Robert King

A regulatory submission marks the end of a long, exciting, and often challenging journey. It’s the final and most pivotal step in clinical development which, if not done correctly, can be a barrier to bringing your valuable product to market. For smaller companies in particular, submission can be a make-or-break situation.

It’s especially true, at this time, when clinical development has never been more complex. Industry guidance is changing for many therapeutic areas and COVID-19 has accelerated the evolution in trial design, giving rise to innovative approaches such as digital, decentralized, and virtual trials. Additionally, there are more data sources than ever before, which demand expert integration prior to submission.

The more things change, however, the more they stay the same. The key to submission success continues to be delivering a clear and effective integrated summary of safety (ISS) and efficacy (ISE) as quickly as possible. This requires a multidisciplinary team of experts—clinical, statistical, regulatory, medical writing and more—who understand the compound and regulatory requirements and can do the hard work of generating and integrating the data to address all safety and efficacy questions.

It’s important to start with the end in mind, though the inherent nature of clinical development is fluid. In many cases the data, not the plan, provide the direction. Teams won’t really know what they’re facing until they see: first, the data from individual studies; and second, those data in an integrated fashion. 

As is standard practice in the clinical trials industry, multiple rigorous studies are required to prove the safety and efficacy of a treatment:

Phase I: First-in-human studies in healthy volunteers to determine if a treatment is safe, determine how it works and understand any side effects

Phase II: Studies in patients with the disease/condition to see if the treatment is effective and to understand the optimal dose/administration

Phase III: Large randomized studies of patients that take place across regions or countries to understand how the treatment compares to any existing treatments

An expert data integration strategy is critical because many forks in the road can and will arise. Product acquisitions are commonplace after a successful Phase II, impacting both plans and data as the scope inevitably changes. Additionally, there may be early-stage trials that are not beneficial to the overall program, so it’s important to be able to consider that data in context and weed it out during data pooling.

Timelines shown are typical but may be compressed in cases such as emergency use authorization or fast track, breakthrough, or other accelerated designations.

Phase III is a critical milestone where positive results mean the product will likely move forward with a submission. Waiting until the end of Phase III to create your data integration strategy is not a feasible option because doing so risks adding months to the process and delaying regulatory approval.

Having the ISS and ISE ready soon after the last Phase III pivotal study database lock can expedite the submission and help speed the new medication to the market. This means that ISS and ISE activities must happen in parallel to the individual pivotal study analysis.

Starting ISS/ISE activities once Phase III pivotal study results are complete can streamline the integrated reporting process, which can reduce effort and costs. But this approach does extend the submission timeline.

Timing is a perennial challenge. Plan too early and you may waste time, money, and resources if initial study results are not optimal. Plan too late, and you lengthen the time it takes to get your product to market.

The accepted industry guidance for study data submission is Clinical Data Interchange Standards Consortium, or CDISC. These standards determine the format data must be submitted in, for both individual studies and integrated reporting. Although CDISC is used industrywide, there are different interpretations by company and region. The process will mean submitting to one region and then using that information to amend and submit, region by region.

Adherence to the data standards/guidance is critical to helping the U.S. Food and Drug Administration and other agency reviewers review and understand the data. If the data are not in the correct format, the submission will be rejected and will need to be configured and submitted again—costing valuable time, money, and loss of sales revenue.

Good data integration means converting and consolidating data from different studies on multiple systems in different formats. This is not as easy as it sounds because it requires resources who know every study, every format, every endpoint, and the full development process. Regulators around the world will want the data in the standard format for quick interpretation and answers to their questions. This takes specialized knowledge.

The compound development plan drives the overarching resourcing approach. It’s a delicate dance, being able to estimate what specific resourcing will be required for a successful submission: how many people, what specific experience level, when?

There are certain roles to cover through the clinical development journey. We will not discuss what is required for individual studies but for integration it is important that a submission-experienced programming leader is in place. This person can set the programming strategy that will be deployed alongside the integration lead statistician. Involving the medical writer preparing the integrated reports and an experienced regulatory lead early also is essential. They along with the statistician and programmer will help drive the appropriate strategy.

