Sony Salzman

Over the past two decades, clinical data managers (CDMs) have seen their responsibilities change dramatically, as the industry shifts toward more complex protocols and elaborate data-collection requirements. “If someone has been in data management for 20 years now, chances are they started working in paper,” says Derek Lawrence, senior clinical data manager with Rho, a contract research organization (CRO). But with the introduction of electronic medical records, CDMs were left to deal with an ever-growing list of new data sources, including eSource, electronic clinical outcome assessment (eCOA), and bring your own device (BYOD), all managed by competing software systems.

	Over time, because clinical data management is one of the only roles in the clinical research industry with the word “data” in the title, many of the new advances in data collection and analytics fell to CDMs to figure out, says Lawrence.

	As the role expanded to match the breakneck pace of technological development, CDMs suddenly began to wear many hats: computer programmer, clinical operations expert, and biostatistician, all in one.

	Making matters worse, “about five years ago, there was a generic flight of data management people into other functional areas,” adds Lawrence. Many gravitated toward project management, and those with technical skills moved into statistical programming.

	Because the role has expanded to include so many different responsibilities, many CDMs today feel overworked and under-resourced. Over time, “our tasks have increased … we are much busier than we were before,” says Michael Goedde, chair of the nonprofit Society for Clinical Data Management (SCDM) Board of Trustees and VP, global data operations, PRA Health Sciences.

	“It’s no surprise today that what we used to do 20 years ago doesn’t work any more,” adds Patrick Nadolny, SCDM innovation committee chair and associate VP and global head, clinical data management and programming, Allergan.

	Against this backdrop, and with the knowledge that more changes are on the horizon, SCDM leadership has embarked on a series of white papers and other educational initiatives to serve as a roadmap for the future of the CDM’s role within the clinical research ecosystem.

	Though their work is still unfolding, one of the key themes that has emerged is the importance of emphasizing the 

 part of a CDM’s role. In the first of three reflection papers, SCDM proposed a new name-clinical data scientist-meant to encompass the growing importance of data mining and extracting value from data and big data, particularly as the field continues to experiment with new technologies like artificial intelligence (AI) and blockchain.

	But even as clinical data science and programming skills grow in importance, the clinical operations piece of a CDM’s role is still a crucial one. That is especially evident as trial designs continue to evolve to include decentralized procedures, synthetic arms, and adaptive trial designs.

	Because of their “hybrid” skills, CDMs are indispensable players at any research organization, says Lawrence. But with so many responsibilities to juggle, the future of this single, solidified role is precarious.

	When asked to define the role of “clinical data manager” as it stands today, Goedde explains that “even though it appears to be a simple question, the answer is quite complex, as it encompasses walking the very fine line of the fundamental (old school) CDM mindset of data integrity as the highest maxim on the one hand, and the exponentially increasing landscape of technology and its potential opportunity on the other hand.” Goedde went on to note that a new SCDM initiative is working to provide more definition to the role of CDM and its transition to clinical data science, to help answer this difficult question.

	The simplest way might be to think of CDMs as “guardians of the data,” says Linda King, immediate past chair of SCDM. Nadolny echoed that sentiment, pointing out that “a clinical data manager is responsible for the lifecycle of the data-from collection to the analysis in support of regulatory submission.”

	Beyond that critical stewardship, responsibilities vary by organization. But the CDM role is difficult to pin down because it touches almost all aspects of the clinical research enterprise-and it’s constantly evolving.

	“In theory, you can have five or more specialists doing all the different areas a clinical data manager would cover,” says Goedde. “But from an operational point of view, that would cause a lot of headaches.”

	Today, he adds, the role is “quite fragmented,” but it’s moving toward a place in which CDMs are expected to proactively manage the risk associated with complex data flows and a decentralized trial environment while ensuring data is credible and reliable.

	““We are at a tipping point,” says King. “We’ve been observing the trends very closely over the last few years, and looking at opportunities for [SCDM] to define and shape the future.”

	“Everybody is scratching their heads on how we are going to cope with the complexity and the speed of change,” adds Nadolny. “What we need to do is define a very clear roadmap of where we are going.”

	The recent SCDM reflection paper on the topic, published in June 2019, is called The Evolution of Clinical Data Management to Clinical Data Science (available at https://scdm.org/white-paper/). Nadolny, its lead author, says forthcoming reflection papers will break down exactly how industry leadership can help facilitate this shift from “data management” to “data science.”

