Natural Product-based Drug Discovery Research Articles

Exploring the potential of marine natural products in drug development: A comprehensive review

The discovery of drugs from marine natural products has witnessed a significant resurgence in recent years. A diverse range of marine organisms, including cone snails, bacteria, cyanobacteria, fungi, and halophytes, have yielded valuable compounds for drug development. These organisms contribute over 90% of the oceanic biomass, offering unique chemical properties with great potential, particularly in the field of anticancer research. The review highlights the historical successes in marine natural product-based drug discovery, emphasizing their novel chemical structures and bioactive properties. It sheds light on critical factors contributing to these discoveries, such as interdisciplinary collaborations between marine biologists, chemists, and pharmacologists, advanced isolation and characterization techniques, and the understanding of marine ecological niches. Challenges include limited supply, environmental concerns, and the complexity of chemical synthesis. Translating marine-derived compounds into viable pharmaceuticals requires rigorous testing, preclinical studies, and clinical trials. Looking forward, advances in research and technology, such as metagenomics and synthetic biology, offer promising avenues for faster and more efficient discovery and development of marine-derived drugs. The potential for uncovering new therapeutic agents remains vast, and the continued exploration of marine natural products holds the promise of significant breakthroughs in drug development while considering sustainable practices and environmental concerns.

Global characterization of biosynthetic gene clusters in non-model eukaryotes using domain architectures

The majority of pharmaceuticals are derived from natural products, bioactive compounds naturally synthesized by organisms to provide evolutionary advantages. Although the rich evolutionary history of eukaryotic algal species implicates a high potential for natural product-based drug discovery, it remains largely untouched. This study investigates 2762 putative biosynthetic gene clusters (BGCs) from 212 eukaryotic algal genomes. To analyze a vast set of structurally diverse BGCs, we employed comparative analysis based on the vectorization of biosynthetic domains, referred to as biosynthetic domain architecture (BDA). By characterizing core biosynthetic machineries through BDA, we identified key BDAs of modular BGCs in diverse eukaryotes and introduced 16 candidate modular BGCs with similar BDAs to previously validated BGCs. This study provides a global characterization of eukaryotic algal BGCs, offering an alternative to laborious manual curation for BGC prioritization.

Synthesis of biologically active compounds derived from natural products

Natural products and their structural analogues have historically made a major contribution to pharmacotherapy, especially for cancer and infectious diseases. Nevertheless, natural products also present challenges for drug discovery, such as technical barriers to screening, isolation, characterization and optimization, which contributed to a decline in their pursuit by the pharmaceutical industry from the 1990s onwards. In recent years, several technological and scientific developments including improved analytical tools, genome mining and engineering strategies, and microbial culturing advances are addressing such challenges and opening up new opportunities. Consequently, interest in natural products as drug leads is being revitalized, particularly for tackling antimicrobial resistance. Here, we summarize recent technological developments that are enabling natural product-based drug discovery, highlight selected applications and discuss key opportunities.The first total synthesis and development of a variety of bioactive natural products have been accomplished by using carbohydrates as a chiral source. In addition, practically useful intermediates have been created, analogs of natural products have been prepared, their structure-activity relationships studied, and the large-scale preparations of medicinally useful compounds established.

Single botanical drugs in the Ayurvedic Pharmacopoeia of India-A quantitative ethnobotanical analysis.

Developing evidence-based uses of herbal medicines and natural product-based drug discovery are two core aims of ethnopharmacology. This requires an understanding of the medicinal plants and the traditional medical knowledge associated with them which is a basis for cross-cultural comparison. The botanical drugs of traditional medical systems are still not understood well, even for well-known and widely respected traditions like Ayurveda. In this study, a quantitative ethnobotanical analysis was performed on the single botanical drugs included in the Ayurvedic Pharmacopoeia of India (API), presenting an overview on the medicinal plants of Ayurveda from perspectives of plant systematics and medical ethnobotany. Part-I of API includes 621 single botanical drugs, which are sourced from 393 species (323 genera in 115 families). Of these, 96 species yield two or more drugs, together accounting for 238 drugs. Taking the traditional concepts, biomedical uses and the pragmatic disease classification into account, therapeutic uses of these botanical drugs are sorted into 20 categories, which meet primary health demands. The therapeutic uses of the drugs sourced from the same species may differ considerably, but 30 of the 238 drugs are used in highly similar way. The comparative phylogenetic analysis identifies 172 species with high potential for specific therapeutic uses. This medical ethnobotanical assessment for the first time provides a comprehensive understanding on the single botanical drugs in API from the perspective of medical botany using an "etic" (scientist-oriented) approach. This study also highlights the importance of quantitative ethnobotanic methods in understanding traditional medical knowledge.

