S. C, Gangadhar S, A. S, Murali M, Shivamallu C, Achar RR, Silina E, Stupin V, Manturova N, Shati AA, Alfaifi MY, Elbehairi SEI, Kollur SP, Amruthesh KN. Exploration of Type III effector Xanthomonas outer protein Q (XopQ) inhibitor from Picrasma quassioides as an antibacterial agent using chemoinformatics analysis

The present study was focused on exploring the efficient inhibitors of closed state (form) of type III effector Xanthomonas outer protein Q (XopQ) (PDB: 4P5F) from the 44 phytochemicals of Picrasma quassioides using cutting-edge computational analysis. Among them, Kumudine B showed excellent binding energy (-11.0 kcal/mol), followed by Picrasamide A, Quassidine I and Quassidine J with the targeted closed state of XopQ protein compared to the reference standard drug (Streptomycin). The molecular dynamics (MD) simulations performed at 300 ns validated the stability of top lead ligands (Kumudine B, Picrasamide A, and Quassidine I)-bound XopQ protein complex with slightly lower fluctuation than Streptomycin. The MM-PBSA calculation confirmed the strong interactions of top lead ligands (Kumudine B and QuassidineI) with XopQ protein, as they offered the least binding energy. The results of absorption, distribution, metabolism, excretion, and toxicity (ADMET) analysis confirmed that Quassidine I, Kumudine B and Picrasamide A were found to qualify most of the drug-likeness rules with excellent bioavailability scores compared to Streptomycin. Results of the computational studies suggested that Kumudine B, Picrasamide A, and Quassidine I could be considered potential compounds to design novel antibacterial drugs against X. oryzae infection. Further in vitro and in vivo antibacterial activities of Kumudine B, Picrasamide A, and Quassidine I are required to confirm their therapeutic potentiality in controlling the X. oryzae infection.

Chronic Behavioral and Neurochemical Effects of Four Novel N-Benzyl-2-phenylethylamine Derivatives Recently Identified as "Psychoactive" in Adult Zebrafish Screens

Potently affecting human and animal brain and behavior, hallucinogenic drugs have recently emerged as potentially promising agents in psychopharmacotherapy. Complementing laboratory rodents, the zebrafish (Danio rerio) is a powerful model organism for screening neuroactive drugs, including hallucinogens. Here, we tested four novel N-benzyl-2-phenylethylamine (NBPEA) derivatives with 2,4- and 3,4-dimethoxy substitutions in the phenethylamine moiety and the -F, -Cl, and -OCF3 substitutions in the ortho position of the phenyl ring of the N-benzyl moiety (34H-NBF, 34H-NBCl, 24H-NBOMe(F), and 34H-NBOMe(F)), assessing their behavioral and neurochemical effects following chronic 14 day treatment in adult zebrafish. While the novel tank test behavioral data indicate anxiolytic-like effects of 24H-NBOMe(F) and 34H-NBOMe(F), neurochemical analyses reveal reduced brain norepinephrine by all four drugs, and (except 34H-NBCl) - reduced dopamine and serotonin levels. We also found reduced turnover rates for all three brain monoamines but unaltered levels of their respective metabolites. Collectively, these findings further our understanding of complex central behavioral and neurochemical effects of chronically administered novel NBPEAs and highlight the potential of zebrafish as a model for preclinical screening of small psychoactive molecules.

A critical assessment of Traditional Chinese Medicine databases as a source for drug discovery

Traditional Chinese Medicine (TCM) has been used for thousands of years to treat human diseases. Recently, many databases have been devoted to studying TCM pharmacology. Most of these databases include information about the active ingredients of TCM herbs and their disease indications. These databases enable researchers to interrogate the mechanisms of action of TCM systematically. However, there is a need for comparative studies of these databases, as they are derived from various resources with different data processing methods. In this review, we provide a comprehensive analysis of the existing TCM databases. We found that the information complements each other by comparing herbs, ingredients, and herb-ingredient pairs in these databases. Therefore, data harmonization is vital to use all the available information fully. Moreover, different TCM databases may contain various annotation types for herbs or ingredients, notably for the chemical structure of ingredients, making it challenging to integrate data from them. We also highlight the latest TCM databases on symptoms or gene expressions, suggesting that using multi-omics data and advanced bioinformatics approaches may provide new insights for drug discovery in TCM. In summary, such a comparative study would help improve the understanding of data complexity that may ultimately motivate more efficient and more standardized strategies towards the digitalization of TCM.

DISPEL: database for ascertaining the best medicinal plants to cure human diseases

Medicinal plants are anticipated to be one of the most valuable resources for the remedial usage in the treatment of various ailments. The data on key medicinal plants and their therapeutic efficacy against various ailments are quite scattered and not available on a single platform. Moreover, currently there is no means/mechanism of finding the best medicinal plant(s) from numerous plants known to cure any disease. DISPEL (Diseases Plants Eliminate) is a compendium of medicinal plants available across the world that are used to cure infectious as well as non-infectious diseases in humans. The association of a medicinal plant with a disease it cures is hereby referred to as 'medicinal plant-disease cured' linkage. The DISPEL database hosts ∼60 000 'medicinal plant-disease cured' linkages encompassing ∼5500 medicinal plants and ∼1000 diseases. This platform provides interactive and detailed visualization of medicinal plants, diseases and their relations using comprehensible network graph representation. The user has the freedom to search the database by specifying the name of disease(s) as well as the scientific/common name(s) of plant. Each 'medicinal plant-disease cured' relation is scored based on the availability of any medicine/product derived from that medicinal plant, information about active compound(s), knowledge regarding the part of plant that is effective and number of distinct articles/books/websites confirming the effectiveness of the medicinal plant. The user can find the best plant(s) that can be used to cure any desired disease(s). The DISPEL database is the first step towards generating the 'most-effective' combination of plants to cure a disease since it delineates as well as ranks all the therapeutic medicinal plants for that disease. The combination of best medicinal plants can then be used to conduct clinical trials and thus pave the way for their use in clinics for treatment of diseases. Database URL https://compbio.iitr.ac.in/dispel.

Towards Novel Potential Molecular Targets for Antidepressant and Antipsychotic Pharmacotherapies

Depression and schizophrenia are two highly prevalent and severely debilitating neuropsychiatric disorders. Both conventional antidepressant and antipsychotic pharmacotherapies are often inefficient clinically, causing multiple side effects and serious patient compliance problems. Collectively, this calls for the development of novel drug targets for treating depressed and schizophrenic patients. Here, we discuss recent translational advances, research tools and approaches, aiming to facilitate innovative drug discovery in this field. Providing a comprehensive overview of current antidepressants and antipsychotic drugs, we also outline potential novel molecular targets for treating depression and schizophrenia. We also critically evaluate multiple translational challenges and summarize various open questions, in order to foster further integrative cross-discipline research into antidepressant and antipsychotic drug development.

