Skip to main content Accessibility help
×
Hostname: page-component-78c5997874-dh8gc Total loading time: 0 Render date: 2024-11-17T14:53:13.838Z Has data issue: false hasContentIssue false

1 - Progress and issues for computationally guided lead discovery and optimization

Published online by Cambridge University Press:  06 July 2010

Kenneth M. Merz, Jr
Affiliation:
University of Florida
Dagmar Ringe
Affiliation:
Brandeis University, Massachusetts
Charles H. Reynolds
Affiliation:
Johnson & Johnson Pharmaceutical Research & Development
Get access

Summary

INTRODUCTION

Since the late 1980s there have been striking advances, fueled by large increases in both industrial and NIH-funded academic research, that have revolutionized drug discovery. This period has seen the introduction of high-throughput screening (HTS), combinatorial chemistry, PC farms, Linux, SciFinder, structure-based design, virtual screening by docking, free-energy methods, absorption/distribution/metabolism/excretion (ADME) software, bioinformatics, routine biomolecular structure determination, structures for ion channels, G-protein-coupled receptors (GPCRs) and ribosomes, structure/activity relationships (SAR) obtained from nuclear magnetic resonance (SAR by NMR), fragment-based design, gene knockouts, proteomics, small interfering RNA (siRNA), and human genome sequences. The result is a much-accelerated progression from identification of biomolecular target to lead compound to clinical candidate. However, a serious concern is that the dramatic increase in drug discovery abilities and expenditures has not been paralleled by an increase in FDA approvals of new molecular entities. High demands for drug safety, broader and longer clinical trials, too much HTS, too little natural products research, and effective generic drugs for many once-pressing afflictions have all been suggested as contributors. Numerous corporate mergers and acquisitions may have also had adverse effects on productivity through distractions of reorganization and integration. Nevertheless, one should consider what the success would have been in the absence of the striking technical advances. Certainly, progress with some critical and challenging target classes such as kinases would have been greatly diminished, and the adverse impact on many cancer patients would have been profound.

Type
Chapter
Information
Drug Design
Structure- and Ligand-Based Approaches
, pp. 1 - 14
Publisher: Cambridge University Press
Print publication year: 2010

