Search Results (36)

Ovarian cancer (OC) remains one of the leading causes of gynecologic cancer mortality, largely due to late diagnosis, frequent relapse, and the emergence of chemoresistance. An important but often-overlooked contributor to treatment failure is the heterogeneous penetration of anticancer drugs within tumors. Structural and biochemical barriers—including abnormal vasculature, elevated interstitial pressure, dense extracellular matrix, drug efflux transporters, and malignant ascites—generate steep intratumoral concentration gradients that conventional preclinical models fail to capture. As a result, systemic pharmacokinetic measurements frequently provide limited insight into tumor-level drug exposure. Patient-derived organoids (PDOs) have emerged as physiologically relevant 3D models that preserve the genetic, architectural, and functional characteristics of the original tumor. These systems enable controlled investigation of pharmacokinetic and pharmacodynamic processes, including drug penetration, metabolism, retention, and exposure–response relationships. Adding cell-free malignant ascites supernatant enhances PDOs’ ability to mimic the metastatic peritoneal microenvironment of OC. This review discusses recent advances in PDO technologies and examines how PDO-derived data can inform intratumoral pharmacokinetics and dosing strategies using physiologically based pharmacokinetic modeling and in vitro–in vivo extrapolation. Emerging hybrid platforms, including organoid-on-chip systems, vascularized co-cultures, and multi-omics integration, are crucial to improve translational prediction and support precision oncology. Full article

Metastatic breast cancer (MBC) remains an overwhelming clinical challenge due to its inherent clonal evolution and the frequent development of drug resistance. A significant hurdle in therapeutic discovery is the reliance on traditional 2D cell cultures and animal models, which often fail to accurately replicate human tumor pathophysiology or predict clinical responses. Consequently, the field of oncology is increasingly exploring a transition towards human-centric research that prioritizes biological data derived directly from patients. Considering the FDA Modernization Act 2.0 and the 2025 FDA Roadmap, frameworks are being established to explore the integration of new approach methodologies (NAMs)—including patient-derived organoids (PDOs) and organ-on-a-chip (OoC) systems—into the drug development pipeline. This review examines how these platforms aim to better simulate the human physiological environment by capturing the complex architecture and microenvironment of the tumor. We further discuss how the integration of these models with Artificial Intelligence (AI), spatial multi-omics, and real-time liquid biopsies is being investigated to enhance the speed and precision of therapeutic testing. While still in the translational phase, emerging evidence suggests that human-centric platforms may eventually support rapid functional drug screening, potentially informing patient treatment responses within clinically relevant timeframes. Strengthening the biological link between the patient and their longitudinal data represents a promising strategy to address the complexities of MBC and improve clinical outcomes. These human-centric platforms preserve patient-specific tumor heterogeneity, recapitulate microenvironmental interactions, and enable functional drug testing under physiologically relevant conditions, thereby improving translational accuracy compared to conventional models. Full article

Background: Precision oncology has traditionally relied on genomic biomarkers to guide therapy selection; however, static molecular profiling often fails to predict real-world responses to cytotoxic chemotherapy. Increasing evidence suggests that treatment outcomes are determined by the interaction between tumor-intrinsic biology and host-specific pharmacology. Functional ex vivo platforms, including patient-derived organoids and tumor slice cultures, provide a complementary phenotypic readout of drug sensitivity that reflects tumor architecture and microenvironmental interactions. Methods: This narrative review integrates recent experimental, translational, and clinical evidence on molecular oncodiagnostics combining tumor transcriptomics, germline pharmacogenetics, and ex vivo drug sensitivity testing. Relevant literature was identified through targeted searches of major biomedical databases, focusing on studies describing multi-omic predictive models, functional precision oncology platforms, and patient-derived tumor models. Results: Converging data indicate that integrated oncodiagnostic strategies can improve prediction of chemotherapy response beyond genomics-only approaches. Transcriptomic profiling captures dynamic pathway activity and resistance programs, pharmacogenetic testing informs host-specific toxicity and dosing constraints, and ex vivo assays enable direct phenotypic validation of drug efficacy. Together, these complementary approaches provide a biologically grounded framework for individualized therapy selection. Conclusions: The convergence of molecular profiling and functional phenotyping represents an emerging paradigm in precision oncology. Integrating multi-omic and functional data may enhance treatment prediction and reduce ineffective therapy, although prospective validation and standardization remain necessary for routine clinical implementation. Full article

