- Email: [email protected]
Response to Froloff: Probing system structure–effect relationships
Update
490
TRENDS in Biotechnology
Implications Fliri et al. show that in vitro pharmacological profiles have the potential to describe broad biological effects of small drug-like organic compounds accurately. This crude and simple description of the biological action of a compound, provided that it is well designed and properly analysed, could give useful insights into more global and integrated biological effects at the level of the cell, the tissue and ultimately the whole living system. If this were the case, in vitro pharmacological profiles could be used to support molecular design and optimization of both efficacy and safety properties of lead compounds. New methods could then be developed to design novel compounds with desired in vitro profiles [23,24]. In vitro pharmacological profiles could also be used to understand the reasons for success or attrition in clinical development. They could add important information for late lead optimization (selection of analogs), for selecting drug candidates for clinical development, for re-orienting drugs in the clinic, and for designing clinical trials. In this respect, Fliri et al. have shown that pharmacological profiles deserve much attention and careful design to explore their full potential both for drug discovery and development. Acknowledgements I thank Mark Crawford and Fre´de´ric Revah for helpful comments in the preparation of this article.
References 1 Patterson, D.E. et al. (1996) Neighborhood behavior: a useful concept for validation of ‘molecular diversity’ descriptors. J. Med. Chem. 39, 3049–3059 2 Barbosa, F. and Horvath, D. (2004) Molecular similarity and property similarity. Curr. Top. Med. Chem. 4, 589–600 3 Hopkins, A.L. and Groom, C.R. (2002) The druggable genome. Nat. Rev. Drug Discov. 1, 727–730 4 Gu¨ner, O.F., ed. (2000) Pharmacophore Perception, Development and Use in Drug Design, International University Line 5 Gohlke, H. and Klebe, G. (2002) Approaches to the description and prediction of the binding affinity of small-molecule ligands to macromolecular receptors. Angew. Chem. Int. Ed. Engl. 41, 2644–2676 6 Kauvar, L.M. et al. (1995) Predicting ligand binding to proteins by affinity fingerprinting. Chem. Biol. 2, 107–118
Vol.23 No.10 October 2005
7 Beroza, P. et al. (2002) Chemoproteomics as a basis for post-genomic drug discovery. Drug Discov. Today 7, 807–814 8 Greenbaum, D.C. et al. (2002) Small molecule affinity fingerprinting: a tool for enzyme family subclassification, target identification, and inhibitor design. Chem. Biol. 9, 1085–1094 9 Hsu, N. et al. (2004) Novel cyclooxygenase-1 inhibitors discovered using affinity fingerprints. J. Med. Chem. 47, 4875–4880 10 Briem, H. and Kuntz, I.D. (1996) Molecular similarity based on DOCK-generated fingerprints. J. Med. Chem. 39, 3401–3408 11 Kitchen, D.B. et al. (2004) Docking and scoring in virtual screening for drug discovery: methods and applications. Nat. Rev. Drug Discov. 3, 935–949 12 Briem, H. and Lessel, U.F. (2000) In vitro and in silico affinity fingerprints: finding similarities beyond structural classes. Perspect. Drug Discov. Des. 20, 231–244 13 Fliri, A.F. et al. (2005) Biological spectra analysis: linking biological activity profiles to molecular structure. Proc. Natl. Acad. Sci. U. S. A. 102, 261–266 14 Krejsa, C.M. et al. (2003) Predicting ADME properties and side effects: the BioPrint approach. Curr. Opin. Drug Discov. Devel. 6, 470–480 15 Di, L. and Kerns, E.H. (2003) Profiling drug-like properties in discovery research. Curr. Opin. Chem. Biol. 7, 402–408 16 van de Waterbeemd, H. and Gifford, E. (2003) ADMET in silico modelling: towards prediction paradise? Nat. Rev. Drug Discov. 2, 192–204 17 Stoughton, R.B. and Friend, S.H. (2005) How molecular profiling could revolutionize drug discovery. Nat. Rev. Drug Discov. 4, 345–350 18 Schuffenhauer, A. et al. (2003) Similarity metrics for ligands reflecting the similarity of the target proteins. J. Chem. Inf. Comput. Sci. 43, 391–405 19 Zamora, I. et al. (2003) Surface descriptors for protein–ligand affinity prediction. J. Med. Chem. 46, 25–33 20 Vieth, M. et al. (2004) Characteristic physical properties and structural fragments of marketed oral drugs. J. Med. Chem. 47, 224–232 21 Butcher, E.C. et al. (2004) Systems biology in drug discovery. Nat. Biotechnol. 22, 1253–1259 22 Csermely, P. et al. (2005) The efficiency of multi-target drugs: the network approach might help drug design. Trends Pharmacol. Sci. 26, 178–182 23 Wermuth, C.G. (2004) Selective optimization of side activities: another way for drug discovery. J. Med. Chem. 47, 1303–1314 24 Morphy, R. et al. (2004) From magic bullets to designed multiple ligands. Drug Discov. Today 9, 641–651
0167-7799/$ - see front matter Q 2005 Elsevier Ltd. All rights reserved. doi:10.1016/j.tibtech.2005.07.004
Research Focus Response
Response to Froloff: Probing system structure–effect relationships Robert A. Volkmann and Anton F. Fliri Pfizer Global Research and Development, Pfizer, Groton, CT 06340, USA
In the Research Focus article by Nicolas Froloff [1], the author speaks of the utility of biological spectra (biospectra) analysis and captures possibilities for its use in advancing systems biology. This method owes its basis to Corresponding author: Volkmann, R.A. ([email protected]).
