Informatics in Medicine Unlocked最新文献

{"title":"Early detection of gynecological malignancies using ensemble deep learning models: ResNet50 and inception V3","authors":"Chetna Vaid Kwatra ,&nbsp;Harpreet Kaur ,&nbsp;Monika Mangla ,&nbsp;Arun Singh ,&nbsp;Swapnali N. Tambe ,&nbsp;Saiprasad Potharaju","doi":"10.1016/j.imu.2025.101620","DOIUrl":"10.1016/j.imu.2025.101620","url":null,"abstract":"<div><h3>Background and objective</h3><div>Improving patient outcomes and lowering death rates depend on the early identification of gynecological cancers. This work intends to improve the accuracy and dependability of early gynecological tumor diagnosis by means of a hybrid deep learning model combining ResNet50 and Inception v3 architectures.</div></div><div><h3>Methods</h3><div>The proposed ensemble model combines multi-scale feature extraction of Inception v3 with the deep residual learning capability of ResNet50. A significant number of gynecological images were employed for training, testing, and assessment of the proposed model. By entailing accuracy, sensitivity, specificity, and F1 score, among other parameters the performance of the model was assessed.</div></div><div><h3>Results</h3><div>The first experiment depicted displays that the ensemble model performed better than single models with a training accuracy of 99.80 %, a validation accuracy of 99.80 %, and a test accuracy of 99.80 %. Comparing the two studies done in the current research, the model has shown to have a high sensitivity of 99 %, specificity of 99 %, and F1 score of 0.99, making it better in the identification of gynecological cancers and significantly reducing low true negatives and low true positives.</div></div><div><h3>Conclusions</h3><div>Ensembling of ResNet50 with Inception v3 for early diagnosis of gynecological cancers is promising and reproducible. Thus, according to the presented results, this method can contribute to the diagnoses of diseases by doctors quickly and accurately and, therefore, improve the treatment outcomes and the patient's health</div></div>","PeriodicalId":13953,"journal":{"name":"Informatics in Medicine Unlocked","volume":"53 ","pages":"Article 101620"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143103460","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Analysis of breast cancer classification and segmentation techniques: A comprehensive review","authors":"Malaya Kumar Nath,&nbsp;Kohilavani Sundararajan,&nbsp;Shanmathi Mathivanan,&nbsp;Bhagyashree Thandapani","doi":"10.1016/j.imu.2025.101642","DOIUrl":"10.1016/j.imu.2025.101642","url":null,"abstract":"<div><div>Breast cancer (BC) is caused by the mutation of breast cells and their uncontrolled proliferation, making diagnosis critical at the chronic stage. Early cancer detection can help plan treatment and reduce its severity and mortality rate. It can be confirmed by the biopsy test. Due to technological advancements, it can be effectively detected by various modalities, such as X-rays, ultrasound, MRI scans, histopathology images, etc. Development in machine learning (ML), data mining, sensors, and signal processing techniques gained popularity in early breast cancer detection and grading. However, these techniques must be improved for better prediction, localization, and grading of cancer tissues. This manuscript discusses the tissue variation due to the propagation of cancer and its havoc in life, along with various AI-based techniques for early identification with their limitations. Publicly available breast cancer databases and performance evaluation metrics used by the researchers have been summarized. Based on the limitations and potential strengths of various techniques, a deep learning (DL) model for multi-class classification of breast cancer for the whole slide image (WSI) is proposed. This study identifies ongoing issues essential for driving future advancements in BC detection and segmentation to improve clinical outcomes.</div></div>","PeriodicalId":13953,"journal":{"name":"Informatics in Medicine Unlocked","volume":"56 ","pages":"Article 101642"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143937872","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Automated lumbar intervertebral disc identification and herniation detection in MR images using cascade CNN architecture","authors":"Md Abu Sayed ,&nbsp;Ashiqur Rahman ,&nbsp;Sadman Mohammad Nasif ,&nbsp;Sudipto Halder ,&nbsp;Akram Hossain ,&nbsp;Hasan Ahmed ,&nbsp;Muhammad Abdul Kadir","doi":"10.1016/j.imu.2025.101648","DOIUrl":"10.1016/j.imu.2025.101648","url":null,"abstract":"<div><h3>Objective</h3><div>Identifying herniated discs in MRI scans is inherently challenging due to the small size, irregular shape, and complex appearance of the affected regions. Conventional approaches typically rely on semi-automated region-of-interest (ROI) selection and single-model classification using either axial or sagittal views, limiting diagnostic performance. This study aims to develop an automated, accurate, and efficient system for the detection and classification of lumbar intervertebral disc herniation using deep learning models applied to axial and sagittal MR images.