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.

  • Analysis
  • Published:

The influence of the 'organizational factor' on compound quality in drug discovery

Nature Reviews Drug Discovery volume 10, pages 749–765 (2011)Cite this article

Subjects

Key Points

  • Using patents published by leading pharmaceutical companies in the 2000–2010 period, the molecular properties of the compounds acting at the drug targets pursued have been analysed.

  • Over the past decade, there has been little overall change in bulk properties that influence absorption, distribution, metabolism, excretion and toxicity (ADMET) outcomes, such as lipophilicity and size.

  • There are marked differences in molecular properties between organizations, which are maintained when the targets pursued are taken into account.

  • Target-unbiased molecular property differences between companies, attributable to divergent corporate drug design strategies, are comparable to the differences between the major drug target classes.

  • Data from patents with single-compound examples suggest that molecular property attrition begins before the selection of candidate drugs.

  • It is concluded that a substantial sector of the pharmaceutical industry has not modified its drug design practices and is still producing compounds with suboptimal physicochemical profiles.

Abstract

Physicochemical properties such as lipophilicity and molecular mass are known to have an important influence on the absorption, distribution, metabolism, excretion and toxicity (ADMET) profile of small-molecule drug candidates. To assess the use of this knowledge in reducing the likelihood of compound-related attrition, the molecular properties of compounds acting at specific drug targets described in patents from leading pharmaceutical companies during the 2000–2010 period were analysed. Over the past decade, there has been little overall change in properties that influence ADMET outcomes, but there are marked differences in molecular properties between organizations, which are maintained when the targets pursued are taken into account. The target-unbiased molecular property differences, which are attributable to divergent corporate drug design strategies, are comparable to the differences between the major drug target classes. On the basis of our analysis, we conclude that a substantial sector of the pharmaceutical industry has not modified its drug design practices and is still producing compounds with suboptimal physicochemical profiles.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Figure 1: Changes in the physicochemical properties of patented compounds by target over time.
Figure 2: Target-unbiased comparisons between companies.
Figure 3: Target-unbiased relative molecular properties for 18 companies.
Figure 4: Radar plots for target-unbiased properties.
Figure 5: Matched-pair analysis of cLogP and molecular mass for the targets that are shared and patented by both Merck and Pfizer, and have ≥50 compounds.
Figure 6: Target-unbiased company property differences versus target-class differences.
Figure 7: Impact of fragment-based drug discovery.

Similar content being viewed by others

References

  1. Paul, S. M. et al. How to improve R&D productivity: the pharmaceutical industry's grand challenge. Nature Rev. Drug Discov. 9, 203–214 (2010).

    CAS  Google Scholar 

  2. Leeson, P. D. & Empfield, J. R. Reducing the risk of drug attrition associated with physicochemical properties. Ann. Reports Med. Chem. 45, 393–407 (2010).

    CAS  Google Scholar 

  3. McGinnity, D. F., Collington, J., Austin, R. P. & Riley, R. J. Evaluation of human pharmacokinetics, therapeutic dose and exposure predictions using marketed oral drugs. Curr. Drug Metab. 8, 463–479 (2007).

    CAS  PubMed  Google Scholar 

  4. Lipinski, C. A., Lombardo, F., Dominy, B. W. & Feeney, P. J. Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Adv. Drug Deliv. Rev. 23, 3–25 (1997).

    CAS  Google Scholar 

  5. Gleeson, M. P. Generation of a set of simple, interpretable ADMET rules of thumb. J. Med. Chem. 51, 817–834 (2008). Based on GlaxoSmithKline's data, the ADMET risk is shown to be reduced when molecular mass is <400 Da and cLogP is <4.

    CAS  PubMed  Google Scholar 

  6. Waring, M. J. Defining optimum lipophilicity and molecular weight ranges for drug candidates — Molecular mass dependent lower log D limits based on permeability. Bioorg. Med. Chem. Lett. 19, 2844–2851 (2009). This study extends the rule-of-five guideline by showing the dependence of permeability on both molecular mass and lipophilicity (LogD) in AstraZeneca's compounds.

