This month, the European Medicines Agency (EMA) published the first summary of a risk management plan (RMP) for a newly authorized medicine. An RMP is a publicly available document that describes all that is known and unknown about a drug’s safety and what actions will be taken to monitor the drug on the market and mitigate any risks. CROS NT explores what this means for your clinical data.
With this news, the EMA published a statement saying, “the Agency will pilot the publishing of RMP summaries for all newly centrally authorized medicines during 2014 and at a later stage will start producing RMP summaries for previously authorized medicines”.
The publication of RMPs is a step towards greater clinical data transparency in the European Union. RMPs include:
- A medicine’s safety profile
- Plan to prevent or minimize risks for patients
- Plans for studies and other activities to gain more knowledge about the safety and efficacy of the medicine
- Risk factors for developing side effects
- Measuring the effectiveness of risk-minimization measures
How can companies prepare their clinical data for greater transparency?
The European Union RMP includes three main components:
- A summary of the drug’s safety based on previous pre-clinical and clinical studies
- The pharmacovigilance plan, and most importantly
- The risk minimization plan which provides preventative actions from doctors, pharmacists and clinical trial professionals, including statisticians.
Centralize Clinical Data from the Start
If one study is assigned to statistical trial design, data management, data analysis and medical communications from the start, common data standards can be applied throughout the drug development process. Continuity of team members creates a consistent style of medical communications and important collaboration between statisticians, data manager and medical writer. All data are stored in a central data warehouse and/or archive which avoids having to keep track of multiple repositories.
Centralizing clinical data in the early phases of drug development facilitates better integration of studies across all phases with common assessment methods, uniform traceability of data as well as the centralization of study metrics and study reports.
Ensuring Traceability for Regulatory Submissions
In order for data to be transparent and to the public, it must also be traceable. Implementing CDISC standards helps both traceability and cross analysis of datasets. There must be clear traceability from analysis results, to analysis datasets, and to SDTM datasets. There are two types of traceability: data-point traceability and metadata traceability. ADaM datasets allow for the creation of variable or observations that are not directly used for the statistical analysis but support traceability. For example, re-allocation of data may happen for early termination visits in accordance with the Statistical Analysis Plan. Metadata traceability includes documentation required to clearly describe information that already exists in the SDTM database together with algorithms and methods used to derive an analysis result.
Invest in Clinical Data Visualization Tools
Conducting a trial generally leads to data being spread across multiple databases, including EDC, CTMS, ePRO, safety databases etc, and if a centralized approach was not employed, such databases can be spread across multiple vendors. Data visualization tools allow the ability to drill-down data and click-through multiple levels of detail, allowing for the analysis of specific subsets and sub-populations. Customizable dashboards allow the clinical team to create ad hoc reports on site performance, data quality, safety and efficacy, drug supply and patient management.
However, perhaps the biggest benefit of data visualization is that clinical metrics from multiple sources can be analyzed in real time. Moreover, decision-makers can identify and fix underperforming sites and make crucial decisions on study progress.