Chemoinformatics for medicinal chemistry: In silico model to enable the discovery of potent and safer anti-cocci agents

Future Medicinal Chemistry

Volumn 6, Issue 18, 2014, Pages 2013-2028

  Speck Planche, Alejandro ^a   Cordeiro, Maria Natália Dias Soeiro ^a  

^a UNIVERSITY OF PORTO   (Portugal)

Author keywords

[No Author keywords available]

Indexed keywords

ACORAFLOXACIN; COCCIDIOSTATIC AGENT; 7-(3-(2-AMINO-1-FLUOROETHYLIDENE)-1-PIPERIDINYL)-1-CYCLOPROPYL-6-FLUORO-1,4-DIHYDRO-8-METHOXY-4-OXO-3-QUINOLINECARBOXYLIC ACID; ANTIINFECTIVE AGENT; QUINOLONE DERIVATIVE;

ACCURACY; ARTICLE; CHEMOINFORMATICS; DRUG POTENCY; DRUG SAFETY; DRUG SCREENING; INFORMATION SCIENCE; MATHEMATICAL MODEL; QUANTITATIVE ANALYSIS; AREA UNDER THE CURVE; CHEMICAL MODEL; CHEMISTRY; DRUG EFFECTS; GRAM POSITIVE BACTERIUM; MEDICINAL CHEMISTRY; METABOLISM; MICROBIAL SENSITIVITY TEST; RECEIVER OPERATING CHARACTERISTIC;

ANTI-BACTERIAL AGENTS; AREA UNDER CURVE; CHEMISTRY, PHARMACEUTICAL; DRUG EVALUATION, PRECLINICAL; FLUOROQUINOLONES; GRAM-POSITIVE BACTERIA; MICROBIAL SENSITIVITY TESTS; MODELS, CHEMICAL; ROC CURVE;

EID: 84919831590     PISSN: 17568919     EISSN: 17568927     Source Type: Journal    
DOI: 10.4155/fmc.14.136     Document Type: Article

References (58)

