Computational models for neglected diseases: Gaps and opportunities

Pharmaceutical Research

Volumn 31, Issue 2, 2014, Pages 271-277

  Ponder, Elizabeth L ^a   Freundlich, Joel S ^b   Sarker, Malabika ^c   Ekins, Sean ^d,e  

^a Center for Emerging and Neglected Diseases   (United States)

^b RUTGERS NEW JERSEY MEDICAL SCHOOL   (United States)

^c SRI INTERNATIONAL   (United States)

^d Collaborations in Chemistry ^*  (United States)

^e Collaborative Drug Discovery ^*  (United States)

Author keywords

African sleeping sickness; Chagas disease; computational models; leishmaniasis; malaria; schistosomiasis; tuberculosis

Indexed keywords

RALTEGRAVIR;

AFRICAN TRYPANOSOMIASIS; BIOINFORMATICS; CHAGAS DISEASE; HIGH THROUGHPUT SCREENING; HUMAN; LEISHMANIASIS; MALARIA; NEGLECTED DISEASE; PRIORITY JOURNAL; REVIEW; SCHISTOSOMIASIS; TROPICAL DISEASE; TUBERCULOSIS;

COMPUTER SIMULATION; DRUG DISCOVERY; HUMANS; NEGLECTED DISEASES;

EID: 84896729192     PISSN: 07248741     EISSN: 1573904X     Source Type: Journal    
DOI: 10.1007/s11095-013-1170-9     Document Type: Review

References (86)

