Adv Sci (Weinh). 2025 Mar 6:e2411325. doi: 10.1002/advs.202411325. Online ahead of print.

ABSTRACT

Drug repurposing identifies new therapeutic uses for the existing drugs originally developed for different indications, aiming at capitalizing on the established safety and efficacy profiles of known drugs. Thus, it is beneficial to bypass of early stages of drug development, and to reduction of the time and cost associated with bringing new therapies to market. Traditional experimental methods are often time-consuming and expensive, making artificial intelligence (AI) a promising alternative due to its lower cost, computational advantages, and ability to uncover hidden patterns. This review focuses on the availability of AI algorithms in drug development, and their positive and specific roles in revealing repurposing of the existing drugs, especially being integrated with virtual screening. It is shown that the existing AI algorithms excel at analyzing large-scale datasets, identifying the complicated patterns of drug responses from these datasets, and making predictions for potential drug repurposing. Building on these insights, challenges remain in developing efficient AI algorithms and future research, including integrating drug-related data across databases for better repurposing, enhancing AI computational efficiency, and advancing personalized medicine.

PMID:40047357 | DOI:10.1002/advs.202411325

Metab Brain Dis. 2025 Mar 6;40(3):136. doi: 10.1007/s11011-025-01560-7.

ABSTRACT

Astaxanthin derived from natural sources has excellent antioxidant and anti-inflammatory effects, and it is currently being widely researched as a neuroprotectant. However, astaxanthin possesses low oral bioavailability, and thus, astaxanthin extract from Haematococcus pluvialis was formulated into a nanoemulsion to improve its bioavailability and administered to Alzheimer's disease (AD)-like rats to study its possible neuroprotective benefits. Astaxanthin nanoemulsion was administered orally once a day for 28 days to streptozotocin (STZ)-induced AD rats at concentrations of 160, 320, and 640 mg/kg of body weight (bw) and subsequently assessed for cognitive function using behavioral assessments. Brain samples were collected for the assessment of AD biomarkers. Astaxanthin nanoemulsion at a dosage of 640 mg/kg bw significantly improved spatial learning, spatial memory, and recognition memory against STZ-AD rats. At 320 and 640 mg/kg bw, astaxanthin nanoemulsion significantly reduced levels of hippocampus synaptosomal amyloid beta and paired-helical fibrillary tau protein while increasing neuron survival. Additionally, astaxanthin nanoemulsion at 640 mg/kg bw significantly increased acetylcholine levels in the hippocampus and cerebellum. Astaxanthin nanoemulsion at all treatment dosages significantly reduced malondialdehyde, a lipid peroxidation product, and neuroinflammatory mediators (GFAP and TNF-α). Astaxanthin nanoemulsion supplementation has the potential to improve cognitive function and synaptic function by lowering amyloid beta and tau levels, as well as preserve neuron integrity by reducing neuroinflammation and lipid peroxidation, indicating that it may be able to treat some of the underlying causes of AD.

PMID:40047916 | DOI:10.1007/s11011-025-01560-7

J Allergy Clin Immunol. 2025 Mar 5:S0091-6749(25)00167-8. doi: 10.1016/j.jaci.2025.01.041. Online ahead of print.

NO ABSTRACT

PMID:40047726 | DOI:10.1016/j.jaci.2025.01.041

Naunyn Schmiedebergs Arch Pharmacol. 2025 Mar 6. doi: 10.1007/s00210-025-03970-x. Online ahead of print.

ABSTRACT

Inflammatory lung diseases (ILDs) such as asthma, acute respiratory distress syndrome, bronchiectasis, chronic obstructive pulmonary disease, COVID-19, cystic fibrosis, and lung cancer impose a substantial worldwide healthcare impact. The pathophysiology of these disorders is primarily influenced by the involvement of neutrophils, which are crucial triggers in the natural immune reaction. Neutrophils participate in pulmonary inflammation and tissue destruction. When neutrophils are activated and recruited, they migrate to inflammatory lung tissues via the chemokine receptor CXCR2. This study explores how neutrophils, directed by CXCR2 signaling, participate in the inflammatory environment in the lung, inducing tissue injury and the development of illness. We investigate both the functional and structural features of CXCR2, emphasizing its relationship with ligands such as IL-8 (CXCL8) and GRO-α (CXCL1) and its involvement in ILDs. The article also explores novel treatment approaches that focus on CXCR2, such as the use of small molecule antagonists. These compounds can regulate neutrophil behavior and reduce signs of the illness. The study provides a detailed analysis of current clinical studies and the results of inhibiting CXCR2, specifically looking at the effectiveness and safety of these new medicines. This study seeks to deliver a thorough analysis of the important function of neutrophils and CXCR2 in ILDs, as well as the possibility of CXCR2-targeted therapeutics to enhance clinical outcomes.