    CAS  PubMed  Google Scholar 

  7. Johnson, T. W., Dress, K. R. & Edwards, M. Using the Golden Triangle to optimize clearance and oral absorption. Bioorg. Med. Chem. Lett. 19, 5560–5564 (2009). This paper shows that combined permeability and metabolic stability are dependent on molecular mass and lipophilicity (LogD) in Pfizer's compounds.

    CAS  PubMed  Google Scholar 

  8. Gleeson, M. P., Hersey, A., Montanari, D. & Overington, J. Probing the links between in vitro potency, ADMET and physicochemical parameters. Nature Rev. Drug Discov. 10, 197–208 (2011). The trade-off between potency and good ADMET properties is shown in this analysis of published data; successful drugs have a modest average potency.

    CAS  Google Scholar 

  9. Hughes, J. D. et al. Physiochemical drug properties associated with in vivo toxicological outcomes. Bioorg. Med. Chem. Lett. 18, 4872–4875 (2008). This study shows that the risk of in vivo toxicity is lower when cLogP<3 and polar surface area >75 in Pfizer's candidate drugs.

    CAS  PubMed  Google Scholar 

  10. Price, D. A., Blagg, J., Jones, L., Greene, N. & Wager, T. Physicochemical drug properties associated with in vivo toxicological outcomes: a review. Expert Opin. Drug Metab. Toxicol. 5, 921–931 (2009).

    CAS  PubMed  Google Scholar 

  11. Greene, N., Aleo, M. D., Louise-May, S., Price, D. A. & Will, Y. Using an in vitro cytotoxicity assay to aid in compound selection for in vivo safety studies. Bioorg. Med. Chem. Lett. 20, 5308–5312 (2010).

    CAS  PubMed  Google Scholar 

  12. Leeson, P. D. & Springthorpe, B. The influence of drug-like concepts on decision-making in medicinal chemistry. Nature Rev. Drug Discov. 6, 881–890 (2007). This analysis relates the decline in pharmaceutical productivity to the pursuit of poor molecular properties, shows that receptor promiscuity is dependent on lipophilicity and ion class, and illustrates variable molecular properties of compounds from different companies.

    CAS  Google Scholar 

  13. Bender, A. et al. Analysis of pharmacology data and the prediction of adverse drug reactions and off-target effects from chemical structure. ChemMedChem 2, 861–873 (2007).

    CAS  PubMed  Google Scholar 

  14. Azzaoui, K. et al. Modeling promiscuity based on in vitro safety pharmacology profiling data. ChemMedChem 2, 874–880 (2007).

    CAS  PubMed  Google Scholar 

  15. Peters, J.-U., Schnider, P., Mattei, P. & Kansy, M. Pharmacological promiscuity: dependence on compound properties and target specificity in a set of recent Roche compounds. ChemMedChem 4, 680–686 (2009).

    CAS  PubMed  Google Scholar 

  16. Waring, M. J. Lipophilicity in drug discovery. Expert Opin. Drug Discov. 5, 235–248 (2010).

    CAS  PubMed  Google Scholar 

  17. Lamanna, C., Bellini, M., Padova, A., Westerberg, G. & Maccari, L. Straightforward recursive partitioning model for discarding insoluble compounds in the drug discovery process. J. Med. Chem. 51, 2891–2897 (2008).

    CAS  PubMed  Google Scholar 

  18. Ritchie, T. J. & MacDonald, S. J. The impact of aromatic ring count on compound developability: are too many aromatic rings a liability in drug design? Drug Discov. Today 14, 1011–1020 (2009). This paper shows that when there are more than three aromatic rings, developability measures in GlaxoSmithKline's compounds — including solubility — are compromised.

    CAS  PubMed  Google Scholar 

  19. Ritchie, T. J., MacDonald, S. J. F., Young, R. J. & Pickett, S. D. The impact of aromatic ring count on compound developability: further insights by examining carbo- and hetero-aromatic and -aliphatic ring types. Drug Discov. Today 16, 164–171 (2011).

    CAS  PubMed  Google Scholar 

  20. Hill, A. P. & Young, R. J. Getting physical in drug discovery: a contemporary perspective on solubility and hydrophobicity. Drug Discov. Today 15, 648–655 (2010).

    CAS  PubMed  Google Scholar 

  21. Yan, A. & Gasteiger, J. Prediction of aqueous solubility of organic compounds by topological descriptors. QSAR Comb. Sci. 22, 821–829 (2003).

