PLoS One. 2025 Apr 29;20(4):e0321094. doi: 10.1371/journal.pone.0321094. eCollection 2025.

ABSTRACT

The prolonged persistence of extracellular chromatin and DNA is a salient feature of diseases like cystic fibrosis, systemic lupus erythematosus and COVID-19 associated microangiopathy. Since deoxyribonuclease I (DNase1) is a major endonuclease involved in DNA-related waste disposal, recombinant DNase1 is an important therapeutic biologic. Recently we described the production of recombinant murine DNase1 (rmDNase1) in Pichia pastoris by employing the α-mating factor prepro signal peptide (αMF-SP) a method, which we now applied to express recombinant human DNASE1 (rhDNASE1). In addition to an impaired cleavage of the αMF pro-peptide, which we also detected previously for mDNase1, expression of hDNASE1 resulted in a 70-80 times lower yield although both orthologues share a high structural and functional homology. Using mDNase1 expression as a guideline, we were able to increase the yield of hDNASE1 fourfold by optimizing parameters like nutrients, cultivation temperature, methanol supply, and codon usage. In addition, post-translational import into the rough endoplasmic reticulum (rER) was changed to co-translational import by employing the signal peptide (SP) of the α-subunit of the Oligosaccharyltransferase complex (Ost1) from Saccharomyces cerevisiae. These improvements resulted in the purification of ~ 8 mg pure mature rmDNase1 and ~ 0.4 mg rhDNASE1 per Liter expression medium of a culture with a cell density of OD600 = 40 in 24 hours. As a main cause for the expression difference, we assume varying folding abilities to reach a native conformation, which induce an elevated unproductive unfolded protein response within the rER during hDNASE1 expression. Concerning functionality, rhDNASE1 expressed in P. pastoris is comparable to Pulmozyme®, i.e. rhDNASE1 produced in Chinese hamster ovary (CHO) cells by Roche - Genentech. With respect to the biochemical effectivity, rmDNase1 is superior to rhDNASE1 due to its higher specific activity in the presence of Ca2 + /Mg2 + and the lower inhibition by monomeric actin.

PMID:40299953 | DOI:10.1371/journal.pone.0321094

PLoS Negl Trop Dis. 2025 Apr 29;19(4):e0012985. doi: 10.1371/journal.pntd.0012985. Online ahead of print.

ABSTRACT

BACKGROUND: The identification of B-cell epitopes (BCEs) is fundamental to advancing epitope-based vaccine design, therapeutic antibody development, and diagnostics, such as in neglected tropical diseases caused by parasitic pathogens. However, the structural complexity of parasite antigens and the high cost of experimental validation present certain challenges. Advances in Artificial Intelligence (AI)-driven protein engineering, particularly through machine learning and deep learning, offer efficient solutions to enhance prediction accuracy and reduce experimental costs.

METHODOLOGY/PRINCIPAL FINDINGS: Here, we present deepBCE-Parasite, a Transformer-based deep learning model designed to predict linear BCEs from peptide sequences. By leveraging a state-of-the-art self-attention mechanism, the model achieved remarkable predictive performance, achieving an accuracy of approximately 81% and an AUC of 0.90 in both 10-fold cross-validation and independent testing. Comparative analyses against 12 handcrafted features and four conventional machine learning algorithms (GNB, SVM, RF, and LGBM) highlighted the superior predictive power of the model. As a case study, deepBCE-Parasite predicted eight BCEs from the leucine aminopeptidase (LAP) protein in Fasciola hepatica proteomic data. Dot-blot immunoassays confirmed the specific binding of seven synthetic peptides to positive sera, validating their IgG reactivity and demonstrating the model's efficacy in BCE prediction.

CONCLUSIONS/SIGNIFICANCE: deepBCE-Parasite demonstrates excellent performance in predicting BCEs across diverse parasitic pathogens, offering a valuable tool for advancing the design of epitope-based vaccines, antibodies, and diagnostic applications in parasitology.