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Hughes, B.2007 FDA drug approvals: a year of flux. Nat. Rev. Drug Discov. 2008, 7, 107–109.Google Scholar
Lahana, R.How many leads from HTS?Drug Discov. Today 1999, 4, 447–448.Google Scholar
Posner, B. A.High-throughput screening-driven lead discovery : Meeting the challenges of finding new therapeutics. Curr. Opin. Drug Disc. Dev. 2005, 8, 487–494.Google Scholar
Ganesan, A.The impact of natural products upon modern drug discovery. Curr. Opin. Chem. Biol. 2008, 12, 306–317.Google Scholar
Barreiro, G.; Kim, J. T.; Guimarães, C. R. W.; Bailey, C. M.; Domaoal, R. A.; Wang, L.; Anderson, K. S.; Jorgensen, W. L.From docking false-positive to active anti-HIV Agent. J. Med. Chem. 2007, 50, 5324–5329.Google Scholar
Friesner, R. A.; Banks, J. L.; Murphy, R. B.; Halgren, T. A.; Klicic, J. J.; Mainz, D. T.; Repasky, M. P.; Knoll, E. H.; Shelley, M.; Perry, J. K.; Shaw, D. E.; Francis, P.; Shenkin, P. S.GLIDE: A new approach for rapid, accurate docking and scoring. 1. Method and assessment of docking accuracy. J. Med. Chem. 2004, 47, 1739–1749.Google Scholar
Leach, A. R.; Hann, M. M.; Burrows, J. N.; Griffen, E. J.Fragment screening: an introduction. Mol. Biosyst. 2006, 2, 429–446.Google Scholar
Congreve, M.; Chessari, G.; Tisi, D.; Woodhead, A. J.Recent developments in fragment-based drug discovery. J. Med. Chem. 2008, 51, 3661–3680.Google Scholar
Rodgers, D. W.; Gamblin, S. J.; Harris, B. A.; Ray, S.; Culp, J. S.; Hellmig, B.; Woolf, D. J.The structure of unliganded reverse transcriptase from the human immunodeficiency virus type 1. Proc. Natl. Acad. Sci. U.S.A. 1995, 92, 1222–1226.Google Scholar
Jorgensen, W. L.; Tirado-Rives, J.Potential energy functions for atomic-level simulations of water and organic and biomolecular systems. Proc. Nat. Acad. Sci U.S.A. 2005, 102, 6665–6670.Google Scholar
Jorgensen, W. L.; Ruiz-Caro, J.; Tirado-Rives, J.; Basavapathruni, A.; Anderson, K. S.; Hamilton, A. D.Computer-aided design of non-nucleoside inhibitors of HIV-1 reverse transcriptase. Bioorg. Med. Chem. Lett. 2006, 16, 663–667.Google Scholar
Ruiz-Caro, J.; Basavapathruni, A.; Kim, J. T.; Wang, L.; Bailey, C. M.; Anderson, K. S.; Hamilton, A. D.; Jorgensen, W. L.Optimization of diarylamines as non-nucleoside inhibitors of HIV-1 reverse transcriptase. Bioorg. Med. Chem. Lett. 2006, 16, 668–671.Google Scholar
Thakur, V. V.; Kim, J. T.; Hamilton, A. D.; Bailey, C. M.; Domaoal, R. A.; Wang, L.; Anderson, K. S.; Jorgensen, W. L.Optimization of pyrimidinyl- and triazinyl-amines as non-nucleoside inhibitors of HIV-1 reverse transcriptase. Bioorg. Med. Chem. Lett. 2006, 16, 5664–5667.Google Scholar
Kim, J. T.; Hamilton, A. D.; Bailey, C. M.; Domaoal, R. A.; Wang, L.; Anderson, K. S.; Jorgensen, W. L.FEP-guided selection of bicyclic heterocycles in lead optimization for non-nucleoside inhibitors of HIV-1 reverse transcriptase. J. Am. Chem. Soc. 2006, 128, 15372–15373.Google Scholar
Jorgensen, W. L.QIKPROP, v 3.0. New York: Schrödinger LLC; 2006.
Jorgensen, W. L.; Tirado-Rives, J.Molecular modeling of organic and biomolecular systems using BOSS and MCPRO. J. Comput. Chem. 2005, 26, 1689–1700.Google Scholar
Jorgensen, W. L.The many roles of computation in drug discovery. Science 2004, 303, 1813–1818.Google Scholar
Kellenberger, E.; Rodrigo, J.; Muller, P.; Rognan, D.Comparative evaluation of eight docking tools for docking and virtual screening accuracy. Proteins 2004, 57, 225–242.Google Scholar
Leach, A. R.; Shoichet, B. K.; Peishoff, C. E.Prediction of protein-ligand interactions. Docking and scoring: successes and gaps. J. Med. Chem. 2006, 49, 5851–5855.Google Scholar
Zhou, Z.; Felts, A. K.; Friesner, R. A.; Levy, R. M.Comparative performance of several flexible docking programs and scoring functions: enrichment studies for a diverse set of pharmaceutically relevant targets. J. Chem. Inf. Model. 2007, 47, 1599–1608.Google Scholar
Tirado-Rives, J.; Jorgensen, W. L.Contribution of conformer focusing to the uncertainty in predicting free energies for protein-ligand binding. J. Med. Chem. 2006, 49, 5880–5884.Google Scholar
