Therapeutic Targets Database

Therapeutic Target Database[1](TTD) is a database provided by the Innovative Drug Research and Bioinformatics Group (IDRB) at Zhejiang University and the Bioinformatics and Drug Design Group at the National University of Singapore, which provides information about known and explored therapeutic protein and nucleic acid targets,[2][3][4] the targeted disease, pathway information and the corresponding drugs directed at each of these targets[5]. Also included in this database are links to relevant databases containing information about target function, sequence, 3D structure, ligand binding properties, enzyme nomenclature and drug structure, therapeutic class, and clinical development status.[6]

Therapeutic Target Database (TTD)
Content
DescriptionDrug target database
Contact
LaboratoryInnovative Drug Research and Bioinformatics Group (IDRB) Bioinformatics and Drug Design Group (BIDD)
Primary citationPMID 31691823
Release date11 Nov, 2019
Access
Websitehttps://db.idrblab.org/ttd/
Miscellaneous
LicenseFree access
Version7.1.01

Content

This database currently contains 2,025 targets, including 364 successful, 286 clinical trial, 44 discontinued and 1,331 research targets, 17,816 drugs, including 1,540 approved, 1,423 clinical trial, 14,853 experimental drugs and 3,681 multi-target agents (14,170 small molecules and 652 antisense drugs with available structure or oligonucleotide sequence). Targets and drugs in this database cover 61 protein biochemical class and 140 drug therapeutic classes respectively.[7]

Target validation data

In the 2011 version of TTD, target validation information has been integrated. Target validation normally requires the determination that the target is expressed in the disease-relevant cells/tissues, it can be directly modulated by a drug or drug-like molecule with adequate potency in biochemical assay, and that target modulation in cell and/or animal models ameliorates the relevant disease phenotype.[8][9] Therefore, TTD collects three types of target validation data: experimentally determined potency of drugs against their primary target or targets, observed potency or effects of drugs against disease models (cell-lines, ex-vivo, in-vivo models) linked to their primary target or targets, and the observed effects of target knockout, knockdown, RNA interference, transgenetic, antibody or antisense treated in-vivo models. Currently, TTD provides complete or partial validation information for 932 targets (351 successful, 252 clinical trial, 34 discontinued and 295 research targets). All validation information can be retrieved from Target Validation Page.

Quantitative structure-activity relationship models against specific target

Knowledge of developed QSAR models for different molecular scaffolds active against different targets is highly useful for facilitating further drug development and lead optimisation efforts.[10][11][12] Current TTD has 841 ligand-based QSAR models for active compounds of 228 chemical types against 121 targets. These QSAR models can be accessed in Target-based QSAR Models Page.

Multi-target agents

Multi-target agents directed at selected multiple targets have been increasingly explored for enhanced therapeutic efficacies, improved safety profiles, and reduced resistance activities by simultaneously modulating the activity of a primary target and the counteractive elements.[13][14][15][16][17] Multi-target agent against a target-pair refers to a compound active against both targets at potency values of ≤ 20 μM regardless of their possible activities against other targets. These multi-target agents can be retrieved from Multi-Target Agents Page.