PMID:40300022 | DOI:10.1371/journal.pntd.0012985

PLoS One. 2025 Apr 29;20(4):e0322052. doi: 10.1371/journal.pone.0322052. eCollection 2025.

ABSTRACT

Ensuring safety and safeguarding indoor properties require reliable fire detection methods. Traditional detection techniques that use smoke, heat, or fire sensors often fail due to false positives and slow response time. Existing deep learning-based object detectors fall short of improved accuracy in indoor settings and real-time tracking, considering the dynamic nature of fire and smoke. This study aimed to address these challenges in fire and smoke detection in indoor settings. It presents a hyperparameter-optimized YOLOv5 (HPO-YOLOv5) model optimized by a genetic algorithm. To cover all prospective scenarios, we created a novel dataset comprising indoor fire and smoke images. There are 5,000 images in the dataset, split into training, validation, and testing samples at a ratio of 80:10:10. It also used the Grad-CAM technique to provide visual explanations for model predictions, ensuring interpretability and transparency. This research combined YOLOv5 with DeepSORT (which uses deep learning features to improve the tracking of objects over time) to provide real-time monitoring of fire progression. Thus, it allows for the notification of actual fire hazards. With a mean average precision (mAP@0.5) of 92.1%, the HPO-YOLOv5 model outperformed state-of-the-art models, including Faster R-CNN, YOLOv5, YOLOv7 and YOLOv8. The proposed model achieved a 2.4% improvement in mAP@0.5 over the original YOLOv5 baseline model. The research has laid the foundation for future developments in fire hazard detection technology, a system that is dependable and effective in indoor scenarios.

PMID:40299940 | DOI:10.1371/journal.pone.0322052

PLoS One. 2025 Apr 29;20(4):e0319499. doi: 10.1371/journal.pone.0319499. eCollection 2025.

ABSTRACT

Non-small cell lung cancer (NSCLC) accounts for the majority of lung cancer cases, making it the most fatal diseases worldwide. Predicting NSCLC patients' survival outcomes accurately remains a significant challenge despite advancements in treatment. The difficulties in developing effective drug therapies, which are frequently hampered by severe side effects, drug resistance, and limited effectiveness across diverse patient populations, highlight the complexity of NSCLC. The machine learning (ML) and deep learning (DL) modelsare starting to reform the field of NSCLC drug disclosure. These methodologies empower the distinguishing proof of medication targets and the improvement of customized treatment techniques that might actually upgrade endurance results for NSCLC patients. Using cutting-edge methods of feature extraction and transfer learning, we present a drug discovery model for the identification of therapeutic targets in this paper. For the purpose of extracting features from drug and protein sequences, we make use of a hybrid UNet transformer. This makes it possible to extract deep features that address the issue of false alarms. For dimensionality reduction, the modified Rime optimization (MRO) algorithm is used to select the best features among multiples. In addition, we design the deep transfer learning (DTransL) model to boost the drug discovery accuracy for NSCLC patients' therapeutic targets. Davis, KIBA, and Binding-DB are examples of benchmark datasets that are used to validate the proposed model. Results exhibit that the MRO+DTransL model outflanks existing cutting edge models. On the Davis dataset, the MRO+DTransL model performed better than the LSTM model by 9.742%, achieved an accuracy of 98.398%. It reached 98.264% and 97.344% on the KIBA and Binding-DB datasets, respectively, indicating improvements of 8.608% and 8.957% over baseline models.

PMID:40299923 | DOI:10.1371/journal.pone.0319499

PLoS Med. 2025 Apr 29;22(4):e1004566. doi: 10.1371/journal.pmed.1004566. eCollection 2025 Apr.

ABSTRACT

BACKGROUND: Postoperative acute kidney injury (PO-AKI) prediction models for non-cardiac major surgeries typically rely solely on preoperative clinical characteristics.