	“One of the things that we’re doing as a society in leading this change is defining what clinical data science is and what the role of a clinical data scientist is,” says King. “It’s not 100% defined at this point, but what we know is what the new skill sets look like.”

	Crucially, a CDM must be “someone who is bringing operation expertise but also scientific insights,” she adds. “They should be using AI/ML (machine learning) to gain insight from the data.”

	According to Nadolny, such tools, along with robotic process automation, could eventually ease the heavy load felt by CDMs, who have struggled to manage dozens of new streams of data, from patient-reported outcomes to wearable sensors to mobile apps.

	“I don’t expect a data manager to be able to look at 16 million data points,” says Nadolny. “You are going to need more advanced analytics, including AI-based analytics, to identify patterns.”

	The next generation of clinical data managers will not only be good data scientists, but they will need to understand how to babysit these new tools. That’s because, left unsupervised, AI and other advanced predictive tools might lead untrained managers down the wrong path. Managers will need to understand how to spot bias, and how to back up decision-making criteria to outside auditors. Even with AI/ML expertise, “you still need a data integrity mindset,” says Goedde.

	Lawrence, meanwhile, believes the CDMs of the future will need to understand how to stitch together legacy systems. “Where I see data capture going is the by hand-constructed [electronic data capture] systems, and more folks who understand how to leverage the strength of [application programming interfaces],” says Lawrence. But he also adds that the most important part of a CDM’s job is “a strong analytical mindset. It’s not a programming language, it’s not an educational degree-it’s anybody that can look at a project with 35 moving pieces and find novel solutions.”

	Current CDMs who possess all these qualities at once are exceedingly rare. In recent years, “they have evolved in their individual organizations out of necessity,” contends Lawrence. “A whole group of disparate people with a pretty diverse skill set just kind of came into being.”

	“We have basically created a new functional area within clinical research,” adds Lawrence. “The role that has emerged are the folks that are involved in integrating these systems and working in a variety of programming languages to generate the safety rails for clinical data.” However, he acknowledged, “it doesn’t have a name on it, and it’s scattered.”

	“I think we have a big opportunity to rebrand, [but] we do not want a cosmetic title change,” says Nadolny, who believes in the future, “this role will be responsible for managing the flow and quality of data. The role is becoming more scientific, as opposed to just managing the data.”

	That’s exactly why Nadolny proposed the term “clinical data scientist,” to encompass this shifting role, although he acknowledges that each organization will likely use different titles to describe the same basic responsibilities.

	In the future, “the title is not going to be data manager,” agrees Lawrence. “It would almost be something akin to ‘database engineer,’” he says, encompassing a job that emphasizes clean data capture, risk minimization, and providing an output that is fit-for-purpose.

	Part of the current uncertainty may stem from the fact that today’s leadership is still working to let go of an “old school” way of doing things, says Goedde. But the next generation-Millennials and Gen Z-grew up in an environment in which fast-moving, disparate data isn’t necessarily seen as a problem.

	Looking to the future, Goedde believes that, to some extent, it’s about “empowering the next generation of data scientists and managers for whom it will be much more natural to work with all this data.” “Embracing this change is fundamental to me from a leadership perspective,” he adds. 

	According to King, there are some universities now that have masters in data science programs and they offer clinical research programs. “I think as the profession evolves, we will get an influx of individuals from those programs,” she says. “However, I also think today’s data manager is well positioned to apply their fundamental data management domain knowledge using new tools and techniques as a clinical data scientist.”

	Despite the ongoing discussion surrounding the exact “rebranding” of the CDM’s role, industry leaders agree that the role will remain laser-focused on data integrity and data quality, meaning data is managed the right way to ensure it is credible and reliable.

	“If you don’t have data integrity, you can’t draw inferences or conclusions,” says King. 

 is a freelance journalist who specializes in health and medical innovation. She can be reached at 

Articles

Breaking down the evolving role of today’s clinical data manager-and its importance as a key cog in the clinical research ecosystem of tomorrow.

Clinical Data Manager: A Roadmap for the Future

Just under a third of the cost to develop an FDA-approved drug is attributable to pre-human laboratory research, reports have found. For decades, FDA and the pharmaceutical industry have agreed that streamlining and optimizing preclinical drug development-which typically includes laboratory toxicology studies-would not only save money but also prevent patient harm by identifying non-viable candidates before they ever enter human trials.