Computer-aided Evaluation of Polyvalent Medications' Pharmacological Potential. Multiphytoadaptogen as a Case Study

Many human diseases including cancer, degenerative and autoimmune disorders, diabetes and others are multifactorial. Pharmaceutical agents acting on a single target do not provide their efficient curation. Multitargeted drugs exhibiting pleiotropic pharmacological effects have certain advantages due to the normalization of the complex pathological processes of different etiology. Extracts of medicinal plants (EMP) containing multiple phytocomponents are widely used in traditional medicines for multifactorial disorders' treatment. Experimental studies of pharmacological potential for multicomponent compositions are quite expensive and time-consuming. In silico evaluation of EMP the pharmacological potential may provide the basis for selecting the most promising directions of testing and for identifying potential additive/synergistic effects. Multiphytoadaptogen (MPhA) containing 70 major phytocomponents of different chemical classes from 40 medicinal plant extracts has been studied in vitro, in vivo and in clinical researches. Antiproliferative and anti-tumor activities have been shown against some tumors as well as evidence-based therapeutic effects against age-related pathologies. In addition, the neuroprotective, antioxidant, antimutagenic, radioprotective, and immunomodulatory effects of MPhA were confirmed. Analysis of the PASS profiles of the biological activity of MPhA phytocomponents showed that most of the predicted anti-tumor and anti-metastatic effects were consistent with the results of laboratory and clinical studies. Antimutagenic, immunomodulatory, radioprotective, neuroprotective and anti-Parkinsonian effects were also predicted for most of the phytocomponents. Effects associated with positive effects on the male and female reproductive systems have been identified too. Thus, PASS and PharmaExpert can be used to evaluate the pharmacological potential of complex pharmaceutical compositions containing natural products.

In silico Study of the Proteins Involved in the Persistence of Brucella spp

BACKGROUND

One of the major problems with Brucella infections is its tendency to become chronic and recurrent, providing a hindrance to the management of this infection. It has been proposed that chronicity is greatly affected by a phenomenon called persistence in bacteria. Several mechanisms are involved in bacterial persistence, including the type II toxin-antitoxin system, the SOS and oxidative and stringent responses.

METHODS

In this in silico study, these persistence mechanisms in Brucella spp. were investigated.

RESULTS

The structure and the interactions between modules involved in these systems were designed, and novel peptides that can interfere with some of these important mechanisms were developed.

CONCLUSION

Since peptide-based therapeutics are a new and evolving field due to their ease of production, we hope that peptides developed in this study, as well as the information about the structure and interactions of modules of persistence mechanisms, can further be used to design drugs against Brucella persister cells in the hope of restraining the chronic nature of Brucellosis.

Computer-aided discovery of pleiotropic effects: Anti-inflammatory action of dithioloquinolinethiones as a case study

Most of pharmaceutical agents exhibit several or even many biological activities. It is clear that testing even one compound for thousands of biological activities is a practically not feasible task. Therefore, computer-aided prediction is the method-of-the-choice to select the most promising bioassays for particular compounds. Using PASS Online software, we determined the likely anti-inflammatory action of the 13 dithioloquinolinethione derivatives with antimicrobial activities. Chemical similarity search in the Cortellis Drug Discovery Intelligence database did not reveal close structural analogues with anti-inflammatory action. Experimental testing of anti-inflammatory activity of the synthesized compounds in carrageenan-induced inflammation mouse model confirmed the computational predictions. The anti-inflammatory activity of the studied compounds was comparable with or higher than the reference drug Indomethacin. Thus, based on the in silico predictions, novel class of the anti-inflammatory agents was discovered.

Profile of Myracrodruon urundeuva Volatile Compounds Ease of Extraction and Biodegradability and In Silico Evaluation of Their Interactions with COX-1 and iNOS

Myracrodruon urundeuva Fr. Allem. (Anacardiaceae) is a tree popularly known as the "aroeira-do-sertão", native to the caatinga and cerrado biomes, with a natural dispersion ranging from the Northeast, Midwest, to Southeast Brazil. Its wood is highly valued and overexploited, due to its characteristics such as durability and resistance to decaying. The diversity of chemical constituents in aroeira seed has shown biological properties against microorganisms and helminths. As such, this work aimed to identify the profile of volatile compounds present in aroeira seeds. Headspace solid phase microextraction was employed (HS-SPME) using semi-polar polydimethylsiloxane-divinylbenzene fiber (PDMS/DVB) for the extraction of VOCs. 22 volatile organic compounds were identified: nine monoterpenes and eight sesquiterpenes, in addition to six compounds belonging to different chemical classes such as fatty acids, terpenoids, salicylates and others. Those that stood out were p-mentha-1,4, 4(8)-diene, 3-carene (found in all samples), caryophyllene and cis-geranylacetone. A virtual docking analysis suggested that around 65% of the VOCs molar content from the aroeiras seeds present moderate a strong ability to bind to cyclooxygenase I (COX-I) active site, oxide nitric synthase (iNOS) active site (iNOSas) or to iNOS cofactor site (iNOScs), corroborating an anti-inflamatory potential. A pharmacophoric descriptor analysis allowed to infer the more determinant characteristics of these compounds' conferring affinity to each site. Taken together, our results illustrate the high applicability for the integrated use of SPME, in silico virtual screening and chemoinformatics tools at the profiling of the biotechnological and pharmaceutical potential of natural sources.

In Silico Prediction of Steroids and Triterpenoids as Potential Regulators of Lipid Metabolism

This review focuses on a rare group of steroids and triterpenoids that share common properties as regulators of lipid metabolism. This group of compounds is divided by the type of chemical structure, and they represent: aromatic steroids, steroid phosphate esters, highly oxygenated steroids such as steroid endoperoxides and hydroperoxides, α,β-epoxy steroids, and secosteroids. In addition, subgroups of carbon-bridged steroids, neo steroids, miscellaneous steroids, as well as synthetic steroids containing heteroatoms S (epithio steroids), Se (selena steroids), Te (tellura steroids), and At (astatosteroids) were presented. Natural steroids and triterpenoids have been found and identified from various sources such as marine sponges, soft corals, starfish, and other marine invertebrates. In addition, this group of rare lipids is found in fungi, fungal endophytes, and plants. The pharmacological profile of the presented steroids and triterpenoids was determined using the well-known computer program PASS, which is currently available online for all interested scientists and pharmacologists and is currently used by research teams from more than 130 countries of the world. Our attention has been focused on the biological activities of steroids and triterpenoids associated with the regulation of cholesterol metabolism and related processes such as anti-hyperlipoproteinemic activity, as well as the treatment of atherosclerosis, lipoprotein disorders, or inhibitors of cholesterol synthesis. In addition, individual steroids and triterpenoids were identified that demonstrated rare or unique biological activities such as treating neurodegenerative diseases, Alzheimer's, and Parkinson's diseases with a high degree of certainty over 95 percent. For individual steroids or triterpenoids or a group of compounds, 3D drawings of their predicted biological activities are presented.

