Methods最新文献

{"title":"Development and validation of a new and rapid molecular diagnostic tool based on RT-LAMP for Hepatitis C virus detection at point-of-care","authors":"Sonia Arca-Lafuente ,&nbsp;Cristina Yépez-Notario ,&nbsp;Pablo Cea-Callejo ,&nbsp;Violeta Lara-Aguilar ,&nbsp;Celia Crespo-Bermejo ,&nbsp;Luz Martín-Carbonero ,&nbsp;Ignacio de los Santos ,&nbsp;Verónica Briz ,&nbsp;Ricardo Madrid","doi":"10.1016/j.ymeth.2024.10.008","DOIUrl":"10.1016/j.ymeth.2024.10.008","url":null,"abstract":"<div><h3>Purpose</h3><div>Globally, it is estimated that 1.0 million individuals are newly infected by Hepatitis C virus (HCV) every year, and nearly 50 million people live with a chronic infection, according to World Health Organization. To overcome underdiagnosis of HCV infection among hard-to-reach populations, it is essential to develop new rapid and easy-to-use molecular diagnostic systems. In this work, we have developed a pangenotypic diagnostic tool based on Loop-Mediated Isothermal Amplification (LAMP), coupled to a direct sample lysis procedure for molecular detection of HCV at point-of-care (POC).</div></div><div><h3>Methods</h3><div>Procedure validation was performed using 129 different samples from HCV infected patients (116 serum samples, and 13 fresh blood samples), 27 individuals who tested negative for HCV but positive for HIV, and 11 healthy donors. Serum was collected, lysed for 10 min at room temperature, and assayed by RT-LAMP. To achieve this, a set of 9 LAMP-primers was used for the first time. Parallel RT-qPCR assays were conducted for HCV to both validate the procedure and quantify viral loads.</div></div><div><h3>Results</h3><div>HCV was detected by RT-LAMP in 109/116 HCV positive serum samples, and in 11/13 positive blood samples in less than 40 min. Compared to RT-qPCR results, our RT-LAMP procedure showed a sensitivity of 94 %, 100 % specificity, and a limit of detection of 3.26 log₁₀ IU/mL (10–20 copies per reaction).</div></div><div><h3>Conclusions</h3><div>We have developed an accurate system, more affordable than the current available rapid tests for HCV. Since no prior RNA purification step from capillary blood is required, we strongly recommend our RT-LAMP system as a valuable and rapid tool for the molecular detection of HCV at POC.</div></div>","PeriodicalId":390,"journal":{"name":"Methods","volume":"232 ","pages":"Pages 43-51"},"PeriodicalIF":4.2,"publicationDate":"2024-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142492288","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"HLA-DR4Pred2: An improved method for predicting HLA-DRB1*04:01 binders","authors":"Sumeet Patiyal ,&nbsp;Anjali Dhall ,&nbsp;Nishant Kumar ,&nbsp;Gajendra P.S. Raghava","doi":"10.1016/j.ymeth.2024.10.007","DOIUrl":"10.1016/j.ymeth.2024.10.007","url":null,"abstract":"<div><div>HLA-DRB1*04:01 is associated with numerous diseases, including sclerosis, arthritis, diabetes, and COVID-19, emphasizing the need to scan for binders in the antigens to develop immunotherapies and vaccines. Current prediction methods are often limited by their reliance on the small datasets. This study presents HLA-DR4Pred2, developed on a large dataset containing 12,676 binders and an equal number of non-binders. It’s an improved version of HLA-DR4Pred, which was trained on a small dataset, containing 576 binders and an equal number of non-binders. All models were trained, optimized, and tested on 80 % of the data using five-fold cross-validation and evaluated on the remaining 20 %. A range of machine learning techniques was employed, achieving maximum AUROC of 0.90 and 0.87, using composition and binary profile features, respectively. The performance of the composition-based model increased to 0.93, when combined with BLAST search. Additionally, models developed on the realistic dataset containing 12,676 binders and 86,300 non-binders, achieved a maximum AUROC of 0.99. Our proposed method outperformed existing methods when we compared the performance of our best model to that of existing methods on the independent dataset. Finally, we developed a standalone tool and a webserver for HLADR4Pred2, enabling the prediction, design, and virtual scanning of HLA-DRB1*04:01 binding peptides, and we also released a Python package available on the Python Package Index (<span><span>https://webs.iiitd.edu.in/raghava/hladr4pred2/</span><svg><path></path></svg></span>; <span><span>https://github.com/raghavagps/hladr4pred2</span><svg><path></path></svg></span>; <span><span>https://pypi.org/project/hladr4pred2/</span><svg><path></path></svg></span>).