The development of current biopharmaceutical industry    have led to the discovery and clinical testing of thousands of targeted agents, and the approval of a large quantity of drugs to treat or diagnose a disease. These agents exert their clinical effect(s) by modulating a molecular structure (chemically definable by at least a molecular mass) in different biological and disease regulatory pathways . The knowledge of these agents, their efficacy targets not only can be filled with numerous hurdles that call for targeted therapeutics  , but also for assisting the research of systems pharmacology   designed at the discovery of multi-target drugs  and drug combinations .
While there is an inadequate coverage of the number and the detailed information of clinical trial drugs in the established drug , efficacy target , pharmacology , bioactive compound , binding , and pathway  databases (Table 1). The shortage of the clinical trial drugs in these databases can be further revealed by the reported drug clinical trial success rates. For instance, among these databases, the previous version of the Therapeutic Target Database (TTD) contains the largest set of 3147 clinical trial drugs vs. 2003 approved drugs. The ratio of the approved and clinical trial drugs is 63.6%, which is much larger than the reported 6.6% - 13.4% phase I clinical trial to drug approval success rates . The update of Therapeutic Target Database (TTD) in 2016 increases markedly current and discontinued clinical trial drugs searchable from the literatures, public reports and clinical trial websites , which can be proven by the number of targets have been reported in 2006 based on the number of innovative targets rates in 2002.
2. Collection of Approved and Clinical Trial Drugs
Approved drugs and clinical trial drugs represent special classes of therapeutic agents in advanced development stages, the knowledge of these agents, their therapeutic targets is significantly important for accelerating the future drug research and development. Hence, there is a strong need to enlarge the coverage of the clinical trial drugs, targets. In our analysis, the primary targets and their corresponding drugs/agents were initially collected from the company websites and publications or review articles in reputable journals (e.g. Journal of Neurochemistry, Current Opinion in Pharmacology, Drug Discovery Today, Nature Reviews Drug Discovery, Current Topics in Medicinal Chemistry, Science, Clinical
Table 1. Examples of well-known drug target database.
Colorectal Cancer, and so on), PhRMA report of Medicines for diabetes, neurological disorders, HIV/AIDS, cancer, children, and rare diseases during 2009- 2015, which explicitly mentioned the targets and theirs corresponding drugs, and the 2014-2015 drug pipeline reports from the websites and annual reports of 183 pharmaceuticals companies (e.g. Abbott, Bayer, Boehringer Ingelheim, Merck, Eli Lilly, Novartis, Pfizer, Roche, Sanofi, Takeda Pharmaceutical, GlaxoSmithKline, and so on). This procedure gave rise to 9528 clinical trial drugs and 2097 approved drugs by FDA in update of TTD .
3. Identification of the Primary Therapeutic Target(s)
Identification of the targets for approved drugs, drugs in clinical trial, and experimental agents is the most critical job. In order to identify the primary efficacy targets through which the drug mediates its clinical therapeutic activities, a comprehensive analysis of the literature for each drug is needed. The criteria for assignment were strict in that strong evidence of cell-based and/or in vivo evidence linking the target (and specific target subtype) to the effect of the drug must exist alongside binding data  . Other criteria for potential drug targets must be related to the corresponding disease tested in clinic (Figure 1). For monoclonal antibodies or recombinant proteins/peptides, their efficacy target(s) are relatively easy to assign because of their specificity.
The literature is often complex in terms of the information provided about efficacy targets. For cases in which a specific target is believed to be the sole or major route through which a drug achieves its efficacy, we accurately identified the drug against the single target at specific protein/mRNA subtype level, such as HER2 targeted by trastuzumab and mRNA of ApoB-100 targeted by mipomersen. For multi-target drugs, their targets were tentatively divided into the pri- mary efficacy targets (play essential roles in the targeted disease) and the secondary targets (play facilitating roles such as drug bypass signaling in the disease). In the latest version of TTD, only the primary efficacy targets were identified and assigned as drug target. For example, INCB018424 is a dual inhibitor of
Figure 1. Criteria for assignment of therapeutic targets.
