Mindfulness is Associated with Better Sleep Quality in Young Adults by Reducing Boredom and Bedtime Procrastination.

OBJECTIVE: Bedtime procrastination is a relatively new topic of research and has been found to compromise sleep. Researchers have studied the predictors, but only a few studies have focused on the ways to reduce bedtime procrastination. Mindfulness, a novel variable in this research area, may shed some light on how to decrease bedtime procrastination. This study examined a serial mediation model and hypothesized that the relationship between mindfulness and better sleep quality would be serially mediated by lower levels of boredom and bedtime procrastination. METHODS: This study employed a correlational approach and recruited a sample of 220 participants aged between 17 and 30 (M = 20.34 years, SD = 2.86). In the Qualtrics online survey, participants completed a series of questionnaires measuring mindfulness, boredom, bedtime procrastination, and sleep quality. RESULTS: The analyses provided support for our serial mediation model. Mindfulness predicted a lower level of boredom, which in turn predicted a lower level of bedtime procrastination and subsequently better sleep quality. CONCLUSION: Our findings highlighted the role of mindfulness in curbing bedtime procrastination, setting a foundation for future research on the interventions for sleep issues associated with bedtime procrastination. We discussed the theoretical and practical implications of the findings.

Prediction of Adverse Drug Reactions by Combining Biomedical Tripartite Network and Graph Representation Model.

As one of the primary contributors to high clinical attrition rates of drugs, toxicity evaluation is of critical significance to new drug discovery. Unsurprisingly, a vast number of computational methods have been developed at various stages of development pipeline to evaluate potential adverse drug reactions (ADRs). Despite previous success of these methods on individual ADR or certain drug family, there are great challenges to toxicity evaluation. In this study, a novel strategy was developed to predict the drug-ADR associations by combining deep learning and the biomedical tripartite network. This heterogeneous network contains biomedical linked data of three entities, for example, drugs, targets, and ADRs. For the first time, GraRep, a deep learning method for distributed representations, is introduced to learn graph representations and identify hidden features from the tripartite network which are further used for ADR prediction. Through this approach, drug-ADR associations could possibly be discovered from a systemic perspective. The accuracy of our method is 0.95 based on internal resource validation and 0.88 based on external resource validation. Moreover, our results show the prediction accuracy using the tripartite network is better than the one with bipartite network, suggesting the model performance can be improved with further enrichment on information. According to the result of 10-fold cross validation, the deep learning model outperforms two traditional methods (topology-based measures and chemical structure-based measures). Additionally, predictive models are also constructed using other deep learning methods, and comparable results are achieved. In summary, the biomedical tripartite network-based deep learning model proposed here proves to offer a promising solution for prediction of ADRs.

Network pharmacology study reveals energy metabolism and apoptosis pathways-mediated cardioprotective effects of Shenqi Fuzheng.

ETHNOPHARMACOLOGICAL RELEVANCE: Shenqi Fuzheng (SQ) is a renowned traditional Chinese medicine extracted from Radix Codonopsis and Radix Astragali. Although SQ is widely used to treat myocardial ischemia-reperfusion (I/R) injury, the molecular mechanisms supporting its clinical application remain elusive. AIM OF THE STUDY: The purpose of current study was to understand its cardioprotective effects at the molecular level using network pharmacology approach. MATERIALS AND METHOD: In an I/R injury animal model, the beneficial pharmacological activities of SQ were confirmed by decreased infarct range observed on drug treated rats versus control group. Additionally, several serum biochemical indicators were in concord with this observation. Subsequently, a microarray experiment was performed to reveal the influence on injured heart at the gene expression level by this TCM injection. We then proposed a network analysis algorithm NTRA to discover the key nodes based on both disease network structure and transcriptomics. Using NRIODN, a method developed by our group previously, the holistic changes on the gene network induced by for I/R injury and SQ treatment were evaluated. RESULTS: Pathway enrichment analysis of highly ranked genes by NTRA showed that PPAR and apoptosis pathways were highly related to I/R injury. Finally, western blot results showed increased level of the PPARα and BAX protein in the heart after injection treatment which confirmed the hypothesis. CONCLUSION: In conclusion, our results suggest that SQ injection exerts protective effect against myocardial ischemia-reperfusion injury through multiple pathways, including myocardial energy metabolism improvement, cell adhesion inhibition, inflammatory reaction perturbation, myocardial apoptosis reduction and ventricular remodeling avoidance.

