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Steady enrollment and optimal trial metrics can become reality with the right processes and tools.
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As the marketplace continues to rapidly change, demanding better access to more cutting edge therapies at lower costs, the pharmaceutical industry is being forced to find more efficient and cost-effective approaches in conducting clinical trials while facing increasing external factors. Rising drug development costs, a limited amount of research participants, increased competition for research participants among pharmaceutical sponsors, and an increasing turnover of new investigators complicate the drug discovery process.1 Additional requirements from regulators (including more diverse subject populations; longer, larger, and more complex clinical trials; exhaustive safety studies; and increased postmarketing safety surveillance) add to the overall study length and increase development costs.
The use of enrollment optimization processes, such as enrollment forecast modeling and trial simulations and the use of clinical investigative networks versus the typical decentralized single site model, should be considered to help improve the current trial model. The addition of well-designed e-clinical processes such as EDC, eSource documents, Web casting, and others should also be considered to realize optimal trial quality and efficiency.
The study protocol is the foundation of the clinical trial recruitment rate. New study protocol designs, such as those for adaptive trials (which is an increasing trend among pharmaceutical sponsors), can have an effect on subject recruitment.
Nearly one third of all clinical trial timelines risk extensions based upon under recruitment or under enrollment. Under enrollment can jeopardize the registration of a drug, while over enrollment risks trial budget overruns.
Protocols that have not undergone a feasibility analysis can be problematic once implemented. Sections of the protocol may be utilized from similar historic studies. This can result in poor recruitment if key changes are not made in relation to the new study outcomes in key areas of the protocol, such as inclusion and exclusion criteria. Other factors affecting recruitment may be study implementation issues at the site or sponsor-related issues. Additionally, poor patient compliance may result due to ambitious study requirements. What solutions are available to this perplexing dilemma?
Clinical trial simulation and modeling tools are emerging in early phase work as a means to identify appropriate subjects and study targets, streamlining a compound's overall clinical study plan by focusing resources on clinical studies having a higher probability of success. Simulations can range from very large-scale mechanistic models that require significant resources to utilize and maintain, to a reusable platform that allows for complex and repeatable in-silica (computer-based) studies. The use of these tools can lead to larger success rates in later stage trials.
The availability of historic trial data is another invaluable tool to develop a data-based projection. It is ideal to have the study projection developed prior to study timeline development. Recruitment activities and acceleration techniques are more effective and less costly when implemented during the initiation of the study rather than as a rescue effort well into the study recruitment period. Key historic data that is essential in forecasting a study projection includes the percentage of sites recruiting zero subjects. Historic metrics, such as the time necessary for sites to become regulatory ready, can occur over weeks or months and need to be considered in study recruitment forecasts. Finally, the rate with which research volunteers randomize into the study can vary depending on the study's inclusion and exclusion criteria, regions of the world involved, therapeutic area, and indication.
A common mistake is to develop a straight-line projection that incorrectly assumes all sites are able to begin recruitment on day one of the clinical trial. Another important consideration in managing the clinical trial recruitment period is to consider timeline knowns including study seasonality, such as the period between Thanksgiving and the new year. In Europe, similar seasonal considerations would include the summer holiday season of July and August. Finally, disease incidence and prevalence, certain treatment settings (i.e., maintenance or adjuvant regimens), and pathophysiology effect recruitment and study duration.
Figure 1 is an example of a projection taking into consideration these important factors. The team projection is a simple straight-line projection used in early forecasting of the study.
Registration studies are increasingly becoming globalized for both increased access to subject populations as well as for economic considerations. Global regions affect the execution of clinical trial recruitment. The Asia-Pacific, Europe, Latin America, and the United States have their own recruitment enrollment performances. Key differences may include disease incidence and prevalence, specific country regulatory requirements, unique Ethical Review Board review periods, and specific investigator performance. There are recent trends of executing clinical trials in developing and emerging markets such as China, India, Latin America, and Russia.
Historic site study execution is another invaluable tool in the acceleration of studies. In the field, there is often anecdotal and reputation-based evaluation of site performance. However, a more empirical method of measuring site performance at the therapeutic, compound, and study level is the use of invaluable tools to select high-performing sites as a means of accelerating not only the subject recruitment but complete study execution. Additionally, historic site study quality is a key factor in site selection.
The methodology used in Figure 2 looks at the total number of subjects recruited at the clinical sites and the mean time between subject randomization at the sites. Using a clustering model, sites are compared to each other in the specific study. The ideal site would recruit a large number of subjects with low mean time between subjects being randomized.
