Rolling Feasibility in Competitive Immuno-Oncology Trials

Immuno-oncology (IO) is a fast-growing field at the frontier of cancer therapy. Data from indicates a fiercely competitive landscape with over 47,000 oncology trials globally and nearly 1,000 IO studies. Many IO studies are precision medicine driven clinical trials that target narrowly defined patient populations with certain biomarker or genetic profiles. To expedite efficacy and safety evaluation of these new treatments, innovative but more complex trial designs have been introduced - this is especially true for early phase clinical studies. As a result, there are significant barriers to effective clinical trial conduct due to difficulties in quickly identifying and recruiting eligible patients, as well as accurately assessing executability of a complex study. In addition, there is a significant shift in perception of value across healthcare, which is increasingly mandating new drugs to demonstrate benefits compared to standard of care (SOC) treatments and proof return on investment. How should biopharmaceutical companies address these new challenges and maintain a competitive edge? Rolling Feasibility provides a new approach. There are two essential components of this strategy – Generating Precision Feasibility Insights and Adaptive Feasibility Monitoring.

Generating Precision Feasibility Insights

Trial feasibility assessment has primarily relied on site feasibility questionnaire and re-using the sites from past trials.

There are three major issues with most site feasibility questionnaires:

  1. Lack of deep insights on patient journey and specific patient population required by an IO study. Where would these patients come from? Would they be seen at a hospital or clinic? Would they be seen first by general practice clinicians? Or by oncology specialists? Do they usually have biomarker or genetic testing results available? Would the biomarker tests be available in routine clinical practice?
  2. Lack of information to provide deep understanding of standard of care treatment. How are different lines of therapies used in today’s cancer care? At different countries? How long would a patient normally stay on a particular line of therapy before switching to a different therapy line and why?
  3. Questionnaires tend to rely on site staffs’ memory and estimation to provide information, which is usually not accurate and resulting in over or under estimates.

In addition to relying on feasibility questionnaires, potential sites are also selected by simply re-using past trial sites. IO drug development landscape is undergoing rapid change. As a result, new treatment approvals constantly impact standard of care, as well as oncologists’ practice and interests. Situations at a site often change over a short period of time and those sites used in past trials may not be suitable for today’s studies any more.

In today’s precision medicine driven IO trials, conducting feasibility has strategic importance for the success of a clinical program from both scientific and operational perspectives. Furthermore, proper feasibility studies can also provide early insights for downstream commercial operations once a potential investigative product gains regulatory approval. There is a paradigm shift from conducting limited and tactical site feasibility assessment to comprehensive, strategic and precise assessment that results in actionable insights for IO studies (Precision Feasibility Insights). Precision Feasibility Insights need to address key aspects of a clinical program covering both science and execution. It includes deep analysis of clinical development strategy, competition for resources required at the site for a given clinical study, right patient population, patient journey and standard of care in complex healthcare settings, country and site selection strategy, enrollment projection and executability at the sites. Specifically, Precision Feasibility Insights needs to answer key feasibility questions:

1. Trial competitive landscape:

    • Are there any competing trials internally and externally?
    • Is a competing product at the regulatory approval stage?
    • Has a competing product gained any approvals from payers to be reimbursed? At which countries?

2. Protocol design, patient population and epidemiology:

    • Do large enough patient populations conforming to inclusion/exclusion criteria exist?
    • What is the latest epidemiology data of a given patient population? At region and country levels?
    • Where and how will they be seen at healthcare settings and how can they be identified?
    • What is the current standard of care treatment?
    • What are the unmet needs that compel patients and clinicians to seek new investigative treatment?
    • Is the required biomarker test available at routine healthcare settings?
    • Do patients have biomarker or genetic testing results available?
    • What is driving the loss ratios across protocol eligibility criteria?
    • What is executability of a protocol at sites?

3. Recruitment rate, trial enrollment projection:

    • What is the latest benchmark recruitment rate for a given indication or specific patient population?
    • What are the recruitment rates across internal and external trials?
    • What is the scenario-based enrollment forecasting and validation?

4. Country selection strategy:

    • What is the investigative treatment’s compatibility with standard of care at country level?
    • What is the situation regarding comparator drug approval and availability?
    • What is competing drug approval and availability?
    • Is this study a registration trial at a country?
    • How many countries?
    • Which countries?

5. Site selection strategy:

    • Can sites access the right patient population?
    • Is there a preferred site network established?
    • How many sites?
    • Which type of sites?
    • Which specific sites?
    • Is there a right balance between academic sites and community or practice group sites?
    • Impact of competing trials at site and investigator levels
    • Who are the right investigators at the sites?
    • What are the research interests and experience of the investigators? What are their specialties? Are the specialties aligned with trial indications or patient cohort groups?
    • How do investigators and other clinicians work and operate at sites?