Virtual Screening and Experimental Testing of B1 Metallo-ß-lactamase Inhibitors.

The global rise of metallo-ß-lactamases (MBLs) is problematic due to their ability to inactivate most ß-lactam antibiotics. MBL inhibitors that could be coadministered with and restore the efficacy of ß-lactams are highly sought after. In this study, we employ virtual screening of candidate MBL inhibitors without thiols or carboxylates to avoid off-target effects using the Avalanche software package, followed by experimental validation of the selected compounds. As target enzymes, we chose the clinically relevant B1 MBLs NDM-1, IMP-1, and VIM-2. Among 32 compounds selected from an approximately 1.5 million compound library, 6 exhibited IC50 values less than 40 µM against NDM-1 and/or IMP-1. The most potent inhibitors of NDM-1, IMP-1, and VIM-2 had IC50 values of 19 ± 2, 14 ± 1, and 50 ± 20 µM, respectively. While chemically diverse, the most potent inhibitors all contain combinations of hydroxyl, ketone, ester, amide, or sulfonyl groups. Docking studies suggest that these electron-dense moieties are involved in Zn(II) coordination and interaction with protein residues. These novel scaffolds could serve as the basis for further development of MBL inhibitors. A procedure for renaming NDM-1 residues to conform to the class B ß-lactamase (BBL) numbering scheme is also included.

A multiple biomarker assay for quality assessment of botanical drugs using a versatile microfluidic chip.

Quality control is critical for ensuring the safety and effectiveness of drugs. Current quality control method for botanical drugs is mainly based on chemical testing. However, chemical testing alone may not be sufficient as it may not capture all constituents of botanical drugs. Therefore, it is necessary to establish a bioassay correlating with the drug's known mechanism of action to ensure its potency and activity. Herein we developed a multiple biomarker assay to assess the quality of botanicals using microfluidics, where enzyme inhibition was employed to indicate the drug's activity and thereby evaluate biological consistency. This approach was exemplified on QiShenYiQi Pills using thrombin and angiotensin converting enzyme as "quality biomarkers". Our results demonstrated that there existed variations in potency across different batches of the intermediates and preparations. Compared with chromatographic fingerprinting, the bioassay provided better discrimination ability for some abnormal samples. Moreover, the chip could function as "affinity chromatography" to identify bioactive phytochemicals bound to the enzymes. This work proposed a multiple-biomarker strategy for quality assessment of botanical drugs, while demonstrating for the first time the feasibility of microfluidics in this field.

Revealing topics and their evolution in biomedical literature using Bio-DTM: a case study of ginseng.

BACKGROUND: Valuable scientific results on biomedicine are very rich, but they are widely scattered in the literature. Topic modeling enables researchers to discover themes from an unstructured collection of documents without any prior annotations or labels. In this paper, taking ginseng as an example, biological dynamic topic model (Bio-DTM) was proposed to conduct a retrospective study and interpret the temporal evolution of the research of ginseng. METHODS: The system of Bio-DTM mainly includes four components, documents pre-processing, bio-dictionary construction, dynamic topic models, topics analysis and visualization. Scientific articles pertaining to ginseng were retrieved through text mining from PubMed. The bio-dictionary integrates MedTerms medical dictionary, the second edition of side effect resource, a dictionary of biology and HGNC database of human gene names (HGNC). A dynamic topic model, a text mining technique, was used to emphasize on capturing the development trends of topics in a sequentially collected documents. Besides the contents of topics taken on, the evolution of topics was visualized over time using ThemeRiver. RESULTS: From the topic 9, ginseng was used in dietary supplements and complementary and integrative health practices, and became very popular since the early twentieth century. Topic 6 reminded that the planting of ginseng is a major area of research and symbiosis and allelopathy of ginseng became a research hotspot in 2007. In addition, the Bio-DTM model gave an insight into the main pharmacologic effects of ginseng, such as anti-metabolic disorder effect, cardioprotective effect, anti-cancer effect, hepatoprotective effect, anti-thrombotic effect and neuroprotective effect. CONCLUSION: The Bio-DTM model not only discovers what ginseng's research involving in but also displays how these topics evolving over time. This approach can be applied to the biomedical field to conduct a retrospective study and guide future studies.

In silico modeling on ADME properties of natural products: Classification models for blood-brain barrier permeability, its application to traditional Chinese medicine and in vitro experimental validation.

