Plant Metabolite Databases: From Herbal Medicines to Modern Drug Discovery

Molecular docking and in silico analysis of the pharmacokinetics, toxicological profile and differential gene expression of bioactive compounds from Cyrtopodium glutiniferum

The genus Cyrtopodium, from the Orchidaceae family, is widely used for its therapeutic properties in the treatment of tuberculosis, abscesses, urinary infection, and colds. C. glutiniferum, one of the species of this genus, is endemic in Brazil and largely used in herbal medicine. Thus, it is of great interest to recognize its composition, the properties of the molecules found in it. This study aimed to perform the in silico analysis of the main compounds from C. glutiniferum, on the platforms pKCSM, SwissADME, LAZAR, CLC-pred, ToxTree, DIGEPred, STRING, and Cytoscape. Further than this, the molecular docking was performed. The compounds present in the aqueous extract of C. glutiniferum were identified by UHPLC-MS/MS, finding Arbutin, Caffeic acid 4-O-glucoside, and Dihydroformononetin as the three most abundant molecules. The evaluation of the gastrointestinal absorption of Dihydroformononetin is given as high, also managing to cross the blood-brain barrier, while Arbutin can only be absorbed by the gastrointestinal tract and Caffeic acid 4-O-glucoside had very low absorption. Further analysis showed that Arbutin and Dihydroformononetin are possible leading molecules for drug synthesis, according to the prediction. Toxicological aspects were analysed, and no adverse effects were noted, but there were divergences in the mutagenic prediction of Arbutin and Dihydroformononetin, having different results in the used platforms, demonstrating that a cautious analysis and data insertion is needed in these tools to optimize them. The analysis of the differentially expressed genes predicted that the compounds can regulate several genes, including some genes associated with carcinogenesis and inflammation. The Molecular docking analysis showed high binding affinities of the molecules with different proteins. Therefore, C. glutiniferum demonstrates the potential to be used as a phytotherapeutic. The same was given through the in silico analysis of the three compounds found in the orchid, that show good individual potential.

Antitumor activity of membranes associated with Acmella oleracea extract

Epithelial cancers, such as epidermoid cancer and some adenocarcinomas, affect surface areas that are generally more accessible to various treatments. However, this group of tumor cells has an aggressive behavior, leading to a high annual mortality rate. The development of a biomaterial that is non-invasive, can kill tumor cells, and prevent opportunistic infections is the basis for the treatment for this type of cancer. Therefore, the objective of this study was to develop a biomaterial from chitosan and A. oleracea extracts that exhibits cytotoxic action against the HEp-2 tumor cell line. Dried crude 90% ethanol extracts were obtained through ultrasound-assisted maceration, followed by liquid-liquid extraction to yield the butanol fraction. From these extracts, chitosan membranes were developed and evaluated for their antitumor activity against HEp-2 using viability tests with crystal violet and MTT (3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide) assay, in addition to a wound healing test. The cytotoxic assays indicated a significant reduction in cell density and mitochondrial activity, especially at the concentration of 1000 µg/mL of crude extract. The butanol fraction had minimal effects on mitochondrial activity. The wound healing test demonstrated that the biomaterial and extract prevented closure of the wound created in the cell monolayer within 48 h of incubation and caused changes in cell morphology. In view of this, we concluded that a chitosan membrane associated with a 90% ethanol extract of Acmella oleracea exhibited cytotoxic activity is a potential alternative treatment for superficial cancers.

Multitask Learning on Graph Convolutional Residual Neural Networks for Screening of Multitarget Anticancer Compounds

Recently, various modern experimental screening pipelines and assays have been developed to find promising anticancer drug candidates. However, it is time-consuming and almost infeasible to screen an immense number of compounds for anticancer activity via experimental approaches. To partially address this issue, several computational advances have been proposed. In this study, we present iACP-GCR, a model based on multitask learning on graph convolutional residual neural networks with two types of shortcut connections, to identify multitarget anticancer compounds. In our architecture, the graph convolutional residual neural networks are shared by all the prediction tasks before being separately customized. The NCI-60 data set, one of the most reliable and well-known sources of experimentally verified compounds, was used to develop our model. From that data set, we collected and refined data about compounds screened across nine cancer types (panels), including breast, central nervous system, colon, leukemia, nonsmall cell lung, melanoma, ovarian, prostate, and renal, for model training and evaluation. The model performance evaluated on an independent test set shows that iACP-GCR surpasses the three advanced computational methods for multitask learning. The integration of two shortcut connection types in the shared networks also improves the prediction efficiency. We also deployed the model as a public web server to assist the research community in screening potential anticancer compounds.