</div></div>","PeriodicalId":390,"journal":{"name":"Methods","volume":"232 ","pages":"Pages 18-28"},"PeriodicalIF":4.2,"publicationDate":"2024-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142455002","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A heterogeneous graph transformer framework for accurate cancer driver gene prediction and downstream analysis","authors":"Shuwen Xiong ,&nbsp;Junming Zhang ,&nbsp;Hong Luo ,&nbsp;Yongqing Zhang ,&nbsp;Qinyin Xiao","doi":"10.1016/j.ymeth.2024.09.018","DOIUrl":"10.1016/j.ymeth.2024.09.018","url":null,"abstract":"<div><div>Accurately predicting cancer driver genes remains a formidable challenge amidst the burgeoning volume and intricacy of cancer genomic data. In this investigation, we propose HGTDG, an innovative heterogeneous graph transformer framework tailored for precisely predicting cancer driver genes and exploring downstream tasks. A heterogeneous graph construction module is central to the framework, which assembles a gene-protein heterogeneous network leveraging the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways and protein-protein interactions sourced from the STRING (search tool for recurring instances of neighboring genes) database. Moreover, our framework introduces a pioneering heterogeneous graph transformer module, harnessing multi-head attention mechanisms for nuanced node embedding. This transformative module proficiently captures distinct representations for both nodes and edges, thereby enriching the model's predictive capacity. Subsequently, the generated node embeddings are seamlessly integrated into a classification module, facilitating the discrimination between driver and non-driver genes. Our experimental findings evince the superiority of HGTDG over existing methodologies, as evidenced by the enhanced performance metrics, including the area under the receiver operating characteristic curves (AUROC) and the area under the precision-recall curves (AUPRC). Furthermore, the downstream analysis utilizing the newly identified cancer driver genes underscores the efficacy and versatility of our proposed framework.</div></div>","PeriodicalId":390,"journal":{"name":"Methods","volume":"232 ","pages":"Pages 9-17"},"PeriodicalIF":4.2,"publicationDate":"2024-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142455001","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multi-view contrastive clustering for cancer subtyping using fully and weakly paired multi-omics data","authors":"Yabin Kuang ,&nbsp;Minzhu Xie ,&nbsp;Zhanhong Zhao ,&nbsp;Dongze Deng ,&nbsp;Ergude Bao","doi":"10.1016/j.ymeth.2024.09.016","DOIUrl":"10.1016/j.ymeth.2024.09.016","url":null,"abstract":"<div><div>The identification of cancer subtypes is crucial for advancing precision medicine, as it facilitates the development of more effective and personalized treatment and prevention strategies. With the development of high-throughput sequencing technologies, researchers now have access to a wealth of multi-omics data from cancer patients, making computational cancer subtyping increasingly feasible. One of the main challenges in integrating multi-omics data is handling missing data, since not all biomolecules are consistently measured across all samples. Current computational models based on multi-omics data for cancer subtyping often struggle with the challenge of weakly paired omics data. To address this challenge, we propose a novel unsupervised cancer subtyping model named Subtype-MVCC. This model leverages graph convolutional networks to extract and represent low-dimensional features from each omics data type, using intra-view and inter-view contrastive learning approaches. By incorporating a weighted average fusion strategy to unify the dimension of each sample, Subtype-MVCC effectively handles weakly paired multi-omics datasets. Comprehensive evaluations on established benchmark datasets demonstrate that Subtype-MVCC outperforms nine leading models in this domain. Additionally, simulations with varying levels of missing data highlight the model's robust performance in handling weakly paired omics data. The clinical relevance and survival outcomes associated with the identified subtypes further validate the interpretability and reliability of the clustering results produced by Subtype-MVCC.