	One of the best ways to streamline pre-human toxicology studies and, therefore, reduce attrition in drug development is to leverage the power of big data-combining thousands of prior toxicology studies into a searchable database that can be used to glean insights and search for potential links among similar drugs.

	But because all these pre-human studies are conducted in different formats by different companies using different terminology, the promise of cross-study analysis has not been realized-until now.

	Today, the industry is on the cusp of a revolution in the way toxicology studies are cataloged, analyzed, and leveraged, experts agree. And it’s all because of a pilot project that was first launched by the FDA back in 2002 called the Standard for Exchange of Nonclinical Data (SEND) initiative.

	For nearly a decade, SEND chugged along under the stewardship of the Clinical Data Interchange Standards Consortium (CDISC), eventually resulting in a data standard that is widely used today. That standard, in turn, fostered new FDA partnerships with groups like BioCelerate, an organization of pharmaceutical companies dedicated to identifying efficiencies in nonclinical research, and the Pharmaceutical Users Software Exchange (PhUSE), a non-profit consortium of data managers, biostatisticians, data scientists, and eClinical IT professionals.

	In 2014, FDA officially mandated that any pharma or biotech company submitting an investigational new drug (IND) application would need to start complying with SEND. The first SEND-compliant toxicology studies started rolling out in 2016, the year of the deadline. Now, as those studies start to accumulate, the industry is waking up to the fact that it may be on the cusp of a dramatic change in the status quo of toxicology data analysis.

	“The earlier you can either adjust a compound to avoid toxicity or stop development on a compound that’s fatally flawed, the more resources a company has to go to other compounds and get more useful drugs on the market,” says William Houser, senior research scientist for Bristol-Myers Squibb, a member of BioCelerate.

	“Usually, preclinical studies are not very large. However, these studies must provide detailed information on dosing and toxicity levels,” says Lilliam Rosario, PhD, director, Office of Computational Science at FDA’s Center for Drug Evaluation and Research (CDER). “After preclinical testing, researchers review their findings and decide whether the drug should be tested in people. For the pharmaceutical industry, it becomes particularly more expensive in the later stages of clinical development.”

	Today, many believe that the holy grail of the SEND initiative will be to enable cross-study analysis, improving the industry’s ability to weed out dud candidates, explains Houser.

	“The SEND standard has been out for a number of years, so why is it that only now people are beginning to do this?” asks Houser. “It’s due to the speed of the pipeline. SEND began for studies in 2016. So now we are three years after that, [meaning] we are just now approaching a critical mass of studies created across the industry that would enable this.”

	“The exciting thing for me is that just not too long ago, I used to measure a submission to FDA on how many boxes went out on a truck,” says Lou Ann Kramer, CDISC’s senior director of standards development. “We paid a lot just to exchange data.”

	But thanks in part to the SEND initiative, there is a new atmosphere of collaboration around preclinical toxicity data among pharmaceutical companies.

	“I don’t think we’ve realized how revolutionary this all is,” says Kramer. “Someday we’re going to look back on this data standard being used as a major turning point in research.”

	At a very basic level, cross-study analysis is the ability to pull studies quickly and compare them on a computer screen, ideally using some sort of visualization tool to understand patterns, explains Todd Page, director of toxicology, Eli Lilly and Company, also a member of BioCelerate.

	This type of analysis would allow scientists to better understand off-target versus on-target effects, and to isolate the effects of “vehicles,” or the inert substances used to deliver a drug that can also sometimes cause toxicity. At some point in the future, cross-study analysis might even be used to correlate chemical structures with specific toxicities, says Page.

	“We’re now beginning to have studies that can be used for cross- study analysis,” says Houser. “It’s approaching 1,000 studies that have been received by the FDA.”

	“I think it’s in its infancy,” adds Kramer, but soon “we are going to get to the point where we get enough data where we’ll start to see things. We never had that line of sight before.”

	FDA’s long-standing partnership with CDISC was a natural result of the SEND collaboration, which CDISC took ownership of shortly after it was ideated at FDA, explains Kramer. In the subsequent years, as the SEND data standards were finalized, FDA also forged additional partnerships to maximize the standard’s reach.

	“FDAs collaboration with BioCelerate is to evaluate and disseminate findings related to the use of [SEND] that can contribute to a more effective analysis of nonclinical studies conducted in support of product development and regulatory applications,” says Rosario.

	“We anticipate that this collaboration will facilitate the discussion and information-sharing among FDA and industry to support shared aims of more efficient, effective, and data-driven decision-making in nonclinical research.”

