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International Clinical Trials

Why Risk It?

To date, risk management has failed to comprehensively capture the actual process of clinical trial delivery, relying on a more ad hoc and generic approach rather than an individually tailored and structured one. Appropriately and actively managing operational risk can, and will, minimise time delays, budgetary over-runs, poor quality delivery and conflicts with outsourcing partners. In summary, acceptance of the statement “if it could go wrong, it can go wrong” underpins the absolute necessity for risk management within a clinical study.

In its broadest sense, risk can simply be defined as “the possibility of suffering harm or loss”. However, this can be further developed into “the potential that a chosen action or activity (including the choice of inaction) will lead to a loss (an undesirable outcome)”, and it is this definition that is most applicable to the delivery of clinical studies. Furthermore, given that planned actions (such as those that form a clinical trial development plan) are subject to large cost and benefit risks, detailed risk assessment and management for all such actions are crucial to ensuring their success. This strategy forms the basis of operational risk management (ORM).

In adopting this approach, ORM becomes a continual cyclic process, which includes a formal risk assessment, risk decisionmaking and implementation of risk controls. By default, this results in the acceptance, mitigation or even avoidance of risk from inadequate or failed internal processes and systems, human factors or external events.

The fundamental principles of ORM include:
  • The acceptance of risk when the benefits outweigh the cost
  • The acceptance of no unnecessary risk
  • The anticipation and management of risk through appropriate planning
  • The making of risk decisions at the right level of operational expertise and responsibility
The structured processes underlying ORM include:
  • Identifying the risks
  • Assessing the risks
  • Making risk decisions
  • Implementing controls
  • Monitoring for change and outcomes
Successful implementation leads to:
  • The reduction of operational loss
  • The reduction of compliance and auditing costs
  • The early detection of time-delaying, cost-increasing and unlawful activities
  • The reduction of exposure to future risks
ORM is, therefore, all about making and managing risk decisions, rather than making risky decisions.

The Uncertainty Principle

Clinical studies inherently involve uncertainty, not only for the product, but also for the development and delivery of the trial itself. Identifying and understanding these uncertainties enable the definition of specific study-associated risks and is, ultimately, critical to successful trial delivery. Ambiguity should be viewed as a continuum, rather than as a binary assessment, as most – if not all elements – may become more or less uncertain, rather than being either uncertain or certain.

Importantly, within the clinical trial process, not all uncertainties can be identified immediately. Nevertheless, the construction of an uncertainty matrix allows for the recognition that some elements may be more important than others, and additional ambiguities may become apparent either as the result of an existing uncertainty or as a de novo event. This may be considered in the context of uncertainty breeds uncertainty. Conversely, however, identification and management of a particular area of doubt may, indeed, result in other potential issues becoming less vague.

So how can uncertainties be identified for a particular clinical trial? In general terms, there should be multiple inputs into the uncertainty matrix, including – among others – commercial, regulatory and operational aspects. In this context, accurate and current clinical trial feasibility should form a critical part of the process. Feasibility should not, however, be considered as creating certainty, but instead as providing information to reduce it and allowing proactive risk identification, management and mitigation.

Enabling feasibility to deliver requires a structured approach, tailored for each trial, and should include at a minimum:
  • Identification of key uncertainties
  • Questioning to acquire sufficient data to determine the interaction between, and the risk associated with, each uncertainty
  • Outputs to allow an assessment of the data through interpretable information
Furthermore, recognising that any one feasibility may generate change or additional uncertainties can require the adoption of a staged or sequential feasibility process to address such issues.

Feasibility Management

It is clear that uncertainty and risk (as well as risk management) are inextricably linked. Detailed feasibility should be an integral part of ORM for a clinical trial, but is more than simply identifying a list of investigators either de novo or from pre-existing database lists. Commonly documented or inferred risks and their management tend to focus on the obvious at a high level, and simply invoke contingency planning rather than detailed and formal assessments, monitoring and response. Indeed, risks assessed in isolation and at a high level may be underor over-weighted, if taken out of the context of the wider trial environment. In reality, for example, identification and selection of a suitable number of investigator sites should always rank as an ‘extreme’ risk, as the consequences of failure have a wider impact on overall trial performance.

