Computers in biology and medicine最新文献

{"title":"Advancing the frontier of artificial intelligence on emerging technologies to redefine cancer diagnosis and care","authors":"Akanksha Vyas ,&nbsp;Krishan Kumar ,&nbsp;Ayushi Sharma ,&nbsp;Damini Verma ,&nbsp;Dhiraj Bhatia ,&nbsp;Nitin Wahi ,&nbsp;Amit K. Yadav","doi":"10.1016/j.compbiomed.2025.110178","DOIUrl":"10.1016/j.compbiomed.2025.110178","url":null,"abstract":"<div><h3>Background</h3><div>Artificial Intelligence (AI) is capable of revolutionizing cancer therapy and advancing precision oncology <em>via</em> integrating genomics data and digitized health information. AI applications show promise in cancer prediction, prognosis, and treatment planning, particularly in radiomics, deep learning, and machine learning for early cancer diagnosis. However, widespread adoption requires comprehensive data and clinical validation. While AI has demonstrated advantages in treating common malignancies like lung and breast cancers, challenges remain in managing rare tumors due to limited datasets. AI's role in processing multi-omics data and supporting precision oncology decision-making is critical as genetic and health data become increasingly digitized.</div></div><div><h3>Method</h3><div>This review article presents current knowledge on AI and associated technologies, which are being utilized in the diagnosis and therapy of cancer. The applications of AI in radiomics, deep learning, and machine learning for cancer screening and treatment planning are examined. The study also explores the capabilities and limitations of predictive AI in diagnosis and prognosis, as well as generative AI, such as advanced chatbots, in patient and provider interactions.</div></div><div><h3>Results</h3><div>AI can improve the early diagnosis and treatment of high-incidence cancers like breast and lung cancer. However, its application in rare cancers is limited by insufficient data for training and validation. AI can effectively process large-scale multi-omics data from DNA and RNA sequencing, enhancing precision oncology. Predictive AI aids in risk assessment and prognosis, while generative AI tools improve patient-provider communication. Despite these advancements, further research and technological progress are needed to overcome existing challenges.</div></div><div><h3>Conclusions</h3><div>AI holds transformative potential for cancer therapy, particularly in precision oncology, early detection, and personalized treatment planning. However, challenges such as data limitations in rare cancers, the need for clinical validation, and regulatory considerations must be addressed. Future advancements in AI could significantly improve decision-support systems in oncology, ultimately enhancing patient care and quality of life. The review highlights both the opportunities and obstacles in integrating AI into cancer diagnostics and therapeutics, calling for continued research and regulatory oversight.</div></div>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"191 ","pages":"Article 110178"},"PeriodicalIF":7.0,"publicationDate":"2025-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143823512","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An integrative study on the effects of Lingguizhugan decoction in treating Alzheimer's disease rats through modulation of multiple pathways involving various components","authors":"Zhe Wang ,&nbsp;Hao-tian Zhang ,&nbsp;Si-yue Li ,&nbsp;Xiu-ping Song ,&nbsp;Chong-zhen Shi ,&nbsp;Ye-wen Zhang ,&nbsp;Fei Han","doi":"10.1016/j.compbiomed.2025.110149","DOIUrl":"10.1016/j.compbiomed.2025.110149","url":null,"abstract":"<div><h3>Objective</h3><div>To explore the active components and mechanisms of Lingguizhugan decoction (LGZGD) in the treatment of Alzheimer's disease (AD) through an integrated approach.</div></div><div><h3>Methods</h3><div>The active components of LGZGD in rat serum were identified using HPLC-FTICR MS. Network pharmacology and molecular docking analyses were conducted, and their findings were validated using an Aβ_1-42-induced AD rat model.</div></div><div><h3>Results</h3><div>Twenty-four active components and 324 common targets were identified and used to construct the networks. KEGG pathway enrichment analysis linked key target genes with MAPK, Rap1, and NF-κB signaling pathways. Molecular docking results indicated that three key targets (IL-6, TNF, and EGFR) and 10 core components are closely associated with LGZGD in the treatment of AD. LGZGD improved the spatial learning and memory abilities of AD rats. LGZGD reduced neuronal damage and increased the number of neurons in the cortex and hippocampal CA1 region of AD rats. LGZGD decreased Aβ_1-42 expression in the rat hippocampus, alleviated oxidative stress in AD rats, and decreased TNF-α, IL-6, IL-1β, and HMGB1 levels in the cerebral cortical tissue. LGZGD markedly decreased Iba-1 and iNOS expression and increased CD206 levels to inhibit M1 activation and promote M2 activation. LGZGD increased the expression of p-GSK-3β, ERK, and p-ERK, while decreasing the expression of p-Tau, IKKβ, p-IκBα, p-p65, p-p38, and p-JNK in the hippocampus of AD rats.