An automated radiation modulator (ARM) was developed to enable a high-throughput radiation sensitivity test using patient-derived organoids (PDOs) as an in vitro diagnostic device. Treatment strategies for head and neck cancer include surgery and radiotherapy. However, patient responses to radiotherapy vary widely. To overcome limitations in efficiency and scalability, the ARM was developed to provide controlled, reproducible, and high-throughput radiation delivery. Its feasibility was validated by comparing it with conventional radiation methods using two HNSCC cell lines, as well as by applying it to PDO-based radiation sensitivity tests. The ARM successfully classified head and neck cancer PDOs into radiation-sensitive and radiation-resistant groups. OncoSensi, a radiation sensitivity screening method utilizing the ARM, was cross-validated with clinical radiotherapy outcomes, including recurrence status, in 14 patients with head and neck cancer. The multi-parameter OncoSensi model achieved a sensitivity of 80% and a specificity of 75%, demonstrating superior predictive performance compared to the single-parameter model, which yielded a sensitivity of 70% and a specificity of 50%. Statistically significant difference in recurrence-free survival (RFS) was observed between the OncoSensi-sensitive and -resistant groups. Therefore, ARM-based radiation sensitivity screening can serve as a practical tool for implementing precision medicine in radiotherapy for patients with head and neck cancer, ultimately contributing to improved treatment efficacy and patient prognosis. • A novel Automated Radiation Modulator (ARM) was developed to enable high-throughput, reproducible radiation delivery for assessing radiotherapy sensitivity using patient-derived organoids (PDOs). • The ARM-based screening platform, OncoSensi, demonstrated strong predictive power for radiotherapy outcomes in HNSCC patients, with 80% sensitivity and 75% specificity. • OncoSensi effectively distinguished between radiation-sensitive and -resistant PDOs, showing significant correlation with patient recurrence-free survival and supporting its clinical utility in precision radiotherapy.

Particulate matter (PM) is a major component of air pollution and is associated with respiratory diseases and lung dysfunction. Physiologically relevant models are essential for studying PM-induced airway responses in vitro. Conventional two-dimensional (2D) monolayer models comprising only bronchial epithelial cells are limited in their ability to mimic the complex structure of airway tissue. In contrast, three-dimensional (3D) airway organoids contain basal, ciliated, and goblet cells, which better reflect airway architecture. However, the standardization of 3D cultures has been a major challenge. In the present study, to address this, we developed the 3D Airway Organoid-Pillar Dish (AO-PD) model, a platform that enables reproducible formation of standardized 3D airway. We compared this model with a 2D monolayer model under PM exposure. The 3D AO-PD model showed higher cell viability and significant inflammatory and epithelial-mesenchymal transition responses than that of the 2D model. These results indicate that the 3D AO-PD model more accurately reflects PM-induced airway damage and provides greater resistance to environmental insults owing to its pseudostratified epithelial structure and enhanced cell-cell interactions. Thus, the 3D AO-PD model serves as a robust in vitro platform for investigating PM-induced airway toxicity and inflammation-related responses.

A pillar dishe for subculture of 3D cultured cells on hydrogel spots (Matrigel and alginate) have been developed. Cells cultured in 3D in an extracellular matrix (ECM) can retain their intrinsic properties, but cells cultured in 2D lose their intrinsic properties as the cells stick to the bottom of the well. Previously, cells and ECM spots were dispensed on a conventional culture dish for 3D cultivation. However, as the spot shape and location depended on user handling, pillars were added to the dish to realize uniform spot shape and stable subculture, supporting 3D cell culture-based high-throughput screening (HTS). Matrigel and alginate were used as ECMs during 6-passage subculture. The growth rate of lung cancer cell (A549) was higher on Matrigel than on alginate. Cancer cell was subcultured in three dimensions in the proposed pillar dish and used for drug screening and differential gene expression analysis. Interestingly, stemness markers, which are unique characteristics of lung cancer cells inducing drug resistance, were upregulated in 3D-subcultured cells compared with those in 2D-subcultured cells. Additionally, the PI3K/Akt/mTOR, VEGFR1/2, and Wnt pathways, which are promising therapeutic targets for lung cancer, were activated, showing high drug sensitivity under 3D-HTS using the 3D-subcultured cells.