High performing sites are illustrated as Cluster 4 performers in Figure 2. Sites having a lower number of total subjects recruited and a longer mean time between patients fall into Cluster 3. Finally, Cluster 2 sites recruited the smaller number of total subjects with the longest mean time between subjects. Sites recruiting one or zero subjects are not represented in the figure. Historical data indicates sites will perform in the same cluster grouping 70% of the time. Selecting historically high performing sites along with new sites can provide a level of assurance in recruitment reliability and study execution.
Another means to accelerate clinical trials is to increase the number of subjects that would consider participating in a clinical trial. While health fairs and disease awareness campaigns can provide a temporary boost to enrollment in targeted areas around clinical investigator sites, the issue remains: The number of people willing to volunteer for a trial is very low.
The Center for Information and Study on Clinical Research Participation (CISCRP) is addressing these issues. CISCRP's recent "Everyday Hero" campaign seeks to address the low rate at which people volunteer for clinical research, as well as to educate the public and enhance its perception of clinical trial participation.2,3 By improving the public's perception of clinical trial volunteerism, there is significant potential for increasing the rate at which clinical trials enroll, thereby enhancing public health.
The clinical network model has been around for more than 15 years; thus, the use of clinical networks is not a novel approach. But in recent years, the clinical network model has shifted from a primarily decentralized structure often lacking streamlined, consistent processes across the network to a more centralized structure governed by core standard operating procedures, enabling the network sites to function in a more consistent and efficient manner.
The old network issue of slower, more complicated processes adding more costs has become an issue of the past. Network processes that were often labor intensive, paper driven, and not properly enforced with network quality assurance staff have all been resolved, as networks have implemented internal quality assurance departments to ensure network core procedures are followed, and implementation of e-clinical processes offer a better approach.
Networks are groups of independent clinical sites managed operationally by a central administrative core group. The central administrative group will often include various departments, such as project management, regulatory, safety, pharmacy, information technology, data management, finance, legal, IRB, and quality assurance. Each of the central departments, as well as the network sites, are governed by a set of standard network operating procedures maintained and enforced by the central quality assurance group. These procedures help to ensure that the network sites function in a consistent, compliant, and efficient manner.
From a scientific or medical perspective, networks usually consist of central disease state committees made up of regional research leaders or disease state experts representing their areas or regions of the network. The disease state committees are led or facilitated by a research advisor or chief medical officer. The central disease state committee conducts scientific reviews to ensure that the best trial designs are run at the right time and with the right regions or sites within the network for optimal trial efficiencies. The disease state committee is assisted by the central network administrative group, which provides historical subject population and network enrollment data so that the best decisions are made for optimal trial placement within the network.
Networks will sometimes differ in some characteristics, such as size, disease state focus (single vs. multiple), community based vs. academic vs. hybrid, and most importantly in their level of centralization (partial vs. complete). Regardless of the characteristics, it is extremely important that the sponsor accurately characterize the network and thus fully understand their organizational structure, capacity, strengths, limitations, level of centralization, and clinical services offered. Several tools and standardized approaches have been successfully used and previously published to assist with properly characterizing clinical networks in a timely manner. 4,5
Networks have always offered various centralized Site Management Organizational (SMO) services, such as site selection, trial placement, trial feasibility surveys, enrollment forecasting, budget and contract development, site reimbursement, and project management along with various clinical site services such as subject recruitment, ICD administration, data collection, safety reporting, and pharmacy and clinical laboratory services. Recently, however, networks have begun to offer various CRO services, such as full data management services and reporting, medical writing, statistical support, regulatory document services, and trial initiation and startup along with quality assurance site assessments and audit services both within and outside of their network.
This expansion of clinical services allows for true, one-stop shopping and thus a more efficient and consistent clinical model. But in order to ensure proper compliance, all networks and services should be assessed prior to any service outsourcing for proper procedures, training, system validations, capacity, privacy compliance, and overall regulatory compliance dependent upon what services are offered.
There are several key elements to look for when analyzing a clinical network. Some of the most important include central standard operating procedures to ensure consistent and efficient network processes that help guarantee proper trial oversight, subject safety, and quality. Central quality assurance is important in order to maintain and enforce the procedures throughout the network. Additional key elements include central data management services that allow for more consistent and efficient data collection and centralized monitoring. Central regulatory document services allow for more consistent and efficient document collection for multiple sites to initiate under one regulatory process vs. multiple site processes, which can be very time consuming and delay trial initiations.