    CAS  Google Scholar 

  22. Lovering, F., Bikker, J. & Humblet, C. Escape from flatland: increasing saturation as an approach to improving clinical success. J. Med. Chem. 52, 6752–6756, (2009). This study shows that marketed drugs have higher fractions of sp3 atoms and are more chiral and soluble than compounds in development.

    CAS  PubMed  Google Scholar 

  23. Ishikawa, M. & Hashimoto, Y. Improvement in aqueous solubility in small molecule drug discovery programs by disruption of molecular planarity and symmetry. J. Med. Chem. 54, 1539–1554 (2011).

    CAS  PubMed  Google Scholar 

  24. Clemons, P. A. et al. Small molecules of different origins have distinct distributions of structural complexity that correlate with protein-binding profiles. Proc. Nat. Acad. Sci. USA 107, 18787–18792 (2010).

    CAS  PubMed  Google Scholar 

  25. Yang, Y., Chen, H., Nilsson, I., Muresan, S. & Engkvist, O. Investigation of the relationship between topology and selectivity for drug-like molecules. J. Med. Chem. 53, 7709–7714 (2010).

    CAS  PubMed  Google Scholar 

  26. Varma, M. V. et al. Physicochemical space for optimum oral bioavailability: contribution of human intestinal absorption and first-pass elimination. J. Med. Chem. 53, 1098–1108 (2010).

    CAS  PubMed  Google Scholar 

  27. Dobson, P. D. & Kell, D. B. Carrier-mediated cellular uptake of pharmaceutical drugs: an exception or the rule? Nature Rev. Drug Discov. 7, 205–220, (2008).

    CAS  Google Scholar 

  28. Varma, M. V. et al. Targeting intestinal transporters for optimizing oral drug absorption. Curr. Drug Metab. 11, 730–742 (2010).

    CAS  PubMed  Google Scholar 

  29. Boecker, A., Bonneau, P. R., Hucke, O., Jakalian, A. & Edwards, P. J. Development of specific “drug-like property” rules for carboxylate-containing oral drug candidates. ChemMedChem 5, 2102–2113 (2010).

    CAS  Google Scholar 

  30. Leeson, P. D., St-Gallay, S. A. & Wenlock, M. C. Impact of ion class and time on oral drug molecular properties. MedChemComm. 2, 91–105 (2011).

    CAS  Google Scholar 

  31. Leeson, P. D. & Davis, A. M. Time-related differences in the physical property profiles of oral drugs. J. Med. Chem. 47, 6338–6348 (2004).

    CAS  PubMed  Google Scholar 

  32. Proudfoot, J. R. The evolution of synthetic oral drug properties. Bioorg. Med. Chem. Lett. 15, 1087–1090 (2005).

    CAS  PubMed  Google Scholar 

  33. Wenlock, M. C., Austin, R. P., Barton, P., Davis, A. M. & Leeson, P. D. A Comparison of physiochemical property profiles of development and marketed oral drugs. J. Med. Chem. 46, 1250–1256 (2003).

    CAS  PubMed  Google Scholar 

  34. Tyrchan, C., Blomberg, N., Engkvist, O., Kogej, T. & Muresan, S. Physicochemical property profiles of marketed drugs, clinical candidates and bioactive compounds. Bioorg. Med. Chem. Lett. 19, 6943–6947 (2009).

    CAS  PubMed  Google Scholar 

  35. Ohno, K., Nagahara, Y., Tsunoyama, K. & Orita, M. Are there differences between launched drugs, clinical candidates, and commercially available compounds? J. Chem. Inf. Model. 50, 815–821 (2010).

    CAS  PubMed  Google Scholar 

  36. Chen, H., Yang, Y. & Engkvist, O. Molecular topology analysis of the differences between drugs, clinical candidate compounds, and bioactive molecules. J. Chem. Inf. Model. 50, 2141–2150 (2010).

    CAS  PubMed  Google Scholar 

  37. Vieth, M. et al. Characteristic physical properties and structural fragments of marketed oral drugs. J. Med. Chem. 47, 224–232 (2004).

    CAS  PubMed  Google Scholar 

  38. Ritchie, T. J., Ertl, P. & Lewis, R. The graphical representation of ADME-related molecule properties for medicinal chemists. Drug Discov. Today 16, 65–72 (2011).

