Multi-chamber cardioids unravel human heart development and cardiac defects.

The number one cause of human fetal death are defects in heart development. Because the human embryonic heart is inaccessible and the impacts of mutations, drugs, and environmental factors on the specialized functions of different heart compartments are not captured by in vitro models, determining the underlying causes is difficult. Here, we established a human cardioid platform that recapitulates the development of all major embryonic heart compartments, including right and left ventricles, atria, outflow tract, and atrioventricular canal. By leveraging 2D and 3D differentiation, we efficiently generated progenitor subsets with distinct first, anterior, and posterior second heart field identities. This advance enabled the reproducible generation of cardioids with compartment-specific in vivo-like gene expression profiles, morphologies, and functions. We used this platform to unravel the ontogeny of signal and contraction propagation between interacting heart chambers and dissect how mutations, teratogens, and drugs cause compartment-specific defects in the developing human heart.

Cannabinoid receptor 1 antagonist genistein attenuates marijuana-induced vascular inflammation.

Epidemiological studies reveal that marijuana increases the risk of cardiovascular disease (CVD); however, little is known about the mechanism. Δ9-tetrahydrocannabinol (Δ9-THC), the psychoactive component of marijuana, binds to cannabinoid receptor 1 (CB1/CNR1) in the vasculature and is implicated in CVD. A UK Biobank analysis found that cannabis was an risk factor for CVD. We found that marijuana smoking activated inflammatory cytokines implicated in CVD. In silico virtual screening identified genistein, a soybean isoflavone, as a putative CB1 antagonist. Human-induced pluripotent stem cell-derived endothelial cells were used to model Δ9-THC-induced inflammation and oxidative stress via NF-κB signaling. Knockdown of the CB1 receptor with siRNA, CRISPR interference, and genistein attenuated the effects of Δ9-THC. In mice, genistein blocked Δ9-THC-induced endothelial dysfunction in wire myograph, reduced atherosclerotic plaque, and had minimal penetration of the central nervous system. Genistein is a CB1 antagonist that attenuates Δ9-THC-induced atherosclerosis.

Pharmacological Targeting of STK19 Inhibits Oncogenic NRAS-Driven Melanomagenesis.

Activating mutations in NRAS account for 20%-30% of melanoma, but despite decades of research and in contrast to BRAF, no effective anti-NRAS therapies have been forthcoming. Here, we identify a previously uncharacterized serine/threonine kinase STK19 as a novel NRAS activator. STK19 phosphorylates NRAS to enhance its binding to its downstream effectors and promotes oncogenic NRAS-mediated melanocyte malignant transformation. A recurrent D89N substitution in STK19 whose alterations were identified in 25% of human melanomas represents a gain-of-function mutation that interacts better with NRAS to enhance melanocyte transformation. STK19D89N knockin leads to skin hyperpigmentation and promotes NRASQ61R-driven melanomagenesis in vivo. Finally, we developed ZT-12-037-01 (1a) as a specific STK19-targeted inhibitor and showed that it effectively blocks oncogenic NRAS-driven melanocyte malignant transformation and melanoma growth in vitro and in vivo. Together, our findings provide a new and viable therapeutic strategy for melanomas harboring NRAS mutations.

Structural Basis for Allosteric Ligand Recognition in the Human CC Chemokine Receptor 7.

The CC chemokine receptor 7 (CCR7) balances immunity and tolerance by homeostatic trafficking of immune cells. In cancer, CCR7-mediated trafficking leads to lymph node metastasis, suggesting the receptor as a promising therapeutic target. Here, we present the crystal structure of human CCR7 fused to the protein Sialidase NanA by using data up to 2.1 Å resolution. The structure shows the ligand Cmp2105 bound to an intracellular allosteric binding pocket. A sulfonamide group, characteristic for various chemokine receptor ligands, binds to a patch of conserved residues in the Gi protein binding region between transmembrane helix 7 and helix 8. We demonstrate how structural data can be used in combination with a compound repository and automated thermal stability screening to identify and modulate allosteric chemokine receptor antagonists. We detect both novel (CS-1 and CS-2) and clinically relevant (CXCR1-CXCR2 phase-II antagonist Navarixin) CCR7 modulators with implications for multi-target strategies against cancer.

Literature-based predictions of Mendelian disease therapies.

