At Veeva Systems’ R&D conference last September, Rob Metcalf, VP of medicines development at Eli Lilly, held a fireside chat with Henry Levy, general manager, Vault CDMS, at Veeva Systems. During the chat, Metcalf discussed his views around the current state of drug development. He shared: “One of the areas that I’m very focused on is the translation of design into delivery, and the role that clinical operations plays in actually informing great design.”

	He continued, “In Lilly, people are hearing me talk less about clinical operations and more about clinical delivery as a capability, as a fundamental drug development capability that has a role in partnering with our therapeutic area groups in terms of great drug development, great study design. And I think we need to position more traditional clinical operations; it’s really a core capability about delivering, delivering great outcomes, delivering great experiences for our investigators, our investigative sites, and ultimately delivering great medicines to patients who are in need.”

	Metcalf said that changes in patient engagement in clinical trials, study designs, and advances in technology, including digital biomarkers, are all ways that will improve clinical development and delivery, which accelerate new medicines to patients in need.

	In the spirit of change in clinical operations to clinical delivery, or maybe something else reimagined, 

 decided to ask a few experts in industry if they think clinical operations needs a makeover.

Anders Persson, Clinical Transformation Leader, Digital Health R&D, AstraZeneca

	Prior to leading the clinical team in digital health R&D, Persson most recently worked as head of clinical operations enablement and has been in various project and line management positions with AstraZeneca since 1995. He is passionate about finding ways to make life better for patients, partners, and society.

	Persson believes that putting our faith in “new technologies, automated processes, and advanced analytics” will create the solid foundation needed, as AstraZeneca steps away from solutions originating from paper-based systems into greater digital working. He cares deeply about the company and pharma and is using his considerable experience and facilitation skills to lead the implementation of a new Unified Clinical System, which includes replacing a spaghetti of outdated clinical trial management systems and reporting processes. 

	â  “Across pharma, the backbone of ClinOps is largely paper-based, which is not sustainable or effective. Embracing technology to digitize and automate is essential to shorten timelines, drive down costs, and build in efficiency, but, most importantly, to deliver improved patient outcomes and experience.” 

	â  “This is easy to say but much harder to deliver. It is paramount that organizations are ready to adopt new technology and approaches and then maintain them in order to ensure success. New system capabilities require very close collaboration across ClinOps and related functions, including IT teams, and with external partners, to achieve constant development and innovation.”

Jim Reilly, VP of Clinical Strategy, Veeva Systems

	Reilly has led teams responsible for sales and delivery of clinical data standards and regulatory technologies at Accenture (formerly as part Octagon Research Solutions), and has extensive consulting experience from his time at Octagon Research, as well as HighPoint Solutions, where he led business process improvement and technology selection programs in clinical and regulatory operations at a number of large pharmaceutical 

organizations. Here are some insights from Reilly:

	â  “Clinical operations need a makeover to overcome the increased complexity of today’s clinical studies. Science is evolving rapidly to bring about breakthrough innovations such as personalized medicines, cell and gene therapies to fight cancers, and novel treatments for rare diseases. While these advancements bring new hope to patients, they also introduce complications, involving more partners, creating supply chain issues, and introducing patient recruitment challenges. At the same time, biopharmaceutical companies are under significant pressure to reduce costs. Speed and efficiency have become as crucial as the science, yet trials are as slow today as they were a decade ago.”

	â  “Collaboration is one of the biggest issues impacting speed. Last year, former FDA Commissioner, Scott Gottlieb challenged the industry to improve collaboration across study partners with modern technologies. However, advancements have been stymied by a lack of technology solutions geared toward the unique needs of sites. While sponsors and CROs have made significant progress in adopting purpose-built technology, clinical research sites often get left out because they may lack the resources needed to invest in infrastructure, or the technology solutions are not designed to meet their specific workflows and business processes. As a result, sites rely on sponsors and CROs to provide technology for managing trial activities and collaborating during a study. With each sponsor providing a unique system to sites running dozens, or even hundreds of trials, the use of technology can become overwhelming, adding unnecessary administrative burden and preventing trials from getting started quickly.” 

	â  “We need to ease the burden on sites. Advanced technology can enable a more collaborative clinical trial environment by seamlessly connecting trial partners, systems, and processes throughout study execution. The resulting gains in efficiency, visibility, and quality will enable trials to run faster, simplify data sharing and collaboration, and ultimately, accelerate delivery of breakthrough drugs and treatments to the market amidst a more complex clinical trial environment.”

Audrey Rossow, Senior Director, Clinical Operations, Pulmatrix, Inc.

	To Audrey Rossow, it’s not so much as clinical operations needing a makeover as needing or setting an expectation level. Rossow’s background is in project management, clinical operations, and consulting for the past 10 years, prior to joining Pulmatrix full time last fall. In her opinion, clinical operations can be defined in different ways; so it basically depends on the background of the individual. Some companies have clinical 

 project leads, clinical trial leads, lead clinical research associates (CRAs), but ultimately, what a clinical operations makeover would truly look like depends on the size of the company and what the expectations are around the ClinOps role as well as the needs of the organization.

	However, Rossow notes, clinical operations is very tactical vs. strategic. “People may be saying ‘I’m too in the weeds, I need to be more strategic,’ but, again, that depends on your background and the size of your company,” she says. In larger companies, clinical operations is less diffused and more defined and integrated within larger teams. In small companies, such as Pulmatrix, where Rossow is a department of one, she has more influence in bringing to bear such solutions as patient recruitment initiatives, selecting CROs and functional service providers to work with, etc.