N(14)-substituted evodiamine derivatives as dual topoisomerase 1/tubulin-Inhibiting anti-gastrointestinal tumor agents

Gastrointestinal tumor is an important factor threatening human health. Natural product-based drug discovery is a popular paradigm for expanding the chemical space and identifying new molecular entities that ameliorate human disease. Evodiamine-inspired medicinal chemistry presents therapeutic potential for treating tumors in different tissues via multi-target inhibition. Here, by focusing on the discovery of anti-gastrointestinal tumor drugs, a series of N(14) alkyl-substituted evodiamine derivatives were designed and synthesized. The structure–activity relationship studies culminated in the identification of the N(14)-propyl-substituted evodiamine analog 6b, which showed low nanomolar inhibitory activity against MGC-803 (IC50 = 0.09 μM) and RKO (IC50 = 0.2 μM) cell lines. Moreover, compound 6b was effective in inducing apoptosis, arresting the cell cycle in the G2/M phase, and inhibiting migration and invasion of MGC-803 and RKO cell lines in a dose-dependent manner in vitro. Further antitumor mechanism studies revealed that compound 6b significantly inhibited topoisomerase 1 (inhibition rate of 58.3% at 50 μM) and tubulin polymerization (IC50 = 5.69 μM). Overall, compound 6b represents a promising dual topoisomerase 1/tubulin-targeting lead structure for the treatment of gastrointestinal tumor.

Systematic exploration of the efficacy marker compounds of Danshen (Salvia miltiorrhiza Bge.) driven by the efficacy network target

IntroductionChinese Materia Medica (CMM) provides medical treatment and healthcare worldwide. However, precision quality control of CMM is considered a bottleneck in Chinese herbal modernization. Considering the multi-efficacy of the clinical applications of CMM, we adopted a novel strategy based on the efficacy network target to screen for efficacy-related constituents and efficacy markers. MethodsNetwork pharmacology and high-throughput molecular docking were integrated to construct efficacy network targets for activating blood (AB), cooling blood (CB), and easing mind (EM) in Danshen (Salvia miltiorrhiza Bge.). Cytoscape with plugins was used to analyze the structure and function of the networks. The efficacy scores of the components were calculated and ranked to identify efficacy markers, which were further verified using the PubChem database. ResultsFollowing 9164 ligand-receptor molecular docking, 328 AB efficacy targets, 88 CB efficacy targets, and 52 EM efficacy targets were validated in silico. Three efficacy network targets were then constructed, and topological and functional analyses showed that the networks were consistent with the corresponding efficacy. Six AB, three CB, and five EM efficacy markers were screened using comprehensive analysis. Most of the indices (13/14) were confirmed to be associated with matching efficacy. ConclusionThe indices selected based on efficacy level could satisfy the clinical requirements of traditional Chinese medicine (TCM). Therefore, the strategy would be conducive to providing a new approach for natural product-based drug discovery and achieving accurate quality control for Danshen following multi-efficacy application.

National Cancer Institute (NCI) Program for Natural Product Discovery: Exploring NCI-60 Screening Data of Natural Product Samples with Artificial Neural Networks.

National Cancer Institute (NCI) Program for Natural Product Discovery is a new initiative aimed at creating new technologies for natural product-based drug discovery. Here, we present the development of a neural network-based bioinformatics platform for visualization and analysis of natural product high-throughput screening data using the NCI's 60 human tumor cell anticancer drug screen. We demonstrate how the tool enables visualization of similar patterns of response that can be parsed both chemically and taxonomically, grouping NCI-60 biological profiles in one easy-to-use bioinformatics interface.

