A mini-review of the anti-SARS-CoV-2 potency of Amaryllidaceae alkaloids.

BACKGROUND: Nature has perennially served as an infinite reservoir of diverse chemicals with numerous applications benefiting humankind. In recent years, due to the emerging COVID-19 pandemic, there has been a surge in studies on repurposing natural products as anti-SARS-CoV-2 agents, including plant-derived substances. Among all types of natural products, alkaloids remain one of the most important groups with various known medicinal values. The current investigation focuses on Amaryllidaceae alkaloids (AAs) since AAs have drawn significant scientific attention as anti-SARS-CoV-2 agents over the past few years. PURPOSE AND STUDY DESIGN: This study serves as a mini-review, summarizing recent advances in studying the anti-SARS-CoV-2 potency of AAs, covering two aspects: structure-activity relationship and mechanism of action (MOA). METHODS: The study covers the period from 2019 to 2023. The information in this review were retrieved from common databases including Web of Science, ScienceDirect, PubMed and Google scholar. Reported anti-SARS-CoV-2 potency, cytotoxicity and possible biological targets of AAs were summarized and classified into different skeletal subclasses. Then, the structure-activity relationship (SAR) was explored, pinpointing the key pharmacophore-related structural moieties. To study the mechanism of action of anti-SARS-CoV-2 AAs, possible biological targets were discussed. RESULTS: In total, fourteen research articles about anti-SARS-CoV-2 was selected. From the SAR point of view, four skeletal subclasses of AAs (lycorine-, galanthamine-, crinine- and homolycorine-types) appear to be promising for further investigation as anti-SARS-CoV-2 agents despite experimental inconsistencies in determining in vitro half maximal inhibitory effective concentration (EC50). Narciclasine, haemanthamine- and montanine-type skeletons were cytotoxic and devoid of anti-SARS-CoV-2 activity. The lycorine-type scaffold was the most structurally diverse in this study and preliminary structure-activity relationships revealed the crucial role of ring C and substituents on rings A, C and D in its anti-SARS-CoV-2 activity. It also appears that two enantiomeric skeletons (haemanthamine- and crinine-types) displayed opposite activity/toxicity profiles regarding anti-SARS-CoV-2 activity. Pharmacophore-related moieties of the haemanthamine/crinine-type skeletons were the substituents on rings B, C and the dioxymethylene moiety. All galanthamine-type alkaloids in this study were devoid of cytotoxicity and it appears that varying substituents on rings C and D could enhance the anti-SARS-CoV-2 potency. Regarding MOAs, initial experimental results suggested Mpro and RdRp as possible viral targets. Dual functionality between anti-inflammatory activity on host cells and anti-SARS-CoV-2 activity on the SARS-CoV-2 virus of isoquinoline alkaloids, including AAs, were suggested as the possible MOAs to alleviate severe complications in COVID-19 patients. This dual functionality was proposed to be related to the p38 MAPK signaling pathway. CONCLUSION: Overall, Amaryllidaceae alkaloids appear to be promising for further investigation as anti-SARS-CoV-2 agents. The skeletal subclasses holding the premise for further investigation are lycorine-, crinine-, galanthamine- and homolycorine-types.

Flindissone, a Limonoid Isolated from Trichilia prieuriana, Is an LXR Agonist.

In this study, the ability of six limonoids from Trichilia prieuriana (Meliaceae) to activate the liver X receptor (LXR) was assessed. One of these limonoids, flindissone, was shown to activate LXR by reporter-gene assays. Flindissone is a ring-intact limonoid, structurally similar to sterol-like LXR ligands. In endogenous cellular settings, flindissone showed an activity profile that is characteristic of LXR agonists. It induced cholesterol efflux in THP-1 macrophages by increasing the cholesterol transporter ABCA1 and ABCG1 gene expression. In HepG2 cells, flindissone induced the expression of IDOL, an LXR-target gene that is associated with the downregulation of the LDL receptor. However, unlike synthetic and similarly to sterol-based LXR agonists, flindissone did not induce the expression of the SREBP1c gene, a major transcription factor regulating de novo lipogenesis. Additionally, flindissone also appeared to be able to inhibit post-translational activation of SREBP1c. The results presented here reveal a natural product as a new LXR agonist and point to an additional property of T. prieuriana and other plant extracts containing flindissone.