iCYP-MFE: Identifying Human Cytochrome P450 Inhibitors Using Multitask Learning and Molecular Fingerprint-Embedded Encoding

The human cytochrome P450 (CYP) superfamily holds responsibilities for the metabolism of both endogenous and exogenous compounds such as drugs, cellular metabolites, and toxins. The inhibition exerted on the CYP enzymes is closely associated with adverse drug reactions encompassing metabolic failures and induced side effects. In modern drug discovery, identification of potential CYP inhibitors is, therefore, highly essential. Alongside experimental approaches, numerous computational models have been proposed to address this biochemical issue. In this study, we introduce iCYP-MFE, a computational framework for virtual screening on CYP inhibitors toward 1A2, 2C9, 2C19, 2D6, and 3A4 isoforms. iCYP-MFE contains a set of five robust, stable, and effective prediction models developed using multitask learning incorporated with molecular fingerprint-embedded features. The results show that multitask learning can remarkably leverage useful information from related tasks to promote global performance. Comparative analysis indicates that iCYP-MFE achieves three predominant tasks, one equivalent task, and one less effective task compared to state-of-the-art methods. The area under the receiver operating characteristic curve (AUC-ROC) and the area under the precision-recall curve (AUC-PR) were two decisive metrics used for model evaluation. The prediction task for CYP2D6-inhibition achieves the highest AUC-ROC value of 0.93 while the prediction task for CYP1A2-inhibition obtains the highest AUC-PR value of 0.92. The substructural analysis preliminarily explains the nature of the CYP-inhibitory activity of compounds. An online web server for iCYP-MFE with a user-friendly interface was also deployed to support scientific communities in identifying CYP inhibitors.

Advancing urban ethnopharmacology: a modern concept of sustainability, conservation and cross-cultural adaptations of medicinal plant lore in the urban environment

The discipline 'urban ethnopharmacology' emerged as a collection of traditional knowledge, ancient civilizations, history and folklore being circulated since generations, usage of botanical products, palaeobotany and agronomy. Non-traditional botanical knowledge increases the availability of healthcare and other essential products to the underprivileged masses. Intercultural medicine essentially involves 'practices in healthcare that bridge indigenous medicine and western medicine, where both are considered as complementary'. A unique aspect of urban ethnopharmacology is its pluricultural character. Plant medicine blossomed due to intercultural interactions and has its roots in major anthropological events of the past. Unani medicine was developed by Khalif Harun Al Rashid and Khalif Al Mansur by translating Greek and Sanskrit works. Similarly, Indo-Aryan migration led to the development of Vedic culture, which product is Ayurveda. Greek medicine reached its summit when it travelled to Egypt. In the past few decades, ethnobotanical field studies proliferated, especially in the developed countries to cope with the increasing demands of population expansion. At the same time, sacred groves continued to be an important method of conservation across several cultures even in the urban aspect. Lack of scientific research, validating the efficiency, messy applications, biopiracy and slower results are the main constrains to limit its acceptability. Access to resources and benefit sharing may be considered as a potential solution. Indigenous communities can copyright their traditional formulations and then can collaborate with companies, who have to provide the original inventors with a fair share of the profits since a significant portion of the health economy is generated by herbal medicine. Search string included the terms 'Urban' + 'Ethnopharmacology', which was searched in Google Scholar to retrieve the relevant literature. The present review aims to critically analyse the global concept of urban ethnopharmacology with the inherent plurality of the cross-cultural adaptations of medicinal plant use by urban people across the world.

Phytochemical library screening reveals betulinic acid as a novel Skp2-SCF E3 ligase inhibitor in non-small cell lung cancer

Skp2 is overexpressed in multiple cancers and plays a critical role in tumor development through ubiquitin/proteasome-dependent degradation of its substrate proteins. Drugs targeting Skp2 have exhibited promising anticancer activity. Here, we identified a plant-derived Skp2 inhibitor, betulinic acid (BA), via high-throughput structure-based virtual screening of a phytochemical library. BA significantly inhibited the proliferation and migration of non-small cell lung cancer (NSCLC) through targeting Skp2-SCF E3 ligase both in vitro and in vivo. Mechanistically, BA binding to Skp2, especially forming H-bonds with residue Lys145, decreases its stability by disrupting Skp1-Skp2 interactions, thereby inhibiting the Skp2-SCF E3 ligase and promoting the accumulation of its substrates; that is, E-cadherin and p27. In both subcutaneous and orthotopic xenografts, BA significantly inhibited the proliferation and metastasis of NSCLC through targeting Skp2-SCF E3 ligase and upregulating p27 and E-cadherin protein levels. Taken together, BA can be considered a valuable therapeutic candidate to inhibit metastasis of NSCLC.

Computational strategies for the discovery of biological functions of health foods, nutraceuticals and cosmeceuticals: a review

Scientific and consumer interest in healthy foods (also known as functional foods), nutraceuticals and cosmeceuticals has increased in the recent years, leading to an increased presence of these products in the market. However, the regulations across different countries that define the type of claims that may be made, and the degree of evidence required to support these claims, are rather inconsistent. Moreover, there is also controversy on the effectiveness and biological mode of action of many of these products, which should undergo an exhaustive approval process to guarantee the consumer rights. Computational approaches constitute invaluable tools to facilitate the discovery of bioactive molecules and provide biological plausibility on the mode of action of these products. Indeed, methodologies like QSAR, docking or molecular dynamics have been used in drug discovery protocols for decades and can now aid in the discovery of bioactive food components. Thanks to these approaches, it is possible to search for new functions in food constituents, which may be part of our daily diet, and help to prevent disorders like diabetes, hypercholesterolemia or obesity. In the present manuscript, computational studies applied to this field are reviewed to illustrate the potential of these approaches to guide the first screening steps and the mechanistic studies of nutraceutical, cosmeceutical and functional foods.

Searching for Scientific Explanations for the Uses of Spanish Folk Medicine: A Review on the Case of Mullein (Verbascum, Scrophulariaceae)

Verbascum species (common mullein) have been widely used in Spanish folk medicine to treat pathologies related to the musculature, skeleton, and circulatory, digestive, and respiratory systems, as well as to treat infectious diseases and organ-sense illnesses. These applications support the potential anti-inflammatory action of Verbascum phytochemicals. Based on the aforementioned facts, and following a deep bibliographic review of the chemical composition of the 10 species of Verbascum catalogued by the Spanish Inventory of Traditional Knowledge related to Biodiversity, we look for scientific evidences to correlate the traditional medical uses with the chemical components of these plants. To support these findings, in silico simulations were performed to investigate molecular interactions between Verbascum phytochemicals and cellular components. Most of common mullein traditional uses could rely on the anti-inflammatory action of phytochemicals, such as quercetin, and it could explain the employment of these plants to treat a wide range of diseases mediated by inflammatory processes such as respiratory diseases, otitis, arthrosis, and rheumatism among others.

