullets: selectively non-selective drugs for mood disorders and schizophrenia. Nat. Rev. Drug Discov. 3, 353–359 (2004).

    CAS  PubMed  Google Scholar 

  41. Alexandrov, V., Brunner, D., Hanania, T. & Leahy, E. High-throughput analysis of behavior for drug discovery. Eur. J. Pharmacol. 750, 82–89 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  42. Rusinova, R., Koeppe, R. E. 2nd & Andersen, O. S. A general mechanism for drug promiscuity: studies with amiodarone and other antiarrhythmics. J. Gen. Physiol. 146, 463–475 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  43. Gillman, P. K. Tricyclic antidepressant pharmacology and therapeutic drug interactions updated. Br. J. Pharmacol. 151, 737–748 (2007).

    CAS  PubMed  PubMed Central  Google Scholar 

  44. Casarotto, P. C. et al. Antidepressant drugs act by directly binding to TRKB neurotrophin receptors. Cell 184, 1299–1313.e19 (2021).

    CAS  PubMed  PubMed Central  Google Scholar 

  45. Maryanoff, B. Phenotypic assessment and the discovery of topiramate. ACS Med. Chem. Lett. 7, 662–665 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  46. Taylor, E. C. et al. A dideazatetrahydrofolate analogue lacking a chiral center at C-6, N-[4-[2-(2-amino-3,4-dihydro-4-oxo-7H-pyrrolo[2,3-d]pyrimidin-5-yl)ethyl]benzoyl]-L-glutamic acid, is an inhibitor of thymidylate synthase. J. Med. Chem. 35, 4450–4454 (1992).

    CAS  PubMed  Google Scholar 

  47. Mendelsohn, L. G. et al. Enzyme inhibition, polyglutamation, and the effect of LY231514 (MTA) on purine biosynthesis. Semin. Oncol. 26, 42–47 (1999).

    CAS  PubMed  Google Scholar 

  48. Mirguet, O. et al. Discovery of epigenetic regulator I-BET762: lead optimization to afford a clinical candidate inhibitor of the BET bromodomains. J. Med. Chem. 56, 7501–7515 (2013).

    CAS  PubMed  Google Scholar 

  49. Piha-Paul, S. A. et al. Phase 1 study of molibresib (GSK525762), a bromodomain and extra-terminal domain protein inhibitor, in NUT carcinoma and other solid tumors. JNCI Cancer Spectr. 4, pkz093 (2020).

    PubMed  Google Scholar 

  50. Han, X. et al. Discovery of RG7834: the first-in-class selective and orally available small molecule hepatitis B virus expression inhibitor with novel mechanism of action. J. Med. Chem. 61, 10619–10634 (2018).

    CAS  PubMed  Google Scholar 

  51. Mueller, H. et al. A novel orally available small molecule that inhibits hepatitis B virus expression. J. Hepatol. 68, 412–420 (2018).

    CAS  PubMed  Google Scholar 

  52. Dedic, N. et al. SEP-363856, a novel psychotropic agent with a unique, non-D2 receptor mechanism of action. J. Pharmacol. Exp. Ther. 371, 1–14 (2019).

    CAS  PubMed  Google Scholar 

  53. Al-Ali, H. et al. Rational polypharmacology: systematically identifying and engaging multiple drug targets to promote axon growth. ACS Chem. Biol. 10, 1939–1951 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  54. Chiarelli, L. R. et al. A multitarget approach to drug discovery inhibiting Mycobacterium tuberculosis PyrG and PanK. Sci. Rep. 8, 3187 (2018).

    PubMed  PubMed Central  Google Scholar 

  55. Sumi, N. J. et al. Divergent polypharmacology-driven cellular activity of structurally similar multi-kinase inhibitors through cumulative effects on individual targets. Cell Chem. Biol. 26, 1240–1252.e11 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  56. Ahn, S. et al. Cyclin-dependent kinase 5 inhibitor butyrolactone I elicits a partial agonist activity of peroxisome proliferator-activated receptor γ. Biomolecules 10, 275 (2020).

    CAS  PubMed Central  Google Scholar 

  57. Sun, D. et al. Dual-target kinase drug design: current strategies and future directions in cancer therapy. Eur. J. Med. Chem. 188, 112025 (2020).

    CAS  PubMed  Google Scholar 

  58. Labrijn, A. F., Janmaat, M. L., Reichert, J. M. & Parren, P. Bispecific antibodies: a mechanistic review of the pipeline. Nat. Rev. Drug Discov. 18, 585–608 (2019).

    CAS  PubMed  Google Scholar 

  59. Proschak, E., Stark, H. & Merk, D. Polypharmacology by design: a medicinal chemist’s perspective on multitargeting compounds. J. Med. Chem. 62, 420–444 (2019).