Scaffold and Structural Diversity of the Secondary Metabolite Space of Medicinal Fungi.

Medicinal fungi, including mushrooms, have well-documented therapeutic uses. In this study, we perform a cheminformatics-based investigation of the scaffold and structural diversity of the secondary metabolite space of medicinal fungi and, moreover, perform a detailed comparison with approved drugs, other natural product libraries, and semi-synthetic libraries. We find that the secondary metabolite space of medicinal fungi has similar or higher scaffold diversity in comparison to other natural product libraries analyzed here. Notably, 94% of the scaffolds in the secondary metabolite space of medicinal fungi are not present in the approved drugs. Further, we find that the secondary metabolites, on the one hand, are structurally far from the approved drugs, while, on the other hand, they are close in terms of molecular properties to the approved drugs. Lastly, chemical space visualization using dimensionality reduction methods showed that the secondary metabolite space has minimal overlap with the approved drug space. In a nutshell, our results underscore that the secondary metabolite space of medicinal fungi is a valuable resource for identifying potential lead molecules for natural product-based drug discovery.

C-H modification of natural products: a minimalist enabling tactic for drug discovery, API processing and bioconjugation.

Intrusion into the C-H chemical space of natural products through the strategic deployment of C-H functionalization reactions could lead to incredibly new molecular diversities with an unforeseen impact on biological functions. Based on this hypothesis, semisynthetic C-H modification of natural products is emerging as a minimalist tactic in natural product based drug discovery. Several examples of C-H modification of natural products, resulting in functional gains in key pharmacological attributes viz. potency, aqueous solubility and DMPK profile, along with opportunities in allied areas such as API processing, bioconjugation, and target deconvolution, continue to surface in the recent literature. The strategy has already recorded commercial success in the development of antineoplastic drugs topotecan and irinotecan and in industrial production of pravastatin, calcitriol and artemisinin. This Feature Article highlights the broad contours of this evolving paradigm at the interface of natural product and synthetic chemistry research to accelerate and widen the scope of natural product-based drug discovery.

Challenges in natural product-based drug discovery assisted with in silico-based methods.

The application of traditional medicine by humans for the treatment of ailments as well as improving the quality of life far outdates recorded history. To date, a significant percentage of humans, especially those living in developing/underprivileged communities still rely on traditional medicine for primary healthcare needs. In silico-based methods have been shown to play a pivotal role in modern pharmaceutical drug discovery processes. The application of these methods in identifying natural product (NP)-based hits has been successful. This is very much observed in many research set-ups that use rationally in silico-based methods in combination with experimental validation techniques. The combination has rendered the use of in silico-based approaches even more popular and successful in the investigation of NPs. However, identifying and proposing novel NP-based hits for experimental validation comes with several challenges such as the availability of compounds by suppliers, the huge task of separating pure compounds from complex mixtures, the quantity of samples available from the natural source to be tested, not to mention the potential ecological impact if the natural source is exhausted. Because most peer-reviewed publications are biased towards "positive results", these challenges are generally not discussed in publications. In this review, we highlight and discuss these challenges. The idea is to give interested scientists in this field of research an idea of what they can come across or should be expecting as well as prompting them on how to avoid or fix these issues.

Combined Bioinformatics and Combinatorial Chemistry Tools to Locate Drug-Able Anti-TB Phytochemicals: A Cost-Effective Platform for Natural Product-Based Drug Discovery.

