Archives: Agenda

Ensuring end-to-end quality standards is arguably the most significant operational challenge for sponsors and CDMOs alike. This complexity is heightened by the reality that a clinical supply network is a potential point of failure, risking not only product quality but the integrity of the entire trial.
确保端到端质量标准，可以说是申办方和 CDMO 面临的最重要运营挑战。由于临床供应网络中的任何环节都可能成为故障点，这一复杂性被进一步放大，不仅会影响产品质量，也会影响整个试验的完整性。

• Rethinking the Role of the Quality Unit
• Risk-Based Supplier Qualification and Lifecycle Management
• Digital Platforms for End-to-End Quality Data Integrity
• Managing Change in a Distributed Network
• Building a Culture of Quality Across Organizational Boundaries

• 重新定义质量部门
• 基于风险的供应商资质认定与生命周期管理
• 用于端到端质量数据完整性的数字平台
• 分布式网络中的变更管理
• 跨组织边界建立质量文化

• Why Poland/CEE delivers: A proven early oncology environment — dense investigator networks, treatment-naive patients, and fast site activation built for FIH-to-Phase-II programs
• The regulatory pathway: What makes CEE data credible for FDA and EMA — and why country-level trial authorization in Europe is getting faster
• The case in practice: What early oncology development anchored in Poland/CEE looks like — and the operational set-up decisions that determine whether it delivers for your global filing

• 为什么波兰/中东欧可交付：成熟的早期肿瘤环境 – 密集的研究者网络、未接受治疗的患者群体，以及为 FIH 至 II 期项目打造的快速中心启动能力。
• 法规路径：是什么让中东欧数据获得 FDA 和 EMA 认可 – 以及为什么欧洲国家层面的临床试验授权正在加速。
• 实践案例：以波兰/中东欧为中心的早期肿瘤开发现状 – 哪些运营决策决定其能否服务全球申报。

• Predicting enrollment trends to ensure uninterrupted clinical trial supply
• Using data-driven forecasting to support agile decision-making
• Managing uncertainty across global, multi-site studies
• Best practices for responding to faster-than-expected recruitment and changing demand

• 预测入组趋势，确保临床试验供应不中断。
• 使用数据驱动的预测支持快速决策。
• 管理全球多中心研究中的不确定性。
• 应对超预期快速招募和需求变化的最佳实践。

Moderator: Daniel Gao, President, ISPE Supply Chain

Brocacef专场

• Moving beyond proof-of-concept to enterprise-wide AI adoption
• Demonstrating measurable impact across study design, recruitment, and trial execution
• Overcoming data, integration, and governance challenges to scale successfully
• Building the foundations for sustainable, AI-enabled clinical development

• 从概念验证迈向企业级 AI 应用。
• 在研究设计、招募和试验执行中展示可衡量影响。
• 克服数据、集成和治理挑战，实现成功规模化。
• 为可持续的 AI 赋能临床开发奠定基础。

• Embedding resilience into supply chain strategy, operations, and quality systems
• Leveraging digital technologies and data visibility to anticipate and mitigate disruptions
• Strengthening supplier networks and risk management frameworks for long-term continuity
• Balancing agility, compliance, and efficiency in an evolving global landscape

• 供应链战略、运营和质量体系的风险管控。
• 利用数字技术与数据可视化提前识别并缓解中断风险。
• 强化供应商网络与风险管理框架，保障弹性供应能力。
• 在不断演变的全球格局中平衡便利性、合规性和效率。