In Silico and In Vitro Approach to Assess Direct Allosteric AMPK Activators from Nature.

The 5'-adenosine monophosphate-activated protein kinase (AMPK) is an important metabolic regulator. Its allosteric drug and metabolite binding (ADaM) site was identified as an attractive target for direct AMPK activation and holds promise as a novel mechanism for the treatment of metabolic diseases. With the exception of lusianthridin and salicylic acid, no natural product (NP) is reported so far to directly target the ADaM site. For the streamlined assessment of direct AMPK activators from the pool of NPs, an integrated workflow using in silico and in vitro methods was applied. Virtual screening combining a 3D shape-based approach and docking identified 21 NPs and NP-like molecules that could potentially activate AMPK. The compounds were purchased and tested in an in vitro AMPK α 1 ß 1 γ 1 kinase assay. Two NP-like virtual hits were identified, which, at 30 µM concentration, caused a 1.65-fold (± 0.24) and a 1.58-fold (± 0.17) activation of AMPK, respectively. Intriguingly, using two different evaluation methods, we could not confirm the bioactivity of the supposed AMPK activator lusianthridin, which rebuts earlier reports.

BonMOLière: Small-Sized Libraries of Readily Purchasable Compounds, Optimized to Produce Genuine Hits in Biological Screens across the Protein Space.

Experimental screening of large sets of compounds against macromolecular targets is a key strategy to identify novel bioactivities. However, large-scale screening requires substantial experimental resources and is time-consuming and challenging. Therefore, small to medium-sized compound libraries with a high chance of producing genuine hits on an arbitrary protein of interest would be of great value to fields related to early drug discovery, in particular biochemical and cell research. Here, we present a computational approach that incorporates drug-likeness, predicted bioactivities, biological space coverage, and target novelty, to generate optimized compound libraries with maximized chances of producing genuine hits for a wide range of proteins. The computational approach evaluates drug-likeness with a set of established rules, predicts bioactivities with a validated, similarity-based approach, and optimizes the composition of small sets of compounds towards maximum target coverage and novelty. We found that, in comparison to the random selection of compounds for a library, our approach generates substantially improved compound sets. Quantified as the "fitness" of compound libraries, the calculated improvements ranged from +60% (for a library of 15,000 compounds) to +184% (for a library of 1000 compounds). The best of the optimized compound libraries prepared in this work are available for download as a dataset bundle ("BonMOLière").

HDAC3i-Finder: A Machine Learning-based Computational Tool to Screen for HDAC3 Inhibitors.

Histone deacetylase 3 (HDAC3) is a potential drug target for treatment of human diseases such as cancer, chronic inflammation, neurodegenerative diseases and diabetes. Machine learning (ML) as an essential cheminformatics approach has been widely used for QSAR modeling. However, none of them has been applied to HDAC3. To this end, we carefully compiled a set of 1098 compounds from the ChEMBL database that have been assayed against HDAC3 and calculated three different sets of molecular features for each compound, i. e. two-dimensional Mordred descriptors, MACCS keys (166 bits) and Morgan2 fingerprints (1024 bits). Five ML classifiers, i. e. k-Nearest Neighbour (KNN), Support Vector Machine (SVM), Random forest (RF), eXtreme Gradient Boosting (XGBoost) and Deep Neural Network (DNN) were trained on each feature set and optimized for classification. A total of 15 models were generated and carefully compared, among which the best-performing one was the XGBoost model based on the Morgan2 fingerprints, i. e. XGBoost_morgan2. Evaluated on a well-curated benchmarking set named MUBD-HDAC3, this model achieved a high early ROC enrichment (ROCE0.5 %: 41.02). A further retrospective screening of an annotated chemical library in PubChem demonstrated that the best model could identify 8 novel-scaffold HDAC3 inhibitors while assaying only 1 % of the compounds. To make this model accessible for the scientific community, we developed a python GUI application named HDAC3i-Finder to facilitate prospective screening for HDAC3 inhibitors. The source code of HDAC3i-Finder is available at https://github.com/jwxia2014/HDAC3i-Finder.