Barreiro, G.; Guimarães, C. R. W.; Tubert-Brohman, I.; Lyons, T. M.; Tirado-Rives, J.; Jorgensen, W. L.Search for non-nucleoside inhibitors of HIV-1 reverse transcriptase using chemical similarity, molecular docking, and MM-GB/SA scoring. J. Chem. Info. Model. 2007, 47, 2416–2428.Google Scholar
Friesner, R. A.; Murphy, R. B.; Repasky, M. P.; Frye, L. L.; Greenwood, J. R.; Halgren, T. A.; Sanschagrin, P. C.; Mainz, D. T.Extra precision GLIDE: Docking and scoring incorporating a model of hydrophobic enclosure for protein-ligand complexes. J. Med. Chem. 2006, 49, 6177–6196.Google Scholar
Zeevaart, J. G.; Wang, L.; Thakur, V. V.; Leung, C. S.; Tirado-Rives, J.; Bailey, C. M.; Domaoal, R. A.; Anderson, K. S.; Jorgensen, W. L.Optimization of azoles as anti-HIV agents guided by free-energy calculations. J. Am. Chem. Soc. 2008, 130, 9492–9499.Google Scholar
Cournia, Z.; Leng, L.; Gandavadi, S.; Du, X.; Bucala, R.; Jorgensen, W. L.Discovery of human macrophage migration inhibitory factor (MIF)-CD74 antagonists via virtual screening. J. Med. Chem. 2009, 52, 416–424.Google Scholar
Irwin, J. J.; Shoichet, B. K.ZINC: a free database of commercially available compounds for virtual screening. J. Chem. Inf. Model. 2005, 45, 177–182.Google Scholar
Morand, E. F.; Leech, M.; Bernhagen, J.MIF: a new cytokine link between rheumatoid arthritis and atherosclerosis. Nat. Rev. Drug Discov. 2006, 5, 399–411.Google Scholar
Hagemann, T.; Robinson, S. C.; Thompson, R. G.; Charles, K.; Kulbe, H.; Balkwill, F. R.Ovarian cancer cell-derived migration inhibitory factor enhances tumor growth, progression, and angiogenesis. Mol. Cancer Ther. 2007, 6, 1993–2002.Google Scholar
Senter, P. D.; Al-Abed, Y.; Metz, C. N.; Benigni, F.; Mitchell, R. A.; Chesney, J.; Han, J.; Gartner, C. G.; Nelson, S. D.; Todaro, G. J.; Bucala, R.Inhibition of macrophage migration inhibitory factor (MIF) tautomerase and biological activities by acetaminophen metabolites. Proc. Natl. Acad. Sci. U.S.A. 2002, 99, 144–149.Google Scholar
Sun, H. W.; Bernhagen, J.; Bucala, R.; Lolis, E.Crystal structure at 2.6-Å resolution of human macrophage migration inhibitory factor. Proc. Natl. Acad. Sci. U.S.A. 1996, 93, 5191–5196.Google Scholar
Egan, W. J.; Merz, K. M.; Baldwin, J. J.Prediction of drug absorption using multivariate statistics. J. Med. Chem. 2000, 43, 3867–3877.Google Scholar
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. 2001, 46, 3–26.Google Scholar
Jorgensen, W. L.; Duffy, E. M. Prediction of solubility from structure. Adv. Drug Deliv. Rev., 54, 355–365.
Norinder, U.; Bergström, C. A. S.Prediction of ADMET properties. ChemMedChem 2006, 1, 920–937.Google Scholar
Proudfoot, J. R.The evolution of synthetic oral drug properties. Bioorg. Med. Chem. Lett. 2005, 15, 1087–1090.Google Scholar
Weber, A.; Casini, A.; Heine, A.; Kuhn, D.; Supuran, C. T.; Scozzafava, A.; Klebe, G.Unexpected nanomolar inhibition of carbonic anhydrase by COX-2-selective celecoxib: new pharmacological opportunities due to related binding site recognition. J. Med. Chem. 2004, 47, 550–557.Google Scholar
Chipot, C.; Pohorille, A. In Springer Series in Chemical Physics: Free Energy Calculations: Theory and Applications in Chemistry and Biology, Vol. 86, Chipot, C.; Pohorille, A.; Eds. Berlin: Springer-Verlag; 2007, 33–75.
Jorgensen, W. L.; Ravimohan, C.Monte Carlo simulation of differences in free energies of hydration. J. Chem. Phys. 1985, 83, 3050–3054.Google Scholar
Wong, C. F.; McCammon, J. A.Dynamics and design of enzymes and inhibitors. J. Am. Chem. Soc. 1986, 108, 3830–3832.Google Scholar
McCammon, J. A.Computer-aided molecular design. Science 1987, 238, 486–491.Google Scholar
Kollman, P. A.Free energy calculations: applications to chemical and biochemical phenomena. Chem. Rev. 1993, 93, 2395–2417.Google Scholar
Merz, K. M.; Kollman, P. A.Free energy perturbation simulations of the inhibition of thermolysin: prediction of the free energy of binding of a new inhibitor. J. Am. Chem. Soc. 1989, 111, 5649–5658.Google Scholar