In the effort to treat Mendelian disorders, correcting the underlying molecular imbalance may be more effective than symptomatic treatment. Identifying treatments that might accomplish this goal requires extensive and up-to-date knowledge of molecular pathways-including drug-gene and gene-gene relationships. To address this challenge, we present "parsing modifiers via article annotations" (PARMESAN), a computational tool that searches PubMed and PubMed Central for information to assemble these relationships into a central knowledge base. PARMESAN then predicts putatively novel drug-gene relationships, assigning an evidence-based score to each prediction. We compare PARMESAN's drug-gene predictions to all of the drug-gene relationships displayed by the Drug-Gene Interaction Database (DGIdb) and show that higher-scoring relationship predictions are more likely to match the directionality (up- versus down-regulation) indicated by this database. PARMESAN had more than 200,000 drug predictions scoring above 8 (as one example cutoff), for more than 3,700 genes. Among these predicted relationships, 210 were registered in DGIdb and 201 (96%) had matching directionality. This publicly available tool provides an automated way to prioritize drug screens to target the most-promising drugs to test, thereby saving time and resources in the development of therapeutics for genetic disorders.

Revolutionizing GPCR-ligand predictions: DeepGPCR with experimental validation for high-precision drug discovery.

G-protein coupled receptors (GPCRs), crucial in various diseases, are targeted of over 40% of approved drugs. However, the reliable acquisition of experimental GPCRs structures is hindered by their lipid-embedded conformations. Traditional protein-ligand interaction models falter in GPCR-drug interactions, caused by limited and low-quality structures. Generalized models, trained on soluble protein-ligand pairs, are also inadequate. To address these issues, we developed two models, DeepGPCR_BC for binary classification and DeepGPCR_RG for affinity prediction. These models use non-structural GPCR-ligand interaction data, leveraging graph convolutional networks and mol2vec techniques to represent binding pockets and ligands as graphs. This approach significantly speeds up predictions while preserving critical physical-chemical and spatial information. In independent tests, DeepGPCR_BC surpassed Autodock Vina and Schrödinger Dock with an area under the curve of 0.72, accuracy of 0.68 and true positive rate of 0.73, whereas DeepGPCR_RG demonstrated a Pearson correlation of 0.39 and root mean squared error of 1.34. We applied these models to screen drug candidates for GPR35 (Q9HC97), yielding promising results with three (F545-1970, K297-0698, S948-0241) out of eight candidates. Furthermore, we also successfully obtained six active inhibitors for GLP-1R. Our GPCR-specific models pave the way for efficient and accurate large-scale virtual screening, potentially revolutionizing drug discovery in the GPCR field.

Reconstitution of the alternative pathway of the complement system enables rapid delineation of the mechanism of action of novel inhibitors.

The complement system plays a critical role in the innate immune response, acting as a first line of defense against invading pathogens. However, dysregulation of the complement system is implicated in the pathogenesis of numerous diseases, ranging from Alzheimer's to age-related macular degeneration and rare blood disorders. As such, complement inhibitors have enormous potential to alleviate disease burden. While a few complement inhibitors are in clinical use, there is still a significant unmet medical need for the discovery and development of novel inhibitors to treat patients suffering from disorders of the complement system. A key hurdle in the development of complement inhibitors has been the determination of their mechanism of action. Progression along the complement cascade involves the formation of numerous multimeric protein complexes, creating the potential for inhibitors to act at multiple nodes in the pathway. This is especially true for molecules that target the central component C3 and its fragment C3b, which serve a dual role as a substrate for the C3 convertases and as a scaffolding protein in both the C3 and C5 convertases. Here, we report a step-by-step in vitro reconstitution of the complement alternative pathway using bio-layer interferometry. By physically uncoupling each step in the pathway, we were able to determine the kinetic signature of inhibitors that act at single steps in the pathway and delineate the full mechanism of action of known and novel C3 inhibitors. The method could have utility in drug discovery and further elucidating the biochemistry of the complement system.

A deep learning based multi-model approach for predicting drug-like chemical compound's toxicity.