Several additional key network elements to look for when analyzing and selecting networks include central pharmacy services, which help to minimize study drug shipments to multiple single sites and study drug waste, since many single sites may never enroll a subject.
The network model offers several efficiencies that benefit not only the sponsor but also the investigator and the subject. The centralized administrative departments take care of most operational tasks, including contract and budget negotiations, trial initiations, regulatory document collection, IRB submissions, central pharmacy, and site payments, and also allow the investigators and their site staff to focus on conducting the trial, enrolling and treating their subjects (which helps with enrollment), data collection, and overall quality. The subjects benefit since the investigators and site staff spend less time on administrative tasks and more on treating and assisting them.
Smaller, more remote clinical sites, which may lack the proper staff to perform all of the administrative tasks, can be considered for clinical trial participation as part of the larger clinical network. This increased opportunity allows for more cutting edge therapies to be offered to more investigators and their subjects while minimizing lengthy trips and costly travel expenses for subjects. In addition, the centralized or partially centralized network model allows for large efficiency and consistency gains since one regulatory document and IRB submission process is conducted to initiate multiple network sites.
Central pharmacy services allow for one central drug shipment and less study drug waste. Central contracts and budgets allow for one budget and contract negotiation and development for multiple sites. Additional efficiency gains have been documented with central data management, allowing for centralized monitoring and more consistent data collection, as well as others which have been discussed in recent clinical network publications.4,5
E-clinical processes are important because they maximize trial process efficiencies while also ensuring enrollment optimization processes, and network model efficiencies are optimized, as documented in recent e-clinical publications.6,7 These processes include virtual training (i.e., Web cast, Web sites, CD, DVD, Web conferences), which offer different options for training the clinical network sites and staff in a thorough, cost-effective, and timely manner.
Document repositories (secure Web sites) are used for secure document delivery and enhanced communication among the network disease state and central administrative committees and at the satellite sites depending on the document type. The enhanced communication can help ensure that proper trial oversight and patient safety occur in the network trials. Secure email systems are helpful for rapid communication among the network and sponsor staff, while e-source documents and EDC systems can have a positive impact on trials by allowing for centralized monitoring and more rapid data access, review, and query resolution.
All of these e-clinical systems and processes also help to ensure that all of the network sites function in a consistent manner and that their records are maintained in a similar fashion. A key element is having a centralized network IT staff that will help to ensure that end-user functionality requirements are met, that system support services are offered, and that the systems are always regulatory compliant. Technical and help desk support and contingency plan development for prompt system and user issue resolution, often referred to as ancillary services, are also crucial for system and process acceptance and success.
As the clinical arena and marketplace continue to change, demanding more efficient clinical processes, it is essential that a streamlined, consistent, and cross-functional analysis process be utilized to ensure that enrollment optimization tools and processes as well as network structures, processes (including e-clinical), and services are properly analyzed, chosen, and/or developed. The sponsor must allow initial time and funding for proper analysis and development.
The resources spent in the early stage of the enrollment optimization or network analysis and e-clinical development will pay off with better structure and process knowledge; better trial designs, placement, and compliance; faster trial initiations; and shorter trial timelines. Overall, quality will also be impacted from the enhanced consistency and communication. Thus, steady enrollment and optimal trial metrics can become reality.
Lee Scheible, * RPh, is a senior medical consultant within the U.S. Medical Affiliate of Eli Lilly and Company, Lilly Corporate Center, Indianapolis, IN 46285, 317-276-8405, email: Scheible_Lee_S@lilly.comMichael Pozsgai is a medical consultant with Global Enrollment Optimization, Eli Lilly and Company.
*To whom all correspondence should be addressed.
1. Tufts Center for the Study of Drug Development, Impact Report, 7 (3) 2005.
2. B. Herskovits, "Returning the Favor," Pharmaceutical Executive, Direct Marketing Edition, January 3, 2007.
3. K. Getz, E. Seargent, J. Kremidas,"Mission Possible: Rebranding Clinical Research," Applied Clinical Trials, April 2007, 40–44.
4. L. Scheible, D. Russell, K. Brinkman, "The Clinical Investigative Site Network," Applied Clinical Trials, March 2005, 42–48.
5. L. Scheible, "The Clinical Network: A Different Approach," Community Oncology, 3 (11) (2006).
6. L. Scheible and M. Pozsgai, "The Virtual Revolution (The Time Is Now)," Applied Clinical Trials, July 2005, 32–37.
7. K. Getz, "Hitching a Ride with the Speed Demons of Drug Development," Applied Clinical Trials, December 2006, 22–24.
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