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International Clinical Trials

Safely Does It



Accurate reporting of serious adverse events is an essential stage in the clinical development process. New data visualisation technology and statistical analysis software can make the process more efficient, reducing the cost for sponsors and increasing safety for patients.

Conducting clinical studies that are safe and effective is a goal shared by all participants in a clinical trial, from the patients to sponsors and regulatory bodies. One cornerstone of clinical trial safety is clear and consistent monitoring of serious adverse events (SAEs). Over the years, the clinical research industry has developed methods for tracking SAEs and determining their cause. However, these traditional methods have a number of shortcomings, including delays in identifying potential problems and obstacles to determining causality or analysing all relevant information. Over the past 10 years the industry has embraced new technology, including electronic data capture (EDC) and safety systems, which are placing growing pressure on sponsors to update their safety procedures in order to fully comply with regulatory requirements. This article will discuss how best practices in SAE reporting are changing, and how companies can use modern analytics and visualisation tools to keep their own SAE reporting systems in compliance with regulations.

SAE Reporting Requirements

Monitoring is the most fundamental component of the safety reporting process. Over the course of a clinical trial, the sponsor is responsible for reviewing all information relevant to the drug, including data derived from the study itself (1). If the sponsor identifies an adverse event that is potentially caused by the treatment that is under investigation, they are obliged to report the risk to the FDA and all investigators within a predetermined period of time. These reports are typically called investigational new drug (IND) safety reports.

The FDA has mandated which potential adverse events are and are not necessary to report – requirements which are fully detailed in 21 CFR Part 312. For example, the sponsor is required to report any SAE that is unexpected if there is evidence suggesting a causal relationship between the treatment and the adverse event, but is not required to report a SAE that does not appear to be related to the treatment. The key to meeting this requirement lies in determining whether or not there is evidence of a causal relationship within the timeframe for reporting.

Meeting the FDA reporting requirement has become more difficult for sponsors in recent years due to mass adoption of electronic data collection systems. Sponsors are collecting data in a format that is immediately accessible, thus their obligation to identify reportable events may begin much earlier in the process than they are used to with paper-based data collection.

Moreover, while sponsors are grappling with more data, they are also now required to analyse it with greater care. Effective from 28th March 2011, the FDA modified its rules with the primary goal of preventing over-reporting by sponsors, who had done so in order to comply but effectively ruined the efficacy of the process. As described by the FDA, the modifications “improve the utility of IND safety reports, reduce the number of reports that do not contribute in a meaningful way to the developing safety profile of the drug, expedite the FDA’s review of critical safety information, better protect human subjects enrolled in clinical trials, subject bioavailability and bioequivalence studies to safety reporting requirements, promote a consistent approach to safety reporting internationally, and enable the agency to better protect and promote public health” (2).

The bottom line for sponsors is that they must implement a process that quickly alerts them to potential adverse events, allows them to see and analyse all of the data being collected in their study, and helps them to readily identify underlying causes and potential confounding factors. Modern visualisation and analytics tools can address all of these challenges.

Timely Reporting

Given the short timeframe in which sponsors are required to make an accurate determination of whether an event needs to be reported under the FDA rule, it is essential that the appropriate stakeholders are aware of the event as soon as possible. Automatic alerting features in today’s visualisation and analytics tools, and ensures this process occurs immediately, without the need for investigator intervention. For example, the analytics software could be configured to analyse data collected by an EDC or safety system in real-time as the data is uploaded. If a certain type of data designated by the sponsor is uploaded, or a specific keyword or an adverse event, the analytics software would automatically send an email or text message to the sponsor’s safety team.

Electronic alerting can increase efficiency even where identifying potential problems is less straightforward. For example, global studies have traditionally suffered from translation issues. Before an adverse event can be properly coded, it may need to be translated from the language in which it was collected. There may be over ten languages used at the various investigator sites, yet once the data is reported it needs to be acted on within the same period of time regardless of its source. Therefore, rather than placing untranslated, collected data on hold for any period of time, alerts could be used to deliver new data to an appropriate translator as soon as it is entered into the EDC or safety system. Additional rules to increase efficiency could be placed on the alert, including changing the recipient based on the time the data is received so that, if translators are located throughout the world, it always goes to a translator during their local business hours, or if no action is taken on the data within a certain period of time, an alert goes to a translation supervisor. A subsequent alert can be set up after translation to go to the coding or safety compliance team.

Alerts are an extremely flexible way to meet the challenge of identifying which events may need to be reported in a timely fashion. They can be put in place before the first site visit of the first patient, or they can be added as the study progresses, giving the sponsor a better understanding of what information is relevant to them. Alerts can be simple or follow complex rules, as demonstrated in the previous example, and ideally they can be triggered by any data source or from combinations of data across a range of sources. The form that alerts can take can also be versatile, ranging from a simple SMS text message to an interactive report accessible on a smart phone or via email.