Background and Objectives: Current research approaches focusing on the human gut microbiota require complex in vitro systems that could provide sufficient viability and similarity with the conditions provided by the human intestine. As critical physiological functions, such as metabolic and inflammatory modulation, are associated with gut microbiota activity, complex host–microbiota interactions represent a pivotal new direction for therapeutic and nutritional interventions. However, there are several limitations to the current development of advanced in vitro models. Materials and Methods: A systematic review was performed according to the PRISMA guidelines for data collection and interpretation. Results: This manuscript summarizes the most advanced in vitro approaches for studying the gut microbiota, including batch fermentation models, dynamic fermentation models, and state-of-the-art technologies, such as organoids and gut-on-a-chip platforms. Each model offers beneficial study backgrounds, advantages, limitations, and the capacity to replicate the physiological complexity of the intestinal environment. However, due to the increased heterogeneity of the reported models, there is an urgent need for standardization. In this way, coherent regulatory frameworks are needed to guide the development and application of in vitro models. Conclusions: By consolidating knowledge and critically addressing current challenges, this study contributes to gut microbiota research by providing a direction for ethical, precise, and high-impact scientific studies. Full article

Background: Local delivery after resection of high-grade glioma, particularly glioblastoma (GBM), aims to increase intratumoral drug exposure while limiting systemic toxicity. The only U.S. Food and Drug Administration (FDA)-approved implantable intracranial chemotherapy is the carmustine (1,3-bis[2-chloroethyl]-1-nitrosourea; BCNU)-impregnated polyanhydride wafer (Gliadel wafer), indicated for newly diagnosed high-grade glioma and recurrent GBM. More than two decades of clinical use and randomized data show that intracavitary chemotherapy is feasible and confers a modest survival benefit in carefully selected patients. Nevertheless, Gliadel wafer has not altered the overall poor prognosis of GBM because of biological resistance to nitrosoureas, constrained brain-parenchymal pharmacokinetics, and device-related adverse effects. Objective: The aim is to synthesize clinical and preclinical evidence defining the current limitations of Gliadel wafer and to outline strategies for next-generation local delivery systems, with emphasis on GBM within the broader high-grade glioma setting. Methods: A narrative review of randomized and observational clinical studies, pharmacokinetic studies, and preclinical investigations evaluating Gliadel wafer and potential next-generation local delivery systems in GBM and other high-grade gliomas was performed. Results: The literature delineates key limitations of Gliadel wafer: short diffusion distances and burst-weighted carmustine release, high tumor cell resistance to carmustine due to heterogeneity, and device-related side effects. Emerging approaches to address these limitations include (i) multidrug systems with synergistic effects against GBM cells; (ii) advanced biomaterials that enable controlled and sustained release; and (iii) targeted agents with minimal off-target effects. Testing newer generations of local drug-delivery systems in more predictive translational models, such as patient-derived organoids and spontaneous large-animal glioma models, is essential to maximize the translatability of preclinical studies to human studies. However, broader adoption of spontaneous large-animal glioma models is constrained by ethical oversight, animal-welfare considerations, cost, and limited availability compared with rodent platforms. Conclusions: Next-generation local drug-delivery systems should include multiple synergistic tumor-selective drugs that can be released in a controlled, sustained manner deep into the residual tumor. Preclinical testing of these systems should be conducted in clinically relevant animal models that are more translatable than those used in early Gliadel wafer studies. Full article

Background: Embryoid bodies (EBs) play a central role in organoid engineering, where their formation fidelity and size critically influence downstream differentiation outcomes. Current EB production workflows primarily rely on retrospective quality assessment, which limits reproducibility in high-throughput culture systems. Objective: This study aimed to develop a prospective, non-invasive framework that integrates early-phase bright-field time-lapse imaging with a three-dimensional convolutional neural network to predict EB formation outcomes and final EB diameter within the microwell platform. Methods: Time-lapse image sequences collected during the first hours after cell seeding on the microwell array were used to train 3D-CNN models for classification (formation vs. non-formation) and regression (final diameter). A balanced dataset was constructed through under-sampling, and five-fold cross-validation with data augmentation was applied to evaluate model performance. Results: The classification model achieved an accuracy of 96.5%, reliably distinguishing between successful and failed EB formation using short-duration image sequences. The regression model predicted the final EB diameter with a mean absolute error of ±7.1 µm, reflecting strong agreement with measured values and capturing seeding-density-dependent size variations. Conclusions: Early aggregation dynamics captured by bright-field time-lapse imaging contain sufficient spatiotemporal information to enable accurate, prospective EB quality prediction. The proposed framework provides a label-free and automation-compatible strategy for improving reproducibility in large-scale EB manufacturing and supports the future development of adaptive and closed-loop organoid culture systems for clinical applications. Full article

