MetNC: Predicting Metabolites in vivo for Natural Compounds.

Natural compounds (NCs) undergo complicated biotransformation in vivo to produce diverse forms of metabolites dynamically, many of which are of high medicinal value. Predicting the profiles of chemical products may help to narrow down possible candidates, yet current computational methods for predicting biotransformation largely focus on synthetic compounds. Here, we proposed a method of MetNC, a tailor-made method for NC biotransformation prediction, after exploring the overall patterns of NC in vivo metabolism. Based on 850 pairs of the biotransformation dataset validated by comprehensive in vivo experiments with sourcing compounds from medicinal plants, MetNC was designed to produce a list of potential metabolites through simulating in vivo biotransformation and then prioritize true metabolites into the top list according to the functional groups in compound structures and steric hindrance around the reaction sites. Among the well-known peers of GLORYx and BioTransformer, MetNC gave the highest performance in both the metabolite coverage and the ability to short-list true products. More importantly, MetNC seemed to display an extra advantage in recommending the microbiota-transformed metabolites, suggesting its potential usefulness in the overall metabolism estimation. In summary, complemented to those techniques focusing on synthetic compounds, MetNC may help to fill the gap of natural compound metabolism and narrow down those products likely to be identified in vivo.

HIT 2.0: an enhanced platform for Herbal Ingredients' Targets.

Literature-described targets of herbal ingredients have been explored to facilitate the mechanistic study of herbs, as well as the new drug discovery. Though several databases provided similar information, the majority of them are limited to literatures before 2010 and need to be updated urgently. HIT 2.0 was here constructed as the latest curated dataset focusing on Herbal Ingredients' Targets covering PubMed literatures 2000-2020. Currently, HIT 2.0 hosts 10 031 compound-target activity pairs with quality indicators between 2208 targets and 1237 ingredients from more than 1250 reputable herbs. The molecular targets cover those genes/proteins being directly/indirectly activated/inhibited, protein binders, and enzymes substrates or products. Also included are those genes regulated under the treatment of individual ingredient. Crosslinks were made to databases of TTD, DrugBank, KEGG, PDB, UniProt, Pfam, NCBI, TCM-ID and others. More importantly, HIT enables automatic Target-mining and My-target curation from daily released PubMed literatures. Thus, users can retrieve and download the latest abstracts containing potential targets for interested compounds, even for those not yet covered in HIT. Further, users can log into 'My-target' system, to curate personal target-profiling on line based on retrieved abstracts. HIT can be accessible at http://hit2.badd-cao.net.

Comparative Network Pharmacology Analysis of Classical TCM Prescriptions for Chronic Liver Disease.

Chronic liver disease (CLD) has become a major global health problem while herb prescriptions are clinically observed with significant efficacy. Three classical Traditional Chinese Medicine (TCM) formulae, Yinchenhao Decoction (YCHT), Huangqi Decoction (HQT), and Yiguanjian (YGJ) have been widely applied in China to treat CLD, but no systematic study has yet been published to investigate their common and different mechanism of action (MOA). Partial limitation may own to deficiency of effective bioinformatics methods. Here, a computational framework of comparative network pharmacology is firstly proposed and then applied to herbal recipes for CLD disease. The analysis showed that, the three formulae modulate CLD mainly through functional modules of immune response, inflammation, energy metabolism, oxidative stress, and others. On top of that, each formula can target additional unique modules. Typically, YGJ ingredients can uniquely target the ATP synthesis and neurotransmitter release cycle. Interestingly, different formulae may regulate the same functional module in different modes. For instance, YCHT and YGJ can activate oxidative stress-related genes of SOD family while HQT are found to inhibit SOD1 gene. Overall, our framework of comparative network pharmacology proposed in our work may not only explain the MOA of different formulae treating CLD, but also provide hints to further investigate the biological basis of CLD subtypes.

In Silico Study of Anti-Insomnia Mechanism for Suanzaoren Prescription.

