Principles of early drug discovery
Open accessAbstract
Developing a new drug from original idea to the launch of a finished product is a complex process which can take 12-15 years and cost in excess of $1 billion. The idea for a target can come from a variety of sources including academic and clinical research and from the commercial sector. It may take many years to build up a body of supporting evidence before selecting a target for a costly drug discovery programme. Once a target has been chosen, the pharmaceutical industry and more recently some academic centres have streamlined a number of early processes to identify molecules which possess suitable characteristics to make acceptable drugs. This review will look at key preclinical stages of the drug discovery process, from initial target identification and validation, through assay development, high throughput screening, hit identification, lead optimization and finally the selection of a candidate molecule for clinical development.
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Accurate prediction of the biochemical properties of small molecules can significantly accelerate drug discovery by reducing the need for expensive lab experiments.
“Accurate prediction of the biochemical properties of small molecules can significantly accelerate drug discovery by reducing the need for expensive lab experiments (Shoichet, 2004; Hughes et al., 2011).”
From WILDS: A Benchmark of in-the-Wild Distribution Shifts (2020) · cites this paper as Hughes et al., 2011
The Structural aided drug design section says virtual screening can identify starting structures without expensive large-library screens, and Screen Description Comments says computer-assisted analysis reduces the number of compounds screened. The paper does not establish that accurate prediction of small molecules’ biochemical properties generally accelerates drug discovery. (LLM, conf=0.84)
Experimental — accuracy not yet measuredDrug discovery and development is an immensely long, costly, and complex process that can often take more than 10 years from identification of molecular targets until a drug product is approved and marketed.
“Drug discovery and development is an immensely long, costly, and complex process that can often take more than 10 years from identification of molecular targets until a drug product is approved and marketed.”
From The rise of artificial intelligence in healthcare applications (2020) · cites this paper as [14]
Most drug candidates fail sometime during development and never make it onto the market.
“Any failure during this process has a large financial impact, and in fact most drug candidates fail sometime during development and never make it onto the market.”
From The rise of artificial intelligence in healthcare applications (2020) · cites this paper as [14]
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Cites
Lipinski, C. A., Lombardo, F., Dominy, B. W., & Feeney, P. J. (2001) · Advanced Drug Delivery Reviews · doi:10.1016/s0169-409x(00)00129-0 · Cited by 18,139 · see citing works
Zhang, J.-H., Chung, T. D. Y., & Oldenburg, K. R. (1999) · SLAS Discovery · doi:10.1177/108705719900400206 · Cited by 6,847 · see citing works
Knocking down barriers: advances in siRNA delivery
Whitehead, K. A., Langer, R., & Anderson, D. G. (2009) · Nature Reviews Drug Discovery · doi:10.1038/nrd2742 · Cited by 3,115 · see citing works
An SCN9A channelopathy causes congenital inability to experience pain
Cox, J. J., Reimann, F. M., Nicholas, A. K., Thornton, G., Roberts, E., Springell, K., Karbani, G., Jafri, H., Mannan, J., Raashid, Y., et al. (2006) · Nature · doi:10.1038/nature05413 · Cited by 1,605 · see citing works
The promises and pitfalls of RNA-interference-based therapeutics
Castanotto, D., & Rossi, J. J. (2009) · Nature · doi:10.1038/nature07758 · Cited by 1,219 · see citing works
Mutations in SCN9A, encoding a sodium channel alpha subunit, in patients with primary erythermalgia
Yang, Y., Wang, Y., Li, S., Xu, Z., Li, H., Ma, L., Fan, J., Bu, D., Liu, B., Fan, Z., et al. (2004) · Journal of Medical Genetics · doi:10.1136/jmg.2003.012153 · Cited by 800 · see citing works
