Biotechnol Adv. 2025 May 3:108592. doi: 10.1016/j.biotechadv.2025.108592. Online ahead of print.

ABSTRACT

Inflammation is the body's response to infection, trauma or injury and is activated in a coordinated fashion to ensure the restoration of tissue homeostasis and healthy physiology. This process requires communication between stromal cells resident to the tissue compartment and infiltrating immune cells which is dysregulated in disease. Clinical innovations in patient diagnosis and stratification include measures of inflammatory activation that support the assessment of patient prognosis and response to therapy. We propose that (i) the recent advances in fast, dynamic monitoring of inflammatory markers (e.g., cytokines) and (ii) data-dependent theoretical and computational modeling of inflammatory marker dynamics will enable the quantification of the inflammatory response, identification of optimal, disease-specific biomarkers and the design of personalized interventions to improve patient outcomes - multidisciplinary efforts in which biomedical engineers may potentially contribute. To illustrate these ideas, we describe the actions of cytokines, acute phase proteins and hormones in the inflammatory response and discuss their role in local wounds, COVID-19, cancer, autoimmune diseases, neurodegenerative diseases and aging, with a central focus on cardiac surgery. We also discuss the challenges and opportunities involved in tracking and modulating inflammation in clinical settings.

PMID:40324661 | DOI:10.1016/j.biotechadv.2025.108592

PLoS One. 2025 May 5;20(5):e0321996. doi: 10.1371/journal.pone.0321996. eCollection 2025.

ABSTRACT

Cystic fibrosis (CF) is the most common genetic diseases in the Caucasian population. CFTR defects, the most common being F508del, lead to abnormal mucus accumulation. Respiratory failure caused by the resulting chronic infections is the leading cause of death in people with cystic fibrosis (pwCF). Pseudomonas aeruginosa is a major pathogen in CF and is responsible for a deterioration of respiratory function in pwCF. The increase of antibiotic-resistant P. aeruginosa strains encourages the search for alternative therapeutics for treating P. aeruginosa infection. In vitro studies have shown an interest in (R)-roscovitine (roscovitine) in the fight against bacterial infection in pwCF. Here we show a nuanced effect of roscovitine on ASL bactericidal activity and CF bronchial epithelium protection against P. aeruginosa. Using a 3D model of fully differentiated and functional F508del-CFTR human bronchial epithelium, we evidenced (i) an enhancement of the bactericidal activity of the airway surface liquid for 25 μM roscovitine but (ii) no limitation of the dynamic of the epithelium destruction upon roscovitine treatment whatever the concentrations. Our findings shed light on reasons for the lack of beneficial effects to prevent P. aeruginosa infection in pwCF treated with roscovitine in the ROSCO-CF clinical trial. We anticipate that our findings would have significant therapeutic implications in seeking to optimize roscovitine analogs.

PMID:40323902 | DOI:10.1371/journal.pone.0321996

J Vis Exp. 2025 Apr 18;(218). doi: 10.3791/67477.

ABSTRACT

Standard pre-clinical testing methods for novel antimicrobial therapeutics used to treat chronic lung infections in people with cystic fibrosis do not reflect the environmental conditions of the hostile lung niche. Current reductionist testing conditions can lead to the progression of compounds along a preclinical pipeline without evidence of their activity under cystic fibrosis lung niche-appropriate conditions. Several approaches used to study traditional antimicrobials may not be suitable for antibiotic alternatives, including anti-virulence therapeutics like anti-quorum sensing agents and siderophore inhibitors. This protocol documents an aggregate biofilm model of Pseudomonas aeruginosa to compare resistance and infection-relevant gene expression in single-species and multi-species cultures (Staphylococcus aureus and Candida albicans), examining colony-forming unit (CFU) reductions and changes in gene expression, using algD as an exemplar. The model was optimized for small, static volumes of bacterial cultures to allow the study of novel compounds in the discovery phase of the drug development pipeline, where compound quantities may be limited. Single-species P. aeruginosa biofilms were formed in Synthetic Cystic Fibrosis Medium 2 (SCFM2) for 24 h before treatment with meropenem at different concentrations (1, 16, and 256 µg/mL) for a further 24 h. Polymicrobial biofilms were established by growing Staphylococcus aureus and Candida albicans together in SCFM2, then inoculating with P. aeruginosa for an additional 24 h and treating with meropenem. The lack of a direct connection between compound efficacy measures in pre-clinical testing and clinical trial results has cast doubt on the applicability of current laboratory screening tools. This model allows us to understand the impact of relevant factors on P. aeruginosa gene expression, including genes contributing to resistance and virulence, thereby bridging this gap.