Other key players to involve are medical experts who will help define pooling approaches that are applicable given the available data and medical need/practice plus other experts depending on the need (e.g., pharmacokinetics, data managers for recoding activities).

For data integration and statistical programming, you’ll need people who know what data can go together and are familiar with how to post and interact with regulators. This includes knowledge of platforms and folder structures to keep everything ticked off and in order.

Once the integration strategy is set and ISS/ISE timelines are determined, a team with the right experience level and the right size becomes key to a successful submission.

Those who take on the challenges of an ISS/ISE must possess compound knowledge, therapeutic experience, and statistical and programming technical expertise, but also have a deep understanding of regulatory requirements and know how these vary from country to country.

Throughout the ISS/ISE production period—especially when it is happening in parallel with individual study reporting effort—there will be times when rapid resourcing ramp up becomes necessary to accommodate new or changing analysis requirements across the program. How can the team effectively deal with the peaks and troughs of the resourcing needs to avoid unnecessary delay of the overall submission timelines?

Once the submission goes out the door, you must plan for the period between the filing and regulatory approval. Some regulatory agencies may require rapid responses to review questions throughout the review period. There also will be 120-day updates. These additional requirements must be resourced by members of the core team to ensure efficiency and speed in responses.

There is no one answer to these questions; rather, various scenarios should be worked through with the submission team to determine the best course of action.

Statistical/data integration expertise is your primary concern at this stage (as opposed to data management, which occurs during the clinical trial process itself—the exception being to update coding terms to the same versions of dictionaries before integration). Even so, there are many other factors that drive the need for having other skilled experts on the team.

The submission strategy for every therapeutic area, indication and region is fully unique—a cardiology study is vastly different from oncology.

If you’re working in rare diseases, there will be a small population and pool of data. Do you have a partner who understands this and has proven success in this area?

There are always follow-ups and updates from regulators! Submission isn’t the end of the road, so resource accordingly.

Experience and relationships with regulators around the world often go hand in hand. Proactive engagement with agencies is a success factor because there are many open channels to engage with regulators in advance. For instance, you can share your preliminary plan, give them early looks at the data and let them point out any potential red flags or concerns. Take advantage of that so you can see questions coming, make any course corrections and plan for a positive response.

Above all, getting to yes with regulatory agencies involves a deep knowledge of the mechanics of the submission as well as how to interact with regulators. This involves the entire process starting from program development and individual study design, all the way to submission and beyond.

is Vice President, Biostatistics and Programming;

is Senior Director, Biostatistics and Programming; and

is Senior Director, Biostatistics and Programming, all of PPD

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The data, not the plan, provide the direction. 

Data Integration Strategy: The Cornerstone Of A Successful Regulatory Submission

	Companies engaged in commercial clinical research have, since the introduction of formalized good clinical practices (GCP), adopted highly defensive practices around the monitoring of clinical trials. The reasons, while diverse, include fear based on perception and interpretation of regulatory requirements. These processes have played a very large role in the high costs associated with clinical trials, without delivering commensurate value. Reasons are many, but surely include propagation of practices established during the years that trials relied on paper-based case report forms (CRFs) without considering optimization of the processes to take advantage of the technology provided by electronic data capture (EDC). All of this has resulted in the institutionalization of high cost, unproven value, manpower-intensive practices that do little to serve the interests of subject safety or quality data. Pharmaceutical, biotech, and medical device industries experiencing unprecedented economic upheaval can no longer justify practices that deliver little value at exorbitant cost. Rather than continuing to spend time, energy, and money focusing on minutiae (e.g., the ability of site personnel to properly transcribe observations from one medium to another), those engaged in clinical trial conduct must re-focus their energies on the aspects of the clinical trial that matter most, such as protocol compliance, subject safety, data timeliness, and data integrity. Through the use of more up-to-date methodologies and technologies, companies can effectively address these concerns while saving both time and money. This article addresses the regulatory and business rationales for adopting monitoring-related tools and processes that, if implemented thoughtfully, should deliver higher-quality trial data, faster, and at significantly lower cost.