</div></div><div><h3>Conclusion</h3><div>LGZGD treats AD by modulating targets like IL-6, TNF, MAPK3, and BCL2, thereby alleviating cognitive impairments in rats. Its neuroprotective effects in treating AD are mediated through the NF-κB/MAPK signaling pathways.</div></div>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"191 ","pages":"Article 110149"},"PeriodicalIF":7.0,"publicationDate":"2025-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143823625","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A novel smart guidewire with an integrated hemodynamic sensor for central catheter placement: Design and simulation","authors":"Huiying Huang ,&nbsp;Peiming Lu ,&nbsp;Minglu Zhong ,&nbsp;Handong Ouyang ,&nbsp;Shengzhao Lin","doi":"10.1016/j.compbiomed.2025.110139","DOIUrl":"10.1016/j.compbiomed.2025.110139","url":null,"abstract":"<div><h3>Objective</h3><div>We analyzed the differences in hemodynamic patterns along the central venous catheterization pathway and constructed a sensor-at-tip guidewire for real-time detection of temperature field changes related to hemodynamic patterns. The design was verified using COSMOL simulation and <em>in vitro</em> simulation tests to evaluate its potential application as a tool to facilitate navigation during catheterization.</div></div><div><h3>Methods</h3><div>Differences in the hemodynamic modes in the central venous catheterization pathway led to changes in the temperature field created with a thermal source. A sensor-at-tip guidewire model was used to detect real-time changes in the temperature field during catheterization. By multiphysical coupling of temperature, heating power, thermistor, and fluid velocity fields, a simulation study based on the intrinsic characteristics of thermistor material winding springs was conducted, wherein the coupling relationship between the blood flow velocity (flow rate) and temperature transfer was obtained and the design was verified by simulation.</div></div><div><h3>Results</h3><div>Based on a multiphysics finite element simulation, the application of a thermal flow sensor composed of a thermistor and power resistor in central vein catheterization was verified. Theoretical calculations suggested that the thermal flow sensor can be composed of a conventional wire-wound spring or a commercially available, inexpensive, small-sized (01005 package) negative thermal coefficient resistor. This study provides a low-cost, portable, and real-time navigation solution for hemodynamic monitoring that is expected to have clinical applications.</div></div><div><h3>Conclusion</h3><div>The sensitivity and resolution of this design met the requirements of difference analysis for heating power vs. temperature fields as well as hemodynamic changes vs. temperature fields, indicating potential applications in navigation for central venous catheterization.</div></div>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"191 ","pages":"Article 110139"},"PeriodicalIF":7.0,"publicationDate":"2025-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143823626","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Uncovering hepatic transcriptomic and circulating proteomic signatures in MASH: A meta-analysis and machine learning-based biomarker discovery","authors":"Elena Cristina Rusu ,&nbsp;Helena Clavero-Mestres ,&nbsp;Mario Sánchez-Álvarez ,&nbsp;Marina Veciana-Molins ,&nbsp;Laia Bertran ,&nbsp;Pablo Monfort-Lanzas ,&nbsp;Carmen Aguilar ,&nbsp;Javier Camaron ,&nbsp;Teresa Auguet","doi":"10.1016/j.compbiomed.2025.110170","DOIUrl":"10.1016/j.compbiomed.2025.110170","url":null,"abstract":"<div><h3>Background</h3><div>Metabolic-associated steatohepatitis (MASH), the progressive form of metabolic-associated steatotic liver disease (MASLD), poses significant risks for liver fibrosis and cardiovascular complications. Despite extensive research, reliable biomarkers for MASH diagnosis and progression remain elusive. This study aimed to identify hepatic transcriptomic and circulating proteomic signatures specific to MASH, and to develop a machine learning-based biomarker discovery model.</div></div><div><h3>Methods</h3><div>A systematic review of RNA-Seq and proteomic datasets was conducted, retrieving 7 hepatic transcriptomics and 3 circulating proteomics studies, encompassing 483 liver samples and 169 serum/plasma samples, respectively. Differential gene and protein expression analyses were performed, and pathways were enriched using gene set enrichment analysis. A machine learning (ML) model was developed to identify MASH-specific biomarkers, utilizing biologically significant protein ratios.