Drug Screening Research Articles

3D in vitro synovial hyperplasia model on polycaprolactone-micropatterned nanofibrous microwells for screening disease-modifying anti-rheumatic drugs

Rheumatoid arthritis (RA) is known to be caused by autoimmune disorders and can be partially alleviated through Disease-Modifying Antirheumatic Drugs (DMARDs) therapy. However, due to significant variations in the physical environment and condition of each RA patient, the types and doses of DMARDs prescribed can differ greatly. Consequently, there is a need for a platform based on patient-derived cells to determine the effectiveness of specific DMARDs for individual patient. In this study, we established an RA three-dimensional (3D) spheroid that mimics the human body's 3D environment, enabling high-throughput assays by culturing patient-derived synovial cells on a macroscale-patterned polycaprolactone (PCL) scaffold. Fibroblast-like synoviocytes (FLSs) from patient and human umbilical vein endothelial cells (HUVECs) were co-cultured to simulate vascular delivery. Additionally, RA characteristics were identified at both the genetic and cytokine levels using real-time polymerase chain reaction (RT-qPCR) and dot blot assay. The similarities in junctions and adhesion were demonstrated in both actual RA patient tissues and 3D spheroids. The 3D RA spheroid was treated with representative DMARDs, observing changes in reactive oxygen species (ROS) levels, lactate dehydrogenase (LDH) levels, and inflammatory cytokine responses to confirm the varying cell reactions depending on the DMARDs used. This study underscores the significance of the 3D drug screening platform, which can be applied to diverse inflammatory disease treatments as a personalized drug screening system. We anticipate that this platform will become an indispensable tool for advancing and developing personalized DMARD treatment strategies.

A pH-Sensitive Double Chromophore Fluorescent Dye for Live-Tracking of Lipophagy.

Lipid droplet (LD) degradation provides metabolic energy and important building blocks for various cellular processes. The two major LD degradation pathways include autophagy (lipophagy), which involves delivery of LDs to autolysosomes, and lipolysis, which is mediated by lipases. While abnormalities in LD degradation are associated with various pathological disorders, our understanding of lipophagy is still rudimentary. In this study, we describe the development of a lipophilic dye containing two fluorophores, one of which is pH-sensitive and the other pH-stable. We further demonstrate that this "Lipo-Fluddy" can be used to visualize and quantify lipophagy in living cells, in an easily applicable and protein label-free approach. After estimating the ability of compound candidates to penetrate LDs, we synthesized several BODIPY and (pH-switchable) rhodol dyes, whose fluorescence properties (incl. their photophysical compatibility) were analyzed. Of three Lipo-Fluddy dyes synthesized, one exhibited the desired properties and allowed observation of lipophagy by fluorescence microscopy. Also, this dye proved to be non-toxic and suitable for the examination of various cell lines. Moreover, a method was developed to quantify the lipophagy process using flow cytometry, which could be applied in the future in the identification of lipophagy-related genes or in the screening of potential drugs against lipophagy-related diseases.

Decoding the Structural Diversity: A New Horizon in Antimicrobial Prospecting and Mechanistic Investigation.

The escalating crisis of antimicrobial resistance (AMR) underscores the urgent need for novel antimicrobials. One promising strategy is the exploration of structural diversity, as diverse structures can lead to diverse biological activities and mechanisms of action. This review delves into the role of structural diversity in antimicrobial discovery, highlighting its influence on factors such as target selectivity, binding affinity, pharmacokinetic properties, and the ability to overcome resistance mechanisms. We discuss various approaches for exploring structural diversity, including combinatorial chemistry, diversity-oriented synthesis, and natural product screening, and provide an overview of the common mechanisms of action of antimicrobials. We also describe techniques for investigating these mechanisms, such as genomics, proteomics, and structural biology. Despite significant progress, several challenges remain, including the synthesis of diverse compound libraries, the identification of active compounds, the elucidation of complex mechanisms of action, the emergence of AMR, and the translation of laboratory discoveries to clinical applications. However, emerging trends and technologies, such as artificial intelligence, high-throughput screening, next-generation sequencing, and open-source drug discovery, offer new avenues to overcome these challenges. Looking ahead, we envisage an exciting future for structural diversity-oriented antimicrobial discovery, with opportunities for expanding the chemical space, harnessing the power of nature, deepening our understanding of mechanisms of action, and moving toward personalized medicine and collaborative drug discovery. As we face the continued challenge of AMR, the exploration of structural diversity will be crucial in our search for new and effective antimicrobials.