Pearlman, D. A.; Charifson, P. S.Are free energy calculations useful in practice? A comparison with rapid scoring functions for the p38 MAP kinase protein system. J. Med. Chem. 2001, 44, 3417–3423.Google Scholar
Pearlman, D. A.Evaluating the molecular mechanics Poisson-Boltzmann surface area free energy method using a congeneric series of ligands to p38 MAP kinase. J. Med. Chem. 2005, 48, 7796–7807.Google Scholar
Reddy, M. R.; Erion, M. D.Calculation of relative binding free energy differences for fructose 1,6-biphosphatase inhibitors using thermodynamic cycle perturbation approach. J. Am. Chem. Soc. 2001, 123, 6246–6252.Google Scholar
Erion, M. D.; Dang, Q.; Reddy, M. R.; Kasibhatla, S. R.; Huang, J.; Lipscomb, W. N.; van Poelje, P. D.Structure-guided design of amp mimics that inhibit fructose-1,6-bisphosphatase with high affinity and specificity. J. Am. Chem. Soc. 2007, 129, 15480–15490.Google Scholar
Pierce, A. C.; Jorgensen, W. L.Computational binding studies of orthogonal cyclosporin-cyclophilin pairs. Angew. Chem. Int. Ed. Engl. 1997, 36, 1466–1469.Google Scholar
Essex, J. W.; Severance, D. L.; Tirado-Rives, J.; Jorgensen, W. L.Monte Carlo simulations for proteins: binding affinities for trypsin-benzamidine complexes via free energy perturbations. J. Phys. Chem. 1997, 101, 9663–9669.Google Scholar
Plount-Price, M. L.; Jorgensen, W. L.Analysis of binding affinities for celecoxib analogs with COX-1 and COX-2 from docking and Monte Carlo simulations and insight into COX-2/COX-1 selectivity. J. Am. Chem. Soc. 2000, 122, 9455–9466.Google Scholar
Plount-Price, M. L.; Jorgensen, W. L.Rationale for the observed COX-2/COX-1 selectivity of celecoxib from Monte Carlo simulations. Bioorg. Med. Chem. Lett. 2001, 11, 1541–1544.Google Scholar
Guimarães, C. R. W; Boger, D. L.; Jorgensen, W. L.Elucidation of fatty acid amide hydrolase inhibition by potent α-ketoheterocycle derivatives from Monte Carlo simulations. J. Am. Chem. Soc. 2005, 127, 17377–17384.Google Scholar
Rizzo, R. C.; Wang, D.-P.; Tirado-Rives, J.; Jorgensen, W. L.Validation of a model for the complex of HIV-1 reverse transcriptase with Sustiva through computation of resistance profiles. J. Am. Chem. Soc. 2000, 122, 12898–12900.Google Scholar
Wang, D.-P.; Rizzo, R. C.; Tirado-Rives, J.; Jorgensen, W. L.Antiviral drug design: Computational analyses of the effects of the L100I mutation for HIV-RT on the binding of NNRTIs. Bioorg. Med. Chem. Lett. 2001, 11, 2799–2802.Google Scholar
Udier-Blagović, M.; Tirado-Rives, J.; Jorgensen, W. L.Validation of a model for the complex of HIV-1 reverse transcriptase with the novel non-nucleoside inhibitor TMC125J. Am. Chem. Soc. 2003, 125, 6016–6017.Google Scholar
Blagović, M. U.; Tirado-Rives, J.; Jorgensen, W. L.Structural and energetic analyses for the effects of the K103N mutation of HIV-1 reverse transcriptase on efavirenz analogs. J. Med. Chem. 2004, 46, 2389–2392.Google Scholar
Das, K.; Clark, A. D.; Lewi, P. J.; Heeres, J.; Jonge, M. R.; Koymans, L. M. H.; Vinkers, H. M.; Daeyaert, F.; Ludovici, D. W.; Kukla, M. J.; Corte, B.; Kavash, R. W.; Ho, C. Y.; Ye, H.; Lichtenstein, M. A.; Andries, K.; Pauwels, R.; Béthune, M.-P.; Boyer, P. L.; Clark, P.; Hughes, S. H.; Janssen, P. A. J.; Arnold, E.Roles of conformational and positional adaptability in structure-based design of TMC125-R165335 (Etravirine) and related non-nucleoside reverse transcriptase inhibitors that are highly potent and effective against wild-type and drug-resistant HIV-1 variants. J. Med. Chem. 2004, 47, 2550–2560.Google Scholar
Jorgensen, W. L.; Thomas, L. T.Perspective on free-energy perturbation calculations for chemical equilibria. J. Chem. Theor. Comput. 2008, 4, 869–876.Google Scholar

Save book to Kindle

To save this book to your Kindle, first ensure [email protected] is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about saving to your Kindle.

Note you can select to save to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

Available formats
×

Save book to Dropbox

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Dropbox.

Available formats
×

Save book to Google Drive

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Google Drive.

Available formats
×