    CAS  PubMed  Google Scholar 

  39. Teague, S. J., Davis, A. M., Leeson, P. D. & Oprea, T. The design of leadlike combinatorial libraries. Angew. Chem. Int. Ed Engl. 38, 3743–3748 (1999).

    CAS  PubMed  Google Scholar 

  40. Oprea, T. I., Davies, A., Teague, S. J. & Leeson, P. D. Is there a difference between leads and drugs? a historical perspective. J. Chem. Inf. Comput. Sci. 41, 1308–1315 (2001).

    CAS  Google Scholar 

  41. Hann, M. M., Leach, A. R. & Harper, G. Molecular complexity and its impact on the probability of finding leads for drug discovery. J. Chem. Inf. Comput. Sci. 41, 856–864 (2001).

    CAS  Google Scholar 

  42. Morphy, R. The influence of target family and functional activity on the physicochemical properties of pre-clinical compounds. J. Med. Chem. 49, 2969–2978 (2006).

    CAS  PubMed  Google Scholar 

  43. Keseru, G. M. & Makara, G. M. The influence of lead discovery strategies on the properties of drug candidates. Nature Rev. Drug Discov. 8 203–212 (2009).

    Google Scholar 

  44. Hajduk, P. J. Fragment-based drug design: how big is too big? J. Med. Chem. 49, 6972–6976 (2006).

    CAS  Google Scholar 

  45. Alex, A. A. & Flocco, M. M. Fragment-based drug discovery: what has it achieved so far? Curr. Top. Med. Chem. 7, 1544–1567 (2007).

    CAS  PubMed  Google Scholar 

  46. Perola, E. An analysis of the binding efficiencies of drugs and their leads in successful drug discovery programs. J. Med. Chem. 53, 2986–2997 (2010). The optimization from a lead compound to a drug in 60 recent examples shows an increase in potency without any change in lipophilicity — an increased lipophilic ligand efficiency.

    CAS  PubMed  Google Scholar 

  47. Wager, T. T. et al. Defining desirable central nervous system drug space through the alignment of molecular properties, in vitro ADME, and safety attributes. ACS Chem. Neurosci. 1, 420–434 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  48. Wager, T. T., Hou, X., Verhoest, P. R. & Villalobos, A. Moving beyond rules: the development of a central nervous system multiparameter optimization (CNS MPO) approach to enable alignment of druglike properties. ACS Chem. Neurosci. 1, 435–449 (2010). This paper assesses multiple drug-like properties, using desirability analysis, for brain-penetrating central nervous system drugs.

    CAS  PubMed  PubMed Central  Google Scholar 

  49. Oashi, T., Ringer, A. L., Raman, E. P. & MacKerell, A. D. Automated selection of compounds with physicochemical properties to maximize bioavailability and druglikeness. J. Chem. Inf. Model. 51, 148–158 (2011).

    CAS  PubMed  Google Scholar 

  50. Vieth, M. & Sutherland, J. J. Dependence of molecular properties on proteomic family for marketed oral drugs. J. Med. Chem. 49, 3451–3453 (2006).

    CAS  PubMed  Google Scholar 

  51. Macarron, R. et al. Impact of high-throughput screening in biomedical research. Nature Rev. Drug Discov. 10, 188–195 (2011).

    CAS  Google Scholar 

  52. Borshell, N. & Congreve, M. Valuation benefits of structure-enabled drug discovery. Nature Rev. Drug Discov. 10, 166 (2011).

    CAS  Google Scholar 

  53. Murray, C. W. & Rees, D. C. The rise of fragment-based drug discovery. Nature Chem. 1, 187–192 (2009).

    CAS  Google Scholar 

  54. Congreve, M., Chessari, G., Tisi, D. & Woodhead, A. J. Recent developments in fragment-based drug discovery. J. Med. Chem. 51, 3661–3680 (2008).

    CAS  PubMed  Google Scholar 

  55. Wyatt, P. G. et al. Identification of N-(4-piperidinyl)-4-(2,6-dichlorobenzoylamino)-1H-pyrazole-3-carboxamide (AT7519), a novel cyclin dependent kinase inhibitor using fragment-based X-ray crystallography and structure based drug design. J. Med. Chem. 51, 4986–4999 (2008).