Microfluidic cell culture systems and organ-on-a-chip platforms provide powerful tools for modeling physiological processes, disease progression, and drug responses under controlled microenvironmental conditions. These technologies rely on diverse cell culture methodologies, including 2D and 3D culture formats, spheroids, scaffold-based systems, hydrogels, and organoid models, to recapitulate tissue-level functions and generate rich, multiparametric datasets through high-resolution imaging, integrated sensors, and biochemical assays. The heterogeneity and volume of these data introduce substantial challenges in pre-processing, feature extraction, multimodal integration, and biological interpretation. Artificial intelligence (AI), particularly machine learning and deep learning, offers solutions to these analytical bottlenecks by enabling automated phenotyping, predictive modeling, and real-time control of microfluidic environments. Recent advances also highlight the importance of technical frameworks such as dimensionality reduction, explainable feature selection, spectral pre-processing, lightweight on-chip inference models, and privacy-preserving approaches that support robust and deployable AI–microfluidic workflows. AI-enabled microfluidic and organ-on-a-chip systems now span a broad application spectrum, including cancer biology, drug screening, toxicity testing, microbial and environmental monitoring, pathogen detection, angiogenesis studies, nerve-on-a-chip models, and exosome-based diagnostics. These platforms also hold increasing potential for precision medicine, where AI can support individualized therapeutic prediction using patient-derived cells and organoids. As the field moves toward more interpretable and autonomous systems, explainable AI will be essential for ensuring transparency, regulatory acceptance, and biological insight. Recent AI-enabled applications in cancer modeling, drug screening, etc., highlight how deep learning can enable precise detection of phenotypic shifts, classify therapeutic responses with high accuracy, and support closed-loop regulation of microfluidic environments. These studies demonstrate that AI can transform microfluidic systems from static culture platforms into adaptive, data-driven experimental tools capable of enhancing assay reproducibility, accelerating drug discovery, and supporting personalized therapeutic decision-making. This narrative review synthesizes current progress, technical challenges, and future opportunities at the intersection of AI, microfluidic cell culture platforms, and advanced organ-on-a-chip systems, highlighting their emerging role in precision health and next-generation biomedical research. Full article

Organoids are self-organizing three-dimensional (3D) cellular structures derived from stem cells. They can mimic the anatomical and functional properties of real organs. They have transformed in vitro disease modeling by closely replicating the structural and functional characteristics of human tissues. The complexity and variability of organoid-derived data pose significant challenges for analysis and clinical translation. Artificial Intelligence (AI) has emerged as a crucial enabler, offering scalable and high-throughput tools for interpreting imaging data, integrating multi-omics profiles, and guiding experimental workflows. This review aims to discuss how AI is reshaping organoid-based research by enhancing morphological image analysis, enabling dynamic modeling of organoid development, and facilitating the integration of genomics, transcriptomics, and proteomics for disease classification. Moreover, AI is increasingly used to support drug screening and personalize therapeutic strategies by analyzing patient-derived organoids. The integration of AI with organoid-on-chip systems further allows for real-time feedback and physiologically relevant modeling. Drawing on peer-reviewed literature from the past decade, Furthermore, CNNs have been used to analyze colonoscopy and histopathological images in colorectal cancer with over 95% diagnostic accuracy. We examine key tools, innovations, and case studies that illustrate this evolving interface. As this interdisciplinary field matures, the future of AI-integrated organoid platforms depends on establishing open data standards, advancing algorithms, and addressing ethical and regulatory considerations to unlock their clinical and translational potential. Full article

The progress of contemporary pharmacology is deeply linked to pharmacokinetics (PK) and its quantitative exploration through PK modeling. By offering a robust mathematical framework to describe and predict drug absorption, distribution, metabolism, and excretion (ADME), PK modeling is essential for designing and optimizing safe and effective dosing regimens and for advancing personalized medicine and model-informed drug development (MIDD). The reliability of population PK (popPK) and physiologically based PK (PBPK) models depends on high-quality experimental data to estimate PK parameters. Traditional PK data sources include clinical studies, preclinical animal models, and human-derived cell lines. Although considered gold standards, these sources have significant drawbacks. Clinical trials are often restricted by ethical, logistical, and financial challenges and often include homogenous populations that fail to reflect real-world interindividual variability. Similarly, animal and cell-based models lack the physiological complexity of humans, leading to discrepancies between preclinical predictions and clinical outcomes. These constraints have stimulated interest in alternative platforms that more faithfully recapitulate human physiology and interindividual diversity. This review explores the potential of organoids as a novel or complementary source of PK-relevant data. Organoids, three-dimensional (3D) stem cell-derived structures, mimic the cellular architecture, functional heterogeneity, and physiological responses of human tissues. In particular, intestinal, liver, and kidney organoids preserve essential cellular features of ADME processes, positioning them as promising tools for integration into popPK and PBPK modeling frameworks. Full article