</div></div><div><h3>Key findings</h3><div>Hepatic transcriptomic analysis identified 5017 differentially expressed genes (DEGs), with significant enrichment of extracellular matrix (ECM) pathways. Serum proteomics revealed six differentially expressed proteins (DEPs), including complement-related proteins. Integration of transcriptomic and proteomic data highlighted the complement cascade as a key pathway in MASH, with discordant regulation between the liver and circulation. The ML-based biomarker discovery model, utilizing protein ratios, achieved an F1 scores of 0.83 and 0.64 in the training sets and 0.67 in an external validation set.</div></div><div><h3>Conclusion</h3><div>Our findings indicate ECM deregulation and complement system involvement in MASH progression. The novel ML model incorporating protein ratios offers a potential tool for MASH diagnosis. However, further refinement and validation across larger and more diverse cohorts is needed to generalize these results.</div></div>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"191 ","pages":"Article 110170"},"PeriodicalIF":7.0,"publicationDate":"2025-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143820284","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evolutionary insights into elongation factor G using AlphaFold and ancestral analysis","authors":"Shawonur Rahaman ,&nbsp;Jacob H. Steele ,&nbsp;Yi Zeng ,&nbsp;Shoujun Xu ,&nbsp;Yuhong Wang","doi":"10.1016/j.compbiomed.2025.110188","DOIUrl":"10.1016/j.compbiomed.2025.110188","url":null,"abstract":"<div><div>Elongation factor G (EF-G) is crucial for ribosomal translocation, a fundamental step in protein synthesis. Despite its indispensable role, the conformational dynamics and evolution of EF-G remain elusive. By integrating AlphaFold structural predictions with multiple sequence alignment (MSA)-based sequence analysis, we explored the conformational landscape, sequence-specific patterns, and evolutionary divergence of EF-G. We identified five high-confidence structural states of wild type (WT) EF-G, revealing broader conformational diversity than previously captured by experimental data. Phylogenetic analysis and MSA-embedded sequence patterns demonstrated that single-point mutations in the switch I loop modulate equilibrium between the two dominant conformational states, con1 and con2, which exhibit distinct functional specializations. Reconstructions of two ancestral EF-Gs revealed minimal GTPase activity and reduced translocase function in both forms, suggesting that robust translocase activity emerged after the divergence of con1 and con2. However, ancestral EF-Gs retained the fidelity of three-nucleotide translocation, underscoring the early evolutionary conservation of accurate mRNA movement. These findings establish a framework for understanding how conformational flexibility shapes EF-G function and specialization. Moreover, our computational pipeline can be extended to other translational GTPases, providing broader insights into the evolution of the translational machinery. This study highlights the power of AlphaFold-assisted structural analysis in revealing the mechanistic and evolutionary relationships involved in protein translation.</div></div>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"191 ","pages":"Article 110188"},"PeriodicalIF":7.0,"publicationDate":"2025-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143820285","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimized multiple instance learning for brain tumor classification using weakly supervised contrastive learning","authors":"Kaoyan Lu ,&nbsp;Shiyu Lin ,&nbsp;Kaiwen Xue ,&nbsp;Duoxi Huang ,&nbsp;Yanghong Ji","doi":"10.1016/j.compbiomed.2025.110075","DOIUrl":"10.1016/j.compbiomed.2025.110075","url":null,"abstract":"<div><div>Brain tumors have a great impact on patients’ quality of life and accurate histopathological classification of brain tumors is crucial for patients’ prognosis. Multi-instance learning (MIL) has become the mainstream method for analyzing whole-slide images (WSIs). However, current MIL-based methods face several issues, including significant redundancy in the input and feature space, insufficient modeling of spatial relations between patches and inadequate representation capability of the feature extractor. To solve these limitations, we propose a new multi-instance learning with weakly supervised contrastive learning for brain tumor classification. Our framework consists of two parts: a cross-detection MIL aggregator (CDMIL) for brain tumor classification and a contrastive learning model based on pseudo-labels (PSCL) for optimizing feature encoder. The CDMIL consists of three modules: an internal patch anchoring module (IPAM), a local structural learning module (LSLM) and a cross-detection module (CDM). Specifically, IPAM utilizes probability distribution to generate representations of anchor samples, while LSLM extracts representations of local structural information between anchor samples. These two representations are effectively fused in CDM. Additionally, we propose a bag-level contrastive loss to interact with different subtypes in the feature space. PSCL uses the samples and pseudo-labels anchored by IPAM to optimize the performance of the feature encoder, which extracts a better feature representation to train CDMIL. We performed benchmark tests on a self-collected dataset and a publicly available dataset. The experiments show that our method has better performance than several existing state-of-the-art methods.