    CAS  PubMed  Google Scholar 

  56. Howard, S. et al. Fragment-based discovery of the pyrazol-4-yl urea (AT9283), a multitargeted kinase inhibitor with potent aurora kinase activity. J. Med. Chem. 52, 379–388 (2009).

    CAS  PubMed  Google Scholar 

  57. Woodhead, A. J. et al. Discovery of (2,4-dihydroxy-5-isopropylphenyl)-[5-(4-methylpiperazin-1-ylmethyl)-1,3-dihydroisoindol-2-yl]methanone (AT13387), a novel inhibitor of the molecular chaperone Hsp90 by fragment based drug design. J. Med. Chem. 53, 5956–5969 (2010).

    CAS  PubMed  Google Scholar 

  58. Gill, A. L., Verdonk, M., Boyle, R. G. & Taylor, R. A comparison of physicochemical property profiles of marketed oral drugs and orally bioavailable anti-cancer protein kinase inhibitors in clinical development. Curr. Top. Med. Chem. 7, 1408–1422 (2007).

    CAS  PubMed  Google Scholar 

  59. Albert, J. S. et al. An integrated approach to fragment-based lead generation: philosophy, strategy and case studies from AstraZeneca's drug discovery programmes. Curr. Top. Med. Chem. 7, 1600–1629 (2007).

    CAS  PubMed  Google Scholar 

  60. Hann, M. M. Molecular obesity, potency and other addictions in drug discovery. MedChemComm. 2, 349–355 (2011). This paper suggests that the cause of poor drug-like properties in contemporary drug discovery primarily derives from the pursuit of optimal potency.

    CAS  Google Scholar 

  61. Lajiness, M. S., Maggiora, G. M. & Shanmugasundaram, V. Assessment of the consistency of medicinal chemists in reviewing sets of compounds. J. Med. Chem. 47, 4891–4896 (2004).

    CAS  PubMed  Google Scholar 

  62. Leeson, P. D., Davis, A. M. & Steele, J. Drug-like properties: guiding principles for design or chemical prejudice? Drug Discov. Today Technol. 1, 189–195 (2004).

    CAS  PubMed  Google Scholar 

  63. Smith, D. A. Discovery and ADMET: where are we now. Curr. Top. Med. Chem. 11, 467–481 (2011).

    CAS  PubMed  Google Scholar 

  64. Gleeson, M. P., Hersey, A. & Hannongbua, S. A. In-silico ADME models: a general assessment of their utility in drug discovery applications. Curr. Top. Med. Chem. 11, 358–381 (2011).

    CAS  PubMed  Google Scholar 

  65. Reynolds, C. H., Tounge, B. A. & Bembenek, S. D. Ligand binding efficiency: trends, physical basis, and implications. J. Med. Chem. 51, 2432–2438 (2008).

    CAS  PubMed  Google Scholar 

  66. Ferenczy, G. G. & Keseru, G. M. Enthalpic efficiency of ligand binding. J. Chem. Inf. Model. 50, 1536–1541 (2010).

    CAS  PubMed  Google Scholar 

  67. Shamovsky, I. et al. Overcoming undesirable hERG potency of chemokine receptor antagonists using baseline lipophilicity relationships. J. Med. Chem. 51, 1162–1178 (2008). This study demonstrates apractical use of lipophilic ligand efficiency and outlines the basic principles of optimizing molecules without increasing lipophilicity.

    CAS  PubMed  Google Scholar 

  68. Muchmore, S. W., Edmunds, J. J., Stewart, K. D. & Hajduk, P. J. Cheminformatic tools for medicinal chemists. J. Med. Chem. 53, 4830–4841 (2010). This is a balanced review of the limitations and applications of available computational chemistry methods.

    CAS  PubMed  Google Scholar 

  69. Smith, G. F. Medicinal chemistry by the numbers: the physicochemistry, thermodynamics and kinetics of modern drug design. Prog. Med. Chem. 48, 1–29 (2009).

    CAS  PubMed  Google Scholar 

  70. Davis, A. M., Keeling, D. J., Steele, J., Tomkinson, N. P. & Tinker, A. C. Components of successful lead generation. Curr. Top. Med. Chem. 5, 421–439 (2005).