PMID:40323888 | DOI:10.3791/67477

Med Image Anal. 2025 Apr 24;103:103600. doi: 10.1016/j.media.2025.103600. Online ahead of print.

ABSTRACT

Aortic stenosis (AS) is a prevalent heart valve disease that requires accurate and timely diagnosis for effective treatment. Current methods for automated AS severity classification rely on black-box deep learning techniques, which suffer from a low level of trustworthiness and hinder clinical adoption. To tackle this challenge, we propose ProtoASNet, a prototype-based neural network designed to classify the severity of AS from B-mode echocardiography videos. ProtoASNet bases its predictions exclusively on the similarity scores between the input and a set of learned spatio-temporal prototypes, ensuring inherent interpretability. Users can directly visualize the similarity between the input and each prototype, as well as the weighted sum of similarities. This approach provides clinically relevant evidence for each prediction, as the prototypes typically highlight markers such as calcification and restricted movement of aortic valve leaflets. Moreover, ProtoASNet utilizes abstention loss to estimate aleatoric uncertainty by defining a set of prototypes that capture ambiguity and insufficient information in the observed data. This feature augments prototype-based models with the ability to explain when they may fail. We evaluate ProtoASNet on a private dataset and the publicly available TMED-2 dataset. It surpasses existing state-of-the-art methods, achieving a balanced accuracy of 80.0% on our private dataset and 79.7% on the TMED-2 dataset, respectively. By discarding cases flagged as uncertain, ProtoASNet achieves an improved balanced accuracy of 82.4% on our private dataset. Furthermore, by offering interpretability and an uncertainty measure for each prediction, ProtoASNet improves transparency and facilitates the interactive usage of deep networks in aiding clinical decision-making. Our source code is available at: https://github.com/hooman007/ProtoASNet.

PMID:40324320 | DOI:10.1016/j.media.2025.103600

Sci Total Environ. 2025 May 4;980:179582. doi: 10.1016/j.scitotenv.2025.179582. Online ahead of print.

ABSTRACT

Given the vital role of saline lakes in supporting ecosystems in arid regions, this study analyzes their long-term changes by assessing their characteristics and spectral reflectance properties. Alongside evaluating the physical and chemical variations of these lakes, the research integrates climate change modeling to predict future shifts in their features and assess ecological impacts on surrounding environments. By employing Super-Resolution Generative Adversarial Network (SRGAN) and Multiresolution Segmentation (MRS), this approach enhances satellite image resolution and enables more precise differentiation of key lake components-such as salt deposits, salinity levels, and moisture fluctuations. The results show that increasing image resolution with SRGAN and using these images as input data for image classification models improves the identification of physical characteristics and the prediction of chemical properties of lakes with greater detail. The proposed method, based on Cellular Automata (CA)-Markov modeling of albedo and infrared wave reflectance, predicts a roughly 15 % increase in salinity of the studied lakes by 2050, driven by rising temperatures, intensified evaporation, and declining moisture levels. Finally, the results of climate change predictions based on the Long Short-Term Memory (LSTM) algorithm, with high accuracy (R2 > 0.9), indicate increasing temperatures and evaporation in the coming years. Consequently, these rising temperatures will elevate salinity, drying, and albedo intensity in Chaka, Tuz, and Razzaza Lakes over the coming decades. This is supported by RCP8.5 scenarios, which project significant increases by 2100 that lead to greater evaporation and salinity. These changes have profound implications for surrounding ecosystems, particularly by affecting plant communities and accelerating desertification around these saline lakes.

PMID:40324314 | DOI:10.1016/j.scitotenv.2025.179582

J Med Internet Res. 2025 May 5;27:e65551. doi: 10.2196/65551.

ABSTRACT

BACKGROUND: Dysphagia affects more than half of older adults with dementia and is associated with a 10-fold increase in mortality. The development of accessible, objective, and reliable screening tools is crucial for early detection and management.

OBJECTIVE: This systematic scoping review aimed to (1) examine the current state of the art in artificial intelligence (AI) and sensor-based technologies for dysphagia screening, (2) evaluate the performance of these AI-based screening tools, and (3) assess the methodological quality and rigor of studies on AI-based dysphagia screening tools.