    CAS  PubMed  Google Scholar 

  60. Besnard, J. et al. Automated design of ligands to polypharmacological profiles. Nature 492, 215–220 (2012).

    CAS  PubMed  Google Scholar 

  61. Da, C. et al. Data-driven construction of antitumor agents with controlled polypharmacology. J. Am. Chem. Soc. 141, 15700–15709 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  62. Sweis, R. F. Target (In)validation: a critical, sometimes unheralded, role of modern medicinal chemistry. ACS Med. Chem. Lett. 6, 618–621 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  63. Monteleone, S., Fuchs, J. E. & Liedl, K. R. Molecular connectivity predefines polypharmacology: aliphatic rings, chirality, and sp3 centers enhance target selectivity. Front. Pharmacol. 8, 552 (2017).

    PubMed  PubMed Central  Google Scholar 

  64. Bendels, S. et al. Safety screening in early drug discovery: an optimized assay panel. J. Pharmacol. Toxicol. Methods 99, 106609 (2019).

    CAS  PubMed  Google Scholar 

  65. Bowes, J. et al. Reducing safety-related drug attrition: the use of in vitro pharmacological profiling. Nat. Rev. Drug. Discov. 11, 909–922 (2012).

    CAS  PubMed  Google Scholar 

  66. Tear, W. F. et al. Selectivity and physicochemical optimization of repurposed pyrazolo[1,5-b]pyridazines for the treatment of human african trypanosomiasis. J. Med. Chem. 63, 756–783 (2020).

    CAS  PubMed  Google Scholar 

  67. Orellana, A. et al. Application of a phenotypic drug discovery strategy to identify biological and chemical starting points for inhibition of TSLP production in lung epithelial cells. PLoS ONE 13, e0189247 (2018).

    PubMed  PubMed Central  Google Scholar 

  68. Subramanian, A. et al. A next generation Connectivity Map: L1000 platform and the first 1,000,000 profiles. Cell 171, 1437–1452.e17 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  69. Berg, E. L. Phenotypic chemical biology for predicting safety and efficacy. Drug Discov. Today Technol. 23, 53–60 (2017).

    PubMed  Google Scholar 

  70. Drawnel, F. M. et al. Molecular phenotyping combines molecular information, biological relevance, and patient data to improve productivity of early drug discovery. Cell Chem. Biol. 24, 624–634.e3 (2017).

    CAS  PubMed  Google Scholar 

  71. Zoffmann, S. et al. Machine learning-powered antibiotics phenotypic drug discovery. Sci. Rep. 9, 5013 (2019).

    PubMed  PubMed Central  Google Scholar 

  72. Connelly, C. M., Moon, M. H. & Schneekloth, J. S. Jr. The emerging role of RNA as a therapeutic target for small molecules. Cell Chem. Biol. 23, 1077–1090 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  73. Warner, K. D., Hajdin, C. E. & Weeks, K. M. Principles for targeting RNA with drug-like small molecules. Nat. Rev. Drug Discov. 17, 547–558 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  74. Shultz, M. D. Two decades under the influence of the rule of five and the changing properties of approved oral drugs. J. Med. Chem. 62, 1701–1714 (2019).

    CAS  PubMed  Google Scholar 

  75. Rainsford, K. D. in Ibuprofen: Discovery, Development and Therapeutics (ed. Rainsford K. D.) Ch. 1 (Wiley-Blackwell, 2015).

  76. Martin, D. Guinter Kahn, inventor of baldness remedy, dies at 80. New York Times (19 September 2014).

  77. Alam, S., Lingenfelter, K. S., Bender, A. M. & Lindsley, C. W. Classics in chemical neuroscience: memantine. ACS Chem. Neurosci. 8, 1823–1829 (2017).

    CAS  PubMed  Google Scholar 

  78. Choi, D., Stables, J. P. & Kohn, H. Synthesis and anticonvulsant activities of N-benzyl-2-acetamidopropionamide derivatives. J. Med. Chem. 39, 1907–1916 (1996).

    CAS  PubMed  Google Scholar 

  79. Shao, L. et al. In vivo phenotypic drug discovery: applying a behavioral assay to the discovery and optimization of novel antipsychotic agents. Med. Chem. Commun. 7, 1093–1101 (2016).

    CAS  Google Scholar 

  80. Koblan, K. S. et al. A non-D2-receptor-binding drug for the treatment of schizophrenia. N. Engl. J. Med. 382, 1497–1506 (2020).

    CAS  PubMed  Google Scholar 

  81. Saporito, M. S., Ochman, A. R., Lipinski, C. A., Handler, J. A. & Reaume, A. G. MLR-1023 is a potent and selective allosteric activator of Lyn kinase in vitro that improves glucose tolerance in vivo. J. Pharmacol. Exp. Ther. 342, 15–22 (2012).

    CAS  PubMed  Google Scholar 

  82. Lipinski, C. A. & Reaume, A. G. High throughput in vivo phenotypic screening for drug repurposing: discovery of MLR-1023 a novel insulin sensitizer and novel Lyn kinase activator with clinical proof of concept. Bioorg. Med. Chem. 28, 115425 (2020).

    CAS  PubMed  Google Scholar 

  83. Faissner, S. & Gold, R. Oral therapies for multiple sclerosis. Cold Spring Harb. Perspect. Med. 9, a032011 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  84. Jhoti, H., Williams, G., Rees, D. C. & Murray, C. W. The ‘rule of three’ for fragment-based drug discovery: where are we now? Nat. Rev. Drug Discov. 12, 644–645 (2013).