Three-dimensional cell model systems such as tumour organoids allow for in vitro modelling of self-organized tissue with functional and histologic similarity to in vivo tissue. However, there is a need for standard protocols and techniques to confirm the presence of cancer within organoids derived from tumour tissue. The aim of this study was to assess the utility of a Nanostring gene expression-based machine learning classifier to determine the presence of cancer or normal organoids in cultures developed from both benign and cancerous stomach biopsies. A prospective cohort of normal and cancer stomach biopsies were collected from 2019 to 2022. Tissue specimens were processed for formalin-fixed paraffin-embedding (FFPE) and a subset of specimens were established in organoid cultures. Specimens were labelled as normal or cancer according to analysis of the FFPE tissue by two pathologists. The gene expression in FFPE and organoid tissue was measured using a 107 gene Nanostring codeset and normalized using the Removal of Unwanted Variation III algorithm. Our machine learning model was developed using five-fold nested cross-validation to classify normal or cancer gastric tissue from publicly available Asian Cancer Research Group (ACRG) gene expression data. The models were externally validated using the Cancer Genome Atlas (TCGA), as well as our own FFPE and organoid gene expression data. A total of 60 samples were collected, including 38 cancer FFPE specimens, 5 normal FFPE specimens, 12 cancer organoids, and 5 normal organoids. The optimal model design used a Least Absolute Shrinkage and Selection Operator model for feature selection and an ElasticNet model for classification, yielding area under the curve (AUC) values of 0.99 [95% CI: 0.99–1], 0.90 [95% CI: 0.87–0.93], and 0.79 [95% CI: 0.74–0.84] for ACRG (internal test), FFPE, and organoid (external test) data, respectively. The performance of our final model on external data achieved AUC values of 0.99 [95% CI: 0.98–1], 0.94 [95% CI: 0.86–1], and 0.85 [95% CI: 0.63–1] for TCGA, FFPE, and organoid specimens, respectively. Using a public database to create a machine learning model in combination with a Nanostring gene expression assay allows us to allocate organoids and their paired whole tissue samples. This platform yielded reasonable accuracy for FFPE and organoid specimens, with the former being more accurate. This study re-affirms that although organoids are a high-fidelity model, there are still limitations in validating the recapitulation of cancer in vitro. Full article

Cancer drug screening is shifting from low-predictive, reductionist assays to human-relevant, data-integrated platforms. This review synthesizes preclinical strategies using a unified lens—Principle, Advantages, Limitations, and Clinical Application—to enable like-for-like comparison. We first appraise traditional two-dimensional (2D) monolayers and animal models, noting scalability and historical utility alongside constrained translational fidelity. We then evaluate advanced systems—patient-derived organoids (PDOs), patient-derived xenografts (PDXs), and organ-on-a-chip—that better recapitulate architecture, microenvironmental cues, and pharmacodynamics (PD), yet face trade-offs in throughput, timelines, costs, and standardization. Functional genomic screens (CRISPR/RNAi) and large-scale pharmacogenomics are summarized as engines for mechanism-based target discovery and resistance mapping, while AI-enabled modeling supports response prediction, biomarker development, and rational combinations. Finally, we discuss trial designs (basket/umbrella), drug repurposing lessons, and regulatory momentum for new approach methodologies. Across platforms, we emphasize cross-model validation, dataset harmonization, and clinically anchored endpoints as prerequisites for real-world impact. We conclude with pragmatic guidance for matching screening modality to study goals, sample constraints, and decision timelines to accelerate precision oncology. Full article

This review aims to highlight how the study of kidney organoids combined with proteomic analysis can deepen our understanding of renal physiology and disease. Proteomics quantifies proteins in a sample, allowing us to determine which proteins are present, how abundant they are, and how they are modified. These data may reveal the pathways that are active in the kidney organoids and how they change in disease, helping to pinpoint candidate biomarkers. Kidney organoids are three-dimensional structures derived from induced pluripotent stem cells (iPS) that recapitulate many architectural and functional features of the adult organ. Because they can be generated in large numbers under defined conditions, organoids provide a promising platform for testing how genetic mutations, environmental stresses, or drugs affect kidney development and pathology. When proteomic profiles are obtained from mature organoids, researchers can directly link protein-level changes to phenotypic outcomes observed in the model. This integration makes it possible to map disease-related networks at the molecular level and to assess the impact of therapeutic interventions in a system that more closely resembles human kidney tissue than traditional cell lines. A current limitation is that many kidney organoids do not reach the full maturation seen in vivo; they often lack complete segmental differentiation and the functional robustness of adult nephrons. Improving the maturation state of organoids will be essential for accurately modeling chronic kidney diseases and for translating findings into clinically relevant therapies. Full article

