Bayesian models leveraging bioactivity and cytotoxicity information for drug discovery

Chemistry and Biology

Volumn 20, Issue 3, 2013, Pages 370-378

  Ekins, Sean ^a,b   Reynolds, Robert C ^c,f   Kim, Hiyun ^d   Koo, Mi Sun ^d   Ekonomidis, Marilyn ^d   Talaue, Meliza ^d   Paget, Steve D ^d   Woolhiser, Lisa K ^e   Lenaerts, Anne J ^e   Bunin, Barry A ^a   Connell, Nancy ^d   Freundlich, Joel S ^d  

^a Collaborative Drug Discovery ^*  (United States)

^b Collaborations in Chemistry ^*  (United States)

^c SOUTHERN RESEARCH INSTITUTE   (United States)

^d RUTGERS NEW JERSEY MEDICAL SCHOOL   (United States)

^e Department of Environmental and Radiological Health Sciences   (United States)

^f University of Alabama at Birmingham   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

6 (2 ((5 NITROFURAN 2 YL)METHYLENE)HYDRAZINYL) N2,N4 DIPHENYL 1,3,5 TRIAZINE 2,4 DIAMINE; TUBERCULOSTATIC AGENT; UNCLASSIFIED DRUG;

ANIMAL CELL; ARTICLE; BAYES THEOREM; CONTROLLED STUDY; CYTOTOXICITY; DRUG ACTIVITY; DRUG SCREENING; GROWTH INHIBITION; HIGH THROUGHPUT SCREENING; HUMAN; HUMAN CELL; IN VITRO STUDY; IN VIVO STUDY; MAMMAL CELL; MINIMUM INHIBITORY CONCENTRATION; MOUSE; NONHUMAN; PRIORITY JOURNAL; WHOLE CELL;

ANIMALS; ANTITUBERCULAR AGENTS; BAYES THEOREM; CERCOPITHECUS AETHIOPS; DRUG DISCOVERY; DRUG EVALUATION, PRECLINICAL; FEMALE; INHIBITORY CONCENTRATION 50; MACROPHAGES; MICE; MYCOBACTERIUM TUBERCULOSIS; VERO CELLS;

MAMMALIA;

EID: 84875428496     PISSN: 10745521     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.chembiol.2013.01.011     Document Type: Article

References (62)