Disruption of the P2X7 purinoceptor gene abolishes chronic inflammatory and neuropathic pain
Chessell, I. P., Hatcher, J. P., Bountra, C., Michel, A. D., Hughes, J. P., Green, P., Egerton, J., Murfin, M., Richardson, J. C., Peck, W. L., et al. (2005) · Pain · doi:10.1016/j.pain.2005.01.002 · Cited by 790 · see citing works
Self-citationThirty years of Alzheimer's disease genetics: the implications of systematic meta-analyses
Bertram, L., & Tanzi, R. E. (2008) · Nature Reviews Neuroscience · doi:10.1038/nrn2494 · Cited by 747 · see citing works
Virtual screening strategies in drug discovery
McInnes, C. (2007) · Current Opinion in Chemical Biology · doi:10.1016/j.cbpa.2007.08.033 · Cited by 469 · see citing works
Honoré, P., Kage, K., Mikusa, J. P., et al. (2002) · Pain · doi:10.1016/s0304-3959(02)00032-5 · Cited by 237 · see citing works
High-Throughput Screening: Update on Practices and Success
Fox, S., Farr-Jones, S., Sopchak, L., Boggs, A. F., Nicely, H. W., Khoury, R., & Biros, M. (2006) · SLAS DISCOVERY · doi:10.1177/1087057106292473 · Cited by 218 · see citing works
Cell-based assays: fuelling drug discovery
Michelini, E., Cevenini, L., Mezzanotte, L., Coppa, A., & Roda, A. (2010) · Analytical and Bioanalytical Chemistry · doi:10.1007/s00216-010-3933-z · Cited by 197 · see citing works
Target discovery from data mining approaches
Yang, Y., Adelstein, S. J., & Kassis, A. I. (2009) · Drug Discovery Today · doi:10.1016/j.drudis.2008.12.005 · Cited by 190 · see citing works
The function neutralizing anti-TrkA antibody MNAC13 reduces inflammatory and neuropathic pain
Ugolini, G., Marinelli, S., Covaceuszach, S., Cattaneo, A., & Pavone, F. (2007) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0611253104 · Cited by 144 · see citing works
HTS and hit finding in academia – from chemical genomics to drug discovery
Frearson, J. A., & Collie, I. T. (2009) · Drug Discovery Today · doi:10.1016/j.drudis.2009.09.004 · Cited by 84 · see citing works
Cell-Based Versus Isolated Target Screening: How Lucky Do You Feel?
Moore, K., & Rees, S. (2001) · SLAS DISCOVERY · doi:10.1177/108705710100600202 · Cited by 83 · see citing works
Self-citationKurosawa, G., Akahori, Y., Morita, M., et al. (2008) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0712202105 · Cited by 78 · see citing works
Abell, A. N., Rivera‐Pérez, J. A., Cuevas, B. D., Uhlik, M. T., Sather, S., Johnson, N. L., Minton, S. K., Lauder, J. M., Winter-Vann, A. M., Nakamura, K., et al. (2005) · Molecular and Cellular Biology · doi:10.1128/mcb.25.20.8948-8959.2005 · Cited by 76 · see citing works
The multiple roles of computational chemistry in fragment-based drug design
Law, R., Barker, O., Barker, J. J., Hesterkamp, T., Godemann, R., Andersen, O. A., Fryatt, T., Courtney, S., Hallett, D., & Whittaker, M. A. (2009) · Journal of Computer-Aided Molecular Design · doi:10.1007/s10822-009-9284-1 · Cited by 57 · see citing works
Boppana, K., Dubey, P. K., Jagarlapudi, S. A. R. P., et al. (2009) · European Journal of Medicinal Chemistry · doi:10.1016/j.ejmech.2009.02.031 · Cited by 45 · see citing works
Probes for chemical genomics by design
Zanders, E. D., Bailey, D. S., & Dean, P. M. (2002) · Drug Discovery Today · doi:10.1016/s1359-6446(02)02325-5 · Cited by 32 · see citing works
Approaches to measuring the activities of protein arginine N-methyltransferases
Lakowski, T. M., Zurita‐Lopez, C. I., Clarke, S. G., et al. (2009) · Analytical Biochemistry · doi:10.1016/j.ab.2009.09.021 · Cited by 27 · see citing works
Use of Primary Human Cells in High-Throughput Screens
Dunne, A., Jowett, M., & Rees, S. (2009) · Methods in molecular biology · doi:10.1007/978-1-60327-258-2_12 · Cited by 26 · see citing works
Self-citationRecombinant aequorin as reporter of changes in intracellular calcium in mammalian cells
Stables, J., Mattheakis, L. C., Chang, R., et al. (2000) · Methods in enzymology on CD-ROM/Methods in enzymology · doi:10.1016/s0076-6879(00)27296-0 · Cited by 24 · see citing works
What constitutes target validation?
