Real-World Data and Real-World Evidence in Support of Drug Development: Will China Soon be Leading the Way?

Author(s)

Moderator: Amanda Pulfer, BA (Hons), Evidera, London, UK
Panelists: Lu Ban, PhD, Evidera, Beijing, China; Haijun Cao, MD, PhD, Happy Life Technology, Shanghai, China; Sunil Garg, PhD, Astellas Pharma Singapore Pte. Ltd, SINGAPORE, 01, Singapore

ISSUE: Similar to the US, China is exploring use of RWE in support of drug development and regulatory decisions. In parallel the data landscape is fast evolving with emerging local RWD sources providing rich data. This session focuses on recent developments in utilising RWE across the product lifecycle and potential challenges and opportunities of China RWD sources. Stakeholders from different organizations will share their perspective and experiences. Dr Lu Ban (Evidera) will provide an up-to-date overview of the regulatory context and the need for RWE generation (15 minutes). Dr Haijun Cao (HLT) will discuss opportunities and challenges of RWD in China and guidance for study success (15 minutes). Dr Sunil Garg (Astellas) will provide an industry perspective on the need and challenges in generating RWE in China and potential mitigation strategies (15 minutes).

OVERVIEW: RWE plays an increasingly significant role in life-cycle management of therapeutic interventions including regulatory and pricing and reimbursement decision making. The Centre for Drug Evaluation (CDE) encourages exploration of RWE to provide supporting evidence for label expansion and effectiveness and safety of products post launch in China. In addition, real-world studies are being undertaken to describe incidence and prevalence of disease, treatment pathway, healthcare resource utilization and clinical and economic burden. RWE is also used to inform payers on National Reimbursement Drug List (NRDL) listing, budget impact analysis, or new round negotiations to limit price cuts in NRDL. In parallel RWE research opportunities are driven by the expanding data landscape. However appropriate data collection, curation, standardization, and validation are required. Advanced analytic techniques including machine learning and AI technology play a crucial role in managing large unstructured data sets. Executing RWE studies requires understanding of unique study approval landscape in China for efficiently delivery of RWE generation programs.

Conference/Value in Health Info

2022-09, ISPOR Asia Pacific 2022, Virtual

Code

IP16

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