	According to Page, the BioCelerate consortium got started on its own standard before it began collaborating with FDA.

	“The FDA collaboration helped things take off because they have a rich data set, but also because they are as dedicated to solving this as we are,” says Page. “Our interests are very much aligned. They want to be able to look across studies just as much as we in industry do. They want to ensure patient safety just as much as we do.”

	According to Rosario, FDA has also partnered with PhUSE to co-sponsor the FDA/PhUSE Computational Science Symposium, during which participants will share best practices around data standards, analytical tool development, and business processes that are driving the development of IT systems.

	“This annual meeting brings together FDA, industry, and academia to work together in a non-competitive environment to find solutions to shared problems and challenges,” she says.

	The SEND standard didn’t solve data harmonization problems overnight. Today, stakeholders are still working out how to best collect and present data for cross-study analysis.

	“When the data standard was designed, it was designed with the intent of looking at one study in isolation,” says Page. The standard allowed a bit more flexibility for industry sponsors, he notes, but ultimately “that’s not a good thing for cross-study analysis.”

	There are subtle differences in the way information is inputted, down to mundane details like which abbreviation a laboratory might use for various measurements.

	FDA and BioCelerate are collaborating on a new harmonization initiative aimed at ironing out the existing kinks in the SEND standard.

	In November, the two parties published a joint manuscript in the journal 

, which identifies common variability in certain SEND domains and describes how variability can be managed to enable cross-study analysis, Rosario explains.

	“We will be making a recommendation on how to populate [each] field, in some cases there will need to be some data extractions, and we will be developing some code on how to make that extraction,” adds Page.

	In the coming years, the insights gathered from SEND-enabled cross-study analysis of toxicology data will benefit industry, regulators, and patients alike.

	“We all want to get into the clinic as quickly as possible. It will make the review process more efficient,” says Page. “It’s all open source. Everyone in the whole industry will be able to benefit.”

	“Pharma companies will be able to more easily distinguish ontarget and off-target tox of compounds they are developing,” says Houser. “They will be able to discover it earlier in the process and be able to bring better medicines to the market faster.”

 is a freelance journalist who specializes in health and medical innovation. She can be reached at 

The long-running SEND initiative has fostered new FDA-industry partnerships, opening the door to dramatic changes in toxicology data analysis.

Unleashing New Insights from Toxicology Studies

The U.S. Department of Veterans Affairs (VA) has a long history of pioneering clinical research, including first definitive tuberculosis trials in the 1940s, and subsequent studies that led to the first CT scan and implantable pacemaker.

	In theory, life science companies should be clamoring to partner with the VA on clinical research. The agency was an early adopter of a unified electronic record system, and its patients are motivated, ethnically diverse former service members-ideal study candidates.

	But in recent decades, the VA’s research efforts have remained inwardly focused, with the agency’s strict rules and protocols making it difficult to partner with the private sector. Today, the VA’s study start-up time is 120 days longer than the industry average, according to two independently verified analysis by contract research organizations (CROs), meaning the VA is overlooked in favor of more nimble academic research centers.

	Now, however, VA is committed to reform. Rachel Ramoni, DMD, ScD, who joined the VA as chief research and development officer in 2017, has made it her mission to cut study start-up times-much to the delight of advocacy groups like the National Association of Veterans Research and Education Foundation (NAVREF) and the Coalition to Heal Invisible Wounds.

	According to Rick Starrs, CEO of NAVREF, VA leadership is committed to change because “they agree with us-that they’re missing out on opportunities by being slow.”

	Reform at the VA will require massive cooperation among the VA’s 170 medical centers, 1,000+ affiliated community medical center, and 80 affiliated non-profit organizations.

	“There are certain things sponsors want: they want you to be quick, they want you to do a really good job, and they want you to be predictable,” says Robin Rusconi, executive director of the Midwest Veterans Biomedical Research Foundation, one of the VA’s affiliated nonprofits.

	In 2019, NAVREF and the Coalition to Heal Invisible Wounds wrote a letter to Congress that encouraged the VA to cut its start-up time by 100 days. Ramoni signed on to this goal, essentially pairing the VA’s existing “Access to Clinical Trials for Veterans (ACT)” initiative with the advocacy groups “100 Days Faster” initiative, pledging to achieve this milestone by 2021.

	For the past year, Ramoni and other VA officials have been listening to feedback from would-be pharma industry partners, and one of the most consistent complaints they receive is that it’s not clear which department at the VA is responsible for clinical research.