    CAS  PubMed  Google Scholar 

  85. Raymer, B. & Bhattacharya, S. K. Lead-like drugs: a perspective. J. Med. Chem. 61, 10375–10384 (2018).

    CAS  PubMed  Google Scholar 

  86. Hopkins, A. L., Keseru, G. M., Leeson, P. D., Rees, D. C. & Reynolds, C. H. The role of ligand efficiency metrics in drug discovery. Nat. Rev. Drug Discov. 13, 105–121 (2014).

    CAS  PubMed  Google Scholar 

  87. Ayotte, Y. et al. Fragment-based phenotypic lead discovery to identify new drug seeds that target infectious diseases. ACS Chem. Biol. 16, 2158–2163 (2021).

    CAS  PubMed  Google Scholar 

  88. Wenchao, L. et al. Fragment-based covalent ligand discovery. RSC Chem. Biol. 9, 354–367 (2021).

    Google Scholar 

  89. Parker, C. G. et al. Ligand and target discovery by fragment-based screening in human cells. Cell 168, 527–541.e29 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  90. Belema, M. & Meanwell, N. A. Discovery of daclatasvir, a pan-genotypic hepatitis C virus NS5A replication complex inhibitor with potent clinical effect. J. Med. Chem. 57, 5057–5071 (2014).

    CAS  PubMed  Google Scholar 

  91. Petersen, D. N. et al. A small-molecule anti-secretagogue of PCSK9 targets the 80S ribosome to inhibit PCSK9 protein translation. Cell Chem. Biol. 23, 1362–1371 (2016).

    CAS  PubMed  Google Scholar 

  92. Lintner, N. G. et al. Selective stalling of human translation through small-molecule engagement of the ribosome nascent chain. PLoS Biol. 15, e2001882 (2017).

    PubMed  PubMed Central  Google Scholar 

  93. Wijayaratne, A. L. & McDonnell, D. P. The human estrogen receptor-α is a ubiquitinated protein whose stability is affected differentially by agonists, antagonists, and selective estrogen receptor modulators. J. Biol. Chem. 276, 35684–35692 (2001).

    CAS  PubMed  Google Scholar 

  94. de Waal, L. et al. Identification of cancer-cytotoxic modulators of PDE3A by predictive chemogenomics. Nat. Chem. Biol. 12, 102–108 (2016).

    PubMed  Google Scholar 

  95. Hughes, R. E., Elliott, R. J. R., Dawson, J. C. & Carragher, N. O. High-content phenotypic and pathway profiling to advance drug discovery in diseases of unmet need. Cell Chem. Biol. 28, 338–355 (2021).

    CAS  PubMed  Google Scholar 

  96. Schenone, M., Dancik, V., Wagner, B. K. & Clemons, P. A. Target identification and mechanism of action in chemical biology and drug discovery. Nat. Chem. Biol. 9, 232–240 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  97. Terstappen, G. C., Schlupen, C., Raggiaschi, R. & Gaviraghi, G. Target deconvolution strategies in drug discovery. Nat. Rev. Drug Discov. 6, 891–903 (2007).

    CAS  PubMed  Google Scholar 

  98. Kosaka, T. et al. Identification of molecular target of AMP-activated protein kinase activator by affinity purification and mass spectrometry. Anal. Chem. 77, 2050–2055 (2005).

    CAS  PubMed  Google Scholar 

  99. Ong, S. E. et al. Identifying the proteins to which small-molecule probes and drugs bind in cells. Proc. Natl Acad. Sci. USA 106, 4617–4622 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  100. Harding, M. W., Galat, A., Uehling, D. E. & Schreiber, S. L. A receptor for the immunosuppressant FK506 is a cistrans peptidyl-prolyl isomerase. Nature 341, 758–760 (1989).

    CAS  PubMed  Google Scholar 

  101. Seneviratne, U. et al. Photoaffinity labeling and quantitative chemical proteomics identify LXRβ as the functional target of enhancers of astrocytic apoE. Cell Chem. Biol. 28, 148–157.e7 (2021).

    CAS  PubMed  Google Scholar 

  102. Huang, Z. et al. Global portrait of protein targets of metabolites of the neurotoxic compound BIA 10-2474. ACS Chem. Biol. 14, 192–197 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  103. Wang, Z. H. et al. C/EBPβ regulates δ-secretase expression and mediates pathogenesis in mouse models of Alzheimer’s disease. Nat. Commun. 9, 1784 (2018).

    PubMed  PubMed Central  Google Scholar 

  104. Martinez Molina, D. et al. Monitoring drug target engagement in cells and tissues using the cellular thermal shift assay. Science 341, 84–87 (2013).

    PubMed  Google Scholar 

  105. Carnero Corrales, M. A. et al. Thermal proteome profiling identifies the membrane-bound purinergic receptor P2X4 as a target of the autophagy inhibitor indophagolin. Cell Chem. Biol. 28, 1750–1757.E5 (2021).

    CAS  PubMed  Google Scholar 

  106. Schmidt, R. et al. CRISPR activation and interference screens decode stimulation responses in primary human T cells. Science 375, eabj4008 (2022).