</div></div><div><h3>Methods</h3><div>A YOLO-based framework was developed to automatically identify lumbar intervertebral discs (IVD_1-5) and extract ROIs from MR images. Attention-enhanced and fine-tuned VGG19 and ResNet50 models were employed to analyze axial and sagittal images for herniation detection. A decision fusion strategy was used to combine the classification probabilities from both models to further enhance accuracy. The dataset underwent extensive preprocessing and augmentation to improve model robustness and generalization.</div></div><div><h3>Results</h3><div>The proposed approach demonstrated exceptional performance in detection and classification tasks. For detection, the model achieved mAP50 scores of 95.18 % (axial IVD_1-5), 99.50 % (lumbar regions), and 94.87 % (sagittal IVD_1-5). Classification accuracy reached 97.05 % for axial images and 97.45 % for sagittal images, increasing to 98.09 % with decision fusion.</div></div><div><h3>Conclusion</h3><div>Designed to assist physicians, especially during high-demand periods such as pandemics, this approach has the potential to improve diagnostic efficiency and reduce clinical workload.</div></div>","PeriodicalId":13953,"journal":{"name":"Informatics in Medicine Unlocked","volume":"55 ","pages":"Article 101648"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143901918","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Machine learning approach to study drug effects and identification of signals from symptomatic and asymptomatic mutation carries using iPSC-derived cardiomyocytes","authors":"Martti Juhola ,&nbsp;Henry Joutsijoki ,&nbsp;Kirsi Penttinen ,&nbsp;Katriina Aalto-Setälä","doi":"10.1016/j.imu.2025.101631","DOIUrl":"10.1016/j.imu.2025.101631","url":null,"abstract":"<div><div>Earlier it has been found that peak data of calcium transient signals originating from human induced pluripotent stem cell-derived cardiomyocytes are possible to be used to study how machine learning methods can be applied to separate which cells respond to a drug. Beating behavior of induced pluripotent stem cell derived cardiomyocytes (iPSC-CMs) from a symptomatic individual and an asymptomatic individual carrying a mutation for Brugada syndrome was analyzed with Ca²⁺ imaging method. Using machine learning methods, it is studied whether it is possible to classify the current peak data successfully and whether differences in the two mutant cell lines could be observed. We applied more machine learning methods than before. Baseline signals were first recorded and they were then exposed to adrenaline and these to an antiarrhythmic drug flecainide which should provoke the disease phenotype. Calcium transient signals derived from induced pluripotent stem cell-derived cardiomyocytes were used for all computational analyses executed. Good classification results were generated with effective machine learning methods. Various test situations were applied to study how different parts of data can be separated to ensure their differences. Good results were gained that support the target so that it is possible to analyze whether the drug impacted on iPSC-CMs. It is also possible to separate which cells were affected by the drug and which were not affected. An important finding was that there were significant differences between calcium transient signals data originated from control subjects and patients and also between responses of the cells from symptomatic and asymptomatic individuals.</div></div>","PeriodicalId":13953,"journal":{"name":"Informatics in Medicine Unlocked","volume":"54 ","pages":"Article 101631"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143579488","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Machine learning-based classification of medication adherence among patients with noncommunicable diseases","authors":"Wellington Kanyongo ,&nbsp;Absalom E. Ezugwu ,&nbsp;Tsitsi Moyo ,&nbsp;Jean Vincent Fonou Dombeu","doi":"10.1016/j.imu.2024.101611","DOIUrl":"10.1016/j.imu.2024.101611","url":null,"abstract":"<div><div>Non-adherence to medication among individuals with non-communicable diseases (NCDs) leads to increased morbidity, mortality, and healthcare costs. The integration of electronic drug prescription and dispensation systems enables comprehensive analysis of medication adherence (MA). Patient-level and medical claims data for 8141 diabetic and hypertensive patients in Harare, Zimbabwe, were analysed. Non-adherence was defined as medication refills falling below 75 % of the intended 12 monthly claims, while adherence required at least 75 % of the refills. Classification employed multiple machine learning algorithms, including SVM, KNN, DT, Naïve Bayes, DNN, LR, and RF in Python 3.11.3. Significant variables for MA were identified through the Random Forest (RF) feature importance mechanism and the information gain technique. These included the annual quantity of medical supplies, annual claim amount, patient age, wellness program subscription, medical aid cover, contribution