	“I know some companies have said, ‘well, we just don’t know how to begin,’” says Allyson Gage, PhD, chief medical officer of Cohen Veterans Biosciences, a nonprofit member of the Coalition to Heal Invisible Wounds. There is no “simple, centralized way of approaching the VA to learn about how to start a clinical trial,” she says.

	According to Grant Huang, PhD, director of the cooperative studies program within VA’s Office of Research and Development (ORD), creating a single point of contact ties in with the VA’s other efforts to centralize key pieces of VA clinical research.

	In fact, “centralization” is the core theme of the VA’s current efforts to become a more attractive clinical research partner to private industry. Now, Ramoni and her colleagues are focusing their efforts on centralizing three main pieces of the VA’s clinical research infrastructure: the institutional review board (IRB) review processes, IT and privacy procedures, and maintaining a single point of contact for industry partners.

	“The first year was about planning,” says Ramoni. “We’re now in year two, which is implementation.”

	The VA’s first and most pressing hold-up includes an official VA policy that prohibits working with commercial IRBs-including IRBs that have been vetted by the Association for the Accreditation of Human Research Protection Programs (AAHRPP).

	“The VA wants to build capacity with [the Office of Research Development] that allows for an enterprise approach,” says Roger P. Murry, senior policy, Akin Gump Strauss Hauer & Feld, the largest lobbying firm in the U.S. “Commercial IRBs are staffed by professionals who do IRBs all day, so let’s inject that expertise into the VA.”

	According to Ramoni, changing the VA’s official policy isn’t the real barrier to working with commercial IRBs. Rather, it’s ensuring that those IRBs meet the level of data security and protection that’s required for government health data.

	Now, for the first time, the VA is exploring partnerships with commercial IRBs. “We are currently pursuing a memorandum of agreement with two major commercial IRBs to be able to rely on them,” says Ramoni, noting the memoranda are not finalized yet, pending review of the VA’s information security office.

	Another important initiative is to standardize the VA’s internal IRB processes through a central IRB. Local VA IRBs meet periodically, so missing a meeting creates major delays.

	“It’s sort of like missing a prerequisite in college,” says Rusconi. “If you miss that first meeting, you have to wait until you can get back into that loop. It can create that slowness and unpredictability in the system.”

	Currently, the VA has “very, very different IRB reviews as you go across the country, each having vastly different review times,” adds Rusconi. “We need to figure out the best way to standardize the timing and the nature of IRB reviews.”

	According to Huang, the VA is in the midst of transitioning to a more robust, central IRB approach for clinical research.

	Streamlined technology review and non-disclosure agreements

	From the outside, the VA appears to be a monolith. But internally, each site operates in a decentralized silo, each with its own procedures, policies, and preferences. Now, the VA is working to create a centralized approach for two specific aspects of clinical research: IT review and non-disclosure agreements.

	All clinical trials require some form of IT setup, including electronic data capture (EDC) systems. At a typical academic medical center, implementing those systems is a “simple review process,” says Gage.

	That’s not the case at the VA, however, where each site “has its own security officer, and that person might not be up to date on the types of digital data capture available,” says Gage, adding that different sites might sign off on different IT systems, creating unpredictability for sponsors.

	In an ideal world, the VA would have a centralized information security analysis to allow for more thorough and consistent reviews of clinical trials to come on board. Now, efforts to streamline the IT security review process are underway.

	“We’ve already seen implemented progress through a new team in the office of IT that is helping reduce delays and inconsistencies related to information security reviews,” says Murry. The VA has hired about a dozen full-time staff members to develop that expertise. “It’s a great example of leadership developing new expertise,” adds Murry.

	Meanwhile, Huang is spearheading a movement to create a unified non-disclosure agreement. Previously, “for non-disclosure agreements, you would have to go to each medical center,” he say. “Now, we have a central authority for non-disclosure agreements.”

	Ultimately, Ramoni believes that bringing industry-backed clinical research to the VA is the right thing to do for the agency as a whole, and for the health of the veterans.

	“Clinical trials are a key part of the national healthcare and research landscape,” adds Starrs, himself a veteran. “They offer opportunities for patients to get cutting-edge therapies. There’s the opportunity with the veteran’s system-with nine million veterans patients it in, and a great electronic health record-to be able to partner with industry to do some novel and groundbreaking type things that other systems couldn’t or wouldn’t do.”