Immune organoids have emerged as a ground-breaking platform in immunology, offering a physiologically relevant and controllable environment to model human immune responses and evaluate immunotherapeutic strategies. Derived from stem cells or primary tissues, these three-dimensional constructs recapitulate key aspects of lymphoid tissue architecture, cellular diversity, and functional dynamics, providing a more accurate alternative to traditional two-dimensional cultures and animal models. Their ability to mimic complex immune microenvironments has positioned immune organoids at the forefront of cancer immunotherapy development, autoimmune disease modeling, and personalized medicine. This narrative review highlights the advances in immune organoid technology, with a focus on their applications in testing immunotherapies, such as checkpoint inhibitors, CAR-T cells, and cancer vaccines. It also explores how immune organoids facilitate the study of autoimmune disease pathogenesis with insights into their molecular basis and support in high-throughput drug screening. Despite their transformative potential, immune organoids face significant challenges, including the replication of systemic immune interactions, standardization of fabrication protocols, scalability limitations, biological heterogeneity, and the absence of vascularization, which restricts organoid size and maturation. Future directions emphasize the integration of immune organoids with multi-organ systems to better replicate systemic physiology, the development of advanced biomaterials that closely mimic lymphoid extracellular matrices, the incorporation of artificial intelligence (AI) to optimize organoid production and data analysis, and the rigorous clinical validation of organoid-derived findings. Continued innovation and interdisciplinary collaboration will be essential to overcome existing barriers, enabling the widespread adoption of immune organoids as indispensable tools for advancing immunotherapy, vaccine development, and precision medicine. Full article

Microfluidic platforms offer a powerful approach for ultimately replicating vascularization in vitro, enabling precise microscale control and manipulation of physical parameters. Despite these advances, the real-time ability to monitor and quantify mechanical forces—particularly pressure—within microfluidic environments remains constrained by limitations in cost and compatibility across diverse device architectures. Our work presents an advanced experimental module for quantifying pressure within a vascularizing microfluidic platform. Equipped with an integrated Arduino microcontroller and image monitoring, the system facilitates real-time remote monitoring to access temporal pressure and flow dynamics within the device. This setup provides actionable insights into the hemodynamic parameters driving vascularization in vitro. In-line pressure sensors, interfaced through I2C communication, are employed to precisely record inlet and outlet pressures during critical stages of microvasculature tubulogenesis. Flow measurements are obtained by analyzing changes in reservoir volume over time (dV/dt), correlated with the change in pressure over time (dP/dt). This quantitative assessment of various pressure conditions in a microfluidic platform offers insights into their impact on microvasculature perfusion kinetics. Data acquisition can help inform and finetune functional vessel network formation and potentially enhance the durability, stability, and reproducibility of engineered in vitro platforms for organoid vascularization in regenerative medicine. Full article

Accurate in vitro models of intestinal permeability are essential for predicting oral drug absorption. Standard models like Caco-2 cells have well-known limitations, including lack of segment-specific physiology, but are widely used. Emerging models such as organoid-derived monolayers and microphysiological systems (MPS) offer enhanced physiological relevance but require comparative validation. We performed a head-to-head evaluation of Caco-2 cells, human jejunal (J2) and duodenal (D109) enteroid-derived cells, and EpiIntestinal^TM tissues cultured on either static Transwell and flow-based MPS platforms. We assessed tissue morphology, barrier function (TEER, dextran leakage), and permeability of three model small molecules (caffeine, propranolol, and indomethacin), integrating the data into a physiologically based gut absorption model (PECAT) to predict human oral bioavailability. J2 and D109 cells demonstrated more physiologically relevant morphology and higher TEER than Caco-2 cells, while the EpiIntestinal^TM model exhibited thicker and more uneven tissue structures with lower TEER and higher passive permeability. MPS cultures offered modest improvements in epithelial architecture but introduced greater variability, especially with enteroid-derived cells. Predictions of human fraction absorbed (F_abs) were most accurate when using static Caco-2 data with segment-specific corrections based on enteroid-derived values, highlighting the utility of combining traditional and advanced in vitro gut models to optimize predictive performance for F_abs. While MPS and enteroid-based systems provide physiological advantages, standard static models remain robust and predictive when used with in silico modeling. Our findings support the need for further refinement of enteroid-MPS integration and advocate for standardized benchmarking across gut model systems to improve translational relevance in drug development and regulatory reviews. Full article