towards medical aid cover, comorbidity, diagnosis, hospital cover type, complications development, gender, and medical aid scheme. The total units of medical supplies dispensed annually emerged as the most significant predictor of MA. Considering the 8-feature subset, which consistently produced the most robust machine learning models, the classification accuracy of the ML classifiers ranged from 84.9 % to 88.2 %, while the AUC values varied between 0.857 and 0.934. RF, an ensemble learning technique, was the most robust classifier, achieving 88.2 % accuracy, an AUC of 0.935, and superior precision, recall, and F1-score. This model shows promise as a prognostic tool for enhancing MA, aiding in identifying adherence levels among patients. These findings contribute to addressing disparities in medication refilling and adherence rates among patients with NCDs. The ML model holds potential for the development of intelligent MA and intervention applications to improve patient adherence to medication in the chronic disease domain.</div></div>","PeriodicalId":13953,"journal":{"name":"Informatics in Medicine Unlocked","volume":"52 ","pages":"Article 101611"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143178787","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Spatiotemporal chest wall movement analysis using depth sensor imaging for detecting respiratory asynchrony","authors":"Masaru Mitsuya ,&nbsp;Hiroki Nishine ,&nbsp;Hiroshi Handa ,&nbsp;Masamichi Mineshita ,&nbsp;Masaki Kurosawa ,&nbsp;Tetsuo Kirimoto ,&nbsp;Shohei Sato ,&nbsp;Takemi Matsui ,&nbsp;Guanghao Sun","doi":"10.1016/j.imu.2025.101619","DOIUrl":"10.1016/j.imu.2025.101619","url":null,"abstract":"<div><h3>Background and objective</h3><div>This study aimed to enhance point-of-care pulmonary function tests by developing a novel method for the spatiotemporal analysis of chest wall movements using a sequence of depth sensor images.</div></div><div><h3>Methods</h3><div>The proposed method employs singular value decomposition (SVD) to extract features from respiratory waveforms, which are then used to cluster pixels while preserving high resolution. The initial validation using simulated thoracic movement data confirmed the validity of the method. Further validation with clinical data capturing the chest wall movements of a patient undergoing interventional bronchology for a right bronchial tumor demonstrated the ability of this method to detect respiratory asynchrony.</div></div><div><h3>Results</h3><div>A phase lag of 867 ms was observed between the left and right sides of the rib cage preoperatively along with notable amplitude differences. These asynchronies resolved postoperatively. These results were consistent with the pulmonary pathophysiology, underscoring the clinical relevance of this method. The proposed system, integrated into an iOS app for an iPhone, is user-friendly and noninvasive and has the potential to become a valuable tool for the real-time assessment of interventional outcomes.</div></div><div><h3>Conclusions</h3><div>The novel method can be applied to various pulmonary diseases to detect the regional ventilation distribution. The method establishes a new generic framework for clinical studies of chest wall motion and pathophysiology.</div></div>","PeriodicalId":13953,"journal":{"name":"Informatics in Medicine Unlocked","volume":"53 ","pages":"Article 101619"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143103203","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Trends in delivery hospitalizations and the impact of ICD-9-CM to ICD-10-CM-PCS transition in Portugal between 2010 and 2018","authors":"Catarina de Paraíso Camarinha ,&nbsp;Maria Miguel Gomes Oliveira ,&nbsp;Cecília Elias ,&nbsp;Miguel de Araújo Nobre ,&nbsp;Leonor Bacelar Costa Nicolau ,&nbsp;Cristina Furtado ,&nbsp;Andreia Silva da Costa ,&nbsp;Paulo Jorge da Silva Nogueira","doi":"10.1016/j.imu.2025.101626","DOIUrl":"10.1016/j.imu.2025.101626","url":null,"abstract":"<div><h3>Background</h3><div>Hospital discharge data are essential for maternal health surveillance, clinical research, and healthcare resource allocation. In 2017, Portuguese hospitals transitioned from the International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) to the International Classification of Diseases, 10th edition, Clinical Modification and Procedure Coding System (ICD-10-CM/PCS), impacting the recording of delivery hospitalizations. This study examines trends in delivery hospitalizations from 2010 to 2018 and assesses the impact of the ICD-10-CM/PCS transition.</div></div><div><h3>Methods</h3><div>We conducted a register-based observational cross-sectional analysis using data from the National Hospital Discharge Database, covering delivery hospitalizations in public hospitals from January 1, 2010, to December 31, 2018. Delivery episodes were identified using diagnosis codes, normal delivery codes, diagnosis-related group (DRG) codes, and procedure codes. Statistical analyses included descriptive statistics, interrupted time series with segmented regression, and Prophet forecasting models to evaluate trends and the impact of the coding transition.