After years of siloed focus-and slow study start-ups-the U.S. Veterans Affairs agency embarks on implementing its multi-year initiative to bolster clinical research.

The VA Gets Real on Reform

Randomized controlled trials (RCTs) have long been held as the gold standard in medical research-and for good reason. By creating a rigid study design and splitting patients up into “treatment” and “control” arms, RCTs are widely accepted as the best way to determine whether an experimental new treatment is indeed better than the standard of care.

	But RCTs have one major drawback: they’re expensive, time-consuming and labor-intensive. To make matters worse, once the trial is finished and the results are tabulated, the entire trial operation is often shuttered, meaning much of the data that was collected will never be used again.

	We live in a data-rich world. Outside healthcare, other industries successfully leverage large datasets and advanced computer science techniques such as machine learning to streamline business operations. But within the clinical research industry, trial design has not been greatly enhanced by the trappings of modern computational technology.

	There are signs that the industry is ready for change, with three different trends converging on the clinical research landscape that promise to slowly improve upon clinical trial design without necessarily undermining the sanctity of the RCT gold standard. The first of these trends is known as a “common protocol template,” or the concept of using one streamlined protocol that is then adapted to specific research programs. Second, is a concept called “quantitative” trial design, which leverages predictive analytics to streamline the planning, execution, and operational side of clinical research efforts.

	Third, is the concept of creating a synthetic placebo arms, or using existing patient data instead of enrolling new patients into the placebo arm of a randomized trial and then giving them a dummy drug. These new approaches are not without their detractors, but most have received tactic if not fully-throated support from the FDA.

	In their own ways, all three concepts-the common protocol, quantitative design, and synthetic placebo arms-are attempting to leverage modern computational firepower to make clinical trials cheaper for sponsors, safer for patients, and better for public health.

	These three concepts “are solutions to the general problem and challenge [of] getting the scientific process to become more efficient,” says David Lee, chief data officer at Medidata Solutions. The idea, Lee adds, is to get results that are “as accurate as possible, as fast as possible, and in the least risky way possible.”

	Today, these new trial concepts are rolling out cautiously in low-stakes settings. But in the future, they may lead to a reimagined world of clinical research.

	Laying a foundational framework with a common protocol template

	A study protocol is basically a roadmap for each clinical trial. Over time, pharmaceutical sponsors have developed their own proprietary study protocols, each of which varies from sponsor to sponsor. Meanwhile, protocols have grown more complex as clinical research questions have become more exacting and precise, driving up the cost and time of conducting a clinical trial.

	The complexity and diversity of these protocols creates headaches for study sites, institutional review boards (IRBs), and regulators who are asked to read and interpret a dizzying array of study protocols from various sponsors.

	Enter the concept of a common protocol, or a single streamlined template that is consistent across study sponsors. “The reason a [common protocol] was needed is every sponsor of research writes their protocols differently,” says Vivian Combs, advisor/process owner of clinical information and process automation at Eli Lilly and Company, and also the lead of the Common Protocol Template Initiative at TransCelerate BioPharma, a non-profit collaboration of more than 19 biopharmaceutical companies.

	Historically, says Combs, “there has not been very much clear guidance around what an ideal protocol looks like.” But over the past decade, pharmaceutical companies (represented by TransCelerate), the FDA, and the National Institutes of Health (NIH) all realized the need to straighten up the protocol mess, and began to separately develop streamlined protocol templates.

	By 2017, all three groups-TransCelerate, FDA, and NIH-teamed up to harmonize their respective templates. Today, the respective templates have been finalized, with each slightly fine-tuned for specific research purposes (for example, the NIH template is geared toward investigators who receive government grants and funding, while TransCelerate’s template is designed for pharmaceutical sponsors).

	Since it was launched in 2017, TransCelerate’s Common Protocol Template (CPT) has been downloaded over 7,600 times, primarily by biopharmaceutical companies, but also by a mix of government agencies, major cancer centers, and independent researchers.

	“TransCelerate is certainly leading the charge here,” says Tom Lehmann, managing director, Accenture Life Sciences. “It’s certainly very beneficial for the industry to create some operational consistency, particularly as you’re moving into more data sharing.”

	When it comes to data sharing, there was plenty of room for improvement. In the 1980s and ‘90s, study protocols were written on paper, but even after pharma sponsors ditched paper and pencil in favor of computers, protocols were formatted in a way that made it difficult to share information. For example, data entered in a study protocol at one site is often manually re-entered at multiple points throughout the life cycle of a clinical trial-a process ripe with errors. Meanwhile, any changes made to the master protocol must be communicated to the study sites in a time-consuming and manual process.