Insomnia is a common and widespread sleeping disorder caused by various risk factors. Though beneficial, conventional treatments of insomnia have significant limitations. As an alternative treatment, Chinese herbal formula Suanzaoren prescription (SZRP), composed of Suanzaoren [seeds of Ziziphus jujuba var. spinosa (Bunge) Hu ex H.F.Chow] and four additional herbs, has been reported with significant anti-insomnia effects. Yet the anti-insomnia mechanism of the herb formulae remains unknown. In this study, we attempted to extrapolate the holistic anti-insomnia mechanism of SZRP through herbal targeting and network pharmacology. The results indicated that the ingredients of Suanzaoren can target multi-neurotransmitter receptors at synapse interface, which was reported to be associated with sedative and hypnotic effects, while the four additional herbs can hit multiple pathways downstream of membrane neurotransmitters. Furthermore, the four additional herbs showed highly cooperative targeting patterns in the paralleled and cross-talked pathways related to inflammatory regulation and endocrine system, which may contribute to the additional relief of insomnia caused by inflammation, anxiety, or endocrine disorder. The interesting complementary mechanism we found among the herbal groups of SZRP may provide an example to study Chinese herbal formula and offers clues to future design of anti-insomnia strategy.

Exploring the Mechanism of Flavonoids Through Systematic Bioinformatics Analysis.

Flavonoids are the largest class of plant polyphenols, with common structure of diphenylpropanes, consisting of two aromatic rings linked through three carbons and are abundant in both daily diets and medicinal plants. Fueled by the recognition of consuming flavonoids to get better health, researchers became interested in deciphering how flavonoids alter the functions of human body. Here, systematic studies were performed on 679 flavonoid compounds and 481 corresponding targets through bioinformatics analysis. Multiple human diseases related pathways including cancers, neuro-disease, diabetes, and infectious diseases were significantly regulated by flavonoids. Specific functions of each flavonoid subclass were further analyzed in both target and pathway level. Flavones and isoflavones were significantly enriched in multi-cancer related pathways, flavan-3-ols were found focusing on cellular processing and lymphocyte regulation, flavones preferred to act on cardiovascular related activities and isoflavones were closely related with cell multisystem disorders. Relationship between chemical constitution fragment and biological effects indicated that different side chain could significantly affect the biological functions of flavonoids subclasses. Results will highlight the common and preference functions of flavonoids and their subclasses, which concerning their pharmacological and biological properties.

In Silico Insight into Potential Anti-Alzheimer's Disease Mechanisms of Icariin.

Herbal compounds that have notable therapeutic effect upon Alzheimer's disease (AD) have frequently been found, despite the recent failure of late-stage clinical drugs. Icariin, which is isolated from Epimedium brevicornum, is widely reported to exhibit significant anti-AD effects in in vitro and in vivo studies. However, the molecular mechanism remains thus far unclear. In this work, the anti-AD mechanisms of icariin were investigated at a target network level assisted by an in silico target identification program (INVDOCK). The results suggested that the anti-AD effects of icariin may be contributed by: attenuation of hyperphosphorylation of tau protein, anti-inflammation and regulation of Ca(2+) homeostasis. Our results may provide assistance in understanding the molecular mechanism and further developing icariin into promising anti-AD agents.

Screening Ingredients from Herbs against Pregnane X Receptor in the Study of Inductive Herb-Drug Interactions: Combining Pharmacophore and Docking-Based Rank Aggregation.

The issue of herb-drug interactions has been widely reported. Herbal ingredients can activate nuclear receptors and further induce the gene expression alteration of drug-metabolizing enzyme and/or transporter. Therefore, the herb-drug interaction will happen when the herbs and drugs are coadministered. This kind of interaction is called inductive herb-drug interactions. Pregnane X Receptor (PXR) and drug-metabolizing target genes are involved in most of inductive herb-drug interactions. To predict this kind of herb-drug interaction, the protocol could be simplified to only screen agonists of PXR from herbs because the relations of drugs with their metabolizing enzymes are well studied. Here, a combinational in silico strategy of pharmacophore modelling and docking-based rank aggregation (DRA) was employed to identify PXR's agonists. Firstly, 305 ingredients were screened out from 820 ingredients as candidate agonists of PXR with our pharmacophore model. Secondly, DRA was used to rerank the result of pharmacophore filtering. To validate our prediction, a curated herb-drug interaction database was built, which recorded 380 herb-drug interactions. Finally, among the top 10 herb ingredients from the ranking list, 6 ingredients were reported to involve in herb-drug interactions. The accuracy of our method is higher than other traditional methods. The strategy could be extended to studies on other inductive herb-drug interactions.