Drug-Online: an online platform for drug-target interaction, affinity, and binding sites identification using deep learning

BackgroundAccurately identifying drug-target interaction (DTI), affinity (DTA), and binding sites (DTS) is crucial for drug screening, repositioning, and design, as well as for understanding the functions of target. Although there are a few online platforms based on deep learning for drug-target interaction, affinity, and binding sites identification, there is currently no integrated online platforms for all three aspects.ResultsOur solution, the novel integrated online platform Drug-Online, has been developed to facilitate drug screening, target identification, and understanding the functions of target in a progressive manner of “interaction-affinity-binding sites”. Drug-Online platform consists of three parts: the first part uses the drug-target interaction identification method MGraphDTA, based on graph neural networks (GNN) and convolutional neural networks (CNN), to identify whether there is a drug-target interaction. If an interaction is identified, the second part employs the drug-target affinity identification method MMDTA, also based on GNN and CNN, to calculate the strength of drug-target interaction, i.e., affinity. Finally, the third part identifies drug-target binding sites, i.e., pockets. The method pt-lm-gnn used in this part is also based on GNN.ConclusionsDrug-Online is a reliable online platform that integrates drug-target interaction, affinity, and binding sites identification. It is freely available via the Internet at http://39.106.7.26:8000/Drug-Online/.

Mining and analysis of security alert signals of valbenazine based on the Food and Drug Administration Adverse Event Reporting System database.

Valbenazine is used for tardive movement disorders in adults. Current studies on its safety are mostly from clinical trials and small case reports, limiting information on rare adverse reactions. This study investigated valbenazine-related adverse event (AE) risk signals using the U.S. Food and Drug Administration Adverse Event Reporting System (FAERS) database. Valbenazine AEs data were collected from the FAERS database from 2017 Q2 to 2023 Q1, employing methods like reporting odds ratio (ROR), proportional reporting ratio (PRR), Bayesian confidence propagation neural network, and empirical Bayesian geometric mean. After data cleaning and drug screening, there were 20,837 AEs primarily suspecting valbenazine, involving 26 system organ classes and 125 AEs related to valbenazine at the preferred terms level. AEs related to valbenazine were mainly concentrated in nervous system disorders, general disorders and administration site conditions, and psychiatric disorders. Eye disorders and gastrointestinal disorders are new AEs not labeled in the valbenazine instructions. In addition, some new potential AE signals were found, such as Tardive dyskinesia and eyelid function disorder. The common AEs of valbenazine in the real world are consistent with the instructions, but there are some newly discovered suspicious AEs.

Comparison of Glioblastoma Cell Culture Platforms Based on Transcriptional Similarity with Paired Tissue.

No standardized in vitro cell culture models for glioblastoma (GBM) have yet been established, excluding the traditional two-dimensional culture. GBM tumorspheres (TSs) have been highlighted as a good model platform for testing drug effects and characterizing specific features of GBM, but a detailed evaluation of their suitability and comparative performance is lacking. Here, we isolated GBM TSs and extracellular matrices (ECM) from tissues obtained from newly diagnosed IDH1 wild-type GBM patients and cultured GBM TSs on five different culture platforms: (1) ordinary TS culture liquid media (LM), (2) collagen-based three-dimensional (3D) matrix, (3) patient typical ECM-based 3D matrix, (4) patient tumor ECM-based 3D matrix, and (5) mouse brain. For evaluation, we obtained transcriptome data from all cultured GBM TSs using microarrays. The LM platform exhibited the most similar transcriptional program to paired tissues based on GBM genes, stemness- and invasiveness-related genes, transcription factor activity, and canonical signaling pathways. GBM TSs can be cultured via an easy-to-handle and cost- and time-efficient LM platform while preserving the transcriptional program of the originating tissues without supplementing the ECM or embedding it into the mouse brain. In addition to applications in basic cancer research, GBM TSs cultured in LM may also serve as patient avatars in drug screening and pre-clinical evaluation of targeted therapy and as standardized and clinically relevant models for precision medicine.

Advances in the Development of Bacterial Bioluminescence Imaging.

