Enabling virtual screening of potent and safer antimicrobial agents against noma: Mtk-QSBER model for simultaneous prediction of antibacterial activities and ADMET properties

Mini-Reviews in Medicinal Chemistry

Volumn 15, Issue 3, 2015, Pages 194-202

  Speck Planche, Alejandro ^a   Cordeiro, M N D S ^a  

^a UNIVERSITY OF PORTO   (Portugal)

Author keywords

ADMET; Antibacterial; Linear discriminant analysis; Noma; Quadratic indices

Indexed keywords

ANTIBIOTIC AGENT; DELAFLOXACIN; ANTIINFECTIVE AGENT; QUINOLONE DERIVATIVE;

ANTIBACTERIAL ACTIVITY; ARTICLE; BACILLUS; COMPUTER ANALYSIS; COMPUTER MODEL; COMPUTER PROGRAM; DRUG ABSORPTION; DRUG DISTRIBUTION; DRUG ELIMINATION; DRUG HALF LIFE; DRUG METABOLISM; DRUG SCREENING; FUSOBACTERIUM; IC50; LD50; MINIMUM INHIBITORY CONCENTRATION; NONHUMAN; PHARMACOKINETICS; PREVOTELLA; QUANTITATIVE STRUCTURE ACTIVITY RELATION; RELIABILITY; STAPHYLOCOCCUS; AREA UNDER THE CURVE; BACTEROIDES FRAGILIS; BIOAVAILABILITY; CHEMISTRY; COMPUTER SIMULATION; DISCRIMINANT ANALYSIS; DRUG EFFECTS; HALF LIFE TIME; HUMAN; MICROBIAL SENSITIVITY TEST; NOMA; PEPTOSTREPTOCOCCUS; RECEIVER OPERATING CHARACTERISTIC; STAPHYLOCOCCUS AUREUS;

ANTI-BACTERIAL AGENTS; ANTI-INFECTIVE AGENTS; AREA UNDER CURVE; BACTEROIDES FRAGILIS; BIOLOGICAL AVAILABILITY; COMPUTER SIMULATION; DISCRIMINANT ANALYSIS; FLUOROQUINOLONES; FUSOBACTERIUM; HALF-LIFE; HUMANS; MICROBIAL SENSITIVITY TESTS; NOMA; PEPTOSTREPTOCOCCUS; QUANTITATIVE STRUCTURE-ACTIVITY RELATIONSHIP; ROC CURVE; STAPHYLOCOCCUS AUREUS;

EID: 84930977627     PISSN: 13895575     EISSN: 18755607     Source Type: Journal    
DOI: 10.2174/138955751503150312120519     Document Type: Article

References (62)