</div></div><div><h3>Results</h3><div>A total of 673,978 delivery hospitalizations were recorded. The transition from ICD-9-CM to ICD-10-CM/PCS in 2017 had minimal overall impact on delivery trends. DRG codes consistently identified the majority of delivery episodes, with outcome of delivery codes and selected procedure codes showing varying trends. An increase in episodes identified by normal delivery codes and a significant decrease in episodes identified by procedure codes was observed immediately after the ICD-10 transition (p &lt; 0.001). The Prophet model indicated improved forecast accuracy for procedure codes when including the ICD-10 transition variable.</div></div><div><h3>Conclusion</h3><div>The transition to ICD-10-CM/PCS had a limited impact on overall delivery hospitalization trends but significantly affected procedure coding. These findings underscore the importance of considering coding system changes in healthcare data analyses. Further research should incorporate private hospital data and continuously monitor coding practices to ensure reliable health data for research and policy-making.</div></div>","PeriodicalId":13953,"journal":{"name":"Informatics in Medicine Unlocked","volume":"53 ","pages":"Article 101626"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143402838","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"DeB5-XNet: An explainable ensemble model for ocular disease classification using feature extraction and Grad-CAM","authors":"Geethanjali Kher ,&nbsp;Suyash Mehra ,&nbsp;Rajni Bala ,&nbsp;Ram Pal Singh","doi":"10.1016/j.imu.2025.101632","DOIUrl":"10.1016/j.imu.2025.101632","url":null,"abstract":"<div><h3>Background and Objective:</h3><div>Vision serves as a window to the world, enabling individuals to fully appreciate various dimensions of everyday life. Some eye diseases can lead to irreversible loss of vision. Developing an algorithm for a clinical decision support system that explains its predictions is essential to assist the limited number of ophthalmologists in managing the increasing patient load with severe ocular diseases. In contrast to earlier models that concentrated on single-disease classification without providing insights into predictions, this approach introduces DeB5-XNet, a novel explainable ensemble model for multi-categorical classification of ocular conditions.</div></div><div><h3>Methods:</h3><div>This study presents an ensemble model developed to categorize images into glaucoma (G), cataract (C), diabetic retinopathy (DR), and healthy condition labeled as Normal(N). This proposal operates on three levels: First, the images are enhanced using CLAHE in LAB color space, which improves the model’s predictive capability. Second, an ensemble model is constructed by concatenating features derived from pairs of seven pre-trained models, utilizing their diverse architectures to capture complex characteristics essential for accurate diagnosis. These extracted features are then fine-tuned using a consistent classifier. Third, it has been observed that trust in any diagnostic method is dependent on explainability. Therefore, the selected approach was validated, and its effectiveness was demonstrated using Grad-CAM. The performance of this diagnostic model was evaluated using recall, precision, F1-score, and accuracy metrics.</div></div><div><h3>Results:</h3><div>The ensemble models outperformed the individual models. DeB5-XNet, an ensemble model that extracted features from DenseNet121 and EfficientNetB5, achieved the highest test accuracy of 95%, notably reducing false negatives compared to standalone models. Remarkably, the model further demonstrated an F1-score of 97% for cataract, 100% for diabetic retinopathy, 90% for glaucoma, and 91% for normal cases.</div></div><div><h3>Conclusion:</h3><div>The proposed ensemble model, DeB5-XNet shows an improvement over the individual pre-trained models. The Grad-CAM technique demonstrates that the features used by the ensemble model for classification closely align with those identified by ophthalmologists for diagnostic purposes. This alignment strengthens the model’s reliability and potential usefulness in clinical settings.</div></div>","PeriodicalId":13953,"journal":{"name":"Informatics in Medicine Unlocked","volume":"54 ","pages":"Article 101632"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143642420","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An optimized data analytics pipeline for improving healthcare diagnosis using ensemble learning","authors":"Lomat Haider Chowdhury ,&nbsp;Shaira Tabassum ,&nbsp;Swakkhar Shatabda ,&nbsp;Ashir Ahmed","doi":"10.1016/j.imu.2025.101623","DOIUrl":"10.1016/j.imu.2025.101623","url":null,"abstract":"<div><div>Healthcare diagnosis is a process physicians follow before prescribing the patients. The medical doctors may make an early prediction by observing the physical signs and symptoms. Imposing a treatment without proper diagnosis cannot guarantee a cure and sometimes may lead the patient