AI-Powered Microfluidics: Shaping the Future of Phenotypic Drug Discovery.

Phenotypic drug discovery (PDD), which involves harnessing biological systems directly to uncover effective drugs, has undergone a resurgence in recent years. The rapid advancement of artificial intelligence (AI) over the past few years presents numerous opportunities for augmenting phenotypic drug screening on microfluidic platforms, leveraging its predictive capabilities, data analysis, efficient data processing, etc. Microfluidics coupled with AI is poised to revolutionize the landscape of phenotypic drug discovery. By integrating advanced microfluidic platforms with AI algorithms, researchers can rapidly screen large libraries of compounds, identify novel drug candidates, and elucidate complex biological pathways with unprecedented speed and efficiency. This review provides an overview of recent advances and challenges in AI-based microfluidics and their applications in drug discovery. We discuss the synergistic combination of microfluidic systems for high-throughput screening and AI-driven analysis for phenotype characterization, drug-target interactions, and predictive modeling. In addition, we highlight the potential of AI-powered microfluidics to achieve an automated drug screening system. Overall, AI-powered microfluidics represents a promising approach to shaping the future of phenotypic drug discovery by enabling rapid, cost-effective, and accurate identification of therapeutically relevant compounds.

Identification of Potential Inhibitors for Drug Resistance in Acute Lymphoblastic Leukemia through Differentially Expressed Gene Analysis and In Silico Screening.

Acute lymphoblastic leukemia (ALL) is a disease of lymphocyte origin predominantly diagnosed in children. While its 5-year survival rate is high, resistance to chemotherapy drugs is still an obstacle. Our aim is to determine differentially expressed genes (DEGs) related to Asparaginase, Daunorubicin, Prednisolone, and Vincristine resistance and identify potential inhibitors via docking. Three datasets were accessed from the Gene Expression Omnibus database; GSE635, GSE19143, and GSE22529. The microarray data was analyzed using R4.2.0 and Bioconductor packages, and pathway and protein-protein interaction analysis were performed. We identified 1294 upregulated DEGs, with 12 genes consistently upregulated in all four resistant groups. KEGG analysis revealed an association with the PI3K-Akt pathway. Among DEGs, 33 hub genes including MDM2 and USP7 were pinpointed. Within common genes, CLDN9 and HS3ST3A1 were subjected to molecular docking against 3556 molecules. Following ADMET analysis, three drugs emerged as potential inhibitors: Flunarizine, Talniflumate, and Eltrombopag. Molecular dynamics analysis for HS3ST3A1 indicated all candidates had the potential to overcome drug resistance, Eltrombopag displaying particularly promising results. This study promotes a further understanding of drug resistance in ALL, introducing novel genes for consideration in diagnostic screening. It also presents potential inhibitor candidates to tackle drug resistance through repurposing.

Using the refractive index of latent fingerprints for the quantification and characterisation of sample deposition.

Latent fingerprints (LFPs) are predominantly used for personal identification, but in recent years research has shown their potential for drug screening. Despite this there is no standardised collection method to allow accurate drug test interpretation. We sought to help address this by characterising different variables related to sweat deposition in LFPs as the knowledge is limited. A series of experiments were conducted firstly to validate a novel tool called the Ridgeway (Intelligent Fingerprint Ltd. UK) to quantify the amount of sweat deposited from a LFP using the refractive index (RI). A significant positive correlation was observed between the Ridgeway score (Rs) and LFP mass [r = 0.868, p < 0.01]. The Rs was used as means to investigate optimal sampling to characterise sample deposition for drug screening purposes. It was found with a consistent disposition pressure (300 - 400 g) and surface (glass slide) no significant difference was observed between the left and right index finger [left: p = 0.938; right: p = 0.838]. Significantly higher Rs [p<0.01] were obtained when 10 cumulative LFPs were deposited compared to a single LFP, suggesting a larger sweat quantity. We also wanted to investigate optimal eccrine sweat sampling to confirm drug ingestion over drug contamination of the fingerprint. We found that wearing gloves did not significantly improve mean difference in Rs when compared to no gloves [p = 0.239]. To produce eccrine only LFPs, external contamination (e.g. sebaceous sweat) needs to be removed. Soap with lint free tissue was significantly better for this compared to antibacterial hand gel [p<0.01]. Our findings showed that the Ridgeway tool effectively quantified LFPs at the point of deposition using a refractive index and enabled us to establish conditions for consistent LFP sampling.