1. Ryan KJ. Streptococci and Enterococci. In: Sherris Medical Microbiology: An Introduction to Infectious Diseases. Ryan KJ, Ray CG (Eds). McGraw-Hill Companies, Inc, London, UK, 273-296 (2004
2. Brachman PS, Abrutyn E. In: Bacterial Infections of Humans: Epidemiology and Control (4th Edition) . Springer Science+Business Media, LLC, New York, NY, USA, (2009
3. Paterson GK, Harrison EM, Craven EF et al Incidence and characterisation of methicillin-resistant Staphylococcus aureus (MRSA) from nasal colonisation in participants attending a cattle veterinary conference in the UK. PLoS ONE 8(7), e68463 (2013
4. Vardakas KZ, Kontopidis I, Gkegkes ID, Rafailidis PI, Falagas ME. Incidence, characteristics, and outcomes of patients with bone and joint infections due to communityassociated methicillin-resistant Staphylococcus aureus: a systematic review. Eur. J. Clin. Microbiol. Infect. Dis. 32(6), 711-721 (2013
5. Silva-Costa C, Friaes A, Ramirez M, Melo-Cristino J. Differences between macrolide-resistant and -susceptible Streptococcus pyogenes: importance of clonal properties in addition to antibiotic consumption. Antimicrob. Agents Chemother. 56(11), 5661-5666 (2012
6. Bidet P, Liguori S, Plainvert C et al. Identification of group A streptococcal emm types commonly associated with invasive infections and antimicrobial resistance by the use of multiplex PCR and high-resolution melting analysis. Eur. J. Clin. Microbiol. Infect. Dis. 31(10), 2817-2826 (2012
7. Arias CA, Contreras GA, Murray BE. Management of multidrug resistant Enterococcal infections. Clin. Microbiol. Infect. 16(6), 555-562 (2010
8. Printsevskaya SS, Reznikova MI, Korolev AM et al. Synthesis and study of antibacterial activities of antibacterial glycopeptide antibiotics conjugated with benzoxaboroles. Future Med. Chem. 5(6), 641-652 (2013
9. Rigottier-Gois L, Alberti A, Houel A et al. Large-scale screening of a targeted Enterococcus faecalis mutant library identifies envelope fitness factors. PLoS ONE 6(12), e29023 (2011
10. Paul SM, Mytelka DS, Dunwiddie CT et al. How to improve R&D productivity: The pharmaceutical industry's grand challenge. Nat. Rev. Drug Discov. 9(3), 203-214 (2010
11. Optimizing the 'Drug-Like' Properties of Leads in Drug Discovery. Borchardt RT, Kerns EH, Hageman MJ, Thakker DR, Stevens JL (Eds). Springer Science+Business Media, LLC, New York, NY, USA, (2006
12. Croes S, Koop AH, van Gils SA, Neef C. Efficacy, nephrotoxicity and ototoxicity of aminoglycosides, mathematically modelled for modelling-supported therapeutic drug monitoring. Eur. J. Pharm. Sci. 45(1-2), 90-100 (2012
13. Hau J, Schapiro SJ. Handbook of Laboratory Animal Science: Essential Principles and Practices (3rd Edition). CRC Press, Taylor & Francis Group, LLC, Boca Raton, FL, USA, 1, (2011
14. Todeschini R, Consonni V. Molecular Descriptors for Chemoinformatics . WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim, Germany, (2009
15. Bunin BA, Bajorath J, Siesel B, Morales G. Chemoinformatics: Theory, Practice and Products . Springer, Dordrecht, Germany, (2007
16. Oprea T. Chemoinformatics in Drug Discovery . WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim, Germany, 23, (2005
17. Tenorio-Borroto E, Penuelas-Rivas CG, Vasquez-Chagoyan JC et al. Model for high-throughput screening of drug immunotoxicity - study of the anti-microbial G1 over peritoneal macrophages using flow cytometry. Eur. J. Med. Chem. 72, 206-220 (2014
18. Tenorio-Borroto E, Garcia-Mera X, Penuelas-Rivas CG et al. Entropy model for multiplex drug-target interaction endpoints of drug immunotoxicity. Curr. Top. Med. Chem. 13(14), 1636-1649 (2013
19. Luan F, Cordeiro MNDS, Alonso N et al. 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. 21(7), 1870-1879 (2013
20. Alonso N, Caamano O, Romero-Duran FJ et al. Model for high-throughput screening of multi-target drugs in chemical neurosciences; synthesis, assay and theoretic study of rasagiline carbamates. ACS Chem. Neurosci. 4(10), 1393-1403 (2013
21. Tenorio-Borroto E, Penuelas Rivas CG, Vasquez Chagoyan JC et al. ANN multiplexing model of drugs effect on macrophages: Theoretical and flow cytometry study on the cytotoxicity of the anti-microbial drug G1 in spleen. Bioorg. Med. Chem. 20(20), 6181-6194 (2012
22. Speck-Planche A, Cordeiro MN. Simultaneous virtual prediction of anti-Escherichia coli activities and ADMET profiles: a chemoinformatic complementary approach for high-throughput screening. ACS Comb. Sci. 16(2), 78-84 (2014
23. Speck-Planche A, Kleandrova VV, Cordeiro MNDS. Chemoinformatics for rational discovery of safe antibacterial drugs: simultaneous predictions of biological activity against streptococci and toxicological profiles in laboratory animals. Bioorg. Med. Chem. 21(10), 2727-2732 (2013
24. Speck-Planche A, Kleandrova VV, Cordeiro MNDS. New insights toward the discovery of antibacterial agents: multitasking QSBER model for the simultaneous prediction of anti-tuberculosis activity and toxicological profiles of drugs. Eur. J. Pharm. Sci. 48(4-5), 812-818 (2013
25. Speck-Planche A, Cordeiro MNDS. Simultaneous modeling of antimycobacterial activities and ADMET profiles: a chemoinformatic approach to medicinal chemistry. Curr. Top. Med. Chem. 13(14), 1656-1665 (2013
26. Speck-Planche A, Cordeiro MNDS. Evolution of graph theory-based QSAR methods and their applications to the search for new antibacterial agents. Curr. Top. Med. Chem. 13(24), 3101-3117 (2013
27. Gaulton A, Bellis LJ, Bento AP et al. ChEMBL: a large-scale bioactivity database for drug discovery. Nucleic Acids Res. 40(Database issue), D1100-D1107 (2012