METHODS AND FINDINGS: In this study, we developed and externally validated a deep-learning-based model that integrates preoperative data with minute-scale intraoperative vital signs to predict PO-AKI. Using data from three hospitals, we constructed a convolutional neural network-based EfficientNet framework to analyze intraoperative data and created an ensemble model incorporating 103 baseline variables of demographics, medication use, comorbidities, and surgery-related characteristics. Model performance was compared with the conventional SPARK model from a previous study. Among 110,696 patients, 51,345 were included in the development cohort, and 59,351 in the external validation cohorts. The median age of the cohorts was 60, 61, and 66 years, respectively, with males comprising 54.9%, 50.8%, and 42.7% of each cohort. The intraoperative vital sign-based model demonstrated comparable predictive power (AUROC (Area Under the Receiver Operating Characteristic Curve): discovery cohort 0.707, validation cohort 0.637 and 0.607) to preoperative-only models (AUROC: discovery cohort 0.724, validation cohort 0.697 and 0.745). Adding 11 key clinical variables (e.g., age, sex, estimated glomerular filtration rate (eGFR), albuminuria, hyponatremia, hypoalbuminemia, anemia, diabetes, renin-angiotensin-aldosterone inhibitors, emergency surgery, and the estimated surgery time) improved the model's performance (AUROC: discovery cohort 0.765, validation cohort 0.716 and 0.761). The ensembled deep-learning model integrating both preoperative and intraoperative data achieved the highest predictive accuracy (AUROC: discovery cohort 0.795, validation cohort 0.762 and 0.786), outperforming the conventional SPARK model. The retrospective design in a single-nation cohort with non-inclusion of some potential AKI-associated variables is the main limitation of this study.

CONCLUSIONS: This deep-learning-based PO-AKI risk prediction model provides a comprehensive approach to evaluating PO-AKI risk prediction by combining preoperative clinical data with real-time intraoperative vital sign information, offering enhanced predictive performance for better clinical decision-making.

PMID:40299885 | DOI:10.1371/journal.pmed.1004566

PLoS One. 2025 Apr 29;20(4):e0321478. doi: 10.1371/journal.pone.0321478. eCollection 2025.

ABSTRACT

Deep learning has significantly advanced in predicting stress-strain curves. However, due to the complex mechanical properties of rock materials, existing deep learning methods have the problem of insufficient accuracy in predicting the stress-strain curves of rock materials. This paper proposes a deep learning method based on a long short-term memory autoencoder (LSTM-AE) for predicting stress-strain curves of rock materials in discrete element numerical simulations. The LSTM-AE approach uses the LSTM network to construct both the encoder and decoder, where the encoder extracts features from the input data and the decoder generates the target sequence for prediction. The mean square error (MSE), root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (R2) of the predicted and true values are used as the evaluation metrics. The proposed LSTM-AE network is compared with the LSTM network, recurrent neural network (RNN), BP neural network (BPNN), and XGBoost model. The results indicate that the accuracy of the proposed LSTM-AE network outperforms LSTM, RNN, BPNN, and XGBoost. Furthermore, the robustness of the LSTM-AE network is confirmed by predicting 10 sets of special samples. However, the scalability of the LSTM-AE network in handling large datasets and its applicability to predicting laboratory datasets need further verification. Nevertheless, this study provides a valuable reference for solving the prediction accuracy of stress-strain curves in rock materials.

PMID:40299820 | DOI:10.1371/journal.pone.0321478

IEEE Trans Neural Syst Rehabil Eng. 2025 Apr 29;PP. doi: 10.1109/TNSRE.2025.3565305. Online ahead of print.