From Organ-on-a-Chip to Human-on-a-Chip: A Review of Research Progress and Latest Applications.

Organ-on-a-Chip (OOC) technology, which emulates the physiological environment and functionality of human organs on a microfluidic chip, is undergoing significant technological advancements. Despite its rapid evolution, this technology is also facing notable challenges, such as the lack of vascularization, the development of multiorgan-on-a-chip systems, and the replication of the human body on a single chip. The progress of microfluidic technology has played a crucial role in steering OOC toward mimicking the human microenvironment, including vascularization, microenvironment replication, and the development of multiorgan microphysiological systems. Additionally, advancements in detection, analysis, and organoid imaging technologies have enhanced the functionality and efficiency of Organs-on-Chips (OOCs). In particular, the integration of artificial intelligence has revolutionized organoid imaging, significantly enhancing high-throughput drug screening. Consequently, this review covers the research progress of OOC toward Human-on-a-chip, the integration of sensors in OOCs, and the latest applications of organoid imaging technologies in the biomedical field.

Drug-induced senescence by aurora kinase inhibitors attenuates innate immune response of macrophages on gastric cancer organoids.

Diffuse-type gastric cancer (DGC) is a subtype of gastric cancer with aggressiveness and poor prognosis. It is of great significance to find sensitive drugs for DGC. In the current study, a total of 20 patient-derived organoids (PDOs) were analyzed for screening the therapeutic efficacy of small molecule kinases inhibitors on gastric cancers, especially the therapeutic difference between intestinal-type gastric cancer (IGCs) and DGCs. The IGCs are sensitive to multiple kinases inhibitors, while DGCs are resistant to most of these kinases inhibitors. It was found that DGCs showed drug-induced senescent phenotype after treatment by aurora kinases inhibitors (AURKi) Barasertib-HQPA and Danusertib. The cell diameter of cancer cells are increased with stronger staining of senescence-associated ß-galactosidase (SA-ß-GAL), and characteristic appearance of multinucleated giant cells. The senescent cancer cells secrete large amounts of chemokine MCP-1/CCL2, which recruit and induce macrophage to M2-type polarization in PDOs of DGC (DPDOs)-macrophage co-culture system. The up-regulation of local MCP-1/CCL2 can interact with MCP-1/CCL2 receptor (CCR2) expressed on macrophages and suppress their innate immunity to cancer cells. Overall, the special response of DGC to AURKi suggests that clinicians should select a sequential therapy with senescent cell clearance after AURKi treatment for DGC.

A clinically compatible in vitro drug-screening platform identifies therapeutic vulnerabilities in primary cultures of brain metastases.

PURPOSE: Brain metastases represent the most common intracranial tumors in adults and are associated with a poor prognosis. We used a personalized in vitro drug screening approach to characterize individual therapeutic vulnerabilities in brain metastases. METHODS: Short-term cultures of cancer cells isolated from brain metastasis patients were molecularly characterized using next-generation sequencing and functionally evaluated using high-throughput in vitro drug screening to characterize pharmacological treatment sensitivities. RESULTS: Next-generation sequencing identified matched genetic alterations in brain metastasis tissue samples and corresponding short-term cultures, suggesting that short-term cultures of brain metastases are suitable models for recapitulating the genetic profile of brain metastases that may determine their sensitivity to anti-cancer drugs. Employing a high-throughput in vitro drug screening platform, we successfully screened the cultures of five brain metastases for response to 267 anticancer compounds and related drug response to genetic data. Among others, we found that targeted treatment with JAK3, HER2, or FGFR3 inhibitors showed anti-cancer effects in individual brain metastasis cultures. CONCLUSION: Our preclinical study provides a proof-of-concept for combining molecular profiling with in vitro drug screening for predictive evaluation of therapeutic vulnerabilities in brain metastasis patients. This approach could advance the use of patient-derived cancer cells in clinical practice and might eventually facilitate decision-making for personalized drug treatment.

Prediction method of pharmacokinetic parameters of small molecule drugs based on GCN network model.

