Skip to main content
SpringerLink
Log in

Successful identification of key chemical structure modifications that lead to improved ADME profiles

  • Published:
Journal of Computer-Aided Molecular Design Aims and scope Submit manuscript

Abstract

The results of a new method developed to identify well defined structural transformations that are key to improve a certain ADME profile are presented in this work. In particular Naïve Bayesian statistics and SciTegic FCFP_6 molecular fingerprints have been used to extract, from a dataset of 1,169 compounds with known in vitro UGT glucuronidation clearance, those changes in chemical structure that lead to a significant increase in this property. The effectiveness in achieving that goal of the thus found 55,987 transformations has been quantified and compared to classical medicinal chemistry transformations. The conclusion is that on average the new transformations found via in silico methods induce increases of UGT clearance by twofold, whilst the classical transformations are on average unable to alter that endpoint significantly in any direction. When both types of transformations are combined via substructural searches (SSS) the average twofold increase in glucuronidation is maintained. The implications of these findings for the drug design process are also discussed, in particular when compared to other methods previously described in the literature to address the question ‘Which compound do I make next?’

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Scheme 1
Fig. 2

Similar content being viewed by others

Abbreviations

ADME:

Absorption distribution metabolism and excretion

QSAR:

Quantitative structure–activity relationships

UGT:

Uridine 5’-diphospho-glucuronosyltransferase

ECFP:

Extended connectivity finger-prints

References

  1. Lipinski CA, Lombardo F, Dominy BW, Feeney PJ (2001) Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Adv Drug Deliv Rev 46:3–26

    Article  CAS  Google Scholar 

  2. Martin YCA (2005) Bioavailability score. J Med Chem 48:3164–3170

    Article  CAS  Google Scholar 

  3. Leach AG, Jones HD, Cosgrove DA, Kenny PW, Ruston L, MacFaul P, Wood JM, Colclough N, Law B (2006) Matched molecular pairs as a guide in the optimization of pharmaceutical properties; a study of aqueous solubility, plasma protein binding and oral exposure. J Med Chem 49:6672–6682

    Article  CAS  Google Scholar 

  4. SciTegic, Inc. (a wholly-owned subsidiary of Accelrys, Inc.), 10188 Telesis Court, Suite 100, San Diego, CA 92121-4779, USA, Pipeline Pilot 7.5, 2008, version 7.5, www.scitegic.com

  5. Uchaipichat V, Winner LK, Mackenzie PI, Elliot DJ, Williams JA, Miners JO (2006) Quantitative prediction of in vivo inhibitory interactions involving glucuronidated drugs from in vitro data: the effect of fluconazole on zidovudine glucuronidation. Br J Clin Pharmacol 61:427–439

    Article  CAS  Google Scholar 

  6. Xia X, Maliski EG, Gallant P, Rogers D (2004) Classification of kinase inhibitors using a Bayesian model. J Med Chem 47:4463–4470

    Article  CAS  Google Scholar 

  7. Rogers D, Brown RD, Hahn M (2005) Using extended-connectivity fingerprints with Laplacian-modified Bayesian analysis in high-throughput screening follow-up. J Biomol Screen 10:682–686

    Article  CAS  Google Scholar 

  8. Nidhi, Glick M, Davies JW, Jenkins JL (2006) Prediction of biological targets for compounds using multiple-category Bayesian models trained on chemogenomics databases. J Chem Inf Model 46:1124–1133

    Article  CAS  Google Scholar 

  9. Hert J, Willett P, Wilton DJ, Acklin P, Azzaoui K, Jacoby E, Schuffenhauer A (2006) New methods for ligand-based virtual screening: use of data fusion and machine learning to enhance the effectiveness of similarity searching. J Chem Inf Model 46:462–470

    Article  CAS  Google Scholar 

  10. Sun HA (2005) Naive Bayes classifier for prediction of multidrug resistance reversal activity on the basis of atom typing. J Med Chem 48:4031–4039

    Article  CAS  Google Scholar 

  11. Stewart KD, Shiroda M, James CA (2006) Drug Guru: a computer software program for drug design using medicinal chemistry rules. Bioorg Med Chem 14:7011–7022

