Progress and issues for computationally guided lead discovery and optimization

doi:10.1017/CBO9780511730412.003

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

Published online by Cambridge University Press: 06 July 2010

William L. Jorgensen

Edited by

Kenneth M. Merz, Jr ,

Dagmar Ringe and

Charles H. Reynolds

Show author details

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

Book contents

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

DOI: https://doi.org/10.1017/CBO9780511730412.003 [Opens in a new window]

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.)

Book purchase

Temporarily unavailable

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