CONTEXT: Accurately predicting plasma protein binding rate (PPBR) and oral bioavailability (OBA) helps to better reveal the absorption and distribution of drugs in the human body and subsequent drug design. Although machine learning models have achieved good results in prediction accuracy, they often suffer from insufficient accuracy when dealing with data with irregular topological structures. METHODS: In view of this, this study proposes a pharmacokinetic parameter prediction framework based on graph convolutional networks (GCN), which predicts the PPBR and OBA of small molecule drugs. In the framework, GCN is first used to extract spatial feature information on the topological structure of drug molecules, in order to better learn node features and association information between nodes. Then, based on the principle of drug similarity, this study calculates the similarity between small molecule drugs, selects different thresholds to construct datasets, and establishes a prediction model centered on the GCN algorithm. The experimental results show that compared with traditional machine learning prediction models, the prediction model constructed based on the GCN method performs best on PPBR and OBA datasets with an inter-molecular similarity threshold of 0.25, with MAE of 0.155 and 0.167, respectively. In addition, in order to further improve the accuracy of the prediction model, GCN is combined with other algorithms. Compared to using a single GCN method, the distribution of the predicted values obtained by the combined model is highly consistent with the true values. In summary, this work provides a new method for improving the rate of early drug screening in the future.

Multireceptor Analysis for Evaluating the Antidiabetic Efficacy of Karanjin: A Computational Approach.

BACKGROUND: Diabetes mellitus, notably type 2, is a rising global health challenge, prompting the need for effective management strategies. Common medications such as metformin, insulin, repaglinide and sitagliptin can induce side effects like gastrointestinal disturbances, hypoglycemia, weight gain and specific organ risks. Plant-derived therapies like Karanjin from Pongamia pinnata present promising alternatives due to their historical use, holistic health benefits and potentially fewer adverse effects. This study employs in silico analysis to explore Karanjin's interactions with diabetes-associated receptors, aiming to unveil its therapeutic potential while addressing the limitations and side effects associated with conventional medications. METHODOLOGY: The research encompassed the selection of proteins from the Protein Data Bank (PDB), followed by structural refinement processes and optimization. Ligands such as Karanjin and standard drugs were retrieved from PubChem, followed by a comprehensive analysis of their ADMET profiling and pharmacokinetic properties. Protein-ligand interactions were evaluated through molecular docking using AutoDockTools 1.5.7, followed by the analysis of structural stability using coarse-grained simulations with CABS Flex 2.0. Molecular dynamics simulations were performed using Desmond 7.2 and the OPLS4 force field to explore how Karanjin interacts with proteins over 100 nanoseconds, focusing on the dynamics and structural stability. RESULTS: Karanjin, a phytochemical from Pongamia pinnata, shows superior drug candidate potential compared to common medications, offering advantages in efficacy and reduced side effects. It adheres to drug-likeness criteria and exhibits optimal ADMET properties, including moderate solubility, high gastrointestinal absorption and blood-brain barrier penetration. Molecular docking revealed Karanjin's highest binding energy against receptor 3L2M (Pig pancreatic alpha-amylase) at -9.1 kcal/mol, indicating strong efficacy potential. Molecular dynamics simulations confirmed stable ligand-protein complexes with minor fluctuations in RMSD and RMSF, suggesting robust interactions with receptors 3L2M. CONCLUSION: Karanjin demonstrates potential in pharmaceutical expansion for treating metabolic disorders such as diabetes, as supported by computational analysis. Prospects for Karanjin in pharmaceutical development include structural modifications for enhanced efficacy and safety. Nanoencapsulation may improve bioavailability and targeted delivery to pancreatic cells, while combination therapies could optimize treatment outcomes in diabetes management. Clinical trials and experimental studies are crucial to validate its potential as a novel therapeutic agent.

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.

Gastric Cancer Assembloids Derived from Patient-Derived Xenografts: A Preclinical Model for Therapeutic Drug Screening.

The construction of reliable preclinical models is crucial for understanding the molecular mechanisms involved in gastric cancer and for advancing precision medicine. Currently, existing in vitro tumor models often do not accurately replicate the human gastric cancer environment and are unsuitable for high-throughput therapeutic drug screening. In this study, droplet microfluidic technology is employed to create novel gastric cancer assembloids by encapsulating patient-derived xenograft gastric cancer cells and patient stromal cells in Gelatin methacryloyl (GelMA)-Gelatin-Matrigel microgels. The usage of GelMA-Gelatin-Matrigel composite hydrogel effectively alleviated cell aggregation and sedimentation during the assembly process, allowing for the handling of large volumes of cell-laden hydrogel and the uniform generation of assembloids in a high-throughput manner. Notably, the patient-derived xenograft assembloids exhibited high consistency with primary tumors at both transcriptomic and histological levels, and can be efficiently scaled up for preclinical drug screening efforts. Furthermore, the drug screening results clearly demonstrated that the in vitro assembloid model closely mirrored in vivo drug responses. Thus, these findings suggest that gastric cancer assembloids, which effectively replicate the in vivo tumor microenvironment, show promise for enabling more precise high-throughput drug screening and predicting the clinical outcomes of various drugs.

