Lead Discovery Research Articles

An artificial intelligence accelerated virtual screening platform for drug discovery

Structure-based virtual screening is a key tool in early drug discovery, with growing interest in the screening of multi-billion chemical compound libraries. However, the success of virtual screening crucially depends on the accuracy of the binding pose and binding affinity predicted by computational docking. Here we develop a highly accurate structure-based virtual screen method, RosettaVS, for predicting docking poses and binding affinities. Our approach outperforms other state-of-the-art methods on a wide range of benchmarks, partially due to our ability to model receptor flexibility. We incorporate this into a new open-source artificial intelligence accelerated virtual screening platform for drug discovery. Using this platform, we screen multi-billion compound libraries against two unrelated targets, a ubiquitin ligase target KLHDC2 and the human voltage-gated sodium channel NaV1.7. For both targets, we discover hit compounds, including seven hits (14% hit rate) to KLHDC2 and four hits (44% hit rate) to NaV1.7, all with single digit micromolar binding affinities. Screening in both cases is completed in less than seven days. Finally, a high resolution X-ray crystallographic structure validates the predicted docking pose for the KLHDC2 ligand complex, demonstrating the effectiveness of our method in lead discovery.

Selective Aurora A-TPX2 Interaction Inhibitors Have In Vivo Efficacy as Targeted Antimitotic Agents.

Aurora A kinase, a cell division regulator, is frequently overexpressed in various cancers, provoking genome instability and resistance to antimitotic chemotherapy. Localization and enzymatic activity of Aurora A are regulated by its interaction with the spindle assembly factor TPX2. We have used fragment-based, structure-guided lead discovery to develop small molecule inhibitors of the Aurora A-TPX2 protein-protein interaction (PPI). Our lead compound, CAM2602, inhibits Aurora A:TPX2 interaction, binding Aurora A with 19 nM affinity. CAM2602 exhibits oral bioavailability, causes pharmacodynamic biomarker modulation, and arrests the growth of tumor xenografts. CAM2602 acts by a novel mechanism compared to ATP-competitive inhibitors and is highly specific to Aurora A over Aurora B. Consistent with our finding that Aurora A overexpression drives taxane resistance, these inhibitors synergize with paclitaxel to suppress the outgrowth of pancreatic cancer cells. Our results provide a blueprint for targeting the Aurora A-TPX2 PPI for cancer therapy and suggest a promising clinical utility for this mode of action.

Natural and synthetic 5-(3'-indolyl)oxazoles: Biological activity, chemical synthesis and advanced molecules.

5-(3'-Indolyl)oxazole moiety is a privileged heterocyclic scaffold, embedded in many biologically interesting natural products and potential therapeutic agents. Compounds containing this scaffold, whether from natural sources or synthesized, have demonstrated a wide array of biological activities. This has piqued the interest of synthetic chemists, leading to a large number of reported synthetic approaches to 5-(3'-indolyl)oxazole scaffold in recent years. In this review, we comprehensively overviewed the different biological activities and chemical synthetic methods for the 5-(3'-indolyl)oxazole scaffold reported in the literatures from 1963 to 2024. The focus of this study is to highlight the significance of 5-(3'-indolyl)oxazole derivatives as the lead compounds for the lead discovery of anticancer, pesticidal, antimicrobial, antiviral, antioxidant and anti-inflammatory agents, to summarize the synthetic methods for the 5-(3'-indolyl)oxazole scaffold. In addition, the reported mechanism of action of 5-(3'-indolyl)oxazoles and advanced molecules studied in animal models are also reviewed. Furthermore, this review offers perspectives on how 5-(3'-indolyl)oxazole scaffold as a privileged structure might be exploited in the future.

Automated Absolute Binding Free Energy Calculation Workflow for Drug Discovery.