</div></div>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"191 ","pages":"Article 110075"},"PeriodicalIF":7.0,"publicationDate":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143815318","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A machine learning approach to differentiate stage IV from stage I colorectal cancer","authors":"Naim Abu-Freha ,&nbsp;Zaid Afawi ,&nbsp;Miar Yousef ,&nbsp;Walid Alamor ,&nbsp;Noor Sanalla ,&nbsp;Simon Esbit ,&nbsp;Malik Yousef","doi":"10.1016/j.compbiomed.2025.110179","DOIUrl":"10.1016/j.compbiomed.2025.110179","url":null,"abstract":"<div><h3>Background and aim</h3><div>The stage at which Colorectal cancer (CRC) diagnosed is a crucial prognostic factor. Our study proposed a novel approach to aid in the diagnosis of stage IV CRC by utilizing supervised machine learning, analyzing clinical history, and laboratory values, comparing them with those of stage I CRC.</div></div><div><h3>Methods</h3><div>We conducted a respective study using patients diagnosed with stage I (n = 433) and stage IV CRC (n = 457). We employed supervised machine learning using random forest. The decision tree is used to visualize the model to identify key clinical and laboratory factors that differentiate between stage IV and stage I CRC.</div></div><div><h3>Results</h3><div>The decision tree classifier revealed that symptoms combined with laboratory values were critical predictors of stage IV CRC. Change in bowel habits was predictive for stage IV CRC among 14 of 22 patients (63 %). Weight loss, constipation, and abdominal pain in combination with different levels of carcinoembryonic antigen (CEA) were predictors for stage IV CRC. A CEA level higher than 260 was indicative for stage IV CRC in all observed patients (61 out of 61 patients). Additionally, a lower CEA level, in combination with hemoglobin, white blood cell count, and platelet count, also predicted stage IV CRC.</div></div><div><h3>Conclusions</h3><div>By applying a machine learning based approach, we identified symptoms and laboratory values (CEA, hemoglobin, white blood cell count, and platelet count), as crucial predictors for stage IV CRC diagnosis. This method holds potential for facilitating the diagnosis of stage IV CRC in clinical practice, even before imaging tests are conducted.</div></div>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"191 ","pages":"Article 110179"},"PeriodicalIF":7.0,"publicationDate":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143820277","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A computed tomography-based deep learning radiomics model for predicting the gender-age-physiology stage of patients with connective tissue disease-associated interstitial lung disease","authors":"Bingqing Long ,&nbsp;Rui Li ,&nbsp;Ronghua Wang ,&nbsp;Anyu Yin ,&nbsp;Ziyi Zhuang ,&nbsp;Yang Jing ,&nbsp;Linning E","doi":"10.1016/j.compbiomed.2025.110128","DOIUrl":"10.1016/j.compbiomed.2025.110128","url":null,"abstract":"<div><h3>Objectives</h3><div>To explore the feasibility of using a diagnostic model constructed with deep learning-radiomics (DLR) features extracted from chest computed tomography (CT) images to predict the gender-age-physiology (GAP) stage of patients with connective tissue disease-associated interstitial lung disease (CTD-ILD).</div></div><div><h3>Materials and methods</h3><div>The data of 264 CTD-ILD patients were retrospectively collected. GAP Stage I, II, III patients are 195, 56, 13 cases respectively. The latter two stages were combined into one group. The patients were randomized into a training set and a validation set. Single-input models were separately constructed using the selected radiomics and DL features, while DLR model was constructed from both sets of features. For all models, the support vector machine (SVM) and logistic regression (LR) algorithms were used for construction. The nomogram models were generated by integrating age, gender, and DLR features.</div></div><div><h3>Results</h3><div>The DLR model outperformed the radiomics and DL models in both the training set and the validation set. The predictive performance of the DLR model based on the LR algorithm was the best among all the feature-based models (AUC = 0.923). The comprehensive models had even greater performance in predicting the GAP stage of CTD-ILD patients. The comprehensive model using the SVM algorithm had the best performance of the two models (AUC = 0.951).