Integrating clinical indexes into four-diagnostic information contributes to the Traditional Chinese Medicine (TCM) syndrome diagnosis of chronic hepatitis B.

Traditional Chinese Medicine (TCM) treatment has been commonly used to treat Chronic Hepatitis B (CHB) in Asian countries based on TCM syndrome diagnosis, also called "ZHENG". The syndrome is identified through the four-diagnostic methods, with certain degree of subjectivity and ambiguity from individual doctors. Normally those CHB patients also receive series of parameters from modern clinical examination, while they are routinely believed to be unrelated with the TCM syndrome diagnosis. In this study, we investigated whether these biomedical indexes in modern medicine could be beneficial to TCM syndrome diagnostics in an integrative way. Based on 634 patient samples from health controls and three subtypes of CHB syndromes, a two-view based hierarchical classification model was tested for TCM syndromes prediction based on totally 222 parameters integrated from both TCM practice and modern clinical tests. The results indicated that the performance of syndrome classification based on a proper integration of TCM and modern clinical indexes was significantly higher than those based on one view of parameters only. Furthermore, those indexes correlated with CHB syndrome diagnosis were successfully identified for CM indexes and biochemical indexes respectively, where potential associations between them were hinted to the MAPK signaling pathway.

Target network differences between western drugs and Chinese herbal ingredients in treating cardiovascular disease.

BACKGROUND: Western drugs have achieved great successes in CVDs treatment. However, they may lead to some side effects and drug resistance. On the other hand, more and more studies found that Traditional Chinese herbs have efficient therapeutic effects for CVDs, while their therapeutic mechanism is still not very clear. It may be a good view towards molecules, targets and network to decipher whether difference exists between anti-CVD western drugs and Chinese herbal ingredients. RESULTS: Anti-CVD western drugs and Chinese herbal ingredients, as well as their targets were thoroughly collected in this work. The similarities and the differences between the herbal ingredients and the western drugs were deeply explored based on three target-based perspectives including biochemical property, regulated pathway and disease network. The biological function of herbal ingredients' targets is more complex than that of the western drugs' targets. The signal transduction and immune system associated signaling pathways, apoptosis associated pathways may be the most important pathway for herbal ingredients, however the western drugs incline to regulate vascular smooth muscle contraction associated pathways. Chinese herbal ingredients prefer to regulate the downstream proteins of apoptosis associated pathway; while the western drugs incline to regulate the upstream proteins of VECC (Vascular Epidermal Cells Contraction) related pathways. CONCLUSION: In summary, the characteristics identified in this study would be valuable for designing new network-based multi-target CVD drugs or vaccine adjuvants.

HIM-herbal ingredients in-vivo metabolism database.

BACKGROUND: Herbal medicine has long been viewed as a valuable asset for potential new drug discovery and herbal ingredients' metabolites, especially the in vivo metabolites were often found to gain better pharmacological, pharmacokinetic and even better safety profiles compared to their parent compounds. However, these herbal metabolite information is still scattered and waiting to be collected. DESCRIPTION: HIM database manually collected so far the most comprehensive available in-vivo metabolism information for herbal active ingredients, as well as their corresponding bioactivity, organs and/or tissues distribution, toxicity, ADME and the clinical research profile. Currently HIM contains 361 ingredients and 1104 corresponding in-vivo metabolites from 673 reputable herbs. Tools of structural similarity, substructure search and Lipinski's Rule of Five are also provided. Various links were made to PubChem, PubMed, TCM-ID (Traditional Chinese Medicine Information database) and HIT (Herbal ingredients' targets databases). CONCLUSIONS: A curated database HIM is set up for the in vivo metabolites information of the active ingredients for Chinese herbs, together with their corresponding bioactivity, toxicity and ADME profile. HIM is freely accessible to academic researchers at http://www.bioinformatics.org.cn/.

