Browse
Submit Data
Databases
API
Help

Dataset Information

8 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Discovery of Drug Candidates for Specific Human Disease Based on Natural Products of Gut Microbes.

ABSTRACT: The beneficial metabolites of the microbiome could be used as a tool for screening drugs that have the potential for the therapy of various human diseases. Narrowing down the range of beneficial metabolite candidates in specific diseases was primarily a key step for further validation in model organisms. Herein, we proposed a reasonable hypothesis that the metabolites existing commonly in multiple beneficial (or negatively associated) bacteria might have a high probability of being effective drug candidates for specific diseases. According to this hypothesis, we screened metabolites associated with seven human diseases. For type I diabetes, 45 out of 88 screened metabolites had been reported as potential drugs in the literature. Meanwhile, 18 of these metabolites were specific to type I diabetes. Additionally, metabolite correlation could reflect disease relationships in some sense. Our results have demonstrated the potential of bioinformatics mining gut microbes' metabolites as drug candidates based on reported numerous microbe-disease associations and the Virtual Metabolic Human database. More subtle methods would be developed to ensure more accurate predictions.

SUBMITTER: Wang CY

PROVIDER: S-EPMC9240467 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Similar Datasets

Fragment-based screening with natural products for novel anti-parasitic disease drug discovery.

Project description:Introduction: Fragment-based drug discovery can identify relatively simple compounds with low binding affinity due to fewer binding interactions with protein targets. FBDD reduces the library size and provides simpler starting points for subsequent chemical optimization of initial hits. A much greater proportion of chemical space can be sampled in fragment-based screening compared to larger molecules with typical molecular weights (MWs) of 250-500 g mol-1 used in high-throughput screening (HTS) libraries. Areas covered: The authors cover the role of natural products in fragment-based drug discovery against parasitic disease targets. They review the approaches to develop fragment-based libraries either using natural products or natural product-like compounds. The authors present approaches to fragment-based drug discovery against parasitic diseases and compare these libraries with the 3D attributes of natural products. Expert opinion: To effectively use the three-dimensional properties and the chemical diversity of natural products in fragment-based drug discovery against parasitic diseases, there needs to be a mind-shift. Library design, in the medicinal chemistry area, has acknowledged that escaping flat-land is very important to increase the chances of clinical success. Attempts to increase sp3 richness in fragment libraries are acknowledged. Sufficient low molecular weight natural products are known to create true natural product fragment libraries.

| S-EPMC6816479 | biostudies-literature

Natural products as drug candidates for breast cancer (Review).

Project description:Progression of early-stage breast cancer to advanced-stage metastatic disease represents a major cause of death in women. Long-term conventional and targeted therapy for breast cancer includes multi-drug combinations of cytotoxic chemotherapeutics and pathway-selective small molecule inhibitors. These treatment options are frequently associated with systemic toxicity, intrinsic/acquired therapy resistance and emergence of a drug-resistant cancer stem cell population. This stem cell population has a chemo-resistant, cancer-initiating, premalignant phenotype that is accompanied by cellular plasticity and metastatic potential. These limitations emphasize an unmet need to identify testable alternatives against therapy-resistant metastatic breast cancer. Natural products such as dietary phytochemicals, nutritional herbs and their constitutive bioactive agents have documented human consumption, and lack detectable systemic toxicity and resultant off-target unfavorable side effects. Because of these advantages, natural products may represent testable alternatives for therapy-resistant breast cancer. The present review discusses published evidence for growth inhibitory efficacy of natural products on cellular models for molecular subtypes of clinical breast cancer and development of drug-resistant stem cell models. Collectively, this evidence validates mechanism-based experimental approaches to screen and prioritize efficacious bioactive agents from natural products as novel drug candidates that may function as therapeutic alternatives for breast cancer.

| S-EPMC10326826 | biostudies-literature

Natural products as leads in schistosome drug discovery.

Project description:Schistosomiasis is a neglected parasitic tropical disease that claims around 200,000 human lives every year. Praziquantel (PZQ), the only drug recommended by the World Health Organization for the treatment and control of human schistosomiasis, is now facing the threat of drug resistance, indicating the urgent need for new effective compounds to treat this disease. Therefore, globally, there is renewed interest in natural products (NPs) as a starting point for drug discovery and development for schistosomiasis. Recent advances in genomics, proteomics, bioinformatics, and cheminformatics have brought about unprecedented opportunities for the rapid and more cost-effective discovery of new bioactive compounds against neglected tropical diseases. This review highlights the main contributions that NP drug discovery and development have made in the treatment of schistosomiasis and it discusses how integration with virtual screening (VS) strategies may contribute to accelerating the development of new schistosomidal leads, especially through the identification of unexplored, biologically active chemical scaffolds and structural optimization of NPs with previously established activity.

| S-EPMC6272663 | biostudies-other

Natural products in drug discovery: advances and opportunities.

Project description:Natural products and their structural analogues have historically made a major contribution to pharmacotherapy, especially for cancer and infectious diseases. Nevertheless, natural products also present challenges for drug discovery, such as technical barriers to screening, isolation, characterization and optimization, which contributed to a decline in their pursuit by the pharmaceutical industry from the 1990s onwards. In recent years, several technological and scientific developments - including improved analytical tools, genome mining and engineering strategies, and microbial culturing advances - are addressing such challenges and opening up new opportunities. Consequently, interest in natural products as drug leads is being revitalized, particularly for tackling antimicrobial resistance. Here, we summarize recent technological developments that are enabling natural product-based drug discovery, highlight selected applications and discuss key opportunities.

| S-EPMC7841765 | biostudies-literature

Glycosylated Natural Products From Marine Microbes.