    CAS  PubMed  Google Scholar 

  107. Neggers, J. E. et al. Target identification of small molecules using large-scale CRISPR–Cas mutagenesis scanning of essential genes. Nat. Commun. 9, 502 (2018).

    PubMed  PubMed Central  Google Scholar 

  108. Deans, R. M. et al. Parallel shRNA and CRISPR–Cas9 screens enable antiviral drug target identification. Nat. Chem. Biol. 12, 361–366 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  109. Matheny, C. J. et al. Next-generation NAMPT inhibitors identified by sequential high-throughput phenotypic chemical and functional genomic screens. Chem. Biol. 20, 1352–1363 (2013).

    CAS  PubMed  Google Scholar 

  110. Cheng, J. et al. Small-molecule probe reveals a kinase cascade that links stress signaling to TCF/LEF and Wnt responsiveness. Cell Chem. Biol. 28, 625–635 (2021).

    CAS  PubMed  PubMed Central  Google Scholar 

  111. Tsherniak, A. et al. Defining a cancer dependency map. Cell 170, 564–576.e16 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  112. Yu, C. et al. High-throughput identification of genotype-specific cancer vulnerabilities in mixtures of barcoded tumor cell lines. Nat. Biotechnol. 34, 419–423 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  113. Lamb, J. et al. The Connectivity Map: using gene-expression signatures to connect small molecules, genes, and disease. Science 313, 1929–1935 (2006).

    CAS  PubMed  Google Scholar 

  114. Keenan, A. B. et al. The Library of Integrated Network-Based Cellular Signatures NIH program: system-level cataloging of human cells response to perturbations. Cell Syst. 6, 13–24 (2018).

    CAS  PubMed  Google Scholar 

  115. Gustafsdottir, S. M. et al. Multiplex cytological profiling assay to measure diverse cellular states. PLoS ONE 8, e80999 (2013).

    PubMed  PubMed Central  Google Scholar 

  116. Bray, M. A. et al. Cell Painting, a high-content image-based assay for morphological profiling using multiplexed fluorescent dyes. Nat. Protoc. 11, 1757–1774 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  117. Pahl, A. & Sievers, S. The Cell Painting assay as a screening tool for the discovery of bioactivities in new chemical matter. Methods Mol. Biol. 1888, 115–126 (2019).

    CAS  PubMed  Google Scholar 

  118. Bray, M. A. et al. A dataset of images and morphological profiles of 30 000 small-molecule treatments using the Cell Painting assay. Gigascience 6, 1–5 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  119. Kunkel, E. J. et al. Rapid structure–activity and selectivity analysis of kinase inhibitors by BioMAP analysis in complex human primary cell-based models. Assay. Drug Dev. Technol. 2, 431–441 (2004).

    CAS  PubMed  Google Scholar 

  120. Kunkel, E. J. et al. An integrative biology approach for analysis of drug action in models of human vascular inflammation. FASEB J. 18, 1279–1281 (2004).

    CAS  PubMed  Google Scholar 

  121. Smith, S. H. et al. Tapinarof is a natural AhR agonist that resolves skin inflammation in mice and humans. J. Invest. Dermatol. 137, 2110–2119 (2017).

    CAS  PubMed  Google Scholar 

  122. Moffat, J. G., Rudolph, J. & Bailey, D. Phenotypic screening in cancer drug discovery — past, present and future. Nat. Rev. Drug Discov. 13, 588–602 (2014).

    CAS  PubMed  Google Scholar 

  123. Kwon, H. & Lok, A. S. Hepatitis B therapy. Nat. Rev. Gastroenterol. Hepatol. 8, 275–284 (2011).

    CAS  PubMed  Google Scholar 

  124. Zhu, D. et al. Clearing persistent extracellular antigen of hepatitis B virus: an immunomodulatory strategy to reverse tolerance for an effective therapeutic vaccination. J. Immunol. 196, 3079–3087 (2016).

    CAS  PubMed  Google Scholar 

  125. Cheung, A. K. et al. Discovery of small molecule splicing modulators of survival motor neuron-2 (SMN2) for the treatment of spinal muscular atrophy (SMA). J. Med. Chem. 61, 11021–11036 (2018).

    CAS  PubMed  Google Scholar 

  126. Ratni, H. et al. Discovery of risdiplam, a selective survival of motor neuron-2 (SMN2) gene splicing modifier for the treatment of spinal muscular atrophy (SMA). J. Med. Chem. 61, 6501–6517 (2018).

    CAS  PubMed  Google Scholar 

  127. Sturm, S. et al. A phase 1 healthy male volunteer single escalating dose study of the pharmacokinetics and pharmacodynamics of risdiplam (RG7916, RO7034067), a SMN2 splicing modifier. Br. J. Clin. Pharmacol. 85, 181–193 (2019).

    CAS  PubMed  Google Scholar 

  128. Vincent, F. in Phenotypic Drug Discovery: Recent Advances and Insights from Chemical and Systems Biology (Keystone Symposia).