PMID:40047857 | DOI:10.1007/s00210-025-03970-x

Int Forum Allergy Rhinol. 2025 Mar 6:e23557. doi: 10.1002/alr.23557. Online ahead of print.

ABSTRACT

BACKGROUND: Elexacaftor/tezacaftor/ivacaftor (ETI) is a Cystic Fibrosis Transmembrane Conductance Regulator (CFTR) therapy that improves pulmonary function and chronic rhinosinusitis (CRS) in cystic fibrosis (CF) adults with at least one copy of the F508del CFTR mutation. The purpose of this study is to evaluate the impact of ETI on CRS symptoms in children and adolescents with CF.

METHODS: The MODUL-CF observational study is a multicenter prospective cohort study enrolling CF children with at least 1 F508del mutation in France. Subjects answered a standardized questionnaire on nasal obstruction and smell and were invited to complete the Sinus and Nasal Quality of Life Survey (SN-5) questionnaire prior to ETI therapy and at 1, 3, 6, and 12 months ETI. Part of the cohort underwent sinus computerized tomography (CT) within an ancillary study, scored by the Lund-Mackay CT score (LMKS).

RESULTS: Of 391 subjects, 94 (24.0%) were aged between 6 and 12 years, and 297 were adolescents. Sixty-four (16.3%) reported nasal obstruction at baseline. One hundred and sixty-three completed the SN-5 questionnaire at M0, 181 at M12, and 123 at M0 and M12. Mean SN-5 global score baseline value was similar to that of a healthy pediatric control cohort. SN-5 global score, nasal obstruction, sinus infection, and emotion domain subscores improved significantly at 1 month and this was sustained over 12 months. LMKS improved significantly in 43 patients who underwent sinus CT at M0 and M12.

CONCLUSION: ETI improves sinonasal symptoms and related quality of life (QOL), including emotion domain, in children and adolescents with CF.

PMID:40047648 | DOI:10.1002/alr.23557

J Physiol. 2025 Mar 6. doi: 10.1113/JP287891. Online ahead of print.

ABSTRACT

Electrogenic transepithelial ion transport can be measured with the short-circuit current technique. Such experiments are frequently used to evaluate the activity of the cystic fibrosis transmembrane conductance regulator (CFTR), a cAMP-activated chloride channel that is defective in cystic fibrosis, one of the most frequent genetic diseases. Typically, CFTR activity is estimated from the effect of CFTRinh-172, a selective CFTR inhibitor. Unexpectedly, we found that CFTRinh-172, in addition to PPQ-102, another CFTR inhibitor, caused only partial inhibition of CFTR function, particularly in epithelia in pro-inflammatory conditions, which are characterized by abundant mucus secretion. We hypothesized that the mucus layer was responsible for the poor activity of CFTR inhibitors. Therefore, we treated the epithelial surface with the reducing agent dithiothreitol to remove mucus. Removal of mucus, confirmed by immunofluorescence, resulted in highly enhanced sensitivity of CFTR to pharmacological inhibition. Our results show that the mucus layer represents an important barrier whose presence limits the activity of pharmacological agents. This is particularly relevant for CFTR and the evaluation of therapeutic approaches for correction of the basic defect in cystic fibrosis. KEY POINTS: Activity of the cAMP-activated cystic fibrosis transmembrane conductance regulator (CFTR) chloride channel can be evaluated by measuring the inhibition elicited by the selective blockers CFTRinh-172 and PPQ-102. In short-circuit current recordings on human airway epithelia, CFTR inhibitors had only a partial effect on cAMP-dependent chloride secretion, suggesting the possible contribution of other ion channels. The mucus layer covering the epithelial surface was removed with the reducing agent dithiothreitol. Treatment of epithelia with dithiothreitol markedly improved the efficacy of CFTR inhibitors. The partial effect of CFTR inhibitors might be explained by the presence of the mucus layer acting as a barrier.