28. Mok NY, Brenk R. Mining the ChEMBL database: an efficient chemoinformatics workflow for assembling an ion channel-focused screening library. J. Chem. Inf. Model. 51(10), 2449-2454 (2011
29. Overington J. ChEMBL. An interview with John Overington, team leader, chemogenomics at the European Bioinformatics Institute Outstation of the European Molecular Biology Laboratory (EMBL-EBI). Interview by Wendy A. Warr. J. Comput. Aided Mol. Des. 23(4), 195-198 (2009
30. EBI-Team: ChEMBL Database (2010). www.ebi.ac.uk/chembl
31. Estrada E. Spectral moments of the edge adjacency matrix in molecular graphs. I. Definition and applications for the prediction of physical properties of alkanes. J. Chem. Inf. Comput. Sci. 36(4), 844-849 (1996
32. Estrada E. Spectral moments of the edge adjacency matrix in molecular graphs. II. Molecules containing heteroatoms and QSAR applications. J. Chem. Inf. Comput. Sci. 37(2), 320-328 (1997
33. Estrada E. Spectral moments of the edge adjacency matrix in molecular graphs. III. Molecules containing cycles. J. Chem. Inf. Comput. Sci. 38(1), 23-27 (1998
34. Estrada E, Quincoces JA, Patlewicz G. Creating molecular diversity from antioxidants in Brazilian propolis. Combination of TOPS-MODE QSAR and virtual structure generation. Mol. Divers. 8(1), 21-33 (2004
35. Estrada E, Uriarte E, Molina E, Simon-Manso Y, Milne GW. An integrated in silico analysis of drug-binding to human serum albumin. J. Chem. Inf. Model. 46(6), 2709-2724 (2006
36. Morales Helguera A, Perez Gonzalez M, Cordeiro MNDS, Cabrera Perez MA. Quantitative structure-carcinogenicity relationship for detecting structural alerts in nitroso compounds: species, rat; sex, female; route of administration, gavage. Chem. Res. Toxicol. 21(3), 633-642 (2008
37. Marzaro G, Chilin A, Guiotto A et al. Using the TOPSMODE approach to fit multi-target QSAR models for tyrosine kinases inhibitors. Eur. J. Med. Chem. 46(6), 2185-2192 (2011
38. Estrada E, Patlewicz G, Chamberlain M, Basketter D, Larbey S. Computer-Aided knowledge generation for understanding skin sensitization mechanisms: The TOPS-MODE approach. Chem. Res. Toxicol. 16(10), 1226-1235 (2003
39. Vilar S, Estrada E, Uriarte E, Santana L, Gutierrez Y. In silico studies toward the discovery of new anti-HIV nucleoside compounds through the use of TOPS-MODE and 2D/3D connectivity indices. II. Purine derivatives. J. Chem. Inf. Model. 45(2), 502-514 (2005
40. Estrada E, Gutiérrez Y. MODESLAB. (v1.5) . Santiago de Compostela, Spain (2002-2004
41. Hill T, Lewicki P. STATISTICS Methods and Applications. In: A Comprehensive Reference for Science, Industry and Data Mining. StatSoft, Tulsa, OK, USA (2006
42. Statsoft-Team. STATISTICA. Data analysis software system. (v6.0). Tulsa, OK, USA (2001
43. Huberty CJ, Olejnik S. Applied MANOVA and discriminant analysis (2nd Edition) . John Wiley & Sons, Inc., Hoboken, NJ, USA (2006
44. Speck-Planche A, Kleandrova VV, Luan F, Cordeiro MNDS. Rational drug design for anti-cancer chemotherapy: multi-target QSAR models for the in silico discovery of anti-colorectal cancer agents. Bioorg. Med. Chem. 20(15), 4848-4855 (2012
45. Hanczar B, Hua J, Sima C, Weinstein J, Bittner M, Dougherty ER. Small-sample precision of ROC-related estimates. Bioinformatics 26(6), 822-830 (2010
46. Jurman G, Riccadonna S, Furlanello C. A comparison of MCC and CEN error measures in multi-class prediction. PLoS ONE 7(8), e41882 (2012
47. Gonzalez-Diaz H, Herrera-Ibata DM, Duardo-Sanchez A, Munteanu CR, Orbegozo-Medina RA, 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. 54(3), 744-755 (2014
48. Duardo-Sanchez A, Munteanu CR, Riera-Fernandez P, Lopez-Diaz A, Pazos A, Gonzalez-Diaz H. Modeling complex metabolic reactions, ecological systems, and financial and legal networks with MIANN models based on Markov-Wiener node descriptors. J. Chem. Inf. Model. 54(1), 16-29 (2013
49. 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. 43(10), 1975-1985 (2000
50. 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. 42(5), 1194-1203 (2002
51. Speck-Planche A, Kleandrova VV, Luan F, Cordeiro MNDS. Fragment-based QSAR model toward the selection of versatile anti-sarcoma leads. Eur. J. Med. Chem. 46(12), 5910-5916 (2011
52. Prado-Prado FJ, Garcia-Mera X, Gonzalez-Diaz H. Multitarget spectral moment QSAR versus ANN for antiparasitic drugs against different parasite species. Bioorg. Med. Chem. 18(6), 2225-2231 (2010
53. Helguera AM, Gonzalez MP, Cordeiro MNDS, Perez MA. Quantitative structure carcinogenicity relationship for detecting structural alerts in nitroso-compounds. Toxicol. Appl. Pharmacol. 221(2), 189-202 (2007
54. Furiex Pharmaceuticals. (2014). www.furiex.com
55. Morrow BJ, He W, Amsler KM et al. In vitro antibacterial activities of JNJ-Q2, a new broad-spectrum fluoroquinolone. Antimicrob. Agents Chemother. 54(5), 1955-1964 (2010
56. Fernandez J, Hilliard JJ, Morrow BJ et al. Efficacy of a new fluoroquinolone, JNJ-Q2, in murine models of Staphylococcus aureus and Streptococcus pneumoniae skin, respiratory, and systemic infections. Antimicrob. Agents Chemother. 55(12), 5522-5528 (2011
57. Davenport JM, Covington P, Gotfried M et al. Summary of pharmacokinetics and tissue distribution of a broadspectrum fluoroquinolone, JNJ-Q2. Clin. Pharmacol. Drug Dev. 1(4), 121-130 (2012
58. Morrow BJ, Abbanat D, Baum EZ et al. Antistaphylococcal activities of the new fluoroquinolone JNJ-Q2. Antimicrob. Agents Chemother. 55(12), 5512-5521 (2011