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Risk Mitigation in Clinical Research: It Starts with Study Startup


2017-bl-risk-mitigation-in-clinical-research-page-imageFor clinical research, Study Startup (SSU) is the pivotal period that lays the groundwork for a successful study. By ensuring that the right sites are selected and target subjects are recruited in a timely and cost-effective manner, mission critical risks can be avoided that lead to runaway costs and delays. Up to now, risk management efforts have focused largely on regulatory compliance, manufacturing, and post-marketing drug safety. However, risks deeply rooted in SSU including: patient enrollment, site-staffing shortage, drug supply logistics, and regulatory delays, have the potential to derail a company’s costly development programs. SSU is one of the most complicated parts of clinical trials[1] and one of the most crucial to meeting site activation timelines and study completion milestones. Yet, its performance scores lag other stages of clinical research[2].

Globalization and trial complexity escalate risk-based challenges while market pressure keeps rising for new therapies at an ever-increasing pace. Overseeing these tasks from dozens or even hundreds of sites requires tracking and storing country-specific documents, ensuring that the most recent versions are being used, and identifying bottlenecks as the study startup process unfolds.

A practical risk management methodology enables stakeholders to identify clinical trial issues early on, which in turn allows for the design of effective mitigation strategies before the risks materialize. As defined by the Project Management Institute, risk mitigation is the process of developing options and actions to enhance opportunities and reduce threats to project objectives. It includes tracking identified risks, identifying new risks, and evaluating risk process effectiveness throughout the project. It is not enough to identify and track risks; risks must also be assessed, which, according to Linda Sullivan, President of Metrics Champion Consortium (MCC), focuses on quantifying the risk based on its potential to occur and become a serious problem. Sullivan asks, “How frequently has the issue occurred in other studies? When the issue occurred, was it a serious problem? Stakeholders may choose to mitigate risk when it is determined that an event is likely to occur and have a significant impact on the study.”  

Both the Food and Drug Administration (FDA)[3] and the European Medicines Agency (EMA)[4] have released documents supporting greater acceptance of risk-based methodologies, which they state should begin from the start of a clinical trial. Each of these documents comment that in a clinical trial, the degree of risk is predictable, and should be anticipated using data-driven approaches.

You can’t manage what you don’t measure

Metrics provide the foundation for business intelligence, affording clinical research teams an opportunity to intervene before the effects of a risk have been incurred. This risk mitigation is therefore optimal using systems which can provide timely, preferably real-time data on trial bottlenecks, which indicate red flags to be reviewed and addressed or at least tracked carefully throughout the trial.

The industry standard risk management framework (taken from the ICH Q9 risk management process[5] (Figure 1)), illustrates a simple cascade of events, starting with risk analysis and ending with risk review. Risk analysis involves first identifying risks or potential sources of harm and estimating the probability of that risk occurring. Organizations may do a study risk assessment during protocol design that is qualitative in nature. For example, an organization may have a high risk in the area of patient recruitment. What is the likelihood of that risk occurring? Does the risk occurrence differ by region, site, investigator, patient cohort, etc.? Moving onto risk evaluation the organization needs to make a decision as to what to do about the risk (i.e., Do they accept the risk? Try to mitigate it? Or change their study plan altogether?) The reality is that organizations have no way of monitoring this potential risk unless they are measuring patient recruitment data in a granular way in real-time. Simply put, you cannot manage what you do not measure. Realizing a sophisticated risk management framework for clinical research for hundreds to thousands of patients simply cannot be done effectively using tools such as Excel.


2017-bl-risk-mitigation-in-clinical-research-chart-page-image (002)

Figure 1. Schematic representation of ICH Q9 risk management process.

Applications that are purpose-built to support SSU improve start-up metrics by facilitating the identification, feasibility assessment, selection, and activation of high performing sites; budget and contract negotiations; and the tracking of protocol amendments and regulatory documents. It is beyond Excel, regrettably still omnipresent in clinical research as the de facto tool, to define and track SSU milestones in real-time, assign risk triggers with milestone re-projections, record the completion of activities, and automatically trigger workflows to begin as others are completed. 

