Knowledge of therapeutic targets and early drug candidates is useful for improved drug discovery. In particular, information about target regulators and the patented therapeutic agents facilitates research regarding druggability, systems pharmacology, new trends, molecular landscapes, and the development of drug discovery tools. To complement other databases, we constructed the Therapeutic Target Database (TTD) with expanded information about (i) target-regulating microRNAs and transcription factors, (ii) target-interacting proteins, and (iii) patented agents and their targets (structures and experimental activity values if available), which can be conveniently retrieved and is further enriched with regulatory mechanisms or biochemical classes. We also updated the TTD with the recently released International Classification of Diseases ICD-11 codes and additional sets of successful, clinical trial, and literature-reported targets that emerged since the last update. TTD is accessible at http://bidd.nus.edu.sg/group/ttd/ttd.asp. In case of possible web connectivity issues, two mirror sites of TTD are also constructed (http://db.idrblab.org/ttd/ and http://db.idrblab.net/ttd/).

Diverse forms of unwanted signal variations in mass spectrometry-based metabolomics data adversely affect the accuracies of metabolic profiling. A variety of normalization methods have been developed for addressing this problem. However, their performances vary greatly and depend heavily on the nature of the studied data. Moreover, given the complexity of the actual data, it is not feasible to assess the performance of methods by single criterion. We therefore developed NOREVA to enable performance evaluation of various normalization methods from multiple perspectives. NOREVA integrated five well-established criteria (each with a distinct underlying theory) to ensure more comprehensive evaluation than any single criterion. It provided the most complete set of the available normalization methods, with unique features of removing overall unwanted variations based on quality control metabolites and allowing quality control samples based correction sequentially followed by data normalization. The originality of NOREVA and the reliability of its algorithms were extensively validated by case studies on five benchmark datasets. In sum, NOREVA is distinguished for its capability of identifying the well performed normalization method by taking multiple criteria into consideration and can be an indispensable complement to other available tools. NOREVA can be freely accessed at http://server.idrb.cqu.edu.cn/noreva/.

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BackgroundBerberine (BBR) is a drug with multiple effects on cellular energy metabolism. The present study explored answers to the question of which CYP450 (Cytochrome P450) isoenzymes execute the phase-I transformation for BBR, and what are the bioactivities of its metabolites on energy pathways.MethodsBBR metabolites were detected using LC-MS/MS. Computer-assistant docking technology as well as bioassays with recombinant CYP450s were employed to identify CYP450 isoenzymes responsible for BBR phase-I transformation. Bioactivities of BBR metabolites in liver cells were examined with real time RT-PCR and kinase phosphorylation assay.ResultsIn rat experiments, 4 major metabolites of BBR, berberrubine (M1), thalifendine (M2), demethyleneberberine (M3) and jatrorrhizine (M4) were identified in rat's livers using LC-MS/MS (liquid chromatography-tandem mass spectrometry). In the cell-free transformation reactions, M2 and M3 were detectable after incubating BBR with rCYP450s or human liver microsomes; however, M1 and M4 were below detective level. CYP2D6 and CYP1A2 played a major role in transforming BBR into M2; CYP2D6, CYP1A2 and CYP3A4 were for M3 production. The hepatocyte culture showed that BBR was active in enhancing the expression of insulin receptor (InsR) and low-density-lipoprotein receptor (LDLR) mRNA, as well as in activating AMP-activated protein kinase (AMPK). BBR's metabolites, M1-M4, remained to be active in up-regulating InsR expression with a potency reduced by 50-70%; LDLR mRNA was increased only by M1 or M2 (but not M3 and M4) with an activity level 35% or 26% of that of BBR, respectively. Similarly, AMPK-α phosphorylation was enhanced by M1 and M2 only, with a degree less than that of BBR.ConclusionsFour major BBR metabolites (M1-M4) were identified after phase-I transformation in rat liver. Cell-free reactions showed that CYP2D6, CYP1A2 and CYP3A4 seemed to be the dominant CYP450 isoenzymes transforming BBR into its metabolites M2 and M3. BBR's metabolites remained to be active on BBR's targets (InsR, LDLR, and AMPK) but with reduced potency.