Absolute binding free energies play a crucial role in drug development, particularly as part of the lead discovery process. In recent work, we showed how in silico predictions directly could support drug development by ranking and recommending favorable ideas over unfavorable ones. Here, we demonstrate a Python workflow that enables the calculation of ABFEs with minimal manual input effort, such as the receptor PDB and ligand SDF files, and outputs a .tsv file containing the ranked ligands and their corresponding binding free energies. The implementation uses Snakemake to structure and control the execution of tasks, allowing for dynamic control of parameters and execution patterns. We provide an example of a benchmark system that demonstrates the effectiveness of the automated workflow.

Structure-based mapping of the histone-binding pocket of KDM4D using functionalized tetrazole and pyridine core compounds

KDM4 histone demethylases became an exciting target for inhibitor development as the evidence linking them directly to tumorigenesis mounts. In this study, we set out to better understand the binding cavity using an X-ray crystallographic approach to provide a detailed landscape of possible interactions within the under-investigated region of KDM4. Our design strategy was based on utilizing known KDM binding motifs, such as nicotinic acid and tetrazolylhydrazides, as core motifs that we decided to enrich with flexible tails to map the distal histone binding site. The resulting X-ray structures of the novel compounds bound to KDM4D, a representative of the KDM4 family, revealed the interaction pattern with distal residues in the histone-binding site. The most prominent protein rearrangement detected upon ligand binding is the loop movement that blocks the accessibility to the histone binding site. Apart from providing new sites that potential inhibitors can target, the novel compounds may prove helpful in exploring the capacity of ligands to bind in sites distal to the cofactor-binding site of other KDMs or 2-oxoglutarate (2OG)-dependent oxygenases. The case study proves that combining a strong small binding motif with flexible tails to probe the binding pocket will facilitate lead discovery in classical drug-discovery campaigns, given the ease of accessing X-ray quality crystals.

Integrating classic AI and agriculture: A novel model for predicting insecticide-likeness to enhance efficiency in insecticide development

The integration of artificial intelligence (AI) into smart agriculture boosts production and management efficiency, facilitating sustainable agricultural development. In intensive agricultural management, adopting eco-friendly and effective pesticides is crucial to promote green agricultural practices. However, exploring new insecticides species is a difficult and time-consuming task that involves significant risks. Enhancing compound druggability in the lead discovery phase could considerably shorten the discovery cycle, accelerating insecticides research and development. The Insecticide Activity Prediction (IAPred) model, a novel classic artificial intelligence-based method for evaluating the potential insecticidal activity of unknown functional compounds, is introduced in this study. The IAPred model utilized 27 insecticide-likeness features from PaDEL descriptors and employed an ensemble of Support Vector Machine (SVM) and Random Forest (RF) algorithms using the hard-vote mechanism, achieving an accuracy rate of 86 %. Notably, the IAPred model outperforms current models by accurately predicting the efficacy of novel insecticides such as nicofluprole, overcoming the limitations inherent in existing insecticide structures. Our research presents a practical approach for discovering and optimizing novel insecticide lead compounds quickly and efficiently.

Breaking the Barriers: Machine-Learning-Based c-RASAR Approach for Accurate Blood-Brain Barrier Permeability Prediction.

The intricate nature of the blood-brain barrier (BBB) poses a significant challenge in predicting drug permeability, which is crucial for assessing central nervous system (CNS) drug efficacy and safety. This research utilizes an innovative approach, the classification read-across structure-activity relationship (c-RASAR) framework, that leverages machine learning (ML) to enhance the accuracy of BBB permeability predictions. The c-RASAR framework seamlessly integrates principles from both read-across and QSAR methodologies, underscoring the need to consider similarity-related aspects during the development of the c-RASAR model. It is crucial to note that the primary goal of this research is not to introduce yet another model for predicting BBB permeability but rather to showcase the refinement in predicting the BBB permeability of organic compounds through the introduction of a c-RASAR approach. This groundbreaking methodology aims to elevate the accuracy of assessing neuropharmacological implications and streamline the process of drug development. In this study, an ML-based c-RASAR linear discriminant analysis (LDA) model was developed using a dataset of 7807 compounds, encompassing both BBB-permeable and -nonpermeable substances sourced from the B3DB database (freely accessible from https://github.com/theochem/B3DB), for predicting BBB permeability in lead discovery for CNS drugs. The model's predictive capability was then validated using three external sets: one containing 276,518 natural products (NPs) from the LOTUS database (accessible from https://lotus.naturalproducts.net/download) for data gap filling, another comprising 13,002 drug-like/drug compounds from the DrugBank database (available from https://go.drugbank.com/), and a third set of 56 FDA-approved drugs to assess the model's reliability. Further diversifying the predictive arsenal, various other ML-based c-RASAR models were also developed for comparison purposes. The proposed c-RASAR framework emerged as a powerful tool for predicting BBB permeability. This research not only advances the understanding of molecular determinants influencing CNS drug permeability but also provides a versatile computational platform for the rapid assessment of diverse compounds, facilitating informed decision-making in drug development and design.