In silico modeling of blood-brain barrier (BBB) permeability plays an important role in early discovery of central nervous system (CNS) drugs due to its high-throughput and cost-effectiveness. Natural products (NP) have demonstrated considerable therapeutic efficacy against several CNS diseases. However, BBB permeation property of NP is scarcely evaluated both experimentally and computationally. It is well accepted that significant difference in chemical spaces exists between NP and synthetic drugs, which calls into doubt on suitability of available synthetic chemical based BBB permeability models for the evaluation of NP. Herein poor discriminative performance on BBB permeability of NP are first confirmed using internal constructed and previously published drug-derived computational models, which warrants the need for NP-oriented modeling. Then a quantitative structure-property relationship (QSPR) study on a NP dataset was carried out using four different machine learning methods including support vector machine, random forest, Naïve Bayes and probabilistic neural network with 67 selected features. The final consensus model was obtained with approximate 90% overall accuracy for the cross-validation study, which is further taken to predict passive BBB permeability of a large dataset consisting of over 10,000 compounds from traditional Chinese medicine (TCM). For 32 selected TCM molecules, their predicted BBB permeability were evaluated by in vitro parallel artificial membrane permeability assay and overall accuracy for in vitro experimental validation is around 81%. Interestingly, our in silico model successfully predicted different BBB permeation potentials of parent molecules and their known in vivo metabolites. Finally, we found that the lipophilicity, the number of hydrogen bonds and molecular polarity were important molecular determinants for BBB permeability of NP. Our results suggest that the consensus model proposed in current work is a reliable tool for prioritizing potential CNS active NP across the BBB, which would accelerate their development and provide more understanding on their mechanisms, especially those with pharmacologically active metabolites.

A Bioactive Chemical Markers Based Strategy for Quality Assessment of Botanical Drugs: Xuesaitong Injection as a Case Study.

Current chemical markers based quality assessment methods largely fail to reflect intrinsic chemical complexity and multiple mechanisms of action of botanical drugs (BD). The development of novel quality markers is greatly needed. Here we propose bioactive chemical markers (BCM), defined as a group of chemo-markers that exhibit similar pharmacological activities comparable to the whole BD, which can therefore be used to effectively assess the quality of BD. As a proof-of-concept, a BCM-based strategy was developed and applied to Xuesaitong Injection (XST) for assessing the efficacy and consistency of different batches. Firstly, systemic characterization of chemical profile of XST revealed a total number of 97 compounds. Secondly, notoginsenoside R1, ginsenoside Rg1, Re, Rb1 and Rd were identified as BCM of XST on treating cardiovascular and cerebrovascular diseases according to Adjusted Efficacy Score following an in vivo validation. Analytical method for quantification of BCM was then developed to ensure the efficacy of XST. Finally, chemical fingerprinting was developed and used to evaluate the batch-to-batch consistency. Our present case study on XST demonstrates that BCM-based strategy offers a rational approach for quality assessment of BD and provides a workflow for chemistry, manufacturing, and controls (CMC) study of BD required by regulatory authority.

Deciphering chemical interactions between Glycyrrhizae Radix and Coptidis Rhizoma by liquid chromatography with transformed multiple reaction monitoring mass spectrometry.

In this study, we propose an integrated strategy for the efficient identification and quantification of herbal constituents using liquid chromatography with mass spectrometry. First, liquid chromatography with quadrupole time-of-flight mass spectrometry was employed for the chemical profiling of herbs, where a targeted following nontargeted approach was developed to detect trace constituents by using structural correlations and extracted ion chromatograms. Next, ion pairs and parameters of MS2 of quadrupole time-of-flight mass spectrometry were selected to design multiple reaction monitoring transitions for the identified compounds on liquid chromatography with triple quadrupole mass spectrometry. The relative concentration of each constituent was then calculated using a semiquantitative calibration curve. The proposed strategy was applied in a study of chemical interactions between Glycyrrhizae Radix and Coptidis Rhizoma. A total of 140 compounds were identified or tentatively characterized from the herbs, 132 of which were relatively quantified. The visualized quantitative results clearly showed codecoction produced significant constituent concentration variations especially for those with a low polarity. The case study also indicated that the present methodology could provide a reliable, accurate, and labor-saving solution for chemical studies of herbal medicines.

Exploring the interaction between Salvia miltiorrhiza and human serum albumin: Insights from herb-drug interaction reports, computational analysis and experimental studies.