Ensuring the safety and efficacy of chemical compounds is crucial in small-molecule drug development. In the later stages of drug development, toxic compounds pose a significant challenge, losing valuable resources and time. Early and accurate prediction of compound toxicity using deep learning models offers a promising solution to mitigate these risks during drug discovery. In this study, we present the development of several deep-learning models aimed at evaluating different types of compound toxicity, including acute toxicity, carcinogenicity, hERG_cardiotoxicity (the human ether-a-go-go related gene caused cardiotoxicity), hepatotoxicity, and mutagenicity. To address the inherent variations in data size, label type, and distribution across different types of toxicity, we employed diverse training strategies. Our first approach involved utilizing a graph convolutional network (GCN) regression model to predict acute toxicity, which achieved notable performance with Pearson R 0.76, 0.74, and 0.65 for intraperitoneal, intravenous, and oral administration routes, respectively. Furthermore, we trained multiple GCN binary classification models, each tailored to a specific type of toxicity. These models exhibited high area under the curve (AUC) scores, with an impressive AUC of 0.69, 0.77, 0.88, and 0.79 for predicting carcinogenicity, hERG_cardiotoxicity, mutagenicity, and hepatotoxicity, respectively. Additionally, we have used the approved drug dataset to determine the appropriate threshold value for the prediction score in model usage. We integrated these models into a virtual screening pipeline to assess their effectiveness in identifying potential low-toxicity drug candidates. Our findings indicate that this deep learning approach has the potential to significantly reduce the cost and risk associated with drug development by expediting the selection of compounds with low toxicity profiles. Therefore, the models developed in this study hold promise as critical tools for early drug candidate screening and selection.

Systematic Identification of MCU Modulators by Orthogonal Interspecies Chemical Screening.

The mitochondrial calcium uniporter complex is essential for calcium (Ca2+) uptake into mitochondria of all mammalian tissues, where it regulates bioenergetics, cell death, and Ca2+ signal transduction. Despite its involvement in several human diseases, we currently lack pharmacological agents for targeting uniporter activity. Here we introduce a high-throughput assay that selects for human MCU-specific small-molecule modulators in primary drug screens. Using isolated yeast mitochondria, reconstituted with human MCU, its essential regulator EMRE, and aequorin, and exploiting a D-lactate- and mannitol/sucrose-based bioenergetic shunt that greatly minimizes false-positive hits, we identify mitoxantrone out of more than 600 clinically approved drugs as a direct selective inhibitor of human MCU. We validate mitoxantrone in orthogonal mammalian cell-based assays, demonstrating that our screening approach is an effective and robust tool for MCU-specific drug discovery and, more generally, for the identification of compounds that target mitochondrial functions.

Drug resistance mechanisms in dopamine agonist-resistant prolactin pituitary neuroendocrine tumors and exploration for new drugs.

BACKGROUND: The treatment of dopamine agonists (DA) resistant prolactinomas remains a formidable challenge, as the mechanism of resistance is still unclear, and there are currently no viable alternative drug therapies available. This study seeks to investigate the mechanism of DA resistance in prolactinomas and identify new potentially effective drugs. METHODS: To explore the mechanism of DA resistance in prolactinomas, this study conducted transcriptome sequencing analysis on 27 cases of DA-resistant prolactinomas and 10 cases of sensitive prolactinomas. In addition, single-cell sequencing analysis was performed on 3 cases of DA-resistant prolactinomas and 3 cases of sensitive prolactinomas. Furthermore, to screen for potential therapeutic drugs, the study successfully established an organoids model for DA-resistant prolactinomas and screened 180 small molecule compounds using 8 organoids. The efficacy of the identified drugs was verified through various assays, including CCK-8, colony formation, CTG, and flow cytometry, and their mechanisms of action were confirmed through WB and IHC. The effectiveness of the identified drugs was evaluated both in vitro and in vivo. RESULTS: The results of transcriptome sequencing and single-cell sequencing analyses showed that DA resistance in prolactinomas is associated with the upregulation of the Focal Adhesion (FA) signaling pathway. Additionally, immunohistochemical validation revealed that FAK and Paxillin were significantly upregulated in DA-resistant prolactinomas. Screening of 180 small molecule compounds using 8 organoids identified Genistein as a potentially effective drug for DA-resistant prolactinomas. Experimental validation demonstrated that Genistein inhibited the proliferation of pituitary tumor cell lines and organoids and promoted apoptosis in pituitary tumor cells. Moreover, both the cell sequencing results and WB validation results of the drug-treated cells indicated that Genistein exerts its anti-tumor effect by inhibiting the FA pathway. In vivo, experiments also showed that Genistein can inhibit subcutaneous tumor formation. CONCLUSION: DA resistance in prolactinomas is associated with upregulation of the Focal Adhesion (FA) signaling pathway, and Genistein can exert its anti-tumor effect by inhibiting the expression of the FA pathway.

Microfluidics guided by deep learning for cancer immunotherapy screening.