Risk management is a team effort

Beyond the data itself, the second major ingredient for effective risk management is collaboration; that is, enabling the risk evaluation and control steps outlined in Figure 1. With data in hand, you must have a means by which to evaluate risks and decide what the team is going to do about them. This requirement highlights one of the key reasons why tools like Excel are ill-suited for this task. Having a tool where you can view trends centrally, assign roles, provide real-time alerts, manage milestones and make projections is a critical factor for successful risk management. 

Identifying poorly performing sites has been a longstanding challenge for the industry. Statistics on typical site enrollment tell the story. Half of investigative sites under-enroll[6], 11 percent of sites fail to enroll a single patient, and a mere 13 percent exceed their enrollment target. In addition, Phase 3-4 study timelines often have to be extended to almost twice their original length to achieve enrollment goals[7].

Purpose-built SSU technologies enable improved decision support by generating target site profiles.  Such profiles, compiled from both internal and external data sources, contain information on start-up time, patient retention, and quality. This technology mitigates risk factors for recruitment and retention by finding the optimum alignment of top-performing sites with substantial patient databases, and quickly assessing which sites have performed best in similar studies.

This data-driven approach enables stakeholders to be proactive in identifying and resolving bottlenecks in real-time by instantly viewing status, and quantifying the clinical research team’s performance. This capability serves to mitigate risk as it removes the task of assembling data manually from multiple systems. Problems can be analyzed and resultant information disseminated quickly.

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Conclusion

Risk is our constant companion, and as clinical trials grow in complexity, so do risk-based challenges to bring new therapies to market at an ever-increasing pace. The continued reliance on Excel, which lacks project- and risk-management functionality, has created an illusion of safety often fueling the rescue study industry.

SSU represents a complex array of processes and without tools designed for risk management planning, each has the potential to cause delays and, ultimately, jeopardize the study. To mitigate this situation, purpose-build SSU tools spanning site identification, feasibility assessment and activation that provide risk management capabilities are a critical improvement over traditional manual processes. Actionable, granular performance metrics empower study managers to keep studies on track and within budget and, ultimately, speed new therapies to market.


2016-nl-bl-author-craig-morgan (002)Craig Morgan currently heads up the marketing and brand development functions at goBalto, a life science SaaS technology organization, working with sponsors, CROs, medical device manufacturers, and sites to reduce cycle times and improve collaboration and oversight in clinical trials.

Craig is a technology and life sciences management professional with over 15 years’ experience in the application of informatics and bioinformatics to drug discovery. He holds degrees in analytical chemistry, information systems, and business administration and is a certified project manager with the Project Management Institute.

 

 

 

[1]   English RA, Lebovitz Y, Giffin RB. Transforming clinical research in the United States: Challenges and opportunities. Workshop Summary. Institute of Medicine. 2009. https://www.ncbi.nlm.nih.gov/books/NBK50892/

[2]   Adelglass, J. The critical elements of study startup. CenterWatch. June 29, 2015. Available at: http://www.centerwatch.com/news-online/2015/06/29/the-critical-elements-of-study-startup/. Accessed February 16, 2017.

[3]   Guidance for Industry: Oversight of clinical investigations — A risk-based approach to monitoring. Food and Drug Administration. August 2013. Available at: http://www.fda.gov/downloads/Drugs/GuidanceComplianceRegulatoryInformation/ Guidances/UCM269919.pdf. Accessed January 15, 2016. 

[4]   European Medicines Agency. Reflection Paper on risk based quality management in clinical trials. November 2013. Available at: http://www.ema.europa.eu/docs/en_GB/document_library/Scientific_guideline/2013/11/WC500155491.pdf. Accessed March 31, 2016. 

[5]   ICH HARMONISED TRIPARTITE GUIDELINE. Quality Risk Management Q9. Available at:  https://www.ich.org/fileadmin/Public_Web_Site/ICH_Products/Guidelines/Quality/Q9/Step4/Q9_Guideline.pdf

[6]   Gwinn B. Metrics for faster clinical trials. PharmaVoice. June 2011. Available at: http://www.pharmavoice.com/article/2278/. Accessed March 29, 2016.

[7]   Getz K, Lamberti MJ. 89% of trials meet enrollment, but timelines slip, half of sites under-enroll. Impact Report. Tufts Center for the Study of Drug Development. January/February 2013, 15(1). http://csdd.tufts.edu/files/uploads/jan-feb_2013_ir_summary.pdf

 

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