【转化医学前沿】FDA法规：从监管规则升级为监管科学的发展

导读

自去年7.22 CFDA暴风骤雨般清理申报项目、稽查临床试验数据以来，最近又公布了一批注册检验不合格的报告，作为旁观者，一直在思考CFDA下一步行动。 

是运动式稽查？是在弥补监管制度上的漏洞？还是籍此建立科学化的法规监管机制？相信大多数人和我一样仍未谙CFDA的套路。那么，不妨借鉴一下FDA的经验：如何从监管审查规则，升级为法规监管科学的发展历程。

监管科学

自2010年以来，FDA为了有效地推动和加速转化医学成果的应用，与NIH合作建立了监管科学（Regulatory Science）。FDA将监管科学定义为新学科领域，旨在开发FDA创新监管工具、规范标准，以及为评估安全性、有效性、毒性、质量监控、公共卫生和健康影响等所制定的各项方案或审评措施。

可见，FDA正在从传统的监管规则升级为法规监管科学，探讨新药发现、药物研发和转化应用，在监管和审评机制上强调科学管理和依据。

这应当是基于既往科学审评规范和标准、现有的大数据信息，以及临床人体试验模式的发展而选择的必然途径。仅仅依靠行政或行政加法规的监管审查模式是否仍然可行？听听同行们的见解。

研讨交流

在过去几年里，FDA与美国医学科学院等组织了多次专题研讨会，交流如何健全监管法规的科学依据和协作评审氛围。最近一期研讨会集中讨论了以下内容：

1. 从监管部门、科研机构和企业三方面，看监管科学的重点和首要策略

2. 作为新学科领域，如何建立监管科学教育体系；如何提供专业培训支持职业化发展

3. 监管科学的创新核心要素：现存差距是什么？创新含义是什么？

4. 在政府与民间机构合作方面，需要面对的困难、合作中的需求等

我们收集了研讨会讲座内容，如各位感兴趣的话，请在公共微信平台上留言索取（请给出自己的Email），我们与您分享下面的内容。

讲座第一部分

SETTINGTHE STAGE FOR INNOVATION IN REGULATORY SCIENCE

1、Valueof Information to Inform Decision Making Under Uncertainty 

VON STACKELBERG

Harvard Center for Risk Analysis

2、Fusing Randomization With EHR ‘Big Data’ For Smarter Evidence Generation On Approved Medical Products

DEREK ANGUS

University of Pittsburgh

讲座第二部分

LEARNING LESSONS THROUGH CONSIDERATION OF REGULATORY SCIENCEAPPLICATIONS

1、Transformation of Our Ability to Generate, Analyze, Integrate and Share Information Across Regulatory Science Applications 

RUSS ALTMAN

Stanford University

2、Basic Science of Measurement: Metrology Principles for Biomarkers

MARC SALIT

National Institute of Standards and Technology (NIST)

3、Opportunities to Develop Meaningful Biomarkers: Polycystic Kidney Disease Biomarker Qualification

SHASHI AMUR

U.S. FDA’s CDER

4、Challenges and Opportunities for Qualifying Biomarkers: An Industry Perspective 

GABRIELA LAVEZZARI

PhRMA

5、Collaborative Approaches for Developing Kidney Safety Biomarkers

JOHN MICHAEL SAUER

The Critical Path Institute

6、Developing Capabilities to Integrate and Use Data from Large Data Sets

MARTIN LANDRAY

Big Data Institute University ofOxford

7、Approaches to Overcoming Variance Due to 

Heterogeneity in Rare Disease

SUSAN WARD

TAP Collaboration

8、AccessTo Patient Level Data From Clinical Trials

PERRY NISEN

Sanford Burnham

9、The Role of Open APIs and the FHIR Platform for Enabling The Integration of Research and Clinical Care Data

CHARLES JAFFE

Health Level Seven International

10、Data Aggregation Across Diseases and Between Stakeholders

ENRIQUE AVILES

The Critical Path Institute

10、Integrating Systems and Capabilities to Enhance Safety Surveillance

RICHARD PLATT

Pilgrim Health Care Institute

11、Harnessing Web Search Data as Complementary Signals for Pharmacovigilance

ERIC HORVITZ

Microsoft Research

12、Online Discussion Forums as Potential Sources of Adverse Drug Event Data

JOHN H. HOLMES

University of Pennsylvania

13、New Frontiers: Surveying Twitter Feeds and Other Social Media

JOHN BROWNSTEIN

Harvard Medical School

14、Statistical Modeling for Efficient and Adaptive Trial Designs Using Composite 

Endpoints

BRIAN ALEXANDER

Harvard Medical School

15、Models of Clinical Trial PK/PD Translated To 

Population Drug Use and Exposure 

SANDY ALLERHEILIGEN

Merck

16、A Quantitative and Integrative Simulation Model for Optimizing Clinical Trial Design to Measure Cognitive Changes of Alzheimer’sDisease

BRIAN CORRIGAN

Pfizer

讲座第三部分

ENVISIONING THE FUTURE OF REGULATORY SCIENCE: AFORWARD-LOOKING AGENDA

1、Core Components of Regulatory Sci Curriculum

SCOTT STEELE

University of Rochester

2、Lessons from Another Sector: Big Data

SAM SHEKAR

Northrop Grumman

3、Training the Regulatory Scientist for Medical 

Product Development

OWEN FIELDS

Pfizer Inc

4、The Future of Regulatory Science at FDA

STEPHEN OSTROFF

U.S. FDA

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