1. Hotez PJ, Molyneux DH, Fenwick A, Kumaresan J, Sachs SE, Sachs JD, et al. Control of neglected tropical diseases. N Engl J Med. 2007;357:1018-27.
2. Guiguemde WA, Shelat AA, Bouck D, Duffy S, Crowther GJ, Davis PH, et al. Chemical genetics of Plasmodium falciparum. Nature. 2010;465:311-5.
3. Ribeiro I, Sevcsik AM, Alves F, Diap G, Don R, Harhay MO, et al. New, improved treatments for Chagas disease: from the R&D pipeline to the patients. PLoS Negl Trop Dis. 2009;3:e484.
4. Bettiol E, Samanovic M, Murkin AS, Raper J, Buckner F, Rodriguez A. Identification of three classes of heteroaromatic compounds with activity against intracellular Trypanosoma cruzi by chemical library screening. PLoS Negl Trop Dis. 2009;3:e384.
5. Magarinos MP, Carmona SJ, Crowther GJ, Ralph SA, Roos DS, Shanmugam D, et al. TDR Targets: a chemogenomics resource for neglected diseases. Nucleic Acids Res. 2012;40:D1118-1127.
6. Gaulton A, Bellis LJ, Bento AP, Chambers J, Davies M, Hersey A, et al. ChEMBL: a large-scale bioactivity database for drug discovery. Nucleic Acids Res. 2012;40:D1100-1107.
7. Ekins S, Freundlich JS, Choi I, Sarker M, Talcott C. Computational databases, pathway and cheminformatics tools for tuberculosis drug discovery. Trends Microbiol. 2011;19:65-74.
8. Miller K. Where tuberculosis meets computation: 10 points of intersection. Biomed Comput Rev. 2012;20-28.
9. Ekins S, Reynolds R, Kim H, Koo M-S, Ekonomidis M, Talaue M, et al. Bayesian models leveraging bioactivity and cytotoxicity information for drug discovery. Chem Biol. 2013;20:370-8.
10. Sarker M, Talcott C, Madrid P, Chopra S, Bunin BA, Lamichhane G, et al. Combining cheminformatics methods and pathway analysis to identify molecules with whole-cell activity against Mycobacterium tuberculosis. Pharm Res. 2012;29:2115-27.
11. Duffy BC, Zhu L, Decornez H, Kitchen DB. Early phase drug discovery: cheminformatics and computational techniques in identifying lead series. Bioorg Med Chem. 2012;20:5324-42.
12. Krueger BA, Weil T, Schneider G. Comparative virtual screening and novelty detection for NMDA-GlycineB antagonists. J Comput Aided Mol Des. 2009;23:869-81.
13. Schames JR, Henchman RH, Siegel JS, Sotriffer CA, Ni H, McCammon JA. Discovery of a novel binding trench in HIV integrase. J Med Chem. 2004;47:1879-81.
14. Kubinyi H. Success stories of computer-aided design. In: Ekins S, editor. Computer applications in pharmaceutical research and development. Hoboken: John Wiley and Sons; 2006. p. 377-424.
15. Sundaramurthi JC, Brindha S, Reddy TB, Hanna LE. Informatics resources for tuberculosis-towards drug discovery. Tuberculosis (Edinburgh, Scotland). 2012;92:133-8.
16. Ekins S, Freundlich JS. Computational models for tuberculosis drug discovery. Methods Mol Biol (Clifton, NJ). 2013;993:245-62.
17. Ekins S, Reynolds RC, Franzblau SG, Wan B, Freundlich JS, Bunin BA. Enhancing hit identification in mycobacterium tuberculosis drug discovery using validated dual-event Bayesian models. PLoS ONE. 2013 (in press).
18. Anderson JW, Sarantakis D, Terpinski J, Kumar TR, Tsai HC, Kuo M, et al. Novel diaryl ureas with efficacy in a mouse model of malaria. Bioorg Med Chem Lett. 2012;23:1022-5.
19. Alvarez G, Martinez J, Aguirre-Lopez B, Cabrera N, Perez-Diaz L, Gomez-Puyou MT, et al. New chemotypes as Trypanosoma cruzi triosephosphate isomerase inhibitors: a deeper insight into the mechanism of inhibition. J Enzym Inhib Med Chem. 2012. doi: 10.3109/14756366.2013.765415.
20. Pires DE, de Melo-Minardi RC, da Silveira CH, Campos FF, Meira Jr W. aCSM: noise-free graph-based signatures to large-scale receptor-based ligand prediction. Bioinformatics (Oxford, England). 2013;29:855-61.
21. Gunatilleke SS, Calvet CM, Johnston JB, Chen CK, Erenburg G, Gut J, et al. Diverse inhibitor chemotypes targeting Trypanosoma cruzi CYP51. PLoS Negl Trop Dis. 2012;6:e1736.
22. Zhang L, Fourches D, Sedykh A, Zhu H, Golbraikh A, Ekins S, et al. Discovery of novel antimalarial compounds enabled by QSAR-based virtual screening. J Chem Inf Model. 2013;53:475-92.