Natural products against acute respiratory infections: Strategies and lessons learned.

ETHNOPHARMACOLOGICAL RELEVANCE: A wide variety of traditional herbal remedies have been used throughout history for the treatment of symptoms related to acute respiratory infections (ARIs). AIM OF THE REVIEW: The present work provides a timely overview of natural products affecting the most common pathogens involved in ARIs, in particular influenza viruses and rhinoviruses as well as bacteria involved in co-infections, their molecular targets, their role in drug discovery, and the current portfolio of available naturally derived anti-ARI drugs. MATERIALS AND METHODS: Literature of the last ten years was evaluated for natural products active against influenza viruses and rhinoviruses. The collected bioactive agents were further investigated for reported activities against ARI-relevant bacteria, and analysed for the chemical space they cover in relation to currently known natural products and approved drugs. RESULTS: An overview of (i) natural compounds active in target-based and/or phenotypic assays relevant to ARIs, (ii) extracts, and (iii) in vivo data are provided, offering not only a starting point for further in-depth phytochemical and antimicrobial studies, but also revealing insights into the most relevant anti-ARI scaffolds and compound classes. Investigations of the chemical space of bioactive natural products based on principal component analysis show that many of these compounds are drug-like. However, some bioactive natural products are substantially larger and have more polar groups than most approved drugs. A workflow with various strategies for the discovery of novel antiviral agents is suggested, thereby evaluating the merit of in silico techniques, the use of complementary assays, and the relevance of ethnopharmacological knowledge on the exploration of the therapeutic potential of natural products. CONCLUSIONS: The longstanding ethnopharmacological tradition of natural remedies against ARIs highlights their therapeutic impact and remains a highly valuable selection criterion for natural materials to be investigated in the search for novel anti-ARI acting concepts. We observe a tendency towards assaying for broad-spectrum antivirals and antibacterials mainly discovered in interdisciplinary academic settings, and ascertain a clear demand for more translational studies to strengthen efforts for the development of effective and safe therapeutic agents for patients suffering from ARIs.

Discovery of Bioactive Natural Products for the Treatment of Acute Respiratory Infections - An Integrated Approach.

In this work, an integrated approach for the identification of new antiviral agents from natural sources for the treatment of acute respiratory infections is presented. The approach comprises (i) the selection of starting material based on traditional knowledge, (ii) phenotypic screening of extracts for antiviral activity, and (iii) the implementation of in silico predictions to identify antiviral compounds and derive the molecular mechanism underlying their biological activity. A variety of starting materials from plants and fungi was selected for the production of 162 extracts. These extracts were tested in cytopathic effect inhibition assays against influenza virus A/Hong Kong/68 (HK/68), rhinovirus A2 (RV-A2), and coxsackie virus B3 (CV-B3). All extracts were also evaluated regarding their cytotoxicity. At an IC50 threshold of 50 µg/mL, 20, 11, and 14% of all tested extracts showed antiviral activity against HK/68, CV-B3, and RV-A2, respectively. Among all active extracts (n = 47), 68% showed antiviral activity against one of the investigated viruses, whereas 31% inhibited at least two viruses. Herein, we present a comprehensive dataset of probed extracts along with their antiviral activities and cytotoxicity. Application examples presented in this work illustrate the phytochemical workflow for the identification of antiviral natural compounds. We also discuss the challenges, pitfalls, and advantages of the integrated approach.

Data Resources for the Computer-Guided Discovery of Bioactive Natural Products.

Natural products from plants, animals, marine life, fungi, bacteria, and other organisms are an important resource for modern drug discovery. Their biological relevance and structural diversity make natural products good starting points for drug design. Natural product-based drug discovery can benefit greatly from computational approaches, which are a valuable precursor or supplementary method to in vitro testing. We present an overview of 25 virtual and 31 physical natural product libraries that are useful for applications in cheminformatics, in particular virtual screening. The overview includes detailed information about each library, the extent of its structural information, and the overlap between different sources of natural products. In terms of chemical structures, there is a large overlap between freely available and commercial virtual natural product libraries. Of particular interest for drug discovery is that at least ten percent of known natural products are readily purchasable and many more natural products and derivatives are available through on-demand sourcing, extraction and synthesis services. Many of the readily purchasable natural products are of small size and hence of relevance to fragment-based drug discovery. There are also an increasing number of macrocyclic natural products and derivatives becoming available for screening.