Evaluating Machine Learning Methods of Analyzing Multiclass Metabolomics

Multiclass metabolomic studies have become popular for revealing the differences in multiple stages of complex diseases, various lifestyles, or the effects of specific treatments. In multiclass metabolomics, there are multiple data manipulation steps for analyzing raw data, which consist of data filtering, the imputation of missing values, data normalization, marker identification, sample separation, classification, and so on. In each step, several to dozens of machine learning methods can be chosen for the given data set, with potentially hundreds or thousands of method combinations in the whole data processing chain. Therefore, a clear understanding of these machine learning methods is helpful for selecting an appropriate method combination for obtaining stable and reliable analytical results of specific data. However, there has rarely been an overall introduction or evaluation of these methods based on multiclass metabolomic data. Herein, detailed descriptions of these machine learning methods in multiple data manipulation steps are reviewed. Moreover, an assessment of these methods was performed using a benchmark data set for multiclass metabolomics. First, 12 imputation methods for imputing missing values were evaluated based on the PSS (Procrustes statistical shape analysis) and NRMSE (normalized root-mean-square error) values. Second, 17 normalization methods for processing multiclass metabolomic data were evaluated by applying the PMAD (pooled median absolute deviation) value. Third, different methods of identifying markers of multiclass metabolomics were evaluated based on the CWrel (relative weighted consistency) value. Fourth, nine classification methods for constructing multiclass models were assessed using the AUC (area under the curve) value. Performance evaluations of machine learning methods are highly recommended to select the most appropriate method combination before performing the final analysis of the given data. Overall, detailed descriptions and evaluation of various machine learning methods are expected to improve analyses of multiclass metabolomic data.

New approaches in developing medicinal herbs databases

Medicinal herbs databases have become a crucial part of organizing new scientific literature generated in medicinal herbs field, as well as new drug discoveries in the information era. The aim of this review was to track the current status of medicinal herbs databases. Search for finding medicinal herbs databases was carried out via Google and PubMed. PubMed was searched for papers introducing medicinal herbs databases by the recruited search strategy. Papers with an active database on the web were included in the review. Google was also searched for medicinal herbs databases. Both retrieved papers and databases were reviewed by the authors. In this review, the current status of 25 medicinal herbs databases was reviewed, and the important characteristics of databases were mentioned. The reviewed databases had a great variety in terms of characteristics and functions. Finally, some recommendations for the efficient development of medicinal herbs databases were suggested. Although contemporary medicinal herbs databases represent much useful information, adding some features to these databases could assist them to have better functionality. This work may not cover all the necessary information, but we hope that our review can provide readers with fundamental concepts, perspectives and suggestions for constructing more useful databases.

Computer Science Technology in Natural Products Research: A Review of Its Applications and Implications

Computational approaches to drug development are rapidly growing in popularity and have been used to produce significant results. Recent developments in information science have expanded databases and chemical informatics knowledge relating to natural products. Natural products have long been well-studied, and a large number of unique structures and remarkable active substances have been reported. Analyzing accumulated natural product knowledge using emerging computational science techniques is expected to yield more new discoveries. In this article, we discuss the current state of natural product research using machine learning. The basic concepts and frameworks of machine learning are summarized. Natural product research that utilizes machine learning is described in terms of the exploration of active compounds, automatic compound design, and application to spectral data. In addition, efforts to develop drugs for intractable diseases will be addressed. Lastly, we discuss key considerations for applying machine learning in this field. This paper aims to promote progress in natural product research by presenting the current state of computational science and chemoinformatics approaches in terms of its applications, strengths, limitations, and implications for the field.

HPLC, FTIR and GC-MS Analyses of Thymus vulgaris Phytochemicals Executing In Vitro and In Vivo Biological Activities and Effects on COX-1, COX-2 and Gastric Cancer Genes Computationally