An automated radiation modulator (ARM) was developed to enable a high-throughput radiation sensitivity test using patient-derived organoids (PDOs) as an in vitro diagnostic device. Treatment strategies for head and neck cancer include surgery and radiotherapy. However, patient responses to radiotherapy vary widely. To overcome limitations in efficiency and scalability, the ARM was developed to provide controlled, reproducible, and high-throughput radiation delivery. Its feasibility was validated by comparing it with conventional radiation methods using two HNSCC cell lines, as well as by applying it to PDO-based radiation sensitivity tests. The ARM successfully classified head and neck cancer PDOs into radiation-sensitive and radiation-resistant groups. OncoSensi, a radiation sensitivity screening method utilizing the ARM, was cross-validated with clinical radiotherapy outcomes, including recurrence status, in 14 patients with head and neck cancer. The multi-parameter OncoSensi model achieved a sensitivity of 80% and a specificity of 75%, demonstrating superior predictive performance compared to the single-parameter model, which yielded a sensitivity of 70% and a specificity of 50%. Statistically significant difference in recurrence-free survival (RFS) was observed between the OncoSensi-sensitive and -resistant groups. Therefore, ARM-based radiation sensitivity screening can serve as a practical tool for implementing precision medicine in radiotherapy for patients with head and neck cancer, ultimately contributing to improved treatment efficacy and patient prognosis. • A novel Automated Radiation Modulator (ARM) was developed to enable high-throughput, reproducible radiation delivery for assessing radiotherapy sensitivity using patient-derived organoids (PDOs). • The ARM-based screening platform, OncoSensi, demonstrated strong predictive power for radiotherapy outcomes in HNSCC patients, with 80% sensitivity and 75% specificity. • OncoSensi effectively distinguished between radiation-sensitive and -resistant PDOs, showing significant correlation with patient recurrence-free survival and supporting its clinical utility in precision radiotherapy.

Particulate matter (PM) is a major component of air pollution and is associated with respiratory diseases and lung dysfunction. Physiologically relevant models are essential for studying PM-induced airway responses in vitro. Conventional two-dimensional (2D) monolayer models comprising only bronchial epithelial cells are limited in their ability to mimic the complex structure of airway tissue. In contrast, three-dimensional (3D) airway organoids contain basal, ciliated, and goblet cells, which better reflect airway architecture. However, the standardization of 3D cultures has been a major challenge. In the present study, to address this, we developed the 3D Airway Organoid-Pillar Dish (AO-PD) model, a platform that enables reproducible formation of standardized 3D airway. We compared this model with a 2D monolayer model under PM exposure. The 3D AO-PD model showed higher cell viability and significant inflammatory and epithelial-mesenchymal transition responses than that of the 2D model. These results indicate that the 3D AO-PD model more accurately reflects PM-induced airway damage and provides greater resistance to environmental insults owing to its pseudostratified epithelial structure and enhanced cell-cell interactions. Thus, the 3D AO-PD model serves as a robust in vitro platform for investigating PM-induced airway toxicity and inflammation-related responses.

A pillar dishe for subculture of 3D cultured cells on hydrogel spots (Matrigel and alginate) have been developed. Cells cultured in 3D in an extracellular matrix (ECM) can retain their intrinsic properties, but cells cultured in 2D lose their intrinsic properties as the cells stick to the bottom of the well. Previously, cells and ECM spots were dispensed on a conventional culture dish for 3D cultivation. However, as the spot shape and location depended on user handling, pillars were added to the dish to realize uniform spot shape and stable subculture, supporting 3D cell culture-based high-throughput screening (HTS). Matrigel and alginate were used as ECMs during 6-passage subculture. The growth rate of lung cancer cell (A549) was higher on Matrigel than on alginate. Cancer cell was subcultured in three dimensions in the proposed pillar dish and used for drug screening and differential gene expression analysis. Interestingly, stemness markers, which are unique characteristics of lung cancer cells inducing drug resistance, were upregulated in 3D-subcultured cells compared with those in 2D-subcultured cells. Additionally, the PI3K/Akt/mTOR, VEGFR1/2, and Wnt pathways, which are promising therapeutic targets for lung cancer, were activated, showing high drug sensitivity under 3D-HTS using the 3D-subcultured cells.