1. S. Ananthan, E.R. Faaleolea, R.C. Goldman, J.V. Hobrath, C.D. Kwong, B.E. Laughon, J.A. Maddry, A. Mehta, L. Rasmussen, and R.C. Reynolds High-throughput screening for inhibitors of Mycobacterium tuberculosis H37Rv Tuberculosis (Edinb.) 89 2009 334 353
2. K. Andries, P. Verhasselt, J. Guillemont, H.W. Göhlmann, J.M. Neefs, H. Winkler, J. Van Gestel, P. Timmerman, M. Zhu, and E. Lee A diarylquinoline drug active on the ATP synthase of Mycobacterium tuberculosis Science 307 2005 223 227
3. L. Ballell, R.A. Field, K. Duncan, and R.J. Young New small-molecule synthetic antimycobacterials Antimicrob. Agents Chemother. 49 2005 2153 2163
4. C.E. Barry III, R.A. Slayden, A.E. Sampson, and R.E. Lee Use of genomics and combinatorial chemistry in the development of new antimycobacterial drugs Biochem. Pharmacol. 59 2000 221 231
5. A. Bender, J. Scheiber, M. Glick, J.W. Davies, K. Azzaoui, J. Hamon, L. Urban, S. Whitebread, and J.L. Jenkins Analysis of pharmacology data and the prediction of adverse drug reactions and off-target effects from chemical structure ChemMedChem 2 2007 861 873
6. H. Bruhin, X. Bühlmann, W.H. Hook, W. Hoyle, B. Orford, and W. Vischer Antituberculosis activity of some nitrofuran derivatives J. Pharm. Pharmacol. 21 1969 423 433
7. A.M. Clark, S. Ekins, and A.J. Williams Redefining cheminformatics with intuitive collaborative mobile apps Mol. Inform. 31 2012 569 584
8. L. Collins, and S.G. Franzblau Microplate Alamar blue assay versus BACTEC 460 system for high-throughput screening of compounds against Mycobacterium tuberculosis and Mycobacterium avium Antimicrob. Agents Chemother. 41 1997 1004 1009
9. S. Ehrt, D. Schnappinger, S. Bekiranov, J. Drenkow, S. Shi, T.R. Gingeras, T. Gaasterland, G. Schoolnik, and C. Nathan Reprogramming of the macrophage transcriptome in response to interferon-γ and Mycobacterium tuberculosis: signaling roles of nitric oxide synthase-2 and phagocyte oxidase J. Exp. Med. 194 2001 1123 1140
10. S. Ekins, and J.S. Freundlich Validating new tuberculosis computational models with public whole cell screening aerobic activity datasets Pharm. Res. 28 2011 1859 1869
11. S. Ekins, J.D. Honeycutt, and J.T. Metz Evolving molecules using multi-objective optimization: applying to ADME/Tox Drug Discov. Today 15 2010 451 460
12. S. Ekins, A.J. Williams, and J.J. Xu A predictive ligand-based Bayesian model for human drug-induced liver injury Drug Metab. Dispos. 38 2010 2302 2308
13. S. Ekins, J. Bradford, K. Dole, A. Spektor, K. Gregory, D. Blondeau, M. Hohman, and B.A. Bunin A collaborative database and computational models for tuberculosis drug discovery Mol. Biosyst. 6 2010 840 851
14. S. Ekins, T. Kaneko, C.A. Lipinski, J. Bradford, K. Dole, A. Spektor, K. Gregory, D. Blondeau, S. Ernst, and J. Yang Analysis and hit filtering of a very large library of compounds screened against Mycobacterium tuberculosis Mol. Biosyst. 6 2010 2316 2324
15. S. Ekins, J.S. Freundlich, I. Choi, M. Sarker, and C. Talcott Computational databases, pathway and cheminformatics tools for tuberculosis drug discovery Trends Microbiol. 19 2011 65 74
16. F.-J. Gamo, L.M. Sanz, J. Vidal, C. de Cozar, E. Alvarez, J.-L. Lavandera, D.E. Vanderwall, D.V.S. Green, V. Kumar, and S. Hasan Thousands of chemical starting points for antimalarial lead identification Nature 465 2010 305 310
17. M. Ganguly, N. Brown, A. Schuffenhauer, P. Ertl, V.J. Gillet, and P.A. Greenidge Introducing the consensus modeling concept in genetic algorithms: application to interpretable discriminant analysis J. Chem. Inf. Model. 46 2006 2110 2124
18. J. Garau Other antimicrobials of interest in the era of extended-spectrum β-lactamases: fosfomycin, nitrofurantoin and tigecycline Clin. Microbiol. Infect. 14 Suppl 1 2008 198 202
19. B. Gold, M. Pingle, S.J. Brickner, N. Shah, J. Roberts, M. Rundell, W.C. Bracken, T. Warrier, S. Somersan, and A. Venugopal Nonsteroidal anti-inflammatory drug sensitizes Mycobacterium tuberculosis to endogenous and exogenous antimicrobials Proc. Natl. Acad. Sci. USA 109 2012 16004 16011
20. J.H. Grosset, T.G. Singer, and W.R. Bishai New drugs for the treatment of tuberculosis: hope and reality Int. J. Tuberc. Lung Dis. 16 2012 1005 1014
