Analysis of drug-induced effect patterns to link structure and side effects of medicines

Nature Chemical Biology volume 1, pages389–397(2005)Cite this article

Abstract

The high failure rate of experimental medicines in clinical trials accentuates inefficiencies of current drug discovery processes caused by a lack of tools for translating the information exchange between protein and organ system networks. Recently, we reported that biological activity spectra (biospectra), derived from in vitro protein binding assays, provide a mechanism for assessing a molecule's capacity to modulate the function of protein-network components. Herein we describe the translation of adverse effect data derived from 1,045 prescription drug labels into effect spectra and show their utility for diagnosing drug-induced effects of medicines. In addition, notwithstanding the limitation imposed by the quality of drug label information, we show that biospectrum analysis, in concert with effect spectrum analysis, provides an alignment between preclinical and clinical drug-induced effects. The identification of this alignment provides a mechanism for forecasting clinical effect profiles of medicines.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

Rent or Buy

All prices are NET prices.

Figure 1: Classification of side effect information for 1,045 medicines.
Figure 2: Comparison of preclinical and clinical drug-induced effect similarity of 40 medicines.
Figure 3: Comparison of biospectra and effect spectra of 19 sedative-hypnotic medicines.
Figure 4: Association between biospectral and side effect similarity for 25 medicines: an independent assessment of similarity distance relationships between side-effect and biospectral profile similarity for compounds in the 872-medicine database.

References

  1. 1

    Sole, R.V., Pastor-Satorras, R., Smith, E. & Kepler, T.B. A model of large-scale proteome evolution. Preprint at http://xxx.lanl.gov/cond-mat/0207311 (2002).

  2. 2

    Tyers, M. & Mann, M. From genomics to proteomics. Nature 422, 193–197 (2003).

    Article  CAS  Google Scholar 

  3. 3

    Petricoin, E.F., Zoon, K.C., Kohn, E.C., Barrett, J.C. & Liotta, L.A. Clinical proteomics:translating benchside promise into bedside reality. Nat. Rev. Drug Discov. 1, 683–695 (2002).

    Article  CAS  Google Scholar 

  4. 4

    Dimpfel, W. Preclinical data base of pharmaco-specific EEG fingerprints (Tele-Stereo-EEG). Eur. J. Med. Res. 8, 199–207 (2003).

    PubMed  CAS  Google Scholar 

  5. 5

    Zajchowski, D.A. et al. Identification of selective estrogen receptor modulators by their gene expression fingerprints. J. Biol. Chem. 275, 15885–15894 (2000).

    Article  CAS  Google Scholar 

  6. 6

    Shi, L.M. et al. Mining the National Cancer Institute Anticancer Drug Discovery Database: cluster analysis of ellipticine analogs with p53-inverse and central nervous system-selective patterns of activity. Mol. Pharmacol. 53, 241–251 (1998).

    Article  CAS  Google Scholar 

  7. 7

    Pellegrini, M. Defining interacting partners for drug discovery. Expert Opin. Ther. Targets 7, 287–297 (2003).

    Article  CAS  Google Scholar 

  8. 8

    Cunningham, M.L., Bogdanffy, M.S., Zacharewski, T.R. & Hines, R.N. Workshop overview: Use of genomic data in risk assessment. Toxicol. Sci. 73, 209–215 (2003).

    Article  CAS  Google Scholar 

  9. 9

    Padrini, R. et al. Pharmacogenetics. N. Engl. J. Med. 348, 2041–2043 (2003).

    Article  Google Scholar 

  10. 10

    Mathew, R.J., Weinman, M.L., Thapar, R., Reck, J.J. & Claghorn, J.L. Somatic symptoms in depression and antidepressants. J. Clin. Psychiatry 44, 10–12 (1983).

    PubMed  CAS  Google Scholar 

  11. 11

    Hamilton, L.W. & Timmons, C.R. Sex differences in response to taste and postingestive consequences of sugar solutions. Physiol. Behav. 17, 221–225 (1976).

    Article  CAS  Google Scholar 

  12. 12

    Antkowiak, B. How do general anaesthetics work? Naturwissenschaften 88, 201–213 (2001).

    Article  CAS  Google Scholar 

  13. 13

    Steiner, S. & Anderson, N.L. Expression profiling in toxicology-potentials and limitations. Toxicol. Lett. 112–113, 467–471 (2000).

    Article  Google Scholar 

  14. 14

    Blower, P.E. et al. Pharmacogenomic analysis: correlating molecular substructure classes with microarray gene expression data. Pharmacogenomics J. 2, 259–271 (2002).

    Article  CAS  Google Scholar 

  15. 15

    Fliri, A.F., Loging, W.T., Thadeio, P.F. & Volkmann, R.A. Biological spectra analysis: linking biological activity profiles to molecular structure. Proc. Natl. Acad. Sci. USA 102, 261–266 (2005).