Alkaloids in Contemporary Drug Discovery to Meet Global Disease Needs

An overview is presented of the well-established role of alkaloids in drug discovery, the application of more sustainable chemicals, and biological approaches, and the implementation of information systems to address the current challenges faced in meeting global disease needs. The necessity for a new international paradigm for natural product discovery and development for the treatment of multidrug resistant organisms, and rare and neglected tropical diseases in the era of the Fourth Industrial Revolution and the Quintuple Helix is discussed.

[Possibilities of in silico estimations for the development of pharmaceutical composition phytoladaptogene cytotoxic for bladder cancer cells]

Based on the prediction of biological activity spectra for several secondary metabolites of medicinal plants using the PASS computer program and validation in vitro of the predictions results the priority direction of the pharmaceutical composition Phytoladaptogene (PLA) development was determined. PLA is a complex of structurally diverse small organic compounds including biologically active substances of phytoadaptogenes (ginsenosides from Panax ginseng, rhodionin from Rhodiola rosea and others) compiled considering previously developed pharmaceutical compositions. Two variants of the pharmaceutical composition were studied: - the major and minor variants included 22 and 13 compounds, respectively. The probability of activity exceeds the probability of inactivity for 1400 out of 1945 pharmacological effects and mechanisms predicted by PASS for the major variant of PLA. The wide range of predicted activities is mainly due to the low structural similarity of constituent compounds. An in silico prediction indicates the possibilities of antitumor properties against bladder, stomach, colon, ovarian and cervical cancers both for minor and major PLA compositions. It was found that the highest probability values of activity were predicted for three mechanisms: apoptosis agonist, caspase-3 stimulant, and transcription factor NF-κB inhibitor. According to the PharmaExpert program they are associated with the antitumor effect against bladder cancer. Experimental validation was using the human bladder cancer cell line RT-112. The results of the MTT test have shown that the cytotoxicity of the major PLA variant is higher than that of the minor PLA variant. In vitro experiments performed using two methods (double staining with annexin V and propidium iodide and detection of active caspase-3 in cells) confirmed that the death of bladder cancer cells occurred via the apoptotic mechanism. The data obtained correspond to the results of the prediction and indicate advantages of the major PLA composition. Thus, PLA can become the basis for the development of a drug with the antitumor activity against bladder cancer. The antitumor activity predicted by PASS for other cancers may be the subject of further studies.

Antitumor Profile of Carbon-Bridged Steroids (CBS) and Triterpenoids

This review focuses on the rare group of carbon-bridged steroids (CBS) and triterpenoids found in various natural sources such as green, yellow-green, and red algae, marine sponges, soft corals, ascidians, starfish, and other marine invertebrates. In addition, this group of rare lipids is found in amoebas, fungi, fungal endophytes, and plants. For convenience, the presented CBS and triterpenoids are divided into four groups, which include: (a) CBS and triterpenoids containing a cyclopropane group; (b) CBS and triterpenoids with cyclopropane ring in the side chain; (c) CBS and triterpenoids containing a cyclobutane group; (d) CBS and triterpenoids containing cyclopentane, cyclohexane or cycloheptane moieties. For the comparative characterization of the antitumor profile, we have added several semi- and synthetic CBS and triterpenoids, with various additional rings, to identify possible promising sources for pharmacologists and the pharmaceutical industry. About 300 CBS and triterpenoids are presented in this review, which demonstrate a wide range of biological activities, but the most pronounced antitumor profile. The review summarizes biological activities both determined experimentally and estimated using the well-known PASS software. According to the data obtained, two-thirds of CBS and triterpenoids show moderate activity levels with a confidence level of 70 to 90%; however, one third of these lipids demonstrate strong antitumor activity with a confidence level exceeding 90%. Several CBS and triterpenoids, from different lipid groups, demonstrate selective action on different types of tumor cells such as renal cancer, sarcoma, pancreatic cancer, prostate cancer, lymphocytic leukemia, myeloid leukemia, liver cancer, and genitourinary cancer with varying degrees of confidence. In addition, the review presents graphical images of the antitumor profile of both individual CBS and triterpenoids groups and individual compounds.

Pathway-based protein-protein association network to explore mechanism of α-glucosidase inhibitors from Scutellaria baicalensis Georgi against type 2 diabetes

Natural products have been widely used in the treatment of type 2 diabetes (T2D). However, their mechanisms are often obscured due to multi-components and multi-targets. The authors constructed a pathway-based protein-protein association (PPA) network for target proteins of 13 α-glucosidase inhibitors (AGIs) identified from Scutellaria baicalensis Georgi (SBG), designed to explore the underlying mechanisms. This network contained 118 nodes and 1167 connections. An uneven degree distribution and small-world property were observed, characterised by high clustering coefficient and short average path length. The PPA network had an inherent hierarchy as C(k)∼k-0.71 . It also exhibited potential weak disassortative mixing pattern, coupled with a decreased function Knn (k) and negative value of assortativity coefficient. These properties indicated that a few nodes were crucial to the network. PGH2, GNAS, MAPK1, MAPK3, PRKCA, and MAOA were then identified as key targets with the highest degree values and centrality indices. Additionally, a core subnetwork showed that chrysin, 5,8,2'-trihydroxy-7-methoxyflavone, and wogonin were the main active constituents of these AGIs, and that the serotonergic synapse pathway was the critical pathway for SBG against T2D. The application of a pathway-based protein-protein association network provides a novel strategy to explore the mechanisms of natural products on complex diseases.

Pharmacoinformatics approaches in the discovery of drug-like antimicrobials of plant origin

Medicinal plants have served as an important source for addressing the ailments of humans and animals alike. The emergence of advanced technologies in the field of drug discovery and development has helped in isolating various bioactive phytochemicals and developing them as drugs. Owing to their significant pharmacological benefits and minimum adverse effects, they not only serve as good candidates for therapeutics themselves but also help in the identification and development of related drug like molecules against various metabolic and infectious diseases. The ever-increasing diversity, severity and incidence of infectious diseases has resulted in an exaggerated mortality and morbidity levels. Geno-proteomic mutations in microbes, irrational prescribing of antibiotics, antimicrobial resistance and human population explosion, all call for continuous efforts to discover and develop alternated therapeutic options against the microbes. This review article describes the pharmacoinformatics tools and methods which are currently used in the discovery of bioactive phytochemicals, thus making the process more efficient and effective. The pharmacological aspects of the drug discovery and development process have also been reviewed with reference to the in silico activities. Communicated by Ramaswamy H. Sarma.