    CAS  Google Scholar 

  71. Muresan, S. & Sadowski, J. in Molecular Drug Properties: Measurement and Prediction Vol. 37 Ch. 17 (ed. Mannhold, R.) 441–461 (Wiley-VCH, Weinheim, 2008).

    Google Scholar 

  72. Carey, J. S., Laffan, D., Thomson, C. & Williams, M. T. Analysis of the reactions used for the preparation of drug candidate molecules. Org. Biomol. Chem. 4, 2337–2347 (2006).

    CAS  PubMed  Google Scholar 

  73. Cooper, T. W. J., Campbell, I. B. & Macdonald, S. J. F. Factors determining the selection of organic reactions by medicinal chemists and the use of these reactions in arrays (small focused libraries). Angew. Chem. Int. Ed. Engl. 49, 8082–8091 (2010).

    CAS  PubMed  Google Scholar 

  74. Roughley, S. D. & Jordan, A. M. The medicinal chemist's toolbox: an analysis of reactions used in the pursuit of drug candidates. J. Med. Chem. 54, 3451–3479 (2011).

    CAS  PubMed  Google Scholar 

  75. Lipkus, A. H. et al. Structural diversity of organic chemistry. A scaffold analysis of the CAS registry. J. Org. Chem. 73, 4443–4451 (2008).

    CAS  PubMed  Google Scholar 

  76. Pitt, W. R., Parry, D. M., Perry, B. G. & Groom, C. R. Heteroaromatic rings of the future. J. Med. Chem. 52, 2952–2963 (2009).

    CAS  PubMed  Google Scholar 

  77. Hajduk, P. J., Galloway, W. R. J. D. & Spring, D. R. Drug discovery: a question of library design. Nature 470, 42–43 (2011). This article presents adebate on the value of chemical complexity versus low molecular mass fragments in the construction of screening libraries.

    CAS  PubMed  Google Scholar 

  78. Cheshire, D. R. How well do medicinal chemists learn from experience? Drug Discov. Today 16, 817–821 (2011). This article shows that candidate drugs often emerge rapidly following structure–activity breakthroughs, and that too many compounds may be synthesized.

    PubMed  Google Scholar 

  79. Teague, S. J. Learning lessons from drugs that have recently entered the market. Drug Discov. Today 16, 398–411 (2011).

    PubMed  Google Scholar 

  80. Keiser, M. J., Irwin, J. J. & Shoichet, B. K. The chemical basis of pharmacology. Biochemistry 49, 10267–10276 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  81. Munos, B. Lessons from 60 years of pharmaceutical innovation. Nature Rev. Drug Discov. 8, 959–968 (2009).

    CAS  Google Scholar 

  82. Smith, G. F. Designing drugs to avoid toxicity. Prog. Med. Chem. 50, 1–47 (2011).

    CAS  PubMed  Google Scholar 

  83. Kalgutkar, A. S. & Didiuk, M. T. Structural alerts, reactive metabolites, and protein covalent binding: how reliable are these attributes as predictors of drug toxicity? Chem. Biodivers. 6, 2115–2137 (2009).

    CAS  PubMed  Google Scholar 

  84. Enoch, S. J. & Cronin, M. T. D. A review of the electrophilic reaction chemistry involved in covalent DNA binding. Crit. Rev. Toxicol. 40, 728–748 (2010).

    CAS  PubMed  Google Scholar 

  85. Park, B. K. et al. Managing the challenge of chemically reactive metabolites in drug development. Nature Rev. Drug Discov. 10, 292–306 (2011).

    CAS  Google Scholar 

  86. Singh, J., Petter, R. C., Baillie, T. A. & Whitty, A. The resurgence of covalent drugs. Nature Rev. Drug Discov. 10, 307–317 (2011).

    CAS  Google Scholar 

  87. Johnstone C., Pairaudeau G. & Pettersson J. A. Creativity, innovation and lean sigma: a controversial combination? Drug Discov. Today 16, 50–57 (2011).

    PubMed  Google Scholar 

  88. Knutsen, L. J. S. Drug discovery management, small is still beautiful: why a number of companies get it wrong. Drug Discov. Today 16, 476–484 (2011).