1. th Ed.; McGraw-Hill Companies, Inc: Arizona, 2004.
2. Mayers, D. L. Antimicrobial Drug Resistance: Mechanisms of Drug Resistance, Humana Press: Dordrecht, Heidelberg, London, New York, 2009.
3. Brachman, P. S.; Abrutyn, E. Bacterial Infections of Humans: Epidemiology and Control, 4th ed.; Springer Science+Business Media, LLC: New York, NY, 2009.
4. Bechtle, M.; Chen, S.; Efferth, T. Neglected diseases caused by bacterial infections. Curr. Med. Chem., 2010, 17(1), 42-60.
5. Feller, L.; Altini, M.; Chandran, R.; Khammissa, R. A.; Masipa, J. N.; Mohamed, A.; Lemmer, J. Noma (cancrum oris) in the South African context. J. Oral Pathol. Med., 2014, 43(1), 1-6.
6. Barmes, D. E.; Enwonwu, C. O.; Leclercq, M. H.; Bourgeois, D.; Falkler, W. A. The need for action against oro-facial gangrene (noma). Trop. Med. Int. Health., 1997, 2(12), 1111-1114.
7. Bourgeois, D. M.; Leclercq, M. H. The World Health Organization initiative on noma. Oral Dis., 1999, 5(2), 172-174.
8. Baratti-Mayer, D.; Pittet, B.; Montandon, D.; Bolivar, I.; Bornand, J. E.; Hugonnet, S.; Jaquinet, A.; Schrenzel, J.; Pittet, D. Noma: an "infectious" disease of unknown aetiology. Lancet Infect. Dis., 2003, 3(7), 419-431.
9. Enwonwu, C. O.; Falkler, W. A., Jr.; Phillips, R. S. Noma (cancrum oris). Lancet, 2006, 368(9530), 147-156.
10. Enwonwu, C. O.; Falkler, W. A.; Idigbe, E. O. Oro-facial gangrene (noma/cancrum oris): pathogenetic mechanisms. Crit. Rev. Oral Biol. Med., 2000, 11(2), 159-171.
11. Paul, S. M.; Mytelka, D. S.; Dunwiddie, C. T.; Persinger, C. C.; Munos, B. H.; Lindborg, S. R.; Schacht, A. L. How to improve R&D productivity: the pharmaceutical industry's grand challenge. Nat. Rev. Drug Discov., 2010, 9(3), 203-214.
12. Todeschini, R.; Consonni, V. Molecular Descriptors for Chemoinformatics, WILEY-VCH Verlag GmbH & Co. KGaA: Weinheim, 2009.
13. Estrada, E.; Uriarte, E.; Montero, A.; Teijeira, M.; Santana, L.; De Clercq, E. A novel approach for the virtual screening and rational design of anticancer compounds. J. Med. Chem., 2000, 43(10), 1975-1985.
14. Estrada, E.; Vilar, S.; Uriarte, E.; Gutierrez, Y. In silico studies toward the discovery of new anti-HIV nucleoside compounds with the use of TOPS-MODE and 2D/3D connectivity indices. 1. Pyrimidyl derivatives. J. Chem. Inf. Comput. Sci., 2002, 42(5), 1194-1203.
15. Marrero-Ponce, Y.; Castillo-Garit, J. A. 3D-chiral atom, atom-type, and total non-stochastic and stochastic molecular linear indices and their applications to central chirality codification. J. Comput. Aided Mol. Des., 2005, 19(6), 369-383.
16. Casanola-Martin, G. M.; Marrero-Ponce, Y.; Tareq Hassan Khan, M.; Torrens, F.; Perez-Gimenez, F.; Rescigno, A. Atom-and bondbased 2D TOMOCOMD-CARDD approach and ligand-based virtual screening for the drug discovery of new tyrosinase inhibitors. J. Biomol. Screen., 2008, 13(10), 1014-1024.
17. Garcia-Garcia, A.; Galvez, J.; de Julian-Ortiz, J. V.; Garcia-Domenech, R.; Munoz, C.; Guna, R.; Borras, R. Search of chemical scaffolds for novel antituberculosis agents. J. Biomol. Screen., 2005, 10(3), 206-214.
18. Gonzalez-Diaz, H.; Olazabal, E.; Santana, L.; Uriarte, E.; Gonzalez-Diaz, Y.; Castanedo, N. QSAR study of anticoccidial activity for diverse chemical compounds: prediction and experimental assay of trans-2-(2-nitrovinyl)furan. Bioorg. Med. Chem., 2007, 15(2), 962-968.
19. Santana, L.; Gonzalez-Diaz, H.; Quezada, E.; Uriarte, E.; Yanez, M.; Vina, D.; Orallo, F. Quantitative structure-activity relationship and complex network approach to monoamine oxidase A and B inhibitors. J. Med. Chem., 2008, 51(21), 6740-6751.
20. Meneses-Marcel, A.; Rivera-Borroto, O. M.; Marrero-Ponce, Y.; Montero, A.; Machado Tugores, Y.; Escario, J. A.; Gomez Barrio, A.; Montero Pereira, D.; Nogal, J. J.; Kouznetsov, V. V.; Ochoa Puentes, C.; Bohorquez, A. R.; Grau, R.; Torrens, F.; Ibarra-Velarde, F.; Aran, V. J. New antitrichomonal drug-like chemicals selected by bond (edge)-based TOMOCOMD-CARDD descriptors. J. Biomol. Screen., 2008, 13(8), 785-794.