Natural Products, the Fourth Industrial Revolution, and the Quintuple Helix

The profound interconnectedness of the sciences and technologies embodied in the Fourth Industrial Revolution is discussed in terms of the global role of natural products, and how that interplays with the development of sustainable and climate-conscious practices of cyberecoethnopharmacolomics within the Quintuple Helix for the promotion of a healthier planet and society.

A Mechanism of Action Study on Danggui Sini Decoction to Discover Its Therapeutic Effect on Gastric Cancer

Danggui Sini Decoction (DSD), a classic Chinese herb medicine (CHM) formula, has been used to treat various diseases in China for centuries. However, it remains challenging to reveal its mechanism of action through conventional pharmacological methods. Here, we first explored the mechanism of action of DSD with the assistance of network pharmacology and bioinformatic analysis tools, and found a potential therapeutic effect of DSD on cancer. Indeed, our in vivo experiment demonstrated that oral administration of DSD could significantly inhibit the growth of xenografted gastric cancer (GC) on mice. The subsequent enrichment analyses for 123 candidate core targets evacuated from the drug/disease-target protein-protein interaction network showed that DSD could affect the key biological processes involving the survival and growth of GC cells, such as apoptosis and cell cycle, and the disturbance of these biological processes is likely attributed to the simultaneous inhibition of multiple signaling pathways, including PI3K/Akt, MAPK, and p53 pathways. Notably, these in silico results were further validated by a series of cellular functional and molecular biological assays in vitro. Moreover, molecular docking analysis suggested an important role of MCM2 in delivering the pharmacological activity of DSD against GC. Together, these results indicate that our network pharmacology and bioinformatics-guided approach is feasible and useful in exploring not only the mechanism of action, but also the "new use" of the old CHM formula.

Machine learning approaches for elucidating the biological effects of natural products

Covering: 2000 to 2020 Machine learning (ML) is an efficient tool for the prediction of bioactivity and the study of structure-activity relationships. Over the past decade, an emerging trend for combining these approaches with the study of natural products (NPs) has developed in order to manage the challenge of the discovery of bioactive NPs. In the present review, we will introduce the basic principles and protocols for using the ML approach to investigate the bioactivity of NPs, citing a series of practical examples regarding the study of anti-microbial, anti-cancer, and anti-inflammatory NPs, etc. ML algorithms manage a variety of classification and regression problems associated with bioactive NPs, from those that are linear to non-linear and from pure compounds to plant extracts. Inspired by cases reported in the literature and our own experience, a number of key points have been emphasized for reducing modeling errors, including dataset preparation and applicability domain analysis.

Chemical Diversity of Soft Coral Steroids and Their Pharmacological Activities

The review is devoted to the chemical diversity of steroids produced by soft corals and their determined and potential activities. There are about 200 steroids that belong to different types of steroids such as secosteroids, spirosteroids, epoxy- and peroxy-steroids, steroid glycosides, halogenated steroids, polyoxygenated steroids and steroids containing sulfur or nitrogen heteroatoms. Of greatest interest is the pharmacological activity of these steroids. More than 40 steroids exhibit antitumor and related activity with a confidence level of over 90 percent. A group of 32 steroids shows anti-hypercholesterolemic activity with over 90 percent confidence. Ten steroids exhibit anti-inflammatory activity and 20 steroids can be classified as respiratory analeptic drugs. Several steroids exhibit rather rare and very specific activities. Steroids exhibit anti-osteoporotic properties and can be used to treat osteoporosis, as well as have strong anti-eczemic and anti-psoriatic properties and antispasmodic properties. Thus, this review is probably the first and exclusive to present the known as well as the potential pharmacological activities of 200 marine steroids.

Prospective pharmacological effects of psoralen photoxidation products and their cycloadducts with aminothiols: chemoinformatic analysis

Psoralens are medicinal photosensitizing furocoumarins which are used in photochemotherapy and photoimmunotherapy of dermatoses. Psoralen photooxidation products may be involved in therapeutic effects, but the possible mechanisms of their action remain unclear. The study was aimed to assess the prospective pharmacological effects and mechanisms of activity for six previously identified ortho–hydroxyformyl-containing psoralen photooxidation products and their cycloadducts with aminothiols, as well as for structurally similar compounds (furocoumaric acid and tucaresol). Chemoinformatic analysis of the prospective pharmacological effects and mechanisms of action of these compounds was performed using the PASS and PharmaExpert software. The predicted pharmacological effects partially confirmed by previous studies highlight the possible involvement of psoralen photooxidation products in the effects of PUVA therapy or photopheresis during the course of dermatoses and proliferative disorders treatment. A broad spectrum of pharmacological effects found for furocoumaric acid and cycloadducts of coumarinic and benzofuranic photoproducts of psoralen with cysteine and homocysteine appoints new directions of research relating to therapeutic use of psoralens.

Pharmacological profile of natural and synthetic compounds with rigid adamantane-based scaffolds as potential agents for the treatment of neurodegenerative diseases

This review is dedicated to the comparative analysis of structure-activity relationships for more than 75 natural and synthetic derivatives of adamantane. Some of these compounds, such as amantadine and memantine, are currently used to treat dementia, Alzheimer's and Parkinson's diseases and other neurodegenerative diseases. The data presented show that the pharmacological potential of 1-fluoro- and 1-phosphonic acid adamantane derivatives against Alzheimer's and Parkinson's diseases and other neurodegenerative diseases exceeds those of well-known amantadine and memantine. The information presented in this review highlights the promising directions of studies for biochemists, pharmacologists, medicinal chemists, physiologists, and neurologists, as well as to the pharmaceutical industry.

Three Alkaloids from an Apocynaceae Species, Aspidosperma spruceanum as Antileishmaniasis Agents by In Silico Demo-case Studies

This paper is focused on demonstrating with a real case that Ethnobotany added to Bioinformatics is a promising tool for new drugs search. It encourages the in silico investigation of "challua kaspi", a medicinal kichwa Amazonian plant (Aspidosperma spruceanum) against a Neglected Tropical Disease, leishmaniasis. The illness affects over 150 million people especially in subtropical regions, there is no vaccination and conventional treatments are unsatisfactory. In attempts to find potent and safe inhibitors of its etiological agent, Leishmania, we recovered the published traditional knowledge on kichwa antimalarials and selected three A. spruceanum alkaloids, (aspidoalbine, aspidocarpine and tubotaiwine), to evaluate by molecular docking their activity upon five Leishmania targets: DHFR-TS, PTR1, PK, HGPRT and SQS enzymes. Our simulation results suggest that aspidoalbine interacts competitively with the five targets, with a greater affinity for the active site of PTR1 than some physiological ligands. Our virtual data also point to the demonstration of few side effects. The predicted binding free energy has a greater affinity to Leishmania proteins than to their homologous in humans (TS, DHR, PKLR, HGPRT and SQS), and there is no match with binding pockets of physiological importance. Keys for the in silico protocols applied are included in order to offer a standardized method replicable in other cases. Apocynaceae having ethnobotanical use can be virtually tested as molecular antileishmaniasis new drugs.