23. Suthram S, Sittler T, Ideker T. The Plasmodium protein network diverges from those of other eukaryotes. Nature. 2005;438:108-12.
24. Schneider G. Virtual screening: an endless staircase? Nat Rev. 2010;9:273-6.
25. Ekins S, Clark AM, Williams AJ. Open drug discovery teams: a chemistry mobile app for collaboration. Mol Inform. 2012;31:585-97.
26. Bunin BA, Ekins S. Alternative business models for drug discovery. Drug Discov Today. 2011;16:643-5.
27. Gamo F-J, Sanz LM, Vidal J, de Cozar C, Alvarez E, Lavandera J-L, et al. Thousands of chemical starting points for antimalarial lead identification. Nature. 2010;465:305-10.
28. Ballell L, Bates RH, Young RJ, Alvarez-Gomez D, Alvarez-Ruiz E, Barroso V, et al. Fueling open-source drug discovery: 177 small-molecule leads against tuberculosis. ChemMedChem. 2013;8:313-21.
29. Reynolds RC, Ananthan S, Faaleolea E, Hobrath JV, Kwong CD, Maddox C, et al. High throughput screening of a library based on kinase inhibitor scaffolds against Mycobacterium tuberculosis H37Rv. Tuberculosis (Edinburgh, Scotland). 2012;92:72-83.
30. Maddry JA, Ananthan S, Goldman RC, Hobrath JV, Kwong CD, Maddox C, et al. Antituberculosis activity of the molecular libraries screening center network library. Tuberculosis (Edinburgh, Scotland). 2009;89:354-63.
31. Ananthan S, Faaleolea ER, Goldman RC, Hobrath JV, Kwong CD, Laughon BE, et al. High-throughput screening for inhibitors of Mycobacterium tuberculosis H37Rv. Tuberculosis (Edinburgh, Scotland). 2009;89:334-53.
32. Mackey ZB, Baca AM, Mallari JP, Apsel B, Shelat A, Hansell EJ, et al. Discovery of trypanocidal compounds by whole cell HTS of Trypanosoma brucei. Chem Biol Drug Des. 2006;67:355-63.
33. Engel JC, Ang KK, Chen S, Arkin MR, McKerrow JH, Doyle PS. Image-based high-throughput drug screening targeting the intracellular stage of Trypanosoma cruzi, the agent of Chagas' disease. Antimicrob Agents Chemother. 2010;54:3326-34.
34. Abdulla MH, Ruelas DS, Wolff B, Snedecor J, Lim KC, Xu F, et al. Drug discovery for schistosomiasis: hit and lead compounds identified in a library of known drugs by medium-throughput phenotypic screening. PLoS Negl Trop Dis. 2009;3:e478.
35. Andriani G, Chessler AD, Courtemanche G, Burleigh BA, Rodriguez A. Activity in vivo of anti-Trypanosoma cruzi compounds selected from a high throughput screening. PLoS Negl Trop Dis. 2011;5:e1298.
36. Ferreira RS, Simeonov A, Jadhav A, Eidam O, Mott BT, Keiser MJ, et al. Complementarity between a docking and a high-throughput screen in discovering new cruzain inhibitors. J Med Chem. 2010;53:4891-905.
37. Ekins S, Clark AM, Sarker M. TB Mobile: a mobile app for anti-tuberculosis molecules with known targets. J Cheminform. 2013;5:13.
38. G-FINDER. https://g-finder.policycures.org/gfinder-report/.
39. Galagan JE, Sisk P, Stolte C, Weiner B, Koehrsen M, Wymore F, et al. TB database 2010: overview and update. Tuberculosis (Edinburgh, Scotland). 2010;90:225-35.
40. Anishetty S, Pulimi M, Pennathur G. Potential drug targets in Mycobacterium tuberculosis through metabolic pathway analysis. Comput Biol Chem. 2005;29:368-78.
41. Raman K, Vashisht R, Chandra N. Strategies for efficient disruption of metabolism in Mycobacterium tuberculosis from network analysis. Mol Biosyst. 2009;5:1740-51.
42. Caspi R, Foerster H, Fulcher CA, Kaipa P, Krummenacker M, Latendresse M, et al. The MetaCyc database of metabolic pathways and enzymes and the BioCyc collection of pathway/genome databases. Nucleic Acids Res. 2008;36:D623-631.
43. Huthmacher C, Hoppe A, Bulik S, Holzhutter HG. Antimalarial drug targets in Plasmodium falciparum predicted by stage-specific metabolic network analysis. BMC Syst Biol. 2010;4:120.
44. Plata G, Hsiao TL, Olszewski KL, Llinas M, Vitkup D. Reconstruction and flux-balance analysis of the Plasmodium falciparum metabolic network. Mol Syst Biol. 2010;6:408.
45. Fatumo S, Plaimas K, Mallm JP, Schramm G, Adebiyi E, Oswald M, et al. Estimating novel potential drug targets of Plasmodium falciparum by analysing the metabolic network of knock-out strains in silico. Infect Genet Evol. 2009;9:351-8.