	The increasing complexity of protocols makes these problems worse, with a 2016 analysis by the Tufts Center for the Study of Drug Development (CSDD) finding that among protocols that had a substantial change during a trial, 23% were completely avoidable, with some due to human error. Protocol non-compliance-in which sites don’t follow the protocol exactly-has also grown rapidly over the past decade, and now accounts for 46% of all site deficiencies, according to research published by Tufts in 2015.

	But thanks to advances in computer programming, it’s now possible to rely on computers to auto-populate fields across different systems and store those fields to be used for different purposes later on.

	“If a machine could ‘read’ the protocol,” explains Combs, “a human wouldn’t have to do the work of structuring the information and feeding it into the next downstream system.”

	“There are a dozen or more downstream processes that are waiting for that information,” says Combs. These include case report forms (CRFs), grant applications, statistical analysis plans, and clinical trial registries. With a truly digital protocol, these downstream systems could be automatically updated, much like an automatic software update, rather than manually re-entered by research staff.

	Still, there is some resistance to the widespread adoption of TransCelerate’s Common Protocol Template-most having to do with industry inertia, says Combs.

	Traditionally, she says, “the protocol has been, within companies, a place to capture that institutional knowledge. It has become a holding ground for those kind of lessons learned.”

	“I think everybody understands the value of having a common template, but it’s hard to let those things go,” adds Combs.

	Looking to the future, Combs says that common protocols will facilitate data sharing between companies-encouraging greater collaboration among pharma companies and reducing unnecessary clinical research costs.

	According to Stuart J. Pocock, professor of medical statistics at the London School of Hygiene and Tropical Medicine, it’s a good idea for pharmaceutical companies to collaborate on a common template.

	“This would enhance the field,” he says, “and at the end of the day, you can combine the results more reliably in a meta-analysis.” Currently, conducting a meta-analysis is a difficult and time-consuming endeavor because biostatisticians must pull data from a myriad of different studies that were all executed on a diverse array of protocols. In the future, when more research is conducted under a common protocol, meta-analysis will become more accurate and reliable, experts predict.

	“The possibilities are almost mind-boggling,” says Combs.

	Optimizing operations and design criteria with quantitative design

	The concept of quantitative design “means many things to many people,” says Lee. But at its core, quantitative design employs recent advancements in data management, computer modeling, and predictive analytics to improve a trial’s operational efficiency and chance of success.

	Quantitative design employs a “data-driven approach” to determining the best path forward in a drug development program, says Venkat Sethuraman, associate principal and the global clinical lead within ZS Associates’ R&D excellence practice.

	It’s like Google Maps, explains Sethuraman. When navigating using Google Maps, “Google tells you there are four ways for you to get to a particular destination,” he says. Once you see all the options laid out, “you can choose which direction you want.”

	Today, through machine-assisted learning, computer algorithms can look at massive amounts of historical clinical trial data. Then, that algorithm can predict-with a high level of accuracy-which trial design features will take the longest, which will be the most expensive, and which are likely to result in trial success.

	That means, instead of generating a trial design based on the experience of a company’s most senior clinical research scientist, studies would be designed by looking at data on what has worked in the past, and using that data to make predictions about the future.

	For example, says Lee, one of the most fundamental questions when planning a new clinical research study is that of statistical power. Chiefly, how many patients need to be enrolled so that the trial can achieve statistical significance? “That is a calculation that is based on assumptions that could be informed quantitatively, or through data,” says Lee. “But often times that’s done in a more informal way,” based on the experience of the lead scientist, he notes.

	Another application could be the use of quantitative analysis to determine which cancer indication to pick first when designing the clinical research program of a new experimental oncology therapy. For example, is the new candidate more likely to succeed in melanoma or lung cancer? Today, those decisions are “primarily based on what the physician thinks,” says Sethuraman. “It’s not very data-driven. I think there’s a huge opportunity for machine assistance.”

	And with so many massive Phase III trial failures in recent years, Lehmann says there has been “a big push” to “do a better job of feasibility assessment.”

	“There also seems to be a shift on the predictive part on the operational side,” says Lehmann. “People are thinking to use their operational data to not only manage the trial but also to [ask], ‘What sites are likely to enroll on time? At what point in the trial do I want to have an intervention because I want to keep things on track?’”