21. M. Hassan, R.D. Brown, S. Varma-O'Brien, and D. Rogers Cheminformatics analysis and learning in a data pipelining environment Mol. Divers. 10 2006 283 299
22. M. Hohman, K. Gregory, K. Chibale, P.J. Smith, S. Ekins, and B. Bunin Novel web-based tools combining chemistry informatics, biology and social networks for drug discovery Drug Discov. Today 14 2009 261 270
23. A.E. Klon, J.F. Lowrie, and D.J. Diller Improved naïve Bayesian modeling of numerical data for absorption, distribution, metabolism and excretion (ADME) property prediction J. Chem. Inf. Model. 46 2006 1945 1956
24. K. Konno, F.M. Feldmann, and W. McDermott Pyrazinamide susceptibility and amidase activity of tubercle bacilli Am. Rev. Respir. Dis. 95 1967 461 469
25. A. Koul, E. Arnoult, N. Lounis, J. Guillemont, and K. Andries The challenge of new drug discovery for tuberculosis Nature 469 2011 483 490
26. S.R. Langdon, J. Mulgrew, G.V. Paolini, and W.P. van Hoorn Predicting cytotoxicity from heterogeneous data sources with Bayesian learning J. Cheminform. 2 2010 11
27. H. Ledford Translational research: 4 ways to fix the clinical trial Nature 477 2011 526 528
28. R.E. Lee, M. Protopopova, E. Crooks, R.A. Slayden, M. Terrot, and C.E. Barry III Combinatorial lead optimization of [1,2]-diamines based on ethambutol as potential antituberculosis preclinical candidates J. Comb. Chem. 5 2003 172 187
29. A.J. Lenaerts, V. Gruppo, J.V. Brooks, and I.M. Orme Rapid in vivo screening of experimental drugs for tuberculosis using γ interferon gene-disrupted mice Antimicrob. Agents Chemother. 47 2003 783 785
30. C. Lienhardt, P. Glaziou, M. Uplekar, K. Lönnroth, H. Getahun, and M. Raviglione Global tuberculosis control: lessons learnt and future prospects Nat. Rev. Microbiol. 10 2012 407 416
31. C. Lienhardt, M. Raviglione, M. Spigelman, R. Hafner, E. Jaramillo, M. Hoelscher, A. Zumla, and J. Gheuens New drugs for the treatment of tuberculosis: needs, challenges, promise, and prospects for the future J. Infect. Dis. 205 Suppl 2 2012 S241 S249
32. R. Macarron, M.N. Banks, D. Bojanic, D.J. Burns, D.A. Cirovic, T. Garyantes, D.V. Green, R.P. Hertzberg, W.P. Janzen, and J.W. Paslay Impact of high-throughput screening in biomedical research Nat. Rev. Drug Discov. 10 2011 188 195
33. J.A. Maddry, S. Ananthan, R.C. Goldman, J.V. Hobrath, C.D. Kwong, C. Maddox, L. Rasmussen, R.C. Reynolds, J.A. Secrist III, and M.I. Sosa Antituberculosis activity of the Molecular Libraries Screening Center Network library Tuberculosis (Edinb.) 89 2009 354 363
34. S. Magnet, R.C. Hartkoorn, R. Székely, J. Pató, J.A. Triccas, P. Schneider, C. Szántai-Kis, L. Orfi, M. Chambon, and D. Banfi Leads for antitubercular compounds from kinase inhibitor library screens Tuberculosis (Edinb.) 90 2010 354 360
35. P.A. Mak, S.P. Rao, M. Ping Tan, X. Lin, J. Chyba, J. Tay, S.H. Ng, B.H. Tan, J. Cherian, and J. Duraiswamy A high-throughput screen to identify inhibitors of ATP homeostasis in non-replicating Mycobacterium tuberculosis ACS Chem. Biol. 7 2012 1190 1197
36. B. Murugasu-Oei, and T. Dick Bactericidal activity of nitrofurans against growing and dormant Mycobacterium bovis BCG J. Antimicrob. Chemother. 46 2000 917 919
37. C. Nathan Making space for anti-infective drug discovery Cell Host Microbe 9 2011 343 348
38. E.L. Nuermberger, M.K. Spigelman, and W.W. Yew Current development and future prospects in chemotherapy of tuberculosis Respirology 15 2010 764 778
39. C.J. O' Connor, H.S. Beckmann, and D.R. Spring Diversity-oriented synthesis: producing chemical tools for dissecting biology Chem. Soc. Rev. 41 2012 4444 4456
40. T.I. Oprea, A.M. Davis, S.J. Teague, and P.D. Leeson Is there a difference between leads and drugs? A historical perspective J. Chem. Inf. Comput. Sci. 41 2001 1308 1315
41. D.J. Payne, M.N. Gwynn, D.J. Holmes, and D.L. Pompliano Drugs for bad bugs: confronting the challenges of antibacterial discovery Nat. Rev. Drug Discov. 6 2007 29 40
42. V. Periwal, J.K. Rajappan Open Source Drug Discovery Consortium A.U. Jaleel, and V. Scaria Predictive models for anti-tubercular molecules using machine learning on high-throughput biological screening datasets BMC Res. Notes 4 2011 504