    Article  CAS  Google Scholar 

  16. 16

    Fliri, A.F., Loging, W.T., Thadeio, P.F. & Volkmann, R.A. Biospectra analysis: model proteome characterizations for linking molecular structure and biological response. J. Med. Chem. 48, 6918–6925 (2005).

    Article  CAS  Google Scholar 

  17. 17

    Hamadeh, H.K., et al. Prediction of compound signature using high density gene expression profiling. Toxicol. Sci. 67, 232–240 (2002).

    Article  CAS  Google Scholar 

  18. 18

    Park, D. et al. Comparative interactomics analysis of protein family interaction networks using PSIMAP (protein structural interactome map). Bioinformatics 21, 3234–3240 (2005).

    Article  CAS  Google Scholar 

  19. 19

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

    PubMed  CAS  Google Scholar 

  20. 20

    Food and Drug Administration. COSTART: Coding Symbols for Thesaurus of Adverse Reaction Terms (3rd edn.) (Food and Drug Administration, Center for Drugs and Biologics, Division of Drug and Biological Products Experience, Rockville, Maryland, 1989).

  21. 21

    Gao, H., Williams, C., Labute, P. & Bajorath, J. Binary quantitative structure-activity relationship (QSAR) analysis of estrogen receptor ligands. J. Chem. Inf. Comput. Sci. 39, 164–168 (1999).

    Article  CAS  Google Scholar 

  22. 22

    Ward, J.H. Hierarchical grouping to optimize an objective function. J. Am. Stat. Assoc. 58, 236–244 (1963).

    Article  Google Scholar 

  23. 23

    German, E.J., Wood, D. & Hurst, M.A. Ocular effects of antimuscarinic compounds: Is clinical effect determined by binding affinity for muscarinic receptors or melanin pigment? J. Ocul. Pharmacol. Ther. 15, 257–269 (1999).

    Article  CAS  Google Scholar 

  24. 24

    Itil, T.M. The discovery of antidepressant drugs by computer-analyzed human cerebral bio-electrical potentials (CEEG). Prog. Neurobiol. 20, 185–249 (1983).

    Article  CAS  Google Scholar 

  25. 25

    Sanger, D.J. The pharmacology and mechanisms of action of new generation, non-benzodiazepine hypnotic agents. CNS Drugs 18 (Suppl.) 9–16 (2004).

    Article  CAS  Google Scholar 

  26. 26

    Hindmarch, I. Myths, medicine and the media. Hum Psychopharmacol. Clin. Exp. 14, 223–224 (1999).

    Article  Google Scholar 

  27. 27

    Becker, R.A., Chambers, J.M. & Wilks, A.R. The New S Language: a Programming Environment for Data Analysis and Graphics (Wadsworth & Brooks/Cole Advanced Books & Software, Monterey, California, 1988.

    Google Scholar 

  28. 28

    Mardia, K.V., Kent, J.T. & Bibby, J.M. Multivariate analysis. Academic Press, (1979).

    Google Scholar 

  29. 29

    Labute, P., Nilar, S. & Williams, C. A probabilistic approach to high throughput drug discovery. Comb. Chem. High Throughput Screen. 5, 135–145 (2002).

    Article  CAS  Google Scholar 

Download references

Acknowledgements

The authors would like to thank D.M. Potter for useful discussions.

Author information

Affiliations

  1. Pfizer Global Research and Development, Eastern Point Road, Groton, 06340, Connecticut, USA

    Anton F Fliri, William T Loging, Peter F Thadeio & Robert A Volkmann

Authors
  1. Anton F Fliri
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. William T Loging
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Peter F Thadeio
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Robert A Volkmann
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Anton F Fliri or Robert A Volkmann.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Supplementary information

Supplementary Fig. 1

The experimental design for investigating the information exchange between drug-induced protein network perturbations to drug-induced organ systems perturbations. (PDF 148 kb)

Supplementary Fig. 2

Positions of ninety-two protein assays in the mammalian proteome network. (PDF 3113 kb)

Supplementary Fig. 3

Sedative-hypnotic cluster. (PDF 718 kb)

Supplementary Fig. 4

Side effect distances compared to BioSpectra distances. (PDF 228 kb)

Supplementary Table 1

List of ninety-two assays. (PDF 50 kb)

Supplementary Table 2

List of 872 medicines. (PDF 48 kb)

Supplementary Table 3

ADRS. (PDF 42 kb)

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Fliri, A., Loging, W., Thadeio, P. et al. Analysis of drug-induced effect patterns to link structure and side effects of medicines. Nat Chem Biol 1, 389–397 (2005). https://doi.org/10.1038/nchembio747

Download citation

Further reading

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