Discovery of prenylated flavonoids with dual activity against influenza virus and Streptococcus pneumoniae.

Influenza virus neuraminidase (NA) is the primary target for influenza therapeutics. Severe complications are often related to secondary pneumonia caused by Streptococcus pneumoniae (pneumococci), which also express NAs. Recently, a NA-mediated lethal synergism between influenza A viruses and pneumococci was described. Therefore, dual inhibitors of both viral and bacterial NAs are expected to be advantageous for the treatment of influenza. We investigated the traditional Chinese herbal drug sang bái pí (mulberry root bark) as source for anti-infectives. Two prenylated flavonoid derivatives, sanggenon G (4) and sanggenol A (5) inhibited influenza A viral and pneumococcal NAs and, in contrast to the approved NA inhibitor oseltamivir, also planktonic growth and biofilm formation of pneumococci. Evaluation of 27 congeners of 5 revealed a correlation between the degree of prenylation and bioactivity. Abyssinone-V 4'-methyl ether (27) inhibited pneumococcal NA with IC50 = 2.18 µM, pneumococcal growth with MIC = 5.63 µM, and biofilm formation with MBIC = 4.21 µM, without harming lung epithelial cells. Compounds 5 and 27 also disrupt the synergism between influenza A virus and pneumococcal NA in vitro, hence functioning as dual-acting anti-infectives. The results warrant further studies on whether the observed disruption of this synergism is transferable to in vivo systems.

How do metabolites differ from their parent molecules and how are they excreted?

Understanding which physicochemical properties, or property distributions, are favorable for successful design and development of drugs, nutritional supplements, cosmetics, and agrochemicals is of great importance. In this study we have analyzed molecules from three distinct chemical spaces (i) approved drugs, (ii) human metabolites, and (iii) traditional Chinese medicine (TCM) to investigate four aspects determining the disposition of small organic molecules. First, we examined the physicochemical properties of these three classes of molecules and identified characteristic features resulting from their distinctive biological functions. For example, human metabolites and TCM molecules can be larger and more hydrophobic than drugs, which makes them less likely to cross membranes. We then quantified the shifts in physicochemical property space induced by metabolism from a holistic perspective by analyzing a data set of several thousand experimentally observed metabolic trees. Results show how the metabolic system aims to retain nutrients/micronutrients while facilitating a rapid elimination of xenobiotics. In the third part we compared these global shifts with the contributions made by individual metabolic reactions. For better resolution, all reactions were classified into phase I and phase II biotransformations. Interestingly, not all metabolic reactions lead to more hydrophilic molecules. We were able to identify biotransformations leading to an increase of logP by more than one log unit, which could be used for the design of drugs with enhanced efficacy. The study closes with the analysis of the physicochemical properties of metabolites found in the bile, faeces, and urine. Metabolites in the bile can be large and are often negatively charged. Molecules with molecular weight >500 Da are rarely found in the urine, and most of these large molecules are charged phase II conjugates.

Novel pharmacological chaperones that correct phenylketonuria in mice.

Phenylketonuria (PKU) is caused by inherited phenylalanine-hydroxylase (PAH) deficiency and, in many genotypes, it is associated with protein misfolding. The natural cofactor of PAH, tetrahydrobiopterin (BH(4)), can act as a pharmacological chaperone (PC) that rescues enzyme function. However, BH(4) shows limited efficacy in some PKU genotypes and its chemical synthesis is very costly. Taking an integrated drug discovery approach which has not been applied to this target before, we identified alternative PCs for the treatment of PKU. Shape-focused virtual screening of the National Cancer Institute's chemical library identified 84 candidate molecules with potential to bind to the active site of PAH. An in vitro evaluation of these yielded six compounds that restored the enzymatic activity of the unstable PAHV106A variant and increased its stability in cell-based assays against proteolytic degradation. During a 3-day treatment study, two compounds (benzylhydantoin and 6-amino-5-(benzylamino)-uracil) substantially improved the in vivo Phe oxidation and blood Phe concentrations of PKU mice (Pah(enu1)). Notably, benzylhydantoin was twice as effective as tetrahydrobiopterin. In conclusion, we identified two PCs with high in vivo efficacy that may be further developed into a more effective drug treatment of PKU.