Antihypoxic Action of Panax Japonicus, Tribulus Terrestris and Dioscorea Deltoidea Cell Cultures: In Silico and Animal Studies

Chemical diversity of secondary metabolites provides a considerable variety of pharmacological actions with a significant extension due to their combinations in plant extracts. Production of plant-derived medicinal products in cell cultures has advantages because of the efficient use of different biotic and abiotic elicitors and better control of the developmental processes. Using PASS software, we predicted biological activity spectra for phytoconstituents identified in cell cultures of Panax japonicus (12 molecules), Tribulus terrestris (4 molecules), and Dioscorea deltoidea (3 molecules). Mechanisms of action associated with the antihypoxic effect were predicted for the majority of molecules. PharmaExpert software allowed analyzing possible synergistic or additive effects of the combinations of phytoconstituents associated with the antihypoxic action. Experimental studies of the antihypoxic effect of the plants' extracts in water and ethanol have been performed in 3 animal models: Acute asphyctic hypoxia (AAH), Acute haemic hypoxia (AHeH), and Acute histotoxic hypoxia (AHtH). Effects of Panax japonicus and Tribulus terrestris preparations exceeded the activity of the reference drug Mexidol in the AHtH model. In the AHeH model, all preparations demonstrated moderate activity; the most potent has been observed for Dioscorea deltoidea. Thus, we found that experimental studies in animal models have confirmed the in silico prediction.

Design of Novel Drug-like Molecules Using Informatics Rich Secondary Metabolites Analysis of Indian Medicinal and Aromatic Plants

BACKGROUND

Several medicinal plants are being used in Indian medicine systems from ancient times. However, in most cases, the specific molecules or the active ingredients responsible for the medicinal or therapeutic properties are not yet known.

OBJECTIVE

This study aimed to report a computational protocol as well as a tool for generating novel potential drug candidates from the bioactive molecules of Indian medicinal and aromatic plants through the chemoinformatics approach.

METHODS

We built a database of the Indian medicinal and aromatic plants coupled with associated information (plant families, plant parts used for the medicinal purpose, structural information, therapeutic properties, etc.) We also developed a Java-based chemoinformatics open-source tool called DoMINE (Database of Medicinally Important Natural products from plantaE) for the generation of virtual library and screening of novel molecules from known medicinal plant molecules. We employed chemoinformatics approaches to in-silico screened metabolites from 104 Indian medicinal and aromatic plants and designed novel drug-like bioactive molecules. For this purpose, 1665 ring containing molecules were identified by text mining of literature related to the medicinal plant species, which were later used to extract 209 molecular scaffolds. Different scaffolds were further used to build a focused virtual library. Virtual screening was performed with cluster analysis to predict drug-like and lead-like molecules from these plant molecules in the context of drug discovery. The predicted drug-like and lead-like molecules were evaluated using chemoinformatics approaches and statistical parameters, and only the most significant molecules were proposed as the candidate molecules to develop new drugs.

RESULTS AND CONCLUSION

The supra network of molecules and scaffolds identifies the relationship between the plant molecules and drugs. Cluster analysis of virtual library molecules showed that novel molecules had more pharmacophoric properties than toxicophoric and chemophoric properties. We also developed the DoMINE toolkit for the advancement of natural product-based drug discovery through chemoinformatics approaches. This study will be useful in developing new drug molecules from the known medicinal plant molecules. Hence, this work will encourage experimental organic chemists to synthesize these molecules based on the predicted values. These synthesized molecules need to be subjected to biological screening to identify potential molecules for drug discovery research.

Natural and synthetic drugs used for the treatment of the dementia

This review is devoted to comparative pharmacological analysis of synthetic drugs such as memantine and its isomers, as well as tacrine, velnacrine, rivastigmine, and donepezil, with natural alkaloids, terpenoids, and triterpenoid peroxides, which are used to treat dementia, Alzheimer's and Parkinson's diseases, myasthenia gravis and other neurodegenerative diseases. Recently discovered by French scientists from Marseille triterpenoid hydroperoxides demonstrate high activity as potential therapeutic agents for the treatment of dementia. The information presented in this review is of great interest to pharmacologists, medical chemists, physiologists, neurologists and doctors, as well as for the pharmaceutical industry.

[Computer-aided drug design: from discovery of novel pharmaceutical agents to systems pharmacology]

New drug discovery is based on the analysis of public information about the mechanisms of the disease, molecular targets, and ligands, which interaction with the target could lead to the normalization of the pathological process. The available data on diseases, drugs, pharmacological effects, molecular targets, and drug-like substances, taking into account the combinatorics of the associative relations between them, correspond to the Big Data. To analyze such data, the application of computer-aided drug design methods is necessary. An overview of the studies in this area performed by the Laboratory for Structure-Function Based Drug Design of IBMC is presented. We have developed the approaches to identifying promising pharmacological targets, predicting several thousand types of biological activity based on the structural formula of the compound, analyzing protein-ligand interactions based on assessing local similarity of amino acid sequences, identifying likely molecular mechanisms of side effects of drugs, calculating the integral toxicity of drugs taking into account their metabolism, have been developed in the human body, predicting sustainable and sensitive options strains and evaluating the effectiveness of combinations of antiretroviral drugs in patients, taking into account the molecular genetic characteristics of the clinical isolates of HIV-1. Our computer programs are implemented as the web-services freely available on the Internet, which are used by thousands of researchers from many countries of the world to select the most promising substances for the synthesis and determine the priority areas for experimental testing of their biological activity.

A systematic analysis of natural α-glucosidase inhibitors from flavonoids of Radix scutellariae using ultrafiltration UPLC-TripleTOF-MS/MS and network pharmacology

BACKGROUND

Flavonoids from plant medicines are supposed to be viable alternatives for the treatment of type 2 diabetes (T2D) as less toxicity and side effects. Radix scutellariae (RS) is a widely used traditional medicine in Asia. It has shown great potential in the research of T2D. However, the pharmacological actions remain obscured due to the complex chemical nature of plant medicines.

METHODS

In the present study, a systematic method combining ultrafiltration UPLC-TripleTOF-MS/MS and network pharmacology was developed to screen α-glucosidase inhibitors from flavonoids of RS, and explore the underlying mechanism for the treatment of T2D.

