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|Title:||Therapeutic target analysis and discovery based on genetic, structural, physicochemical and system profiles of successful targets||Authors:||ZHU FENG||Keywords:||Therapeutic target, Database construction, Sequence similarity, Structural fold comparison, Machine learning||Issue Date:||11-Aug-2010||Citation:||ZHU FENG (2010-08-11). Therapeutic target analysis and discovery based on genetic, structural, physicochemical and system profiles of successful targets. ScholarBank@NUS Repository.||Abstract:||Knowledge from established therapeutic targets is expected to be invaluable goldmine for target discovery. To facilitate access to target information, publicly accessible databases have been developed. Information about the primary drug target(s) of comprehensive sets of approved, clinical trial, and experimental drugs is highly useful for facilitating focused investigation and discovery effort. However, none of those databases can accurately provide such data. Thus, a significant update to the Therapeutic Targets Database (TTD) in 2010 was conducted by expanding target data to include 348 successful, 292 clinical trial and 1,254 research targets, and added drug data for 1,514 approved, 1,212 clinical trial and 2,302 experimental drugs linked to their primary target(s). Comprehensive analysis on successful and clinical trial targets is able to reveal their common features. As found, analysis of therapeutic, biochemical, physicochemical, and systems features of clinical trial targets and drugs reveal areas of focuses, progresses and distinguished features. Many new targets, particularly G protein-coupled receptors (GPCRs) and kinases in the upstream signaling pathways are in advanced trial phases against cancer, inflammation, and nervous and circulatory systems diseases. The majority of the clinical trial targets show sequence and system profiles similar to successful targets, but fewer of them show overall sequence, structure, physicochemical, and system features resembling successful ones. Drugs in advanced trial phase show improved potency but increased lipophilicity and molecular weight with respect to approved drugs, and improved potency and lipophilicity but increased molecular weight compared to high thoughput screening (HTS) leads. These suggest a need for further improvement in drug-like and target-like features. Based on information from TTD and other sources, and statistical analysis results on successful and clinical trial targets, a collective approach combining 4 in silico methods to identify targets was proposed. These methods include (1) machine learning used for identifying physicochemical properties embedded in target primary structure; (2) sequence similarity in drug-binding domains; (3) 3-D structural fold of drug-binding domains; and (4) simple system level druggability rules. This combination identified 50%, 25%, 10% and 4% of the phase III, II, I, and non-clinical targets as promising, it enriched phase II and III target identification rate by 4.0~6.0 fold over random selection. The phase III targets identified include 7 of the 8 targets with positive phase III results. Recent emergence of swine and avian influenza A H1N1 and H5N1 outbreaks and various drug-resistant influenza strains underscores the urgent need for developing new anti-influenza drugs. As an application, target discovery approach is used to identify promising targets from the genomes of influenza A (H1N1, H5N1, H2N2, H3N2, H9N2), B and C. The identified promising drug targets are neuraminidase of influenza A and B, polymerase of influenza A, B and C, and matrix protein 2 of influenza A. The identified marginally promising therapeutic targets are haemagglutinin of influenza A and B, and hemagglutinin-esterase of influenza C. The identified promising targets show fair drug discovery productivity level compared to a modest level for the marginally promising targets and low level for unpromising targets. Thus, the results are highly consistent with the current drug discovery productivity levels against these proteins.||URI:||http://scholarbank.nus.edu.sg/handle/10635/22811|
|Appears in Collections:||Ph.D Theses (Open)|
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