	It’s still early days for quantitative trial design, with companies like Medidata and ZS offering their clients tools and apps to strengthen and streamline studies from the very first design decisions.

	“Civilization is increasingly driven by models and data, so I think this theme is being adapted into the clinical trial space as well,” says Lee. “How do we do what Netflix or Amazon has done on the consumer side?”

	“The technology is there, says Lehmann, but  “the question is: is it actually changing the way [companies] operate, or are they still relying on instincts?”

	Enhancing an ethical obligation to patients with a synthetic placebo

	Every year, thousands of hopeful patients enroll in clinical research studies, only to be given a placebo intervention. This feature of randomized trials-the placebo control arm-is necessary to determine whether an intervention has a real effect on patient outcomes.

	But placebo-controlled trials have long created hang-ups in clinical research. Most patients would rather take an active drug than a placebo. In addition, there are ethical implications to consider when giving a placebo treatment to a patient with a potentially deadly disease.

	Over the past several decades, pharmaceutical sponsors have collected an unprecedented amount of placebo-controlled data. This data indicates how different types of patients-from cancer patients to diabetes patients to psoriasis patients-fare while taking nothing more than a sugar pill.

	Today the industry is hoping to leverage that historical data, comparing those outcomes against the experimental therapy in lieu of a placebo-control arm of a trial. In fact, in 2017, researchers used Medidata’s de-identified database of 3,000+ trials to create a “synthetic control arm” for a Phase I/II single-arm trial in acute myeloid leukemia (AML).

	Although study authors noted that synthetic controls are “not ideal,” they also pointed out that they may be “much more efficient and economical” than traditional placebo-controlled studies. They concluded that using synthetic control arms to predict which patients will respond “can help build more efficient and more informative adaptive clinical trials.”

	In addition to Medidata’s efforts, TransCelerate is also working on an initiative to maximize the value of existing placebo and standard-of-care data through an initiative called DataCelerate. According to TransCelerate, this initiative could improve trial design, improve researcher’s understanding of specific patient populations, and streamline trial execution.

	And in December 2018, then-FDA Commissioner Scott Gottlieb indicated the agency’s willingness to explore the use of “real-world” electronic medical record (EMR) data in the context of clinical research.

	Unlike data from the placebo arm of prior clinical trials, so-called real-world data (RWD) is typically de-identified patient data gleaned from the EMR. RWD tends to be messier than highly-sanitized clinical trial data. Nevertheless, there is so much RWD currently sitting unused in EMR systems that researchers are working out how to build computer analytics models that can safely interpret and leverage that data.

	As Gottlieb wrote in his statement last year, “these opportunities are already being recognized. In the oncology setting, for example, we currently have new drug applications under review where [RWD] and [real-world evidence] are helping to inform our ongoing evaluation as one component of the total complement of information that we’re evaluating. This is especially relevant when it comes to the evaluation of treatments for uncommon conditions, such as very rare tumor types.”

	Lee adds that the evaluation of synthetic data or RWD is something the FDA is growing increasingly willing to consider, particularly in oncology and other high-stakes settings.

	Lehmann, meanwhile, notes that the use of RWD could also be extremely helpful in common diseases, such as cardiovascular disease, diabetes, and other indications “where there is already a significant dataset that is out there to use as a comparison.” Ultimately, Lehmann says, “it comes down to the ability for the biostatistician and the sponsor to have trust in the data and believe it is a relevant comparison to their therapy.”

Not everyone shares this optimistic outlook when it comes to synthetic and real-world trial data. “You can’t replace randomization,” says Pocock. “In order to get a fair comparison of two treatments, you need to randomize patients one to another. Comparative effectiveness via big data is a bit of a con.”

	But synthetic control arms might come into play for pharma sponsors during very early stage trials, long before a regulatory decision needs to be made, says Lee. “At the end of [Phase I] trials, you need a go/no-go decision,” says Lee. “But if you don’t have a control arm, it’s kind of hard to assess whether or not you think the new drug will perform better than standard of care. The internal go/no-go decisions can be greatly informed by the use of a synthetic control arm.”

	“The key is, the pendulum cannot go completely to the other end,” says Sethuraman. “The RCT cannot be completely replaced.”

 is freelance journalist who specializes in health and medical innovation. She can be reached at 

A look at three contemporary trends that though integrated cautiously at first, may open up a reimagined world of clinical research.

Author | Sony Salzman