Bioluminescence imaging (BLI) is a powerful method for visualizing biological processes and tracking cells. Engineered bioluminescent bacteria that utilize luciferase-catalyzed biochemical reactions to generate luminescence have become useful analytical tools for in vitro and in vivo bacterial imaging. Accordingly, this review initially introduces the development of engineered bioluminescent bacteria that use different luciferase-luciferin pairs as analytical tools and their applications for in vivo BLI, including real-time bacterial tracking of infection, probiotic investigation, tumor-targeted therapy, and drug screening. Applications of engineered bioluminescent bacteria as whole-cell biosensors for sensing biological changes in vitro and in vivo are then discussed. Finally, we review the optimizations and future directions of bioluminescent bacteria for imaging. This review aims to provide fundamental insights into bacterial BLI and highlight the potential development of this technique in the future.

Weighted gene co-expression network analysis reveals key biomarkers and immune infiltration characteristics for bronchial epithelial cells from asthmatic patients.

Asthma ranks among the most prevalent non-communicable diseases worldwide. Previous studies have elucidated the significant role of the immune system in its pathophysiology. Nevertheless, the immune-related mechanisms underlying asthma are complex and still inadequately understood. Thus, our objective was to investigate novel key biomarkers and immune infiltration characteristics associated with asthma by employing integrated bioinformatics tools. In this study, we conducted a weighted gene co-expression network analysis (WGCNA) to identify key modules and genes potentially implicated in asthma. Functional annotation of these key modules and genes was carried out through gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. Additionally, we constructed a protein-protein interaction (PPI) network using the STRING database to identify 10 hub genes. Furthermore, we evaluated the relative proportion of immune cells in bronchial epithelial cell samples from 20 healthy individuals and 88 asthmatic patients using CIBERSORT. Finally, we validated the hub genes and explored their correlation with immune infiltration. Furthermore, 20 gene expression modules and 10 hub genes were identified herein. Among them, complement component 3 (C3), prostaglandin I2 receptor (PTGIR), parathyroid hormone-like hormone (PTHLH), and C-X3-C motif chemokine ligand 1 (CX3CL1) were closely correlated with the infiltration of immune cells. They may be novel candidate biomarkers or therapeutic targets for asthma. Furthermore, B cells memory, and plasma cells might play an important role in immune cell infiltration after asthma. C3, PTGIR, CX3CL1, and PTHLH have important clinical diagnostic values and are correlated with infiltration of multiple immune cell types in asthma. These hub genes, B cells memory, and plasma cells may become important biological targets for therapeutic asthma drug screening and drug design.

CLDN18: Clinical, Pathological, and Genetic Signatures with Drug Screening in Gastric Adenocarcinoma.

The CLDN18 gene, encoding claudin 18.1 and claudin 18.2, is a key component of tight junction strands in epithelial cells that form a paracellular barrier that is critical in Stomach Adenocarcinoma (STAD). Our study included 1,095 patients with proven STAD, 415 from The Cancer Genome Atlas (TCGA) cohort and 680 from the Gene Expression Omnibus database. We applied various analyses, including gene set enrichment analysis, pathway analysis, and in vitro drug screening to evaluate survival, immune cells, and genes and gene sets associated with cancer progression, based on CLDN18 expression levels. Gradient boosting machine learning (70% for training, 15% for validation, and 15% for testing) was used to evaluate the impact of CLDN18 on survival and develop a survival prediction model. High CLDN18 expression correlated with worse survival in lymphocyte-poor STAD, accompanied by decreased helper T cells, altered metabolic genes, low necrosis-related gene expression, and increased tumor proliferation. CLDN18 expression showed associations with gene sets associated with various stomach, breast, ovarian, and esophageal cancers, while pathway analysis linked CLDN18 to immunity. Incorporating CLDN18 expression improved survival prediction in a machine learning model. Notably, nutlin-3a and niraparib effectively inhibited high CLDN18-expressing gastric cancer cells in drug screening. Our study provides a comprehensive understanding of the biological role of CLDN18-based bioinformatics and machine learning analysis in STAD, shedding light on its prognostic significance and potential therapeutic implications. To fully elucidate the molecular intricacies of CLDN18, further investigation is warranted, particularly through in vitro and in vivo studies.