	The “extras”-the efficiencies promised by technology vendors, the getting rid of some archaic practice, or implementing a more modern one-are well-intentioned, but lost on the resources and bandwidth of the smaller biopharma. “For a company as small as mine, and highly dependent on a CRO, there is a limit to what we can implement,” Rossow told

. “With technology solutions, sometimes people bite off more than they can chew, and if it’s not adopted and consistently utilized across the organization, then it won’t be successful.”

	But a makeover? Not so much, Rossow believes. She says resources and bandwidth are where clinical operations needs to find a middle ground based on the company size and the true trial needs.

Mike McLaughlin, Associate Director, Clinical Operations, Dermavant Sciences, Inc.

	Amid the global COVID-19 pandemic and current social and physical distancing measures implemented to attempt to quell the spread, Mike McLaughlin, who helps lead clinical operations at Dermavant Sciences, noted that, “As a technology fan, I appreciate that innovations in clinical operations allow us to continue working in a 

safe environment. However, it is during the new daily norm of remote working and distance living that I’ve experienced feelings of missing closer human interaction.” He said he began to ask himself the following questions about technology’s impact on clinical trials.

		Is the amount of human interaction decreasing due to technological advances in clinical trials?

		Is there a gradual extinction of the human elements of clinical trials?

		Is there a reduction of valuable and quality working personal relationships?

		Are we creating shallow and false relationships between sponsors, CROs, and investigators/sites, and, more importantly, are we emotionally or socially disconnecting study participants? 

		Is there a need to refocus on growing meaningful human interactions within clinical trial operations to reconnect people and create memorable experiences? 

	McLaughlin said: “We are working at a time when technology is useful and can accomplish great things, but cultivating a positive work experience that connects study participants and allows clinical operations to improve people’s health should be valued in the long run. When the clouds of the COVID-19 pandemic clear, I believe that we’ll refocus our efforts, utilizing the best components of technology with the valuable aspects of human interaction. After all, human clinical trials are about people.”

Darcy Forman, Vice President, Clinical Operations, Science 37

	Darcy Forman, who earlier this year joined Science 37 from Firma Clinical Research, a CRO, believes clinical operations is “definitely in need of a makeover.” The following are her thoughts:

	â  "Embracing innovation and change is difficult, especially for pharma and biotech clinical operations teams that are tasked with delivering studies within timelines. Implementing innovation or piloting something new can be perceived as a disruption, can make project teams feel that they are being tasked with additional work, on top of

 the original goal of delivering their study, and could be perceived to put timelines at risk. This stagnant thinking has caused innovation to stall.”

	â  "There are two areas that are ripe for disruption: decentralized trial execution and clinical monitoring. Decentralized trial execution includes digitalizing clinical endpoints to provide broader use of telemedicine and adoption of mobile nursing support. The progression of these areas will accelerate the acceptance of decentralized study operations, which results in greater clinical trial enrollment potential by providing broadened geographies and accelerated drug development timelines.”

	â  "Embracing technology to allow for greater direct data capture and capitalizing and solving for electronic medical record (EMR) data mining should reduce the burden and spend on classic clinical monitoring activity. Clinical operation teams needs to empower remote monitoring tasks, reduce source document verification (SDV) requirements, and innovate the way clinical trial data is monitored. By innovating clinical monitoring, the industry should see cost savings with a reduction and/or near elimination of travel costs for monitors.”

Articles

Industry experts weigh in on how much traditional approaches in clinical operations need to change to meet new expectations for clinical delivery.

Does Clinical Operations Need a Makeover?

As of the beginning of the month, in many parts of the world, the number of COVID-19 cases continued to rise rapidly as countries enacted strict lockdown and social-distancing countermeasures. Like many sectors of the world economy, the biopharmaceutical industry has been impacted with significant disruptions to clinical development, many of which have been well-documented in recent weeks.

 Patients are less able to travel to sites and comply with studies. Enrollments are drying up. Many resources, supplies, and personnel aren’t available. Studies are missing critical data points and are delayed. Ultimately, important treatments are delayed.

	To combat these disruptions, FDA recently released guidance on the conduct of clinical trials during the COVID-19 pandemic.

 In particular, the agency repeatedly emphasized prioritizing safety for all participants. For example, due to global travel restrictions, the agency recommended that sponsors look for substitute methods to conduct patient safety assessments, and use remote technology where possible. FDA also recommended that sponsors explore alternative delivery methods for investigational products where feasible, such as patients self-administering drugs or visiting other sites, and pursue site monitoring remotely. For patients unable to access trial sites or necessary medications, the agency advised sponsors to put additional safety monitoring programs in place.

	In accordance with FDA’s recommendations, there are four key actions that biopharmaceutical sponsors can take immediately to offset disruptions.

 A survey of U.S. and EU sites have reported that many of their patients are less willing to participate in the clinical trials that they’re already enrolled in. This patient sentiment is only worsening as the crisis continues.

 For clinical studies currently in flight, sponsors need to virtualize their trial operations to avoid jeopardizing the safety of patients as well as site staff.

 In a purely virtual setting, patients are in contact with study teams and trial physicians through smartphone technologies

, and investigational products are shipped directly to patients’ homes. Patients can self-administer therapies, if feasible. For more complex procedures and interventions, sponsors can collaborate with third-party home-trial vendors

 or incorporate at-home nurse visits. Obviously, the feasibility and logistics of this approach need to be analyzed thoroughly. For example, does a protocol involve procedures that cannot be conducted outside of a hospital setting?