both JAK1 and JAK2. About half of patients with myelofibrosis carry a gain-of- function mutation (V617F) in JAK2 gene that is the major contributor to the pathophysiology of this disease . Thus, only JAK2 was identified as the primary efficacy target of INCB018424. Other than those targets of well-defined subtype, the remaining can be group into three categories. Firstly, some are imprecisely defined as targets with ambiguous subtype/subunit. For example, it is difficult to identify the specific target subtype/subunit for benzocaine, therefore the sodium channel was assigned as target of this drug. Secondly, for cases in which targets are imprecisely defined as target with ambiguous target species. These targets are especially for infection related diseases. For example, teicoplanin is an antibiotic treating broad spectrum bacterial infections by inhibiting bacterial cell wall synthesis. Thirdly, the others targets are imprecisely defined as only to the pathway/biological process level. For example, the mode of action of tecfidera  is not clear so far. However, it was reported that this drug exerts its therapeutic effect by activating NRF2 pathway, thus, NRF2 pathway is imprecisely assigned as tecfidera’s target.
There is a relatively small, but clinically significant, class of drugs that bind to DNA, or that have no an unknown or distinct mode of action. The literature changes frequently in terms of the knowledge available about drug indications and mechanisms of action, and so this information needs to be reviewed regularly.
The need for improved drug discovery productivity and innovation   has led to intensifying efforts by employing advanced technologies   , novel therapeutic strategies such as RNA therapeutics and monoclonal antibodies, and the knowledge of target druggability features , and the systems-level profiles  learnt from the studies of the approved and clinical trial drugs and targets. The enriched information and search facilities in TTD complement the other established drug and target databases in promoting the drug discovery research.
This work was funded by the research support of National Natural Science Foundation of China (81202459, 21505009 and 21302102); by Innovation Project on Industrial Generic Key Technologies of Chongqing (cstc2015zdcy-ztzx120003); by the Chongqing Graduate Student Research Innovation Project (CYB14027); by the Fundamental Research Funds for the Central Universities (CDJZR14468801, CDJKXB14011, 2015CDJXY).
 Zheng, C.J., Han, L.Y., Yap, C.W., Ji, Z.L., Cao, Z.W. and Chen, Y.Z. (2006) Therapeutic Targets: Progress of Their Exploration and Investigation of Their Characteristics. Pharmacological Reviews, 58, 259-279. https://doi.org/10.1124/pr.58.2.4
 Engelman, J.A., Zejnullahu, K., Mitsudomi, T., Song, Y., Hyland, C., Park, J.O., Lindeman, N., Gale, C.M., Zhao, X., Christensen, J., et al. (2007) MET Amplification Leads to Gefitinib Resistance in Lung Cancer by Activating ERBB3 Signaling. Science, 316, 1039-1043. https://doi.org/10.1126/science.1141478
 Chen, Z., Cheng, K., Walton, Z., Wang, Y., Ebi, H., Shimamura, T., Liu, Y., Tupper, T., Ouyang, J., Li, J., et al. (2012) A Murine Lung Cancer Co-Clinical Trial Identifies Genetic Modifiers of Therapeutic Response. Nature, 483, 613-617. https://doi.org/10.1038/nature10937
 Zhao, S. and Iyengar, R. (2012) Systems Pharmacology: Network Analysis to Identify Multiscale Mechanisms of Drug Action. Annual Review of Pharmacology and Toxicology, 52, 505-521. https://doi.org/10.1146/annurev-pharmtox-010611-134520
 Csermely, P., Agoston, V. and Pongor, S. (2005) The Efficiency of Multi-Target Drugs: The Network Approach Might Help Drug Design. Trends in Pharmacological Sciences, 26, 178-182. https://doi.org/10.1016/j.tips.2005.02.007