Therapeutic potential of targeting Nrf2 by panobinostat in pituitary neuroendocrine tumors

We aimed to identify the druggable cell-intrinsic vulnerabilities and target-based drug therapies for PitNETs using the high-throughput drug screening (HTS) and genomic sequencing methods. We examined 9 patient-derived PitNET primary cells in HTS. Based on the screening results, the potential target genes were analyzed with genomic sequencing from a total of 180 PitNETs. We identified and verified one of the most potentially effective drugs, which targeted the Histone deacetylases (HDACs) both in in vitro and in vivo PitNET models. Further RNA sequencing revealed underlying molecular mechanisms following treatment with the representative HDACs inhibitor, Panobinostat. The HTS generated a total of 20,736 single-agent dose responses which were enriched among multiple inhibitors for various oncogenic targets, including HDACs, PI3K, mTOR, and proteasome. Among these drugs, HDAC inhibitors (HDACIs) were, on average, the most potent drug class. Further studies using in vitro, in vivo, and isolated PitNET primary cell models validated HDACIs, especially Panobinostat, as a promising therapeutic agent. Transcriptional surveys revealed substantial alterations to the Nrf2 signaling following Panobinostat treatment. Moreover, Nrf2 is highly expressed in PitNETs. The combination of Panobinostat and Nrf2 inhibitor ML385 had a synergistic effect on PitNET suppression. The current study revealed a class of effective anti-PitNET drugs, HDACIs, based on the HTS and genomic sequencing. One of the representative compounds, Panobinostat, may be a potential drug for PitNET treatment via Nrf2-mediated redox modulation. Combination of Panobinostat and ML385 further enhance the effectiveness for PitNET treatment.

PERCEPTION predicts patient response and resistance to treatment using single-cell transcriptomics of their tumors.

Tailoring optimal treatment for individual cancer patients remains a significant challenge. To address this issue, we developed PERCEPTION (PERsonalized Single-Cell Expression-Based Planning for Treatments In ONcology), a precision oncology computational pipeline. Our approach uses publicly available matched bulk and single-cell (sc) expression profiles from large-scale cell-line drug screens. These profiles help build treatment response models based on patients' sc-tumor transcriptomics. PERCEPTION demonstrates success in predicting responses to targeted therapies in cultured and patient-tumor-derived primary cells, as well as in two clinical trials for multiple myeloma and breast cancer. It also captures the resistance development in patients with lung cancer treated with tyrosine kinase inhibitors. PERCEPTION outperforms published state-of-the-art sc-based and bulk-based predictors in all clinical cohorts. PERCEPTION is accessible at https://github.com/ruppinlab/PERCEPTION . Our work, showcasing patient stratification using sc-expression profiles of their tumors, will encourage the adoption of sc-omics profiling in clinical settings, enhancing precision oncology tools based on sc-omics.

Improving the power of drug toxicity measurements by quantitative nuclei imaging

Imaging-based anticancer drug screens are becoming more prevalent due to development of automated fluorescent microscopes and imaging stations, as well as rapid advancements in image processing software. Automated cell imaging provides many benefits such as their ability to provide high-content data, modularity, dynamics recording and the fact that imaging is the most direct way to access cell viability and cell proliferation. However, currently most publicly available large-scale anticancer drugs screens, such as GDSC, CTRP and NCI-60, provide cell viability data measured by assays based on colorimetric or luminometric measurements of NADH or ATP levels. Although such datasets provide valuable data, it is unclear how well drug toxicity measurements can be integrated with imaging data. Here we explored the relations between drug toxicity data obtained by XTT assay, two quantitative nuclei imaging methods and trypan blue dye exclusion assay using a set of four cancer cell lines with different morphologies and 30 drugs with different mechanisms of action. We show that imaging-based approaches provide high accuracy and the differences between results obtained by different methods highly depend on drug mechanism of action. Selecting AUC metrics over IC50 or comparing data where significantly drugs reduced cell numbers noticeably improves consistency between methods. Using automated cell segmentation protocols we analyzed mitochondria activity in more than 11 thousand drug-treated cells and showed that XTT assay produces unreliable data for CDK4/6, Aurora A, VEGFR and PARP inhibitors due induced cell size growth and increase in individual mitochondria activity. We also explored several benefits of image-based analysis such as ability to monitor cell number dynamics, dissect changes in total and individual mitochondria activity from cell proliferation, and ability to identify chromatin remodeling drugs. Finally, we provide a web tool that allows comparing results obtained by different methods.