From in silico target prediction to multi-target drug design: current databases, methods and applications.

Given the tremendous growth of bioactivity databases, the use of computational tools to predict protein targets of small molecules has been gaining importance in recent years. Applications span a wide range, from the 'designed polypharmacology' of compounds to mode-of-action analysis. In this review, we firstly survey databases that can be used for ligand-based target prediction and which have grown tremendously in size in the past. We furthermore outline methods for target prediction that exist, both based on the knowledge of bioactivities from the ligand side and methods that can be applied in situations when a protein structure is known. Applications of successful in silico target identification attempts are discussed in detail, which were based partly or in whole on computational target predictions in the first instance. This includes the authors' own experience using target prediction tools, in this case considering phenotypic antibacterial screens and the analysis of high-throughput screening data. Finally, we will conclude with the prospective application of databases to not only predict, retrospectively, the protein targets of a small molecule, but also how to design ligands with desired polypharmacology in a prospective manner.

Predicting Cyclooxygenase Inhibition by Three-Dimensional Pharmacophoric Profiling. Part I: Model Generation, Validation and Applicability in Ethnopharmacology.

3D pharmacophore modeling has evolved as an established and state-of-the-art method for performing in-silico predictions of biological activity. Using one single model is limited to single binding modes, while the combination of several models bears a broader application scope. We demonstrate the generation of a complete and predictive 3D model set for cyclooxygenase 1 and 2 inhibitors, along with a selection and validation protocol optimized for parallel virtual screening. This model set was applied to explain the cyclooxygenase activity of an ethnopharmacologically known mixture of natural products, the Thai traditional medicine "Prasaplai". Results show that rationalizing natural product activity by modern in-silico approaches is promising and can be tremendously useful in the identification of the mechanisms of action for known biological effects of complex herbal remedies.

Antiviral potential and molecular insight into neuraminidase inhibiting diarylheptanoids from Alpinia katsumadai.

At present, neuraminidase (NA) inhibitors are the mainstay of pharmacological strategies to fight against global pandemic influenza. In the search for new antiviral drug leads from nature, the seed extract of Alpinia katsumadai has been phytochemically investigated. Among the six isolated constituents, four diarylheptanoids showed in vitro NA inhibitory activities in low micromolar ranges against human influenza virus A/PR/8/34 of subtype H1N1. The most promising constituent, katsumadain A (4; IC(50) = 1.05 +/- 0.42 microM), also inhibited the NA of four H1N1 swine influenza viruses, with IC(50) values between 0.9 and 1.64 muM, and showed antiviral effects in plaque reduction assays. Considering the flexible loop regions of NA, extensive molecular dynamics (MD) simulations were performed to study the putative binding mechanism of the T-shaped diarylheptanoid 4. Docking results showed well-established interactions between the protein and the core of this novel NA-inhibiting natural scaffold, excellent surface complementarity to the simulated binding pocket, and concordance with experimentally derived SAR data.

Critical comparison of virtual screening methods against the MUV data set.

In the current work, we measure the performance of seven ligand-based virtual screening tools--five similarity search methods and two pharmacophore elucidators--against the MUV data set. For the similarity search tools, single active molecules as well as active compound sets clustered in terms of their chemical diversity were used as templates. Their score was calculated against all inactive and active compounds in their target class. Subsequently, the scores were used to calculate different performance metrics including enrichment factors and AUC values. We also studied the effect of data fusion on the results. To measure the performance of the pharmacophore tools, a set of active molecules was picked either random- or chemical diversity-based from each target class to build a pharmacophore model which was then used to screen the remaining compounds in the set. Our results indicate that template sets selected by their chemical diversity are the best choice for similarity search tools, whereas the optimal training sets for pharmacophore elucidators are based on random selection underscoring that pharmacophore modeling cannot be easily automated. We also suggest a number of improvements for future benchmark sets and discuss activity cliffs as a potential problem in ligand-based virtual screening.