	Most areas of business and government-including finance, insurance, public health, agrochemicals, manufacturing, and pharmacovigilance-apply risk-management principles, leveraging mathematical principals (namely statistical inference) in circumstances where it is simply impossible to ensure the quality of a large volume of services or products by exhaustingly checking the accuracy of every item. Clinical research-related industries, operating under GCP, appear as a notable exception for the following reasons:

		The honest and honorable intent to ensure the highest possible quality and safety for products that have a direct impact on the life and well-being of us all

		The pre-GxP beginnings of pharmaceutical research based on the use of paper CRFs

		Fear of failure based on results of regulatory inspections

	Quality risk management has been rather limited in the area of GCP. Until recently, research and development personnel (as well as their representatives/CROs) have believed that they were required to engage in 100% verification of all records and reports obtained in the context of Phase I-IV clinical research.

	These deeply ingrained beliefs have led to practices that are not only prohibitively expensive, but also fail to deliver value that can justify these costs. As a result, the pharmaceutical and device industries have institutionalized practices and behaviors that deliver low value at exorbitant cost. These costs include both the direct and indirect expenses associated with monitoring investigative sites, as well as the opportunity costs associated with these antiquated practices. Exhaustive manual verifications have shown their limits in particular when it comes to identifying (in a timely manner):

		Safety related signals and trends across large datasets

	The almost concurrent release in 2011 of draft guidance documents from two of the three major GCP regulatory agencies related to "risk-based monitoring" has triggered numerous discussions among clinical research operatives and peers, including sponsors of drug and device clinical research, contract research organizations (CROs), and academic clinical research organizations.

	A survey conducted by the Clinical Trials Transformation Initiative

 indicated that there was a large and somewhat inconsistent range of monitoring practices during the conduct of clinical trials. The survey found that monitoring practices vary in intensity, focus, and methodology and include:

		Frequent, comprehensive on-site visits to all clinical investigator sites by company personnel or representatives (e.g., clinical monitors or clinical research associates)

		Targeted on-site visits to higher-risk clinical investigators (e.g., where centralized monitoring indicates problems at a site)

		Centralized monitoring of clinical data by clinical, data management, and statistical personnel at some location other than the study site

	For major efficacy and safety trials, companies typically conduct on-site monitoring visits at approximately four to eight week intervals, at least partly because of the perception that frequent on-site monitoring with 100% source document verification (SDV) is the regulator's preferred way for sponsors to meet their monitoring obligations. In addition, overall source document review (SDR) remains as an on-site monitoring obligation. However, FDA also recognizes that data from critical outcome studies (e.g., National Institutes of Health-sponsored trials, Medical Research Council-sponsored trials in the United Kingdom, and International Study of Infarct Survival), which had no regular on-site monitoring and relied largely on centralized and other alternative monitoring methods, have been relied on by regulators and practitioners.

	The general nature of the guidance documents is a potential gift to sponsor organizations. It encourages the industry to define strategies for risk-based monitoring, and perhaps create standards, that can be customized for individual protocols. Sponsors can now take full advantage of EDC as centralized monitoring tools are developed to capture intra- and inter-site outcomes, as a quality window on GCP.

	The unrealistic fear of regulatory agency findings at the time of pre-approval inspections leads to a more conservative approach that is both necessary and required. A paradigm shift must take place whereby sponsors realize that inspection findings are not to be feared, but are ways to improve processes, and collaborate with the regulatory agencies to better define clinical trial operations and patient safety.

	The eClinical Forum in the context of its chartered mission to serve the pharmaceutical and device industries focuses on those systems, processes, and roles relevant to electronic data capture, data handling, and regulatory submission of clinical trial data. The eClinical Forum has already submitted comments directly to both the FDA and EMA, and released a white paper to promote awareness of the concepts shared by both regulatory documents. By doing so, the eClinical Forum can assuage any apprehension expressed by the more traditionalist organizations which continue to interpret the regulations and predicate rules as calling for full verification and monitoring. We propose that by discussing the key elements of the guidance documents and by proposing best practices on actual tasks (whats and how-tos), we can greatly contribute to demystifying and adopting risk-based quality management of clinical trials. This approach should also allow the regulatory authorities to get access to honest, unbiased, substantiated previews of the organizational and procedural response by industry stakeholders as well as provide previews that are the product of reflective listening to regulatory expectations and that underline the challenges and work effort required by organizations as they adopt, adapt, and measure up to the guidance documents. As a result, the eClinical Forum should contribute actively to the further alignment between the regulatory agencies and the pharmaceutical and device industries.