www.sciencedirect.com
the notion that characterization of an ‘interactome’ obtained by measuring the capacity of molecules to interact with a model proteome substitutes for knowledge on affinity constants for individual drug targets. As indicated in the PNAS paper [2] and discussed by Froloff, this approach provides us with the ability to obtain information about the system effects of ligands even in
Update
TRENDS in Biotechnology
the absence of information on putative drug targets. This capacity is articulated in part by the observation that biospectra analysis identifies similarities between ligand structures and pharmacology (system response). What distinguishes our work and that performed by John Weinstein’s group at the National Cancer Institute (http://www.nci.nih.gov/) from the affinity-based studies that Froloff describes is the use of broad biological effect patterns for deriving structure–activity relationships (SARs). Our method expands on Weinstein’s pioneering studies [3] by adding the ability to translate complex biological response information into model proteome characterizations. This, in turn, provides medicinal chemists with concise instructions for ligand–structure design using probabilistic SAR concepts. The fact that a model proteome provides information about the pharmacology of ligands even in the absence of information about putative drug targets, implies that ‘interactome’ characterizations can be used to investigate manifestations of
Vol.23 No.10 October 2005
protein and organ system network properties such as the pharmacology of medicines. Tests of this utility will become available as additional work on this subject is published. We believe that probabilistic SAR concepts, such as biospectra analysis, will have an important role in investigations of complex structure–effect relationships (i.e. systems biology).
References 1 Froloff, N. (2005) Probing drug action using in vitro pharmacological profiles. Trends Biotechnol. doi: 10.1016/j.tibtech.2005.07.004 2 Fliri, A.F. et al. (2005) Biological spectra analysis: linking biological activity profiles to molecular structure. Proc. Natl. Acad. Sci. U. S. A. 102, 261–266 3 Weinstein, J.N. et al. (1997) An information-intensive approach to the molecular pharmacology of cancer. Science 275, 343–349
0167-7799/$ - see front matter Q 2005 Elsevier Ltd. All rights reserved. doi:10.1016/j.tibtech.2005.07.003
Endeavour the quarterly magazine for the history and philosophy of science You can access Endeavour online via ScienceDirect, where you’ll find a collection of beautifully illustrated articles on the history of science, book reviews and editorial comment.
