A chemo-centric view of human health and disease

Nature Communications

Volumn 5, Issue , 2014, Pages

  Duran Frigola, Miquel ^a   Rossell, David ^a,b   Aloy, Patrick ^a,c  

^a INSTITUTE FOR RESEARCH IN BIOMEDICINE   (Spain)

^b UNIVERSITY OF WARWICK   (United Kingdom)

^c INSTITUCIÓ CATALANA DE RECERCA I ESTUDIS AVANÇATS ICREA   (Spain)

Author keywords

[No Author keywords available]

Indexed keywords

ALPRENOLOL; ANTIBIOTIC AGENT; BIOLOGICAL MARKER; BRONCHODILATING AGENT; CYCLOTHIAZIDE; ELVUCITABINE; ENVIRONMENTAL CHEMICAL; HYDROGEN; METHYCLOTHIAZIDE; NEBIVOLOL; NORADRENALIN; SUFENTANIL; TOXIN;

DISEASE PREVALENCE; PHENOTYPE; PHYSIOLOGICAL RESPONSE; PUBLIC HEALTH;

ALGORITHM; ALKYLATION; ARTICLE; BIOAVAILABILITY; CHEMICAL STRUCTURE; CHEMO CENTRIC DISEASE NETWORK; CLINICAL OBSERVATION; COMORBIDITY; DISEASE ASSOCIATION; DISEASES; DRUG REPOSITIONING; DRUG REPOSITIONING NETWORK; DRUG SIDE EFFECT NETWORK; HEALTH STATUS; HEART ARRHYTHMIA; HUMAN; HYDROGEN BOND; HYPERPITUITARISM; HYPERTENSION; MOLECULAR WEIGHT; MUSCLE HYPERTONIA; OSTEOMYELITIS; OXIDATIVE PHOSPHORYLATION; PARANOID SCHIZOPHRENIA; PHENOTYPE; PHYSICAL CHEMISTRY; PSEUDOMONAS INFECTION; SEIZURE; SEROTONIN SYNDROME; STRUCTURE ACTIVITY RELATION; SUBURETHRAL SLING; SUPRAVENTRICULAR TACHYCARDIA; THIOL ALKYLATION; TREATMENT RESPONSE; CHEMISTRY; DISEASE; DRUG THERAPY; HEALTH;

CHEMISTRY; DISEASE; DRUG THERAPY; HEALTH; HUMANS; PHENOTYPE;

EID: 84922811728     PISSN: None     EISSN: 20411723     Source Type: Journal    
DOI: 10.1038/ncomms6676     Document Type: Article

References (68)