The role of corticosterone in nevirapine-induced idiosyncratic drug-induced liver injury.

Nevirapine, an antiretroviral used in the treatment of HIV, is associated with idiosyncratic drug-induced liver injury (IDILI), a potentially life-threatening adverse drug reaction. Its usage has decreased due to this concern, but it is still widely used in lower-resource settings. In general, the mechanisms underlying idiosyncratic drug reactions (IDRs) are poorly understood, but evidence indicates that most are immune-mediated. There is very limited understanding of the early immune response following administration of drugs associated with IDRs, which likely occurs due to reactive metabolite formation. In this work, we aimed to characterize the links between covalent binding of nevirapine, the development of an early immune response, and the subsequent liver injury using a mouse model. We describe initial attempts to characterize an early immune response to nevirapine followed by the discovery that nevirapine induced the release of corticosterone. Corticosterone release was partially associated with the degree of drug covalent binding in the liver but was also likely mediated by additional mechanisms at higher drug doses. Transcriptomic analysis confirmed metabolic activation, glucocorticoid signaling, and decreased immune activation; GDF-15 also warrants further investigation as part of the immune response to nevirapine. Finally, glucocorticoid blockade preceding the first dose of nevirapine attenuated nevirapine-induced liver injury at 3 weeks, suggesting that acute glucocorticoid signaling is harmful in the context of nevirapine-induced liver injury. This work demonstrates that nevirapine induces acute corticosterone release, which contributes to delayed-onset liver injury. It also has implications for screening drug candidates for IDILI risk and preventing nevirapine-induced IDILI.

Spatial and single-cell explorations uncover prognostic significance and immunological functions of mitochondrial calcium uniporter in breast cancer

The mitochondrial calcium uniporter (MCU) is a transmembrane protein facilitating the entry of calcium ions into mitochondria from the cell cytosol. Maintaining calcium balance is crucial for enhancing cellular energy supply and regulating cell death. The interplay of calcium balance through MCU and the sodium-calcium exchanger is known, but its regulation in the breast cancer tumor microenvironment remains elusive. Further investigations are warranted to explore MCU’s potential in BRCA clinical pathology, tumor immune microenvironment, and precision oncology. Our study, employing a multi-omics approach, identifies MCU as an independent diagnostic biomarker for breast cancer (BRCA), correlated with advanced clinical status and poor overall survival. Utilizing public datasets from GEO and TCGA, we discern differentially expressed genes in BRCA and examine their associations with immune gene expression, overall survival, tumor stage, gene mutation status, and infiltrating immune cells. Spatial transcriptomics is employed to investigate MCU gene expression in various regions of BRCA, while spatial transcriptomics and single-cell RNA-sequencing methods explore the correlation between MCUs and immune cells. Our findings are validated through the analysis of 59 BRCA patient samples, utilizing immunohistochemistry and bioinformatics to examine the relationship between MCU expression, clinicopathological features, and prognosis. The study uncovers the expression of key gene regulators in BRCA associated with genetic variations, deletions, and the tumor microenvironment. Mutations in these regulators positively correlate with different immune cells in six immune datasets, playing a pivotal role in immune cell infiltration in BRCA. Notably, high MCU performance is linked to CD8 + T cells infiltration in BRCA. Furthermore, pharmacogenomic analysis of BRCA cell lines indicates that MCU inactivation is associated with increased sensitivity to specific small molecule drugs. Our findings suggest that MCU alterations may be linked to BRCA progression, unveiling new diagnostic and prognostic implications for MCU in BRCA. The study underscores MCU's role in the tumor immune microenvironment and cell cycle progression, positioning it as a potential tool for BRCA precision medicine and drug screening.