Immunocyte infiltration and cytotoxicity play critical roles in both inflammation and immunotherapy. However, current cancer immunotherapy screening methods overlook the capacity of the T cells to penetrate the tumor stroma, thereby significantly limiting the development of effective treatments for solid tumors. Here, we present an automated high-throughput microfluidic platform for simultaneous tracking of the dynamics of T cell infiltration and cytotoxicity within the 3D tumor cultures with a tunable stromal makeup. By recourse to a clinical tumor-infiltrating lymphocyte (TIL) score analyzer, which is based on a clinical data-driven deep learning method, our platform can evaluate the efficacy of each treatment based on the scoring of T cell infiltration patterns. By screening a drug library using this technology, we identified an epigenetic drug (lysine-specific histone demethylase 1 inhibitor, LSD1i) that effectively promoted T cell tumor infiltration and enhanced treatment efficacy in combination with an immune checkpoint inhibitor (anti-PD1) in vivo. We demonstrated an automated system and strategy for screening immunocyte-solid tumor interactions, enabling the discovery of immuno- and combination therapies.

Phenotypic screening of the ReFRAME drug repurposing library to discover new drugs for treating sickle cell disease.

Stem cell transplantation and genetic therapies offer potential cures for patients with sickle cell disease (SCD), but these options require advanced medical facilities and are expensive. Consequently, these treatments will not be available for many years to the majority of patients suffering from this disease. What is urgently needed now is an inexpensive oral drug in addition to hydroxyurea, the only drug approved by the FDA that inhibits sickle-hemoglobin polymerization. Here, we report the results of the first phase of our phenotypic screen of the 12,657 compounds of the Scripps ReFRAME drug repurposing library using a recently developed high-throughput assay to measure sickling times following deoxygenation to 0% oxygen of red cells from sickle trait individuals. The ReFRAME library is a very important collection because the compounds are either FDA-approved drugs or have been tested in clinical trials. From dose-response measurements, 106 of the 12,657 compounds exhibit statistically significant antisickling at concentrations ranging from 31 nM to 10 µM. Compounds that inhibit sickling of trait cells are also effective with SCD cells. As many as 21 of the 106 antisickling compounds emerge as potential drugs. This estimate is based on a comparison of inhibitory concentrations with free concentrations of oral drugs in human serum. Moreover, the expected therapeutic potential for each level of inhibition can be predicted from measurements of sickling times for cells from individuals with sickle syndromes of varying severity. Our results should motivate others to develop one or more of these 106 compounds into drugs for treating SCD.

Establishment of a hepatitis B virus reporter system harboring a HiBiT-tag in the PreS2 region.

BACKGROUND: Approximately 296 million people suffer from chronic hepatitis B (CHB) caused by hepatitis B virus (HBV). Current standard treatment, nucleos(t)ide analogs, are not efficient enough to eradicate HBV from the hepatocytes. Thus, developing new drugs for CHB is desired to achieve complete cure. METHODS: Here we established a novel HBV reporter system, HBV-HiBiT-PS2, to screen new drugs for CHB. HBV-HiBiT-PS2 was constructed by introducing a HiBiT-tag at the 5'-end of PreS2 and introduced into HepG2-NTCP cells. Culture supernatant containing HBV-HiBiT-PS2 virions was fractionated by a sucrose density gradient ultracentrifugation to characterize their components. Replication kinetics and reporter function of HBV-HiBiT-PS2 were determined by analyzing the parameters for HBV replication in the presence or absence of HBV inhibitors. RESULTS: HBV-HiBiT-PS2 could be used for monitoring most of the replication cycle of HBV. The effects of well-characterized HBV inhibitors could be evaluated by the HiBiT activity. HBV-HiBiT-PS2 could be specialized for screening secretion inhibitors for hepatitis B surface antigen (HBsAg) because most of the HiBiT activity was derived from subviral particles which are the multimers of HBsAg. CONCLUSIONS: We demonstrated that HBV-HiBiT-PS2 would be a robust tool for screening novel drugs, especially HBsAg secretion inhibitors, targeted for CHB.

Cracking the code of the cardiovascular enigma: hPSC-derived endothelial cells unveil the secrets of endothelial dysfunction.