Project description:A growing body of evidence indicates that glycosylated natural products have become vital platforms for the development of many existing first-line drugs. This review covers 205 new glycosides over the last 22 years (1997-2018), from marine microbes, including bacteria, cyanobacteria, and fungi. Herein, we discuss the structures and biological activities of these compounds, as well as the details of their source organisms.

| S-EPMC6965366 | biostudies-literature

Systems Chemical Genetics-Based Drug Discovery: Prioritizing Agents Targeting Multiple/Reliable Disease-Associated Genes as Drug Candidates.

Project description:Genetic disease genes are considered a promising source of drug targets. Most diseases are caused by more than one pathogenic factor; thus, it is reasonable to consider that chemical agents targeting multiple disease genes are more likely to have desired activities. This is supported by a comprehensive analysis on the relationships between agent activity/druggability and target genetic characteristics. The therapeutic potential of agents increases steadily with increasing number of targeted disease genes, and can be further enhanced by strengthened genetic links between targets and diseases. By using the multi-label classification models for genetics-based drug activity prediction, we provide universal tools for prioritizing drug candidates. All of the documented data and the machine-learning prediction service are available at SCG-Drug (http://zhanglab.hzau.edu.cn/scgdrug).

| S-EPMC6549477 | biostudies-literature

Project description:Intestinal bacteria can metabolize polyphenols into highly bioavailable derivatives, which provide potential health-promoting effects to the host. However, the metabolic pathways and related products in this process are still largely unclear. Polyphenols are generally characterized by the presence of many phenolic structural units, which makes it possible to explore correlations among compounds based on similar molecular networks. In this study, we developed a standard-oriented/database-assisted molecular networking (SODA-MN) method for iterative compound annotation analysis to explore the metabolic profiles of polyphenol-rich black raspberry extract metabolized by representative gut bacteria. Starting from a group of polyphenol metabolites, the SODA-MN method predicted the possible polyphenol derivatives by adding or deducting common biotransformation groups and iterative annotating of structure similarity based on fragmentation spectra. Our results showed that 48 polyphenol derivatives in the first round of analysis alone (fragmentation match≥5, spec score >0.5) can be annotated, which were associated with 13 detected polyphenol standards that served as seed compounds. Meanwhile, this method was applied to a time-course study to show the time-dependent changes of polyphenols metabolized by a mix of gut bacteria. In addition, the metabolic capabilities of polyphenols among four representative gut bacteria were compared via our newly developed method and differential polyphenol metabolites were detected. In summary, the SODA-MN method provides a new approach for the annotation of unknown compounds by structure similarity and molecular networking analysis. Our analysis results could provide identification of key polyphenol derivatives that may contribute to the mechanistic investigations of their functions and assist our understanding of how polyphenols and gut bacteria interact to promote human health.

| S-EPMC10324525 | biostudies-literature

Fragment Library of Natural Products and Compound Databases for Drug Discovery.

Project description:Natural products and semi-synthetic compounds continue to be a significant source of drug candidates for a broad range of diseases, including coronavirus disease 2019 (COVID-19), which is causing the current pandemic. Besides being attractive sources of bioactive compounds for further development or optimization, natural products are excellent substrates of unique substructures for fragment-based drug discovery. To this end, fragment libraries should be incorporated into automated drug design pipelines. However, public fragment libraries based on extensive collections of natural products are still limited. Herein, we report the generation and analysis of a fragment library of natural products derived from a database with more than 400,000 compounds. We also report fragment libraries of a large food chemical database and other compound datasets of interest in drug discovery, including compound libraries relevant for COVID-19 drug discovery. The fragment libraries were characterized in terms of content and diversity.

| S-EPMC7694623 | biostudies-literature

Cheminformatics in Natural Product-based Drug Discovery.

Project description:This review seeks to provide a timely survey of the scope and limitations of cheminformatics methods in natural product-based drug discovery. Following an overview of data resources of chemical, biological and structural information on natural products, we discuss, among other aspects, in silico methods for (i) data curation and natural products dereplication, (ii) analysis, visualization, navigation and comparison of the chemical space, (iii) quantification of natural product-likeness, (iv) prediction of the bioactivities (virtual screening, target prediction), ADME and safety profiles (toxicity) of natural products, (v) natural products-inspired de novo design and (vi) prediction of natural products prone to cause interference with biological assays. Among the many methods discussed are rule-based, similarity-based, shape-based, pharmacophore-based and network-based approaches, docking and machine learning methods.

| S-EPMC7757247 | biostudies-literature

Automated high-throughput system to fractionate plant natural products for drug discovery.

Project description:The development of an automated, high-throughput fractionation procedure to prepare and analyze natural product libraries for drug discovery screening is described. Natural products obtained from plant materials worldwide were extracted and first prefractionated on polyamide solid-phase extraction cartridges to remove polyphenols, followed by high-throughput automated fractionation, drying, weighing, and reformatting for screening and storage. The analysis of fractions with UPLC coupled with MS, PDA, and ELSD detectors provides information that facilitates characterization of compounds in active fractions. Screening of a portion of fractions yielded multiple assay-specific hits in several high-throughput cellular screening assays. This procedure modernizes the traditional natural product fractionation paradigm by seamlessly integrating automation, informatics, and multimodal analytical interrogation capabilities.

| S-EPMC2866159 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data