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 via your institution
Access options
Access Nature and 54 other Nature Portfolio journals
Get Nature+, our best-value online-access subscription
$29.99 per month
cancel any time
Subscribe to this journal
Receive 12 print issues and online access
$189.00 per year
only $15.75 per issue
Rent or buy this article
Get just this article for as long as you need it
$39.95
Prices may be subject to local taxes which are calculated during checkout





Change history
10 June 2022
A Correction to this paper has been published: https://doi.org/10.1038/s41573-022-00503-6
References
Swinney, D. C. & Anthony, J. How were new medicines discovered? Nat. Rev. Drug Discov. 10, 507–519 (2011).
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).
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).
Eder, J., Sedrani, R. & Wiesmann, C. The discovery of first-in-class drugs: origins and evolution. Nat. Rev. Drug Discov. 13, 577–587 (2014).
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).
Vincent, F. et al. Developing predictive assays: the phenotypic screening “rule of 3”. Sci. Transl. Med. 7, 293ps215 (2015).
Haasen, D. et al. How phenotypic screening influenced drug discovery: lessons from five years of practice. Assay. Drug Dev. Technol. 15, 239–246 (2017).
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).
Vincent, F. et al. Hit triage and validation in phenotypic screening: considerations and strategies. Cell Chem. Biol. 27, 1332–1346 (2020).
Zajac, M. et al. Hepatitis C — new drugs and treatment prospects. Eur. J. Med. Chem. 165, 225–249 (2019).
Lemm, J. A. et al. Identification of hepatitis C virus NS5A inhibitors. J. Virol. 84, 482–491 (2010).
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).
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).
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).
Middleton, P. G. et al. Elexacaftor–tezacaftor–ivacaftor for cystic fibrosis with a single Phe508del allele. N. Engl. J. Med. 381, 1809–1819 (2019).
Singhal, S. et al. Antitumor activity of thalidomide in refractory multiple myeloma. N. Engl. J. Med. 341, 1565–1571 (1999).
Millrine, D. & Kishimoto, T. A brighter side to thalidomide: its potential use in immunological disorders. Trends Mol. Med. 23, 348–361 (2017).
Lindner, S. & Kronke, J. The molecular mechanism of thalidomide analogs in hematologic malignancies. J. Mol. Med. 94, 1327–1334 (2016).
Urquhart, L. Top companies and drugs by sales in 2020. Nat. Rev. Drug Discov. 20, 253 (2021).
Lu, G. et al. The myeloma drug lenalidomide promotes the cereblon-dependent destruction of Ikaros proteins. Science 343, 305–309 (2014).
Schreiber, S. L. The rise of molecular glues. Cell 184, 3–9 (2021).
Palacino, J. et al. SMN2 splice modulators enhance U1-pre-mRNA association and rescue SMA mice. Nat. Chem. Biol. 11, 511–517 (2015).
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).
Sivaramakrishnan, M. et al. Binding to SMN2 pre-mRNA–protein complex elicits specificity for small molecule splicing modifiers. Nat. Commun. 8, 1476 (2017).
Campagne, S. et al. Structural basis of a small molecule targeting RNA for a specific splicing correction. Nat. Chem. Biol. 15, 1191–1198 (2019).
Hopkins, A. L. Network pharmacology: the next paradigm in drug discovery. Nat. Chem. Biol. 4, 682–690 (2008).
Reddy, A. S. & Zhang, S. Polypharmacology: drug discovery for the future. Expert Rev. Clin. Pharmacol. 6, 41–47 (2013).
Keiser, M. J. et al. Predicting new molecular targets for known drugs. Nature 462, 175–181 (2009).
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).
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).
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).
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).
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).
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).
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).
Crystal, A. S. et al. Patient-derived models of acquired resistance can identify effective drug combinations for cancer. Science 346, 1480–1486 (2014).
Ianevski, A. et al. Identification and tracking of antiviral drug combinations. Viruses 12, 1178 (2020).
van Hasselt, J. G. C. & Iyengar, R. Systems pharmacology: defining the interactions of drug combinations. Annu. Rev. Pharmacol. Toxicol. 59, 21–40 (2019).