Rolling Feasibility Monitoring

Traditional feasibility assessment is conducted at the beginning of a study. It is usually a one-time, static effort. As competition in IO drug development has become fiercer with new treatments approved, reimbursed and available at healthcare practices, the impact on competing clinical trials’ recruitment is significant. Investigators, clinicians and patients may lose interest or momentum on a given study due to approvals or positive results of competing trials. The newly approved IO treatments could also change the standard of care and attract patients and clinicians to quickly adopt new treatments to meet previously unmet medical needs. Feasibility needs to be monitored using a rolling and adaptive approach (Rolling Feasibility Monitoring). It is an ongoing effort throughout the clinical trial delivery process from trial planning and design, study startup, and the whole enrollment and monitoring period. In other words, Rolling Feasibility Monitoring not only focuses on first patient in but also helps to maintain enrollment performance until last patient in, taking into consideration ever-changing macro environment and all the other factors that may have an impact on a given study’s feasibility and executability.

How to Implement Rolling Feasibility Monitoring?

Immuno-oncology is a highly competitive therapeutic area. As we bring therapies to treat various tumor types under a new precision medicine paradigm, we can’t only rely on old approaches to recruit the required precisely targeted patients. We must seek novel data sources, processes and methodologies to bring new ways of conducting clinical studies. Following are some enabling capabilities:

  • Advanced data science and analytics: Informatics and data science play a big role in enabling evidence-based precision feasibility insight generation. New data sources and analytical approaches need to be adopted to enable precision feasibility.
    • Real World Evidence (RWE): data-driven analytics with healthcare data generated in routine clinical practice and testing labs has proven to increase physicians’ referral rate by 10-fold thus doubling enrollment rate, and reduce per patient recruitment cost by 36.9% and phase time by 17.6%.

With adoption of electronic medical records (EMR), more healthcare data sources have become available in US and Europe for secondary use to support pharmaceutical research and development, including clinical trials. These data sources include general EMR, specialist EMR such as oncology EMR, physician office EMR, registries, health plan (payer) claims, pharmacy databases, biomarker and genetics testing results databases, etc. These data sources are available through healthcare providers such as hospital systems, lab test providers, pharmacy providers and technology data brokers.

Depending on specific coverage and patient-level details, RWE data can be used to assess executability of eligibility criteria of a trial protocol, gain insights into standard of care, and perform analysis of therapy lines and cancer patient journey. It can also be used to identify potential patient pool and which physician practice groups or hospitals can access targeted patient pool. Such insights can provide more accurate evidence for precision medicine trial patient recruitment and referral, which reduces the risk of overestimate of patient enrollment rate by sites through only traditional site questionnaires.

Advanced trial intelligence data sources and past trial records can provide visibility to trials and competitive landscape across the internal sponsor company and the wider industry. Trial intelligence data includes published information about clinical trials, pipeline development, drug approval and reimbursement, investigators and hospitals, publications and conference presentations, etc. With new informatics technology and tools, biopharma companies need to establish a trial feasibility data portal that automates information gathering, aggregation and analysis from various trial data sources. This portal can provide instant global access to study epidemiology information, regulatory approval and reimbursement information, hospital and site profiles, investigator and key opinion leader profiles, etc. Clinical trial teams can then easily identify internal and external competition at sites across the portfolio by indication and communicate rationale for prioritization of studies to investigators. If priority of studies and assets change, an action plan can be quickly developed to reduce internal and external competition.