PMID:40047394 | DOI:10.1113/JP287891

Invest Ophthalmol Vis Sci. 2025 Mar 3;66(3):13. doi: 10.1167/iovs.66.3.13.

ABSTRACT

PURPOSE: To determine the factors cross-sectionally and longitudinally associated with retinal vessel diameter, total area, and tortuosity in the Canadian Longitudinal Study on Aging (CLSA).

METHODS: Of the 30,097 adults between ages 45 and 85 years old in the CLSA Comprehensive Cohort, 26,076 had at least one retinal image gradable by QUARTZ, a deep-learning algorithm that automatically assessed image quality, distinguished between arterioles and venules, and estimated retinal vessel traits over the entire retina. Questions were asked about demographic, lifestyle, and medical factors. Blood pressure, cholesterol, and C-reactive protein were measured. Participants returned for follow-up 3 years later. Multiple linear regression was used to provide adjusted estimates.

RESULTS: Current smoking was strongly associated with wider arteriolar and venular diameters and their widening over 3 years (P < 0.05). Current smoking was also associated with a larger arteriolar and venular area and a 3-year increase in venular area (P < 0.05). Obesity was positively associated with venular diameter, total venular area, 3-year change in total venular area, and venular tortuosity (P < 0.05). Diastolic blood pressure was negatively associated with both arteriolar and venular diameter, area, and tortuosity, both cross-sectionally and longitudinally (P < 0.05). Diabetes was associated with wider arteriolar diameters cross-sectionally, and type 1 diabetes was associated with 3-year widening of arteriolar diameters (P < 0.05).

CONCLUSIONS: This work provides comprehensive information on the factors associated with retinal vessel traits and their change. Factors such as smoking, obesity, blood pressure, and diabetes were longitudinally related to retinal vessel traits, which play a role in the development of eye disease.

PMID:40048189 | DOI:10.1167/iovs.66.3.13

Abdom Radiol (NY). 2025 Mar 6. doi: 10.1007/s00261-025-04860-9. Online ahead of print.

ABSTRACT

Computed Tomography (CT) imaging captures detailed cross-sectional images of the pancreas and surrounding structures and provides valuable information for medical professionals. The classification of pancreatic CT images presents significant challenges due to the complexities of pancreatic diseases, especially pancreatic cancer. These challenges include subtle variations in tumor characteristics, irregular tumor shapes, and intricate imaging features that hinder accurate and early diagnosis. Image noise and variations in image quality also complicate the analysis. To address these classification problems, advanced medical imaging techniques, optimization algorithms, and deep learning methodologies are often employed. This paper proposes a robust classification model called DeepOptimalNet, which integrates optimization algorithms and deep learning techniques to handle the variability in imaging characteristics and subtle variations associated with pancreatic tumors. The model uses a comprehensive approach to enhance the analysis of medical CT images, beginning with the application of the Gaussian smoothing filter (GSF) for noise reduction and feature enhancement. It introduces the Modified Remora Optimization Algorithm (MROA) to improve the accuracy and efficiency of pancreatic cancer tissue segmentation. The adaptability of modified optimization algorithms to specific challenges such as irregular tumor shapes is emphasized. The paper also utilizes Deep Transfer CNN with ResNet-50 (DTCNN) for feature extraction, leveraging transfer learning to enhance prediction accuracy in CT images. ResNet-50's strong feature extraction capabilities are particularly relevant to fault diagnosis in CT images. The focus then shifts to a Deep Cascade Convolutional Neural Network with Multimodal Learning (DCCNN-ML) for classifying pancreatic cancer in CT images. The DeepOptimalNet approach underscores the advantages of deep learning techniques, multimodal learning, and cascade architectures in addressing the complexity and subtle variations inherent in pancreatic cancer imaging, ultimately leading to more accurate and robust classifications. The proposed DeepOptimalNet achieves 99.3% accuracy, 99.1% sensitivity, 99.5% specificity, and 99.3% F-score, surpassing existing models in pancreatic tumor classification. Its MROA-based segmentation improves boundary delineation, while DTCNN with ResNet-50 enhances feature extraction for small and low-contrast tumors. Benchmark validation confirms its superior classification performance, reduced false positives, and improved diagnostic reliability compared to traditional deep learning methods.

PMID:40047871 | DOI:10.1007/s00261-025-04860-9

Abdom Radiol (NY). 2025 Mar 6. doi: 10.1007/s00261-025-04858-3. Online ahead of print.