</div></div>","PeriodicalId":390,"journal":{"name":"Methods","volume":"232 ","pages":"Pages 1-8"},"PeriodicalIF":4.2,"publicationDate":"2024-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142455004","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"DGSIST: Clustering spatial transcriptome data based on deep graph structure Infomax","authors":"Yu-Han Xiu ,&nbsp;Si-Lin Sun ,&nbsp;Bing-Wei Zhou ,&nbsp;Ying Wan ,&nbsp;Hua Tang ,&nbsp;Hai-Xia Long","doi":"10.1016/j.ymeth.2024.10.002","DOIUrl":"10.1016/j.ymeth.2024.10.002","url":null,"abstract":"<div><div>Although spatial transcriptomics data provide valuable insights into gene expression profiles and the spatial structure of tissues, most studies rely solely on gene expression information, underutilizing the spatial data. To fully leverage the potential of spatial transcriptomics and graph neural networks, the DGSI (Deep Graph Structure Infomax) model is proposed. This innovative graph data processing model uses graph convolutional neural networks and employs an unsupervised learning approach. It maximizes the mutual information between graph-level and node-level representations, emphasizing flexible sampling and aggregation of nodes and their neighbors. This effectively captures and incorporates local information from nodes into the overall graph structure. Additionally, this paper developed the DGSIST framework, an unsupervised cell clustering method that integrates the DGSI model, SVD dimensionality reduction algorithm, and k-means++ clustering algorithm. This aims to identify cell types accurately. DGSIST fully uses spatial transcriptomics data and outperforms existing methods in accuracy. Demonstrations of DGSIST’s capability across various tissue types and technological platforms have shown its effectiveness in accurately identifying spatial domains in multiple tissue sections. Compared to other spatial clustering methods, DGSIST excels in cell clustering and effectively eliminates batch effects without needing batch correction. DGSIST excels in spatial clustering analysis, spatial variation identification, and differential gene expression detection and directly applies to graph analysis tasks, such as node classification, link prediction, or graph clustering. Anticipation lies in the contribution of the DGSIST framework to a deeper understanding of the spatial organizational structures of diseases such as cancer.</div></div>","PeriodicalId":390,"journal":{"name":"Methods","volume":"231 ","pages":"Pages 226-236"},"PeriodicalIF":4.2,"publicationDate":"2024-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142455003","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Determination of adipogenesis stages of human umbilical cord-derived mesenchymal stem cells using three-dimensional label-free holotomography","authors":"Mahesh Prakash Bhatta ,&nbsp;Gun-Woo Won ,&nbsp;Seung Hoon Lee ,&nbsp;Seung-Hyeon Choi ,&nbsp;Cheong-Hae Oh ,&nbsp;Ji Hyun Moon ,&nbsp;Hong-Hoa Hoang ,&nbsp;Jaehyeok Lee ,&nbsp;Sang Do Lee ,&nbsp;Jong-Il Park","doi":"10.1016/j.ymeth.2024.10.005","DOIUrl":"10.1016/j.ymeth.2024.10.005","url":null,"abstract":"<div><div>Adipogenesis involves complex changes in gene expression, morphology, and cytoskeletal organization. However, the quantitative analysis of live cell images to identify their stages through morphological markers is limited. Distinct adipogenesis markers on human umbilical cord-derived mesenchymal stem cells (UC-MSCs) were identified through holotomography, a label-free live cell imaging technique. In the MSC-to-preadipocyte transition, the nucleus-to-cytoplasm ratio (0.080 vs. 0.052) and lipid droplet (LD) refractive index variation decreased (0.149 % vs. 0.061 %), whereas the LD number (20 vs. 65) increased. This event was also accompanied by the downregulation and upregulation of THY1 and Preadipocyte Factor-1 (PREF-1), respectively. In the preadipocyte to immature adipocyte shift, cell sphericity (0.20 vs. 0.43) and LD number (65 vs. 200) surged, large LDs (&gt;10 μm³) appeared, and the major axis of the cell was reduced (143.7 μm vs. 83.12 μm). These findings indicate features of preadipocyte and immature adipocyte stages, alongside the downregulation of PREF-1 and upregulation of Peroxisome Proliferator-Activated Receptor gamma (PPARγ). In adipocyte maturation, along with PPARγ and Fatty Acid-Binding Protein 4 upregulation, cell compactness (0.15 vs. 0.29) and sphericity (0.43 vs. 0.59) increased, and larger LDs (&gt;30 μm³) formed, marking immature and mature adipocyte stages. The study highlights the distinct adipogenic morphological biomarkers of adipogenesis stages in UC-MSCs, providing potential applications in biomedical and clinical settings, such as fostering innovative medical strategies for treating metabolic disease.