If clinical research associates (CRAs) are unable to access sites because of global travel restrictions or local quarantines, sponsors should enable remote monitoring and oversight as the FDA recommends. If not already in place, sponsors may need to enable virtual workspaces for sites to upload the necessary documents for centralized monitoring. The Association of Clinical Research Organizations (ACRO) recently laid out emergency guidelines for sponsors, contract research organizations (CROs), and sites on conducting trial oversight during the pandemic.

		Continuing periodic communication with sites over telephone.

		Using key risk indicators (KRIs) and key performance indicators (KPIs) to identify trends that may require short-term interventions. Indicators could include protocol deviations, missed visits, adverse event reporting cadence, etc.

		Using an electronic data capture (EDC) system to enable remote reviews of patient visits, with particular emphasis on patient safety data and data quality issues. ACRO notes that it does not recommend accessing patient electronic health records remotely unless protocols and procedures for maintaining patient privacy are already in place. Additionally, it does not recommend using fax machines for critical source documents.

3. Engage patients and investigators through proactive communication.

 Developing a communication and remote engagement strategy with patients and investigators that can be sustained throughout the pandemic is vital to ensure motivation and reduce burden. Virtualizing trials or monitoring sites will only be successful if

 sponsors frequently engage and empathize with patients and sites in order to assuage concerns caused by COVID-19.

4. Assess the overall clinical portfolio and evaluate study design, feasibility, and operations as the pandemic evolves.

 For each clinical trial in the portfolio, sponsors need to conduct a thorough assessment exercise aimed at answering key questions related to design, feasibility, and operations in the immediate term, short term, and long term. For example, in the immediate term, sponsors should determine which of their in-flight trials need to pause enrollment and which of their new planned trials need delayed starts. In the short and long term, sponsors should think through:

		Protocol modifications to incorporate remote patient and site assessments or other virtual elements.

		Patient visit requirements and anticipated data collection challenges.

		Effective resumption of activities when the pandemic situation improves.

		Improvements to risk management planning.

		Overall impact on clinical trial and clinical program timelines (including time to approval).

	These are just a few questions among many that need to be sorted out. Moreover, each passing day brings new information about the spread of COVID-19 globally. China and South Korea seemed to have gotten a grip on the spread of the virus (as of early April), while the U.S. and countries in Western Europe continued to struggle as cases rose exponentially. Therefore, to plan for the short and long term, sponsors should go back to the drawing board and reevaluate overall clinical study design, including patient eligibility, feasibility, enrollment, timelines, and budgets against the evolving global landscape.

	Sponsors should note that these strategies may require amendments to study protocols, and FDA guidelines urge sponsors to engage with institutional review boards (IRBs) as soon as possible. However, in cases where protocol changes are made to directly eliminate hazards to patients, these may be implemented without IRB approval. Finally, alarming projections indicate that social-distancing countermeasures may need to be in place well into 2021

, which means that biopharmaceutical sponsors should make urgent decisions now to plan for continued clinical research in the coming months.

is a manager in ZS’s San Francisco office; 

is a manager in ZS’s Los Angeles office; 

is ZS’s global clinical lead, based out of the company’s Princeton office; 

is an associate consultant in ZS’s San Francisco office

https://www.jdsupra.com/legalnews/the-global-impact-of-covid-19-on-72027/

https://www.fda.gov/regulatory-information/search-fda-guidance-documents/fda-guidance-conduct-clinical-trials-medical-products-during-covid-19-pandemic

https://continuumclinical.com/blog/covid-19-live-updates

https://www.science37.com/wp-content/uploads/2020/03/12161417/Navigating-Clinical-Trials-in-the-Coronavirus-Era_Science37.pdf

https://www.science37.com/blog/siteless-clinical-trials-changing-the-game-for-patients/

https://www.clinicalleader.com/doc/the-benefits-and-challenges-of-siteless-clinical-trials-0001

https://www.acrohealth.org/wp-content/uploads/2020/03/ACRO-Statement-on-Monitoring-Oversight-FINAL-3.13.20.pdf

https://info.zs.com/activeingredient/clinical-development-risks-and-issues-in-a-covid-19-world

https://www.washingtonpost.com/health/2020/03/19/coronavirus-projections-us/

Lockstep with regulatory guidance on conducting trials amid crisis, here are four key actions sponsors can take to minimize disruptions. 

Moving Clinical Operations Forward During Pandemic

Click the title above to open the Applied Clinical Trials April 2020 issue in an interactive PDF format.

Applied Clinical Trials, April 2020 Issue (PDF)

	Our own contributor, Moe Alsumidae, recently 

, at the precipice of U.S.-based COVID-19, where clinical trials were already heading. Then, in a 

 from Madrid, he shared on LinkedIn what many were beginning to think…this had to be the beginning of the end of clinical trials as we know them.

	COVID-19 will not only leave a long legacy on public health (and a plethora of other institutions) as we know it, but will it also force companies that have been lagging behind the disruption curve to adopt technologies and processes that will make clinical trials more efficient?

	Moe’s was not the only rallying cry. From 

, they posted, issued press releases, and spoke from stay-at-home lockdown to say, “This is the Time.”

 from only the last two weeks of March. Some of it may seem self-serving, but there was no end to the number of companies offering some kind of service, software, or toolkit to help clinical trials during the pandemic. As I write this column at the end of March, we still don’t have a total picture of how many or what types of trials have been suspended by investigators or sponsors in response to the crisis. Ethically, some most go on; others, ethically also, must be 

Most of those services, software, or toolkits were from providers that, not surprisingly, have a remote, centralized, or patient-centric aspect. Decentralized and hybrid trials had been the words of day coming into 2020; the pandemic is bringing virtual and remote to new heights. If you thought remote monitoring of a clinical trial was too much change for your organization, how did the complete inability to get into a quarantined site to verify source data go?

