Prediction of anti-inflammatory plants and discovery of their biomarkers by machine learning algorithms and metabolomic studies

Planta Medica

Volumn 81, Issue 6, 2015, Pages 450-458

  Chagas Paula, Daniela Aparecida ^a   Oliveira, Tiago Branquinho ^a   Zhang, Tong ^b   Edrada Ebel, Ruangelie ^b   Da Costa, Fernando Batista ^a  

^a UNIVERSITY OF SÃO PAULO   (Brazil)

^b UNIVERSITY OF STRATHCLYDE   (United Kingdom)

Author keywords

anti inflammatory; Asteraceae; cyclooxygenase; lipoxygenase; machine learning algorithms; metabolomics

Indexed keywords

ANTIINFLAMMATORY AGENT; ARACHIDONATE 5 LIPOXYGENASE; ASTERACEAE EXTRACT; BIOLOGICAL MARKER; CYCLOOXYGENASE 1; LIPOXYGENASE; PLANT EXTRACT; PROSTAGLANDIN SYNTHASE; UNCLASSIFIED DRUG;

ANTIINFLAMMATORY ACTIVITY; ARTICLE; ASTERACEAE; BACK PROPAGATION; GENETIC ALGORITHM; HIGH PERFORMANCE LIQUID CHROMATOGRAPHY; INFLAMMATION; LEARNING ALGORITHM; LIQUID CHROMATOGRAPHY; MACHINE LEARNING; MASS SPECTROMETRY; METABOLOMICS; NONHUMAN; PREDICTION; ALGORITHM; CHEMISTRY; ISOLATION AND PURIFICATION; MEDICINAL PLANT;

ASTERACEAE;

ALGORITHMS; ANTI-INFLAMMATORY AGENTS; BIOMARKERS; CHROMATOGRAPHY, HIGH PRESSURE LIQUID; MACHINE LEARNING; MASS SPECTROMETRY; METABOLOMICS; PLANTS, MEDICINAL;

EID: 84928956231     PISSN: 00320943     EISSN: 14390221     Source Type: Journal    
DOI: 10.1055/s-0034-1396206     Document Type: Article

References (62)