    Article  CAS  Google Scholar 

  12. Lewis M, Cucurull-Sanchez L (2009) Structural pairwise comparisons of HLM stability of phenyl derivatives: introduction of the Pfizer metabolism index (PMI) and metabolism-lipophilicity efficiency (MLE). J Comput-Aided Mol Des 23:97–103

    Article  CAS  Google Scholar 

  13. Sheridan RP, Hunt P, Culberson JC (2006) Molecular transformations as a way of finding and exploiting consistent local QSAR. J Chem Inf Model 46:180–192

    Article  CAS  Google Scholar 

  14. Lewis RAA (2005) General method for exploiting QSAR models in lead optimization. J Med Chem 48:1638–1648

    Article  CAS  Google Scholar 

  15. Wolohan PRN, Akella LB, Dorfman RJ, Nell PG, Mundt SM, Clark RD (2006) Structural unit analysis identifies lead series and facilitates scaffold hopping in combinatorial chemistry. J Chem Inf Model 46:1188–1193

    Article  CAS  Google Scholar 

  16. Hajduk PJ, Sauer DR (2008) Statistical analysis of the effects of common chemical substituents on ligand potency. J Med Chem 51:553–564

    Article  CAS  Google Scholar 

  17. Wagener M, Lommerse JPM (2006) The quest for bioisosteric replacements. J Chem Inf Model 46:677–685

    Article  CAS  Google Scholar 

  18. Patani GA, LaVoie EJ (1996) Bioisosterism: a rational approach in drug design. Chem Rev 96:3147–3176

    Article  CAS  Google Scholar 

  19. Olesen PH (2001) The use of bioisosteric groups in lead optimization. Curr Opin Drug Discov Devel 4:471–478

    CAS  Google Scholar 

  20. Wermuth CG, Camille GW (2003) Molecular variations based on isosteric replacements. In the practice of medicinal chemistry, 2nd edn. Academic Press, London, pp 189–214

    Google Scholar 

  21. Lima LM, Barreiro EJ (2005) Bioisosterism: a useful strategy for molecular modification and drug design. Curr Med Chem 12:23–49

    CAS  Google Scholar 

  22. Lewell XQ, Judd DB, Watson SP, Hann MM (1998) RECAP-retrosynthetic combinatorial analysis procedure: a powerful new technique for identifying privileged molecular fragments with useful applications in combinatorial chemistry. J Chem Inf Comput Sci 38:511–522

    CAS  Google Scholar 

  23. Southall N, Ajay T (2006) Kinase patent space visualization using chemical replacements. J Med Chem 49:2103–2109

    Article  CAS  Google Scholar 

  24. Sheridan RP (2002) The most common chemical replacements in drug-like compounds. J Chem Inf Comput Sci 42:103–108

    CAS  Google Scholar 

  25. Haubertin DY, Bruneau PA (2007) Database of historically-observed chemical replacements. J Chem Inf Model 47:1294–1302

    Article  CAS  Google Scholar 

  26. Lewell XQ, Jones AC, Bruce CL, Harper G, Jones MM, McLay IM, Bradshaw J (2003) Drug rings database with web interface. A tool for identifying alternative chemical rings in lead discovery programs. J Med Chem 46:3257–3274

    Article  CAS  Google Scholar 

Download references

Acknowledgments

The author thanks Marcel de Groot and Willem van Hoorn for useful discussions about pair wise comparisons and Bayesian statistics. She also thanks Chad Stoner, Inaki Morao and Willem van Hoorn for reviewing the manuscript and for their helpful suggestions.

Author information

Authors and Affiliations

  1. Department of Pharmacokinetics, Dynamics and Metabolism, Pfizer PGRD, Sandwich, Kent, CT13 9NJ, UK

    Lourdes Cucurull-Sanchez

Authors
  1. Lourdes Cucurull-Sanchez
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Lourdes Cucurull-Sanchez.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (DOC 3397 kb)

Rights and permissions

Reprints and permissions

About this article

Cite this article

Cucurull-Sanchez, L. Successful identification of key chemical structure modifications that lead to improved ADME profiles. J Comput Aided Mol Des 24, 449–458 (2010). https://doi.org/10.1007/s10822-010-9361-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10822-010-9361-5

Keywords

Search

Navigation