	But, it’s only early April. We are all still scrambling to maintain some semblance of a work life, let alone a personal or family life for the next maybe 30 days.

isn’t your news go-to, we know that. We are the ones that provide practical, hands-on solutions to the current clinical trials industry.

	Suffice to say, we are already reshaping our future content to bring the lessons learned from COVID-19 to bear on this next phase of clinical trials. Stay healthy.

COVID-19 will not only leave a long legacy on public health, but it may also force companies that have been lagging behind the disruption curve to adopt technologies and processes that will make clinical trials more efficient.

The Beginning of the End of Clinical Trials As We Know Them?

	Amid the daily competition for attention in a public space overwhelmed by messaging about COVID-19, the European medicines industry has released a series of “commitments” to demonstrate how hard it is working toward solutions.

	The European Federation of Pharmaceutical Industries and Associations (EFPIA), which represents the research-based pharmaceutical industry in Europe, has published its promises to “working collaboratively across the research and healthcare communities, utilizing our world-leading science, people, and resources to tackle this outbreak.”

	According to Jean-Christophe Tellier, EFPIA president and CEO of UCB Pharma, the industry is “continuing to donate money and in-kind support to health systems, governments, and organizations on the ground, pulling out every stop to ensure the safe supply of medicines to the patients that need them,” and “working around the clock to find new vaccines, diagnostics and treatments.”

	The announcement-released April 1-will have to find an audience among the public and the media that are ceaselessly battered by statements, strategies, and scenarios from governments, international organizations, health professionals, and patient representatives all vying to offer the definitive view of where we are, where we are going, and what we should be doing.

	The drug industry itself has been in overdrive, offering details of the latest scientific and medical advances that may or may not constitute progress toward a solution. The European Union, facing the prospect of a renewed migrant crisis and the meltdown of the continent’s economic and fiscal stability, has been meeting in almost continuous emergency mode at the highest level, pouring out a string of contradictory communiqués that alternate between reassurance and dire warnings. And the background drumbeat swells all the time with repeated demands for more muscular cooperation-at times akin to a form of dirigisme-at EU level.

	The industry commitments include “bringing together the best scientists from industry and academia through the Innovative Medicines Initiative to accelerate the development of new diagnostics and treatments,” and “sharing the learnings from clinical trials in real time with governments and other companies to advance the development of new therapies.” It says it is launching clinical development of potential vaccines against the coronavirus, screening its global libraries of medicines to identify potential treatments, and running “numerous clinical trials” to test new and existing therapies. And it is “coordinating with governments and diagnostic partners to increase COVID-19 testing capability and capacity.”

	Industry is expanding its manufacturing capabilities and sharing available capacity to ramp up production “once a successful medicine or vaccine is developed,” and in the meantime, it is directly linking with regulatory authorities to provide information about stock, manufacturing capacity, and market tensions so as to head off potential disruptions or shortages. At the same time, it is-it boasts-“protecting our workforce and the communities where we live and work, having employees working from home whenever possible.”

	But the industry commitment most likely to strike a chord with many of the industry’s inveterate critics, who have not ceased to push for patent limitations and price controls in a post-COVID world, is “working with governments and health systems to ensure that when new treatments and vaccines are approved they are available and affordable.” That rather timid commitment could turn out in a year’s time to be a Trojan horse inside the industry’s hitherto impregnable citadel.

 is a freelance journalist who specializes in European health affairs and is based in Brussels, Belgium

That question will likely hinge on EFPIA's ability to uphold its commitment to "work with governments and health systems to ensure that when new treatments and vaccines are approved, they are available and affordable.”

How Far Will European Drug Industry 'Commitments' on COVID Resonate?

	Observational cohort clinical studies, or patient registries, are common studies that generate data required not only by regulators, but, also payers and prescribers. While patient registries seem simple, a poorly designed one can lead to the collection of poor-quality data that ultimately results in poor-quality evidence. In this interview, Ravi Jandhyala, consultant pharmaceutical physician at Medialis and an expert in rare disease patient registries, who is also responsible for clarifying the formal definition of a patient registry, discusses how to overcome clinical trial registry challenges.

What is the most difficult challenge of setting up a registry?

 To my mind, there are a number of them, however, the challenge that stands out most relates to data ownership, so who has the ultimate control of the data that goes into the study. We know from our own research and experience that this is a major concern from the investigators’ perspective in the observational

 clinical study or patient registry setting. In an interventional clinical study, it’s rather cut and dry, in that the sponsor is the data owner-or data controller. However, investigators are clear that they do not want their data “stolen” and withheld from them for their own research in the observational setting.

	The second biggest challenge is the data that is to be collected. Essentially, an observational clinical study-of which a patient registry is a type-is limited by the fact that no data can be mandated by the protocol. Therefore, all data must be collected as part of standard diagnostic and monitoring practice for the center. As a result, the data collected versus that which is requested is smaller in volume, and therefore reduces the quality of the overall data set through the missing data. Missing data is a problem across the board; however, it is of greater impact in rare diseases where patient numbers are smaller. The core data set is something I like to call the Holy Grail of observational clinical studies. I’m pleased to say that we have addressed this and have developed a reliable approach to develop such a data set; a particularly high-profile example will be published soon.