Human serum albumin (HSA) binding is one of important pharmacokinetic properties of drug, which is closely related to in vivo distribution and may ultimately influence its clinical efficacy. Compared to conventional drug, limited information on this transportation process is available for medicinal herbs, which significantly hampers our understanding on their pharmacological effects, particularly when herbs and drug are co-administrated as polytherapy to the ailment. Several lines of evidence suggest the existence of Salvia miltiorrhiza-Warfarin interaction. Since Warfarin is highly HSA bound in the plasma with selectivity to site I, it is critical to evaluate the possibility of HSA-related herb-drug interaction. Herein an integrated approach was employed to analyze the binding of chemicals identified in S. miltiorrhiza to HSA. Molecular docking simulations revealed filtering criteria for HSA site I compounds that include docking score and key molecular determinants for binding. For eight representative ingredients from the herb, their affinity and specificity to HSA site I was measured and confirmed fluorometrically, which helps to improve the knowledge of interaction mechanisms between this herb and HSA. Our results indicated that several compounds in S. miltiorrhiza were capable of decreasing the binding constant of Warfarin to HSA site I significantly, which may increase free drug concentration in vivo, contributing to the herb-drug interaction observed clinically. Furthermore, the significance of HSA mediated herb-drug interactions was further implied by manual mining on the published literatures on S. miltiorrhiza.

Qualitative analysis of chemical constituents in traditional Chinese medicine analogous formula cheng-Qi decoctions by liquid chromatography-mass spectrometry.

Cheng-Qi decoctions (CQs), a group of analogous formulas, are well-known traditional Chinese preparations used as purgative remedies to treat 'internal heat'-induced symptoms, which manifest as a bloated and painful abdomen, hard stools, fever and other clinical observations. In this study, HPLC-ESI-MS/MS and UPLC-TOF-MS were employed for separation and structural identification of constituents in CQs. As a result, a total of 90 compounds, including seven anthraquinones, 39 flavones, 21 glycosides, 11 stilbene glycosides, six organic acids, five coumarins and one lignans, were detected and tentatively identified in CQs extracts. The characterization results shed some light on the scientific foundation for clinical application of the CQ analogous formulas. Our results also indicate that the HPLC-MS method is useful for the systemic identification of major constituents in traditional Chinese medicine formulas.

[Toxicogenomics and its application in safety evaluation of traditional Chinese medicine].

Toxicogenomics (TGx) refers to a set of technologies that assess genome-wide responses after toxic agent exposure. Altered gene expression patterns that are caused by specific exposures reveal how toxicants may disrupt cellular processes and lead to side effects. Development and application of " omics" technology facilitate the toxicogenomic research which sharing and interpretation of the enormous amount of biological information generated in toxicologic field. In recent years TGx has been widely valued and successfully applied as an effective research tool to evaluate the toxic effects of traditional Chinese medicine (TCM). Here we reviewed current progress in the field of TGx and focused on its application in traditional Chinese medicine safety evaluation, especially in revealing the mechanism, finding potential toxic biomarkers and studying compatibility detoxification of TCM.

Dissecting functional chemome of Xiaoqinglong decoction analogous formulae using network formulaology approach.

Analogous formulae (AF) refer to a set of traditional Chinese medicine (TCM) formulae sharing similar herbs and/or indications. Dissecting functional chemome of analogous formulae could enhance the understanding of the intrinsic nature of TCM. In this study, taking 5 Xiaoqinglong decoction analogous formulae (XQL AF) including Xiaoqinglong decoction, Mahuang Xingren Shigao Gancao decoction, Mahuang Fuzi decoction, Houpu Mahuang decoction and Daqinglong decoction as example, we systematically investigated the relationship between compounds and indications using network formulaology approach. The functional chemome of XQL AF were revealed by network analysis and molecular docking. This successful application in XQL AF suggests network formulaology could be a useful tool for AF-related research and therefore provide a new way to discover the scientific foundation of Zhang Zhongjing's herbal formulae.

Transcriptomics: a sword to cut the Gordian knot of traditional Chinese medicine.

The systemic effects of traditional Chinese medicine (TCM) seem to be a Gordian knot, impossible to untie for decades. With the advent of transcriptomics, a useful sword is provided to cut the knot and shed some light on complex bioprocesses and intrinsic connections among them. Here, we revisit studies on TCM ZHENGs using this approach, highlight its applications on elucidating the potential scientific basis of ZHENG and investigating mechanisms of action for the TCM formula, and demonstrating its unique role in novel TCM drug design and discovery through active ingredient detection from TCM and compatibility theory study of TCM. The limitations and future perspectives of transcriptomics approaches to TCM study are also discussed.