METHODS: We conducted a systematic literature search across CINAHL, Embase, PubMed, and Web of Science from inception to July 4, 2024, following the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) framework. In total, 2 independent researchers conducted the search, screening, and data extraction. Eligibility criteria included original studies using sensor-based instruments with AI to identify individuals with dysphagia or unsafe swallow events. We excluded studies on pediatric, infant, or postextubation dysphagia, as well as those using non-sensor-based assessments or diagnostic tools. We used a modified Quality Assessment of Diagnostic Accuracy Studies-2 tool to assess methodological quality, adding a "model" domain for AI-specific evaluation. Data were synthesized narratively.

RESULTS: This review included 24 studies involving 2979 participants (1717 with dysphagia and 1262 controls). In total, 75% (18/24) of the studies focused solely on per-individual classification rather than per-swallow event classification. Acoustic (13/24, 54%) and vibratory (9/24, 38%) signals were the primary modality sources. In total, 25% (6/24) of the studies used multimodal approaches, whereas 75% (18/24) used a single modality. Support vector machine was the most common AI model (15/24, 62%), with deep learning approaches emerging in recent years (3/24, 12%). Performance varied widely-accuracy ranged from 71.2% to 99%, area under the receiver operating characteristic curve ranged from 0.77 to 0.977, and sensitivity ranged from 63.6% to 100%. Multimodal systems generally outperformed unimodal systems. The methodological quality assessment revealed a risk of bias, particularly in patient selection (unclear in 18/24, 75% of the studies), index test (unclear in 23/24, 96% of the studies), and modeling (high risk in 13/24, 54% of the studies). Notably, no studies conducted external validation or domain adaptation testing, raising concerns about real-world applicability.

CONCLUSIONS: This review provides a comprehensive overview of technological advancements in AI and sensor-based dysphagia screening. While these developments show promise for continuous long-term tele-swallowing assessments, significant methodological limitations were identified. Future studies can explore how each modality can target specific anatomical regions and manifestations of dysphagia. This detailed understanding of how different modalities address various aspects of dysphagia can significantly benefit multimodal systems, enabling them to better handle the multifaceted nature of dysphagia conditions.

PMID:40324167 | DOI:10.2196/65551

JCO Precis Oncol. 2025 May;9:e2400859. doi: 10.1200/PO-24-00859. Epub 2025 May 5.

ABSTRACT

PURPOSE: Endometrial cancer (EC) is the most common gynecologic cancer in the United States with rising incidence and mortality. Despite optimal treatment, 15%-20% of all patients will recur. To better select patients for adjuvant therapy, it is important to accurately predict patients at risk for recurrence. Our objective was to train, validate, and test models of EC recurrence using lasso regression and other machine learning (ML) and deep learning (DL) analytics in a large, comprehensive data set.

METHODS: Data from patients with EC were downloaded from the Oncology Research Information Exchange Network database and stratified into low risk, The International Federation of Gynecology and Obstetrics (FIGO) grade 1 and 2, stage I (N = 329); high risk, or FIGO grade 3 or stages II, III, IV (N = 324); and nonendometrioid histology (N = 239) groups. Clinical, pathologic, genomic, and genetic data were used for the analysis. Genomic data included microRNA, long noncoding RNA, isoforms, and pseudogene expressions. Genetic variation included single-nucleotide variation (SNV) and copy-number variation (CNV). In the discovery phase, we selected variables informative for recurrence (P < .05), using univariate analyses of variance. Then, we trained, validated, and tested multivariate models using selected variables and lasso regression, MATLAB (ML), and TensorFlow (DL).

RESULTS: Recurrence clinic models for low-risk, high-risk, and high-risk nonendometrioid histology had AUCs of 56%, 70%, and 65%, respectively. For training, we selected models with AUC >80%: five for the low-risk group, 20 models for the high-risk group, and 20 for the nonendometrioid group. The two best low-risk models included clinical data and CNVs. For the high-risk group, three of the five best-performing models included pseudogene expression. For the nonendometrioid group, pseudogene expression and SNV were overrepresented in the best models.

CONCLUSION: Prediction models of EC recurrence built with ML and DL analytics had better performance than models with clinical and pathologic data alone. Prospective validation is required to determine clinical utility.

PMID:40324114 | DOI:10.1200/PO-24-00859

PLoS One. 2025 May 5;20(5):e0319776. doi: 10.1371/journal.pone.0319776. eCollection 2025.