	The final challenge I’d like to convey here is data entry. Again, in contrast to a clinical study, there is not as much of a draw in terms of return on investment for investigators, and therefore the sites don’t spend much time with a patient registry; so there is a need to rely very much on their goodwill for their buy-in. This can be supported with appropriate reimbursement for data entry for either an external function or a pre-existing internal researcher at the site.

 How did you overcome the challenges with so much hesitation from study sites and investigators?

 First, we really needed to listen to the concerns and understand the background to them. This involved many face-to-face meetings and a fair degree of research into “failed” examples of patient registries. The solution in this case was actually quite straightforward, which was to define an access agreement that accommodated their concerns and assign the data controller, or to use the old-fashioned term “data owner,” as the patient. This meant that ownership of the aggregated data set remained with the individual patients and consent was provided for its use under certain predefined circumstances. All these materials required both national regulatory and local ethics approval, and importantly, the consent to use an individual’s data could be withdrawn at any stage.

	The control and the consent allowed each patient’s data to be used by the investigator, and then the sponsor, in this aggregated dataset.

	This dataset could then have research conducted on it and, of course, the investigators or steering committee-drawn from all of the participating European countries-would sit on the adjudication committee that would review applications for research to be conducted on the larger aggregated European data set.

	They would feel that they could contribute to, or at least have some understanding and control over, what research is then done on the aggravated data set; the process of reviewing these research studies would be transparent, alongside the results, or outcomes, of these applications.

	Once research questions have been submitted and anonymized, aggregated tables provided, any evidence that is generated must be published. It can’t be left on a hard drive somewhere. It must be made available as part of the transparency associated with a pharmaceutical company primarily sponsoring this type of research. It must also be made available, i.e. published within a peer review journal, or made available as a report on a company website, within a certain time period.

	This meant that the scenario of a sponsor preventing researchers conducting data on the aggregated data set would not arise.

 When you go into designing these studies, how do you decide what data to collect? How do you set up the study designed to create this data?

 Well, we need to look at this from the perspective of the stakeholder whose requirements the evidence must meet. We promote a multi-stakeholder approach to medical planning and real-world evidence generation, and have identified the patient, prescriber, payer, regulator. and sponsor as relevant stakeholders, each with discrete needs.

	Examples on how these needs may differ are as follows:

		Payers are traditionally not so concerned about changes in blood markers but are more focused on improvements in quality of life and burden of disease.

		Regulators may need reassurance on certain safety aspects, such as cardiovascular risk, and require these to be assessed as post approval safety commitments.

		Prescribers are driven by evidence of effectiveness and safety outside the clinical trial population and the knowledge that their peers are using the medicine and generating real-world evidence.

	We spend a significant amount of our time and resource in generating this evidence from expert opinion; we’ll be publishing this later this year.

	The real skill here lies in understanding how to build the market for the medicine and differentiate it either within its class or against current standard of care.

	So, going back to your question, the evidence needs must be defined first from a stakeholders’ perspective and then the parameters are selected through desk research and direct stakeholder engagement.

The stakeholders select themselves. For example, if we are preparing a real-world evidence generating medical plan for a rare disease, we will work through the list.

	Patient stakeholder engagement will involve identifying any relevant patient organizations, and key personnel within them, who would have the experience to be able to help. They may, on occasion have developed a list of research priorities for their disease with their members and this type of document is invaluable for us in ensuring we generate data that is aligned to it. If this list doesn’t currently exist, we may develop one with the relevant patient organization which will feed into this real-world evidence medical plan; indeed, we are currently in the process of developing one of these documents.

	Prescribers tend to be identified from the clinical trialists network or their recommendations. With any new launch, centers that treat a certain disease of interest can be identified through desk research, including literature reviews, but also through national and international organizations and their congresses. In terms of engagement of this particular stakeholder group, we do need to work with the other demands on their time, something that does add significantly to delivery timelines.

	Payers are even easier to identify, for example NICE in the UK. There will be a clear understanding by the market access function within the sponsor as to the routes of approaching each of these in their territory. It is important to remember that reimbursement varies between North America and the constituent countries of the EU.

Moe Alsumidaie, MBA, MSF, is a thought leader and expert in the application of business analytics toward clinical trials, and Editorial Advisory Board member for and regular contributor to Applied Clinical Trials.

Expert in rare disease patient registries discusses ways to address hurdles in setting up observational cohort clinical studies.

Overcoming Challenges in Observational Patient Registries

	Big pharma joins forces against covid-19

	Novartis and a consortium of life sciences companies announced a collaboration to accelerate the development, manufacture, and delivery of vaccines, diagnostics, and treatments for COVID-19 in response to the pandemic. They have joined forces with the Bill & Melinda Gates Foundation.

	As a first step, the 15 companies have agreed to share their proprietary libraries of molecular compounds that already have some degree of safety and activity data-with the COVID-19 Therapeutics Accelerator launched by the Gates Foundation, Wellcome, and Mastercard two weeks ago. Accelerator will quickly screen the compounds for potential against COVID-19. Successful hits would move rapidly into 

	Accelerated Enrollment Solutions (AES), a business of PPD, Inc., and Science 37 are collaborating to recruit and enroll clinical trial participants who live beyond the geographic location of traditional research sites. The companies also are working to rapidly deliver continuity solutions for clinical trials affected by the COVID-19 pandemic, including the use of telehealth to remotely screen patients and conduct study visits.

