The Interpretation of Clinical Guidances & Regulations Initiative shares expertise to more efficiently and effectively meet the intent of ambiguous regulations/guidances and Health Authority operational expectations around the world. Secondarily, the initiative will take opportunities to raise Health Authority awareness of the real-life impact of ambiguous regulations/guidelines. While addressing these aims, the initiative will consider the highest priority to be continued enhancement of patient safety.
Quality Tolerance Limits: Integrated Addendum to ICH E6(R1): Guideline for Good Clinical
Guidances: Integrated Addendum to ICH E6(R1): Guideline for Good Clinical Practice E6(R2)
Agency: ICH
Guidance Date: November 2016
Approach
- The topic team reviewed the 2016 Guidance and identified areas that may need clarification.
- The topic team then provided greater clarity on how to interpret and operationalize QTLs as per ICH E6 R2 through deliverables (see below).
Value
This topic’s objective was to improve the systematic quality of Clinical Research by facilitating the implementation of Quality Tolerance Limits (QTLs) in ways that advance compliance and efficiency but minimize confusion and duplication of effort.
Deliverables
| Asset Type | Title | Description |
|---|---|---|
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Historical Benchmarks for Quality Tolerance Limits Parameters in Clinical Trials | A peer-reviewed publication showing that historical parameter values can provide helpful benchmark information for quality control activities in future trials. |
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Quality Tolerance Limits Framework | A framework including a proposed definition of the term ‘QTL’ , process maps/steps and examples of QTL parameters. |
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What You Need to Know About Quality Tolerance Limits Infographic | An infographic introducing how a QTL is defined, how they fit into the broader context of a Quality Management System and when action may be taken because of a QTL. |
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Quality Tolerance Limits: Framework for Successful Implementation in Clinical Development | A peer-reviewed publication sharing the work done on the definition of the term ‘QTL’ and parameter guides. |
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Quality Tolerance Limits Plan Template | A template intended to aide sponsors in developing their own QTL monitoring plan |
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Quality Tolerance Limits FAQs | A compilation of FAQs and responses. |
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Quality Tolerance Limits Text for the Common Protocol Template (CPT) and Clinical Study Report (CSR) | Model instructional text for inclusion in the TransCelerate Clinical Content & Reuse Initiative CPT and CSR Templates. |
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Demystifying Quality Tolerance Limits Webinar | This webinar reviews guidance and considerations for QTL implementation in Clinical Development through highlights from the TransCelerate Quality Tolerance Limits Framework, including but not limited to a proposed definition of the term “QTL,” process map/steps, and examples of QTL parameters. |
GDPR Data Reuse
A Privacy Framework for Clinical Data Reuse: Secondary Data Use in the Pharmaceutical Industry
This framework has been developed to decrease the time companies spend addressing privacy questions related to research and increase the potential reuse of clinical data. An increase in this data reuse would ultimately reduce the burden on patients, with less data needing to be collected as the use of existing data was maximized.
With a focus on privacy the framework introduces six principles and a best practice process model for reuse of clinical data for research purposes. Furthermore, the framework provides examples of potential primary and secondary uses of research data in the pharmaceutical industry.
As the EU General Data Protection Regulation (GDPR) has become a global inspiration for data protection legislation it has been used as the basis for developing the framework. However, the framework can also be utilized with other privacy regulations and requirements.
Guidance: EU General Data Protection Regulation
Agency: European Commission
Effective Date: May 2018
Approach
- The team reviewed the 2018 regulation and relevant opinions and guidance in regards to data reuse.
- The team then developed a framework to provide greater clarity on proposed principles for management of data reuse.
Value
Data reuse has the potential to decrease the time companies spend addressing privacy questions related to research and increase the potential reuse of clinical data. An increase in this data reuse would ultimately reduce the burden on patients, with less data needing to be collected as the use of existing data was maximized.
This framework is meant for companies developing their internal data reuse processes in compliance with privacy regulations. However, it could also be relevant for cross company research collaboration and subsequent sharing of data.
Deliverables
| Asset Type | Title | Description |
|---|---|---|
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A Privacy Framework for Clinical Data Reuse: Secondary Data Use in the Pharmaceutical Industry | A framework intended to provide a set of principles and best practice processes for secondary research based on clinical data, a glossary, and reuse use cases. |
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Overview Video | A video on the key aspects of the framework, including principles for reuse of clinical data, a glossary, use cases and a best practice process. |
| Tools for Reuse of Clinical Data | ||
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Glossary | A glossary, which provides definitions relevant to key terms within the framework. |
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Definition of Primary and Secondary Use of Clinical Data | Examples of potential primary and secondary uses of clinical data within the pharmaceutical industry. |
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Step-By-Step Best Practice Process | A quick overview of a process, which companies can use when considering a data reuse project, containing a table aligning the stages and steps with the principles outlined in the framework. |
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Reuse Assessment Form | A template for assessing the reuse of data for a possible research activity. |