ABSTRACT

Accurate prediction of gold prices is crucial for investment decision-making and national risk management. The time series data of gold prices exhibits random fluctuations, non-linear characteristics, and high volatility, making prediction extremely challenging. Various methods, from classical statistics to machine learning techniques like Random Forests, Convolutional Neural Networks (CNN), and Recurrent Neural Networks (RNN), have achieved high accuracy, but they also have inherent limitations. To address these issues, a model that combines Temporal Convolutional Networks (TCN) with Query (Q) and Keys (K) attention mechanisms (TCN-QV) is proposed to enhance the accuracy of gold price predictions. The model begins by employing stacked dilated causal convolution layers within the TCN framework to effectively extract temporal features from the sequence data. Subsequently, an attention mechanism is introduced to enable adaptive weight distribution according to the information features. Finally, the predicted results are generated through a dense layer. This method is used to predict the time series data of gold prices in Shanghai. The optimized model demonstrates a substantial improvement in Mean Absolute Error (MAE) compared to the baseline model, achieving reductions of approximately 5.47% in the least favorable case and up to 33.69% in the most favorable scenario across four experimental datasets. Additionally, the model is tested across different time steps and shows satisfactory performance in long sequence predictions. To validate the necessity of the model components, this paper conducts ablation experiments to confirm the significance of each segment.

PMID:40324013 | DOI:10.1371/journal.pone.0319776

PLoS One. 2025 May 5;20(5):e0321404. doi: 10.1371/journal.pone.0321404. eCollection 2025.

ABSTRACT

The mutation status of isocitrate dehydrogenase1 (IDH1) in glioma is critical information for the diagnosis, treatment, and prognosis. Accurately determining such information from MRI data has emerged as a significant research challenge in recent years. Existing techniques for this problem often suffer from various limitations, such as the data waste and instability issues. To address such issues, we present a semisupervised adaptive deep learning model based on radiomics and rough sets for predicting the mutation status of IDH1 from MRI data. Firstly, our model uses a rough set algorithm to remove the redundant medical image features extracted by radiomics, while adding pseudo-labels for non-labeled data via statistical. T-tests to mitigate the common issue of insufficient datasets in medical imaging analysis. Then, it applies a Sand Cat Swarm Optimization (SCSO) algorithm to optimize the weight of pseudo-label data. Finally, our model adopts U-Net and CRNN to construct UCNet, a semisupervised classification model for classifying IDH1 mutation status. To validate our models, we use a preoperative MRI dataset with 316 glioma patients to evaluate the performance. Our study suggests that the prediction accuracy of glioma IDH1 mutation status reaches 95.63%. Our experimental results suggest that the study can effectively improve the utilization of glioma imaging data and the accuracy of intelligent diagnosis of glioma IDH1 mutation status.

PMID:40323991 | DOI:10.1371/journal.pone.0321404

PLoS One. 2025 May 5;20(5):e0322695. doi: 10.1371/journal.pone.0322695. eCollection 2025.

ABSTRACT

BACKGROUND: Food classification is the foundation for developing food vision tasks and plays a key role in the burgeoning field of computational nutrition. Due to the complexity of food requiring fine-grained classification, the Convolutional Neural Networks (CNNs) backbone needs additional structural design, whereas Vision Transformers (ViTs), containing the self-attention module, has increased computational complexity.

METHODS: We propose a ResVMamba model and validate its performance on processing complex food dataset. Unlike previous fine-grained classification models that heavily rely on attention mechanisms or hierarchical feature extraction, our method leverages a novel residual learning strategy within a state-space framework to improve representation learning. This approach enables the model to efficiently capture both global and local dependencies, surpassing the computational efficiency of Vision Transformers (ViTs) while maintaining high accuracy. We introduce an academically underestimated food dataset CNFOOD-241, and compare the CNFOOD-241 with other food databases.

RESULTS: The proposed ResVMamba surpasses current state-of-the-art (SOTA) models, achieving a Top-1 classification accuracy of 81.70% and a Top-5 accuracy of 96.83%. Our findings elucidate that our proposed methodology establishes a new benchmark for SOTA performance in food recognition on the CNFOOD-241 dataset.

CONCLUSIONS: We pioneer the integration of a residual learning framework within the VMamba model to concurrently harness both global and local state features. The code can be obtained on GitHub: https://github.com/ChiShengChen/ResVMamba.

PMID:40323945 | DOI:10.1371/journal.pone.0322695

IEEE J Biomed Health Inform. 2025 May 5;PP. doi: 10.1109/JBHI.2025.3567141. Online ahead of print.