ABSTRACT

sEMG signals hold significant potential for motion prediction, with promising applications in areas such as rehabilitation, sports training, and human-computer interaction. However, achieving robust prediction accuracy remains a critical challenge, as even minor inaccuracies in motion prediction can severely affect the reliability and practical utility of sEMG-based systems. In this study, we propose a novel framework, muscle synergy (MS)-based graph attention networks (MSGAT-LSTM), specifically designed to address the challenges of continuous motion prediction using sparse sEMG electrodes. By leveraging MS theory and graph-based learning, the framework effectively compensates for the limitations of sparse sEMG setups and achieves significant improvements in prediction accuracy compared to existing methods. Based on MS theory, the framework calculates cosine similarity between sEMG signal features from different muscles to assign edge weights, effectively capturing their coordinated contributions to motion. The proposed framework integrates GAT for relational feature learning with LSTM networks for temporal dependency modeling, leveraging the strengths of both architectures. Experimental results on the public dataset Ninapro DB2 and a self-collected dataset demonstrate that MSGAT-LSTM achieves superior performance compared to state-of-the-art methods, including the muscle anatomy and MS-based 3DCNN, GCN-LSTM, and classic models such as CNN-LSTM, CNN, and LSTM, in terms of RMSE and R2. Furthermore, experimental results reveal that incorporating MS into GCN reduces training time by 13% compared to GCN-LSTM, significantly enhancing computational efficiency and scalability. This study highlights the potential of integrating MS theory with graph-based deep learning methods for motion prediction based on sEMG.

PMID:40299730 | DOI:10.1109/TNSRE.2025.3565305

Biomedicines. 2025 Apr 13;13(4):951. doi: 10.3390/biomedicines13040951.

ABSTRACT

Cancer remains one of the leading causes of mortality worldwide, driving the need for innovative approaches in research and treatment. Artificial intelligence (AI) has emerged as a powerful tool in oncology, with the potential to revolutionize cancer diagnosis, treatment, and management. This paper reviews recent advancements in AI applications within cancer research, focusing on early detection through computer-aided diagnosis, personalized treatment strategies, and drug discovery. We survey AI-enhanced diagnostic applications and explore AI techniques such as deep learning, as well as the integration of AI with nanomedicine and immunotherapy for cancer care. Comparative analyses of AI-based models versus traditional diagnostic methods are presented, highlighting AI's superior potential. Additionally, we discuss the importance of integrating social determinants of health to optimize cancer care. Despite these advancements, challenges such as data quality, algorithmic biases, and clinical validation remain, limiting widespread adoption. The review concludes with a discussion of the future directions of AI in oncology, emphasizing its potential to reshape cancer care by enhancing diagnosis, personalizing treatments and targeted therapies, and ultimately improving patient outcomes.

PMID:40299653 | DOI:10.3390/biomedicines13040951

Pac Symp Biocomput. 2025;30:675-689. doi: 10.1142/9789819807024_0048.

ABSTRACT

Alzheimer's disease (AD) is a neurocognitive disorder that deteriorates memory and impairs cognitive functions. Mild Cognitive Impairment (MCI) is generally considered as an intermediate phase between normal cognitive aging and more severe conditions such as AD. Although not all individuals with MCI will develop AD, they are at an increased risk of developing AD. Diagnosing AD once strong symptoms are already present is of limited value, as AD leads to irreversible cognitive decline and brain damage. Thus, it is crucial to develop methods for the early prediction of AD in individuals with MCI. Recurrent Neural Networks (RNN)-based methods have been effectively used to predict the progression from MCI to AD by analyzing electronic health records (EHR). However, despite their widespread use, existing RNN-based tools may introduce increased model complexity and often face difficulties in capturing long-term dependencies. In this study, we introduced a novel Dynamic deep learning model for Early Prediction of AD (DyEPAD) to predict MCI subjects' progression to AD utilizing EHR data. In the first phase of DyEPAD, embeddings for each time step or visit are captured through Graph Convolutional Networks (GCN) and aggregation functions. In the final phase, DyEPAD employs tensor algebraic operations for frequency domain analysis of these embeddings, capturing the full scope of evolutionary patterns across all time steps. Our experiments on the Alzheimer's Disease Neuroimaging Initiative (ADNI) and National Alzheimer's Coordinating Center (NACC) datasets demonstrate that our proposed model outperforms or is in par with the state-of-the-art and baseline methods.