  • Site network strategy: A network of the right sites and investigators that is aligned with a sponsor company’s therapeutic focus, clinical development strategy and operational approaches is an important component of building the rolling feasibility roadmap. Five areas need to be considered when developing a robust site network strategy:
    1. Deep understanding of site profiles: Use trial intelligence tools and data sources including academic and hospital web sites to gather key information such as therapeutic and disease indication experience, past trial experience, patient population access, healthcare setting (research academic center, community hospital, or physician practice group), publications and presentations, relationships with competing sponsor companies in drug development, trending of investigators’ research interests, growth of new investigators, etc.
    2. Prioritization of the sites: Top sites that are aligned with the organization’s clinical strategy need to be ranked at different levels based on a set of key criteria, such as strategic alliance, volume of trials, patient access, site trial performance, etc.
    3. Site/investigator placement strategy: Based on clinical program or study requirements, an approach to identify key opinion leaders and select academic sites vs. community practice groups needs to be defined in order to ensure the right balance of different types of trial sites. A clinical trial is also a good venue to foster a mini-clinical research community among the investigators. When identifying potential investigators, analyze their past and current research collaborations. We have observed that if the potential investigators have previous collaboration experience, they will more likely form a supportive minicommunity during a clinical trial, which could result in better trial performance and patient enrollment.
    4. New site/investigator development: While maintaining good relationship with existing sites and investigators, effort should also be invested in developing new sites and upcoming investigators. This is very important for conducting IO clinical trials. With rapid development of new IO treatments and more regulatory approvals, IO treatments have seen adoption at community based practices or other hospitals where a strong research interest has been fostered. In the US, community based practices and hospitals account for 80% of cancer patient access. Developing new potential investigators from community based practices and hospitals could strengthen the preferred clinical trial site network for the long run. Strategically this approach could also be beneficial to allow more cancer patients to receive new treatments via early exposure to the large pool of patients at the community practice setting.
    5. Patient referral: A sophisticated patient referral strategy should be considered as well when building a robust site network. This requires a deep understanding of healthcare setting, patient journey and motivations and concerns of physicians. Dedicated staff with such knowledge and experience is required to provide patient referral support services to ensure this process is smooth and concern free from patients and referral physicians and sites.
  • Operating model and process: Rolling feasibility process takes four major steps when it is implemented in a clinical trial. Figure 1 shows a process outline.
Figure 1.
  • New skills and organizational setup: To maximize the benefits of rolling feasibility, a dedicated feasibility team with new skills, knowledge and tools needs to be in place.
    • Precision feasibility lead: The right lead should have advanced knowledge and experience in clinical research and translational science, including clinical trial operations.
    • Feasibility data scientists: Data scientists need to use new tools, new data sources from RWE and trial intelligence databases and past trial performance records to conduct deep data mining and analytics to provide evidence at each step of rolling feasibility.
    • Site liaisons: New role of site liaison needs to be established to be a knowledge broker for top sites within the preferred site network. They are also the liaisons for clinical trial teams and sites to address strategic and site level needs, concerns and issues. This role can be considered as a counterpart to Medical Science Liaison except in the preapproval setting.
    • All these roles need to work cohesively with clinical trial teams to ensure successful delivery of a study.
    • Collaborations with external providers: When implementing rolling feasibility, a biopharma sponsor company can collaborate with external CROs and technology providers. This will allow the organization to expand their own knowledge, information and technology capability by leveraging additional information and technology that external CROs and technology vendors have obtained.

How to Measure Success?

It’s a journey to change traditional feasibility to rolling feasibility. Take a practical approach to measure success when implementing rolling feasibility. Figure 2 provides a method to assess the effectiveness of implementation. Metrics should be developed to assess the progress and effectiveness of the implementation. Examples of the key metrics include:

Figure 2.
  • Leading indicators:
    • % of studies conducting rolling feasibility
    • # of decisions/recommendations taken per protocol to change country and site allocation or recruitment strategy
  • Lagging indicators:
    • Maximized country enrollment
    • Decrease in # of poor performing sites and # of non-enrolling sites
    • Improved recruitment rate
    • Reduced recruitment period


Precision medicine drug development, by definition, increases the difficulty of patient recruitment and enrollment. As such, in today’s highly competitive IO clinical trials, feasibility has become much more important than before in order to ensure robust planning and successful development of a clinical program and associated studies. Feasibility is undergoing a paradigm shift from the traditional limited and static site feasibility questionnaire approach to precision and rolling feasibility. This new approach helps to address scientific merit and executability of clinical studies at the strategic as well as operational levels.

Biopharma companies competing in oncology and IO can benefit greatly by investing in establishing a dedicated rolling feasibility team with new skills, novel data sources and innovative tools to help turbo charge precision clinical development and operations.


  1. Sertkaya A, Birkenbach A, Berlind A, Eyraud J, on behalf of the Eastern Research Group. Examination of Clinical Trial Costs and Barriers for Drug Development [report to the U.S. Department of Health and Human Services, Assistant Secretary of Planning and Evaluation] Lexington, MA: Eastern Research Group; 2014.
  2. The State of Cancer Care in America, 2017: A Report by the American Society of Clinical Oncology. Journal of Oncology Practice, Volume 13, Issue 4, April 2017, 256 – e394.
  3. Mulvey, T. (2008). Challenges for Community-Based Clinical Trials. Journal of Oncology Practice, January 2008.

Jane Fang is the head of AZ/MedImmune R&D Information for Clinical Biologics. She is a physician scientist with training in healthcare management and informatics. She has 25 years of broad experiences across from medical practice, clinical and immunology research to eClinical strategy and advanced analytics in healthcare and biopharmaceutical industries. Jane has provided various leadership roles in building clinical innovations and digital capabilities to enable better clinical research and trial delivery.

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