ABSTRACT

Monitoring physiological parameters such as respiratory rate (f$_{R}$) is essential for diagnosing and managing various pathological conditions. Thermal imaging offers a promising contactless alternative to traditional methods, which often rely on partially invasive sensors or obtrusive wearable systems. However, existing approaches for f$_{R}$ estimation from thermal signals typically require extensive pre-processing and manual or semi-automatic region-of-interest (ROI) tracking, limiting their practical applicability. This study proposes a deep learning-based method for estimating f$_{R}$ directly from thermal videos, eliminating the need for complex pre-processing and ROI tracking. A 3D Convolutional Neural Network (3D-CNN) is developed to operate on raw thermal video data. To address challenges related to small datasets, the model is trained using data augmentation and transfer learning from synthetic datasets. Experimental results demonstrate that the proposed approach achieves a validation $R^{2}$ score of approximately 0.61 on both pre-processed and raw thermal videos. By simplifying the workflow, this method holds promise for enhancing the feasibility of thermal imaging in real-world applications, such as remote healthcare and driver monitoring in automotive applications.

PMID:40323749 | DOI:10.1109/JBHI.2025.3567141

IEEE Trans Med Imaging. 2025 May 5;PP. doi: 10.1109/TMI.2025.3562756. Online ahead of print.

ABSTRACT

Generative modelling for shapes is a prerequisite for In-Silico Clinical Trials (ISCTs), which aim to cost-effectively validate medical device interventions using synthetic anatomical shapes, often represented as 3D surface meshes. However, constructing AI models to generate shapes closely resembling the real mesh samples is challenging due to variable vertex counts, connectivities, and the lack of dense vertex-wise correspondences across the training data. Employing graph representations for meshes, we develop a novel unsupervised geometric deep-learning model to establish refinable shape correspondences in a latent space, construct a population-derived atlas and generate realistic synthetic shapes. We additionally extend our proposed base model to a joint shape generative-clustering multi-atlas framework to incorporate further variability and preserve more details in the generated shapes. Experimental results using liver and left-ventricular models demonstrate the approach's applicability to computational medicine, highlighting its suitability for ISCTs through a comparative analysis.

PMID:40323742 | DOI:10.1109/TMI.2025.3562756

PLoS One. 2025 May 5;20(5):e0322498. doi: 10.1371/journal.pone.0322498. eCollection 2025.

ABSTRACT

Embeddings are semantically meaningful representations of words in a vector space, commonly used to enhance downstream machine learning applications. Traditional biomedical embedding techniques often replace all synonymous words representing biological or medical concepts with a unique token, ensuring consistent representation and improving embedding quality. However, the potential impact of replacing non-biomedical concept synonyms has received less attention. Embedding approaches often employ concept replacement to replace concepts that span multiple words, such as non-small-cell lung carcinoma, with a single concept identifier (e.g., D002289). Also, all synonyms of each concept are merged into the same identifier. Here, we additionally leveraged WordNet to identify and replace sets of non-biomedical synonyms with their most common representatives. This combined approach aimed to reduce embedding noise from non-biomedical terms while preserving the integrity of biomedical concept representations. We applied this method to 1,055 biomedical concept sets representing molecular signatures or medical categories and assessed the mean pairwise distance of embeddings with and without non-biomedical synonym replacement. A smaller mean pairwise distance was interpreted as greater intra-cluster coherence and higher embedding quality. Embeddings were generated using the Word2Vec algorithm applied to a corpus of 10 million PubMed abstracts. Our results demonstrate that the addition of non-biomedical synonym replacement reduced the mean intra-cluster distance by an average of 8%, suggesting that this complementary approach enhances embedding quality. Future work will assess its applicability to other embedding techniques and downstream tasks. Python code implementing this method is provided under an open-source license.

PMID:40324016 | DOI:10.1371/journal.pone.0322498

PLoS One. 2025 May 5;20(5):e0322308. doi: 10.1371/journal.pone.0322308. eCollection 2025.

ABSTRACT

Riboswitches are structured elements predominantly found in the 5'-untranslated region of many bacterial mRNA. These noncoding RNA regions play a vital role in bacterial metabolism and overall function. Each riboswitch binds to a specific small molecule and causes conformational changes in the mRNA leading to regulation of transcription or translation. In this work, we have synthesized SK4, a novel nucleoside analog that binds to the guanine riboswitch mRNA of the xanthine phosphoribosyl transferase gene in Bacillus subtilis and terminates transcription of the riboswitch mRNA to a greater extent than the native ligand guanine. Molecular dynamics simulations of SK4 with riboswitch mRNA reveal an overall stable complex with additional bonding interactions in comparison to guanine. Our work with SK4 demonstrates that specific genes in bacteria can be effectively controlled by ligand analogs, providing an alternative mechanism to regulate the function of bacteria.