Evaluation of In vitro anti-inflammatory activity and In-silico pharmacokinetics and molecular docking study of Horsfieldia iryaghedhi

Background: Phytochemicals are still a valuable source to develop clinically important drugs in treating chronic and acute diseases. Inflammation is a response to an injurious stimulus of the body and novel therapeutic agents are needed to alleviate the condition with minimum side effects. Aims and Objectives: To investigate in vitro anti-inflammatory activity of methanol and aqueous leaf, bark, and combination extracts of plant Horsfieldia iryaghedhi by heat-induced egg albumin denaturation method and to analyze the phytochemicals of Horsfieldia iryaghedhi for their anti-inflammatory potential against cyclooxygenase- 2 (COX-2) using molecular docking. Methodology: Matured and fully expanded fresh leaves and barks of H. iryaghedhi were collected, and the extractions were obtained cold maceration using 99.9% methanol and distilled water as solvents. A concentration series was then developed, and the anti-inflammatory activity was evaluated against Diclofenac sodium as the positive control, using the heat-induced egg albumin denaturation method. Further, selected phytochemicals were tested against COX-2 enzyme (PDB ID: 5IKR) using site-specific molecular docking with autodock vina and the binding energies and pharmacokinetic and toxicity parameters were evaluated. Results: The methanol and aqueous extracts have shown a moderate to strong concentration-dependent anti-inflammatory activity with reference to standard Diclofenac sodium (IC50 116.4 μg/ml) and Methanol bark extract exhibited potent anti-inflammatory activity compared to other extracts (IC50 293 μg/ml). Further, Methanol and aqueous extracts showed a statistically significant correlation between concentration and percentage inhibition (p<0.05, R2 ≈1). The molecular docking results suggest that the phytochemicals available on the plant have possible COX-2 inhibitory activity and the compounds selected (Methyl 2,4-dihydroxy-6-methylbenzoate and N, N-Dimethyl-5-methoxy tryptamine) even got favourable toxicity and pharmacokinetic parameters confirming their drugability. Conclusion: Methanol bark extract of Horsfieldia iryaghedhi have marked in vitro anti-inflammatory activity. The results indicate a solid possibility of lead discovery of anti- inflammatory agents from the bark and leaves of Horsfieldia iryaghedhi. However, further molecular dynamics studies and in vivo tests may be required to confirm the findings.

Facile Synthesis and First Antifungal Exploration of Tetracyclic Meroterpenoids: (+)-Aureol, (-)-Pelorol, and Its Analogs.

As an important bioactive molecular backbone, drimane meroterpenoids have drawn a great deal of attention from both pharmacologists and chemists. Inspired by the prevalidated success of conformational restriction in the discovery of novel pharmaceutical leads, two distinct tetracyclic drimane meroterpenoids, (-)-pelorol and (+)-aureol, were synthesized from the inexpensive starting material (-)-sclareol through 10 and 8 steps with 5.6% and 5.4% overall yield, respectively. The mild conditions, operational facility, and scalability enabled the expedient synthesis and biological exploration of not only natural products themselves but also their mimics. The first agrochemical exploration showed (-)-pelorol and (+)-aureol possessed good antifungal activity against Rhizoctonia solani, with EC50 values of 7.7 and 6.9 μM, respectively. This revealed that tetracyclic drimane meroterpenoids are valuable models for antifungal lead discovery.