Medicinal plants have played an essential role in the treatment of various diseases. Thymus vulgaris, a medicinal plant, has been extensively used for biological and pharmaceutical potential. The current study was performed to check the biopotential of active biological compounds. The GC-MS analysis identified 31 compounds in methanolic crude extract, among which thymol, carvacrol, p-cymene, and eugenol are the main phytoconstituents present in T. vulgaris. The HPLC analysis quantified that flavonoids and phenolic acids are present in a good concentration in the active fraction of ethyl acetate and n-butanol. FTIR confirmed the presence of functional groups such as phenols, a carboxylic group, hydroxy group, alcohols, and a benzene ring. Among both fractions, ethyl acetate showed high antioxidant activity in the DPPH (84.1 0.88) and ABTS (87.1 0.89) assays, respectively. The anti-inflammatory activity of the fractions was done in vitro and in vivo by using a carrageenan-induced paw edema assay, while the hexane-based extract showed high anti-inflammatory activity (57.1 0.54) in a dose-response manner. Furthermore, the lead compound responsible for inhibition in the denaturation of proteins is thymol, which exhibits the highest binding affinity with COX1 (-6.4 KJ/mol) and COX2 (-6.3 KJ/mol) inflammatory proteins. The hepatotoxicity analysis showed that plant-based phytoconstituents are safe to use and have no toxicity, with no necrosis, fibrosis, and vacuolar degeneration, even at a high concentration of 800 mg/kg body weight. Furthermore, the in silico analysis of HPLC phytochemical compounds against gastric cancer genes showed that chlorogenic acid exhibited anticancer activity and showed good drug-designing characteristics. Thrombolysis and hemolysis are the major concerns of individuals suffering from gastric cancer. However, the T. vulgaris fractions showed thrombolysis from 17.6 to 5.4%; similarly, hemolysis ranged from 9.73 to 7.1% at a concentration of 12 mg/mL. The phytoconstituents present in T. vulgaris have the potential for multiple pharmacological applications. This should be further investigated to isolate bioactive compounds that can be used for the treatment of different ailments.

Lead/Drug Discovery from Natural Resources

Natural products and their derivatives have been shown to be effective drug candidates against various diseases for many years. Over a long period of time, nature has produced an abundant and prosperous source pool for novel therapeutic agents with distinctive structures. Major natural-product-based drugs approved for clinical use include anti-infectives and anticancer agents. This paper will review some natural-product-related potent anticancer, anti-HIV, antibacterial and antimalarial drugs or lead compounds mainly discovered from 2016 to 2022. Structurally typical marine bioactive products are also included. Molecular modeling, machine learning, bioinformatics and other computer-assisted techniques that are very important in narrowing down bioactive core structural scaffolds and helping to design new structures to fight against key disease-associated molecular targets based on available natural products are considered and briefly reviewed.

Artificial intelligence and machine-learning approaches in structure and ligand-based discovery of drugs affecting central nervous system

CNS disorders are indications with a very high unmet medical needs, relatively smaller number of available drugs, and a subpar satisfaction level among patients and caregiver. Discovery of CNS drugs is extremely expensive affair with its own unique challenges leading to extremely high attrition rates and low efficiency. With explosion of data in information age, there is hardly any aspect of life that has not been touched by data driven technologies such as artificial intelligence (AI) and machine learning (ML). Drug discovery is no exception, emergence of big data via genomic, proteomic, biological, and chemical technologies has driven pharmaceutical giants to collaborate with AI oriented companies to revolutionise drug discovery, with the goal of increasing the efficiency of the process. In recent years many examples of innovative applications of AI and ML techniques in CNS drug discovery has been reported. Research on therapeutics for diseases such as schizophrenia, Alzheimer's and Parkinsonism has been provided with a new direction and thrust from these developments. AI and ML has been applied to both ligand-based and structure-based drug discovery and design of CNS therapeutics. In this review, we have summarised the general aspects of AI and ML from the perspective of drug discovery followed by a comprehensive coverage of the recent developments in the applications of AI/ML techniques in CNS drug discovery.

In vitro efficacy of Capparis spinosa extraction against larvae viability of Echinococcus granulosus sensu stricto

Cystic echinococcosis (CE) is a chronic zoonotic parasitic disease caused by infection with the larvae of the Echinococcus granulosus sensu lato (s.l.) cluster. Currently, new drugs are urgently required due to the poor therapeutic effect of the existing drugs albendazole and mebendazole. Capparis spinosa, a traditional medicinal plant, has potential therapeutic effects on various diseases based on extracts from its fruit and other parts. The results of this study demonstrated that the water-soluble and ethanolic extracts of C. spinosa fruit had in vitro killing effects on the larvae of E. granulosus sensu stricto (s.s.) and disrupted the ultrastructure of protoscoleces and metacestodes. In vitro cytotoxicity assays showed that the water-soluble and ethanolic extracts of C. spinosa fruit were not significantly toxic to primary mouse hepatocytes at an effective dose to CE. In conclusion, water-soluble and ethanolic extracts of C. spinosa fruit have great potential for the development of new drugs for the treatment of CE.