1. Yildirim, M. A., Goh, K. I., Cusick, M. E., Barabasi, A. L., & Vidal, M. Drugtarget network. Nat. Biotechnol. 25, 1119-1126 (2007).
2. Bauer-Mehren, A. et al. Automatic filtering and substantiation of drug safety signals. PLoS Comput. Biol. 8, e1002457 (2012).
3. Pujol, A., Mosca, R., Farres, J., & Aloy, P. Unveiling the role of network and systems biology in drug discovery. Trends Pharmacol. Sci. 31, 115-123 (2010).
4. Keiser, M. J., Irwin, J. J., & Shoichet, B. K. The chemical basis of pharmacology. Biochemistry 49, 10267-10276 (2010).
5. Davis, A. P. et al. The comparative toxicogenomics database: Update 2013. Nucleic Acids Res. 41, D1104-D1114 (2013).
6. Wishart, D. S. Chapter 3: Small molecules and disease. PLoS Comput. Biol. 8, e1002805 (2012).
7. Davis, A. P., Wiegers, T. C., Rosenstein, M. C., & Mattingly, C. J. MEDIC: A practical disease vocabulary used at the comparative toxicogenomics database. Database 2012, bar065 (2012).
8. Varin, T., Schuffenhauer, A., Ertl, P., & Renner, S. Mining for bioactive scaffolds with scaffold networks: Improved compound set enrichment from primary screening data. J. Chem. Inf. Model. 51, 1528-1538 (2011).
9. Wetzel, S. et al. Interactive exploration of chemical space with Scaffold Hunter. Nat. Chem. Biol. 5, 581-583 (2009).
10. Shultz, M. D. Setting expectations in molecular optimizations: Strengths and limitations of commonly used composite parameters. Bioorg. Med. Chem. Lett. 231, 5980-5991 (2013).
11. Congreve, M., Carr, R., Murray, C., & Jhoti, H. A -rule of three- for fragment-based lead discovery? Drug Discov. Today 8, 876-877 (2003).
12. Langdon, S. R., Brown, N., & Blagg, J. Scaffold diversity of exemplified medicinal chemistry space. J. Chem. Inf. Model. 51, 2174-2185 (2011).
13. Akritopoulou-Zanze, I., & Hajduk, P. J. Kinase-targeted libraries: The design and synthesis of novel, potent, and selective kinase inhibitors. Drug Discov. Today 14, 291-297 (2009).
14. Lameijer, E. W., Kok, J. N., Back, T., & Ijzerman, A. P. Mining a chemical database for fragment co-occurrence: Discovery of -chemical cliches-. J. Chem. Inf. Model. 46, 553-562 (2006).
15. Bemis, G. W., & Murcko, M. A. The properties of known drugs. 1. Molecular frameworks. J. Med. Chem. 39, 2887-2893 (1996).
16. Lipkus, A. H. et al. Structural diversity of organic chemistry. A scaffold analysis of the CAS Registry. J. Org. Chem. 73, 4443-4451 (2008).
17. Pitt, W. R., Parry, D. M., Perry, B. G., & Groom, C. R. Heteroaromatic rings of the future. J. Med. Chem. 52, 2952-2963 (2009).
18. Williams, D. P. Toxicophores: Investigations in drug safety. Toxicology 226, 1-11 (2006).
19. Welsch, M. E., Snyder, S. A., & Stockwell, B. R. Privileged scaffolds for library design and drug discovery. Curr. Opin. Chem. Biol. 14, 347-361 (2010).
20. Wester, M. J. et al. Scaffold topologies. 2. Analysis of chemical databases. J. Chem. Inf. Model. 48, 1311-1324 (2008).
21. Knox, C. et al. DrugBank 3.0: A comprehensive resource for -omics- research on drugs. Nucleic Acids Res. 39, D1035-D1041 (2011).
22. Dupont, E., Mikkelsen, B., & Jakobsen, J. Mesulergine in early Parkinson-s disease: A double blind controlled trial. J. Neurol. Neurosurg. Psychiatry 49, 390-395 (1986).
23. Mavridis, L., Hudson, B. D., & Ritchie, D. W. Toward high throughput 3D virtual screening using spherical harmonic surface representations. J. Chem. Inf. Model. 47, 1787-1796 (2007).
24. Takigawa, I., & Mamitsuka, H. Graph mining: Procedure, application to drug discovery and recent advances. Drug Discov. Today 18, 50-57 (2013).
25. Wang, Y. et al. Estimation of carcinogenicity using molecular fragments tree. J. Chem. Inf. Model. 52, 1994-2003 (2012).
26. Greene, N., Judson, P. N., Langowski, J. J., & Marchant, C. A. Knowledge-based expert systems for toxicity and metabolism prediction: DEREK, StAR and METEOR. SAR QSAR Environ. Res. 10, 299-314 (1999).
27. Berger, S. I., Ma-ayan, A., & Iyengar, R. Systems pharmacology of arrhythmias. Sci. Signal. 3, ra30 (2010).
28. Nakajima, A. et al. Investigation of glucocorticoid-induced side effects in patients with autoimmune diseases. J. Pharm. Soc. Jpn 129, 445-450 (2009).
29. Yang, P., Li, X. L., Mei, J. P., Kwoh, C. K., & Ng, S. K. Positive-unlabeled learning for disease gene identification. Bioinformatics 28, 2640-2647 (2012).
30. Zhang, B., & Zuo, W. Reliable negative extracting based on kNN for learning from positive and unlabeled examples. J. Comput. 4, 94-101 (2009).
31. Liu, B., Lee, W. S., Yu, P., & Li, X. Partially supervised classification of text documents. in Proceedings of the Nineteenth International Conference on Machine Learning. ICML-02, 387-394 (2002).
32. Davis, A. P. et al. A CTD-Pfizer collaboration: Manual curation of 88,000 scientific articles text mined for drug-disease and drug-phenotype interactions. Database 2013, bat080 (2013).
33. Liew, C. Y., Pan, C., Tan, A., Ang, K. X., & Yap, C. W. QSAR classification of metabolic activation of chemicals into covalently reactive species. Mol. Divers. 16, 389-400 (2012).
34. Casalegno, M., & Sello, G. Determination of toxicant mode of action by augmented top priority fragment class. J. Chem. Inf. Model. 53, 1113-1126 (2013).