21. Borchardt, R. T.; Kerns, E. H.; Hageman, M. J.; Thakker, D. R.; Stevens, J. L. Optimizing the "Drug-Like" Properties of Leads in Drug Discovery. Springer Science+Business Media, LLC: New York, NY, 2006; Vol. IV.
22. Croes, S.; Koop, A. H.; van Gils, S. A.; Neef, C. Efficacy, nephrotoxicity and ototoxicity of aminoglycosides, mathematically modelled for modelling-supported therapeutic drug monitoring. Eur. J. Pharm. Sci., 2012, 45(1-2), 90-100.
23. rd ed.; CRC Press, Taylor & Francis Group, LLC: Boca Raton, FL, 2011.
24. Tenorio-Borroto, E.; Penuelas-Rivas, C. G.; Vasquez-Chagoyan, J. C.; Castanedo, N.; Prado-Prado, F. J.; Garcia-Mera, X.; Gonzalez-Diaz, H. Model for high-throughput screening of drug immunotoxicity-Study of the anti-microbial G1 over peritoneal macrophages using flow cytometry. Eur. J. Med. Chem., 2014, 72, 206-220.
25. Gonzalez-Diaz, H.; Herrera-Ibata, D. M.; Duardo-Sanchez, A.; Munteanu, C. R.; Orbegozo-Medina, R. A.; Pazos, A. ANN Multiscale Model of Anti-HIV Drugs Activity vs AIDS Prevalence in the US at County Level Based on Information Indices of Molecular Graphs and Social Networks. J. Chem. Inf. Model., 2014, 54(3), 744-755.
26. Tenorio-Borroto, E.; Garcia-Mera, X.; Penuelas-Rivas, C. G.; Vasquez-Chagoyan, J. C.; Prado-Prado, F. J.; Castanedo, N.; Gonzalez-Diaz, H. Entropy model for multiplex drug-target interaction endpoints of drug immunotoxicity. Curr. Top. Med. Chem., 2013, 13(14), 1636-1649.
27. Luan, F.; Cordeiro, M. N. D. S.; Alonso, N.; Garcia-Mera, X.; Caamano, O.; Romero-Duran, F. J.; Yanez, M.; Gonzalez-Diaz, H. TOPS-MODE model of multiplexing neuroprotective effects of drugs and experimental-theoretic study of new 1, 3-rasagiline derivatives potentially useful in neurodegenerative diseases. Bioorg. Med. Chem., 2013, 21(7), 1870-1879.
28. Alonso, N.; Caamano, O.; Romero-Duran, F. J.; Luan, F.; Cordeiro, M. N. D. S.; Yanez, M.; Gonzalez-Diaz, H.; Garcia-Mera, X. Model for High-Throughput Screening of Multi-Target Drugs in Chemical Neurosciences; Synthesis, Assay and Theoretic Study of Rasagiline Carbamates. ACS Chem. Neurosci., 2013, 4(10), 1393-1403.
29. Speck-Planche, A.; Kleandrova, V. V.; Cordeiro, M. N. D. S. New insights toward the discovery of antibacterial agents: Multi-tasking QSBER model for the simultaneous prediction of anti-tuberculosis activity and toxicological profiles of drugs. Eur. J. Pharm. Sci., 2013, 48(4-5), 812-818.
30. Speck-Planche, A.; Kleandrova, V. V.; Cordeiro, M. N. D. S. Chemoinformatics for rational discovery of safe antibacterial drugs: Simultaneous predictions of biological activity against streptococci and toxicological profiles in laboratory animals. Bioorg. Med. Chem., 2013, 21(10), 2727-2732.
31. Speck Planche, A.; Cordeiro, M. N. D. S. In Chemoinformatics in drug design. Artificial neural networks for simultaneous prediction of anti-enterococci activities and toxicological profiles, Proceedings of the 5th International Joint Conference on Computational Intelligence, NCTA-International Conference on Neural Computation Theory and Applications, Vilamoura, Algarve, Portugal, September 20-22, 2013; Institute for Systems and Technologies of Information, Control and Communication (INSTICC): Vilamoura, Algarve, Portugal, 2013; pp 458-465.
32. Speck-Planche, A.; Cordeiro, M. N. D. S. Simultaneous modeling of antimycobacterial activities and ADMET profiles: a chemoinformatic approach to medicinal chemistry. Curr. Top. Med. Chem., 2013, 13(14), 1656-1665.
33. Speck-Planche, A.; Cordeiro, M. N. D. S. Simultaneous virtual prediction of anti-Escherichia coli activities and ADMET profiles: A chemoinformatic complementary approach for high-throughput screening. ACS Comb. Sci., 2014, 16(2), 78-84.