A class of chemical compounds enhances clustering of muscle nicotinic acetylcholine receptor in cultured myogenic cells.

Neuromuscular signal transmission is affected in various diseases including myasthenia gravis, congenital myasthenic syndromes, and sarcopenia. We used an ATF2-luciferase system to monitor the phosphorylation of MuSK in HEK293 cells introduced with MUSK and LRP4 cDNAs to find novel chemical compounds that enhanced agrin-mediated acetylcholine receptor (AChR) clustering. Four compounds with similar chemical structures carrying benzene rings and heterocyclic rings increased the luciferase activities 8- to 30-folds, and two of them showed continuously graded dose dependence. The effects were higher than that of disulfiram, a clinically available aldehyde dehydrogenase inhibitor, which we identified to be the most competent preapproved drug to enhance ATF2-luciferase activity in the same assay system. In C2C12 myotubes, all the compounds increased the area, intensity, length, and number of AChR clusters. Three of the four compounds increased the phosphorylation of MuSK, but not of Dok7, JNK. ERK, or p38. Monitoring cell toxicity using the neurite elongation of NSC34 neuronal cells as a surrogate marker showed that all the compounds had no effects on the neurite elongation up to 1 µM. Extensive docking simulation and binding structure prediction of the four compounds with all available human proteins using AutoDock Vina and DiffDock showed that the four compounds were unlikely to directly bind to MuSK or Dok7, and the exact target remained unknown. The identified compounds are expected to serve as a seed to develop a novel therapeutic agent to treat defective NMJ signal transmission.

Pneumatic extrusion bioprinting-based high throughput fabrication of a melanoma 3D cell culture model for anti-cancer drug screening.

The high incidence of malignant melanoma highlights the need for in vitro models that accurately represent the tumour microenvironment, enabling developments in melanoma therapy and drug screening. Despite several advancements in 3D cell culture models, appropriate melanoma models for evaluating drug efficacy are still in high demand. The 3D pneumatic extrusion-based bioprinting technology offers numerous benefits, including the ability to achieve high-throughput capabilities. However, there is a lack of research that combines pneumatic extrusion-based bioprinting with analytical assays to enable efficient drug screening in 3D melanoma models. To address this gap, this study developed a simple and highly reproducible approach to fabricate a 3D A375 melanoma cell culture model using the pneumatic extrusion-based bioprinting technology. To optimise this method, the bioprinting parameters for producing 3D cell cultures in a 96-well plate were adjusted to improve reproducibility while maintaining the desired droplet size and a cell viability of 92.13± 6.02%. The cross-linking method was optimised by evaluating cell viability and proliferation of the 3D bioprinted cells in three different concentrations of calcium chloride. The lower concentration of 50 mM resulted in higher cell viability and increased cell proliferation after 9 days of incubation. The A375 cells exhibited a steadier proliferation rate in the 3D bioprinted cell cultures, and tended to aggregate into spheroids, whereas the 2D cell cultures generally formed monolayered cell sheets. In addition, we evaluated the drug responses of four different anti-cancer drugs on the A375 cells in both the 2D and 3D cell cultures. The 3D cell cultures exhibited higher levels of drug resistance in all four tested anti-cancer drugs. This method presents a simple and cost-effective method of producing and analysing 3D cell culture models that do not add additional complexity to current assays and shows considerable potential for advancing 3D cell culture models' drug efficacy evaluations.

Using the Zebrafish Larval Model of Infection to Investigate Antibiotic Efficacy and Combination Treatments Against Staphylococcus aureus.

There is an increasing need for new treatment regimens to combat antibiotic-resistant strains of bacteria. Staphylococcus aureus is a clinically important, opportunist pathogen that has developed resistance to a range of antibiotics. The zebrafish larval model of systemic disease has been increasingly utilized to elucidate S. aureus virulence mechanisms and host-pathogen interactions. Here, we outline how this model can be used to investigate the effects of different antibiotics alone and in combination against S. aureus.

