Barbara Bonnesen, Jens-Ulrik S. Jensen, Alexander G. Mathioudakis, Alexandru Corlateanu, Pradeesh Sivapalan
{"title":"Promising treatment biomarkers in asthma","authors":"Barbara Bonnesen, Jens-Ulrik S. Jensen, Alexander G. Mathioudakis, Alexandru Corlateanu, Pradeesh Sivapalan","doi":"10.3389/fdsfr.2023.1291471","DOIUrl":"https://doi.org/10.3389/fdsfr.2023.1291471","url":null,"abstract":"Asthma is a highly heterogenous disease which researchers over time have attempted to classify into different phenotypes and endotypes to improve diagnosis, prognosis and treatment. Earlier classifications based on reaction to environmental allergens, age, sex and lung function have evolved, and today, the use of precision medicine guided by biomarkers offers new perspectives on asthma management. Identifying biomarkers that may reveal the underlying pathophysiology of the disease will help to select the patients who will benefit most from specific treatments. This review explores the classification of asthma phenotypes and focuses on the most recent advances in using biomarkers to guide treatment.","PeriodicalId":489826,"journal":{"name":"Frontiers in drug safety and regulation","volume":"24 6","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135476648","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":"Identification of small cell lung cancer patients who are at risk of developing common serious adverse event groups with machine learning","authors":"Linda Wanika, Neil D. Evans, Michael J. Chappell","doi":"10.3389/fdsfr.2023.1267623","DOIUrl":"https://doi.org/10.3389/fdsfr.2023.1267623","url":null,"abstract":"Introduction: Across multiple studies, the most common serious adverse event groups that Small Cell Lung Cancer (SCLC) patients experience, whilst undergoing chemotherapy treatment, are: Blood and Lymphatic Disorders, Infections and Infestations together with Metabolism and Nutrition Disorders. The majority of the research that investigates the relationship between adverse events and SCLC patients, focuses on specific adverse events such as neutropenia and thrombocytopenia. Aim: This study aims to utilise machine learning in order to identify those patients who are at risk of developing common serious adverse event groups, as well as their specific adverse event classification grade. Methods: Data from five clinical trial studies were analysed and 12 analysis groups were formed based on the serious adverse event group and grade. Results: The best test runs for each of the models were able to produce an area under the curve (AUC) score of at least 0.714. The best model was the Blood and Lymphatic Disorder group, SAE grade 0 vs. grade 3 (best AUC = 1, sensitivity rate = 0.84, specificity rate = 0.96). Conclusion: The top features that contributed to this prediction were total bilirubin, alkaline phosphatase, and age. Future work should investigate the relationship between these features and common SAE groups.","PeriodicalId":489826,"journal":{"name":"Frontiers in drug safety and regulation","volume":"207 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135438634","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}