Morphy, R. Selectively nonselective kinase inhibition: striking the right balance. J. Med. Chem. 53, 1413–1437 (2010).
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).
Alexandrov, V., Brunner, D., Hanania, T. & Leahy, E. High-throughput analysis of behavior for drug discovery. Eur. J. Pharmacol. 750, 82–89 (2015).
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).
Gillman, P. K. Tricyclic antidepressant pharmacology and therapeutic drug interactions updated. Br. J. Pharmacol. 151, 737–748 (2007).
Casarotto, P. C. et al. Antidepressant drugs act by directly binding to TRKB neurotrophin receptors. Cell 184, 1299–1313.e19 (2021).
Maryanoff, B. Phenotypic assessment and the discovery of topiramate. ACS Med. Chem. Lett. 7, 662–665 (2016).
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).
Mendelsohn, L. G. et al. Enzyme inhibition, polyglutamation, and the effect of LY231514 (MTA) on purine biosynthesis. Semin. Oncol. 26, 42–47 (1999).
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).
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).
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).
Mueller, H. et al. A novel orally available small molecule that inhibits hepatitis B virus expression. J. Hepatol. 68, 412–420 (2018).
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).
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).
Chiarelli, L. R. et al. A multitarget approach to drug discovery inhibiting Mycobacterium tuberculosis PyrG and PanK. Sci. Rep. 8, 3187 (2018).
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).
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).
Sun, D. et al. Dual-target kinase drug design: current strategies and future directions in cancer therapy. Eur. J. Med. Chem. 188, 112025 (2020).
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).
Proschak, E., Stark, H. & Merk, D. Polypharmacology by design: a medicinal chemist’s perspective on multitargeting compounds. J. Med. Chem. 62, 420–444 (2019).
Besnard, J. et al. Automated design of ligands to polypharmacological profiles. Nature 492, 215–220 (2012).
Da, C. et al. Data-driven construction of antitumor agents with controlled polypharmacology. J. Am. Chem. Soc. 141, 15700–15709 (2019).
Sweis, R. F. Target (In)validation: a critical, sometimes unheralded, role of modern medicinal chemistry. ACS Med. Chem. Lett. 6, 618–621 (2015).
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).
Bendels, S. et al. Safety screening in early drug discovery: an optimized assay panel. J. Pharmacol. Toxicol. Methods 99, 106609 (2019).
Bowes, J. et al. Reducing safety-related drug attrition: the use of in vitro pharmacological profiling. Nat. Rev. Drug. Discov. 11, 909–922 (2012).
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).
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).
Subramanian, A. et al. A next generation Connectivity Map: L1000 platform and the first 1,000,000 profiles. Cell 171, 1437–1452.e17 (2017).
Berg, E. L. Phenotypic chemical biology for predicting safety and efficacy. Drug Discov. Today Technol. 23, 53–60 (2017).
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).
Zoffmann, S. et al. Machine learning-powered antibiotics phenotypic drug discovery. Sci. Rep. 9, 5013 (2019).
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).
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).
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).
Rainsford, K. D. in Ibuprofen: Discovery, Development and Therapeutics (ed. Rainsford K. D.) Ch. 1 (Wiley-Blackwell, 2015).
Martin, D. Guinter Kahn, inventor of baldness remedy, dies at 80. New York Times (19 September 2014).
Alam, S., Lingenfelter, K. S., Bender, A. M. & Lindsley, C. W. Classics in chemical neuroscience: memantine. ACS Chem. Neurosci. 8, 1823–1829 (2017).
Choi, D., Stables, J. P. & Kohn, H. Synthesis and anticonvulsant activities of N-benzyl-2-acetamidopropionamide derivatives. J. Med. Chem. 39, 1907–1916 (1996).
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).
Koblan, K. S. et al. A non-D2-receptor-binding drug for the treatment of schizophrenia. N. Engl. J. Med. 382, 1497–1506 (2020).
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).
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).
Faissner, S. & Gold, R. Oral therapies for multiple sclerosis. Cold Spring Harb. Perspect. Med. 9, a032011 (2019).
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).