PMID:40299624 | DOI:10.1142/9789819807024_0048

Pac Symp Biocomput. 2025;30:565-579. doi: 10.1142/9789819807024_0040.

ABSTRACT

Precision medicine significantly enhances patients prognosis, offering personalized treatments. Particularly for metastatic cancer, incorporating primary tumor location into the diagnostic process greatly improves survival rates. However, traditional methods rely on human expertise, requiring substantial time and financial resources. To address this challenge, Machine Learning (ML) and Deep Learning (DL) have proven particularly effective. Yet, their application to medical data, especially genomic data, must consider and encompass privacy due to the highly sensitive nature of data. In this paper, we propose OGHE, a convolutional neural network-based approach for privacy-preserving cancer classification designed to exploit spatial patterns in genomic data, while maintaining confidentiality by means of Homomorphic Encryption (HE). This encryption scheme allows the processing directly on encrypted data, guaranteeing its confidentiality during the entire computation. The design of OGHE is specific for privacy-preserving applications, taking into account HE limitations from the outset, and introducing an efficient packing mechanism to minimize the computational overhead introduced by HE. Additionally, OGHE relies on a novel feature selection method, VarScout, designed to extract the most significant features through clustering and occurrence analysis, while preserving inherent spatial patterns. Coupled with VarScout, OGHE has been compared with existing privacy-preserving solutions for encrypted cancer classification on the iDash 2020 dataset, demonstrating their effectiveness in providing accurate privacy-preserving cancer classification, and reducing latency thanks to our packing mechanism. The code is released to the scientific community.

PMID:40299616 | DOI:10.1142/9789819807024_0040

Pac Symp Biocomput. 2025;30:167-184. doi: 10.1142/9789819807024_0013.

ABSTRACT

Accurate prediction of suicide risk is crucial for identifying patients with elevated risk burden, helping ensure these patients receive targeted care. The US Department of Veteran Affairs' suicide prediction model primarily leverages structured electronic health records (EHR) data. This approach largely overlooks unstructured EHR, a data format that could be utilized to enhance predictive accuracy. This study aims to enhance suicide risk models' predictive accuracy by developing a model that incorporates both structured EHR predictors and semantic NLP-derived variables from unstructured EHR. XGBoost models were fit to predict suicide risk- the interactions identified by the model were extracted using SHAP, validated using logistic regression models, added to a ridge regression model, which was subsequently compared to a ridge regression approach without the use of interactions. By introducing a selection parameter, α, to balance the influence of structured (α=1) and unstructured (α=0) data, we found that intermediate α values achieved optimal performance across various risk strata, improved model performance of the ridge regression approach and uncovered significant cross-modal interactions between psychosocial constructs and patient characteristics. These interactions highlight how psychosocial risk factors are influenced by individual patient contexts, potentially informing improved risk prediction methods and personalized interventions. Our findings underscore the importance of incorporating nuanced narrative data into predictive models and set the stage for future research that will expand the use of advanced machine learning techniques, including deep learning, to further refine suicide risk prediction methods.

PMID:40299589 | DOI:10.1142/9789819807024_0013

Biomedicines. 2025 Apr 17;13(4):989. doi: 10.3390/biomedicines13040989.

ABSTRACT

Increased exposure to particulate matter (PM) from air pollution causes lung inflammation and increases morbidity and mortality due to respiratory diseases. Pirfenidone is an anti-fibrotic agent used to treat idiopathic pulmonary fibrosis. Background/Objectives: In this experiment, we studied the therapeutic effects of pirfenidone on PM-induced pulmonary fibrosis. Methods: Pulmonary fibrosis was induced by the intratracheal application of 100 μg/kg PM10 mixed with 200 μL saline. After 42 days of PM10 infusion, 0.2 mL of distilled water with pirfenidone was orally administered to the pirfenidone-treated groups (200 and 400 mg/kg) every other day for a total of 15 times over 30 days. Results: The intratracheal administration of PM resulted in lung injury and a significant decrease in the number of bronchoalveolar lavage fluid cells. PM administration increased the lung injury score, level of lung fibrosis, and production of pro-inflammatory cytokines. Pirfenidone treatment effectively suppressed transforming growth factor-β-activated kinase 1 in PM-induced pulmonary fibrosis. The present changes inhibited the expressions of mitogen-activated protein kinase kinase 3 and p38, which suppressed transforming growth factor-β, ultimately alleviating lung fibrosis. PM exposure upregulated the expressions of fibronectin and type 1 collagen. PM exposure enhanced connective tissue growth factor and hydroxyproline levels in the lung tissue. The levels of these fibrosis-related factors were inhibited by pirfenidone treatment. Conclusions: These results suggest that pirfenidone is therapeutically effective against PM-induced pulmonary fibrosis.