	The collaboration enables AES to provide increased patient access by reaching beyond its existing, physical site infrastructure of more than 180 research sites. These patients can instead participate in AES studies through Science 37’s Metasite, a hybrid clinical trial methodology that combines traditional site visits with remote trial capabilities, allowing patients located virtually anywhere to participate in a clinical trial. 

	Asklepios BioPharmaceutical, Inc. (AskBio), a a clinical-stage adeno-associated virus (AAV) gene therapy company, has entered into a research collaboration and licensing agreement with the University of North Carolina at Chapel Hill (UNC) for the development and commercialization of gene therapy for Angelman syndrome..

	Angelman syndrome is a rare neurogenetic disorder caused by the loss of function of the UBE3A gene. The disorder occurs in approximately one in 15,000 people, or about 500,000 individuals worldwide, and there is currently no cure. In addition to life-altering symptoms such as speech and motor deficits, more than 80% of Angelman syndrome patients experience epilepsy.

	Zymewire, a SaaS company, has launched  CovidCollaborators.org, an online directory of expert, pro-bono, and discounted clinical development services being offered in the fight against COVID-19. Some of the services being offered include clinical research, clinical software, EDC, manufacturing, investigator training, patient recruitment, use of facilities, virtual services, and clinical trial management.

	Pivotal, a privately held European CRO, has acquired Akcelis BVBA, a full-service provider of patient enrollment and retention solutions for clinical trials in the life sciences industry. Akcelis was founded in 2015 in Belgium.

A compilation of recent notable news developments that pertain to the clinical trials industry.

News Notes

Functional service provision, provider, and/or partnerships-otherwise known as FSP-is a concept that has been used in the life sciences industry for over 20 years. Even so, the understanding of FSP, how it is evolving, and how to get the best results from the model still are a puzzle to many. For the purposes of this article, we define FSP as an outsourcing strategy where vendors provide a single service (statistics, pharmacovigilance, or clinical monitoring, etc.) or a limited number of highly related services (site contracting, site payments, etc.). Regardless of your definition, clients regularly communicate three concerns with the FSP model: resource forecasting, quality and training of staff, and innovation.

The norm for much of the last 20 years has been for sponsors to estimate the number of people needed when, where, and in what roles, and then inform their FSP vendors to provide the needed resources. Forecasting creates a burden on predicting needs, magnified by constant updates to projects, protocols, and priorities. Moreover, there are incentives within companies that can impact the accuracy of resource forecasts.

	Sponsors may underestimate or overestimate the number of people they need, an issue that can occur with their own internal hires as well as FSP vendor staff and services. Requesting more resources than needed may be seen as a hedge to reduce the risk of missing operational deadlines. Underestimating scope is incentivized to meet budget targets, at least in the short run. In either case, clients hesitate to ask FSP vendors to forecast

 resources, fearing they inflate the scope of services in order to increase revenue.

	However, best-in-class FSP providers have become adept at maximizing resource forecasting so as to require fewer resources to deliver a function or task. Therefore, FSP clients should require their vendors to shoulder capacity management, while demanding utilization and performance metrics to verify the FSP workforce is right-sized.

 A common concern is that FSP vendors-in an attempt to reduce costs-provide less experienced and qualified people who receive lower salaries and fewer benefits. The net result is cost savings, which are very important, but may be overshadowed by higher turnover, lower quality, repeat work, and operational delivery problems.

	In the face of these issues, training and mentoring become even more important. Historically, and particularly so for clinical monitoring FSPs, FSP vendor staff use the client’s systems and processes. Their systems harmonize the way a study or portfolio of studies is conducted around the world, when FSP staff collaborate with client employees and/or staff from multiple vendors. The tradeoff is that sponsors often have to take on the burden of training vendor staff on their proprietary systems and SOPs. This, coupled with the traditionally higher turnover associated with some FSP vendors, magnifies the training burden on customers.

	In best-of-class FSP operations, the solution is to use vendor systems and SOPs whenever practicable, but then shift the responsibility of training on client systems and SOPs to vendors using a train-the-trainer model.

 When using sponsor systems and procedures, FSP vendors are left with little leverage to address process improvement, as any enhancements typically require changes from the client-often at an additional cost in terms of investments in new software, tools, and so forth. As a result, FSP vendors typically place the responsibility for innovation on their clients. Moreover, the traditional full-time equivalency (FTE)-based model for FSP de-emphasizes innovation, whereas moving to unitized models financially incentivizes FSP vendors to do better, faster work with fewer people.

	Therefore, best-in-class FSP vendors are moving away from simply providing headcount, to coupling their FSP services with vendor-driven innovation. In clinical monitoring, for example, FSP vendors are providing access to proprietary site networks, risk-based and remote monitoring services, and protocol optimization services as “add-ons.” These innovations drive the need for fewer or less expensive headcount, not more. The intended result may be less revenue for the FSP vendor on a project-by-project basis, but often more satisfied customers and repeat business.

, Associate Director, Functional Service Partnerships, PPD

Outlining the three concerns communicated the most when it comes to implementing an FSP outsourcing strategy.

Building A Better FSP

Data is one of the most valuable assets in clinical development. For a new therapy to reach patients, the clinical evidence package must be of the highest quality to gain approval from key decision-makers, including regulators, prescribers, and healthcare payers. Therefore, adopting a program-wide strategy that protects data quality and ensures its optimal utility across all developmental stages and throughout the product’s lifecycle is an essential requirement for pharmaceutical companies.

