The pharmaceutical industry’s increasing adoption of cloud-based technologies has introduced new challenges in computerized systems validation (CSV). This paper explores the evolving landscape of cloud validation in ...The pharmaceutical industry’s increasing adoption of cloud-based technologies has introduced new challenges in computerized systems validation (CSV). This paper explores the evolving landscape of cloud validation in pharmaceutical manufacturing, focusing on ensuring data integrity and regulatory compliance in the digital era. We examine the unique characteristics of cloud-based systems and their implications for traditional validation approaches. A comprehensive review of current regulatory frameworks, including FDA and EMA guidelines, provides context for discussing cloud-specific validation challenges. The paper introduces a risk-based approach to cloud CSV, detailing methodologies for assessing and mitigating risks associated with cloud adoption in pharmaceutical environments. Key considerations for maintaining data integrity in cloud systems are analyzed, particularly when applying ALCOA+ principles in distributed computing environments. The article presents strategies for adapting traditional Installation Qualification (IQ), Operational Qualification (OQ), and Performance Qualification (PQ) models to cloud-based systems, highlighting the importance of continuous validation in dynamic cloud environments. The paper also explores emerging trends, including integrating artificial intelligence and edge computing in pharmaceutical manufacturing and their implications for future validation strategies. This research contributes to the evolving body of knowledge on cloud validation in pharmaceuticals by proposing a framework that balances regulatory compliance with the agility offered by cloud technologies. The findings suggest that while cloud adoption presents unique challenges, a well-structured, risk-based approach to validation can ensure the integrity and compliance of cloud-based systems in pharmaceutical manufacturing.展开更多
文摘The pharmaceutical industry’s increasing adoption of cloud-based technologies has introduced new challenges in computerized systems validation (CSV). This paper explores the evolving landscape of cloud validation in pharmaceutical manufacturing, focusing on ensuring data integrity and regulatory compliance in the digital era. We examine the unique characteristics of cloud-based systems and their implications for traditional validation approaches. A comprehensive review of current regulatory frameworks, including FDA and EMA guidelines, provides context for discussing cloud-specific validation challenges. The paper introduces a risk-based approach to cloud CSV, detailing methodologies for assessing and mitigating risks associated with cloud adoption in pharmaceutical environments. Key considerations for maintaining data integrity in cloud systems are analyzed, particularly when applying ALCOA+ principles in distributed computing environments. The article presents strategies for adapting traditional Installation Qualification (IQ), Operational Qualification (OQ), and Performance Qualification (PQ) models to cloud-based systems, highlighting the importance of continuous validation in dynamic cloud environments. The paper also explores emerging trends, including integrating artificial intelligence and edge computing in pharmaceutical manufacturing and their implications for future validation strategies. This research contributes to the evolving body of knowledge on cloud validation in pharmaceuticals by proposing a framework that balances regulatory compliance with the agility offered by cloud technologies. The findings suggest that while cloud adoption presents unique challenges, a well-structured, risk-based approach to validation can ensure the integrity and compliance of cloud-based systems in pharmaceutical manufacturing.
文摘目的:开发和验证乳腺癌患者新发心血管疾病(cardiovascular disease,CVD)的3年预测模型。方法:基于内蒙古区域医疗数据,纳入接受抗肿瘤治疗的18岁以上乳腺癌女性患者。多因素Fine&Gray模型纳入预测因子后,使用Lasso回归筛选变量,在训练集上拟合Cox比例风险、Logistic回归、Fine&Gray、随机森林和XGBoost模型,在测试集上分别用受试者工作特征(receiver operating characteristics,ROC)曲线下面积(area under the curve,AUC)和校准曲线评价模型区分度和校准度。结果:共纳入19325例接受抗肿瘤治疗的乳腺癌患者,平均年龄(52.76±10.44)岁,中位随访时间1.18年[四分位距(interquartile range,IQR):2.71]。7856例患者(40.65%)在乳腺癌诊断3年内发生CVD。Lasso回归筛选的预测因子为乳腺癌诊断年龄、居住地国内生产总值(gross domestic product,GDP)、肿瘤分期、高血压、缺血性心脏病及脑血管疾病既往史、手术类型、化疗类型、放疗类型。不考虑生存时间时,XGBoost模型的AUC显著高于随机森林模型[0.660(95%CI:0.644~0.675)vs.0.608(95%CI:0.591~0.624),P<0.001]和Logistic回归[0.609(95%CI:0.593~0.625),P<0.001],Logistic回归和XGBoost模型的校准度更好。考虑生存时间时,Cox比例风险模型和Fine&Gray模型的AUC差异无统计学意义[0.600(95%CI:0.584~0.616)vs.0.615(95%CI:0.599~0.631),P=0.188],但Fine&Gray模型的校准度更好。结论:基于区域医疗数据建立乳腺癌新发CVD的预测模型具有可行性。不考虑生存时间时,Logistic回归和XGBoost模型的预测性能更好;考虑生存时间时,Fine&Gray模型的预测性能更好。