Plant based radioprotectors as an adjunct to radiotherapy: advantages and limitations

Radioprotectors are agents that have the potential to act against radiation damage to cells. These are equally invaluable in radiation protection, both in intentional and unintentional radiation exposure. It is however, complex to use a universal radioprotector that could be beneficial in diverse contexts such as in radiotherapy, nuclear accidents, and space travel, as each of these circumstances have unique requirements. In a clinical setting such as in radiotherapy, a radioprotector is used to increase the efficacy of cancer treatment. The protective agent must act against radiation damage selectively in normal healthy cells while enhancing the radiation damage imparted on cancer cells. In the context of radiotherapy, plant-based compounds offer a more reliable solution over synthetic ones as the former are less expensive, less toxic, possess synergistic phytochemical activity, and are environmentally friendly. Phytochemicals with both radioprotective and anticancer properties may enhance the treatment efficacy by two-fold. Hence, plant based radioprotective agents offer a promising field to progress forward, and to expand the boundaries of radiation protection. This review is an account on radioprotective properties of phytochemicals and complications encountered in the development of the ideal radioprotector to be used as an adjunct in radiotherapy.

Virtual screening of phytochemicals from Indian medicinal plants against the endonuclease domain of SFTS virus L polymerase

Severe fever with thrombocytopenia syndrome virus (SFTSV) causes a highly infectious disease with reported mortality in the range 2.8% to 47%. The replication and transcription of the SFTSV genome is performed by L polymerase, which has both an RNA dependent RNA polymerase domain and an N-terminal endonuclease (endoN) domain. Due to its crucial role in the cap-snatching mechanism required for initiation of viral RNA transcription, the endoN domain is an ideal antiviral drug target. In this virtual screening study for the identification of potential inhibitors of the endoN domain of SFTSV L polymerase, we have used molecular docking and molecular dynamics (MD) simulation to explore the natural product space of 14 011 phytochemicals from Indian medicinal plants. After generating a heterogeneous ensemble of endoN domain structures reflecting conformational diversity of the corresponding active site using MD simulations, ensemble docking of the phytochemicals was performed against the endoN domain structures. Apart from the ligand binding energy from docking, our virtual screening workflow imposes additional filters such as drug-likeness, non-covalent interactions with key active site residues, toxicity and chemical similarity with other hits, to identify top 5 potential phytochemical inhibitors of endoN domain of SFTSV L polymerase. Further, the stability of the protein–ligand docked complexes for the top 5 potential inhibitors was analyzed using MD simulations. The potential phytochemical inhibitors, predicted in this study using contemporary computational methods, are expected to serve as lead molecules in future experimental studies towards development of antiviral drugs against SFTSV.

Virtual screening of a large phytochemical library from Indian medicinal plants to identify potential endonuclease inhibitors against emerging virus SFTSV.

iANP-EC: Identifying Anticancer Natural Products Using Ensemble Learning Incorporated with Evolutionary Computation

Cancer is one of the most deadly diseases that annually kills millions of people worldwide. The investigation on anticancer medicines has never ceased to seek better and more adaptive agents with fewer side effects. Besides chemically synthetic anticancer compounds, natural products are scientifically proved as a highly potential alternative source for anticancer drug discovery. Along with experimental approaches being used to find anticancer drug candidates, computational approaches have been developed to virtually screen for potential anticancer compounds. In this study, we construct an ensemble computational framework, called iANP-EC, using machine learning approaches incorporated with evolutionary computation. Four learning algorithms (k-NN, SVM, RF, and XGB) and four molecular representation schemes are used to build a set of classifiers, among which the top-four best-performing classifiers are selected to form an ensemble classifier. Particle swarm optimization (PSO) is used to optimise the weights used to combined the four top classifiers. The models are developed by a set of curated 997 compounds which are collected from the NPACT and CancerHSP databases. The results show that iANP-EC is a stable, robust, and effective framework that achieves an AUC-ROC value of 0.9193 and an AUC-PR value of 0.8366. The comparative analysis of molecular substructures between natural anticarcinogens and nonanticarcinogens partially unveils several key substructures that drive anticancerous activities. We also deploy the proposed ensemble model as an online web server with a user-friendly interface to support the research community in identifying natural products with anticancer activities.

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.

Cheminformatics in Natural Product-based Drug Discovery

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.