Endothelial dysfunction is a central contributor to the development of most cardiovascular diseases and is characterised by the reduced synthesis or bioavailability of the vasodilator nitric oxide together with other abnormalities such as inflammation, senescence, and oxidative stress. The use of patient-specific and genome-edited human pluripotent stem cell-derived endothelial cells (hPSC-ECs) has shed novel insights into the role of endothelial dysfunction in cardiovascular diseases with strong genetic components such as genetic cardiomyopathies and pulmonary arterial hypertension. However, their utility in studying complex multifactorial diseases such as atherosclerosis, metabolic syndrome and heart failure poses notable challenges. In this review, we provide an overview of the different methods used to generate and characterise hPSC-ECs before comprehensively assessing their effectiveness in cardiovascular disease modelling and high-throughput drug screening. Furthermore, we explore current obstacles that will need to be overcome to unleash the full potential of hPSC-ECs in facilitating patient-specific precision medicine. Addressing these challenges holds great promise in advancing our understanding of intricate cardiovascular diseases and in tailoring personalised therapeutic strategies.

Library screening identifies commercial drugs as potential structure correctors of abnormal apolipoprotein A-I.

AapoA-I, the main protein of high-density lipoprotein, plays a key role in the biogenesis and atheroprotective properties of high-density lipoprotein. We showed previously that a naturally occurring apoA-I mutation, L178P, induces major defects in protein's structural integrity and functions that may underlie the increased cardiovascular risk observed in carriers of the mutation. Here, a library of marketed drugs (956 compounds) was screened against apoA-I[L178P] to identify molecules that can stabilize the normal conformation of apoA-I. Screening was performed by the thermal shift assay in the presence of fluorescent dye SYPRO Orange. As an orthogonal assay, we monitored the change in fluorescence intensity of 8-anilinonaphthalene-1-sulfonic acid upon binding on hydrophobic sites on apoA-I. Screening identified four potential structure correctors. Subsequent analysis of the concentration-dependent effect of these compounds on secondary structure and thermodynamic stability of WT apoA-I and apoA-I[L178P] (assessed by thermal shift assay and circular dichroism spectroscopy), as well as on macrophage viability, narrowed the potential structure correctors to two, the drugs atorvastatin and bexarotene. Functional analysis showed that these two compounds can restore the defective capacity of apoA-I[L178P] to promote cholesterol removal from macrophages, an important step for atheroprotection. Computational docking suggested that both drugs target a positively charged cavity in apoA-I, formed between helix 1/2 and helix 5, and make extensive interactions that could underlie thermodynamic stabilization. Overall, our findings indicate that small molecules can correct defective apoA-I structure and function and may lead to novel therapeutic approaches for apoA-I-related dyslipidemias and increased cardiovascular risk.

Early-phase drug discovery of ß-III-spectrin actin-binding modulators for treatment of spinocerebellar ataxia type 5.

ß-III-Spectrin is a key cytoskeletal protein that localizes to the soma and dendrites of cerebellar Purkinje cells and is required for dendritic arborization and signaling. A spinocerebellar ataxia type 5 L253P mutation in the cytoskeletal protein ß-III-spectrin causes high-affinity actin binding. Previously we reported a cell-based fluorescence assay for identification of small-molecule actin-binding modulators of the L253P mutant ß-III-spectrin. Here we describe a complementary, in vitro, fluorescence resonance energy transfer (FRET) assay that uses purified L253P ß-III-spectrin actin-binding domain (ABD) and F-actin. To validate the assay for high-throughput compatibility, we first confirmed that our 50% FRET signal was responsive to swinholide A, an actin-severing compound, and that this yielded excellent assay quality with a Z' value > 0.77. Second, we screened a 2684-compound library of US Food and Drug Administration-approved drugs. Importantly, the screening identified numerous compounds that decreased FRET between fluorescently labeled L253P ABD and F-actin. The activity and target of multiple Hit compounds were confirmed in orthologous cosedimentation actin-binding assays. Through future medicinal chemistry, the Hit compounds can potentially be developed into a spinocerebellar ataxia type 5-specific therapeutic. Furthermore, our validated FRET-based in vitro high-throughput screening platform is poised for screening large compound libraries for ß-III-spectrin ABD modulators.

A novel machine learning-based screening identifies statins as inhibitors of the calcium pump SERCA.