Analysing Relevant Data

One potential problem in the reporting process is that safety data is collected in a variety of ways during a clinical study.

Depending on the method of collection, data from the same study may end up in different systems or formats. The challenge for sponsors is to reconcile data across the systems.

Ideally, an investigator collects the data, recognises it as a potential SAE, and files it with the appropriate clinical safety group so that it is properly entered into the safety system. However, it is not uncommon for an SAE to be captured only in the EDC system or written on a case report form (CRF). Eventually the SAE will be discovered and properly filed in the safety system, but in the interim the sponsor’s ability to meet its reporting requirements, and even in extreme cases, the study itself, can be jeopardised.

Traditionally, the process of reconciling disparate data sources to ensure complete reporting of SAEs is a slow, expensive and error-prone process, as it generally involves the human resource-intensive process of regularly comparing data, often by manually evaluating printouts for the various systems side-by-side. Such methods not only rely on a human reviewer being sharp-eyed enough to identify issues, but also increase inefficiency because the reviewer is dealing with a static report, and therefore has to the take the time to request an additional report if he or she wants to drill down on any information in order to reconcile a piece of data.

Automated analytics tools can solve a number of these problems and provide huge productivity gains; today’s technology can create an automated reconciliation report that quickly points a reviewer to potential issues and reduces the likelihood of overlooking filing the proper procedures for a SAE. The technology that provides this functionality comes in many forms. One solution is a data warehouse that aggregates all data from the various systems into one place. Another option is software capable of querying data where they reside, across all formats or schemas.

Regardless of the form of the technology, any of today’s methods of automatically reconciling SAEs across data systems is likely to reduce costs for sponsors and increase safety for patients.

Understanding Causality

One of the most important aspects of safety reporting is determining whether or not there is a potentially causal relationship between an event and the treatment under study. As already noted, the FDA is looking to sponsors to reduce the number of unnecessary reports that they file in order to allow the FDA and investigators to focus on the most important safety concerns. Doing this requires sophisticated tools that provide reviewers with robust analytical functions.

Ideally, an analytics and visualisation tool should connect to all relevant data sources and provide the reviewer with the ability to explore and understand the data through a nontechnical (that is, a non-computer programmer) interface. A tool with this capability eliminates the downtime that can occur if data across sources needs to be reconciled or merged, or if a reviewer needs to wait for a programmer to create and run the reports he or she wants to see.

Such tools should contain a number of features. Ad hoc querying and drill-down capabilities are extremely helpful when providing a reviewer with the ability to understand causality. The reviewer must be able to identify and compare patient subpopulations to determine whether or not one subpopulation is at risk over another. They must be able to find outliers and trends – potential concerns that are worth further interrogation. They must be able to quickly look at all relevant data from a patient, for example all of the data from that patient’s CRF, in order to determine whether or not something else is happening. Statistical analysis capabilities are also essential. The reviewer must have access to statistical analysis systems (SAS) and the ability to bring up analysis such as comparative incidence and more intense statistical analysis across all relevant data.

Understanding causality is an analysis-driven process, so the right tools are critical for reducing the time reviewers spend setting up and waiting for data reports and increasing the time they have to review the contents of those reports.

Conclusion

In today’s clinical research landscape, sponsors are under more regulatory pressure than ever before. The use of modern analytics and visualisation software is critical to ensuring sponsors can deliver timely SAE reporting to regulatory bodies, making sure that all relevant data is included in this analysis, and helping to understand whether or not there is a causal relationship between an incoming SAE and the treatment. Ultimately, these tools will empower sponsors with the information they need to make the changes necessary to reduce risk, and ultimately, save studies.

References

  1. US FDA, Code of Federal Regulations Title 21, available at www. accessdata.fda.gov/scripts/cdrh/cfdocs/cfcfr/CFRSearch. cfm?fr=312.32
  2. 21 CFR Parts 213 and 320, Federal Register, 75(188): pp59,935- 59,963, 29th September 2010, available at www.gpo.gov/fdsys/pkg/ FR-2010-09-29/pdf/2010-24296.pdf

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Rick Morrison is the co-founder and Chief Executive Officer of Comprehend Systems. Prior to founding Comprehend Systems, Rick served as the Chief Technology Officer of an internet-based data aggregator, where he was responsible for product development and operations. He has over a decade of experience writing software for clinical trials, including tools that are now used by the FDA and top pharma companies. Rick holds a BSc in Computer Science from Carnegie Mellon University.
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