A Novel NMR-Based Protocol to Screen Ultralow Molecular Weight Fragments.

Fragment-based lead discovery has emerged as one of the most efficient screening strategies for finding hit molecules in drug discovery. Recently, a novel strategy based on a class of fragments characterized by an ultralow molecular weight (ULMW) has been proposed. These fragments bind to the target with a very low affinity, requiring reliable biophysical methods for detection. The most notable application of ULMW used a set of 81 fragments, named MiniFrags, and screened them by X-ray crystallography. We extended the utilization of this novel class of fragments to another gold standard technique for fragment-based screening: nuclear magnetic resonance (NMR). Here, we present a novel NMR protocol to detect and analyze such weak interactions in a challenging real-world scenario: a flexible target with a flat, water-exposed binding site. We identified a subset of 69 highly water-soluble MiniFrags that were screened against the antiapoptotic protein human Bfl-1.

Discovery of the First‐in‐Class Inhibitors of Hypoxia Up‐Regulated Protein 1 (HYOU1) Suppressing Pathogenic Fibroblast Activation

AbstractFibroblasts are key regulators of inflammation, fibrosis, and cancer. Targeting their activation in these complex diseases has emerged as a novel strategy to restore tissue homeostasis. Here, we present a multidisciplinary lead discovery approach to identify and optimize small molecule inhibitors of pathogenic fibroblast activation. The study encompasses medicinal chemistry, molecular phenotyping assays, chemoproteomics, bulk RNA‐sequencing analysis, target validation experiments, and chemical absorption, distribution, metabolism, excretion and toxicity (ADMET)/pharmacokinetic (PK)/in vivo evaluation. The parallel synthesis employed for the production of the new benzamide derivatives enabled us to a) pinpoint key structural elements of the scaffold that provide potent fibroblast‐deactivating effects in cells, b) discriminate atoms or groups that favor or disfavor a desirable ADMET profile, and c) identify metabolic “hot spots”. Furthermore, we report the discovery of the first‐in‐class inhibitor leads for hypoxia up‐regulated protein 1 (HYOU1), a member of the heat shock protein 70 (HSP70) family often associated with cellular stress responses, particularly under hypoxic conditions. Targeting HYOU1 may therefore represent a potentially novel strategy to modulate fibroblast activation and treat chronic inflammatory and fibrotic disorders.

Discovery of the First-in-Class Inhibitors of Hypoxia Up-Regulated Protein 1 (HYOU1) Suppressing Pathogenic Fibroblast Activation.

Fibroblasts are key regulators of inflammation, fibrosis, and cancer. Targeting their activation in these complex diseases has emerged as a novel strategy to restore tissue homeostasis. Here, we present a multidisciplinary lead discovery approach to identify and optimize small molecule inhibitors of pathogenic fibroblast activation. The study encompasses medicinal chemistry, molecular phenotyping assays, chemoproteomics, bulk RNA-sequencing analysis, target validation experiments, and chemical absorption, distribution, metabolism, excretion and toxicity (ADMET)/pharmacokinetic (PK)/in vivo evaluation. The parallel synthesis employed for the production of the new benzamide derivatives enabled us to a) pinpoint key structural elements of the scaffold that provide potent fibroblast-deactivating effects in cells, b) discriminate atoms or groups that favor or disfavor a desirable ADMET profile, and c) identify metabolic "hot spots". Furthermore, we report the discovery of the first-in-class inhibitor leads for hypoxia up-regulated protein 1 (HYOU1), a member of the heat shock protein 70 (HSP70) family often associated with cellular stress responses, particularly under hypoxic conditions. Targeting HYOU1 may therefore represent a potentially novel strategy to modulate fibroblast activation and treat chronic inflammatory and fibrotic disorders.