Chemical and Metabolic Profiling of Si-Ni Decoction Analogous Formulae by High performance Liquid Chromatography-Mass Spectrometry.

Along with an indispensable role in healthcare system of China for centuries, Traditional Chinese Medicine (TCM) shows increasing usages as complementary therapy in western countries. To improve our understanding on their therapeutic effects, it's critical to unveil chemical compositions of TCM formula, the predominant form of therapy in TCM. However, intrinsic chemical complexity makes it a challenging task to perform analysis on each individual TCM formula even with most current state-of-art analytic techniques available. In this work we approached this question by focusing on analogous formulae, a unique category of TCM formulae grouped together based on shared herbs and/or similar TCM syndromes. Systematic chemical profiling on five Si-Ni decoctions (SNs) for cardiovascular diseases was performed by multistage MS and high-resolution MS (HR-MS) experiments. A total of 83 compounds, including alkaloids, flavonoids, ginsenosides, bile acids and triterpenoids, were described. Analysis on SNs-treated rats detected 55 prototype compounds and 39 metabolites in the systemic circulation in vivo, which may contribute directly to their observed clinical efficacies. This approach offers great advantage to speed up identification of chemical compositions of formula and reveal the difference among these analogous formulae that may be related to diverse clinical effects.

Fabrication of enzyme-immobilized halloysite nanotubes for affinity enrichment of lipase inhibitors from complex mixtures.

Lipase is the key enzyme for catalyzing triglyceride hydrolysis in vivo, and lipase inhibitors have been used in the management of obesity. We present the first report on the use of lipase-adsorbed halloysite nanotubes as an efficient medium for the selective enrichment of lipase inhibitors from natural products. A simple and rapid approach was proposed to fabricate lipase-adsorbed nanotubes through electrostatic interaction. Results showed that more than 85% lipase was adsorbed into nanotubes in 90 min, and approximately 80% of the catalytic activity was maintained compared with free lipase. The specificity and reproducibility of the proposed approach were validated by screening a known lipase inhibitor (i.e., orlistat) from a mixture that contains active and inactive compounds. Moreover, we applied this approach with high performance liquid chromatography-mass spectrometry technique to screen lipase inhibitors from the Magnoliae cortex extract, a medicinal plant used for treating obesity. Two novel biphenyl-type natural lipase inhibitors magnotriol A and magnaldehyde B were identified, and their IC50 values were determined as 213.03 and 96.96 µM, respectively. The ligand-enzyme interactions of magnaldehyde B were further investigated by molecular docking. Our findings proved that enzyme-adsorbed nanotube could be used as a feasible and selective affinity medium for the rapid screening of enzyme inhibitors from complex mixtures.

Derivative multiple reaction monitoring and single herb calibration approach for multiple components quantification of traditional Chinese medicine analogous formulae.

In comparison with monotherapy in western medicine, traditional Chinese medicine (TCM) advocates combinational therapy for treating diseases and TCM formula is a representative for this approach. Despite of extensive clinical applications of TCM formulae, knowledge about their pharmacological activities, mechanisms of action and cellular targets remains limited. A main contributing factor to these unanswered questions is unavailability of chemical compositions and their contents in the formulae. Several challenges hinder global qualitative and quantitative analysis of the formulae, including large quantities of constituents, potential physicochemical changes during decoction and lack of authentic standards. Herein we introduced an integrated strategy based on liquid chromatography coupled with mass spectrometry (LC-MS) systems to address such challenges. First, liquid chromatography-ion trap and quadrupole time-of-flight mass spectrometry (LC-IT/MS and LC-QTOF/MS) were utilized to characterize chemical profiling of the formulae. Meanwhile, MS(2) of IT/MS produced major parameters for derivative multiple reaction monitoring (DeMRM) on liquid chromatography-triple quadrupole mass spectrometry (LC-TQ/MS), which offered rapid and direct transition design in the quantitative assay. Instead of authentic standards, serial dilutions of single herbs were employed in this study to construct calibration curves necessary for calculating relative concentrations of components. Xiao-Banxia decoction and its four analogous formulae were then taken to exemplify the feasibility of currently proposed methodology. Among the 160 qualitatively identified components, a total of 138 components were semi-quantified for these decoctions. Based on these results, we demonstrated that co-decoction of different herbs could result in concentration variations of components and this effect was more prominent when certain herbs were combined. Our results indicated that the present strategy would significantly contribute to chemical studies on TCM and its utilities could be extended to other research fields, such as metabolomics and comparative chemistry.