Drug Evaluation of Parkinson's Disease Patient-Derived Midbrain Organoids Using Mesoporous Au Nanodot-Patterned 3D Concave Electrode.

Brain organoids are being recognized as valuable tools for drug evaluation in neurodegenerative diseases due to their similarity to the human brain's structure and function. However, a critical challenge is the lack of selective and sensitive electrochemical sensing platforms to detect the response of brain organoids, particularly changes in the neurotransmitter concentration upon drug treatment. This study introduces a 3D concave electrode patterned with a mesoporous Au nanodot for the detection of electrochemical signals of dopamine in response to drugs in brain organoids for the first time. The mesoporous Au nanodot-patterned film was fabricated using laser interference lithography and electrochemical deposition. Then, the film was attached to a polymer-based 3D concave mold to obtain a 3D concave electrode. Midbrain organoids generated from Parkinson's disease (PD) patient-derived iPSCs with gene mutations (named as PD midbrain organoid) or normal midbrain organoids were positioned on the developed 3D concave electrode. The 3D concave electrode showed a 1.4 times higher electrochemical signal of dopamine compared to the bare gold electrode. And the dopamine secreted from normal midbrain organoids or PD midbrain organoids on the 3D concave electrode could be detected electrochemically. After the treatment of PD midbrain organoids with levodopa, the drug for PD, the increase in dopamine level was detected due to the activation of dopaminergic neurons by the drug. The results suggest the potential of the proposed 3D concave electrode combined with brain organoids as a useful tool for assessing drug efficacy. This sensing system can be applied to a variety of organoids for a comprehensive drug evaluation.

Dual-driven AND molecular logic gates for label-free and sensitive ratiometric fluorescence sensing and inhibitors screening.

Due to the complexity of regulatory networks of disease-related biomarkers, developing simple, sensitive, and accurate methods has remained challenging for precise diagnosis. Herein, an "AND" logic gates DNA molecular machine (LGDM) was constructed, which was powered by the catalytic hairpin assembly (CHA). It was coupled with dual-emission CdTe quantum dots (QDs)-based cation exchange reaction (CER) for label-free, sensitive, and ratiometric fluorescence detection of APE1 and miRNA biomarkers. Benefiting from synergistic signal amplification strategies and a ratiometric fluorometric output mode, this LGDM enables accurate logic computing with robust and significant output signals from weak inputs. It offers improved sensitivity and selectivity even in cell extracts. Using dual-emission spectra CdTe QDs, with a ratiometric signal output mode, ensured good stability and effectively prevented false-positive signals from intrinsic biological interferences compared to the approach relying on a single signal output mode, which enabled the LGDM to achieve rapid, efficient, and accurate natural drug screening against APE1 inhibitors in vitro and cells. The developed method provides impetus to streamline research related to miRNA and APE1, offering significant promise for widespread application in drug development and clinical analysis.

Diving deep: zebrafish models in motor neuron degeneration research.

In the dynamic landscape of biomedical science, the pursuit of effective treatments for motor neuron disorders like hereditary spastic paraplegia (HSP), amyotrophic lateral sclerosis (ALS), and spinal muscular atrophy (SMA) remains a key priority. Central to this endeavor is the development of robust animal models, with the zebrafish emerging as a prime candidate. Exhibiting embryonic transparency, a swift life cycle, and significant genetic and neuroanatomical congruencies with humans, zebrafish offer substantial potential for research. Despite the difference in locomotion-zebrafish undulate while humans use limbs, the zebrafish presents relevant phenotypic parallels to human motor control disorders, providing valuable insights into neurodegenerative diseases. This review explores the zebrafish's inherent traits and how they facilitate profound insights into the complex behavioral and cellular phenotypes associated with these disorders. Furthermore, we examine recent advancements in high-throughput drug screening using the zebrafish model, a promising avenue for identifying therapeutically potent compounds.

Improving iPSC Differentiation Using a Nanodot Platform.