PMID:40299673 | DOI:10.3390/biomedicines13040989

Biomedicines. 2025 Apr 14;13(4):959. doi: 10.3390/biomedicines13040959.

ABSTRACT

Background/Objectives: Idiopathic pulmonary fibrosis (IPF) is a chronic lung disease characterized by excessive extracellular matrix (ECM) production and tissue stiffening, resulting in impaired lung function. Sodium hydrogen exchanger isoform 1 (NHE1) is a key mediator of intracellular and extracellular pH regulation, influencing fibroblast activation, motility, and proliferative pathways. This study investigates the role of NHE1 in actin stress fiber formation, fibroblast-to-myofibroblast differentiation, and cytokine secretion in IPF progression. Methods: Fibroblasts were treated with profibrotic agonists, including transforming growth factor-beta (TGFβ), lysophosphatidic acid (LPA), and serotonin (THT), in the presence or absence of the NHE1-specific inhibitor, EIPA. Actin stress fibers were visualized using phalloidin staining, while α-smooth muscle actin (α-SMA) expression and cytokine secretion (TGFβ, IL-6, and IL-8) were quantified using immunostaining and ELISA. Intracellular pH changes were measured using BCECF-AM fluorescence. Results: Profibrotic agonists induced significant actin stress fiber formation and α-SMA expression in fibroblasts, both of which were abolished by EIPA. NHE1 activity was shown to mediate intracellular alkalization, a critical factor for fibroblast activation. Cytokine secretion, including TGFβ, IL-6, and IL-8, was enhanced by agonist treatments but reduced with NHE1 inhibition. Chronic TGFβ exposure increased intracellular pH and sustained myofibroblast differentiation, which was partially reversed by EIPA. Conclusions: NHE1 is indicated to play a novel and potential role in processes supporting profibrotic agonists driving fibroblast activation and IPF progression. Targeting NHE1 could present a potential therapeutic approach to disrupt profibrotic pathways and mitigate IPF severity.

PMID:40299552 | DOI:10.3390/biomedicines13040959

Pac Symp Biocomput. 2025;30:473-487. doi: 10.1142/9789819807024_0034.

ABSTRACT

CDK4/6 inhibitors such as palbociclib block cell cycle progression and improve outcomes for many ER+/HER2- breast cancer patients. Unfortunately, many patients are initially resistant to the drug or develop resistance over time in part due to heterogeneity among individual tumor cells. To better understand these mechanisms of resistance, we used multiplex, single-cell imaging to profile cell cycle proteins in ER+ breast tumor cells under increasing palbociclib concentrations. We then applied spherical principal component analysis (SPCA), a dimensionality reduction method that leverages the inherently cyclical nature of the high-dimensional imaging data, to look for changes in cell cycle behavior in resistant cells. SPCA characterizes data as a hypersphere and provides a framework for visualizing and quantifying differences in cell cycles across treatment-induced perturbations. The hypersphere representations revealed shifts in the mean cell state and population heterogeneity. SPCA validated expected trends of CDK4/6 inhibitor response such as decreased expression of proliferation markers (Ki67, pRB), but also revealed potential mechanisms of resistance including increased expression of cyclin D1 and CDK2. Understanding the molecular mechanisms that allow treated tumor cells to evade arrest is critical for identifying targets of future therapies. Ultimately, we seek to further SPCA as a tool of precision medicine, targeting treatments by individual tumors, and extending this computational framework to interpret other cyclical biological processes represented by high-dimensional data.