</div></div><div><h3>Conclusion</h3><div>The DLR model extracted from CT images can assist in the clinical prediction of the GAP stage of CTD-ILD patients. A nomogram showed even greater performance in predicting the GAP stage of CTD-ILD patients.</div></div>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"191 ","pages":""},"PeriodicalIF":7.0,"publicationDate":"2025-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143806934","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Analysis of protein determinants of genotype-specific properties of group a rotaviruses using machine learning","authors":"Myeongji Cho ,&nbsp;Nara Been ,&nbsp;Hyeon S. Son","doi":"10.1016/j.compbiomed.2025.110143","DOIUrl":"10.1016/j.compbiomed.2025.110143","url":null,"abstract":"<div><div>Group A rotaviruses (RVAs) are the leading cause of viral diarrhoea across various host species, including mammals and birds. The VP7 and VP4 proteins of these viruses play critical roles in determining genotype specificity, influencing viral infectivity and host adaptation. This study employed machine-learning techniques to classify RVA genotypes based on the molecular and physicochemical properties of these proteins. A dataset of 94 VP7 and 68 VP4 protein sequences was collected from various host species. Seven machine-learning algorithms—Naïve Bayes (NB), logistic regression (LR), decision tree (DT), random forest (RF), k-nearest neighbour (kNN), support vector machine (SVM), and artificial neural network (ANN)—were used for genotype classification. Feature subsets were configured using ranking-based attribute selection, and classification performance was evaluated using accuracy (ACC), precision, recall, Matthews’ correlation coefficient (MCC), and the area under the curve (AUC). kNN demonstrated the highest classification accuracy for both VP7 (ACC = 97.87 %) and VP4 (ACC = 100 %), outperforming NB, LR, DT, RF, SVM, and ANN. For VP7 sequences, key properties influencing genotype classification included hydrophobicity, normalised van der Waals volume, and leucine composition. For VP4, polarity, normalised van der Waals volume, and polarizability were the most significant factors. In summary, the genotype-specific molecular features of VP7 and VP4 proteins served as reliable markers for RVA classification. Our findings highlight the potential of machine-learning approaches to predict RVA genotypes based on the physicochemical properties of amino acids, providing valuable insights into the molecular mechanisms that drive viral evolution, host specificity, and immune evasion.</div></div>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"191 ","pages":""},"PeriodicalIF":7.0,"publicationDate":"2025-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143806994","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Precision medicine approach for in vitro modeling and computational screening of anti-epileptic drugs in pediatric epilepsy patients with SCN2A (R1629L) mutation","authors":"Jihun Kim ,&nbsp;Bilal Shaker ,&nbsp;Ara Ko ,&nbsp;Sunggon Yoo ,&nbsp;Dokyun Na ,&nbsp;Hoon-Chul Kang","doi":"10.1016/j.compbiomed.2025.110100","DOIUrl":"10.1016/j.compbiomed.2025.110100","url":null,"abstract":"<div><div>This study aimed to develop personalized anti-epileptic drugs for pediatric patients with an <em>SCN2A</em> (R1629L) mutation, which is unresponsive to conventional sodium channel blockers. The mutation was identified using genomic DNA sequencing, and patient-derived induced pluripotent stem cells (iPSCs) were differentiated into the neuronal network to mimic seizure activity. A total of 1.6 million compounds were screened using computational methods, identifying five candidates with high affinity to the mutant <em>SCN2A</em> protein, low potential toxicity, and high blood–brain barrier permeability. These compounds were pharmacologically evaluated using the patient-derived <em>in vitro</em> seizure model, which replicated the abnormal electrophysiological characteristics of epilepsy. Two of the five candidate compounds effectively modulated electrophysiological activities; moreover, these compounds were 100 times more potent than phenytoin. Therefore, this study demonstrates the feasibility of precision medicine in epilepsy treatment, emphasizing the benefits of patient-derived <em>in vitro</em> seizure models and computational drug screening. Additionally, this study highlights the potential of targeted therapeutic development for patients unresponsive to conventional therapies, showcasing a promising approach for personalized medical interventions in epilepsy.</div></div>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"191 ","pages":""},"PeriodicalIF":7.0,"publicationDate":"2025-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143807109","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}