RESULTS

The n-butanol part of ethanol extract from RS showed a strong α-glucosidase inhibition activity (90.55%, IC50 0.551 mg/mL) against positive control acarbose (90.59%, IC50 1.079 mg/mL). A total of 32 kinds of flavonoids were identified from the extract, and their ESI-MS/MS behaviors were elucidated. Thirteen compounds were screened as α-glucosidase inhibitors, including viscidulin III, 2',3,5,6',7-pentahydroxyflavanone, and so on. A compound-target-pathway (CTP) network was constructed by integrating these α-glucosidase inhibitors, target proteins, and related pathways. This network exhibited an uneven distribution and approximate scale-free property. Chrysin (k = 87), 5,8,2'-trihydroxy-7-methoxyflavone (k = 21) and wogonin (k = 20) were selected as the main active constituents with much higher degree values. A protein-protein interaction (PPI) weighted network was built for target proteins of these α-glucosidase inhibitors and drug targets of T2D. PPARG (Cd = 0.165, Cb = 0.232, Cc = 0.401), ACACB (Cd = 0.155, Cb = 0.184, Cc = 0.318), NFKB1 (Cd = 0.233, Cb = 0.161, Cc = 0.431), and PGH2 (Cd = 0.194, Cb = 0.157, Cc = 0.427) exhibited as key targets with the highest scores of centrality indices. Furthermore, a core subnetwork was extracted from the CTP and PPI weighted network. Type II diabetes mellitus (hsa04930) and PPAR signaling pathway (hsa03320) were confirmed as the critical pathways.

CONCLUSIONS

These results improved current understanding of natural flavonoids on the treatment of T2D. The combination of ultrafiltration UPLC-TripleTOF-MS/MS and network pharmacology provides a novel strategy for the research of plant medicines and complex diseases.

Prediction of modes of action of components of traditional medicinal preparations

Traditional medicine preparations are used to treat many ailments in multiple regions across the world. Despite their widespread use, the mode of action of these preparations and their constituents are not fully understood. Traditional methods of elucidating the modes of action of these natural products (NPs) can be expensive and time consuming e. g. biochemical methods, bioactivity guided fractionation, etc. In this review, we discuss some methods for the prediction of the modes of action of traditional medicine preparations, both in mixtures and as isolated NPs. These methods are useful to predict targets of NPs before they are experimentally validated. Case studies of the applications of these methods are also provided herein.

Plant Metabolite Databases: From Herbal Medicines to Modern Drug Discovery

Traditional herbal medicine has been an inseparable part of the traditional medical science in many countries throughout history. Nowadays, the popularity of using herbal medicines in daily life, as well as clinical practices, has gradually expanded to numerous Western countries with positive impacts and acceptance. The continuous growth of the herbal consumption market has promoted standardization and modernization of herbal-derived products with present pharmacological criteria. To store and extensively share this knowledge with the community and serve scientific research, various herbal metabolite databases have been developed with diverse focuses under the support of modern advances. The advent of these databases has contributed to accelerating research on pharmaceuticals of natural origins. In the scope of this study, we critically review 30 herbal metabolite databases, discuss different related perspectives, and provide a comparative analysis of 18 accessible noncommercial ones. We hope to provide you with fundamental information and multidimensional perspectives from herbal medicines to modern drug discovery.

Combined network pharmacology and virtual reverse pharmacology approaches for identification of potential targets to treat vascular dementia

Dementia is a major cause of disability and dependency among older people. If the lives of people with dementia are to be improved, research and its translation into druggable target are crucial. Ancient systems of healthcare (Ayurveda, Siddha, Unani and Sowa-Rigpa) have been used from centuries for the treatment vascular diseases and dementia. This traditional knowledge can be transformed into novel targets through robust interplay of network pharmacology (NetP) with reverse pharmacology (RevP), without ignoring cutting edge biomedical data. This work demonstrates interaction between recent and traditional data, and aimed at selection of most promising targets for guiding wet lab validations. PROTEOME, DisGeNE, DISEASES and DrugBank databases were used for selection of genes associated with pathogenesis and treatment of vascular dementia (VaD). The selection of new potential drug targets was made by methods of NetP (DIAMOnD algorithm, enrichment analysis of KEGG pathways and biological processes of Gene Ontology) and manual expert analysis. The structures of 1976 phytomolecules from the 573 Indian medicinal plants traditionally used for the treatment of dementia and vascular diseases were used for computational estimation of their interactions with new predicted VaD-related drug targets by RevP approach based on PASS (Prediction of Activity Spectra for Substances) software. We found 147 known genes associated with vascular dementia based on the analysis of the databases with gene-disease associations. Six hundred novel targets were selected by NetP methods based on 147 gene associations. The analysis of the predicted interactions between 1976 phytomolecules and 600 NetP predicted targets leaded to the selection of 10 potential drug targets for the treatment of VaD. The translational value of these targets is discussed herewith. Twenty four drugs interacting with 10 selected targets were identified from DrugBank. These drugs have not been yet studied for the treatment of VaD and may be investigated in this field for their repositioning. The relation between inhibition of two selected targets (GSK-3, PTP1B) and the treatment of VaD was confirmed by the experimental studies on animals and reported separately in our recent publications.

Innovative omics-based approaches for prioritisation and targeted isolation of natural products - new strategies for drug discovery

Covering: 2013 to 2019 The exploration of the chemical diversity of extracts from various biological sources has led to major drug discoveries. Over the past two decades, despite the introduction of advanced methodologies for natural product (NP) research (e.g., dereplication and high content screening), successful accounts of the validation of NPs as lead therapeutic candidates have been limited. In this context, one of the main challenges faced is related to working with crude natural extracts because of their complex composition and the inadequacies of classical bioguided isolation studies given the pace of high-throughput screening campaigns. In line with the development of metabolomics, genomics and chemometrics, significant advances in metabolite profiling have been achieved and have generated high-quality massive genome and metabolome data on natural extracts. The unambiguous identification of each individual NP in an extract using generic methods remains challenging. However, the establishment of structural links among NPs via molecular network analysis and the determination of common features of extract composition have provided invaluable information to the scientific community. In this context, new multi-informational-based profiling approaches integrating taxonomic and/or bioactivity data can hold promise for the discovery and development of new bioactive compounds and return NPs back to an exciting era of development. In this article, we examine recent studies that have the potential to improve the efficiency of NP prioritisation and to accelerate the targeted isolation of key NPs. Perspectives on the field's evolution are discussed.

Capturing the Geoherbalism Differentiation in Wild Paris polyphylla var. yunnanensis Raw Materials through the Application of Multispectral Information Fusion Combined with Chemometrics

Paris polyphylla var. yunnanensis is a famous medicinal plant distributed in some Asian countries. This species has attracted a great deal of attention and is often used as raw materials in traditional medicine practices. With the purpose of gaining insight into the geoherbalism of wild P. polyphylla var. yunnanensis, a total of 183 dried rhizome samples from eight different regions including 16 typical or nontypical natural habitats have been analyzed by multispectral information fusion based on ultraviolet and Fourier transform infrared spectroscopies combined with partial least squares discriminant analysis (PLS-DA) and hierarchical cluster analysis. From the results, the use of multispectral information fusion strategy could improve the correct classification of samples, and good classification performances have been shown according to PLS-DA models. The discrimination of samples was obtained successfully with respect to the typical and nontypical natural habitats, different collection areas of typical natural habitats, and various sampling sites in nontypical natural habitats. Additionally, the similarities among samples were presented as well. Overall, the rhizome of wild P. polyphylla var. yunnanensis exhibited various regional dependence and individual differences according to the geographical origins, and the relatively appropriate growth region with better quality consistency of samples was preliminarily selected. This study also revealed that the developed multispectral information fusion method has the potential to be a reliable analytical methodology for capturing the geoherbalism differentiation in wild P. polyphylla var. yunnanensis. Furthermore, it could provide more chemical evidence for the critical supplement of quality evaluation on P. polyphylla var. yunnanensis.