Breaking Boundaries in Malaria Research: Design of a Genetic Tool for High-Throughput Gametocidal Drug Screening

Breaking Boundaries in Malaria Research: Design of a Genetic Tool for High-Throughput Gametocidal Drug Screening

506 Methamphetamine Intoxication and Inpatient Outcomes for Burn Patients

Abstract Introduction Methamphetamine intoxication frequently complicates inpatient burn admissions. While single-institution studies describe adverse outcomes during resuscitation, less is known about the associated inpatient complications and perioperative management in users. This analysis aims to identify these trends on a national level. Methods The US National Trauma Data Bank was queried to identify burn patients between 2017-2021. Methamphetamine intoxication was identified on admission and compared to those who screen negative. Primary outcomes included death, stroke, and myocardial infarction (MI). Secondary outcomes included organ failure, timing of surgery, admission vitals, length of stay (LOS), and days in the intensive care unit (ICU). Multivariable regressions modeled the effects on outcomes adjusting for available covariates including demographics, total body surface area (TBSA) burned, and inhalation injury. Bonferroni adjustments were applied. Results 326,614 burn encounters were identified, and 5,380 (1.7%) had positive methamphetamine drug screens upon admission. Users were significantly older (35.2 versus 31.9 years, p < .001), had a greater percentage of males (85.3 vs. 82.0, p < .001), were more likely to have inhalation injury and overall had larger %TBSA burns (16% vs. 13%, (p < .001). Users had significantly higher but clinically insignificant ED systolic blood pressures (132.1 vs. 129.9, p < .001). There was no significant difference between the two groups regarding pulse rate >120 (1.7% vs. 1.6%, p=.611). Users were not more likely to die, sustain an MI, or stroke during admission. In contrast, users were significantly more likely to have alcohol withdrawal (0.8% vs. 0.5%, p=.019), drug withdrawal (0.3% vs. 0.2%, p=.003), a pulmonary embolism (PE) (0.9% vs. 0.7%, p=.039), and delirium (1.2% vs. 0.4%, p=.003). Users had longer times to surgery adjusting for burn severity and demographics (1.6 vs. 1.4 days, p=.033). There were no significant differences in the average number of wound coverage procedures (1.50 vs. 1.45, p=.947). Users had significantly longer hospitalization (8.7 vs. 6.2 days, p < .001); however, there was no significant difference between days in the ICU (6.7 vs. 6.4 days, p=.072). Conclusions While not associated with mortality, methamphetamine intoxication was associated with an increased risk of many complications including PE, drug withdrawal, and delirium. Methamphetamine use was associated with delays in surgery and hospitalization. Applicability of Research to Practice Recognize the increased risk of complications and the need for tailored perioperative care for methamphetamine users. Additionally, burn centers may consider how to avoid delaying care for users and support interventions for withdrawal.

Should contingency management protocols and dissemination practices be modified to accommodate rising stimulant use and harm reduction frameworks?

AbstractBackgroundStimulant‐related overdoses have increased dramatically, with almost 50% of overdoses in the United States now involving stimulants. Additionally, harm‐reduction approaches are increasingly seen as key to reducing the negative impact of substance use. Contingency management (CM), the provision of financial incentives for abstinence, is the most effective treatment for stimulant use disorder, but historically has not been widely implemented. Many recent, large‐scale implementation efforts have relied upon foundational CM protocols that may not sufficiently account for recent increases in the prevalence and lethality of stimulant use nor the growing preference for harm reduction versus abstinence‐only frameworks.ArgumentWe argue the need to (1) consider whether and how CM protocols might be modified to address rising stimulant use and harm reduction frameworks and (2) make CM widely accessible so that it can reduce population‐level stimulant use while ensuring that it is delivered with fidelity to its basic principles. Proposed changes include changing CM reinforcement schedules to emphasize treatment engagement and reductions in use in addition to abstinence, changing guidelines on the duration of and re‐engagement in CM, investing in research on virtual CM, incentivizing providers and health systems to deliver CM, making it easier to purchase and use point‐of‐care drug screens, using direct‐to‐consumer marketing to increase demand for CM and adapting CM to the community in which it is being implemented.ConclusionsOur proposed modifications to contingency management (CM) protocols and accessibility may more effectively address rising stimulant use and align CM more closely with harm‐reduction frameworks. Given the urgent need to reduce overdose deaths, developing and testing modified CM protocols may need to rely upon methods other than randomized controlled trials. Efforts to disseminate CM widely to reduce population‐level stimulant use must be balanced with the need to maintain fidelity to CM’s basic principles.

The potential molecular markers of inflammatory response in KOA with AD based on single-cell transcriptome sequencing analysis and identification of ligands by virtual screening.

