Review of natural product databases

Novel applications of mass spectrometry-based metabolomics in herbal medicines and its active ingredients: Current evidence

Current evidence shows that herbal medicines could be beneficial for the treatment of various diseases. However, the complexities present in chemical compositions of herbal medicines are currently an obstacle for the progression of herbal medicines, which involve unclear bioactive compounds, mechanisms of action, undetermined targets for therapy, non-specific features for drug metabolism, etc. To overcome those issues, metabolomics can be a great to improve and understand herbal medicines from the small-molecule metabolism level. Metabolomics could solve scientific difficulties with herbal medicines from a metabolic perspective, and promote drug discovery and development. In recent years, mass spectrometry-based metabolomics was widely applied for the analysis of herbal constituents in vivo and in vitro. In this review, we highlight the value of mass spectrometry-based metabolomics and metabolism to address the complexity of herbal medicines in systems pharmacology, and to enhance their biomedical value in biomedicine, to shed light on the aid that mass spectrometry-based metabolomics can offer to the investigation of its active ingredients, especially, to link phytochemical analysis with the assessment of pharmacological effect and therapeutic potential. © 2019 Wiley Periodicals, Inc. Mass Spec Rev.

The antitumor effect of hinesol, extract from Atractylodes lancea (Thunb.) DC. by proliferation, inhibition, and apoptosis induction via MEK/ERK and NF-κB pathway in non-small cell lung cancer cell lines A549 and NCI-H1299

Lung cancer (especially, non-small cell lung cancer [NSCLC]) is one of the most malignant cancers in the world. Hinesol is the major component of the essential oil of Atractylodes lancea (Thunb.) DC and possesses the most promising anticancer function. However, the effects and molecular mechanism of hinesol on antiproliferation in NSCLC cells has not been well understood. In this study, we found that hinesol effectively inhibited the A549 and NCI-H1299 cells in a dose- and time-dependent manner by 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazoliumbromide assay. In addition, hinesol induced cell cycle arrest at G0/G1 phase and apoptosis assessed by flow cytometry in A549 cells. Furthermore, Western blot analysis showed that hinesol decreased phosphorylation of mitogen-activated protein kinase, extracellular signal-regulated kinase, IκBα, and p65 inhibited the expressions of Bcl-2, cyclin D1 and upregulated the expression of Bax. Based on these results, hinesol might be a potential drug candidate of anti-NSCLC for therapy.

Informatics and Computational Methods in Natural Product Drug Discovery: A Review and Perspectives

The discovery of new pharmaceutical drugs is one of the preeminent tasks-scientifically, economically, and socially-in biomedical research. Advances in informatics and computational biology have increased productivity at many stages of the drug discovery pipeline. Nevertheless, drug discovery has slowed, largely due to the reliance on small molecules as the primary source of novel hypotheses. Natural products (such as plant metabolites, animal toxins, and immunological components) comprise a vast and diverse source of bioactive compounds, some of which are supported by thousands of years of traditional medicine, and are largely disjoint from the set of small molecules used commonly for discovery. However, natural products possess unique characteristics that distinguish them from traditional small molecule drug candidates, requiring new methods and approaches for assessing their therapeutic potential. In this review, we investigate a number of state-of-the-art techniques in bioinformatics, cheminformatics, and knowledge engineering for data-driven drug discovery from natural products. We focus on methods that aim to bridge the gap between traditional small-molecule drug candidates and different classes of natural products. We also explore the current informatics knowledge gaps and other barriers that need to be overcome to fully leverage these compounds for drug discovery. Finally, we conclude with a "road map" of research priorities that seeks to realize this goal.

Botanical Therapeutics: Phytochemical Screening and Biological Assessment of Chamomile, Parsley and Celery Extracts against A375 Human Melanoma and Dendritic Cells

Chamomile, parsley, and celery represent major botanical sources of apigenin, a well-known flavone with chemopreventive properties. The aim of this study was to assess the phytochemical composition, antioxidant, and anti-inflammatory potential of methanol extracts obtained from chamomile, parsley, and celery collected from Romania, as well as the biological activity against A375 human melanoma and human dendritic cells. Results have shown that all three extracts are rich in polyphenolic compounds and flavonoids, and they generate a radical scavenger capacity, iron chelation potential, as well as lipoxygenase inhibition capacity. Chamomile and celery extracts present weak antiproliferative and pro-apoptotic properties in the set experimental conditions, while parsley extract draws out significant pro-apoptotic potential against A375 human melanoma cells. Parsley and chamomile extracts affected the fibroblast-like morphology of the screened tumor cell line. On the other hand, chamomile and celery extracts abrogated the expansion of LPS-activated dendritic cells, while the metabolic activity was attenuated by stimulation with celery extract; chamomile and parsley extracts had no effect upon this parameter. Chamomile and parsley extracts incubation with naive dendritic cells did not trigger cytokine secretion (TNF-alpha, IL-6, IL-10), but celery extract stimulation significantly reduced the anti-inflammatory, cytokine IL-10.

Pharmacokinetic profiling of anticancer phytocompounds using computational approach

INTRODUCTION

Natural products exhibit diverse scaffolds and are considered as suitable candidates for development of leads. However, poor pharmacokinetics often acts as a hindrance during the drug discovery process.

OBJECTIVE

With a view of exploring the absorption, distribution, metabolism, excretion and toxicity (ADMET) profile of plant-based anticancer compounds, open-access databases (NPACT, CancerHSP and TaxKB) were analysed to identify molecules having properties favourable for drug ability.

METHODOLOGY

Our workflow involved identification of molecules capable of passing each of the ADMET barriers based on physicochemical properties of molecules, and physiological barriers and factors.