ABSTRACT

BACKGROUND: To explore the predictive value of radiomics features extracted from anatomical ROIs in differentiating the International Society of Urological Pathology (ISUP) grading in prostate cancer patients.

METHODS: This study included 1,500 prostate cancer patients from a multi-center study. The peripheral zone (PZ) and central gland (CG, transition zone + central zone) of the prostate were segmented using deep learning algorithms and were defined as the regions of interest (ROI) in this study. A total of 12,918 image-based features were extracted from T2-weighted imaging (T2WI), apparent diffusion coefficient (ADC), and diffusion-weighted imaging (DWI) images of these two ROIs. Synthetic minority over-sampling technique (SMOTE) algorithm was used to address the class imbalance problem. Feature selection was performed using Pearson correlation analysis and random forest regression. A prediction model was built using the random forest classification algorithm. Kruskal-Wallis H test, ANOVA, and Chi-Square Test were used for statistical analysis.

RESULTS: A total of 20 ISUP grading-related features were selected, including 10 from the PZ ROI and 10 from the CG ROI. On the test set, the combined PZ + CG radiomics model exhibited better predictive performance, with an AUC of 0.928 (95% CI: 0.872, 0.966), compared to the PZ model alone (AUC: 0.838; 95% CI: 0.722, 0.920) and the CG model alone (AUC: 0.904; 95% CI: 0.851, 0.945).

CONCLUSION: This study demonstrates that radiomic features extracted based on anatomical sub-region of the prostate can contribute to enhanced ISUP grade prediction. The combination of PZ + GG can provide more comprehensive information with improved accuracy. Further validation of this strategy in the future will enhance its prospects for improving decision-making in clinical settings.

PMID:40047870 | DOI:10.1007/s00261-025-04858-3

Int J Legal Med. 2025 Mar 6. doi: 10.1007/s00414-025-03453-x. Online ahead of print.

ABSTRACT

INTRODUCTION: Forensic Odontology plays a crucial role in medicolegal identification by comparing dental evidence in antemortem (AM) and postmortem (PM) dental records, including orthopantomograms (OPGs). Due to the complexity and time-consuming nature of this process, imaging analysis optimization is an urgent matter. Convolutional neural networks (CNN) are promising artificial intelligence (AI) structures in Forensic Odontology for their efficiency and detail in image analysis, making them a valuable tool in medicolegal identification. Therefore, this study focused on the development of a CNN algorithm capable of comparing AM and PM dental evidence in OPGs for the medicolegal identification of unknown cadavers.

MATERIALS AND METHODS: The present study included a total sample of 1235 OPGs from 1050 patients from the Stomatology Department of Unidade Local de Saúde Santa Maria, aged 16 to 30 years. Two algorithms were developed, one for age classification and another for positive identification, based on the pre-trained model VGG16, and performance was evaluated through predictive metrics and heatmaps.

RESULTS: Both developed models achieved a final accuracy of 85%, reflecting high overall performance. The age classification model performed better at classifying OPGs from individuals aged between 16 and 23 years, while the positive identification model was significantly better at identifying pairs of OPGs from different individuals.

CONCLUSIONS: The developed AI model is useful in the medicolegal identification of unknown cadavers, with advantage in mass disaster victim identification context, by comparing AM and PM dental evidence in OPGs of individuals aged 16 to 30 years.

PMID:40047854 | DOI:10.1007/s00414-025-03453-x

J Wound Care. 2025 Mar 2;34(3):205-214. doi: 10.12968/jowc.2021.0171.

ABSTRACT

OBJECTIVE: The purpose of this research was to develop an automatic wound segmentation method for a pressure ulcer (PU) monitoring system (PrUMS) using a depth camera to provide automated, non-contact wound measurements.

METHOD: The automatic wound segmentation method, which combines multiple convolutional neural network classifiers, was developed to segment the wound region to improve PrUMS accuracy and to avoid the biased decision from a single classifier. Measurements from PrUMS were compared with the standardised manual measurements (ground truth) of two clinically trained wound care nurses for each wound.

RESULTS: Compared to the average ground truth measurement (38×34×15mm), measurement errors for length, width and depth were 9.27mm, 5.89mm and 5.79mm, respectively, for the automatic segmentation method, and 4.72mm, 4.34mm, and 5.71mm, respectively, for the semi-automatic segmentation method. There were no significant differences between the segmentation methods and ground truth measurements for length and width; however, the depth measurement was significantly different (p<0.001) from the ground truth measurement.