PMID:40299610 | DOI:10.1142/9789819807024_0034

Pac Symp Biocomput. 2025;30:412-425. doi: 10.1142/9789819807024_0030.

ABSTRACT

Down syndrome (DS), caused by the triplication of chromosome 21 (T21), is a prevalent genetic disorder with a higher incidence of obesity. Traditional approaches have struggled to differentiate T21-specific molecular dysregulation from general obesity-related processes. This study introduces the omni-PLIER framework, combining the Pathway-Level Information ExtractoR (PLIER) with the omnigenic model, to uncover molecular mechanisms underlying obesity in DS. The PLIER framework aligns gene expression data with biological pathways, facilitating the identification of relevant molecular patterns. Using RNA sequencing data from the Human Trisome Project, omni-PLIER identified latent variables (LVs) significantly associated with both T21 and body mass index (BMI). Elastic net regression and causal mediation analysis revealed LVs mediating the effect of karyotype on BMI. Notably, LVs involving glutathione peroxidase-1 (GPX1) and MCL1 apoptosis regulator, BCL2 family members emerged as crucial mediators. These findings provide insights into the molecular interplay between DS and obesity. The omni-PLIER model offers a robust methodological advancement for dissecting complex genetic disorders, with implications for understanding obesity-related processes in both DS and the general population.

PMID:40299606 | DOI:10.1142/9789819807024_0030

Pac Symp Biocomput. 2025;30:360-376. doi: 10.1142/9789819807024_0026.

ABSTRACT

Patients experiencing adverse drug events (ADE) from polypharmaceutical regimens present a huge challenge to modern healthcare. While computational efforts may reduce the incidence of these ADEs, current strategies are typically non-generalizable for standard healthcare systems. To address this, we carried out a retrospective study aimed at developing a statistical approach to detect and quantify potential ADEs. The data foundation comprised of almost 2 million patients from two health regions in Denmark and their drug and laboratory data during the years 2011 to 2016. We developed a series of multistate Cox models to compute hazard ratios for changes in laboratory test results before and after drug exposure. By linking the results to data from a drug-drug interaction database, we found that the models showed potential for applications for medical safety agencies and improved efficiency for drug approval pipelines.

PMID:40299602 | DOI:10.1142/9789819807024_0026

Mol Cancer Ther. 2025 Apr 29. doi: 10.1158/1535-7163.MCT-24-0955. Online ahead of print.

ABSTRACT

The Warburg effect is a shift from oxidative phosphorylation to anaerobic glycolysis, accompanied by an enormous increase in glucose uptake into cancer cells. We have utilized this effect to design a new group of targeted 1,4-naphthoquinone-glucose derivatives conjugated with a novel thiomethylene linker, which are cytotoxic to prostate cancer cells. Compound PeS-9 revealed the highest efficacy and selectivity, which was conditioned by a GLUT-1-mediated uptake. PeS-9 induced androgen receptor degradation followed by downregulation of its signaling. In addition, it increased reactive oxygen species production and induced DNA double-strand breaks. Combinational therapy with PARP-inhibitor olaparib resulted in synergistic effects in homologous recombination deficient cells. The underlying mode of PeS-9's cytotoxic action involved mitochondrial targeting, leading to a loss of mitochondrial membrane potential, release of cytochrome C and AIF, activation of caspases-3 and -9, PARP cleavage, and apoptotic cell death. This process was stipulated by down-regulation of several antiapoptotic factors and induction of endoplasmic reticulum stress. Moreover, drug-induced activation of signaling pathway mediated by p38, JNK1/2, and ERK1/2 kinases was identified as an important factor of the cytotoxic activity. The anticancer activity of PeS-9 could be confirmed ex vivo using patients-derived tumoroids as well as in vivo in xenografts demonstrating suppression of tumor growth and decreased dissemination of prostate cancer cells to the lungs. No serious side effects were observed in animal models. This unique combination of anticancer properties makes PeS-9 an attractive candidate for targeted monotherapy against GLUT-1-overexpressing tumors and as a potential combination partner, especially with PARP inhibitors.