Rapid screening natural-origin lipase inhibitors from hypolipidemic decoctions by ultrafiltration combined with liquid chromatography-mass spectrometry.

Lipase inhibitors generate hypolipidemic effect that is helpful to control or treat some obesity diseases by inactivating catalytic activity of human pancreatic lipase, a key enzyme involved in triglyceride hydrolysis in vivo. Many traditional Chinese medicine (TCM) formulae have been effectively used to treat obesity and other fat related diseases for centuries and modern biological experiments demonstrate therapeutic effect of these formulae can be linked to their lipid-lowering capability in blood. These observations suggest that these hypolipidemic decoctions (HDs) could be a promising resource of natural-origin lipase inhibitors. This work described a rapid approach for screening lipase inhibitors from four widely used HDs, including Wu-Ling-San (WLS), Ze-Xie decoction (ZX), Xiao-Xian-Xiong decoction (XXX) and Xiao-Chai-Hu decoction (XCH), by ultrafiltration combing with high performance liquid chromatography-mass spectrometry (HPLC-MS). Our results showed sixteen natural-origin lipase inhibitors were discovered and identified by high resolution and multistage mass spectrometry. Inhibitory activities of two compounds were confirmed by a functional assay of lipase, which validated the reliability of our approach. Molecular docking simulation was then performed to investigate potential mechanism of action for these compounds. Together we present an efficient method for rapid screening lipase inhibitors from complex natural products, which can be easily accommodated to other important enzymatic system with therapeutic values.

Dissecting active ingredients of Chinese medicine by content-weighted ingredient-target network.

Chinese medicine has been widely used in clinical practice, but its mode of action often remains obscure. This has seriously hindered further development and better clinical applications of Chinese medicine. Among the most critical questions to be addressed, the identification of active ingredients is an important one requiring more research. Existing methods are only concerned the potential pharmacological effects of the individual purified chemical ingredients without consideration of the contents of these ingredients, which is critical to the comprehensive effect of Chinese medicine. A novel approach was proposed here to integrate network pharmacology analysis and ingredient content in Chinese medicine to identify active ingredients. The therapeutic action of Xuesaitong (XST) injection on myocardial infarction was analyzed as an example in this study. Firstly, we built a cardiovascular disease (CVD) related protein-protein interaction (PPI) network. Secondly, the potential targets of the ingredients of XST were identified by integrating microarray data, text mining and pharmacophore model-based prediction. The target-ingredient relationships were then mapped to the network. Topological attributes related to the targets of these ingredients, together with the ingredients' contents, were combined to calculate a composition-weighted index for integrative evaluation of ingredient efficacy. Our results indicated that major active ingredients in XST were notoginsenoside R1, ginsenoside Rg1, Rb1, Rd and Re, which was further validated on myocardial infarction rat models. In conclusion, this study presented a novel approach to identify active ingredients in Chinese medicine.

LTMap: a web server for assessing the potential liver toxicity by genome-wide transcriptional expression data.

Toxicogenomics (TGx) has played a significant role in mechanistic research related with hepatotoxicity as well as liver toxicity prediction. Currently, several large-scale preclinical TGx data sets were made freely accessible to the public, such as Open TG-GATEs. With the availability of a sufficient amount of microarray data, it is important to integrate this information to provide new insights into the risk assessment of potential drug-induced liver toxicity. Here we developed a web server for evaluating the potential liver toxicity based on genome-wide transcriptomics data, namely LTMap. In LTMap, researchers could compare signatures of query compounds against a pregenerated signature database of 20 123 Affymetrix arrays associated with about 170 compounds retrieved from the largest public toxicogenomics data set Open TG-GATEs. Results from this comparison may lead to the unexpected discovery of similar toxicological responses between chemicals. We validated our computational approach for similarity comparison using three example drugs. Our successful applications of LTMap in these case studies demonstrated its utility in revealing the connection of chemicals according to similar toxicological behaviors. Furthermore, a user-friendly web interface is provided by LTMap to browse and search toxicogenomics data (http://tcm.zju.edu.cn/ltmap).