43. P. Prathipati, N.L. Ma, and T.H. Keller Global Bayesian models for the prioritization of antitubercular agents J. Chem. Inf. Model. 48 2008 2362 2370
44. R.C. Reynolds, S. Ananthan, E. Faaleolea, J.V. Hobrath, C.D. Kwong, C. Maddox, L. Rasmussen, M.I. Sosa, E. Thammasuvimol, and E.L. White High throughput screening of a library based on kinase inhibitor scaffolds against Mycobacterium tuberculosis H37Rv Tuberculosis (Edinb.) 92 2012 72 83
45. D. Rogers, R.D. Brown, and M. Hahn Using extended-connectivity fingerprints with Laplacian-modified Bayesian analysis in high-throughput screening follow-up J. Biomol. Screen. 10 2005 682 686
46. D.A. Rozwarski, G.A. Grant, D.H. Barton, W.R. Jacobs Jr., and J.C. Sacchettini Modification of the NADH of the isoniazid target (InhA) from Mycobacterium tuberculosis Science 279 1998 98 102
47. J.C. Sacchettini, E.J. Rubin, and J.S. Freundlich Drugs versus bugs: in pursuit of the persistent predator Mycobacterium tuberculosis Nat. Rev. Microbiol. 6 2008 41 52
48. L. Sandegren, A. Lindqvist, G. Kahlmeter, and D.I. Andersson Nitrofurantoin resistance mechanism and fitness cost in Escherichia coli J. Antimicrob. Chemother. 62 2008 495 503
49. M. Sarker, C. Talcott, P. Madrid, S. Chopra, B.A. Bunin, G. Lamichhane, J.S. Freundlich, and S. Ekins Combining cheminformatics methods and pathway analysis to identify molecules with whole-cell activity against Mycobacterium tuberculosis Pharm. Res. 29 2012 2115 2127
50. J. Saunders Non-nucleoside inhibitors of HIV reverse transcriptase: screening successes - clinical failures Drug Des. Discov. 8 1992 255 263
51. G. Schneider Virtual screening: an endless staircase? Nat. Rev. Drug Discov. 9 2010 273 276
52. D. Schuster, C. Laggner, and T. Langer Why drugs fail - a study on side effects in new chemical entities Curr. Pharm. Des. 11 2005 3545 3559
53. A. Scorpio, and Y. Zhang Mutations in pncA, a gene encoding pyrazinamidase/nicotinamidase, cause resistance to the antituberculous drug pyrazinamide in tubercle bacillus Nat. Med. 2 1996 662 667
54. R. Singh, U. Manjunatha, H.I. Boshoff, Y.H. Ha, P. Niyomrattanakit, R. Ledwidge, C.S. Dowd, I.Y. Lee, P. Kim, and L. Zhang PA-824 kills nonreplicating Mycobacterium tuberculosis by intracellular NO release Science 322 2008 1392 1395
55. S.A. Stanley, S.S. Grant, T. Kawate, N. Iwase, M. Shimizu, C. Wivagg, M. Silvis, E. Kazyanskaya, J. Aquadro, and A. Golas Identification of novel inhibitors of M. tuberculosis growth using whole cell based high-throughput screening ACS Chem. Biol. 7 2012 1377 1384
56. C.K. Stover, P. Warrener, D.R. VanDevanter, D.R. Sherman, T.M. Arain, M.H. Langhorne, S.W. Anderson, J.A. Towell, Y. Yuan, and D.N. McMurray A small-molecule nitroimidazopyran drug candidate for the treatment of tuberculosis Nature 405 2000 962 966
57. C. Vilchèze, A.D. Baughn, J. Tufariello, L.W. Leung, M. Kuo, C.F. Basler, D. Alland, J.C. Sacchettini, J.S. Freundlich, and W.R. Jacobs Jr. Novel inhibitors of InhA efficiently kill Mycobacterium tuberculosis under aerobic and anaerobic conditions Antimicrob. Agents Chemother. 55 2011 3889 3898
58. P. Volarath, R.W. Harrison, and I.T. Weber Structure based drug design for HIV protease: from molecular modeling to cheminformatics Curr. Top. Med. Chem. 7 2007 1030 1038
59. W.P. Walters, and M.A. Murcko Prediction of 'drug-likeness.' Adv. Drug Deliv. Rev. 54 2002 255 271
60. J. Whiteway, P. Koziarz, J. Veall, N. Sandhu, P. Kumar, B. Hoecher, and I.B. Lambert Oxygen-insensitive nitroreductases: analysis of the roles of nfsA and nfsB in development of resistance to 5-nitrofuran derivatives in Escherichia coli J. Bacteriol. 180 1998 5529 5539
61. Z. Xie, N. Siddiqi, and E.J. Rubin Differential antibiotic susceptibilities of starved Mycobacterium tuberculosis isolates Antimicrob. Agents Chemother. 49 2005 4778 4780
62. M. Zientek, C. Stoner, R. Ayscue, J. Klug-McLeod, Y. Jiang, M. West, C. Collins, and S. Ekins Integrated in silico-in vitro strategy for addressing cytochrome P450 3A4 time-dependent inhibition Chem. Res. Toxicol. 23 2010 664 676