Raymer, B. & Bhattacharya, S. K. Lead-like drugs: a perspective. J. Med. Chem. 61, 10375–10384 (2018).
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).
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).
Wenchao, L. et al. Fragment-based covalent ligand discovery. RSC Chem. Biol. 9, 354–367 (2021).
Parker, C. G. et al. Ligand and target discovery by fragment-based screening in human cells. Cell 168, 527–541.e29 (2017).
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).
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).
Lintner, N. G. et al. Selective stalling of human translation through small-molecule engagement of the ribosome nascent chain. PLoS Biol. 15, e2001882 (2017).
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).
de Waal, L. et al. Identification of cancer-cytotoxic modulators of PDE3A by predictive chemogenomics. Nat. Chem. Biol. 12, 102–108 (2016).
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).
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).
Terstappen, G. C., Schlupen, C., Raggiaschi, R. & Gaviraghi, G. Target deconvolution strategies in drug discovery. Nat. Rev. Drug Discov. 6, 891–903 (2007).
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).
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).
Harding, M. W., Galat, A., Uehling, D. E. & Schreiber, S. L. A receptor for the immunosuppressant FK506 is a cis–trans peptidyl-prolyl isomerase. Nature 341, 758–760 (1989).
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).
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).
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).
Martinez Molina, D. et al. Monitoring drug target engagement in cells and tissues using the cellular thermal shift assay. Science 341, 84–87 (2013).
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).
Schmidt, R. et al. CRISPR activation and interference screens decode stimulation responses in primary human T cells. Science 375, eabj4008 (2022).
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).
Deans, R. M. et al. Parallel shRNA and CRISPR–Cas9 screens enable antiviral drug target identification. Nat. Chem. Biol. 12, 361–366 (2016).
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).
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).
Tsherniak, A. et al. Defining a cancer dependency map. Cell 170, 564–576.e16 (2017).
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).
Lamb, J. et al. The Connectivity Map: using gene-expression signatures to connect small molecules, genes, and disease. Science 313, 1929–1935 (2006).
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).
Gustafsdottir, S. M. et al. Multiplex cytological profiling assay to measure diverse cellular states. PLoS ONE 8, e80999 (2013).
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).
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).
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).
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).
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).
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).
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).
Kwon, H. & Lok, A. S. Hepatitis B therapy. Nat. Rev. Gastroenterol. Hepatol. 8, 275–284 (2011).
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).
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).
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).
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).
Vincent, F. in Phenotypic Drug Discovery: Recent Advances and Insights from Chemical and Systems Biology (Keystone Symposia).
Cassar, S. et al. Use of zebrafish in drug discovery toxicology. Chem. Res. Toxicol. 33, 95–118 (2020).
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).
Kleinstreuer, N. C. et al. Phenotypic screening of the ToxCast chemical library to classify toxic and therapeutic mechanisms. Nat. Biotechnol. 32, 583–591 (2014).
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).
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).
Javanbakht, H. in Phenotypic Drug Discovery: Recent Advances and Insights from Chemical and Systems Biology (Keystone Symposia, 2019).
Pandika, M. Mining gene expression data for drug discovery. ACS Cent. Sci. 4, 944–947 (2018).
Berger, A. H. et al. High-throughput phenotyping of lung cancer somatic mutations. Cancer Cell 30, 214–228 (2016).
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).
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).
Corsello, S. M. et al. The Drug Repurposing Hub: a next-generation drug library and information resource. Nat. Med. 23, 405–408 (2017).
Chen, B. et al. Reversal of cancer gene expression correlates with drug efficacy and reveals therapeutic targets. Nat. Commun. 8, 16022 (2017).
Mendez, D. et al. ChEMBL: towards direct deposition of bioassay data. Nucleic Acids Res. 47, D930–D940 (2019).
Barretina, J. et al. The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity. Nature 483, 603–607 (2012).
Nassiri, I. & McCall, M. N. Systematic exploration of cell morphological phenotypes associated with a transcriptomic query. Nucleic Acids Res. 46, e116 (2018).
Recursion Pharmaceuticals, Inc. Amendment No. 2 to Form S-1 (Securities and Exchange Commission, 2021); https://sec.report/Document/0001193125-21-117033/.