References

  1. Wang, Yunxia; Zhang, Song; Li, Fengcheng; Zhou, Ying; Zhang, Ying; Wang, Zhengwen; Zhang, Runyuan; Zhu, Jiang; Ren, Yuxiang; Tan, Ying; Qin, Chu (6 November 2019). "Therapeutic target database 2020: enriched resource for facilitating research and early development of targeted therapeutics". Nucleic Acids Research. doi:10.1093/nar/gkz981. ISSN 1362-4962. PMID 31691823.
  2. Hopkins AL, Groom CR (2002). "The druggable genome". Nature Reviews Drug Discovery. 1 (9): 727–30. doi:10.1038/nrd892. PMID 12209152.
  3. Overington JP, Al-Lazikani B, Hopkins AL (2006). "How many drug targets are there?". Nature Reviews Drug Discovery. 5 (12): 993–6. doi:10.1038/nrd2199. PMID 17139284.
  4. Zheng CJ, Han LY, Yap CW, Ji ZL, Cao ZW, Chen YZ (2006). "Therapeutic targets: progress of their exploration and investigation of their characteristics". Pharmacol. Rev. 58 (2): 259–79. doi:10.1124/pr.58.2.4. PMID 16714488.
  5. Li, Ying Hong; Yu, Chun Yan; Li, Xiao Xu; Zhang, Peng; Tang, Jing; Yang, Qingxia; Fu, Tingting; Zhang, Xiaoyu; Cui, Xuejiao; Tu, Gao; Zhang, Yang (01 04, 2018). "Therapeutic target database update 2018: enriched resource for facilitating bench-to-clinic research of targeted therapeutics". Nucleic Acids Research. 46 (D1): D1121–D1127. doi:10.1093/nar/gkx1076. ISSN 1362-4962. PMC 5753365. PMID 29140520. Check date values in: |date= (help)
  6. Li YH, Yu CY, Li XX, Zhang P, Tang J, Yang Q, Fu T, Zhang X, Cui X, Tu G, Zhang Y, Li S, Yang F, Sun Q, Qin C, Zeng X, Chen Z, Chen YZ, Zhu F (2018). "Therapeutic target database update 2018: enriched resource for facilitating bench-to-clinic research of targeted therapeutics". Nucleic Acids Res. 46 (D1): D1121–D1127. doi:10.1093/nar/gkx1076. PMC 5753365. PMID 29140520.
  7. Chen X, Ji ZL, Chen YZ (2002). "TTD: Therapeutic Target Database". Nucleic Acids Res. 30 (1): 412–5. doi:10.1093/nar/30.1.412. PMC 99057. PMID 11752352.
  8. Lindsay MA. (2003). "Target discovery". Nature Reviews Drug Discovery. 2 (10): 831–8. doi:10.1038/nrd1202. PMID 14526386.
  9. Vidalin O, Muslmani M, Estienne C, Echchakir H, Abina AM (2009). "In vivo target validation using gene invalidation, RNA interference and protein functional knockout models: it is the time to combine". Current Opinion in Pharmacology. 9 (5): 669–76. doi:10.1016/j.coph.2009.06.017. PMID 19646923.
  10. Sprous DG, Palmer RK, Swanson JT, Lawless M (2010). "QSAR in the pharmaceutical research setting: QSAR models for broad, large problems". Curr Top Med Chem. 10 (6): 619–37. doi:10.2174/156802610791111506. PMID 20337590.
  11. Mittal RR, McKinnon RA, Sorich MJ (2009). "Comparison data sets for benchmarking QSAR methodologies in lead optimization". J. Chem. Inf. Model. 49 (7): 1810–20. doi:10.1021/ci900117m. PMID 19569715.
  12. Dudek AZ, Arodz T, Gálvez J (2006). "Computational methods in developing quantitative structure-activity relationships (QSAR): a review". Comb Chem High Throughput Screen. 9 (3): 213–28. CiteSeerX 10.1.1.591.1216. doi:10.2174/138620706776055539. PMID 16533155.
  13. Matera, Carlo; Pucci, Luca; Fiorentini, Chiara; Fucile, Sergio; Missale, Cristina; Grazioso, Giovanni; Clementi, Francesco; Zoli, Michele; De Amici, Marco; Gotti, Cecilia; Dallanoce, Clelia (2015). "Bifunctional compounds targeting both D 2 and non-α7 nACh receptors: Design, synthesis and pharmacological characterization". European Journal of Medicinal Chemistry. 101: 367–383. doi:10.1016/j.ejmech.2015.06.039. ISSN 0223-5234. PMID 26164842.
  14. Matera, Carlo; Bono, Federica; Pelucchi, Silvia; Collo, Ginetta; Bontempi, Leonardo; Gotti, Cecilia; Zoli, Michele; De Amici, Marco; Missale, Cristina; Fiorentini, Chiara; Dallanoce, Clelia (2019). "The novel hybrid agonist HyNDA-1 targets the D3R-nAChR heteromeric complex in dopaminergic neurons". Biochemical Pharmacology. 163: 154–168. doi:10.1016/j.bcp.2019.02.019. ISSN 0006-2952. PMID 30772268.
  15. Keith CT, Borisy AA, Stockwell BR (2005). "Multicomponent therapeutics for networked systems". Nature Reviews Drug Discovery. 4 (1): 71–8. doi:10.1038/nrd1609. PMID 15688074.
  16. Smalley KS, Haass NK, Brafford PA, Lioni M, Flaherty KT, Herlyn M (2006). "Multiple signaling pathways must be targeted to overcome drug resistance in cell lines derived from melanoma metastases". Mol. Cancer Ther. 5 (5): 1136–44. doi:10.1158/1535-7163.MCT-06-0084. PMID 16731745.
  17. Larder BA, Kemp SD, Harrigan PR (1995). "Potential mechanism for sustained antiretroviral efficacy of AZT-3TC combination therapy". Science. 269 (5224): 696–9. doi:10.1126/science.7542804. PMID 7542804.
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