 Jia, J., Zhu, F., Ma, X., Cao, Z., Li, Y. and Chen, Y.Z. (2009) Mechanisms of Drug Combinations: Interaction and Network Perspectives. Nature Reviews. Drug Discovery, 8, 111-128. https://doi.org/10.1038/nrd2683
 Law, V., Knox, C., Djoumbou, Y., Jewison, T., Guo, A.C., Liu, Y., Maciejewski, A., Arndt, D., Wilson, M., Neveu, V., et al. (2014) DrugBank 4.0: Shedding New Light on Drug Metabolism. Nucleic Acids Research, 42, D1091-1097. https://doi.org/10.1093/nar/gkt1068
 Qin, C., Zhang, C., Zhu, F., Xu, F., Chen, S.Y., Zhang, P., Li, Y.H., Yang, S.Y., Wei, Y.Q., Tao, L., et al. (2014) Therapeutic Target Database Update 2014: A Resource for Targeted Therapeutics. Nucleic Acids Research, 42, D1118-1123. https://doi.org/10.1093/nar/gkt1129
 Pawson, A.J., Sharman, J.L., Benson, H.E., Faccenda, E., Alexander, S.P., Buneman, O.P., Davenport, A.P., McGrath, J.C., Peters, J.A., Southan, C., et al. (2014) The IUPHAR/BPS Guide to PHARMACOLOGY: An Expert-Driven Knowledgebase of Drug Targets and Their Ligands. Nucleic Acids Research, 42, D1098-1106. https://doi.org/10.1093/nar/gkt1143
 Bento, A.P., Gaulton, A., Hersey, A., Bellis, L.J., Chambers, J., Davies, M., Kruger, F.A., Light, Y., Mak, L., McGlinchey, S., et al. (2014) The ChEMBL Bioactivity Database: An Update. Nucleic Acids Research, 42, D1083-1090. https://doi.org/10.1093/nar/gkt1031
 Liu, T., Lin, Y., Wen, X., Jorissen, R.N. and Gilson, M.K. (2007) BindingDB: A Web-Accessible Database of Experimentally Determined Protein-Ligand Binding Affinities. Nucleic Acids Research, 35, D198-201. https://doi.org/10.1093/nar/gkl999
 Kanehisa, M., Goto, S., Sato, Y., Kawashima, M., Furumichi, M. and Tanabe, M. (2014) Data, Information, Knowledge and Principle: Back to Metabolism in KEGG. Nucleic Acids Research, 42, D199-205. https://doi.org/10.1093/nar/gkt1076
 Yang, H., Qin, C., Li, Y.H., Tao, L., Zhou, J., Yu, C.Y., Xu, F., Chen, Z., Zhu, F. and Chen, Y.Z. (2016) Therapeutic Target Database Update 2016: Enriched Resource for Bench to Clinical Drug Target and Targeted Pathway Information. Nucleic Acids Research, 44, D1069-1074. https://doi.org/10.1093/nar/gkv1230
 Verstovsek, S., Kantarjian, H., Mesa, R.A., Pardanani, A.D., Cortes-Franco, J., Thomas, D.A., Estrov, Z., Fridman, J.S., Bradley, E.C., Erickson-Viitanen, S., et al. (2010) Safety and Efficacy of INCB018424, a JAK1 and JAK2 Inhibitor, in Myelofibrosis. The New England Journal of Medicine, 363, 1117-1127. https://doi.org/10.1056/NEJMoa1002028
 Gao, B., Doan, A. and Hybertson, B.M. (2014) The Clinical Potential of Influencing Nrf2 Signaling in Degenerative and Immunological Disorders. Clinical Pharmacology: Advances and Applications, 6, 19-34.
 Paul, S.M., Mytelka, D.S., Dunwiddie, C.T., Persinger, C.C., Munos, B.H., Lindborg, S.R. and Schacht, A.L. (2010) How to Improve R&D Productivity: the Pharmaceutical Industry’s Grand Challenge. Nature Reviews. Drug Discovery, 9, 203-214. https://doi.org/10.1038/nrd3078
 Lipinski, C.A., Lombardo, F., Dominy, B.W. and Feeney, P.J. (2001) Experimental and Computational Approaches to Estimate Solubility and Permeability in Drug Discovery and Development Settings. Advanced Drug Delivery Reviews, 46, 3-26. https://doi.org/10.1016/S0169-409X(00)00129-0
 Zhu, F., Han, L., Zheng, C., Xie, B., Tammi, M.T., Yang, S., Wei, Y. and Chen, Y. (2009) What Are Next Generation Innovative Therapeutic Targets? Clues from Genetic, Structural, Physicochemical, and Systems Profiles of Successful Targets. The Journal of Pharmacology and Experimental Therapeutics, 330, 304-315. https://doi.org/10.1124/jpet.108.149955