RESULTS

The results revealed that out of 5086 phytomolecules, 63% were orally absorbable and 52% distributable. Also, an appreciable proportion of these compounds (45%) could be metabolised and excreted. Furthermore, 28% were found to be non-toxic for cardio toxicity and central nervous system (CNS) activity. Additionally, comparison against known anticancer drugs (reference dataset) revealed that the three libraries exhibit similar trends, thus providing additional confidence to the predictions. Overall, 28% of the molecular dataset was found to have suitable pharmacokinetic properties. We have also discussed a few natural products which exhibit favourable ADMET as well as low nano-micromolar in vitro anticancer activity.

CONCLUSION

We have created an interactive database (ADMETCan), which provides access to predicted ADMET of these anticancer phytomolecules. The ease of availability of this dataset is expected to minimise failure rate of these compounds and thus is expected to be beneficial to the scientific community involved in anticancer identification and development.

Online structure-based screening of purchasable approved drugs and natural compounds: retrospective examples of drug repositioning on cancer targets

Drug discovery is a long and difficult process that benefits from the integration of virtual screening methods in experimental screening campaigns such as to generate testable hypotheses, accelerate and/or reduce the cost of drug development. Current drug attrition rate is still a major issue in all therapeutic areas and especially in the field of cancer. Drug repositioning as well as the screening of natural compounds constitute promising approaches to accelerate and improve the success rate of drug discovery. We developed three compounds libraries of purchasable compounds: Drugs-lib, FOOD-lib and NP-lib that contain approved drugs, food constituents and natural products, respectively, that are optimized for structure-based virtual screening studies. The three compounds libraries are implemented in the MTiOpenScreen web server that allows users to perform structure-based virtual screening computations on their selected protein targets. The server outputs a list of 1,500 molecules with predicted binding scores that can then be processed further by the users and purchased for experimental validation. To illustrate the potential of our service for drug repositioning endeavours, we selected five recently published drugs that have been repositioned in vitro and/or in vivo on cancer targets. For each drug, we used the MTiOpenScreen service to screen the Drugs-lib collection against the corresponding anti-cancer target and we show that our protocol is able to rank these drugs within the top ranked compounds. This web server should assist the discovery of promising molecules that could benefit patients, with faster development times, and reduced costs and risk.

Application of Metabolomics in the Study of Natural Products

LC-MS-based metabolomics could have a major impact in the study of natural products, especially in its metabolism, toxicity and activity. This review highlights recent applications of metabolomics approach in the study of metabolites and toxicity of natural products, and the understanding of their effects on various diseases. Metabolomics has been employed to study the in vitro and in vivo metabolism of natural compounds, such as osthole, dehydrodiisoeugenol, and myrislignan. The pharmacological effects of natural compounds and extracts were determined using metabolomics technology combined with diseases models in animal, including osthole and nutmeg extracts. It has been demonstrated that metabolomics is a powerful technology for the investigation of xenobiotics-induced toxicity. The metabolism of triptolide and its hepatotoxicity were discussed. LC-MS-based metabolomics has a great potential in the druggability of natural products. The application of metabolomics should be broadened in the field of natural products in the future.

Omics based approach for biodiscovery of microbial natural products in antibiotic resistance era

The need for a new antibiotic pipeline to confront threat imposed by resistant pathogens has become a major global concern for human health. To confront the challenge there is a need for discovery and development of new class of antibiotics. Nature which is considered treasure trove, there is re-emerged interest in exploring untapped microbial to yield novel molecules, due to their wide array of negative effects associated with synthetic drugs. Natural product researchers have developed many new techniques over the past few years for developing diverse compounds of biopotential. Taking edge in the advancement of genomics, genetic engineering, in silico drug design, surface modification, scaffolds, pharmacophores and target-based approach is necessary. These techniques have been economically sustainable and also proven efficient in natural product discovery. This review will focus on recent advances in diverse discipline approach from integrated Bioinformatics predictions, genetic engineering and medicinal chemistry for the synthesis of natural products vital for the discovery of novel antibiotics having potential application.

Identification of Estrogen Receptor α Antagonists from Natural Products via In Vitro and In Silico Approaches

Estrogen receptor α (ERα) is a successful target for ER-positive breast cancer and also reported to be relevant in many other diseases. Selective estrogen receptor modulators (SERMs) make a good therapeutic effect in clinic. Because of the drug resistance and side effects of current SERMs, the discovery of new SERMs is given more and more attention. Virtual screening is a validated method to high effectively to identify novel bioactive small molecules. Ligand-based machine learning methods and structure-based molecular docking were first performed for identification of ERα antagonist from in-house natural product library. Naive Bayesian and recursive partitioning models with two kinds of descriptors were built and validated based on training set, test set, and external test set and then were utilized for distinction of active and inactive compounds. Totally, 162 compounds were predicted as ER antagonists and were further evaluated by molecular docking. According to docking score, we selected 8 representative compounds for both ERα competitor assay and luciferase reporter gene assay. Genistein, daidzein, phloretin, ellagic acid, ursolic acid, (-)-epigallocatechin-3-gallate, kaempferol, and naringenin exhibited different levels for antagonistic activity against ERα. These studies validated the feasibility of machine learning methods for predicting bioactivities of ligands and provided better insight into the natural products acting as estrogen receptor modulator, which are important lead compounds for future new drug design.

Importance of Rigidity in Designing Small Molecule Drugs To Tackle Protein-Protein Interactions (PPIs) through Stabilization of Desired Conformers

Tackling PPIs, particularly by stabilizing clinically favored conformations of target proteins, with orally available, bona fide small molecules remains a significant but immensely worthwhile challenge for the pharmaceutical industry. Success may be more likely through the application of nature's learnings to build intrinsic rigidity into the design of clinical candidates.