46. Anon. PathCase for metabolic analysis. http://nashua.case.edu/ PathwaysMAW-Trypanosoma/web/. Accessed 3 Aug 2013.
47. Raman K, Bhat AG, Chandra N. A systems perspective of host-pathogen interactions: predicting disease outcome in tuberculosis. Mol Biosyst. 2010;6:516-30.
48. Wuchty S. Computational prediction of host-parasite protein interactions between P. falciparum and H. sapiens. PLoS ONE. 2011;6:e26960.
49. Davis FP, Barkan DT, Eswar N, McKerrow JH, Sali A. Host pathogen protein interactions predicted by comparative modeling. Protein Sci. 2007;16:2585-96.
50. Dyer MD, Murali TM, Sobral BW. Computational prediction of host-pathogen protein-protein interactions. Bioinformatics (Oxford, England). 2007;23:i159-166.
51. Kushwaha SK, Shakya M. Protein interaction network analysis-approach for potential drug target identification in Mycobacterium tuberculosis. J Theor Biol. 2010;262:284-94.
52. Cui T, Zhang L, Wang X, He ZG. Uncovering new signaling proteins and potential drug targets through the interactome analysis of Mycobacterium tuberculosis. BMC Genomics. 2009;10:118.
53. Ramaprasad A, Pain A, Ravasi T. Defining the protein interaction network of human malaria parasite Plasmodium falciparum. Genomics. 2012;99:69-75.
54. Rodriguez-Soca Y, Munteanu CR, Dorado J, Pazos A, Prado-Prado FJ, Gonzalez-Diaz H. Trypano-PPI: a web server for prediction of unique targets in trypanosome proteome by using electrostatic parameters of protein-protein interactions. J Proteome Res. 2010;9:1182-90.
55. Ioerger TR, Koo S, No EG, Chen X, Larsen MH, Jacobs Jr WR, et al. Genome analysis of multi- and extensively-drug-resistant tuberculosis from KwaZulu-Natal, South Africa. PLoS ONE. 2009;4:e7778.
56. Gething PW, Patil AP, Smith DL, Guerra CA, Elyazar IR, Johnston GL, et al. A new world malaria map: Plasmodium falciparum endemicity in 2010. Malar J. 2011;10:378.
57. Computational epidemiologic models developed. http://compepid.tuskegee. edu/CCEBRA/compmod.htm.
58. Gurarie D, King CH, Wang X. A new approach to modelling schistosomiasis transmission based on stratified worm burden. Parasitology. 2010;137:1951-65.
59. Raso G, Vounatsou P, McManus DP, Utzinger J. Bayesian risk maps for Schistosoma mansoni and hookworm mono-infections in a setting where both parasites co-exist. Geospat Health. 2007;2:85-96.
60. Crowther GJ, Shanmugam D, Carmona SJ, Doyle MA, Hertz-Fowler C, Berriman M, et al. Identification of attractive drug targets in neglected-disease pathogens using an in silico approach. PLoS Negl Trop Dis. 2010;4:e804.
61. Capriles PV, Guimaraes AC, Otto TD, Miranda AB, Dardenne LE, Degrave WM. Structural modelling and comparative analysis of homologous, analogous and specific proteins from Trypanosoma cruzi versus Homo sapiens: putative drug targets for chagas' disease treatment. BMC Genomics. 2010;11:610.
62. Kinnings SL, Liu N, Buchmeier N, Tonge PJ, Xie L, Bourne PE. Drug discovery using chemical systems biology: repositioning the safe medicine Comtan to treat multi-drug and extensively drug resistant tuberculosis. PLoS Comput Biol. 2009;5:e1000423.
63. Kinnings SL, Xie L, Fung KH, Jackson RM, Xie L, Bourne PE. The Mycobacterium tuberculosis drugome and its polypharmacological implications. PLoS Comput Biol. 2010;6:e1000976.
64. Prathipati P, Ma NL, Manjunatha UH, Bender A. Fishing the target of antitubercular compounds: in silico target deconvolution model development and validation. J Proteome Res. 2009;8:2788-98.
65. Raman K, Yeturu K, Chandra N. targetTB: a target identification pipeline for Mycobacterium tuberculosis through an interactome, reactome and genome-scale structural analysis. BMC Syst Biol. 2008;2:109.
66. Jensen K, Plichta D, Panagiotou G, Kouskoumvekaki I. Mapping the genome of Plasmodium falciparum on the drug-like chemical space reveals novel anti-malarial targets and potential drug leads. Mol Biosyst. 2012;8:1678-85.