CONCLUSION: The novel PrUMS device used in this study provided objective, non-contact wound measurement and was demonstrated to be usable in clinical wound care practice. Images taken with a regular camera can improve the classifier's performance. With a dataset of 70 PUs for single and multiple (four images per PU) measurements, the differences between length and width measurements of the PrUMS and the manual measurement by nurses were not statistically significant (p>0.05). A statistical difference (p=0.04) was found between depth measurements obtained manually and with PrUMS, due to limitations of the depth camera within PrUMS, causing missing depth measurements for small wounds.

PMID:40047814 | DOI:10.12968/jowc.2021.0171

Micromachines (Basel). 2025 Feb 7;16(2):193. doi: 10.3390/mi16020193.

ABSTRACT

The deep learning object detection algorithm has been widely applied in the field of synthetic aperture radar (SAR). By utilizing deep convolutional neural networks (CNNs) and other techniques, these algorithms can effectively identify and locate targets in SAR images, thereby improving the accuracy and efficiency of detection. In recent years, achieving real-time monitoring of regions has become a pressing need, leading to the direct completion of real-time SAR image target detection on airborne or satellite-borne real-time processing platforms. However, current GPU-based real-time processing platforms struggle to meet the power consumption requirements of airborne or satellite applications. To address this issue, a low-power, low-latency deep learning SAR object detection algorithm accelerator was designed in this study to enable real-time target detection on airborne and satellite SAR platforms. This accelerator proposes a Process Engine (PE) suitable for multidimensional convolution parallel computing, making full use of Field-Programmable Gate Array (FPGA) computing resources to reduce convolution computing time. Furthermore, a unique memory arrangement design based on this PE aims to enhance memory read/write efficiency while applying dataflow patterns suitable for FPGA computing to the accelerator to reduce computation latency. Our experimental results demonstrate that deploying the SAR object detection algorithm based on Yolov5s on this accelerator design, mounted on a Virtex 7 690t chip, consumes only 7 watts of dynamic power, achieving the capability to detect 52.19 512 × 512-sized SAR images per second.

PMID:40047666 | DOI:10.3390/mi16020193

Adv Sci (Weinh). 2025 Mar 6:e2411191. doi: 10.1002/advs.202411191. Online ahead of print.

ABSTRACT

As an animal matures, its neural circuit undergoes alterations, yet the developmental changes in intracellular organelles to facilitate these changes is less understood. Using 3D electron microscopy and deep learning, the study develops semi-automated methods for reconstructing mitochondria in C. elegans and collected mitochondria reconstructions from normal reproductive stages and dauer, enabling comparative study on mitochondria structure within the neuromuscular system. It is found that various mitochondria structural properties in neurons correlate with synaptic connections and these properties are preserved across development in different neural circuits. To test the necessity of these universal mitochondria properties, the study examines the behavior in drp-1 mutants with impaired mitochondria fission and discovers that it causes behavioral deficits. Moreover, it is observed that dauer neurons display distinctive mitochondrial features, and mitochondria in dauer muscles exhibit unique reticulum-like structure. It is proposed that these specialized mitochondria structures may serve as an adaptive mechanism to support stage-specific behavioral and physiological needs.

PMID:40047328 | DOI:10.1002/advs.202411191

ACS Synth Biol. 2025 Mar 6. doi: 10.1021/acssynbio.4c00869. Online ahead of print.

ABSTRACT

Cupriavidus necator H16 is a facultative chemolithotroph capable of using CO2 as a carbon source, making it a promising organism for carbon-negative biomanufacturing of petroleum-based product alternatives. In contrast to model microbes, genetic engineering technologies are limited in C. necator, constraining its utility in basic and applied research. Here, we developed a genome engineering technology to efficiently mobilize, integrate, and express synthetic genetic elements (SGEs) in C. necator. We tested the chromosomal expression of four inducible promoters to optimize an engineered genetic landing pad for tunable gene expression. To demonstrate utility, we employed the SGE system to design, mobilize, and express eight heterologous inorganic carbon uptake pathways in C. necator. We demonstrated all inorganic carbon uptake systems' upregulated intracellular bicarbonate concentrations under heterotrophic conditions. This work establishes the utility of the SGE strategy for expedited integration and tunable expression of heterologous pathways, and enhances intracellular bicarbonate concentrations in C. necator.