</div></div>","PeriodicalId":390,"journal":{"name":"Methods","volume":"231 ","pages":"Pages 204-214"},"PeriodicalIF":4.2,"publicationDate":"2024-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142441517","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multi-kernel clustering with tensor fusion on Grassmann manifold for high-dimensional genomic data","authors":"Fei Qi ,&nbsp;Jin Guo ,&nbsp;Junyu Li ,&nbsp;Yi Liao ,&nbsp;Wenxiong Liao ,&nbsp;Hongmin Cai ,&nbsp;Jiazhou Chen","doi":"10.1016/j.ymeth.2024.09.015","DOIUrl":"10.1016/j.ymeth.2024.09.015","url":null,"abstract":"<div><div>The high dimensionality and noise challenges in genomic data make it difficult for traditional clustering methods. Existing multi-kernel clustering methods aim to improve the quality of the affinity matrix by learning a set of base kernels, thereby enhancing clustering performance. However, directly learning from the original base kernels presents challenges in handling errors and redundancies when dealing with high-dimensional data, and there is still a lack of feasible multi-kernel fusion strategies. To address these issues, we propose a Multi-Kernel Clustering method with Tensor fusion on Grassmann manifolds, called MKCTM. Specifically, we maximize the clustering consensus among base kernels by imposing tensor low-rank constraints to eliminate noise and redundancy. Unlike traditional kernel fusion approaches, our method fuses learned base kernels on the Grassmann manifold, resulting in a final consensus matrix for clustering. We integrate tensor learning and fusion processes into a unified optimization model and propose an effective iterative optimization algorithm for solving it. Experimental results on ten datasets, comparing against 12 popular baseline clustering methods, confirm the superiority of our approach. Our code is available at <span><span>https://github.com/foureverfei/MKCTM.git</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":390,"journal":{"name":"Methods","volume":"231 ","pages":"Pages 215-225"},"PeriodicalIF":4.2,"publicationDate":"2024-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142446912","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Protocol for a pilot study: Feasibility of a web-based platform to improve nutrition, mindfulness, and physical function in people living with Post COVID-19 condition (BLEND)","authors":"Montserrat Montes-Ibarra ,&nbsp;Kristine Godziuk ,&nbsp;Richard B Thompson ,&nbsp;Catherine B. Chan ,&nbsp;Edith Pituskin ,&nbsp;Douglas P. Gross ,&nbsp;Grace Lam ,&nbsp;Mathias Schlögl ,&nbsp;João Felipe Mota ,&nbsp;D. Ian Paterson ,&nbsp;Carla M. Prado","doi":"10.1016/j.ymeth.2024.10.004","DOIUrl":"10.1016/j.ymeth.2024.10.004","url":null,"abstract":"<div><div>Individuals with Post COVID-19 condition (PCC), or long COVID, experience symptoms such as fatigue, muscle weakness, and psychological distress, including anxiety, depression, or sleep disorders that persist after recovery from COVID-19. These ongoing symptoms significantly compromise quality of life and diminish functional capacity and independence. Multimodal digital interventions targeting behavioural factors such as nutrition and mindfulness have shown promise in improving health outcomes of people with chronic health conditions and may be beneficial for those with PCC. <em>The BLEND study (weB-based pLatform to improve nutrition, mindfulnEss, and physical function, in patients with loNg COVID)</em> study is an 8-week pilot randomized controlled trial evaluating the feasibility of a digital wellness platform compared to usual care among individuals with PCC. The web-based wellness platform employed in this study, My Viva Plan (MVP)®, integrates a holistic, multicomponent approach to promote wellness. The intervention group receives access to the digital health platform for 8 weeks with encouragement for frequent interactions to improve dietary intake and mindfulness. The control group receives general content focusing on improvements in dietary intake and mindfulness. Assessments are conducted at baseline and week 8. The primary outcome is the feasibility of platform use. Secondary and exploratory outcomes include a between-group comparison of changes in body composition, nutritional status, quality of life, mindfulness, physical activity, and physical performance after 8 weeks. Findings of this study will inform the development of effective web-based wellness programs tailored for individuals with PCC to promote sustainable behavioural changes and improved health outcomes.