  129. Cassar, S. et al. Use of zebrafish in drug discovery toxicology. Chem. Res. Toxicol. 33, 95–118 (2020).

    CAS  PubMed  Google Scholar 

  130. Shah, F. et al. Mechanisms of skin toxicity associated with metabotropic glutamate receptor 5 negative allosteric modulators. Cell Chem. Biol. 24, 858–869.e5 (2017).

    CAS  PubMed  Google Scholar 

  131. Kleinstreuer, N. C. et al. Phenotypic screening of the ToxCast chemical library to classify toxic and therapeutic mechanisms. Nat. Biotechnol. 32, 583–591 (2014).

    CAS  PubMed  Google Scholar 

  132. Rogawski, M. A., Tofighy, A., White, H. S., Matagne, A. & Wolff, C. Current understanding of the mechanism of action of the antiepileptic drug lacosamide. Epilepsy Res. 110, 189–205 (2015).

    CAS  PubMed  Google Scholar 

  133. Labau, J. I. R. et al. Lacosamide Inhibition of NaV1.7 channels depends on its interaction with the voltage sensor domain and the channel pore. Front. Pharmacol. 12, 791740 (2021).

    CAS  PubMed  PubMed Central  Google Scholar 

  134. Javanbakht, H. in Phenotypic Drug Discovery: Recent Advances and Insights from Chemical and Systems Biology (Keystone Symposia, 2019).

  135. Pandika, M. Mining gene expression data for drug discovery. ACS Cent. Sci. 4, 944–947 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  136. Berger, A. H. et al. High-throughput phenotyping of lung cancer somatic mutations. Cancer Cell 30, 214–228 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  137. Gu, M. et al. iPSC–endothelial cell phenotypic drug screening and in silico analyses identify tyrphostin-AG1296 for pulmonary arterial hypertension. Sci. Transl. Med. 13, eaba6480 (2021).

    CAS  PubMed  PubMed Central  Google Scholar 

  138. Alvarez, M. J. et al. A precision oncology approach to the pharmacological targeting of mechanistic dependencies in neuroendocrine tumors. Nat. Genet. 50, 979–989 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  139. Corsello, S. M. et al. The Drug Repurposing Hub: a next-generation drug library and information resource. Nat. Med. 23, 405–408 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  140. Chen, B. et al. Reversal of cancer gene expression correlates with drug efficacy and reveals therapeutic targets. Nat. Commun. 8, 16022 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  141. Mendez, D. et al. ChEMBL: towards direct deposition of bioassay data. Nucleic Acids Res. 47, D930–D940 (2019).

    CAS  PubMed  Google Scholar 

  142. Barretina, J. et al. The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity. Nature 483, 603–607 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  143. Nassiri, I. & McCall, M. N. Systematic exploration of cell morphological phenotypes associated with a transcriptomic query. Nucleic Acids Res. 46, e116 (2018).

    PubMed  PubMed Central  Google Scholar 

  144. Recursion Pharmaceuticals, Inc. Amendment No. 2 to Form S-1 (Securities and Exchange Commission, 2021); https://sec.report/Document/0001193125-21-117033/.

  145. Vamathevan, J. et al. Applications of machine learning in drug discovery and development. Nat. Rev. Drug Discov. 18, 463–477 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  146. Schneider, P. et al. Rethinking drug design in the artificial intelligence era. Nat. Rev. Drug Discov. 19, 353–364 (2020).

    CAS  PubMed  Google Scholar 

  147. Callaway, E. ‘It will change everything’: DeepMind’s AI makes gigantic leap in solving protein structures. Nature 588, 203–204 (2020).

    CAS  PubMed  Google Scholar 

  148. Cova, T. & Pais, A. Deep learning for deep chemistry: optimizing the prediction of chemical patterns. Front. Chem. 7, 809 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  149. Idakwo, G. et al. A review on machine learning methods for in silico toxicity prediction. J. Env. Sci. Health C. Env. Carcinog. Ecotoxicol. Rev. 36, 169–191 (2018).

    CAS  Google Scholar 

  150. Issa, N. T., Stathias, V., Schurer, S. & Dakshanamurthy, S. Machine and deep learning approaches for cancer drug repurposing. Semin. Cancer Biol. 68, 132–142 (2020).

    PubMed  PubMed Central  Google Scholar 

  151. Keshavarzi Arshadi, A., Salem, M., Collins, J., Yuan, J. S. & Chakrabarti, D. DeepMalaria: artificial intelligence driven discovery of potent antiplasmodials. Front. Pharmacol. 10, 1526 (2019).

    PubMed  Google Scholar 

  152. Chandrasekaran, S. N., Ceulemans, H., Boyd, J. D. & Carpenter, A. E. Image-based profiling for drug discovery: due for a machine-learning upgrade? Nat. Rev. Drug Discov. 20, 145–159 (2021).

    CAS  PubMed  Google Scholar 

  153. Rohban, M. H. et al. Systematic morphological profiling of human gene and allele function via Cell Painting. eLife 6, e24060 (2017).

    PubMed  PubMed Central  Google Scholar 

  154. Caicedo, J. C., Singh, S. & Carpenter, A. E. Applications in image-based profiling of perturbations. Curr. Opin. Biotechnol. 39, 134–142 (2016).