A Comprehensive Review on Medicinal Plants as Antimicrobial Therapeutics: Potential Avenues of Biocompatible Drug Discovery

The war on multidrug resistance (MDR) has resulted in the greatest loss to the world's economy. Antibiotics, the bedrock, and wonder drug of the 20th century have played a central role in treating infectious diseases. However, the inappropriate, irregular, and irrational uses of antibiotics have resulted in the emergence of antimicrobial resistance. This has resulted in an increased interest in medicinal plants since 30-50% of current pharmaceuticals and nutraceuticals are plant-derived. The question we address in this review is whether plants, which produce a rich diversity of secondary metabolites, may provide novel antibiotics to tackle MDR microbes and novel chemosensitizers to reclaim currently used antibiotics that have been rendered ineffective by the MDR microbes. Plants synthesize secondary metabolites and phytochemicals and have great potential to act as therapeutics. The main focus of this mini-review is to highlight the potential benefits of plant derived multiple compounds and the importance of phytochemicals for the development of biocompatible therapeutics. In addition, this review focuses on the diverse effects and efficacy of herbal compounds in controlling the development of MDR in microbes and hopes to inspire research into unexplored plants with a view to identify novel antibiotics for global health benefits.

Closing the Gap Between Therapeutic Use and Mode of Action in Remedial Herbs

The ancient tradition of taking parts of a plant or preparing plant extracts for treating certain discomforts and maladies has long been lacking a scientific rationale to support its preparation and still widespread use in several parts of the world. In an attempt to address this challenge, we collected and integrated data connecting metabolites, plants, diseases, and proteins. A mechanistic hypothesis is generated when a metabolite is known to be present in a given plant, that plant is known to be used to treat a certain disease, that disease is known to be linked to the function of a given protein, and that protein is finally known or predicted to interact with the original metabolite. The construction of plant–protein networks from mutually connected metabolites and diseases facilitated the identification of plausible mechanisms of action for plants being used to treat analgesia, hypercholesterolemia, diarrhea, catarrh, and cough. Additional concrete examples using both experimentally known and computationally predicted, and subsequently experimentally confirmed, metabolite–protein interactions to close the connection circle between metabolites, plants, diseases, and proteins offered further proof of concept for the validity and scope of the approach to generate mode of action hypotheses for some of the therapeutic uses of remedial herbs.

Therapeutic value of steroidal alkaloids in cancer: Current trends and future perspectives

Discovery and development of new potentially selective anticancer agents are necessary to prevent a global cancer health crisis. Currently, alternative medicinal agents derived from plants have been extensively investigated to develop anticancer drugs with fewer adverse effects. Among them, steroidal alkaloids are conventional secondary metabolites that comprise an important class of natural products found in plants, marine organisms and invertebrates, and constitute a judicious choice as potential anti-cancer leads. Traditional medicine and modern science have shown that representatives from this compound group possess potential antimicrobial, analgesic, anticancer and anti-inflammatory effects. Therefore, systematic and recapitulated information about the bioactivity of these compounds, with special emphasis on the molecular or cellular mechanisms, is of high interest. In this review, we methodically discuss the in vitro and in vivo potential of the anticancer activity of natural steroidal alkaloids and their synthetic and semi-synthetic derivatives. This review focuses on cumulative and comprehensive molecular mechanisms, which will help researchers understand the molecular pathways involving steroid alkaloids to generate a selective and safe new lead compound with improved therapeutic applications for cancer prevention and therapy. In vitro and in vivo studies provide evidence about the promising therapeutic potential of steroidal alkaloids in various cancer cell lines, but advanced pharmacokinetic and clinical experiments are required to develop more selective and safe drugs for cancer treatment.

Untargeted LC-MS metabolomic studies of Asteraceae species to discover inhibitors of Leishmania major dihydroorotate dehydrogenase

INTRODUCTION

Interesting data about the family Asteraceae as a new source of Leishmania major dihydroorotate dehydrogenase (LmDHODH) inhibitors are presented. This key macromolecular target for parasites causing neglected diseases catalyzes the fourth reaction of the de novo pyrimidine biosynthetic pathway, which takes part in major cell functions, including DNA and RNA biosynthesis.

OBJECTIVES

We aimed to (1) determine LmDHODH inhibitor candidates, revealing the type of chemistry underlying such bioactivity, and (2) predict the inhibitory potential of extracts from new untested plant species, classifying them as active or inactive based on their LC-MS based metabolic fingerprints.

METHODS

Extracts from 150 species were screened for the inhibition of LmDHODH, and untargeted UHPLC-(ESI)-HRMS metabolomic studies were carried out in combination with in silico approaches.

RESULTS

The IC₅₀ values determined for a subset of 59 species ranged from 148 µg mL^-1 to 9.4 mg mL^-1. Dereplication of the metabolic fingerprints allowed the identification of 48 metabolites. A reliable OPLS-DA model (R² > 0.9, Q² > 0.7, RMSE_CV < 0.3) indicated the inhibitor candidates; nine of these metabolites were identified using data from isolated chemical standards, one of which-4,5-di-O-E-caffeoylquinic acid (IC₅₀ 73 µM)-was capable of inhibiting LmDHODH. The predictive OPLS model was also effective, with 60% correct predictions for the test set.

CONCLUSION

Our approach was validated for (1) the discovery of LmDHODH inhibitors or interesting starting points for the optimization of new leishmanicides from Asteraceae species and (2) the prediction of extracts from untested species, classifying them as active or inactive.

Hydroperoxides derived from marine sources: origin and biological activities

Hydroperoxides are a small and interesting group of biologically active natural marine compounds. All these metabolites contain a group (R-O-O-H). In this mini-review, studies of more than 80 hydroperoxides isolated from bacteria, fungi, algae, and marine invertebrates are described. Hydroperoxides from the red, brown, and green algae exhibit high antineoplastic, anti-inflammatory, and antiprotozoal activity with a confidence of 73 to 94%. Hydroperoxides produced by soft corals showed antineoplastic and antiprotozoal activity with confidence from 81 to 92%. Metabolites derived from sea sponges, mollusks, and other invertebrates showed antineoplastic and antiprotozoal (Plasmodium) activity with confidence from 80 to 90%.