ORM can, therefore, be regarded as a two-dimensional matrix, with risks concerning the group or sub-group of investigative sites as a whole and those associated with individual sites. Importantly, both dimensions can act in concert or independently. For example, the risks of not meeting anticipated group recruitment rates can be influenced by either individual and discrete risks at each site (for instance, lack of appropriately trained personnel, or of the availability of suitable subjects) or factors that affect all sites globally (like inappropriate protocol design or failure to gain regulatory approval). This further highlights the observation that managing a single clinical trial operational risk within a sub-group, such as an individual site, can have much wider implications, and clinical trial operational risks as a whole need to be managed as such.

As implied from the above, performing an ‘uncertaintydriven’ feasibility analysis and managing the associated operational risk calls for a clear and structured process to be in place. Assessing these risks requires a clear map as to where each risk arises and how these interrelate and impact on the global risk profile. Figure 1 (see full PDF) shows how different groups of risk interrelate, influence and impact on others within the ORM of a clinical trial – feasibility risk management (shown in purple) and overall subject recruitment (shown in red) form two such overlapping groups within the overall risks regarding a clinical trial (shown in grey). Such a construct readily allows risk to be managed appropriately.

Implementing ORM

Managing and delivering a study is fundamentally driven by people and processes. The increasing pressure to deliver cost-effective, timely and quality results continues to drive change within the industry and is leading to, for example, running larger and more complex clinical trials in emerging regions, and the greater use of outsourced services.

It can be argued that ORM is merely a more disciplined, formal approach to the everyday tasks that project managers already perform, albeit sometimes unconsciously. This may be, in part, correct – but risk management should be regarded as the umbrella of a clinical trial: its purpose, function and necessity understood by all parties involved. For a study to be run in its entirety by a single individual would be classified by most as being an extremely high-risk proposition. However, paradoxically, the converse can also be true. More resources do not necessarily equate to less operational risk. Poor communication, for example, can amplify and even conceal risk within larger teams.

So how can ORM plans work at a practical level without becoming over-burdensome for pressured and busy clinical operations teams, or incurring unacceptable additional layers of management? Lessons can be learned from specific groups – such as pharmacovigilance teams – and also from other industry sectors like finance and construction. In the latter, there is a strong focus on regulated processes, and significant advantage has been gained by incorporating and integrating trained risk management personnel into delivery teams.

Be Certain

Uncertainty is an inherent part of clinical trials, and feasibility forms a critical strategy in the documentation of perceived or actual risk. Active – rather than passive – risk management is, therefore, important and, despite not necessarily having prescriptive standards in the pharma industry, is a universal concept. Identification of uncertainty and operational risk is best managed in a framework of responsibility and communication. These are within the domain of all those involved in clinical trial activities and should be managed in the same way as, for example, clinical monitoring or biometrics. Importantly, upward communication within a team is critical, and fostering such a culture will ensure that risk decisions are made by the right person at the right time.

ORM is not difficult and, with relatively little effort, can be readily embedded within clinical operations. The consequences of this approach will not only support faster, more cost-effective and higher quality results, but will also facilitate better team work and more informed and structured responses to those events that will undoubtedly arise in any clinical trial.

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Dr Guy Patrick, MD, PhD, is the Director, Chief Medical Officer and co-founder of Centrical Global, a specialist clinical trial feasibility and ORM company providing services to a broad range of pharma and biotech businesses. Having trained as a Consultant Nephrologist and Renal Transplant Immunologist, he formerly worked for Omnicare Clinical Research prior to co-founding Premier Research Group, a leading full-service CRO.
Dr Guy Patrick
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