1. Villas-Bôas S. G., Mas S., Akesson M., Smedsgaard J., Nielsen J. Mass spectrometry in metabolome analysis. Mass Spectrom Rev: 2005; 24 613 646
2. Weckwerth W., Morgenthal K. Metabolomics: from pattern recognition to biological interpretation. Drug Discov Today: 2005; 10 1551 1558
3. Wolfender J., Marti G., Ferreira Queiroz E. Advances in techniques for profiling crude extracts and for the rapid identification of natural products: dereplication, quality control and metabolomics. Curr Org Chem: 2010; 14 1808 1832
4. Sumner L. W., Mendes P., Dixon R. A. Plant metabolomics: large-scale phytochemistry in the functional genomics era. Phytochemistry: 2003; 62 817 836
5. Bedair M., Sumner L. W. Current and emerging mass-spectrometry technologies for metabolomics. Trends Anal Chem: 2008; 27 238 250
6. Newman D. J. Natural products as leads to potential drugs: an old process or the new hope for drug discovery? J Med Chem: 2008; 51 2589 2599
7. Newman D. J., Cragg G. M. Natural products as sources of new drugs over the last 30 years from 1981 to 2010. J Nat Prod: 2012; 75 311 335
8. Harvey A. L. Natural products in drug discovery. Drug Discov Today: 2008; 13 894 901
9. De Donno A., Grassi T., Idolo A., Guido M., Papadia P., Caccioppola A., Villanova L., Merendino A., Bagordo F., Fanizzi F. P. First-time comparison of the in vitro antimalarial activity of Artemisia annua herbal tea and artemisinin. Trans R Soc Trop Med Hyg: 2012; 106 696 700
10. Phillipson J. D. Phytochemistry and medicinal plants. Phytochemistry: 2001; 56 237 243
11. Wagner H. Synergy research: approaching a new generation of phytopharmaceuticals. Fitoterapia: 2011; 82 34 37
12. Yuliana N. D., Khatib A., Choi Y. H., Verpoorte R. Metabolomics for bioactivity assessment of natural products. Phyther Res: 2011; 25 157 169
13. Verpoorte R., Choi Y. H., Kim H. K. Ethnopharmacology and systems biology: a perfect holistic match. J Ethnopharmacol: 2005; 100 53 56
14. Abdelmohsen U. R., Cheng C., Viegelmann C., Zhang T., Grkovic T., Ahmed S., Quinn R. J., Hentschel U., Edrada-Ebel R. Dereplication strategies for targeted isolation of new antitrypanosomal actinosporins A and B from a marine sponge associated- Actinokineospora sp. EG49. Mar Drugs: 2014; 12 1220 1244
15. Shyur L. F., Yang N. Metabolomics for phytomedicine research and drug development. Curr Opin Chem Biol: 2008; 12 66 71
16. Lang G., Mayhudin N. A., Mitova M. I., Sun L., van der Sar S., Blunt J. W., Cole A. LJ, Ellis G., Laatsch H., Munro M. HG. Evolving trends in the dereplication of natural product extracts: new methodology for rapid, small-scale investigation of natural product extracts. J Nat Prod: 2008; 71 1595 1599
17. Nicholson J. K., Lindon J. C. Metabonomics. Nature: 2008; 455 1054 1056
18. Viegelmann C., Margassery L. M., Kennedy J., Zhang T., O'Brien C., O'Gara F., Morrissey J. P., Dobson A. DW, Edrada-Ebel R. Metabolomic profiling and genomic study of a marine sponge-associated Streptomyces sp. Mar Drugs: 2014; 12 3323 3351
19. Madsen R., Lundstedt T., Trygg J. Chemometrics in metabolomics - a review in human disease diagnosis. Anal Chim Acta: 2010; 659 23 33
20. Trygg J., Holmes E., Lundstedt T. Chemometrics in metabonomics. J Proteome Res: 2007; 6 469 479
21. Trygg J., Wold S. Orthogonal projections to latent structures (O-PLS). J Chemom: 2002; 16 119 128
22. Bylesjö M., Rantalainen M., Cloarec O., Nicholson J. K., Holmes E., Trygg J. OPLS discriminant analysis: combining the strengths of PLS-DA and SIMCA classification. J Chemom: 2006; 20 341 351
23. Boccard J., Kalousis A., Hilario M., Lantéri P., Hanafi M., Mazerolles G., Wolfender J. L., Carrupt P. A., Rudaz S. Standard machine learning algorithms applied to UPLC-TOF/MS metabolic fingerprinting for the discovery of wound biomarkers in Arabidopsis thaliana. Chemom Intell Lab Syst: 2010; 104 20 27
24. Back B., Laitinen T., Sere K. Neural networks and genetic algorithms for bankruptcy predictions. Expert Syst Appl: 1996; 11 407 413
25. Anbarasi M., Anupriya E. Enhanced prediction of heart disease with feature subset selection using genetic algorithm. Int J Eng Sci Technol: 2010; 2 5370 5376
26. Leardi R. Genetic algorithms in chemometrics and chemistry: a review. J Chemom: 2001; 15 559 569
27. Witten I. H., Frank E., Hall M. A. Data mining: practical machine learning tools and techniques. 3rd edition. Burlington Morgan Kaufmann Publishers: 2011; 490 493
28. Tong D. L., Mintram R. Genetic Algorithm-Neural Network (GANN): a study of neural network activation functions and depth of genetic algorithm search applied to feature selection. Int J Mach Learn Cybern: 2010; 1 75 87
29. D'heygere T., Goethals P. LM, Pauw N. D. Use of genetic algorithms to select input variables in decision tree models for the prediction of benthic macroinvertebrates. Ecol Model: 2003; 160 291 300
30. Karegowda A. G., Manjunath A. S., Jayaram M. A. Comparative study of attribute selection using gain ration and correlation based feature selection. Int J Inf Technol Knowl Manag: 2010; 2 271 227
31. Machine Learning Group Class J48. Available at http://weka.sourceforge.net/doc.dev/weka/classifiers/trees/J48.html Accessed October 10, 2014
32. Kotsiantis S. B. Supervised machine learning: a review of classification techniques. Informatica: 2007; 31 249 268
33. Quinlan J. R. Improved use of continuous attributes in C4.5. J Artif Intell Res: 2006; 4 77 90
34. Endo A., Shibata T., Tanaka H. Comparison of seven algorithms to predict breast cancer survival. Biomed Soft Comput Hum Sci: 2008; 13 11 16