Differentiation of induced pluripotent stem cells (iPSCs) is an extremely complex process that has proven difficult to study. In this research, we utilized nanotopography to elucidate details regarding iPSC differentiation by developing a nanodot platform consisting of nanodot arrays of increasing diameter. Subjecting iPSCs cultured on the nanodot platform to a cardiomyocyte (CM) differentiation protocol revealed several significant gene expression profiles that were associated with poor differentiation. The observed expression trends were used to select existing small-molecule drugs capable of modulating differentiation efficiency. BRD K98 was repurposed to inhibit CM differentiation, while iPSCs treated with NSC-663284, carmofur, and KPT-330 all exhibited significant increases in not only CM marker expression but also spontaneous beating, suggesting improved CM differentiation. In addition, quantitative polymerase chain reaction was performed to determine the gene regulation responsible for modulating differentiation efficiency. Multiple genes involved in extracellular matrix remodeling were correlated with a CM differentiation efficiency, while genes involved in the cell cycle exhibited contrasting expression trends that warrant further studies. The results suggest that expression profiles determined via short time-series expression miner analysis of nanodot-cultured iPSC differentiation can not only reveal drugs capable of enhancing differentiation efficiency but also highlight crucial sets of genes related to processes such as extracellular matrix remodeling and the cell cycle that can be targeted for further investigation. Our findings confirm that the nanodot platform can be used to reveal complex mechanisms behind iPSC differentiation and could be an indispensable tool for optimizing iPSC technology for clinical applications.

Personalized drug screening using patient-derived organoid and its clinical relevance in gastric cancer.

The efficacy of chemotherapy varies significantly among patients with gastric cancer (GC), and there is currently no effective strategy to predict chemotherapeutic outcomes. In this study, we successfully establish 57 GC patient-derived organoids (PDOs) from 73 patients with GC (78%). These organoids retain histological characteristics of their corresponding primary GC tissues. GC PDOs show varied responses to different chemotherapeutics. Through RNA sequencing, the upregulation of tumor suppression genes/pathways is identified in 5-fluorouracil (FU)- or oxaliplatin-sensitive organoids, whereas genes/pathways associated with proliferation and invasion are enriched in chemotherapy-resistant organoids. Gene expression biomarker panels, which could distinguish sensitive and resistant patients to 5-FU and oxaliplatin (area under the dose-response curve [AUC] >0.8), are identified. Moreover, the drug-response results in PDOs are validated in patient-derived organoids-based xenograft (PDOX) mice and are consistent with the actual clinical response in 91.7% (11/12) of patients with GC. Assessing chemosensitivity in PDOs can be utilized as a valuable tool for screening chemotherapeutic drugs in patients with GC.

3D Models of Sarcomas: The Next-generation Tool for Personalized Medicine.

Sarcoma is a complex and heterogeneous cancer that has been difficult to study in vitro. While two-dimensional (2D) cell cultures and mouse models have been the dominant research tools, three-dimensional (3D) culture systems such as organoids have emerged as promising alternatives. In this review, we discuss recent developments in sarcoma organoid culture, with a focus on their potential as tools for drug screening and biobanking. We also highlight the ways in which sarcoma organoids have been used to investigate the mechanisms of gene regulation, drug resistance, metastasis, and immune interactions. Sarcoma organoids have shown to retain characteristics of in vivo biology within an in vitro system, making them a more representative model for sarcoma research. Our review suggests that sarcoma organoids offer a potential path forward for translational research in this field and may provide a platform for developing personalized therapies for sarcoma patients.

Application and challenge of pancreatic organoids in therapeutic research.

The in-vivo non-human primate animal and in-vitro cell disease models play a crucial part in the study of the mechanisms underlying the occurrence and development of pancreatic diseases, but with increasingly prominent limitations with in-depth research. Organoids derived from human pluripotent and adult stem cells resemble human in-vivo organs in their cellular composition, spatial tissue structure and physiological function, making them as an advantageous research tool. Up until now, numerous human organoids, including pancreas, have been effectively developed, demonstrating significant potential for research in organ development, disease modeling, drug screening, and regenerative medicine. However, different from intestine, liver and other organs, the pancreas is the only special organ in the human body, consisting of an exocrine gland and an endocrine gland. Thus, the development of pancreatic organoid technology faces greater challenges, and how to construct a composite pancreatic organoid with exocrine and endocrine gland is still difficult in current research. By reviewing the fundamental architecture and physiological role of the human pancreas, along with the swiftly developing domain of pancreatic organoids, we summarize the method and characteristics of human pancreatic organoids, and its application in modeling pancreatic diseases, as a platform for individualized drug screening and in regenerative medicine study. As the first comprehensive review that focus on the pharmacological study of human pancreatic organoid, the review hopes to help scholars to have a deeper understanding in the study of pancreatic organoid.