Structure-Based Identification of Organoruthenium Compounds as Nanomolar Antagonists of Cannabinoid Receptors.

Metal complexes exhibit a diverse range of coordination geometries, representing novel privileged scaffolds with convenient click types of preparation inaccessible for typical carbon-centered organic compounds. Herein, we explored the opportunity to identify biologically active organometallic complexes by reverse docking of a rigid, minimum-size octahedral organoruthenium scaffold against thousands of protein-binding pockets. Interestingly, cannabinoid receptor type 1 (CB1) was identified based on the docking scores and the degree of overlap between the docked organoruthenium scaffold and the hydrophobic scaffold of the cocrystallized ligand. Further structure-based optimization led to the discovery of organoruthenium complexes with nanomolar binding affinities and high selectivity toward CB2. Our work indicates that octahedral organoruthenium scaffolds may be advantageous for targeting the large and hydrophobic binding pockets and that the reverse docking approach may facilitate the discovery of novel privileged scaffolds, such as organometallic complexes, for exploring chemical space in lead discovery.

Predicting novel targets with Bayesian machine learning by integrating multiple biological signatures.

The identification of targets for candidate molecules is a pivotal stride in the drug development journey, encompassing lead discovery, drug repurposing, and the scrutiny of potential off-target or side effects. Consequently, enhancing the precision of target prediction has significant implications. Moreover, current target prediction methods primarily rely on the principle of ligand-based chemical similarity, lacking the capture of novel compound-target relationships based on ligand high-level characterization similarity. Therefore, in this context, we introduce a pioneering algorithm known as the Fused Multiple Biological Signatures (FMBS) strategy. This approach leverages a Bayesian framework to amalgamate 25 predictable biological space characterizations of molecules to predict novel targets through scaffold hopping, thereby improving target prediction accuracy and providing a versatile tool for a wide range of small-molecule target prediction. When juxtaposed with alternative target prediction methods, FMBS showcases notable efficacy, outperforming traditional descriptors. Through an analysis of scaffold hopping cases, we elucidate how FMBS attains heightened accuracy by assimilating comprehensive and complementary high-dimensional signatures, thereby underscoring its potential in unearthing novel compound-target relationships. The findings underscore that our approach adeptly pinpoints promising candidate targets, thereby expediting drug mechanism exploration through the integration of multiple high-level characterizations.

Computational Approaches for Lead Discovery against SARS-CoV-2 3C-Like Protease: Virtual Screening and Molecular Dynamics Studies

Background: The COVID-19 pandemic has caused significant difficulties in multiple emotional, social, and financial areas. Despite the positive effects of vaccination in reducing infection and fatality rates, the need for efficient antiviral medications, particularly those that can be taken orally, remains a critical concern. Methods: A virtual screening method based on structure, referred to as SBVS, was used to identify potential inhibitory small molecules that specifically target the 3C-like protease (3CLPRO) found in SARS-CoV-2. Results: The filtering process for potential ligands involved strict criteria based on their molecular properties, including a molecular weight limit of 500 g/mol, maximum of five hydrogen bond donors, maximum of 10 hydrogen bond acceptors, and logP limit of 5. This was done to identify five candidates with lower ∆G values than the reference drugs lopinavir (-8.19 kcal/mol) and ritonavir (-8.04 kcal/mol). Three hits were identified through further evaluation using the hydrogen bond criteria and the BOILED-Egg model. The pharmacokinetic attributes of these two hits were compared with those of the reference drugs lopinavir and ritonavir. Conclusion: The molecular dynamics simulation (20 ns) outcomes unequivocally demonstrated the stability and promising nature of MCULE-2367618737 as a possible lead compound against the targeted 3CLPRO.

Synthesis and in Silico Studies of Some New 1,2,3-Triazolyltetrazole Bearing Indazole Derivatives as Potent Antimicrobial Agents.