Based on extensive experimental studies, a huge number of phytochemicals showed potential activity against tuberculosis (TB) at a lower minimum inhibitory concentration (MIC) and fewer toxicity profiles. However, these promising drugs have not been able to convert from 'lead' to 'mainstream' due to inadequate drug-ability profiles. Thus, early drug-prospective analyses are required at the primary stage to accelerate natural-product-based drug discovery with limited resources and time. In the present study, we have selected seventy-three potential anti-TB phytochemicals (MIC value ≤10 μg/mL) and assessed the drug-ability profiles using bioinformatics and combinatorial chemistry tools, systematically. Primarily, the molecular docking study was done against two putative drug targets, catalase-peroxidase enzyme (katG) and RNA polymerase subunit-β (rpoB) of Mycobacterium tuberculosis (Mtb) using AutoDock 4.2 software. Further, assessed the drug-ability score from Molsoft, toxicity profiles from ProTox, pharmacokinetics from SwisADME, hierarchical cluster analysis (HCA) by ChemMine tools and frontier molecular orbitals (FMOs) with Avogadro and structural activity relationships (SAR) analysis with ChemDraw 18.0 software. Above analyses indicated that, lower MIC exhibited anti-TB phytochemicals, abietane, 12-demethylmulticaulin exhibited poor docking and drug-ability scores, while tiliacorinine, 2-nortiliacorinine showed higher binding energy and drug-ability profiles. Overall, tiliacorinine, 2-nortiliacorinine, 7α-acetoxy-6β-hydroxyroyleanone (AHR), (2S)-naringenin and isovachhalcone were found as the most active and drug-able anti-TB candidates from 73 candidates. Phytochemicals are always a vital source of mainstream drugs, but the MIC value of a phytochemical is not sufficient for it to be promoted. An ideal drug-ability profile is therefore essential for achieving clinical success, where advanced bioinformatics tools help to assess and analyse that profile. Additionally, several natural pharmacophores found in existing anti-TB drugs in SAR analyses also provide crucial information for developing potential anti-TB drug. As a conclusion, combined bioinformatics and combinatorial chemistry are the most effective strategies to locate potent-cum-drug-able candidates in the current drug-development module.

A case history in natural product-based drug discovery: discovery of vorapaxar (Zontivity™)

A case history in natural product-based drug discovery: discovery of vorapaxar (Zontivity™)

Artemisinin derivative SM934 in the treatment of autoimmune and inflammatory diseases: therapeutic effects and molecular mechanisms.

Artemisinin and its derivatives are the well-known anti-malarial drugs derived from a traditional Chinese medicine. In addition to antimalarial, artemisinin and its derivatives possess distinguished anti-cancer, anti-oxidant, anti-inflammatory and anti-viral activities, but the poor solubility and low bioavailability hinder their clinical application. In the last decades a series of new water-soluble and oil-soluble derivatives were synthesized. Among them, we have found a water-soluble derivative β-aminoarteether maleate (SM934) that exhibits outstanding suppression on lymphocytes proliferation in immunosuppressive capacity and cytotoxicity screening assays with 35-fold higher potency than dihydroartemisinin. SM934 displays significant therapeutic effects on various autoimmune and inflammatory diseases, including systemic lupus erythematosus, antiphospholipid syndrome nephropathy, membranous nephropathy, rheumatoid arthritis, multiple sclerosis, inflammatory bowel disease, and dry eye disease. Here, we summarize the immunomodulatory effects, anti-inflammatory, anti-oxidative and anti-fibrosis activities of SM934 in disease-relevant animal models and present the probable pharmacological mechanisms involved in its therapeutic efficacy. This review also delineates a typical example of natural product-based drug discovery, which might further vitalize natural product exploration and development in pharmacotherapy.

Activating cryptic biosynthetic gene cluster through a CRISPR-Cas12a-mediated direct cloning approach.

Direct cloning of biosynthetic gene clusters (BGCs) from microbial genomes facilitates natural product-based drug discovery. Here, by combining Cas12a and the advanced features of bacterial artificial chromosome library construction, we developed a fast yet efficient in vitro platform for directly capturing large BGCs, named CAT-FISHING (CRISPR/Cas12a-mediated fast direct biosynthetic gene cluster cloning). As demonstrations, several large BGCs from different actinomycetal genomic DNA samples were efficiently captured by CAT-FISHING, the largest of which was 145 kb with 75% GC content. Furthermore, the directly cloned, 110 kb long, cryptic polyketide encoding BGC from Micromonospora sp. 181 was then heterologously expressed in a Streptomyces chassis. It turned out to be a new macrolactam compound, marinolactam A, which showed promising anticancer activity. Our results indicate that CAT-FISHING is a powerful method for complicated BGC cloning, and we believe that it would be an important asset to the entire community of natural product-based drug discovery.