A Screening of Neuraminidase Inhibition Activities of Isoquinolone Alkaloids in Coptis chinensis Using Molecular Docking and Pharmacophore Analysis

Coptis chinensis has been long used as the potential herbal remedy for the treatment of influenza A infection. The six isoquinolone alkaloids extracted from C. chinensis rhizomes are reported to have good inhibition activity on neuraminidase (NA) of Clostridium perfringens, A/H1N1/1918, and recombinant NA-1; however, the study of the effect of these candidates on other NAs of threatening influenza A causing pandemic and seasonal flu recently has not considered yet. The purpose of this study is to investigate the interaction between these compounds and NAs of different wild and mutant subtypes of influenza A. This process involved the molecular docking of 3D structures of those compounds (ligand) into target proteins NA of A/H1N1/1918, A/H1N1/2009pdm, H3N2/2010 wild type, H3N2/2010 D151G mutant, H5N1 wild type, and H5N1 H274Y mutant. Then, the Protein-Ligand Interaction Profiler (PLIP) was utilized to demonstrate the bond formed between the ligand and the binding pocket of receptors of interest. The results showed that six candidates including palmatine, berberine, jatrorrhizine, epiberberine, columbamine, and coptisine have a higher affinity to all six selected proteins than commercial drugs such as oseltamivir, zanamivir, and natural binding ligand sialic acid. The results could be explained via the 2D picture, which showed the hydrophobic interaction and hydrogen bonding forming between the oxygen molecules of the ligand with the free residue of proteins.

Bioactive Compounds in Anti-Diabetic Plants: From Herbal Medicine to Modern Drug Discovery

Natural products, including organisms (plants, animals, or microorganisms) have been shown to possess health benefits for animals and humans. According to the estimation of the World Health Organization, in developing countries, 80% of the population has still depended on traditional medicines or folk medicines which are mostly prepared from the plant for prevention or treatment diseases. Traditional medicine from plant extracts has proved to be more affordable, clinically effective and relatively less adverse effects than modern drugs. Literature shows that the attention on the application of phytochemical constituents of medicinal plants in the pharmaceutical industry has increased significantly. Plant-derived secondary metabolites are small molecules or macromolecules biosynthesized in plants including steroids, alkaloids, phenolic, lignans, carbohydrates and glycosides, etc. that possess a diversity of biological properties beneficial to humans, such as their antiallergic, anticancer, antimicrobial, anti-inflammatory, antidiabetic and antioxidant activities Diabetes mellitus is a chronic disease result of metabolic disorders in pancreas β-cells that have hyperglycemia. Hyperglycemia can be caused by a deficiency of insulin production by pancreatic (Type 1 diabetes mellitus) or insufficiency of insulin production in the face of insulin resistance (Type 2 diabetes mellitus). The current medications of diabetes mellitus focus on controlling and lowering blood glucose levels in the vessel to a normal level. However, most modern drugs have many side effects causing some serious medical problems during a period of treating. Therefore, traditional medicines have been used for a long time and play an important role as alternative medicines. Moreover, during the past few years, some of the new bioactive drugs isolated from plants showed antidiabetic activity with more efficacy than oral hypoglycemic agents used in clinical therapy. Traditional medicine performed a good clinical practice and is showing a bright future in the therapy of diabetes mellitus. World Health Organization has pointed out this prevention of diabetes and its complications is not only a major challenge for the future, but essential if health for all is to be attained. Therefore, this paper briefly reviews active compounds, and pharmacological effects of some popular plants which have been widely used in diabetic treatment. Morphological data from V-herb database of each species was also included for plant identification.

Plants Metabolites: Possibility of Natural Therapeutics Against the COVID-19 Pandemic

COVID-19, a disease induced by SARS-CoV-2 (Severe Acute Respiratory Syndrome Coronavirus-2), has been the cause of a worldwide pandemic. Though extensive research works have been reported in recent days on the development of effective therapeutics against this global health crisis, there is still no approved therapy against SARS-CoV-2. In the present study, plant-synthesized secondary metabolites (PSMs) have been prioritized to make a review focusing on the efficacy of plant-originated therapeutics for the treatment of COVID-19. Plant metabolites are a source of countless medicinal compounds, while the diversity of multidimensional chemical structures has made them superior to treat serious diseases. Some have already been reported as promising alternative medicines and lead compounds for drug repurposing and discovery. The versatility of secondary metabolites may provide novel antibiotics to tackle MDR (Multi-Drug Resistant) microbes too. This review attempted to find out plant metabolites that have the therapeutic potential to treat a wide range of viral pathogens. The study includes the search of remedies belonging to plant families, susceptible viral candidates, antiviral assays, and the mode of therapeutic action; this attempt resulted in the collection of an enormous number of natural therapeutics that might be suggested for the treatment of COVID-19. About 219 plants from 83 families were found to have antiviral activity. Among them, 149 plants from 71 families were screened for the identification of the major plant secondary metabolites (PSMs) that might be effective for this pandemic. Our investigation revealed that the proposed plant metabolites can serve as potential anti- SARS-CoV-2 lead molecules for further optimization and drug development processes to combat COVID-19 and future pandemics caused by viruses. This review will stimulate further analysis by the scientific community and boost antiviral plant-based research followed by novel drug designing.