Towards a bioinformatics analysis of anti-Alzheimer's herbal medicines from a target network perspective.

With the growth of aging population all over the world, a rising incidence of Alzheimer's disease (AD) has been recently observed. In contrast to FDA-approved western drugs, herbal medicines, featured as abundant ingredients and multi-targeting, have been acknowledged with notable anti-AD effects although the mechanism of action (MOA) is unknown. Investigating the possible MOA for these herbs can not only refresh but also extend the current knowledge of AD pathogenesis. In this study, clinically tested anti-AD herbs, their ingredients as well as their corresponding target proteins were systematically reviewed together with applicable bioinformatics resources and methodologies. Based on above information and resources, we present a systematically target network analysis framework to explore the mechanism of anti-AD herb ingredients. Our results indicated that, in addition to the binding of those symptom-relieving targets as the FDA-approved drugs usually do, ingredients of anti-AD herbs also interact closely with a variety of successful therapeutic targets related to other diseases, such as inflammation, cancer and diabetes, suggesting the possible cross-talks between these complicated diseases. Furthermore, pathways of Ca(2+) equilibrium maintaining upstream of cell proliferation and inflammation were densely targeted by the anti-AD herbal ingredients with rigorous statistic evaluation. In addition to the holistic understanding of the pathogenesis of AD, the integrated network analysis on the MOA of herbal ingredients may also suggest new clues for the future disease modifying strategies.

Discrimination of approved drugs from experimental drugs by learning methods.

BACKGROUND: To assess whether a compound is druglike or not as early as possible is always critical in drug discovery process. There have been many efforts made to create sets of 'rules' or 'filters' which, it is hoped, will help chemists to identify 'drug-like' molecules from 'non-drug' molecules. However, among the chemical space of the druglike molecules, the minority will be approved drugs. Classifying approved drugs from experimental drugs may be more helpful to obtain future approved drugs. Therefore, discrimination of approved drugs from experimental ones has been done in this paper by analyzing the compounds in terms of existing drugs features and machine learning methods. RESULTS: Four methodologies were compared by their performance to classify approved drugs from experimental ones. The best results were obtained by SVM, in which the accuracy is 0.7911, the sensitivity is 0.5929, and the specificity is 0.8743. Based on the results, consensus model was developed to effectively discriminate drugs, which further pushed the correct classification rate up to 0.8517, sensitivity up to 0.7242, specificity up to 0.9352. The applications on the Traditional Chinese Medicine Ingredients Database (TCM-ID) tested the methods. Therefore this model has been proven to be a potent tool for identifying drug molecules. CONCLUSION: The studies would have potential applications in the research of combinatorial library design and virtual high throughput screening for drug discovery.

HIT: linking herbal active ingredients to targets.

The information of protein targets and small molecule has been highly valued by biomedical and pharmaceutical research. Several protein target databases are available online for FDA-approved drugs as well as the promising precursors that have largely facilitated the mechanistic study and subsequent research for drug discovery. However, those related resources regarding to herbal active ingredients, although being unusually valued as a precious resource for new drug development, is rarely found. In this article, a comprehensive and fully curated database for Herb Ingredients' Targets (HIT, http://lifecenter.sgst.cn/hit/) has been constructed to complement above resources. Those herbal ingredients with protein target information were carefully curated. The molecular target information involves those proteins being directly/indirectly activated/inhibited, protein binders and enzymes whose substrates or products are those compounds. Those up/down regulated genes are also included under the treatment of individual ingredients. In addition, the experimental condition, observed bioactivity and various references are provided as well for user's reference. Derived from more than 3250 literatures, it currently contains 5208 entries about 1301 known protein targets (221 of them are described as direct targets) affected by 586 herbal compounds from more than 1300 reputable Chinese herbs, overlapping with 280 therapeutic targets from Therapeutic Targets Database (TTD), and 445 protein targets from DrugBank corresponding to 1488 drug agents. The database can be queried via keyword search or similarity search. Crosslinks have been made to TTD, DrugBank, KEGG, PDB, Uniprot, Pfam, NCBI, TCM-ID and other databases.