BACKGROUND: value of the dose-response curve (DRC) is used to differentiate between sensitive and resistant patient's groups. This study proposes a multi-parameter analysis method (cancer organoid-based diagnosis reactivity prediction, CODRP) that considers the cancer stage and cancer cell growth rate, which represent the severity of cancer patients, in the sensitivity test. METHODS: On the CODRP platform, patient-derived organoids (PDOs) that recapitulate patients with lung cancer were implemented by applying a mechanical dissociation method capable of high yields and proliferation rates. A disposable nozzle-type cell spotter with efficient high-throughput screening (HTS) has also been developed to dispense a very small number of cells due to limited patient cells. A drug sensitivity test was performed using PDO from the patient tissue and the primary cancer characteristics of PDOs were confirmed by pathological comparision with tissue slides. RESULTS: The conventional index of drug sensitivity is the AUC of the DRC. In this study, the CODRP index for drug sensitivity test was proposed through multi-parameter analyses considering cancer cell proliferation rate, the cancer diagnosis stage, and AUC values. We tested PDOs from eight patients with lung cancer to verify the CODRP index. According to the anaplastic lymphoma kinase (ALK) rearrangement status, the conventional AUC index for the three ALK-targeted drugs (crizotinib, alectinib, and brigatinib) did not classify into sensitive and resistant groups. The proposed CODRP index-based drug sensitivity test classified ALK-targeted drug responses according to ALK rearrangement status and was verified to be consistent with the clinical drug treatment response. CONCLUSIONS: Therefore, the PDO-based HTS and CODRP index drug sensitivity tests described in this paper may be useful for predicting and analyzing promising anticancer drug efficacy for patients with lung cancer and can be applied to a precision medicine platform.

Predicting individual responses to radiotherapy remains a major challenge in esophageal cancer. This study evaluated an cancer organoid-based diagnosis and reactivity prediction (CODRP) approach to estimate pathological response and recurrence risk after neoadjuvant chemoradiotherapy (NCRT) in esophageal squamous cell carcinoma. Organoids were established from endoscopic biopsy specimens and irradiated with doses of 2, 4, or 8 Gy. Growth inhibition-based area under the curve (GI-AUC) and cell growth rates were quantified and integrated with the clinical cancer stage to generate the CODRP index. Compared with single-parameter models, the CODRP showed improved apparent predictive performance for pathological response prediction in this feasibility cohort (sensitivity, 87.5%; specificity, 100%). Recurrence-free survival was significantly higher in the CODRP-classified radiation-sensitive group (71.43%) than in the radiation-resistant group (10%) ( p = 0.0103). These findings support the feasibility of applying CODRP to radiotherapy response and recurrence risk prediction in esophageal squamous cell carcinoma.

Introduction Central nervous system (CNS) drug development remains challenging because effective delivery across the blood–brain barrier (BBB) is difficult to predict preclinically. In vitro BBB models are increasingly used to evaluate barrier properties and drug transport, but platform variability and limited standardization hinder consistent interpretation and translational use.Areas covered This review summarizes major cell-based in vitro BBB models for CNS drug development, focusing on static, dynamic, microfluidic, induced pluripotent stem cell-derived, and three-dimensional (3D) BBB platforms. Emphasis is placed on model applicability for permeability assessment, transporter activity, exposure kinetics, spatial distribution, and translational prediction. Relevant literature was searched using PubMed, Web of Science, and Google Scholar up to 2025.Expert opinion Although in vitro BBB models have advanced substantially, no single system fully reproduces the complexity of the in vivo BBB. The major limitation is persistent inconsistency across models, which complicates interpretation and limits confidence in their translational application. Static models are practical but provide only partial representation of BBB function, whereas stem cell-based and microfluidic platforms offer greater physiological relevance but face reproducibility and scalability challenges. Future progress should prioritize context-of-use-driven model selection, tiered validation, and harmonized reporting to support reliable decision-making in CNS drug development.

This file provides documentation for calculating the Cancer Organoid Functional Assay (COFA) indexes used for recurrence-risk stratification in epithelial ovarian cancer. It describes the required input variables, Z-score standardization, subgroup-specific logistic recurrence and Cox-based RFS prognostic models for R0 and R1/R2 patients, model coefficients, and exploratory classification thresholds. The documentation is intended to support reproducibility of the analyses reported in the associated study.

Recently, three-dimensional (3D) cell culture technology has been developing rapidly, and disease-specific organoid models that can simulate human diseases are being developed. These models are being studied as a valuable tool that can be applied to pathogen biology research and drug screening analysis platforms to obtain fast, reliable, and reproducible results. Organoids are 3D cell aggregates formed from embryonic stem cells (ESCs), adult stem cells (ASCs), or induced pluripotent stem cells (iPSCs) through self-renewal and self-organization. They are also called mini-organs and have a structure and function similar to those of real organs, providing a more physiologically relevant model compared to traditional 2D cultures. In particular, due to the recent epidemics of infectious diseases such as coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), many studies are using organoids for infectious disease research, enabling more accurate pathogen-host interaction modeling. In this review, we briefly introduce organoids and discuss research trends in developing organoid-based models of infectious diseases, focusing on organoids derived from the brain, liver, intestines, lung, kidney, skin, and blood vessels. These models hold significant potential for advancing our understanding of disease mechanisms and therapeutic development.