Metabolomics of the secondary metabolites of Ammi visnaga L. roots (family Apiaceae) and evaluation of their biological potential

Recent scientific advancements in biotechnological, nanobiotechnological, and omics approaches have blossomed into a plethora of applications that hold the potential to revolutionize natural product-based drug discovery. In this context, the present work was undertaken to unveil the secondary metabolites of the under-explored roots of Ammi visnaga L. (family Apiaceae) using HPLC‒HESI‒HRMS-based metabolomics, along with evaluating their anti-proliferative potential against a number of tumor cells in comparison with their green synthesized silver nanoparticles (SNPs), for the first time. Overall, the total ethanol extract of A. visnaga roots (TEEAVR) revealed potent inhibitory actions against colon cancer (Caco-2), breast cancer (Mcf-7), and hepatocellular carcinoma (HepG-2) cell lines, with IC50 values of 3.19 ± 0.07, 12.36 ± 0.13, and 18.38 ± 0.09 µg/mL, respectively. More interestingly, the prepared biogenic SNPs of TEEAVR exhibited more prominent inhibitory potential, particularly against Caco-2 tumor cells (IC50= 0.24 ± 0.05 µg/mL), which was even higher than that of doxorubicin (IC50= 2.12 ± 0.04 µg/mL). Moreover, HPLC‒HESI‒HRMS metabolic profiling of TEEAVR resulted in the characterization of a group of chemically varied metabolites, including phenylpropanoids, flavonoids, isobenzofuranones, coumarins, and chromones. Among them, junipediol A 4-O-glucoside (1a), junipediol A 8-O-glucoside (1b), acacetin (2), and apiumetin-O-glucoside (7) exhibited greater binding affinities to the Epidermal Growth Factor Receptor (EGFR) tyrosine kinase compared to the reference drug, erlotinib; suggesting their possible contribution as EGFR inhibitors to the promising anti-proliferative properties of TEEAVR. These findings cast a new light on the metabolic diversity of A. visnaga roots as an overlooked underground treasure of pharmaceutically relevant phytochemicals that could be incorporated into future anticancer drug research.

Chalcone Scaffolds, Bioprecursors of Flavonoids: Chemistry, Bioactivities, and Pharmacokinetics.

Chalcones are secondary metabolites belonging to the flavonoid (C6-C3-C6 system) family that are ubiquitous in edible and medicinal plants, and they are bioprecursors of plant flavonoids. Chalcones and their natural derivatives are important intermediates of the flavonoid biosynthetic pathway. Plants containing chalcones have been used in traditional medicines since antiquity. Chalcones are basically α,β-unsaturated ketones that exert great diversity in pharmacological activities such as antioxidant, anticancer, antimicrobial, antiviral, antitubercular, antiplasmodial, antileishmanial, immunosuppressive, anti-inflammatory, and so on. This review provides an insight into the chemistry, biosynthesis, and occurrence of chalcones from natural sources, particularly dietary and medicinal plants. Furthermore, the pharmacological, pharmacokinetics, and toxicological aspects of naturally occurring chalcone derivatives are also discussed herein. In view of having tremendous pharmacological potential, chalcone scaffolds/chalcone derivatives and bioflavonoids after subtle chemical modification could serve as a reliable platform for natural products-based drug discovery toward promising drug lead molecules/drug candidates.

An Update on Pharmacological Relevance and Chemical Synthesis of Natural Products and Derivatives with Anti SARS-CoV-2 Activity.

Natural products recognized traditionally as a vital source of active constituents in pharmacotherapy. The COVID‐19 infection caused by severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) is highly transmissible, pathogenic, and considered an ongoing global health emergency. The emergence of COVID‐19 globally and the lack of adequate treatment brought attention towards herbal medicines, and scientists across the globe instigated the search for novel drugs from medicinal plants and natural products to tackle this deadly virus. The natural products rich in scaffold diversity and structural complexity are an excellent source for antiviral drug discovery. Recently the investigation of several natural products and their synthetic derivatives resulted in the identification of promising anti SARS‐CoV‐2 agents. This review article will highlight the pharmacological relevance and chemical synthesis of the recently discovered natural product and their synthetic analogs as SARS‐CoV‐2 inhibitors. The summarized information will pave the path for the natural product‐based drug discovery of safe and potent antiviral agents, particularly against SARS‐CoV‐2.