67. Durrant JD, Amaro RE, Xie L, Urbaniak MD, Ferguson MA, Haapalainen A, et al. A multidimensional strategy to detect polypharmacological targets in the absence of structural and sequence homology. PLoS Comput Biol. 2010;6:e1000648.
68. Krasky A, Rohwer A, Schroeder J, Selzer PM. A combined bioinformatics and chemoinformatics approach for the development of new antiparasitic drugs. Genomics. 2007;89:36-43.
69. Ballester PJ, Mangold M, Howard NI, Robinson RL, Abell C, Blumberger J, et al. Hierarchical virtual screening for the discovery of new molecular scaffolds in antibacterial hit identification. J R Soc Interface. 2012;9:3196-207.
70. Ekins S, Bradford J, Dole K, Spektor A, Gregory K, Blondeau D, et al. A collaborative database and computational models for tuberculosis drug discovery. Mol BioSyst. 2010;6:840-51.
71. Periwal V, Rajappan JK, Jaleel AU, Scaria V. Predictive models for anti-tubercular molecules using machine learning on high-throughput biological screening datasets. BMC Res Notes. 2011;4:504.
72. Scheich C, Szabadka Z, Vertessy B, Putter V, Grolmusz V, Schade M. Discovery of novel MDR-Mycobacterium tuberculosis inhibitor by new FRIGATE computational screen. PLoS ONE. 2011;6:e28428.
73. Lamichhane G, Freundlich JS, Ekins S, Wickramaratne N, Nolan S, Bishai WR. Essential metabolites of M. tuberculosis and their mimics. Ambio. 2011;2:e00301-00310.
74. Marrero-Ponce Y, Iyarreta-Veitia M, Montero-Torres A, Romero-Zaldivar C, Brandt CA, Avila PE, et al. Ligand-based virtual screening and in silico design of new antimalarial compounds using nonstochastic and stochastic total and atom-type quadratic maps. J Chem Inf Model. 2005;45:1082-100.
75. Go Fight Against Malaria. http://gofightagainstmalaria.scripps.edu/.
76. Freymann DM, Wenck MA, Engel JC, Feng J, Focia PJ, Eakin AE, et al. Efficient identification of inhibitors targeting the closed active site conformation of the HPRT from Trypanosoma cruzi. Chem Biol. 2000;7:957-68.
77. Castillo-Garit JA, Vega MC, Rolon M, Marrero-Ponce Y, Gomez-Barrio A, Escario JA, et al. Ligand-based discovery of novel trypanosomicidal drug-like compounds: in silico identification and experimental support. Eur J Med Chem. 2011;46:3324-30.
78. Khanna V, Ranganathan S. In silico approach to screen compounds active against parasitic nematodes of major socio-economic importance. BMC Bioinforma. 2011;12 Suppl 13:S25.
79. Carmona SJ, Sartor P, Leguizamon MS, Campetella O, Aguero F. A computational pipeline for diagnostic biomarker discovery in the human pathogen Trypanosoma cruzi. BMC Bioinforma. 2010;11 Suppl 10:O11.
80. Carmona SJ, Sartor PA, Leguizamon MS, Campetella OE, Aguero F. Diagnostic peptide discovery: prioritization of pathogen diagnostic markers using multiple features. PLoS ONE. 2012;7:e50748.
81. Lin HH, Langley I, Mwenda R, Doulla B, Egwaga S, Millington KA, et al. A modelling framework to support the selection and implementation of new tuberculosis diagnostic tools. Int J Tuberc Lung Dis. 2011;15:996-1004.
82. Smith T, Ross A, Maire N, Chitnis N, Studer A, Hardy D, et al. Ensemble modeling of the likely public health impact of a pre-erythrocytic malaria vaccine. PLoS Med. 2012;9:e1001157.
83. Lee BY, Bacon KM, Shah M, Kitchen SB, Connor DL, Slayton RB. The economic value of a visceral leishmaniasis vaccine in Bihar state, India. Am J Trop Med Hyg. 2012;86:417-25.
84. de Araujo Pereira G, Louzada F, de Fatima Barbosa V, Ferreira-Silva MM, Moraes-Souza H. A general latent class model for performance evaluation of diagnostic tests in the absence of a gold standard: an application to Chagas disease. Computational Math methods Med. 2012;2012:487502.
85. Abu-Raddad LJ, Sabatelli L, Achterberg JT, Sugimoto JD, Longini Jr IM, Dye C, et al. Epidemiological benefits of more-effective tuberculosis vaccines, drugs, and diagnostics. Proc Natl Acad Sci U S A. 2009;106:13980-5.
86. Aandahl RZ, Reyes JF, Sisson SA, Tanaka MM. A model-based Bayesian estimation of the rate of evolution of VNTR loci in Mycobacterium tuberculosis. PLoS Comput Biol. 2012;8:e1002573.