PMID:40323922 | DOI:10.1371/journal.pone.0322308

Anesthesiology. 2025 May 5. doi: 10.1097/ALN.0000000000005535. Online ahead of print.

ABSTRACT

BACKGROUND: Sugammadex is well-tolerated and effective for reversing neuromuscular blockade (NMB) in adults and children ≥2 years old. There is little information on its use in younger children. The aim of this study was to evaluate the efficacy and tolerability of sugammadex in children under 2 years of age.

METHODS: Phase IV, randomized, parallel-group, multicenter, clinical trial of sugammadex in participants aged birth to <2 years. Part A was open-label and included pharmacokinetic assessments to determine whether sugammadex dose-adjustment for Part B was necessary based on age. Part B was double-blind and evaluated sugammadex 2 mg/kg and 4 mg/kg. Participants were randomized to: 1) moderate NMB and reversal with 2 mg/kg sugammadex, or 2) moderate NMB and reversal with neostigmine + glycopyrrolate or atropine (hereafter, called neostigmine), or 3) deep NMB and reversal with 4 mg/kg sugammadex. The primary efficacy endpoint was time to neuromuscular recovery (TTNMR). The primary efficacy hypothesis was that sugammadex 2mg/kg would be superior to neostigmine for the reversal of moderate NMB as measured by TTNMR in Part B.

RESULTS: 138 participants aged 1 to 720 days were treated in Parts A+B (sugammadex 2mg/kg n=44, sugammadex 4 mg/kg n=63, neostigmine n=31). Based on pharmacokinetic assessments in Part A, no dose-adjustments for age were needed. In Part B, TTNMR for reversal of moderate NMB was faster with sugammadex 2 mg/kg than neostigmine (median of 1.4 minutes vs 4.4 minutes, hazard ratio = 2.40, 95% CI: 1.37, 4.18; p=0.0002). Sugammadex 4 mg/kg achieved rapid TTNMR for reversal of deep NMB with a median of 1.1 minutes (Parts A+B). The percentage of participants with ≥1 adverse event (Parts A+B) was similar for sugammadex and neostigmine. No deaths, drug-related serious adverse events, or hypersensitivity or anaphylaxis events were reported.

CONCLUSIONS: In children <2 years old, sugammadex 2 mg/kg reversed moderate NMB faster than neostigmine, and sugammadex 4 mg/kg rapidly reversed deep NMB. Sugammadex 2 mg/kg and 4 mg/kg were well-tolerated.

PMID:40324166 | DOI:10.1097/ALN.0000000000005535

JAMA Netw Open. 2025 May 1;8(5):e258339. doi: 10.1001/jamanetworkopen.2025.8339.

NO ABSTRACT

PMID:40323606 | DOI:10.1001/jamanetworkopen.2025.8339

JAMA Netw Open. 2025 May 1;8(5):e258330. doi: 10.1001/jamanetworkopen.2025.8330.

ABSTRACT

IMPORTANCE: Treatments are urgently needed for the more than 9500 rare diseases with no US Food and Drug Administration-approved therapies. Although repurposing can be less time- and cost-intensive compared with novel drug development, hurdles have impeded systematic repurposing. Rare disease nonprofit organizations (RDNPs) are well-positioned to overcome barriers and have spearheaded rare disease repurposing efforts for decades. However, no comprehensive data are available on the state of rare disease repurposing or features of successful efforts.

OBJECTIVE: To characterize the state of rare disease drug repurposing, identify factors associated with successful outcomes, and share thematic insights using the interactive Repurposing of All Drugs, Mapping All Paths (ROADMAP) Project web tool.

DESIGN, SETTING, AND PARTICIPANTS: The ROADMAP study was a qualitative study using a mixed-methods analysis of US-based RDNP leaders and their stakeholders, including a national survey and semistructured interviews of RDNP leaders, conducted from September 29, 2021, to January 6, 2022. Surveys and interviews revealed themes associated with RDNP strategies, timelines, and support mechanisms. Data were analyzed from January 22, 2024, to April 23, 2024.

MAIN OUTCOMES AND MEASURES: The primary survey outcome was the repurposing project stage (abandoned, early, clinical, late, or successful). Qualitative outcomes included themes characterizing repurposing experiences. Two random forest models of drug- and disease- specific as well as organization-specific variables were used to evaluate factor importance toward inferring the project stage. Orthogonal significance testing was conducted using Spearman rank correlation, and P values in each model were corrected for multiple hypothesis testing using a Benjamini-Hochberg procedure.