Discovery of novel PPAR ligands by a virtual screening approach based on pharmacophore modeling, 3D shape, and electrostatic similarity screening.

Peroxisome proliferator-activated receptors (PPARs) are important targets for drugs used in the treatment of atherosclerosis, dyslipidaemia, obesity, type 2 diabetes, and other diseases caused by abnormal regulation of the glucose and lipid metabolism. We applied a virtual screening workflow based on a combination of pharmacophore modeling with 3D shape and electrostatic similarity screening techniques to discover novel scaffolds for PPAR ligands. From the resulting 10 virtual screening hits, five tested positive in human PPAR ligand-binding domain (hPPAR-LBD) transactivation assays and showed affinities for PPAR in a competitive binding assay. Compounds 5, 7, and 8 were identified as PPAR-alpha agonists, whereas compounds 2 and 9 showed agonistic activity for hPPAR-gamma. Moreover, compound 9 was identified as a PPAR-delta antagonist. These results demonstrate that our virtual screening protocol is able to enrich novel scaffolds for PPAR ligands that could be useful for drug development in the area of atherosclerosis, dyslipidaemia, and type 2 diabetes.

Discovery of novel cathepsin S inhibitors by pharmacophore-based virtual high-throughput screening.

The cysteine protease cathepsin S (CatS) is involved in the pathogenesis of autoimmune disorders, atherosclerosis, and obesity. Therefore, it represents a promising pharmacological target for drug development. We generated ligand-based and structure-based pharmacophore models for noncovalent and covalent CatS inhibitors to perform virtual high-throughput screening of chemical databases in order to discover novel scaffolds for CatS inhibitors. An in vitro evaluation of the resulting 15 structures revealed seven CatS inhibitors with kinetic constants in the low micromolar range. These compounds can be subjected to further chemical modifications to obtain drugs for the treatment of autoimmune disorders and atherosclerosis.

Discovery of nonsteroidal 17beta-hydroxysteroid dehydrogenase 1 inhibitors by pharmacophore-based screening of virtual compound libraries.

17Beta-hydroxysteroid dehydrogenase type 1 (17beta-HSD1) plays a pivotal role in the local synthesis of the most potent estrogen estradiol. Its expression is a prognostic marker for the outcome of patients with breast cancer and inhibition of 17beta-HSD1 is currently under consideration for breast cancer prevention and treatment. We aimed to identify nonsteroidal 17beta-HSD1 inhibitor scaffolds by virtual screening with pharmacophore models built from crystal structures containing steroidal compounds. The most promising model was validated by comparing predicted and experimentally determined inhibitory activities of several flavonoids. Subsequently, a virtual library of nonsteroidal compounds was screened against the 3D pharmacophore. Analysis of 14 selected compounds yielded four that inhibited the activity of human 17beta-HSD1 (IC 50 below 50 microM). Specificity assessment of identified 17beta-HSD1 inhibitors emphasized the importance of including related short-chain dehydrogenase/reductase (SDR) members to analyze off-target effects. Compound 29 displayed at least 10-fold selectivity over the related SDR enzymes tested.

Structure-based virtual screening for the discovery of natural inhibitors for human rhinovirus coat protein.

Inhibitors of the human rhinovirus (HRV) coat protein are promising candidates to treat and prevent a number of upper respiratory diseases. The aim of this study was to find antiviral compounds from nature, focusing on the HRV coat protein. Through computational structure-based screening of an in-house 3D database containing 9676 individual plant metabolites from ancient herbal medicines, combined with knowledge from traditional use, we selected sesquiterpene coumarins from the gum resin asafetida as promising natural products. Chromatographic separation steps resulted in the isolation of microlobidene (1), farnesiferol C (2), farnesiferol B (3), and kellerin (4). Determination of the inhibition of the HRV-induced cytopathic effect for serotypes 1A, 2, 14, and 16 revealed a dose-dependent and selective antirhinoviral activity against serotype 2 for asafetida (IC50 = 11.0 microg/mL) and its virtually predicted constituents 2 (IC50 = 2.5 microM) and 3 (IC50 = 2.6 microM). Modeling studies helped to rationalize the retrieved results.