Vamathevan, J. et al. Applications of machine learning in drug discovery and development. Nat. Rev. Drug Discov. 18, 463–477 (2019).
Schneider, P. et al. Rethinking drug design in the artificial intelligence era. Nat. Rev. Drug Discov. 19, 353–364 (2020).
Callaway, E. ‘It will change everything’: DeepMind’s AI makes gigantic leap in solving protein structures. Nature 588, 203–204 (2020).
Cova, T. & Pais, A. Deep learning for deep chemistry: optimizing the prediction of chemical patterns. Front. Chem. 7, 809 (2019).
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).
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).
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).
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).
Rohban, M. H. et al. Systematic morphological profiling of human gene and allele function via Cell Painting. eLife 6, e24060 (2017).
Caicedo, J. C., Singh, S. & Carpenter, A. E. Applications in image-based profiling of perturbations. Curr. Opin. Biotechnol. 39, 134–142 (2016).
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).
O’Duibhir, E. et al. Machine learning enables live label-free phenotypic screening in three dimensions. Assay. Drug. Dev. Technol. 16, 51–63 (2018).
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).
Simm, J. et al. Repurposing high-throughput image assays enables biological activity prediction for drug discovery. Cell Chem. Biol. 25, 611–618.e3 (2018).
Stokes, J. M. et al. A deep learning approach to antibiotic discovery. Cell 181, 475–483 (2020).
Scannell, J. W. & Bosley, J. When quality beats quantity: decision theory, drug discovery, and the reproducibility crisis. PLoS ONE 11, e0147215 (2016).
Lam, P. Y. & Peterson, R. T. Developing zebrafish disease models for in vivo small molecule screens. Curr. Opin. Chem. Biol. 50, 37–44 (2019).
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).
Anzalone, A. V. et al. Search-and-replace genome editing without double-strand breaks or donor DNA. Nature 576, 149–157 (2019).
le Sage, C., Lawo, S. & Cross, B. C. S. CRISPR: a screener’s guide. SLAS Discov. 25, 233–240 (2020).
Friese, A. et al. The convergence of stem cell technologies and phenotypic drug discovery. Cell Chem. Biol. 26, 1050–1066 (2019).
Horvath, P. et al. Screening out irrelevant cell-based models of disease. Nat. Rev. Drug Discov. 15, 751–769 (2016).
Benam, K. H. et al. Engineered in vitro disease models. Annu. Rev. Pathol. 10, 195–262 (2015).
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).
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).
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).
Carragher, N. et al. Concerns, challenges and promises of high-content analysis of 3D cellular models. Nat. Rev. Drug Discov. 17, 606 (2018).
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).
Thery, M. Micropatterning as a tool to decipher cell morphogenesis and functions. J. Cell Sci. 123, 4201–4213 (2010).
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).
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).
Kostrzewski, T. et al. A microphysiological system for studying nonalcoholic steatohepatitis. Hepatol. Commun. 4, 77–91 (2020).
Vunjak-Novakovic, G., Ronaldson-Bouchard, K. & Radisic, M. Organs-on-a-chip models for biological research. Cell 184, 4597–4611 (2021).
Abbott, R. D. & Kaplan, D. L. Strategies for improving the physiological relevance of human engineered tissues. Trends Biotechnol. 33, 401–407 (2015).
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).
Williams, M. Target validation. Curr. Opin. Pharmacol. 3, 571–577 (2003).
Kostrzewski, T. et al. Modelling human liver fibrosis in the context of non-alcoholic steatohepatitis using a microphysiological system. Commun. Biol. 4, 1080 (2021).
Ganesh, K. et al. A rectal cancer organoid platform to study individual responses to chemoradiation. Nat. Med. 25, 1607–1614 (2019).
Tiriac, H. et al. Organoid profiling identifies common responders to chemotherapy in pancreatic cancer. Cancer Discov. 8, 1112–1129 (2018).
Vlachogiannis, G. et al. Patient-derived organoids model treatment response of metastatic gastrointestinal cancers. Science 359, 920–926 (2018).
Yao, Y. et al. Patient-derived organoids predict chemoradiation responses of locally advanced rectal cancer. Cell Stem Cell 26, 17–26.E16 (2020).