to a more detrimental scenario. However, the cost of healthcare diagnosis makes people indifferent to going through the process. Big data and machine learning are already in use to contribute to the healthcare diagnosis sector with the available data which is enormously growing through the digitalization of the system. Yet the difficulty remains since the raw data contains noise including missing values, outliers, and an imbalanced number of samples. These properties in a dataset make it challenging to implement any diagnosis model. A complete patient profile cannot be generated due to missing values, which may affect the final prediction. Outliers in a medical dataset represent extreme cases and rare conditions, or they may even be generated due to data entry errors. An excessive number of outliers may lead to a skewed and incorrect prediction. An imbalanced dataset makes it challenging to identify the minority classes appropriately and mostly generates a biased model for majority class instances. A combination of advanced preprocessing techniques and reliable model selection are required to address these challenges effectively. This paper proposes a data analytics pipeline on a Portable Health Clinic (PHC) dataset. The paper systematically evaluates different preprocessing methods for missing value imputation, outliers detection, and data balancing and offers a comprehensive preprocessing framework. Later, five state-of-the-art ensemble models for healthcare diagnosis were implemented along with a proposed ensemble machine learning model, KNN-XGBoost-SVM-Random Forest (KNN-X-SVM-R). The proposed model achieved an accuracy of 97.03% which supersedes all the other state-of-the-art models. To reaffirm the rectification of our model, we experimented with it on another COVID-19 routine blood test dataset. In both cases, our proposed model acquired better results regarding different performance measures. Validating the approach on a secondary dataset strengthens the robustness of the proposed methodology. The recommended preprocessing and modeling approach can be adopted to enhance diagnostic systems and improve patient outcomes.</div></div>","PeriodicalId":13953,"journal":{"name":"Informatics in Medicine Unlocked","volume":"53 ","pages":"Article 101623"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143377159","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Targeting KRAS G12C and G12S mutations in lung cancer: In silico drug repurposing and antiproliferative assessment on A549 cells","authors":"Mansour S. Alturki ,&nbsp;Nada Tawfeeq ,&nbsp;Amal Alissa ,&nbsp;Zahra Ahbail ,&nbsp;Mohamed S. Gomaa ,&nbsp;Abdulaziz H. Al Khzem ,&nbsp;Thankhoe A. Rants'o ,&nbsp;Mohammad J. Akbar ,&nbsp;Waleed S. Alharbi ,&nbsp;Bayan Y. Alshehri ,&nbsp;Amjad N. Alotaibi ,&nbsp;Fahad A. Almughem ,&nbsp;Abdullah A. Alshehri","doi":"10.1016/j.imu.2024.101612","DOIUrl":"10.1016/j.imu.2024.101612","url":null,"abstract":"<div><div>The RAS protein is a notable target in cancer research, being the most often mutated oncogene in human malignancies. The RAS G12X mutation is predominantly found in non-small cell lung cancer, including G12C and G12S variants, which are associated with a poor prognosis. Despite the approval of two inhibitors for the KRAS G12C mutation (sotorasib and adagrasib), the necessity persists due to the emergence of resistance to these inhibitors, which has become a substantial concern. This work involved the repurposing of FDA-approved drugs through <em>in silico</em> methods to identify compounds capable of covalently binding to KRAS G12C (PDB entry: 6OIM) and G12S (PDB entry: 7TLG). The computational studies involved virtual screening, induced fit, and covalent docking, and molecular dynamics simulations, and identified five promising candidates, the antibiotics; capreomycin, cefadroxil, and Cefdinir, the antifungal; natamycin, and the anti-inflammatory, cortisone. The hits exhibited binding affinities between −9.98 and −11.35 kcal/mol compared to −9.81 for sotorasib and were found to be covalent binders targeting KRAS G12C and G12S. The computational results were supported with <em>in vitro</em> evaluation on A549 malignant cells and HFF-1 non-cancerous cells. The antiproliferative efficacy of these drugs was evaluated by MTS tests, and their IC₅₀ values were determined in which natamycin, although non-selective, and cortisone showed the highest activity with IC₅₀ of 53.42 and 53.51 μg/mL, respectively, followed by cefadroxil (84.63 μg/mL). This study promisingly repurposed five drugs for KRAS mutant lung cancer, of which cefadroxil, and cortisone are particularly warranting further assessment either as a standalone or combination therapy while capreomycin is still an effective inhibitor for KRAS G12C mutant as evident from <em>in silico</em> and <em>in vitro</em> studies.</div></div>","PeriodicalId":13953,"journal":{"name":"Informatics in Medicine Unlocked","volume":"52 ","pages":"Article 101612"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143178788","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}