	While planning a data strategy undoubtedly requires the right expertise, an equally crucial requirement is effective timing-something that many organizations can overlook. Attempting to implement a data strategy partway through a clinical trial, when copious amounts of data have already been generated, increases the risk of overlooking or even losing critical information. Such errors are costly and can significantly impact development timelines, ultimately reducing the chances of bringing valuable new therapies to patients. Thus, a comprehensive, program-wide data strategy is the essential first step, and indeed the basis on which the success of the whole clinical development program depends.

	This article outlines the best practices and tactics for planning such a data strategy, including what’s involved, who’s responsible, and when it should take place. The key benefits that can be gained from a well-planned, program-wide data strategy will also be discussed, highlighting why such an approach is vital for successful high-efficiency clinical development.

	The growing importance of data strategies

	Pharmaceutical companies are striving to bring ever-greater speed and efficiency to the drug discovery process while still ensuring the growing regulatory focus on data reliability and accuracy is satisfied. As a result, data has become a powerful asset that can be used to maximize every opportunity in clinical trials, from analytics and adaptations, prediction and simulation, and biomarker substratification, through dose-response modeling and QPP, data quality, and easy access to good metadata structures.

	Data quality is a vital component for compliance with FDA and the European Medicines Agency (EMA), with both regulators putting a high priority on recording and reporting data and data transparency. Meanwhile, clinical data is no less important for other stakeholders, including trial participants, funders and sponsors, research institutions, journals, and professional societies.

	Even small errors in trial data can be detrimental to a company’s reputation or for the advancement of a promising new drug candidate. Deprioritization of a clinical data strategy jeopardizes the chances of regulatory approval and market acceptance, which puts company viability and assets at risk. It can also lead to trial

 outcomes that are ultimately difficult to translate to clear evidence of effectiveness and benefits to patients.

	To address these emerging demands, data management systems are becoming increasingly sophisticated, enabling the collection of higher quality data on which to base subsequent analyses, while also increasing the chances of detecting unanticipated trends. Higher quality data and easy access to good meta-data structures create opportunities to optimize clinical studies, ultimately increasing the chances of trial success- whether bringing a therapy to market or licensing it to another company.

	While a robust data strategy enables risks to be managed and minimized, the importance of implementing it at the earliest stages of clinical trials cannot be overstated. Indeed, to obtain a database of the highest quality, clinical data management should start early, even prior to the completion of the study protocol.

 A well-planned data strategy should cover each of the four stages of the traditional clinical development process (i.e., be program-wide), identify where data will be sourced from, and define the approaches to data collection, storage, and analysis. Figure 1 summarizes the best practices for data strategy formulation. The crucial issue of timing is explored in more detail ahead, but first we examine what exactly is meant by a well-planned, program-wide clinical data management strategy.

	Planning for the duration, from the outset

	Although most companies formulate data strategies for their clinical programs, they often don’t adopt a program-wide approach, and they are put into action too late in the development process. In these late-on implementations, even the best-case scenario means that a program will not reap the full potential of a well-planned data strategy. In the worst case, the evidence package will be of insufficient quality to gain regulatory approval for a promising new therapy.

	The traditional four-phase clinical trial process is becoming increasingly connected. This means that a data issue encountered during one phase can now easily have significant downstream consequences. Mitigating this potential issue is best achieved by planning a data strategy for the entire program, from start to finish, i.e., a program-wide strategy.

	Instead of planning trial by trial, formulating a program-wide strategy for data management and trial design well before the commencement of Phase I enables the right data to be collected, at the right time, and in the right way. This approach will generate an evidence package of the highest quality and demonstrate to stakeholders that data integrity has been maintained throughout the entire program.

	The following sections discuss the key considerations and best practices in implementing a data management plan, with an emphasis on Phases I and II of the clinical trial, given the importance of implementing a data strategy at the very earliest opportunity-and the tendency for implementation at these stages to be overlooked.

	Phase I trials can involve unique and unforeseen challenges, such as the timely introduction of data standardization processes and the requirement for frequent protocol amendments during trial setup. There is also a need to manage and integrate data from multiple vendors-a task that requires significant time and organizational resources. Having a data strategy in place that predicts and manages these issues can thus help mitigate risks and ultimately expedite the development pathway. The contribution of dedicated statistical programmers in addressing these challenges is particularly important at this stage. Their work is to build databases to strengthen the evidence package, to assess existing data to inform trial design, and to perform pharmacokinetic analyses, all of which are critical to avoid spiralling costs and unwanted delays.

	As discussed, the key aims of a data strategy are to protect the quality of clinical data, to identify and plug information gaps (such as when assessing target product profile), and to define the trial’s approach to data collection, storage, and analysis (such as the selection of suitable electronic data capture [EDC] systems). A well-planned data strategy will also ensure that early phase trials generate the evidence necessary for later phases of development.

	Another crucial objective of a data strategy is to inform the choice of adaptations in trial design. While there are many types of trial designs to suit a variety of programs, a well-planned data strategy implemented at the earliest stages has the advantage of facilitating the necessary flexibility to implement innovative, adaptive trial designs. Trials with adaptive designs are often more efficient, informative, and ethical than trials with a traditional fixed design, since they can maximize the use of time and money, and may require fewer participants.

 Some adaptive trial designs allow newly enrolled participants to be assigned to a more promising treatment arm after interim analysis, whereas others change the recruitment eligibility criteria to enroll patients who are more likely to benefit. Changing parameters midstream in this way can create complex statistical challenges, which a well-planned data strategy will help organizations adequately prepare for and resolve.