featuring
Selling the silver: country house libraries and the history of science by Roger Gaskell and Patricia Fara Carl Schmidt – a chemical tourist in Victorian Britain by R. Stefan Ross The rise, fall and resurrection of group selection by M.E. Borello Mary Anning: the fossilist as exegete by T.W. Goodhue Caroline Herschel: ‘the unquiet heart’ by M. Hoskin Science in the 19th-century zoo by Oliver Hochadel The melancholy of anatomy by P. Fara and coming soon Etienne Geoffroy St-Hillaire, Napoleon’s Egyptian campaign and a theory of everything by P. Humphries Losing it in New Guinea: The voyage of HMS Rattlesnake by J. Goodman The accidental conservationist by M.A. Andrei Powering the porter brewery by J. Sumner Female scientists in films by B.A. Jones and much, much more . . . Locate Endeavour on ScienceDirect (http://www.sciencedirect.com) www.sciencedirect.com
491
490
TRENDS in Biotechnology
Implications Fliri et al. show that in vitro pharmacological profiles have the potential to describe broad biological effects of small drug-like organic compounds accurately. This crude and simple description of the biological action of a compound, provided that it is well designed and properly analysed, could give useful insights into more global and integrated biological effects at the level of the cell, the tissue and ultimately the whole living system. If this were the case, in vitro pharmacological profiles could be used to support molecular design and optimization of both efficacy and safety properties of lead compounds. New methods could then be developed to design novel compounds with desired in vitro profiles [23,24]. In vitro pharmacological profiles could also be used to understand the reasons for success or attrition in clinical development. They could add important information for late lead optimization (selection of analogs), for selecting drug candidates for clinical development, for re-orienting drugs in the clinic, and for designing clinical trials. In this respect, Fliri et al. have shown that pharmacological profiles deserve much attention and careful design to explore their full potential both for drug discovery and development. Acknowledgements I thank Mark Crawford and Fre´de´ric Revah for helpful comments in the preparation of this article.
References 1 Patterson, D.E. et al. (1996) Neighborhood behavior: a useful concept for validation of ‘molecular diversity’ descriptors. J. Med. Chem. 39, 3049–3059 2 Barbosa, F. and Horvath, D. (2004) Molecular similarity and property similarity. Curr. Top. Med. Chem. 4, 589–600 3 Hopkins, A.L. and Groom, C.R. (2002) The druggable genome. Nat. Rev. Drug Discov. 1, 727–730 4 Gu¨ner, O.F., ed. (2000) Pharmacophore Perception, Development and Use in Drug Design, International University Line 5 Gohlke, H. and Klebe, G. (2002) Approaches to the description and prediction of the binding affinity of small-molecule ligands to macromolecular receptors. Angew. Chem. Int. Ed. Engl. 41, 2644–2676 6 Kauvar, L.M. et al. (1995) Predicting ligand binding to proteins by affinity fingerprinting. Chem. Biol. 2, 107–118
Vol.23 No.10 October 2005
7 Beroza, P. et al. (2002) Chemoproteomics as a basis for post-genomic drug discovery. Drug Discov. Today 7, 807–814 8 Greenbaum, D.C. et al. (2002) Small molecule affinity fingerprinting: a tool for enzyme family subclassification, target identification, and inhibitor design. Chem. Biol. 9, 1085–1094 9 Hsu, N. et al. (2004) Novel cyclooxygenase-1 inhibitors discovered using affinity fingerprints. J. Med. Chem. 47, 4875–4880 10 Briem, H. and Kuntz, I.D. (1996) Molecular similarity based on DOCK-generated fingerprints. J. Med. Chem. 39, 3401–3408 11 Kitchen, D.B. et al. (2004) Docking and scoring in virtual screening for drug discovery: methods and applications. Nat. Rev. Drug Discov. 3, 935–949 12 Briem, H. and Lessel, U.F. (2000) In vitro and in silico affinity fingerprints: finding similarities beyond structural classes. Perspect. Drug Discov. Des. 20, 231–244 13 Fliri, A.F. et al. (2005) Biological spectra analysis: linking biological activity profiles to molecular structure. Proc. Natl. Acad. Sci. U. S. A. 102, 261–266 14 Krejsa, C.M. et al. (2003) Predicting ADME properties and side effects: the BioPrint approach. Curr. Opin. Drug Discov. Devel. 6, 470–480 15 Di, L. and Kerns, E.H. (2003) Profiling drug-like properties in discovery research. Curr. Opin. Chem. Biol. 7, 402–408 16 van de Waterbeemd, H. and Gifford, E. (2003) ADMET in silico modelling: towards prediction paradise? Nat. Rev. Drug Discov. 2, 192–204 17 Stoughton, R.B. and Friend, S.H. (2005) How molecular profiling could revolutionize drug discovery. Nat. Rev. Drug Discov. 4, 345–350 18 Schuffenhauer, A. et al. (2003) Similarity metrics for ligands reflecting the similarity of the target proteins. J. Chem. Inf. Comput. Sci. 43, 391–405 19 Zamora, I. et al. (2003) Surface descriptors for protein–ligand affinity prediction. J. Med. Chem. 46, 25–33 20 Vieth, M. et al. (2004) Characteristic physical properties and structural fragments of marketed oral drugs. J. Med. Chem. 47, 224–232 21 Butcher, E.C. et al. (2004) Systems biology in drug discovery. Nat. Biotechnol. 22, 1253–1259 22 Csermely, P. et al. (2005) The efficiency of multi-target drugs: the network approach might help drug design. Trends Pharmacol. Sci. 26, 178–182 23 Wermuth, C.G. (2004) Selective optimization of side activities: another way for drug discovery. J. Med. Chem. 47, 1303–1314 24 Morphy, R. et al. (2004) From magic bullets to designed multiple ligands. Drug Discov. Today 9, 641–651
0167-7799/$ - see front matter Q 2005 Elsevier Ltd. All rights reserved. doi:10.1016/j.tibtech.2005.07.004
Research Focus Response
Response to Froloff: Probing system structure–effect relationships Robert A. Volkmann and Anton F. Fliri Pfizer Global Research and Development, Pfizer, Groton, CT 06340, USA
In the Research Focus article by Nicolas Froloff [1], the author speaks of the utility of biological spectra (biospectra) analysis and captures possibilities for its use in advancing systems biology. This method owes its basis to Corresponding author: Volkmann, R.A. ([email protected]).