</div></div>","PeriodicalId":390,"journal":{"name":"Methods","volume":"231 ","pages":"Pages 186-194"},"PeriodicalIF":4.2,"publicationDate":"2024-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142398994","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Study protocol for a single-arm pilot trial investigating the feasibility of a multimodal digital technology for managing metabolic syndrome in patients with chronic obstructive pulmonary disease","authors":"Bruna R. da Silva ,&nbsp;Amanda I. Radil ,&nbsp;Liam Collins ,&nbsp;Nathanial Maeda ,&nbsp;Carla M. Prado ,&nbsp;Martin Ferguson-Pell ,&nbsp;Doug Klein","doi":"10.1016/j.ymeth.2024.10.003","DOIUrl":"10.1016/j.ymeth.2024.10.003","url":null,"abstract":"<div><div>Individuals diagnosed with Chronic Obstructive Pulmonary Disease (COPD) are exposed to an increased risk of metabolic syndrome (MetS), which negatively affects their health outcomes and quality of life. Lifestyle interventions have shown promise in managing MetS. This study outlines the protocol for a web-based multimodal self-care program, Digital Metabolic Rehabilitation, for managing MetS in patients with COPD. The Digital Metabolic Rehabilitation is a single-arm pilot trial that integrates the Canadian Health Advanced by Nutrition and Graded Exercise (CHANGE) Program and a web-based wellness platform. The web-based wellness platform employed in this study is My Viva Plan (MVP)®, which integrates a holistic, multicomponent approach to promote wellness. The intervention will primarily focus on lifestyle changes for patients with COPD. Over 6 months, participants will use the web-based wellness platform and engage in weekly online support group sessions. Fifty patients diagnosed with stage I-II COPD and MetS will participate. Blood tests, anthropometrics, body composition, physical function, muscle strength, physical activity, energy metabolism, quality of life and mental health will be assessed at baseline, 3, and 6 months. The Digital Metabolic Rehabilitation program aims to explore whether a multimodal integrative intervention delivered through a web-based wellness platform can be implemented by patients with COPD with MetS. By combining the expertise of the CHANGE Program with the digital delivery format, the intervention seeks to enhance self-monitoring and foster better self-management practices. The protocol outlines a novel and potentially impactful intervention for managing MetS in patients with COPD.</div></div>","PeriodicalId":390,"journal":{"name":"Methods","volume":"231 ","pages":"Pages 195-203"},"PeriodicalIF":4.2,"publicationDate":"2024-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142398995","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Development and validation of a stability-indicating HPLC method for assay of tonabersat in pharmaceutical formulations","authors":"Santosh Bhujbal,&nbsp;Ilva D. Rupenthal,&nbsp;Priyanka Agarwal","doi":"10.1016/j.ymeth.2024.10.001","DOIUrl":"10.1016/j.ymeth.2024.10.001","url":null,"abstract":"<div><div>A stability-indicating reversed-phase high-performance liquid chromatography (RP-HPLC) method was developed to assay tonabersat and assess its stability in pharmaceutical formulations. Chromatographic separation was achieved using a Kinetex® C18 column (2.6 µm, 150 x 3 mm, 100 Å) at 50 °C, with a 20 µL injection volume. A linear gradient of acetonitrile in water (5 – 33.5 %) was applied for 1 min, followed by a gradual increase to 100 % over 26 min at a flow rate of 0.5 mL/min. Tonabersat and its degradation products were detected at 275 nm and 210 nm, respectively. The optimized method was used to evaluate the stability of tonabersat in lipid-based pharmaceutical formulations at 5 ± 3 °C, 25 ± 2°C/60 ± 5 % RH, and 40 ± 2 °C/75 ± 5 % RH over 3 months. The method was validated as per ICH guidelines and demonstrated linearity in the range of 5 – 200 µg/mL (R² = 0.99994) with good accuracy (98.25 – 101.58 % recovery) and precision (% RSD &lt; 2.5 %). The limits of detection and quantitation were 0.8 µg/mL and 5 µg/mL, respectively. Forced degradation studies showed significant degradation on exposure to alkaline (90.33 ± 0.80 %), acidic (70.60 ± 1.57 %), and oxidative stress (33.95 ± 0.69 %) at 70 °C, but no degradation was observed on exposure to thermal or photolytic stress. No chemical degradation was observed in either formulation on storage. Thus, the method was sensitive, specific, and suitable for stability testing of tonabersat in pharmaceutical formulations.</div></div>","PeriodicalId":390,"journal":{"name":"Methods","volume":"231 ","pages":"Pages 178-185"},"PeriodicalIF":4.2,"publicationDate":"2024-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142378942","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}