Archives: Agenda

• Understanding country-specific site application and approval requirements
• Reducing start-up timelines through effective planning and local partnerships
• Common challenges and pitfalls when activating sites across multiple regions
• Best practices for successful collaboration with investigators, sites, and regulators

• 了解各国特定的中心申请与审批要求。
• 通过有效规划和本地合作伙伴关系缩短启动时间。
• 跨多个区域启动中心时常见的挑战和陷阱。
• 与研究者、中心和监管方成功协作的最佳实践

Moderator:

Julia Leng Multitude Therapeutics

A single biopharma portfolio today may encompass a diverse mix of modalities—small molecules, monoclonal antibodies, ADCs, cell therapies, and more—each with vastly different and often contradictory supply chain requirements. This new reality demands a fundamental shift towards platform-based supply chain strategies.

• What Are We Actually Building?
• Orchestrating Complexity with Data & AI
• From Transactional Vendors to Strategic Capability Partners
• Upskilling Teams for a Multi-Modality World

如今，单一生物医药企业的研发管线可能涵盖多种不同的药物模态——小分子、单克隆抗体（单抗）、抗体偶联药物（ADCs）、细胞疗法等。这些药物对供应链的要求截然不同，甚至相互冲突。这一新现状要求供应链管理必须发生根本性转变，全面走向平台化策略。

• 我们究竟在构建什么？
• 利用数据与人工智能（AI）协调整合复杂流程
• 从传统的交易型供应商转型为战略能力合作伙伴
• 赋能团队升级，全面迎接多模态时代

Expanding clinical trials into China requires more than a global strategy, it requires local execution. In this session, Emsere will share how a smarter equipment rental and management strategy can simplify clinical trial operations, reduce logistical complexity, and ensure equipment is available when and where it’s needed. Learn how combining global standards with local expertise helps sponsors execute successful clinical trials in China. 将临床试验拓展至中国，不仅需要具备全球化的战略眼光，更需要扎实的本土化执行力。在本次专题中，Emsere 将分享如何通过更智慧的设备租赁与管理策略来简化临床试验运营、降低物流复杂度，并确保设备能够在所需的时间和地点精准交付。您将了解到，如何将全球统一标准与本土专业经验相结合，从而助力申办方在中国成功执行临床试验。

AI is reshaping the entire lifecycle of GLP-1 drug development. This presentation will systematically examine the specific applications of AI technology across the entire R&D chain, covering early-stage molecular design, activity and toxicity prediction, clinical trial design optimization, and optimized scheduling within the clinical supply chain. Using a case study of MindRank’s MAP platform, the session will analyze how AI-driven approaches improve R&D efficiency, reduce costs, and shorten timelines, ultimately advancing a candidate drug into Phase III clinical trials within 4.5 years.

AI正在重塑GLP-1药物的研发全链条。本报告将系统阐述AI技术在药物研发全生命周期中的具体应用，涵盖早期分子设计、活性与毒性预测、临床试验设计优化，以及临床供应端的优化调度。报告将结合德睿智药MAP平台的实际案例，分析如何通过AI驱动的方案提升研发效率、降低成本并缩短周期，并在4.5年内将相关候选药物推进至三期临床试验。

• Selecting the right countries and sites to support global development goals
• Using real-world data and feasibility insights to improve decision-making
• Aligning site selection with recruitment, operational, and regulatory strategies
• Creating scalable site networks for long-term global clinical development success

• 选择合适的国家和中心，以支持全球开发目标。
• 利用真实世界数据和可行性洞察改善决策。
• 将中心选择与招募、运营和法规策略相结合。
• 建立可扩展的中心网络，实现长期全球临床开发成功。

This session outlines an end-to-end framework for Multi-Regional Clinical Trials (MRCTs). It covers critical regional epidemiology and regulatory planning, third-party operational models, the impact of inter-ethnicity differences on future labeling, and navigating health agency registration decisions.

Plan

• Clinical epidemiology in different regions
• Regulatory requirements on protocol design and patient recruitment
• Timeline and financial models

Do

• Operational models involving third parties

Check

• Influence of inter-ethnicity differences and response differences on future label statements

Access

• Agency‘s decision on registration

• The 3+3 design is simple but has limited statistical performance, failing to meet modern Phase I R&D needs.
• BOIN is a model-assisted design whose core centers on constructing optimal decision intervals around the target toxicity rate.
• BOIN combines strong statistical performance with high clinical feasibility.
• Compared to 3+3, BOIN allocates patients more rationally and improves MTD accuracy.
• BOIN has evolved into a design family that extends to efficacy, combination therapies, late-onset toxicities, and OBD optimization.

• 3+3 简单但统计性能有限，已难满足现代 I 期研发需求。
• BOIN 是模型辅助设计，核心是围绕目标毒性率构建最优决策区间。
• BOIN 兼具较强统计性能与较高临床可实施性。
• 相比 3+3，BOIN 能更合理分配患者并提高 MTD 准确率。
• BOIN 已发展为设计家族，可扩展到疗效、联合用药、迟发毒性和 OBD 优化等场景。

• How to build a “Predict and Prevent” Supply Mindset
• Dynamic Re-supply Models for a Dynamic Clinical Reality
• A Step-by-Step Guide to Smoother Cross-Border Movements
• Rapid Response Protocols and Effective Contingency Planning
• Integrating Partners into a Single, Seamless Team

• 如何建立“预测并预防”的供应思维。
• 面向动态临床现实的动态补货模型。
• 更顺畅跨境运输的分步指南。
• 快速响应流程与有效的应急规划。
• 将合作伙伴整合为一个无缝协作团队。

Moderator: Jae-Young Ha, CBO, AriBio

下午茶歇及交流

下午茶歇及交流