34. Gaulton, A.; Bellis, L. J.; Bento, A. P.; Chambers, J.; Davies, M.; Hersey, A.; Light, Y.; McGlinchey, S.; Michalovich, D.; Al-Lazikani, B.; Overington, J. P. ChEMBL: a large-scale bioactivity database for drug discovery. Nucleic Acids Res., 2012, 40(Database issue), D1100-1107.
35. O'Boyle, N. M.; Banck, M.; James, C. A.; Morley, C.; Vandermeersch, T.; Hutchison, G. R. Open Babel: An open chemical toolbox. J. Cheminform., 2011, 3, 33.
36. Marrero-Ponce, Y.; Valdes-Martini, J. R.; Garcia-Jacas, C. R.; Silveira-Vaz 'd Almeida, Y.; Morell, C.; Cubillan, N. TOMOCOMD-CARDD. Module QUBILs-MAS, v1. 0; CAMD-BIR Unit: Santa Clara, Villa Clara, 2012.
37. Marrero-Ponce, Y. Total and Local Quadratic Indices of the Molecular Pseudograph's Atom Adjacency Matrix: Applications to the Prediction of Physical Properties of Organic Compounds. Molecules, 2003, 8(9), 687-726.
38. Marrero-Ponce, Y.; Cabrera Perez, M. A.; Romero Zaldivar, V.; Ofori, E.; Montero, L. A. Total and Local Quadratic Indices of the "Molecular Pseudograph's Atom Adjacency Matrix". Application to Prediction of Caco-2 Permeability of Drugs. Int. J. Mol. Sci., 2003, 4(8), 512-536.
39. Marrero-Ponce, Y.; Castillo-Garit, J. A.; Olazabal, E.; Serrano, H. S.; Morales, A.; Castanedo, N.; Ibarra-Velarde, F.; Huesca-Guillen, A.; Jorge, E.; del Valle, A.; Torrens, F.; Castro, E. A. TOMOCOMD-CARDD, a novel approach for computer-aided 'rational' drug design: I. Theoretical and experimental assessment of a promising method for computational screening and in silico design of new anthelmintic compounds. J. Comput. Aided Mol. Des., 2004, 18(10), 615-634.
40. Montero-Torres, A.; Garcia-Sanchez, R. N.; Marrero-Ponce, Y.; Machado-Tugores, Y.; Nogal-Ruiz, J. J.; Martinez-Fernandez, A. R.; Aran, V. J.; Ochoa, C.; Meneses-Marcel, A.; Torrens, F. Nonstochastic quadratic fingerprints and LDA-based QSAR models in hit and lead generation through virtual screening: theoretical and experimental assessment of a promising method for the discovery of new antimalarial compounds. Eur. J. Med. Chem., 2006, 41(4), 483-493.
41. Marrero-Ponce, Y.; Medina-Marrero, R.; Torrens, F.; Martinez, Y.; Romero-Zaldivar, V.; Castro, E. A. Atom, atom-type, and total nonstochastic and stochastic quadratic fingerprints: a promising approach for modeling of antibacterial activity. Bioorg. Med. Chem., 2005, 13(8), 2881-2899.
42. Marrero-Ponce, Y.; Iyarreta-Veitia, M.; Montero-Torres, A.; Romero-Zaldivar, C.; Brandt, C. A.; Avila, P. E.; Kirchgatter, K.; Machado, Y. 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(4), 1082-1100.
43. Marrero-Ponce, Y.; Siverio-Mota, D.; Galvez-Llompart, M.; Recio, M. C.; Giner, R. M.; Garcia-Domenech, R.; Torrens, F.; Aran, V. J.; Cordero-Maldonado, M. L.; Esguera, C. V.; de Witte, P. A.; Crawford, A. D. Discovery of novel anti-inflammatory drug-like compounds by aligning in silico and in vivo screening: the nitroindazolinone chemotype. Eur. J. Med. Chem., 2011, 46(12), 5736-5753.
44. Marrero-Ponce, Y. Linear indices of the "molecular pseudograph's atom adjacency matrix": definition, significance-interpretation, and application to QSAR analysis of flavone derivatives as HIV-1 integrase inhibitors. J. Chem. Inf. Comput. Sci., 2004, 44(6), 2010-2026.
45. Marrero-Ponce, Y.; Meneses-Marcel, A.; Castillo-Garit, J. A.; Machado-Tugores, Y.; Escario, J. A.; Barrio, A. G.; Pereira, D. M.; Nogal-Ruiz, J. J.; Aran, V. J.; Martinez-Fernandez, A. R.; Torrens, F.; Rotondo, R.; Ibarra-Velarde, F.; Alvarado, Y. J. Predicting antitrichomonal activity: a computational screening using atombased bilinear indices and experimental proofs. Bioorg. Med. Chem., 2006, 14(19), 6502-6524.
46. Casanola-Martin, G. M.; Marrero-Ponce, Y.; Khan, M. T.; Ather, A.; Sultan, S.; Torrens, F.; Rotondo, R. TOMOCOMD-CARDD descriptors-based virtual screening of tyrosinase inhibitors: evaluation of different classification model combinations using bond-based linear indices. Bioorg. Med. Chem., 2007, 15(3), 1483-1503.