	Although the guidance document was assessed versus previous and current corporate procedures and best practices, it was necessary or timely to advocate specifically how individual companies might choose to implement changes or adaptations to their quality/risk management. We expect that the roles within each organization that will be asked to perform those tasks will differ, with some companies deciding to assign responsibilities to monitoring, programming or data management job profiles.

	From its inception, EDC provided an opportunity for clinical research operations to re-evaluate the tasks traditionally performed by the data management and site monitoring roles. Moving towards adoption of integrated quality risk management systems calls for further, bolder, and continuous adaptation of systems, workflows, and people. Moving from the ingrained notions of traditional source document review and point-to-point SDV to one of quality processes (focus on optimizing site processes, QC of site compliance, detection of fraud, IMP distribution chains, etc.) will encourage us to:

		Delineate QA from QC in a risk-based quality system.

		Identify standard approaches to centralized and on-site monitoring activities.

		Assess how information technology tools and associated processes can best support centralized and risk-based data review.

		Determine which metrics (thresholds/error rates) and alerts will drive the documentation of quality as well as trigger corrective actions when the quality thresholds are not met.

		Evaluate the impact on the preparation and planning for protocol-driven research.

	Risk is someone or something that creates or suggests a hazard. Sponsors of clinical investigations are required to provide oversight to ensure adequate protection of the rights, welfare, and safety of human subjects and the quality and integrity of the resulting data submitted to regulatory agencies; in other words, avoidance of a hazardous situation.

 As a result, risk must be defined and maintained across all stakeholders.

	Quality is degree of excellence. According to the FDA guidance, quality is a systems property that must be built into an enterprise and cannot be achieved by oversight or monitoring alone.

 Quality is something that needs to be maintained throughout the lifecycle of the product. The evaluation of the risk to quality should be based on scientific knowledge and connected to the protection of the patient.

	There is a need to pause to consider the difference between quality control and quality management. Quality control allows the ability to outline a process to spot check data and operation by reviewing a slice in time. Quality management on the other hand, is used to direct, control, and monitor quality as a more fluid and dynamic activity.

	The ICH Q9 guideline merges the two control processes as it speaks to quality risk management as a standard practice.

 Q9 provides guidance with examples that attempt to outline risk management across the pharmaceutical industry. Figure 1, taken from the EMA Reflection Paper,

 is an excellent starting place for organizations to turn as they design quality risk management; a systematic process for the assessment, control, communication, and review of risks to the quality of the drug (medicinal) product across the product lifecycle.

	The ICH Q9 and EMA reflection paper guidance documents provide good direction on what to address when crafting a quality risk-management plan. However, neither truly addresses regulatory expectations for minimal requirements to accomplish this in a holistic manner. While it is one thing to talk about managing risk through traditional data management and site monitoring processes, it is quite another to propose a concrete way to manage risk through trend setting (electronic transparency on entry time, account use, etc.) and standard metrics (time between tasks, accuracy of input, etc.). Variability in the extent to which organizations approach quality risk management may confuse the situation even more. Fear of having decisions not meet regulatory approval also may keep some companies from updating their approach to quality management. In addition, the payoff may not be as large as indicated.

	While there is no clear indication that quality is inadequate today, there is evidence that the industry overspends to perform tedious reconciliation between source and sponsor data that do not significantly affect the outcome of clinical trials.

	Traditional monitoring plans outline what the sponsor considers to be their responsibility and action plan for ensuring site compliance with the protocol including GCP and other applicable regulations. We should consider the value of combining elements of monitoring, data management, and statistical plans into a comprehensive integrated quality plan. It is important not to jeopardize the relationship between sponsor and the clinical sites and care should be taken to ensure that this remains in the forefront of the plan. For example, a communication plan is useful to document standard communication when there is no need for a site visit. This could be used to praise the site for good work and maintain monthly communication. Minimally, a consistent point of contact should be assigned to the site to represent the face of the sponsor.