Peet, N. P. (2003) · TARGETS · doi:10.1016/s1477-3627(03)02317-1 · Cited by 10 · see citing works
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Zhu, H. (2020) · Annual Review of Pharmacology and Toxicology · doi:10.1146/annurev-pharmtox-010919-023324 · Cited by 455 · see citing works
Low-N protein engineering with data-efficient deep learning
Biswas, S., Khimulya, G., Alley, E. C., Esvelt, K. M., & Church, G. M. (2021) · Nature Methods · doi:10.1038/s41592-021-01100-y · Cited by 422 · see citing works
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Cao, Y., Romero, J., & Aspuru-Guzik, A. (2018) · IBM Journal of Research and Development · doi:10.1147/jrd.2018.2888987 · Cited by 357 · see citing works
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Gaudelet, T., Day, B., Jamasb, A. R., Soman, J., Regep, C., Liu, G., Hayter, J. B. R., Vickers, R., Roberts, C., Tang, J., et al. (2021) · Briefings in Bioinformatics · doi:10.1093/bib/bbab159 · Cited by 313 · see citing works
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Koh, P. W., Sagawa, S., Marklund, H., Xie, S. M., Zhang, M., Balsubramani, A., Hu, W., Yasunaga, M., Phillips, R. L., Gao, I., et al. (2020) · arXiv · doi:10.48550/arxiv.2012.07421 · Cited by 518 · see citing works
“…across different scaffolds Drug discovery is hugely expensive and time-consuming: the entire process typically takes more than 10 years, during which many expensive wet lab experiments need to be performed to find a potent molecule (Hughes et al., 2011), before clinical trials can even start.”
Organs-on-a-chip: a new tool for drug discovery
Polini, A., Prodanov, L., Bhise, N. S., Manoharan, V., Dokmeci, M. R., & Khademhosseini, A. (2014) · Expert Opinion on Drug Discovery · doi:10.1517/17460441.2014.886562 · Cited by 223 · see citing works
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- Authors
- Hughes, J., Rees, S., Kalindjian, S., & Philpott, K.
- Affiliations
- GlaxoSmithKline (United Kingdom) (GB)
- King's College London (GB)
- Venue
- British Journal of Pharmacology
- Publisher
- Wiley
- Published
- 2011-02-22
- Volume
- 162
- Issue
- 6
- Pages
- 1239-1249
- ISSN
- 0007-1188 (ISSN-L)
- 1476-5381
- PMC
- PMC3058157
- OpenAlex
- W1500036797
- Cited by
- 2,859
- References
- 25
- Citations by year
- 2026: 261 · 2025: 346 · 2024: 374 · 2023: 349 · 2022: 298 · 2021: 289 · 2020: 226 · 2019: 144 · 2018: 161 · 2017: 130 · 2016: 104 · 2015: 85 · 2014: 57 · 2013: 19 · 2012: 10
- FWCI
- 10.83
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- British Journal of Pharmacology (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Wellcome Trust
- Medical Research Council
- Topics
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Health Sciences › Veterinary › Small Animals › Animal testing and alternatives
- Health Sciences › Medicine › Reproductive Medicine › Science, Research, and Medicine
- Keywords
- early drug discovery, target identification, target validation, assay development, high-throughput screening, hit identification, lead optimization, preclinical drug development, candidate selection, pharmaceutical industry
- MeSH terms
- Drug Industry, Drug Evaluation, Preclinical, Humans, Drug Design, Drug Discovery, High-Throughput Screening Assays, Molecular Targeted Therapy
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