PMID:40299783 | DOI:10.1158/1535-7163.MCT-24-0955

Pac Symp Biocomput. 2025;30:360-376. doi: 10.1142/9789819807024_0026.

ABSTRACT

Patients experiencing adverse drug events (ADE) from polypharmaceutical regimens present a huge challenge to modern healthcare. While computational efforts may reduce the incidence of these ADEs, current strategies are typically non-generalizable for standard healthcare systems. To address this, we carried out a retrospective study aimed at developing a statistical approach to detect and quantify potential ADEs. The data foundation comprised of almost 2 million patients from two health regions in Denmark and their drug and laboratory data during the years 2011 to 2016. We developed a series of multistate Cox models to compute hazard ratios for changes in laboratory test results before and after drug exposure. By linking the results to data from a drug-drug interaction database, we found that the models showed potential for applications for medical safety agencies and improved efficiency for drug approval pipelines.

PMID:40299602 | DOI:10.1142/9789819807024_0026

Biomedicines. 2025 Mar 31;13(4):839. doi: 10.3390/biomedicines13040839.

ABSTRACT

Background: Although mTOR has long been regarded as a promising target for cancer treatment, the efficacy of mTOR inhibitors in most clinical trials has been rather limited. Nevertheless, their favorable safety profile has opened up opportunities for drug repurposing, even as their potential applications across various diseases remain largely unexplored. Methods: We performed an MR-PheWAS analysis across 1431 phenotypes to explore drug repurposing opportunities. We analyzed GWAS data of 452 plasma metabolites, 731 immune traits, and 412 gut microbiota to uncover potential mechanisms for the causal link between the mTOR gene and body mass index (BMI). Results: A causal link between mTOR gene expression and BMI has been established. Additionally, mTOR-related vulnerabilities associated with BMI, including alterations in metabolites, immune traits, and gut microbiota, were identified. Conclusions: The identified causal relationship between mTOR and BMI suggests novel potential non-cancer applications for mTOR inhibitors.

PMID:40299431 | DOI:10.3390/biomedicines13040839

J Proteome Res. 2025 Apr 29. doi: 10.1021/acs.jproteome.4c00885. Online ahead of print.

ABSTRACT

Cisplatin-based chemotherapy is a cornerstone in treating cervical cancer, yet the efficacy is frequently limited by the rapid onset of drug resistance, a major challenge in clinical management. To investigate this, we employed HPV16+ human cervix squamous carcinoma cells, SiHa (CIS/S), and their cisplatin-resistant subline (CIS/R) as a model. Using DIA-based proteomics, we identified 5152 protein groups and over 50,000 peptides with a global FDR <1%. Comparative analysis revealed 123 differentially expressed proteins. Gene Set Enrichment Analysis (GSEA) highlighted proteins involved in DNA damage, metabolism, and repair pathways (RFC4, RFC3, RFC2, DUT, DDX54, CDCA8, CDK7, CHAF1B, and GTF2F1), suggesting a role in developing acquired cisplatin resistance. Pathways related to mitotic spindle assembly and P53 signaling were found to be perturbed in resistant cells. Next, we screened a library of approx. 240 FDA-approved drugs against three protein targets and found four small-molecular ligands as potential hits for further in vitro validation. Cabozantinib and sorafenib gave us positive results in terms of increasing the cisplatin sensitivity of CIS/R cells. In conclusion, our findings provide insights into the molecular mechanisms underpinning cisplatin resistance in cervical cancer and propose novel strategies for combating this resistance through targeted therapies and drug repurposing.

PMID:40298920 | DOI:10.1021/acs.jproteome.4c00885