RESULTS: Representative organizations submitted survey responses, including 147 of 698 potential US-based RDNPs. The median RDNP age was 10 years (IQR, 5-20 years), and the median annual revenue was $355 390 (IQR, $90 028-$946 108). Among 34 leaders who were interviewed, representing 25 RDNPs, 23 were female (67.6%), and the RDNPs had a median age of 15 years (IQR, 6-19 years) and a median revenue of $670 719 (IQR, $193 587-$1 830 890). Among the surveyed RDNPs, 58 of 138 (42.0%) specifically identifying their involvement in repurposing supported repurposing projects, and 94 drugs were in various stages of repurposing, of which 23 met success criteria (5 with US Food and Drug Administration approval and 18 with off-label use with subjective benefit). Survey factors associated with successful outcomes included nonprofit-supported patient recruitment into trials (Gini importance, 3.90; ρ = 0.50; adjusted P < .001) and provision of nonfinancial research support (Gini importance, 0.69; ρ = 0.33; adjusted P = .02). Interview themes were synthesized into a 5-stage repurposing framework with roadblocks and recommendations that included (1) enabling drug repurposing, (2) identifying a drug therapy, (3) validating a drug therapy, (4) clinical use and testing, and (5) reaching an optimal end point for clinical practice.

CONCLUSIONS AND RELEVANCE: The findings of this qualitative study of RDNP repurposing suggest that several opportunities were associated with successful outcomes and can be considered to optimize systematic repurposing among RDNPs, external collaborators, and policymakers with the use of an interactive tool showcasing insights to facilitate data-driven drug repurposing.

PMID:40323602 | DOI:10.1001/jamanetworkopen.2025.8330

Naunyn Schmiedebergs Arch Pharmacol. 2025 May 5. doi: 10.1007/s00210-025-04155-2. Online ahead of print.

ABSTRACT

The modulation of gut microbiota presents promising therapeutic possibilities for various health conditions, ranging from gastrointestinal infections to neurodegenerative and mental health disorders. Among the available interventions, gut microbiota modulators (GMMs) such as probiotics and prebiotics have demonstrated significant potential in infection prevention and neuroprotection. Despite these encouraging findings, the clinical application of GMMs remains challenging due to safety concerns and inconsistent effectiveness across diverse patient populations. These factors create substantial barriers to the widespread adoption of microbiota-based therapies in clinical practice. To overcome these challenges and fully leverage the therapeutic potential of microbiota modulation, this review explores the feasibility of repurposing GMMs for managing multiple health disorders. A broad spectrum of microbiota-targeted strategies is examined, including dietary modifications, fecal microbiota transplantation, bacteriophage therapy, microbiome engineering, and immune system modulation. A particularly innovative approach involves integrating GMMs with pharmaceutical delivery systems to enhance therapeutic efficacy while mitigating potential adverse effects. This integrative strategy underscores the pivotal role of the gut microbiome in health and disease, supporting the development of precision medicine tailored to individual patient needs. By combining GMMs with targeted delivery mechanisms, this approach not only improves treatment effectiveness but also addresses critical concerns regarding safety and patient variability. Furthermore, this review outlines future research directions within the rapidly evolving field of microbiota modulation, emphasizing the necessity of comprehensive clinical trials and long-term safety evaluations. By critically assessing both the challenges and opportunities associated with microbiota-based interventions, this study provides a strategic framework for translating experimental research into viable clinical applications. A holistic approach to gut microbiota modulation has the potential to redefine treatment paradigms, offering personalized therapeutic strategies for a wide range of disorders and advancing the broader field of precision medicine.

PMID:40323507 | DOI:10.1007/s00210-025-04155-2

JAMA Netw Open. 2025 May 1;8(5):e258330. doi: 10.1001/jamanetworkopen.2025.8330.

ABSTRACT

IMPORTANCE: Treatments are urgently needed for the more than 9500 rare diseases with no US Food and Drug Administration-approved therapies. Although repurposing can be less time- and cost-intensive compared with novel drug development, hurdles have impeded systematic repurposing. Rare disease nonprofit organizations (RDNPs) are well-positioned to overcome barriers and have spearheaded rare disease repurposing efforts for decades. However, no comprehensive data are available on the state of rare disease repurposing or features of successful efforts.

OBJECTIVE: To characterize the state of rare disease drug repurposing, identify factors associated with successful outcomes, and share thematic insights using the interactive Repurposing of All Drugs, Mapping All Paths (ROADMAP) Project web tool.