    CAS  PubMed  Google Scholar 

  155. Hofmarcher, M., Rumetshofer, E., Clevert, D. A., Hochreiter, S. & Klambauer, G. Accurate prediction of biological assays with high-throughput microscopy images and convolutional networks. J. Chem. Inf. Model. 59, 1163–1171 (2019).

    CAS  PubMed  Google Scholar 

  156. O’Duibhir, E. et al. Machine learning enables live label-free phenotypic screening in three dimensions. Assay. Drug. Dev. Technol. 16, 51–63 (2018).

    PubMed  Google Scholar 

  157. Gautam, P., Jaiswal, A., Aittokallio, T., Al-Ali, H. & Wennerberg, K. Phenotypic screening combined with machine learning for efficient identification of breast cancer-selective therapeutic targets. Cell Chem. Biol. 26, 970–979 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  158. Simm, J. et al. Repurposing high-throughput image assays enables biological activity prediction for drug discovery. Cell Chem. Biol. 25, 611–618.e3 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  159. Stokes, J. M. et al. A deep learning approach to antibiotic discovery. Cell 181, 475–483 (2020).

    CAS  PubMed  Google Scholar 

  160. Scannell, J. W. & Bosley, J. When quality beats quantity: decision theory, drug discovery, and the reproducibility crisis. PLoS ONE 11, e0147215 (2016).

    PubMed  PubMed Central  Google Scholar 

  161. Lam, P. Y. & Peterson, R. T. Developing zebrafish disease models for in vivo small molecule screens. Curr. Opin. Chem. Biol. 50, 37–44 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  162. Ciallella, J. R. & Reaume, A. G. In vivo phenotypic screening: clinical proof of concept for a drug repositioning approach. Drug Discov. Today Technol. 23, 45–52 (2017).

    PubMed  Google Scholar 

  163. Anzalone, A. V. et al. Search-and-replace genome editing without double-strand breaks or donor DNA. Nature 576, 149–157 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  164. le Sage, C., Lawo, S. & Cross, B. C. S. CRISPR: a screener’s guide. SLAS Discov. 25, 233–240 (2020).

    PubMed  Google Scholar 

  165. Friese, A. et al. The convergence of stem cell technologies and phenotypic drug discovery. Cell Chem. Biol. 26, 1050–1066 (2019).

    CAS  PubMed  Google Scholar 

  166. Horvath, P. et al. Screening out irrelevant cell-based models of disease. Nat. Rev. Drug Discov. 15, 751–769 (2016).

    CAS  PubMed  Google Scholar 

  167. Benam, K. H. et al. Engineered in vitro disease models. Annu. Rev. Pathol. 10, 195–262 (2015).

    CAS  PubMed  Google Scholar 

  168. Berg, E. L., Hsu, Y.-C. & Lee, J. A. Consideration of the cellular microenvironment: physiologically relevant co-culture systems in drug discovery. Adv. Drug Deliv. Rev. 69-70, 190–204 (2014).

    Google Scholar 

  169. Hetheridge, C., Mavria, G. & Mellor, H. Uses of the in vitro endothelial-fibroblast organotypic co-culture assay in angiogenesis research. Biochem. Soc. Trans. 39, 1597–1600 (2011).

    CAS  PubMed  Google Scholar 

  170. Thelu, A., Catoire, S. & Kerdine-Romer, S. Immune-competent in vitro co-culture models as an approach for skin sensitisation assessment. Toxicol. Vitr. 62, 104691 (2020).

    Google Scholar 

  171. Carragher, N. et al. Concerns, challenges and promises of high-content analysis of 3D cellular models. Nat. Rev. Drug Discov. 17, 606 (2018).

    CAS  PubMed  Google Scholar 

  172. Kelm, J. M., Lal-Nag, M., Sittampalam, G. S. & Ferrer, M. Translational in vitro research: integrating 3D drug discovery and development processes into the drug development pipeline. Drug Discov. Today 24, 26–30 (2019).

    CAS  PubMed  Google Scholar 

  173. Thery, M. Micropatterning as a tool to decipher cell morphogenesis and functions. J. Cell Sci. 123, 4201–4213 (2010).

    CAS  PubMed  Google Scholar 

  174. Jalili-Firoozinezhad, S. et al. A complex human gut microbiome cultured in an anaerobic intestine-on-a-chip. Nat. Biomed. Eng. 3, 520–531 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  175. Maoz, B. M. et al. A linked organ-on-chip model of the human neurovascular unit reveals the metabolic coupling of endothelial and neuronal cells. Nat. Biotechnol. 36, 865–874 (2018).

    CAS  PubMed  Google Scholar 

  176. Kostrzewski, T. et al. A microphysiological system for studying nonalcoholic steatohepatitis. Hepatol. Commun. 4, 77–91 (2020).

    CAS  PubMed  Google Scholar 

  177. Vunjak-Novakovic, G., Ronaldson-Bouchard, K. & Radisic, M. Organs-on-a-chip models for biological research. Cell 184, 4597–4611 (2021).