1,2,3-Triazole and tetrazole derivatives bearing pyrrolidines are found to exhibit notable biological activity and have become useful scaffolds in medicinal chemistry for application in lead discovery and optimization. Novel indazole bearing 1,2,3-triazolyltetrazoles were designed as potential antimicrobial candidates. The structure of duel heterocyclics was validated by a spectroscopic technique of infrared (IR), nuclear magnetic resonance (1 H and 13 C NMR), and mass spectral data. Compounds 4b, 4c, 4d, and 4h were found to have a stronger antibacterial effect against Gram-positive (S. aureus, B. subtilis, M. Luteus) and Gram-negative (E. coli, P. aeruginosa) microorganisms with MICs ranging from 5±0.03-18±0.02 μM, respectively. Moreover, scaffolds 4a, 4h showed potent antifungal activity against A. flavus, M. gypsuem strains with MIC values of 10±0.02, 11±0.01 μM, which are similar activity that of the standard Itraconazole (MIC=8±0.02, 10±0.01 μM). The binding mode for compound 4 inside the catalytic pocket of S. aureus complexed with nicotinamide adenine dinucleotide phosphate and trimethoprim and produced a network of hydrophobic and hydrophilic interactions (3FRE). From in silico results, 4b demonstrated highly stable hydrogen binding amino acids Leu62(X) [N18…O, 2.47 Å], Arg44(X) [N17…N, 3.11 Å], Thr96(X) [N10…OG1, 3.05 Å], Gly94(X) [F7…N, 2.82 Å], and Gly43(X) [F7…N, 2.90 Å], which are plays a crucial role in ensuring efficient binding of the ligand in a crystal structure of antibacterial receptor. Furthermore, the physicochemical and ADME filtration molecular properties, estimation of toxicity, and bioactivity scores of these novel scaffolds were evaluated by using SwissADME and ADMETlab2.0 online protocols. Thus, the significant antimicrobial activity of indazole linked to duel heterocyclic compounds can be used for development of new antimicrobial agents with further modifications.

Designing of fragment based inhibitors with improved activity against E. coli AmpC β-lactamase compared to the conventional antibiotics

One of the most common primary resistance mechanism of multi-drug resistant (MDR) Gram negative pathogenic bacteria to combat β-lactam antibiotics, such as penicillins, cephalosporins and carbapenems is the generation of β- lactamases. The uropathogenic E. coli is mostly getting multi-drug resistance due to the synthesis of AmpC β-lactamases and therefore new antibiotics and inhibitors are needed to treat the evolving infections. The current study was designed for targetting AmpC β-lactamase of E. coli using molecular docking based virtual screening, linking fragments for designing novel compounds and binding mode analysis using molecular dynamic simulation with target protein. The FCH group all-purpose fragment library consisting of 9388 fragments has been screened against AmpC β-lactamase protein of E. coli and the antibiotics and anti-infectives used in treatment of Urinary tract Infections (UTIs) were also screened with AmpC β-lactamase protein. Among the 9388 fragments, 339 fragment candidates were selected and linked with cefepime antibiotic having maximum binding affinity for AmpC target protein. Computational analysis of interactions as well as molecular dynamics (MD) simulations were also conducted for identifying the most promising ligand-pocket complexes from docking investigations to comprehend their thermodynamic properties and verify the docking outcomes as well. Overall, the linked complexes (LCs) showed good binding interactions with AmpC β-lactamase. Interestingly, our fragment-based LCs remained relatively stable in comparison with cefepime antibiotic. Moreover, S12 fragment linked complex remained the most stable during 50 ns with remarkable number of interactions indicating it as promising candidate in novel lead discovery against MDR E. coli infections.