35. Barabasi, A. L., Gulbahce, N., & Loscalzo, J. Network medicine: A network-based approach to human disease. Nat. Rev. Genet. 12, 56-68 (2011).
36. Jacunski, A., & Tatonetti, N. Connecting the dots: Applications of network medicine in pharmacology and disease. Clin. Pharmacol. Ther. 94, 659-669 (2013).
37. Goh, K. I. et al. The human disease network. Proc. Natl Acad. Sci. USA 104, 8685-8690 (2007).
38. Hidalgo, C. A., Blumm, N., Barabasi, A. L., & Christakis, N. A. A dynamic network approach for the study of human phenotypes. PLoS Comput. Biol. 5, e1000353 (2009).
39. Lee, D. S. et al. The implications of human metabolic network topology for disease comorbidity. Proc. Natl Acad. Sci. USA 105, 9880-9885 (2008).
40. Ashburn, T. T., & Thor, K. B. Drug repositioning: Identifying and developing new uses for existing drugs. Nat. Rev. Drug discov. 3, 673-683 (2004).
41. Chiang, A. P., & Butte, A. J. Systematic evaluation of drug-disease relationships to identify leads for novel drug uses. Clin. Pharmacol. Ther. 86, 507-510 (2009).
42. Gottlieb, A., Stein, G. Y., Ruppin, E., & Sharan, R. PREDICT: A method for inferring novel drug indications with application to personalized medicine. Mol. Syst. Biol. 7, 496 (2011).
43. Kuhn, M., Campillos, M., Letunic, I., Jensen, L. J., & Bork, P. A side effect resource to capture phenotypic effects of drugs. Mol. Syst. Biol. 6, 343 (2010).
44. Gielen, W., Cleophas, T. J., & Agrawal, R. Nebivolol: A review of its clinical and pharmacological characteristics. Int. J. Clin. Pharmacol. Ther. 44, 344-357 (2006).
45. Campillos, M., Kuhn, M., Gavin, A. C., Jensen, L. J., & Bork, P. Drug target identification using side-effect similarity. Science 321, 263-266 (2008).
46. Duran-Frigola, M., & Aloy, P. Recycling side-effects into clinical markers for drug repositioning. Genome Med. 4, 3 (2012).
47. Russell, R. B., & Aloy, P. Targeting and tinkering with interaction networks. Nat. Chem. Biol. 4, 666-673 (2008).
48. Duran-Frigola, M., & Aloy, P. Analysis of chemical and biological features yields mechanistic insights into drug side effects. Chem. Biol. 20, 594-603 (2013).
49. Pammolli, F., Magazzini, L., &D Riccaboni, M. The productivity crisis in pharmaceutical R&D. Nature reviews. Drug Discov. 10, 428-438 (2011).
50. Audouze, K. et al. Deciphering diseases and biological targets for environmental chemicals using toxicogenomics networks. PLoS Comput. Biol. 6, e1000788 (2010).
51. Keiser, M. J. et al. Relating protein pharmacology by ligand chemistry. Nat. Biotechnol. 25, 197-206 (2007).
52. Bellis, L. J. et al. Collation and data-mining of literature bioactivity data for drug discovery. Biochem. Soc. Trans. 39, 1365-1370 (2011).
53. Liu, T., Lin, Y., Wen, X., Jorissen, R. N., & Gilson, M. K. BindingDB: A webaccessible database of experimentally determined protein-ligand binding affinities. Nucleic Acids Res. 35, D198-D201 (2007).
54. Davis, A. P. et al. Text mining effectively scores and ranks the literature for improving chemical-gene-disease curation at the comparative toxicogenomics database. PLoS ONE 8, e58201 (2013).
55. Grego, T., & Couto, F. M. Enhancement of chemical entity identification in text using semantic similarity validation. PLoS ONE 8, e62984 (2013).
56. Ding, Y. et al. Entitymetrics: Measuring the impact of entities. PLoS ONE 8, e71416 (2013).
57. Hu, Z. et al. VisANT 4.0: Integrative network platform to connect genes, drugs, diseases and therapies. Nucleic Acids Res. 41, W225-W231 (2013).
58. Oprea, T. I., May, E. E., Leitao, A., & Tropsha, A. Computational systems chemical biology. Methods Mol. Biol. 672, 459-488 (2011).
59. King, B. L., Davis, A. P., Rosenstein, M. C., Wiegers, T. C., & Mattingly, C. J. Ranking transitive chemical-disease inferences using local network topology in the comparative toxicogenomics database. PLoS ONE 7, e46524 (2012).
60. Butina, D. Unsupervised data base clustering based on daylight-s fingerprint and tanimoto similarity: A fast and automated way to cluster small and large data sets. J. Chem. Inf. Comput. Sci. 39, 747-750 (1999).
61. Chawla, N. V., Bowyer, K. W., Hall, L. O., & Kegelmeyer, W. P. Smote: Synthetic minority over-sampling technique. J. Artif. Intell. Res. 16, 321-357 (2002).
62. Torgo, L. Data Mining with R, Learning with Case Studies (Chapman and Hall/CRC, 2010).
63. Müllner, D. Fastcluster: Fast hierarchical, agglomerative clustering routines for R and Python. J. Stat. Softw. 53, 18 (2013).
64. Langfelder, P., Zhang, B., & Horvath, S. Defining clusters from a hierarchical cluster tree: The Dynamic Tree Cut package for R. Bioinformatics 24, 719-720 (2008).
65. Wildenhain, J., Fitzgerald, N., & Tyers, M. MolClass: A web portal to interrogate diverse small molecule screen datasets with different computational models. Bioinformatics 28, 2200-2201 (2012).
66. Liaw, A., & Wiener, M Classification and regression by random Forest. R News. 2, 18-22 (2002)
67. McInnes, B. T., Pedersen, T., & Pakhomov, S. V. UMLS-Interface and UMLSSimilarity: Open source software for measuring paths and semantic similarity. AMIA Annu. Symp. Proc. 2009, 431-435 (2009).
68. Yang, L., & Agarwal, P. Systematic drug repositioning based on clinical sideeffects. PLoS ONE 6, e28025 (2011).