Alzheimer's disease (AD) and osteoarthritis (OA) are both senile degenerative diseases.Clinical studies have found that OA patients have a significantly increased risk of AD in their later life. This study hypothesized that chronic aseptic inflammation might lead to AD in KOA patients. However, current research has not yet clarified the potential mechanism between AD and KOA. Therefore, this study intends to use KOA transcriptional profiling and single-cell sequencing analysis technology to explore the molecular mechanism of KOA affecting AD development, and screen potential molecular biomarkers and drugs for the prediction, diagnosis, and prognosis of AD in KOA patients. It was found that the higher the expression of TXNIP, MMP3, and MMP13, the higher the risk coefficient of AD was. In addition, the AUC of TXNIP, MMP3, and MMP13 were all greater than 0.70, which had good diagnostic significance for AD. Finally, through the virtual screening of core proteins in FDA drugs and molecular dynamics simulation, it was found that compound Cobicistat could be targeted to TXNIP, Itc could be targeted to MMP3, and Isavuconazonium could be targeted to MMP13.To sum up, TXNIP, MMP3, and MMP13 are prospective molecular markers in KOA with AD, which could be used to predict, diagnose, and prognosis.

Organoids Modeling Stroke in a Petri Dish.

Stroke is a common neurological disorder, the second leading cause of death, and the third leading cause of disability. Unfortunately, the only approved drug for it is tissue plasminogen, but the therapeutic window is limited. In this context, preclinical studies are relevant to better dissect the underlying mechanisms of stroke and for the drug screening of potential therapies. Brain organoids could be relevant in this setting. They are derived from pluripotent stem cells or isolated organ progenitors that differentiate to form an organ-like tissue, exhibiting multiple cell types that self-organize to form a structure not unlike the organ in vivo. Brain organoids mimic many key features of early human brain development at molecular, cellular, structural, and functional levels and have emerged as novel model systems that can be used to investigate human brain diseases including stroke. Brain organoids are a promising and powerful tool for ischemic stroke studies; however, there are a few concerns that need to be addressed, including the lack of vascularization and the many cell types that are typically present in the human brain. The aim of this review is to discuss the potential of brain organoids as a novel model system for studying ischemic stroke, highlighting both the advantages and disadvantages in the use of this technology.

Testing of putative antiseizure medications in a preclinical Dravet syndrome zebrafish model.

Dravet syndrome is a severe genetic epilepsy primarily caused by de novo mutations in a voltage-activated sodium channel gene (SCN1A). Patients face life-threatening seizures that are largely resistant to available anti-seizure medications. Preclinical Dravet syndrome animal models are a valuable tool to identify candidate anti-seizure medications for these patients. Among these, scn1lab mutant zebrafish, exhibiting spontaneous seizure-like activity, are particularly amenable to large-scale drug screening. Thus far, we have screened more than 3000 drug candidates in scn1lab zebrafish mutants, identifying valproate, stiripentol, and fenfluramine e.g. Food and Drug Administration-approved drugs, with clinical application in the Dravet syndrome population. Successful phenotypic screening in scn1lab mutant zebrafish is rigorous and consists of two stages: (i) a locomotion-based assay measuring high-velocity convulsive swim behaviour and (ii) an electrophysiology-based assay, using in vivo local field potential recordings, to quantify electrographic seizure-like events. Historically, nearly 90% of drug candidates fail during translation from preclinical models to the clinic. With such a high failure rate, it becomes necessary to address issues of replication and false positive identification. Leveraging our scn1lab zebrafish assays is one approach to address these problems. Here, we curated a list of nine anti-seizure drug candidates recently identified by other groups using preclinical Dravet syndrome models: 1-Ethyl-2-benzimidazolinone, AA43279, chlorzoxazone, donepezil, lisuride, mifepristone, pargyline, soticlestat and vorinostat. First-stage locomotion-based assays in scn1lab mutant zebrafish identified only 1-Ethyl-2-benzimidazolinone, chlorzoxazone and lisuride. However, second-stage local field potential recording assays did not show significant suppression of spontaneous electrographic seizure activity for any of the nine anti-seizure drug candidates. Surprisingly, soticlestat induced frank electrographic seizure-like discharges in wild-type control zebrafish. Taken together, our results failed to replicate clear anti-seizure efficacy for these drug candidates highlighting a necessity for strict scientific standards in preclinical identification of anti-seizure medications.