We report a novel small-molecule screening approach that combines data augmentation and machine learning to identify Food and Drug Administration (FDA)-approved drugs interacting with the calcium pump (Sarcoplasmic reticulum Ca2+-ATPase, SERCA) from skeletal (SERCA1a) and cardiac (SERCA2a) muscle. This approach uses information about small-molecule effectors to map and probe the chemical space of pharmacological targets, thus allowing to screen with high precision large databases of small molecules, including approved and investigational drugs. We chose SERCA because it plays a major role in the excitation-contraction-relaxation cycle in muscle and it represents a major target in both skeletal and cardiac muscle. The machine learning model predicted that SERCA1a and SERCA2a are pharmacological targets for seven statins, a group of FDA-approved 3-hydroxy-3-methylglutaryl coenzyme A reductase inhibitors used in the clinic as lipid-lowering medications. We validated the machine learning predictions by using in vitro ATPase assays to show that several FDA-approved statins are partial inhibitors of SERCA1a and SERCA2a. Complementary atomistic simulations predict that these drugs bind to two different allosteric sites of the pump. Our findings suggest that SERCA-mediated Ca2+ transport may be targeted by some statins (e.g., atorvastatin), thus providing a molecular pathway to explain statin-associated toxicity reported in the literature. These studies show the applicability of data augmentation and machine learning-based screening as a general platform for the identification of off-target interactions and the applicability of this approach extends to drug discovery.

Targeting autophagy impairment improves the phenotype of a novel CLN8 zebrafish model.

CLN8 is an endoplasmic reticulum cargo receptor and a regulator of lysosome biogenesis whose loss of function leads to neuronal ceroid lipofuscinosis. CLN8 has been linked to autophagy and lipid metabolism, but much remains to be learned, and there are no therapies acting on the molecular signatures in this disorder. The present study aims to characterize the molecular pathways involved in CLN8 disease and, by pinpointing altered ones, to identify potential therapies. To bridge the gap between cell and mammalian models, we generated a new zebrafish model of CLN8 deficiency, which recapitulates the pathological features of the disease. We observed, for the first time, that CLN8 dysfunction impairs autophagy. Using autophagy modulators, we showed that trehalose and SG2 are able to attenuate the pathological phenotype in mutant larvae, confirming autophagy impairment as a secondary event in disease progression. Overall, our successful modeling of CLN8 defects in zebrafish highlights this novel in vivo model's strong potential as an instrument for exploring the role of CLN8 dysfunction in cellular pathways, with a view to identifying small molecules to treat this rare disease.

Bioinformatics Analysis of Mutations Sheds Light on the Evolution of Dengue NS1 Protein With Implications in the Identification of Potential Functional and Druggable Sites.

Non-structural protein (NS1) is a 350 amino acid long conserved protein in the dengue virus. Conservation of NS1 is expected due to its importance in dengue pathogenesis. The protein is known to exist in dimeric and hexameric states. The dimeric state is involved in its interaction with host proteins and viral replication, and the hexameric state is involved in viral invasion. In this work, we performed extensive structure and sequence analysis of NS1 protein, and uncovered the role of NS1 quaternary states in its evolution. A three-dimensional modeling of unresolved loop regions in NS1 structure is performed. "Conserved" and "Variable" regions within NS1 protein were identified from sequences obtained from patient samples and the role of compensatory mutations in selecting destabilizing mutations were identified. Molecular dynamics (MD) simulations were performed to extensively study the effect of a few mutations on NS1 structure stability and compensatory mutations. Virtual saturation mutagenesis, predicting the effect of every individual amino acid substitution on NS1 stability sequentially, revealed virtual-conserved and variable sites. The increase in number of observed and virtual-conserved regions across NS1 quaternary states suggest the role of higher order structure formation in its evolutionary conservation. Our sequence and structure analysis could enable in identifying possible protein-protein interfaces and druggable sites. Virtual screening of nearly 10,000 small molecules, including FDA-approved drugs, permitted us to recognize six drug-like molecules targeting the dimeric sites. These molecules could be promising due to their stable interactions with NS1 throughout the simulation.

Establishment of a novel minigenome system for the identification of drugs targeting Nipah virus replication.

Nipah virus (NiV) is a deadly zoonotic pathogen with high potential to cause another pandemic. Owing to biosafety concerns, studies on living NiV must be performed in biosafety level 4 (BSL-4) laboratories, which greatly hinders the development of anti-NiV drugs. To overcome this issue, minigenome systems have been developed to study viral replication and screen for antiviral drugs. This study aimed to develop two minigenome systems (transient and stable expression) based on a helper cell line expressing the NiV P, N and L proteins required to initiate NiV RNA replication. Stable minigenome cells were resistant to ribavirin, remdesivir and favipiravir but sensitive to interferons. Cells of the transient replication system were sensitive to ribavirin and favipiravir and suitable for drug screening. Our study demonstrates a feasible and effective platform for studying NiV replication and shows great potential for high-throughput drug screening in a BSL-2 laboratory environment.