www.sciencedirect.com
the notion that characterization of an ‘interactome’ obtained by measuring the capacity of molecules to interact with a model proteome substitutes for knowledge on affinity constants for individual drug targets. As indicated in the PNAS paper [2] and discussed by Froloff, this approach provides us with the ability to obtain information about the system effects of ligands even in
Update
TRENDS in Biotechnology
the absence of information on putative drug targets. This capacity is articulated in part by the observation that biospectra analysis identifies similarities between ligand structures and pharmacology (system response). What distinguishes our work and that performed by John Weinstein’s group at the National Cancer Institute (http://www.nci.nih.gov/) from the affinity-based studies that Froloff describes is the use of broad biological effect patterns for deriving structure–activity relationships (SARs). Our method expands on Weinstein’s pioneering studies [3] by adding the ability to translate complex biological response information into model proteome characterizations. This, in turn, provides medicinal chemists with concise instructions for ligand–structure design using probabilistic SAR concepts. The fact that a model proteome provides information about the pharmacology of ligands even in the absence of information about putative drug targets, implies that ‘interactome’ characterizations can be used to investigate manifestations of
Vol.23 No.10 October 2005
protein and organ system network properties such as the pharmacology of medicines. Tests of this utility will become available as additional work on this subject is published. We believe that probabilistic SAR concepts, such as biospectra analysis, will have an important role in investigations of complex structure–effect relationships (i.e. systems biology).
References 1 Froloff, N. (2005) Probing drug action using in vitro pharmacological profiles. Trends Biotechnol. doi: 10.1016/j.tibtech.2005.07.004 2 Fliri, A.F. et al. (2005) Biological spectra analysis: linking biological activity profiles to molecular structure. Proc. Natl. Acad. Sci. U. S. A. 102, 261–266 3 Weinstein, J.N. et al. (1997) An information-intensive approach to the molecular pharmacology of cancer. Science 275, 343–349
0167-7799/$ - see front matter Q 2005 Elsevier Ltd. All rights reserved. doi:10.1016/j.tibtech.2005.07.003
Endeavour the quarterly magazine for the history and philosophy of science You can access Endeavour online via ScienceDirect, where you’ll find a collection of beautifully illustrated articles on the history of science, book reviews and editorial comment.
featuring
Selling the silver: country house libraries and the history of science by Roger Gaskell and Patricia Fara Carl Schmidt – a chemical tourist in Victorian Britain by R. Stefan Ross The rise, fall and resurrection of group selection by M.E. Borello Mary Anning: the fossilist as exegete by T.W. Goodhue Caroline Herschel: ‘the unquiet heart’ by M. Hoskin Science in the 19th-century zoo by Oliver Hochadel The melancholy of anatomy by P. Fara and coming soon Etienne Geoffroy St-Hillaire, Napoleon’s Egyptian campaign and a theory of everything by P. Humphries Losing it in New Guinea: The voyage of HMS Rattlesnake by J. Goodman The accidental conservationist by M.A. Andrei Powering the porter brewery by J. Sumner Female scientists in films by B.A. Jones and much, much more . . . Locate Endeavour on ScienceDirect (http://www.sciencedirect.com) www.sciencedirect.com
491