    PubMed  Google Scholar 

  89. Pammolli, F., Magazzini, L. & Riccaboni, M. The productivity crisis in pharmaceutical R&D. Nature Rev. Drug Discov. 10, 428–438 (2011).

    CAS  Google Scholar 

  90. Bunnage, M. E. Getting pharmaceutical R&D back on target. Nature Chem. Biol. 7, 335–339 (2011).

    CAS  Google Scholar 

  91. Arrowsmith, J. Phase III and submission failures: 2007–2010. Nature Rev. Drug. Discov. 10, 87 (2011).

    CAS  Google Scholar 

  92. Arrowsmith, J. Phase II failures: 2008–2010. Nature Rev. Drug Discov. 10, 328–329 (2011).

    CAS  Google Scholar 

  93. Jagarlapudi, S. A. & Kishan, K. V. Database systems for knowledge-based discovery. Methods Mol. Biol. 575, 159–172 (2009).

    CAS  PubMed  Google Scholar 

  94. Southan, C., Boppana, K., Jagarlapudi, S. A. & Muresan, S. Analysis of in vitro bioactivity data extracted from drug discovery literature and patents: ranking 1654 human protein targets by assayed compounds and molecular scaffolds. J. Cheminform. 3, 14 (2011).

    PubMed  PubMed Central  Google Scholar 

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

    CAS  PubMed  Google Scholar 

  96. Luker, T. et al. Strategies to improve in vivo toxicology outcomes for basic candidate drug molecules. Bioorg. Med. Chem. Lett. 21, 5673–5679 (2011).

    CAS  PubMed  Google Scholar 

Download references

Acknowledgements

We are grateful to the following individuals for numerous discussions on medicinal chemistry practice and molecular properties: A. Davis, S. Teague, J. Empfield, M. Wenlock, J. Steele, R. Bonnert, D. Cheshire, G. Pairaudeau, L. Alcaraz, T. Luker, J. Dixon, D. Lathbury and the wider AstraZeneca chemistry community, T. Oprea, D. Rees, T. Hart, M. Hann, T. Wood, R. Young, J. Mason, T. Ritchie and A. Hopkins.

Author information

Authors and Affiliations

  1. AstraZeneca R&D Charnwood, Bakewell Road, Loughborough, LE11 5RH, Leicestershire, UK

    Paul D. Leeson & Stephen A. St-Gallay

Authors
  1. Paul D. Leeson
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Stephen A. St-Gallay
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Paul D. Leeson.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Supplementary information

Supplementary information S1 (table)

(XLS 481 kb)

Supplementary information S2 (figure)

Target-unbiased mean molecular mass (top) and cLogP (bottom) for the major target classes (kinases, proteases and peptidic GPCRs) by company. (PDF 287 kb)

Supplementary information S3 (figure)

Target classes distribution by company (all targets ≥50 compounds). (PDF 238 kb)

Supplementary information S4 (figure)

Distribution of patents restricted to 100 or 200 examples. (PDF 190 kb)

Supplementary information S5 (figure)

Shared single gene target distribution between 18 Companies (with ≥50 compounds per Company per target). (PDF 180 kb)

Supplementary information S6 (table)

Single-gene target counts with ≥50 compounds and numbers of the targets shared between all pairs of 18 companies. (PDF 175 kb)

Supplementary information S7 (figure)

Target class distribution of shared targets (≥50 compounds). (PDF 190 kb)

Related links

Related links

FURTHER INFORMATION

Astex Therapeutics press release (16 May 2009)

Astex Therapeutics — Product Overview

BioByte website

GVK BIO — Informatics services

JMP software website

OpenEye Scientific Software website

TIBCO Spotfire website

Rights and permissions

Reprints and permissions

About this article

Cite this article

Leeson, P., St-Gallay, S. The influence of the 'organizational factor' on compound quality in drug discovery. Nat Rev Drug Discov 10, 749–765 (2011). https://doi.org/10.1038/nrd3552

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/nrd3552

This article is cited by

Search

Advanced search

Quick links

Nature Briefing: Translational Research

Sign up for the Nature Briefing: Translational Research newsletter — top stories in biotechnology, drug discovery and pharma.

Get what matters in translational research, free to your inbox weekly. Sign up for Nature Briefing: Translational Research