DESIGN, SETTING, AND PARTICIPANTS: The ROADMAP study was a qualitative study using a mixed-methods analysis of US-based RDNP leaders and their stakeholders, including a national survey and semistructured interviews of RDNP leaders, conducted from September 29, 2021, to January 6, 2022. Surveys and interviews revealed themes associated with RDNP strategies, timelines, and support mechanisms. Data were analyzed from January 22, 2024, to April 23, 2024.

MAIN OUTCOMES AND MEASURES: The primary survey outcome was the repurposing project stage (abandoned, early, clinical, late, or successful). Qualitative outcomes included themes characterizing repurposing experiences. Two random forest models of drug- and disease- specific as well as organization-specific variables were used to evaluate factor importance toward inferring the project stage. Orthogonal significance testing was conducted using Spearman rank correlation, and P values in each model were corrected for multiple hypothesis testing using a Benjamini-Hochberg procedure.

RESULTS: Representative organizations submitted survey responses, including 147 of 698 potential US-based RDNPs. The median RDNP age was 10 years (IQR, 5-20 years), and the median annual revenue was $355 390 (IQR, $90 028-$946 108). Among 34 leaders who were interviewed, representing 25 RDNPs, 23 were female (67.6%), and the RDNPs had a median age of 15 years (IQR, 6-19 years) and a median revenue of $670 719 (IQR, $193 587-$1 830 890). Among the surveyed RDNPs, 58 of 138 (42.0%) specifically identifying their involvement in repurposing supported repurposing projects, and 94 drugs were in various stages of repurposing, of which 23 met success criteria (5 with US Food and Drug Administration approval and 18 with off-label use with subjective benefit). Survey factors associated with successful outcomes included nonprofit-supported patient recruitment into trials (Gini importance, 3.90; ρ = 0.50; adjusted P < .001) and provision of nonfinancial research support (Gini importance, 0.69; ρ = 0.33; adjusted P = .02). Interview themes were synthesized into a 5-stage repurposing framework with roadblocks and recommendations that included (1) enabling drug repurposing, (2) identifying a drug therapy, (3) validating a drug therapy, (4) clinical use and testing, and (5) reaching an optimal end point for clinical practice.

CONCLUSIONS AND RELEVANCE: The findings of this qualitative study of RDNP repurposing suggest that several opportunities were associated with successful outcomes and can be considered to optimize systematic repurposing among RDNPs, external collaborators, and policymakers with the use of an interactive tool showcasing insights to facilitate data-driven drug repurposing.

PMID:40323602 | DOI:10.1001/jamanetworkopen.2025.8330

Naunyn Schmiedebergs Arch Pharmacol. 2025 May 5. doi: 10.1007/s00210-025-04155-2. Online ahead of print.

ABSTRACT

The modulation of gut microbiota presents promising therapeutic possibilities for various health conditions, ranging from gastrointestinal infections to neurodegenerative and mental health disorders. Among the available interventions, gut microbiota modulators (GMMs) such as probiotics and prebiotics have demonstrated significant potential in infection prevention and neuroprotection. Despite these encouraging findings, the clinical application of GMMs remains challenging due to safety concerns and inconsistent effectiveness across diverse patient populations. These factors create substantial barriers to the widespread adoption of microbiota-based therapies in clinical practice. To overcome these challenges and fully leverage the therapeutic potential of microbiota modulation, this review explores the feasibility of repurposing GMMs for managing multiple health disorders. A broad spectrum of microbiota-targeted strategies is examined, including dietary modifications, fecal microbiota transplantation, bacteriophage therapy, microbiome engineering, and immune system modulation. A particularly innovative approach involves integrating GMMs with pharmaceutical delivery systems to enhance therapeutic efficacy while mitigating potential adverse effects. This integrative strategy underscores the pivotal role of the gut microbiome in health and disease, supporting the development of precision medicine tailored to individual patient needs. By combining GMMs with targeted delivery mechanisms, this approach not only improves treatment effectiveness but also addresses critical concerns regarding safety and patient variability. Furthermore, this review outlines future research directions within the rapidly evolving field of microbiota modulation, emphasizing the necessity of comprehensive clinical trials and long-term safety evaluations. By critically assessing both the challenges and opportunities associated with microbiota-based interventions, this study provides a strategic framework for translating experimental research into viable clinical applications. A holistic approach to gut microbiota modulation has the potential to redefine treatment paradigms, offering personalized therapeutic strategies for a wide range of disorders and advancing the broader field of precision medicine.

PMID:40323507 | DOI:10.1007/s00210-025-04155-2