Optimisation-based modelling for explainable lead discovery in malaria

Background:The search for new antimalarial treatments is urgent due to growing resistance to existing therapies. The Open Source Malaria (OSM) project offers a promising starting point, having extensively screened various compounds for their effectiveness. Further analysis of the chemical space surrounding these compounds could provide the means for innovative drugs. Methods:We report an optimisation-based method for quantitative structure–activity relationship (QSAR) modelling that provides explainable modelling of ligand activity through a mathematical programming formulation. The methodology is based on piecewise regression principles and offers optimal detection of breakpoint features, efficient allocation of samples into distinct sub-groups based on breakpoint feature values, and insightful regression coefficients. Analysis of OSM antimalarial compounds yields interpretable results through rules generated by the model that reflect the contribution of individual fingerprint fragments in ligand activity prediction. Using knowledge of fragment prioritisation and screening of commercially available compound libraries, potential lead compounds for antimalarials are identified and evaluated experimentally via a Plasmodium falciparum asexual growth inhibition assay (PfGIA) and a human cell cytotoxicity assay. Conclusions:Three compounds are identified as potential leads for antimalarials using the methodology described above. This work illustrates how explainable predictive models based on mathematical optimisation can pave the way towards more efficient fragment-based lead discovery as applied in malaria.

Unprecedented anti-inflammatory biscembranoids with 3-hydroperoxy-3-methylcyclohex-1-ene moiety isolated from aquaculture Sarcophyton trocheliophorum

Recent advancements in the discovery of natural products have made significant progress in rapidly identifying known compounds from complex extracts using advanced spectroscopic technologies. Among these methods, one powerful approach called MS/MS molecular networking (MN) organizes chemical similarity based on MS/MS fragmentation data, serving as a valuable complement to traditional dereplication strategies. In our ongoing research focused on lead discovery from a sustainable medicinal source, an aquaculture soft coral Sarcophyton trocheliophorum, the MN profiling approach revealed the chemical diversity of biscembranoid analogs. By analyzing the MN pattern, biscembranoid derivatives with unique MS/MS fragments were prioritized for further investigation. Two novel biscembranoids, sarcotroxide A (1) and B (2), were successfully obtained through targeted isolation. These novel biscembranes contained an unprecedented 3-hydroperoxy-3-methylcyclohex-1-ene moiety compared with all biscembranoid analogs. The chemical structures of the two new biscembranoids were confirmed through spectroscopic analyses and in silico calculations. Their anti-inflammatory properties were assessed against the generation of superoxide anion (O2•−) and elastase release in activated human neutrophils. Sarcotroxides A (1) and B (2) exhibited anti-inflammatory activity by inhibiting O2•− accumulation (with 43.67% and 61.62% inhibition) and elastase release (with 38.21% and 61.62% inhibition) at 10 µM. These findings validated the potential of these biscembranoids from the cultured S. trocheliophorum as promising candidates for the future development of anti-inflammatory agents specifically designed to target neutrophils.

Genetically Engineered Cell Membrane-Coated Nanoparticles with High-Density Customized Membrane Receptor for High-Performance Drug Lead Discovery.

Cell membrane coating strategies have been increasingly researched due to their unique capabilities of biomimicry and biointerfacing, which can mimic the functionality of the original source cells in vivo but fail to provide customized nanoparticle surfaces with new or enhanced capabilities beyond natural cells. However, the field of drug lead discovery necessitates the acquisition of sufficient surface density of specific target membrane receptors, presenting a heightened demand for this technology. In this study, we developed a novel approach to fabricate high density of fibroblast growth factor receptor 4 (FGFR4) cell membrane-coated nanoparticles through covalent site-specific immobilization between genetically engineered FGFR4 with HaloTag anchor on cell membrane and chloroalkane-functionalized magnetic nanoparticles. This technique enables efficient screening of tyrosine kinase inhibitors from natural products. And the enhanced density of FGFR4 on the surface of nanoparticles were successfully confirmed by Western blot assay and confocal laser scanning microscopy. Further, the customized nanoparticles demonstrated exceptional sensitivity (limit of detection = 0.3 × 10-3 μg mL-1). Overall, the proposed design of a high density of membrane receptors, achieved through covalent site-specific immobilization with a HaloTag anchor, demonstrates a promising strategy for the development of cell membrane surface engineering. This approach highlights the potential of cell membrane coating technology for facilitating the advanced extraction of small molecules for drug discovery.