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).
Irmisch, A. et al. The Tumor Profiler Study: integrated, multi-omic, functional tumor profiling for clinical decision support. Cancer Cell 39, 288–293 (2021).
Bolker, J. A. Animal models in translational research: rosetta stone or stumbling block? Bioessays https://doi.org/10.1002/bies.201700089 (2017).
Hooijmans, C. R. & Ritskes-Hoitinga, M. Progress in using systematic reviews of animal studies to improve translational research. PLoS Med. 10, e1001482 (2013).
van der Worp, H. B. et al. Can animal models of disease reliably inform human studies? PLoS Med. 7, e1000245 (2010).
Kim, S. et al. Anticancer flavonoids are mouse-selective STING agonists. ACS Chem. Biol. 8, 1396–1401 (2013).
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).
Clohessy, J. G. & Pandolfi, P. P. The mouse hospital and its integration in ultra-precision approaches to cancer care. Front. Oncol. 8, 340 (2018).
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).
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).
Manguso, R. T. et al. In vivo CRISPR screening identifies Ptpn2 as a cancer immunotherapy target. Nature 547, 413–418 (2017).
Kuhn, M. S., Antonio, J. & Platt, R. J. Moving from in vitro to in vivo CRISPR screens. Gene Genome Editing 2, 100008 (2021).
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).
Rodgers, G. et al. Glimmers in illuminating the druggable genome. Nat. Rev. Drug Discov. 17, 301–302 (2018).
Carter, A. J. et al. Target 2035: probing the human proteome. Drug Discov. Today 24, 2111–2115 (2019).
Muller, S. et al. Donated chemical probes for open science. eLife 7, e34311 (2018).
Arrowsmith, C. H. et al. The promise and peril of chemical probes. Nat. Chem. Biol. 11, 536–541 (2015).
Swinney, D. C. & Lee, J. A. Recent advances in phenotypic drug discovery. F1000Res https://doi.org/10.12688/f1000research.25813.1 (2020).
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).
Jones, L. H. & Bunnage, M. E. Applications of chemogenomic library screening in drug discovery. Nat. Rev. Drug Discov. 16, 285–296 (2017).
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).
Savi, P. et al. Identification and biological activity of the active metabolite of clopidogrel. Thromb. Haemost. 84, 891–896 (2000).
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).
Savi, P. et al. P2y12, a new platelet ADP receptor, target of clopidogrel. Biochem. Biophys. Res. Commun. 283, 379–383 (2001).
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).
Nicodeme, E. et al. Suppression of inflammation by a synthetic histone mimic. Nature 468, 1119–1123 (2010).
Chung, C. W. et al. Discovery and characterization of small molecule inhibitors of the BET family bromodomains. J. Med. Chem. 54, 3827–3838 (2011).
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).
Brown, W. A. & Rosdolsky, M. The clinical discovery of imipramine. Am. J. Psychiatry 172, 426–429 (2015).
Taylor, E. C. The discovery and synthesis of Alimta. Chem. Int. 33, 4–9 (2011).
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).
Mori, G. et al. Thiophenecarboxamide derivatives activated by EthA kill Mycobacterium tuberculosis by inhibiting the CTP synthetase PyrG. Chem. Biol. 22, 917–927 (2015).
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).
Rossi, A. et al. Minoxidil use in dermatology, side effects and recent patents. Recent. Pat. Inflamm. Allergy Drug Discov. 6, 130–136 (2012).
Clader, J. W. The discovery of ezetimibe: a view from outside the receptor. J. Med. Chem. 47, 1–9 (2004).
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).
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).
Krejsa, C. M. et al. Predicting ADME properties and side effects: the BioPrint approach. Curr. Opin. Drug Discov. Devel 6, 470–480 (2003).
Author information
Authors and Affiliations
Corresponding author
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
About this article
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 21, 899–914 (2022). https://doi.org/10.1038/s41573-022-00472-w
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1038/s41573-022-00472-w
This article is cited by
-
Repurposing drugs to treat cardiovascular disease in the era of precision medicine
Nature Reviews Cardiology (2022)