Building Natural Product Libraries Using Quantitative Clade-Based and Chemical Clustering Strategies.

ABSTRACTThe success of natural product-based drug discovery is predicated on having chemical collections that offer broad coverage of metabolite diversity. We propose a simple set of tools combining genetic barcoding and metabolomics to help investigators build natural product libraries aimed at achieving predetermined levels of chemical coverage. It was found that such tools aided in identifying overlooked pockets of chemical diversity within taxa, which could be useful for refocusing collection strategies. We have used fungal isolates identified as Alternaria from a citizen-science-based soil collection to demonstrate the application of these tools for assessing and carrying out predictive measurements of chemical diversity in a natural product collection. Within Alternaria, different subclades were found to contain nonequivalent levels of chemical diversity. It was also determined that a surprisingly modest number of isolates (195 isolates) was sufficient to afford nearly 99% of Alternaria chemical features in the data set. However, this result must be considered in the context that 17.9% of chemical features appeared in single isolates, suggesting that fungi like Alternaria might be engaged in an ongoing process of actively exploring nature’s metabolic landscape. Our results demonstrate that combining modest investments in securing internal transcribed spacer (ITS)-based sequence information (i.e., establishing gene-based clades) with data from liquid chromatography-mass spectrometry (i.e., generating feature accumulation curves) offers a useful route to obtaining actionable insights into chemical diversity coverage trends in a natural product library. It is anticipated that these outcomes could be used to improve opportunities for accessing bioactive molecules that serve as the cornerstone of natural product-based drug discovery.IMPORTANCE Natural product drug discovery efforts rely on libraries of organisms to provide access to diverse pools of compounds. Actionable strategies to rationally maximize chemical diversity, rather than relying on serendipity, can add value to such efforts. Readily implementable biological (i.e., ITS sequence analysis) and chemical (i.e., mass spectrometry-based feature and scaffold measurements) diversity assessment tools can be employed to monitor and adjust library development tactics in real time. In summary, metabolomics-driven technologies and simple gene-based specimen barcoding approaches have broad applicability to building chemically diverse natural product libraries.

BIOACTIVE PHYTOCONSTITUENTS WITH HEPATOPROTECTIVE POTENTIAL: A REVIEW

The liver is a requisite organ in the body, maintaining various physiological functions. Liver damage or toxicity is due to consuming certain chemicals, dietary supplements, drugs, and alcohol at high doses in day-to-day life. Several phytoconstituents acts as hepatoprotective agents. Research advances in analytical techniques and knowledge of active components have allowed research scientists to study the relationship between the phytoconstituents and their activity on the human liver. The review article aims to compile data on promising active constituents that act as lead molecules for future natural products-based drug discovery. The online database, including Web of Science, PubMed, Scopus, and Science Direct, was explored for papers and studied the hepatoprotective activity. Some phytoconstituents act against the chemicals such as CCl4 , paracetamol, alcohol, oxidative stress-induced hepatotoxicity, etc.

Target Prediction Model for Natural Products Using Transfer Learning.

A large proportion of lead compounds are derived from natural products. However, most natural products have not been fully tested for their targets. To help resolve this problem, a model using transfer learning was built to predict targets for natural products. The model was pre-trained on a processed ChEMBL dataset and then fine-tuned on a natural product dataset. Benefitting from transfer learning and the data balancing technique, the model achieved a highly promising area under the receiver operating characteristic curve (AUROC) score of 0.910, with limited task-related training samples. Since the embedding distribution difference is reduced, embedding space analysis demonstrates that the model’s outputs of natural products are reliable. Case studies have proved our model’s performance in drug datasets. The fine-tuned model can successfully output all the targets of 62 drugs. Compared with a previous study, our model achieved better results in terms of both AUROC validation and its success rate for obtaining active targets among the top ones. The target prediction model using transfer learning can be applied in the field of natural product-based drug discovery and has the potential to find more lead compounds or to assist researchers in drug repurposing.