	It is important to emphasize that monitoring plans are not unique to the CRA role but also can provide an overall cross functional plan that emphasizes what must be done in order to assure that the data received from clinical research sites are of high quality and integrity. The monitoring plans include not only what is expected to occur at a site level, but also within the data system and program level.

	A quality monitoring plan, which can be part of the clinical monitoring plan, may include information from traditional plans that had previously been separated into role or task based activity, for example, each therapeutic area may be different so several quality plans may be needed on that basis. In considering the monitoring plan, adequate risk assessment must be performed prior to the start of the study, in order to ensure patient protection as well as the quality and integrity of data. Our recommendation is that monitoring plans should exist at the program, protocol, and site levels, and outline what will be included in on-site monitoring as well as central monitoring.

This plan addresses overall quality aspects of the program and should be developed at the start of the clinical program. Risks that are known or anticipated for the disease population and investigational product under study should be documented and clear instruction given as to what, why, and how those risks should be monitored. As a general outline, a program level plan could minimally include the following:

		Planning-a priori identification of risks of the investigational product.

		Design-define protocol eligibility criteria, efficacy, and safety risk factors during design; for inclusion in the eCRF and edit build, as well as define key protocol deviations anticipated for study.

		Execution-clear direction in the oversight and monitoring of parameters leading up to study endpoints, integrated study level monitoring plan, clear direction on evaluating fraud.

		Analysis-clear definition of data measurement and reporting, defined elements of statistical analysis of protocol endpoints, safety, etc.

		Disclosure-publish outcome data, bringing all the above together in the clinical study report (CSR).

	Companies may wish to consider creating standard quality checks pertinent to all programs, and which may also include therapeutic area specific sections to be selected as relevant. Ensuring that the entire study team understands the principles and purpose of the program level monitoring plan will help avoid duplication and encourage study teams focus effort where it is most effective.

	This is an ideal opportunity to take a step back from familiar routines and look hard at current processes; assess what is valuable and what is not; invest in processes that bring most value from a quality perspective. The goal is to develop an integrated clinical data monitoring plan, based on compound and historical knowledge, which can respond proactively to data signals during trial execution.

 This plan should be specific to a particular protocol and draw upon the information from the program level plan as to what is needed to ensure quality. We suggest that the study level plans are categorized as those activities that occur during study startup, study execution, and study closure. This plan allows flexibility for site level variations, (e.g., highly compliant and performing sites, new sites with little experience, high volume sites, and non-compliant sites). The following specific minimal list of topics should be addressed in such a plan:

		Site selection factors-include key protocol requirements that affect decision making on site selection.

		Site training considerations-include information on how the site is prepared to conduct trial.

		Information on all forms of data review.

		Identification of critical data-safety, endpoint related.

		Delineation of what is reviewed on-site versus central.

		Assessment of site understanding of protocol.

	By focusing on study level tasks, rather than roles, study managers will be able to assign responsibilities to the most appropriate team member(s). This adds flexibility to the process, while ensuring that there are no omissions. It also will allow for role changes/combinations in the future. Plans should be easily readable and referable-limit bulk text; use graphics and bullet points. If a suitable electronic tool can be used, it should be feasible to pull out a sub-set of tasks for each function on the study from the overall plan.

 This plan should be specific to each investigative site, combining information from Program/TA level plans and the protocol(s) that the specific site is involved with. There is a large degree of variety in the way regional, country, or hub monitoring units are being organized, therefore a critical aspect of the risk assessment will be the concurrent (and possibly competitive) workload and commitments that shape the possibilities and constraints of both the sponsor/CRO and site staff when collecting and reviewing data. Another key consideration in defining a site level risk management plan is to never project any task or activity over time as being flat or a product of the volume of data flow only. What's more, it is an oversimplification (with potentially disastrous results) to even consider data quality as being inversely proportionate to its volume. Some of the minimally required aspects to cover in the site level plan are:

The eClinical Forum Risk Based Monitoring Taskforce offers some best practices for ensuring clinical data quality.

Robert King

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Data Integration Strategy: The Cornerstone Of A Successful Regulatory Submission

Risk-Based Approaches