35. Zarkami R. Application of classification trees-J48 to model the presence of roach (Rutilus rutilus) in rivers. Casp J Environ Sci: 2011; 9 189 198
36. Hecht-Nielsen R. Theory of the backpropagation neural network. International Joint Conference on Neural Networks, Vol. 1. Washington IEEE: 1989; 593 605
37. Parente L. Pros and cons of selective inhibition of cyclooxygenase-2 versus dual lipoxygenase/cyclooxygenase inhibition: is two better than one? J Rheumatol: 2001; 28 2375 2382
38. Fiorucci S., Meli R., Bucci M., Cirino G. Dual inhibitors of cyclooxygenase and 5-lipoxygenase. A new avenue in anti-inflammatory therapy? Biochem Pharmacol: 2001; 62 1433 1438
39. Gaddi A., Cicero A. FG, Pedro E. J. Clinical perspectives of anti-inflammatory therapy in the elderly: the lipoxigenase (LOX)/cycloxigenase (COX) inhibition concept. Arch Gerontol Geriatr: 2004; 38 201 212
40. Schneider I., Bucar F. Lipoxygenase inhibitors from natural plant sources. Part 1: Medicinal plants with inhibitory activity on arachidonate 5-lipoxygenase and 5-lipoxygenase[sol]cyclooxygenase. Phyther Res: 2005; 19 81 102
41. Rao P. NP, Knaus E. E. Evolution of nonsteroidal anti-inflammatory drugs (NSAIDs): cyclooxygenase (COX) inhibition and beyond. J Pharm Pharm Sci: 2008; 11 81 110
42. Celotti F., Durand T. The metabolic effects of inhibitors of 5-lipoxygenase and of cyclooxygenase 1 and 2 are an advancement in the efficacy and safety of anti-inflammatory therapy. Prostag Oth Lipid M: 2003; 71 147 162
43. Schmidt T. J. Toxic activities of sesquiterpene lactones: structural and biochemical aspects. Curr Org Chem: 1999; 3 577 608
44. Frédérich M., Jansen C., de Tullio P., Tits M., Demoulin V., Angenot L. Metabolomic analysis of Echinacea spp. by 1H nuclear magnetic resonance spectrometry and multivariate data analysis technique. Phytochem Anal: 2010; 21 61 65
45. Li Z., Zhi H., Zhang F., Sun H., Zhang L., Jia J., Xing J., Qin X. Metabolomic profiling of the antitussive and expectorant plant Tussilago farfara L. by nuclear magnetic resonance spectroscopy and multivariate data analysis. J Pharmacol Biomed Anal: 2013; 75 158 164
46. Modarai M., Yang M., Suter A., Kortenkamp A., Heinrich M. Metabolomic profiling of liquid Echinacea medicinal products with in vitro inhibitory effects on cytochrome P450 3A4 (CYP3A4). Planta Med: 2010; 76 378 385
47. Bórquez J., Kennelly E. J., Simirgiotis M. J. Activity guided isolation of isoflavones and hyphenated HPLC-PDA-ESI-ToF-MS metabolome profiling of Azorella madreporica Clos. from northern Chile. Food Res Int: 2013; 52 288 297
48. Inui T., Wang Y., Pro S. M., Franzblau S. G., Pauli G. F. Unbiased evaluation of bioactive secondary metabolites in complex matrices. Fitoterapia: 2012; 83 1218 1225
49. Ching J., Soh W. L., Tan C. H., Lee J., Tan J. C., Yang J., Yap C., Koh H. Identification of active compounds from medicinal plant extracts using gas chromatography-mass spectrometry and multivariate data analysis. J Sep Sci: 2012; 35 53 59
50. Yuliana N. D., Khatib A., Verpoorte R., Choi Y. H. Comprehensive extraction method integrated with NMR metabolomics: a new bioactivity screening method for plants, adenosine A1 receptor binding compounds in Orthosiphon stamineus Benth. Anal Chem: 2011; 83 6902 6906
51. Cabrera A. C., Prieto J. M. Application of artificial neural networks to the prediction of the antioxidant activity of essential oils in two experimental in vitro models. Food Chem: 2010; 118 141 146
52. Khan J., Wei J. S., Ringnér M., Saal L. H., Ladanyi M., Westermann F., Berthold F., Schwab M., Antonescu C. R., Peterson C., Meltzer P. S. Classification and diagnostic prediction of cancers using gene expression profiling and artificial neural networks. Nat Med: 2001; 7 673 679
53. Kuhn M. Building predictive models in R using the caret package. J Stat Softw: 2008; 28 1 26
54. Macintyre L., Zhang T., Viegelmann C., Martinez I. J., Cheng C., Dowdells C., Abdelmohsen U. R., Gernert C., Hentschel U., Edrada-Ebel R. Metabolomic tools for secondary metabolite discovery from marine microbial symbionts. Mar Drugs: 2014; 12 3416 3448
55. Eriksson L., Trygg J., Wold S. CV-ANOVA for significance testing of PLS and OPLS® models. J Chemom: 2008; 22 594 600
56. Tropsha A., Golbraikh A. Predictive QSAR modeling workflow, model applicability domains, and virtual screening. Curr Pharm Des: 2007; 13 3494 3504
57. Golbraikh A., Tropsha A. Beware of q2! J Mol Graph Model: 2002; 20 269 276
58. Corley D. G., Durley R. C. Strategies for database dereplication of natural products. J Nat Prod: 1994; 57 1484 1490
59. García-Mediavilla V., Crespo I., Collado P. S., Esteller A., Sánchez-Campos S., Tuñón M. J., Gonzállez-Gallego J. The anti-inflammatory flavones quercetin and kaempferol cause inhibition of inducible nitric oxide synthase, cyclooxygenase-2 and reactive C-protein, and down-regulation of the nuclear factor kappaB pathway in Chang Liver cells. Eur J Pharmacol: 2007; 557 221 229
60. Chagas-Paula D. A., Oliveira R. B., Rocha B. A., Da Costa F. B. Ethnobotany, chemistry, and biological activities of the genus Tithonia (Asteraceae). Chem Biodivers: 2012; 9 210 235
61. Sabir S. M., Ahmad S. D., Hamid A., Khan M. Q., Athayde M. L., Santos D. B., Boligon A. A., Rocha J. BT. Antioxidant and hepatoprotective activity of ethanolic extract of leaves of Solidago microglossa containing polyphenolic compounds. Food Chem: 2012; 131 741 747
62. Heinrich M., Robles M., West J. E., Ortiz de Montellano B. R., Rodriguez E. Ethnopharmacology of Mexican Asteraceae (Compositae). Annu Rev Pharmacol Toxicol: 1998; 38 539 565