47. Marrero-Ponce, Y.; Khan, M. T.; Casanola Martin, G. M.; Ather, A.; Sultankhodzhaev, M. N.; Torrens, F.; Rotondo, R. Prediction of tyrosinase inhibition activity using atom-based bilinear indices. Chem. Med. Chem., 2007, 2(4), 449-478.
48. Castillo-Garit, J. A.; Marrero-Ponce, Y.; Torrens, F.; Garcia-Domenech, R. Estimation of ADME properties in drug discovery: predicting Caco-2 cell permeability using atom-based stochastic and non-stochastic linear indices. J. Pharm. Sci., 2008, 97(5), 1946-1976.
49. Ibarra-Velarde, F.; Vera-Montenegro, Y.; Huesca-Guillen, A.; Canto-Alarcon, G.; Alcala-Canto, Y.; Marrero-Ponce, Y. In silico fasciolicide activity of three experimental compounds in sheep. Ann. N. Y. Acad. Sci., 2008, 1149, 183-185.
50. Casanola-Martin, G. M.; Marrero-Ponce, Y.; Khan, M. T.; Khan, S. B.; Torrens, F.; Perez-Jimenez, F.; Rescigno, A.; Abad, C. Bondbased 2D quadratic fingerprints in QSAR studies: virtual and in vitro tyrosinase inhibitory activity elucidation. Chem. Biol. Drug Des., 2010, 76(6), 538-545.
51. Hill, T.; Lewicki, P. STATISTICS Methods and Applications. A Comprehensive Reference for Science, Industry and Data Mining, StatSoft: Tulsa, 2006.
52. Statsoft-Team. STATISTICA. Data analysis software system, v6. 0; Tulsa, 2001.
53. Huberty, C. J.; Olejnik, S. Applied MANOVA and discriminant analysis, 2nd ed.; John Wiley & Sons, Inc.: Hoboken, New Jersey, 2006.
54. Speck-Planche, A.; Kleandrova, V. V.; Luan, F.; Cordeiro, M. N. D. S. Rational drug design for anti-cancer chemotherapy: multi-target QSAR models for the in silico discovery of anticolorectal cancer agents. Bioorg. Med. Chem., 2012, 20(15), 4848-4855.
55. Hanczar, B.; Hua, J.; Sima, C.; Weinstein, J.; Bittner, M.; Dougherty, E. R. Small-sample precision of ROC-related estimates. Bioinformatics, 2010, 26(6), 822-830.
56. Kleandrova, V. V.; Luan, F.; Gonzalez-Diaz, H.; Ruso, J. M.; Speck-Planche, A.; Cordeiro, M. N. D. S. Computational tool for risk assessment of nanomaterials: Novel QSTR-perturbation model for simultaneous prediction of ecotoxicity and cytotoxicity of uncoated and coated nanoparticles under multiple experimental conditions. Environ. Sci. Technol., 2014, 48(24), 14686-14694.
57. Remy, J. M.; Tow-Keogh, C. A.; McConnell, T. S.; Dalton, J. M.; Devito, J. A. Activity of delafloxacin against methicillin-resistant Staphylococcus aureus: resistance selection and characterization. J. Antimicrob. Chemother., 2012, 67(12), 2814-2820.
58. Nilius, A. M.; Shen, L. L.; Hensey-Rudloff, D.; Almer, L. S.; Beyer, J. M.; Balli, D. J.; Cai, Y.; Flamm, R. K. In vitro antibacterial potency and spectrum of ABT-492, a new fluoroquinolone. Antimicrob. Agents Chemother., 2003, 47(10), 3260-3269.
59. Harnett, S. J.; Fraise, A. P.; Andrews, J. M.; Jevons, G.; Brenwald, N. P.; Wise, R. Comparative study of the in vitro activity of a new fluoroquinolone, ABT-492. J. Antimicrob. Chemother., 2004, 53(5), 783-792.
60. Goldstein, E. J.; Citron, D. M.; Merriam, C. V.; Warren, Y. A.; Tyrrell, K. L.; Fernandez, H. T. In vitro activities of ABT-492, a new fluoroquinolone, against 155 aerobic and 171 anaerobic pathogens isolated from antral sinus puncture specimens from patients with sinusitis. Antimicrob. Agents Chemother., 2003, 47(9), 3008-3011.
61. Sillerstrom, E. In Comparative in vitro activity of ABT-492 and six other antimicrobial agents against anaerobic bacteria, 14th European Congress of Clinical Microbiology and Infectious Diseases, Prague, Czech Republic, May 1-4, 2004; European Society of Clinical Microbiology and Infectious Diseases: Prague, Czech Republic, 2004; p 119.
62. Lawrence, L.; Benedict, M.; Hart, J.; Hawkins, A.; Li, D.; Medlock, M.; Hopkins, S.; Burak, E. In Pharmacokinetics (PK) and safety of single doses of delafloxacin administered intravenously in healthy human subjects, 51st ICAAC (Poster session), Chicago, IL, USA, September 17-20, 2011; Chicago, IL, USA, 2011.