PMID:40048245 | DOI:10.1021/acssynbio.4c00869

Metabolomics. 2025 Mar 6;21(2):38. doi: 10.1007/s11306-025-02234-2.

NO ABSTRACT

PMID:40048010 | DOI:10.1007/s11306-025-02234-2

Elife. 2025 Mar 6;14:e104432. doi: 10.7554/eLife.104432.

ABSTRACT

The RAS-MAPK system plays an important role in regulating various cellular processes, including growth, differentiation, apoptosis, and transformation. Dysregulation of this system has been implicated in genetic diseases and cancers affecting diverse tissues. To better understand the regulation of this system, we employed information flow analysis based on transfer entropy (TE) between the activation dynamics of two key elements in cells stimulated with EGF: SOS, a guanine nucleotide exchanger for the small GTPase RAS, and RAF, a RAS effector serine/threonine kinase. TE analysis allows for model-free assessment of the timing, direction, and strength of the information flow regulating the system response. We detected significant amounts of TE in both directions between SOS and RAF, indicating feedback regulation. Importantly, the amount of TE did not simply follow the input dose or the intensity of the causal reaction, demonstrating the uniqueness of TE. TE analysis proposed regulatory networks containing multiple tracks and feedback loops and revealed temporal switching in the reaction pathway primarily responsible for reaction control. This proposal was confirmed by the effects of an MEK inhibitor on TE. Furthermore, TE analysis identified the functional disorder of a SOS mutation associated with Noonan syndrome, a human genetic disease, of which the pathogenic mechanism has not been precisely known yet. TE assessment holds significant promise as a model-free analysis method of reaction networks in molecular pharmacology and pathology.

PMID:40047537 | DOI:10.7554/eLife.104432

J Food Sci. 2025 Mar;90(3):e70074. doi: 10.1111/1750-3841.70074.

ABSTRACT

Obesity prevalence has steadily increased over the past decades. Standard approaches, such as increased energy expenditure, lifestyle changes, a balanced diet, and the use of specific drugs, are the conventional strategies for preventing or treating the disease and its associated complications. Fermented foods and their subsequent bioactive constituents are now believed to be a novel strategy that can complement already existing approaches for managing and preventing this disease. Recent developments in systems biology and bioinformatics have made it possible to model and simulate compounds and disease interactions. The adoption of such in silico models has contributed to the discovery of novel fermented product targets and helped in testing hypotheses regarding the mechanistic impact and underlying functions of fermented food components. From the studies explored, key findings suggest that fermented foods affect adipogenesis, lipid metabolism, appetite regulation, gut microbiota composition, insulin resistance, and inflammation related to obesity, which could lead to new ways to treat these conditions. These outcomes were linked to probiotics, prebiotics, metabolites, and complex bioactive substances produced during fermentation. Overall, fermented foods and their bioactive compounds show promise as innovative tools for obesity management by influencing metabolic pathways and overall gut health.

PMID:40047326 | DOI:10.1111/1750-3841.70074

J Patient Rep Outcomes. 2025 Mar 6;9(1):27. doi: 10.1186/s41687-025-00855-8.

ABSTRACT

BACKGROUND: Monitoring for the side effects of novel therapies using patient-reported outcomes (PROs) is critical for ensuring patient safety. Existing static patient-reported outcome measures may not provide adequate coverage of novel side effects. Item libraries provide a flexible approach to monitoring for side effects using customized item lists, but the ideal process for matching side effects to items sourced from multiple item libraries is yet to be established. We sought to develop a pragmatic process for mapping side effects to items from three major item libraries using immune checkpoint inhibitor (ICI) side effects as an example.

METHODS: Using a consumer- and clinician-driven list of 36 ICI side effects, two authors independently mapped side effects to Common Terminology Criteria for Adverse Event (CTCAE) terms, and then to three item libraries: the Patient-Reported Outcome version of the Common Terminology Criteria for Adverse Events (PRO-CTCAE), the European Organisation for Research and Treatment of Cancer (EORTC) Item Library, and the Functional Assessment of Chronic Illness Therapy (FACIT) searchable library. The rates of inter-rater agreement were recorded. Following item collation from the item libraries, we devised criteria for selecting the optimal item for each side effect for inclusion in a future electronic PRO system based on guidance from the above groups.