    CAS  PubMed  Google Scholar 

  178. Abbott, R. D. & Kaplan, D. L. Strategies for improving the physiological relevance of human engineered tissues. Trends Biotechnol. 33, 401–407 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  179. Ainslie, G. R. et al. Microphysiological lung models to evaluate the safety of new pharmaceutical modalities: a biopharmaceutical perspective. Lab. Chip 19, 3152–3161 (2019).

    CAS  PubMed  Google Scholar 

  180. Williams, M. Target validation. Curr. Opin. Pharmacol. 3, 571–577 (2003).

    CAS  PubMed  Google Scholar 

  181. Kostrzewski, T. et al. Modelling human liver fibrosis in the context of non-alcoholic steatohepatitis using a microphysiological system. Commun. Biol. 4, 1080 (2021).

    CAS  PubMed  PubMed Central  Google Scholar 

  182. Ganesh, K. et al. A rectal cancer organoid platform to study individual responses to chemoradiation. Nat. Med. 25, 1607–1614 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  183. Tiriac, H. et al. Organoid profiling identifies common responders to chemotherapy in pancreatic cancer. Cancer Discov. 8, 1112–1129 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  184. Vlachogiannis, G. et al. Patient-derived organoids model treatment response of metastatic gastrointestinal cancers. Science 359, 920–926 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  185. Yao, Y. et al. Patient-derived organoids predict chemoradiation responses of locally advanced rectal cancer. Cell Stem Cell 26, 17–26.E16 (2020).

    CAS  PubMed  Google Scholar 

  186. Mittal, S. et al. β2-Adrenoreceptor is a regulator of the α-synuclein gene driving risk of Parkinson’s disease. Science 357, 891–898 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  187. Irmisch, A. et al. The Tumor Profiler Study: integrated, multi-omic, functional tumor profiling for clinical decision support. Cancer Cell 39, 288–293 (2021).

    CAS  PubMed  Google Scholar 

  188. Bolker, J. A. Animal models in translational research: rosetta stone or stumbling block? Bioessays https://doi.org/10.1002/bies.201700089 (2017).

    Article  PubMed  Google Scholar 

  189. Hooijmans, C. R. & Ritskes-Hoitinga, M. Progress in using systematic reviews of animal studies to improve translational research. PLoS Med. 10, e1001482 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  190. van der Worp, H. B. et al. Can animal models of disease reliably inform human studies? PLoS Med. 7, e1000245 (2010).

    PubMed  PubMed Central  Google Scholar 

  191. Kim, S. et al. Anticancer flavonoids are mouse-selective STING agonists. ACS Chem. Biol. 8, 1396–1401 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  192. Clohessy, J. G. & Pandolfi, P. P. Mouse hospital and co-clinical trial project — from bench to bedside. Nat. Rev. Clin. Oncol. 12, 491–498 (2015).

    PubMed  Google Scholar 

  193. Clohessy, J. G. & Pandolfi, P. P. The mouse hospital and its integration in ultra-precision approaches to cancer care. Front. Oncol. 8, 340 (2018).

    PubMed  PubMed Central  Google Scholar 

  194. Kuhn, A. et al. Mutant huntingtin’s effects on striatal gene expression in mice recapitulate changes observed in human Huntington’s disease brain and do not differ with mutant huntingtin length or wild-type huntingtin dosage. Hum. Mol. Genet. 16, 1845–1861 (2007).

    CAS  PubMed  Google Scholar 

  195. Roberds, S. L., Filippov, I., Alexandrov, V., Hanania, T. & Brunner, D. Rapid, computer vision-enabled murine screening system identifies neuropharmacological potential of two new mechanisms. Front. Neurosci. 5, 103 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  196. Manguso, R. T. et al. In vivo CRISPR screening identifies Ptpn2 as a cancer immunotherapy target. Nature 547, 413–418 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  197. Kuhn, M. S., Antonio, J. & Platt, R. J. Moving from in vitro to in vivo CRISPR screens. Gene Genome Editing 2, 100008 (2021).

    Google Scholar 

  198. Patton, E. E., Zon, L. I. & Langenau, D. M. Zebrafish disease models in drug discovery: from preclinical modelling to clinical trials. Nat. Rev. Drug Discov. 20, 611–628 (2021).

    CAS  PubMed  Google Scholar 

  199. Rodgers, G. et al. Glimmers in illuminating the druggable genome. Nat. Rev. Drug Discov. 17, 301–302 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  200. Carter, A. J. et al. Target 2035: probing the human proteome. Drug Discov. Today 24, 2111–2115 (2019).

    CAS  PubMed  Google Scholar 

  201. Muller, S. et al. Donated chemical probes for open science. eLife 7, e34311 (2018).

    PubMed  PubMed Central  Google Scholar 

  202. Arrowsmith, C. H. et al. The promise and peril of chemical probes. Nat. Chem. Biol. 11, 536–541 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  203. Swinney, D. C. & Lee, J. A. Recent advances in phenotypic drug discovery. F1000Res https://doi.org/10.12688/f1000research.25813.1 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  204. Spear, K. L. & Brown, S. P. The evolution of library design: crafting smart compound collections for phenotypic screens. Drug Discov. Today Technol. 23, 61–67 (2017).