	While frequentist methodologies are used to design most clinical trials, adaptive trials also accommodate Bayesian methods. The flexibility of Bayesian approaches enables the integration of different sources of information and uncertainty

, which allow for smaller trials, more precise dose-escalation, and better patient care. They also enable the prediction of timelines and help determine how a patient cohort will respond to treatment (based on historical data or available patient-outcome information), all while minimizing the risk of introducing bias or impairing interpretability. However, Bayesian approaches are only available to investigators who have access to high quality data. A Bayesian trial requires a well-designed data strategy that is implemented from the outset, without which the scientific validity of results may be put at risk.

	Phase I and II trials concern the study of patient populations with the disease of interest to generate preliminary evidence, particularly on safety. The key challenge at this stage is to ensure that adequate data is collected to fill any knowledge gaps in the target product profile, as well as plan for future evidence needs. A range of models are available to address any such knowledge gaps, from non-compartmental analysis (NCA) to more advanced statistical models such as pharmacokinetics/pharmacodynamics (PK/PD) and model-based meta-analysis (MBMA), which optimize the use of available data.

	When conducting early phase studies, global regulators require submission of NCAs that measure factors such as extent and rate of exposure to a drug, without the complexity of strenuous assumptions or complex models. Through the use of rudimentary methods such as linear trapezoidal rules, NCAs make it relatively easy to measure the concentration of a drug in a body over time. They can capture exposure length, and time of peak exposure, without the challenges of models that require independent validation.

	NCAs are a common subject of regulatory inquiries and can complement other models such as PK/PD analysis. PK/PD models enable researchers to evaluate and present data on drug safety and potency, and to test the impact of trial designs on the potential outcomes. Reliable data at this stage also ensures that Phase III decisions about dosage are based on knowledge gathered during the entire Phase I to III analysis. PK/PD modeling can maximize the chances of success by ensuring that the correct data on safety is recorded.

	PK/PD models are becoming more commonplace in quantitative pharmacometrics, to build strong dose-response models for Phase II. It is well known that unreliable dose-response models in Phase II can create problems for Phase III tests: only 13.2% of Phase III trials accepted after initial rejection are rejected on grounds of efficacy. More common reasons are dose selection, choice of endpoints, and other challenges that better Phase II modeling can prevent.

 Working with statistical experts from Phase I onwards can therefore ensure that knowledge gleaned from NCAs is used to build stronger Phase II models, thus avoiding these Phase III pitfalls.

	Another important tool is MBMA, which allows investigators to take advantage of knowledge gained across several clinical trials, showcasing the benefits and challenges of confirming safety and efficacy from various endpoints. Trial sponsors move forward knowing they’ve made the best available choice from perspectives as wide ranging as patient safety and expected financial value. When coupled with forecasting and simulation tools, MBMAs also highlight possible pitfalls, allowing trial planners to plan ahead.

	MBMAs also help with the construction of surrogate endpoints, which are useful for detecting early signs of efficacy, and go/no-go rules that allow statisticians to benefit from expertise gleaned across the therapeutic sector. The accumulation of this broad range of expertise means that studies moving to Phase III not only have early phase knowledge that validates the enterprise, but they can also rely on numerous other studies that signal the probability of Phase III success.

	A program-wide data management approach is incredibly important to the success of a clinical trial. However, given the advantages of establishing the right data strategy as early as possible, the focus here has been on solutions for Phases I and II, which will have an immense impact on the success of the later stages. While a similar examination of Phases III and IV is beyond the scope of this article, a brief mention of some of the key data requirements for these phases follows here:

		Phase III studies, which are designed to assess drug efficacy, effectiveness, and safety, become increasingly time pressured, as sponsors often engage in a “race to market.” This means that observance of timelines in data management is crucial since delays may be costly. EDC systems, although they may cause initial delays due to setup time, will lead to higher quality data and swifter database locking at the end of the study.

		In Phase IV, marketing and safety groups are the key stakeholders. These studies often involve following patients for long periods to examine treatment effectiveness and use in clinical practice. Given that these studies observe events in real-life settings, the data collection approach needs to reflect the fact that those collecting and storing the data might not be as trial savvy as those who normally work in clinical trial settings. Therefore, ease of use of any EDC system needs to be a primary factor.

	A predetermined strategy on what data should be collected, how it should be formatted and organized, and how it should be analyzed, simplifies the whole process from database submission to demonstrating regulatory compliance.

	However, data strategy planning often occurs quite late in development, which means there is rarely enough time to address the complex considerations involved in the planning process. Consequently, fewer benefits will be gained and, in the worst case, the quality of the clinical evidence could be compromised.

	A well-planned data strategy can strengthen clinical evidence by ensuring that the data is efficiently collected, handled properly, and interpreted correctly. Moreover, it can minimize risk by identifying, quantifying, and mitigating data issues. A clear plan also allows any problems and roadblocks to be resolved quickly, helping to streamline the development pathway and potentially expedite a therapy’s time to market.

	1. Sharing Clinical Trial Data: Maximizing Benefits, Minimizing Risk. Committee on Strategies for Responsible Sharing of Clinical Trial Data; Board on Health Sciences Policy; Institute of Medicine. Washington (DC): National Academies Press (US); 2015 Apr 20. 

https://www.ncbi.nlm.nih.gov/books/NBK286000/

	2. Why clinical trial outcomes fail to translate into benefits for patients. Carl Heneghan, Ben Goldacre, and Kamal R. Mahtani. 

. 2017; 18: 122. Published online 2017 Mar 14. doi: 10.1186/s13063-017-1870-2 

Examining the advantages of a program-wide data strategy in advancing high-efficiency drug development.

Applied Clinical Trials-04-01-2020