RESULTS: All 36 side effects mapped to at least one CTCAE term, with eight mapping to more than one term. Twenty-three side effects mapped to at least one PRO-CTCAE term, 35 side effects mapped to at least one EORTC item, and 31 side effects mapped to at least one FACIT item. The inter-rater agreement rate was 100% (PRO-CTCAE), 83% (EORTC) and 75% (FACIT). Pre-determined criteria were applied to select the optimal item for each side effect from the three item libraries, producing a final 61-item list.

CONCLUSION: Using ICI side effects as an example, we developed a pragmatic approach to creating customized item lists from three major item libraries to monitor for side effects of novel therapies in routine care. This process highlighted the challenges of using item libraries and priorities for future work to improve their usability.

PMID:40048089 | DOI:10.1186/s41687-025-00855-8

Front Pharmacol. 2025 Feb 19;16:1519066. doi: 10.3389/fphar.2025.1519066. eCollection 2025.

ABSTRACT

BACKGROUND: Considering the role PARPs play in inflammation, we assessed the effect of PARP inhibition in an inflammatory skin condition, psoriasis, to explore novel avenues for the potential repurposing of PARP inhibitors that are currently used in tumour therapy.

METHODS: The imiquimod (IMQ)-induced model of psoriasis was applied in BALB/c mice. Mice received daily intraperitoneal injection of either one of four PARP inhibitors or their vehicle prior to treatment of the shaved back skin of mice with IMQ-containing cream or control cream for four days. The appearance of the skin of mice was scored daily according to the extent of erythema, induration and scaling. The most effective PARP inhibitor was selected for detailed studies on mouse skin and in a human keratinocyte cell line.

RESULTS: Of the PARP inhibitors, talazoparib and rucaparib improved the imiquimod-induced symptoms on mouse skin. Application of talazoparib in the psoriasis model resulted in maintained terminal differentiation and reduced proliferation of epidermal keratinocytes. Conversely, talazoparib also enhanced the production of pro-inflammatory chemokines in the skin of mice. These effects of talazoparib was associated with increased mitochondrial production of reactive oxygen species and a consequent activation of pro-apoptotic and pro-inflammatory pathways in keratinocytes.

CONCLUSION: PARP inhibition by talazoparib promotes terminal differentiation of epidermal keratinocytes that may be beneficial in psoriasis. Despite the fact that talazoparib exerted a pro-inflammatory effect in the skin, which is not unprecedented in anti-psoriatic therapy, these findings may advance the conduction of pre-clinical and clinical trials with PARP inhibitors in psoriasis management.

PMID:40046735 | PMC:PMC11879949 | DOI:10.3389/fphar.2025.1519066

Cancer Manag Res. 2025 Mar 1;17:429-440. doi: 10.2147/CMAR.S483151. eCollection 2025.

ABSTRACT

Pancreatic cancer (PC) remains one of the most challenging malignancies to treat. Current therapeutic options are unsatisfactory, and there is an urgent need for more effective and less toxic drugs to improve the dismal prognosis of PC. In recent years, drug repurposing (DR) has emerged as an attractive strategy to identify novel treatments for PC by leveraging existing drugs approved for other indications. Through the use of electronic medical records, Artificial Intelligence, study of metabolic pathways, signalling pathways, and many other approaches, it has become much easier in recent years to identify potential novel uses for old drugs. Although policy, funding and research attention in this area are steadily growing, major challenges to efficient and effective patient-centric DR in PC need to be addressed. These include but are not limited to regulatory, financial and funding barriers and the lack of coordination and collaboration among several sectors and stakeholders. To explore the opportunities and challenges associated with DR in PC, a one-day multi-stakeholder meeting was held on 14th of November 2024 in Brussels, Belgium as part of the REMEDi4ALL project. This meeting provided a platform for researchers, clinicians, industry representatives, funders, regulatory experts, and patient advocates to discuss and propose actions to optimize and accelerate DR in PC. Insights from this meeting support the potential of DR to enhance PC treatment options while highlighting the importance of systemic and supportive changes in the regulatory, policy and funding landscapes, interdisciplinary collaboration, data sharing, and patient involvement in driving therapeutic innovation. This summary highlights key outcomes and recommendations from the meeting in informing future efforts to advance DR initiatives in the context of PC.

PMID:40046652 | PMC:PMC11881603 | DOI:10.2147/CMAR.S483151