    PubMed  Google Scholar 

  205. Jones, L. H. & Bunnage, M. E. Applications of chemogenomic library screening in drug discovery. Nat. Rev. Drug Discov. 16, 285–296 (2017).

    CAS  PubMed  Google Scholar 

  206. Van Goor, F. et al. Rescue of ΔF508-CFTR trafficking and gating in human cystic fibrosis airway primary cultures by small molecules. Am. J. Physiol. Lung Cell Mol. Physiol. 290, L1117–L1130 (2006).

    PubMed  Google Scholar 

  207. Savi, P. et al. Identification and biological activity of the active metabolite of clopidogrel. Thromb. Haemost. 84, 891–896 (2000).

    CAS  PubMed  Google Scholar 

  208. Maffrand, J. P. The story of clopidogrel and its predecessor, ticlopidine: could these major antiplatelet and antithrombotic drugs be discovered and developed today? Comptes Rendus Chim. 15, 737–743 (2012).

    CAS  Google Scholar 

  209. Savi, P. et al. P2y12, a new platelet ADP receptor, target of clopidogrel. Biochem. Biophys. Res. Commun. 283, 379–383 (2001).

    CAS  PubMed  Google Scholar 

  210. Tokarski, J. S. et al. Tyrosine kinase 2-mediated signal transduction in T lymphocytes is blocked by pharmacological stabilization of its pseudokinase domain. J. Biol. Chem. 290, 11061–11074 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  211. Nicodeme, E. et al. Suppression of inflammation by a synthetic histone mimic. Nature 468, 1119–1123 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  212. Chung, C. W. et al. Discovery and characterization of small molecule inhibitors of the BET family bromodomains. J. Med. Chem. 54, 3827–3838 (2011).

    CAS  PubMed  Google Scholar 

  213. Ochman, A. R., Lipinski, C. A., Handler, J. A., Reaume, A. G. & Saporito, M. S. The Lyn kinase activator MLR-1023 is a novel insulin receptor potentiator that elicits a rapid-onset and durable improvement in glucose homeostasis in animal models of type 2 diabetes. J. Pharmacol. Exp. Ther. 342, 23–32 (2012).

    CAS  PubMed  Google Scholar 

  214. Brown, W. A. & Rosdolsky, M. The clinical discovery of imipramine. Am. J. Psychiatry 172, 426–429 (2015).

    PubMed  Google Scholar 

  215. Taylor, E. C. The discovery and synthesis of Alimta. Chem. Int. 33, 4–9 (2011).

    Google Scholar 

  216. Al-Ali, H., Schurer, S. C., Lemmon, V. P. & Bixby, J. L. Chemical interrogation of the neuronal kinome using a primary cell-based screening assay. ACS Chem. Biol. 8, 1027–1036 (2013).

    CAS  PubMed  Google Scholar 

  217. Mori, G. et al. Thiophenecarboxamide derivatives activated by EthA kill Mycobacterium tuberculosis by inhibiting the CTP synthetase PyrG. Chem. Biol. 22, 917–927 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  218. Yoshida, T. et al. Identification and characterization of a novel chemotype MEK inhibitor able to alter the phosphorylation state of MEK1/2. Oncotarget 3, 1533–1545 (2012).

    PubMed  PubMed Central  Google Scholar 

  219. Rossi, A. et al. Minoxidil use in dermatology, side effects and recent patents. Recent. Pat. Inflamm. Allergy Drug Discov. 6, 130–136 (2012).

    CAS  PubMed  Google Scholar 

  220. Clader, J. W. The discovery of ezetimibe: a view from outside the receptor. J. Med. Chem. 47, 1–9 (2004).

    CAS  PubMed  Google Scholar 

  221. Schwab, R. S., England, A. C. Jr, Poskanzer, D. C. & Young, R. R. Amantadine in the treatment of Parkinson’s disease. JAMA 208, 1168–1170 (1969).

    CAS  PubMed  Google Scholar 

  222. Ghofrani, H. A., Osterloh, I. H. & Grimminger, F. Sildenafil: from angina to erectile dysfunction to pulmonary hypertension and beyond. Nat. Rev. Drug Discov. 5, 689–702 (2006).

    CAS  PubMed  PubMed Central  Google Scholar 

  223. Krejsa, C. M. et al. Predicting ADME properties and side effects: the BioPrint approach. Curr. Opin. Drug Discov. Devel 6, 470–480 (2003).

    CAS  PubMed  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Fabien Vincent.

Ethics declarations

Competing interests

The following authors are employees of and may own shares in Pfizer (F.V., M.S.), Roche (M.P.) or Almirall (A.N.). The other authors (J.L. and M.M.) declare no competing interests.

Peer review

Peer review information

Nature Reviews Drug Discovery thanks Bridget Wagner, Jeremy Jenkins and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Related links

The Cancer Genome Atlas (TCGA): https://www.cancer.gov/about-nci/organization/ccg/research/structural-genomics/tcga

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Vincent, F., Nueda, A., Lee, J. et al. Phenotypic drug discovery: recent successes, lessons learned and new directions. Nat Rev Drug Discov (2022). https://doi.org/10.1038/s41573-022-00472-w

Download citation

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1038/s41573-022-00472-w

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing